Podcasts about naively

Lauren has had three very different births. She had a peaceful C-section due to breech presentation with a difficult recovery, a wild, unmedicated VBAC, and a calm, medicated 2VBAC. Due to her baby's large size, she had to have an extra incision made during her Cesarean leaving her with a special J scar. Though her provider was hesitant to support a TOLAC with a special scar, Lauren advocated for herself by creating a special relationship with her OB and they were able to move forward together to help Lauren achieve both of her VBACs. Lauren talks about the importance of having an open mind toward interventions as she was firmly against many of the things that ended up making her second VBAC the most redemptive and healing experience of all. How to VBAC: The Ultimate Prep Course for ParentsFull Transcript under Episode Details Meagan: Hey, hey everybody. Guess what? We have our friend Lauren and her 11-day-old baby. Is that right? Lauren: Yeah. Meagan: 11 days old. You guys, I actually didn't even know that this baby existed until we popped up on the Zoom and she was holding this precious little newborn. She was like, “Surprise! I had another VBAC.” So we will be sharing, well she will be sharing her two VBACs so 2VBAC and something kind of unique about Lauren is that she has a special scar, a special J scar, correct? Lauren: Yep. Meagan: Yeah, so that happened in her first C-section. If you are listening and you have a special scar or have been told that you have a special scar, this is definitely an episode that you are going to want to put on repeat and save because I know that there are so many people out there who are told that they have a special scar and that they should never or can never VBAC again. I know we're not even getting into the story quite yet, Lauren, but did you have any flack with that? Did anyone talk about your special scar at all? Lauren: Yes. Advocating for the VBAC is probably the overarching theme of my VBAC because I really had to go to bat for myself for that without switching providers. Meagan: Yeah. We know that's so common. We see it a lot in our community just in general trying to get a VBAC let alone a VBAC with a special scar. We are going to get into her story but I have a review and I didn't even know that this was a review. It was left in a Baby Bump Canada group on Reddit actually so that was kind of fun to find. It's really nice. It says, “Seriously, I'm addicted. I find them so healing. I had an unplanned and very much unwanted C-section and I have been unknowingly carrying around all of these emotions and trauma about it since. I thought I was empowered going into my first birth, but I wasn't strong enough to stop the medical staff with all of their interventions. Don't get me wrong, I believe interventions are necessary in some instances. But looking back now, I realize those interventions were put in place to make things easier involved in delivering my baby. Anyway, I won't get into all of that here, maybe in a separate post. The point of my post is checking out The VBAC Link podcast. I listen to them all day now while caring for my babe. They also have a course you can take focusing on preparing for VBACs. Even if you just like birth stories, they have CBAC stories I believe as well. On the podcast, a guest also pointed out that what do you want for a VBAC birth– peace, redemption, etc.? She talked about how you can still feel those things if you need a Cesarean.” I love that point of view right there that you can still have peace and redemption even if you have a scheduled C-section or if your VBAC ends in a Cesarean. It says, “Another mom pointed out when she was feeling hesitant about saying okay to a C-section, her midwife said, ‘You have permission to get a C-section,' not in a way that a midwife was giving her permission, but telling this mom, ‘C-section is okay and you shouldn't feel like having one is wrong.' My baby is 8.5 months and we aren't going to try for a baby until they're about 18-24 months mostly to increase my chances of VBAC, but I really love these podcasts.” Then she says, “Okay, I'll stop raving now.” I love that. Her title is, “If you're considering a VBAC, I highly recommend The VBAC Link.” Thank you so much to– I don't actually know what your name is. Catasuperawesome on this Baby Bump Canada group. Just thank you so much for your review. As always, these reviews brighten our day here at The VBAC Link but most importantly, they help other Women of Strength find these stories like what we are going to be sharing today with Lauren's story. They help people feel empowered and educated and motivated and even first-time moms. They are really truly helping people learn how to avoid unnecessary Cesareans. I truly believe that from the bottom of my heart. Meagan: Okay, Lauren. As you are rocking your sweet, precious babe, I would love to turn the time over to you to share your stories. Lauren: Awesome, thank you. It's so nice to be here finally. I'm so excited because this podcast truly is the reason why I had my VBAC. I am kind of weirdly unique in that I didn't really feel like I had any mothering instincts. My husband and I had been married for 6.5 years before we decided to get pregnant because I always swore off children. I said, “It's not for me. I'm never going to have children. I want to travel and I want to do all of these things and children are for other people. I can't imagine myself as a mom.” My husband said, “Well, let's wait until we are 30,” because we got married really young. He was like, “Let's just wait until we are 30 and we will revisit the discussion.” I always find it kind of nice when I hear stories of women who feel similarly to the way I did because it's so relatable and I feel like we are very few and far between. That's another reason I wanted to share my story because I know there are other women out there like me. So anyway, it just so happened that at this time, my sister was pregnant. My brother was pregnant. My husband's brother was pregnant. We were like, “You know, we're almost 30. We've waited a long time. If we're going to have kids, we might as well have a kid when he or she is going to have all of these cousins.” My husband was like, “Let's start trying.” I'm like, “Great. I'm going to give it two months and if we don't get pregnant, we're not going forward with this. I'm going to say I tried and I can tell everyone I tried and that it didn't work.” Well, God has a sense of humor because two weeks later, I had a positive pregnancy test. Meagan: Two weeks later? Lauren: Yes. Meagan: So you were already pregnant when you had this conversation. Lauren: I was already taking birth control. I was multiple days into the pack. I just threw it in the trash and was like, “Let's just see what happens.” I guess when you do that, you can get pregnant. I don't know. I didn't really have a cycle. I got pregnant. I was so naive about how it all worked. I'm like, “Okay. The test is positive. I'm pregnant. It is what it is. I'm very much pregnant.” I had not doubt. I had no worry about miscarriage, nothing because I had a positive pregnancy test. That's sort of how I went through my pregnancy, kind of disconnected, very naive, and a little bit in denial that I was actually pregnant all the way up until the end. I read one book and it was called The Girlfriend's Guide to Pregnancy and it's this really sarcastic, funny book. She's very flippant about pregnancy and very straightforward with my sense of humor. I liked it because I felt the same way. I wasn't mushy or emotional. I had no connection to the pregnancy. I am pregnant. That's a fact. Anyway, at 26 weeks, my doctor was like, “You know, I think he's breech.” I was like, “Okay.” I knew what breech was, but I'm like, “Okay, what does that mean?” She was like, “Well, I would start doing some Spinning Babies exercises. Let's just keep an eye on it. I was going to a chiropractor this whole time. This is important for people to know. I was going to a chiropractor before I even got pregnant regularly. This chiropractor was seeing me. I told her that the baby was breech. “Can you help me flip him? Can we do some bodywork?” I continued to see her. I don't know if it was once or twice a week but it was often. 36 weeks rolls around and I see the midwife in the practice. She is not finding the heartbeat where it should be. She finds it up higher and she goes, “Lauren, I think your baby is still breech.” I thought there was no way. I had been seeing a chiropractor. I had been doing body work and stuff. She was like, “Well, why don't you go see the chiropractor that our practice recommends?” I'm like, “Okay.” I call this chiropractor on the phone. I leave her a message and I'm like, “I've been seeing another chiropractor, but my baby is breech.” She immediately called me right back and she was like, “What has the chiropractor been doing?” I'm like, “It feels like a normal adjustment like nothing different from before I was pregnant.” She was like, “So you've been on your side and she's been twisting your back and your pelvis away from each other?” You know how they do those kinds of adjustments? I said, “Yes.” She was like, “Oh my gosh.” She's like, “How soon can you come see me?” I started seeing her. My OB actually also recommended moxibustion. She got me set up with an acupuncturist in the area which I thought was really cool that she was like, “Some people say they swear by this. You need to do more Spinning Babies. I want you to go to acupuncture.” I saw this chiropractor and she was like, “What that chiropractor is doing to you is not pregnancy-safe. She's not Webster-certified and you needed to be seeing a Webster-certified chiropractor.” That's one of my regrets because I feel like had I known, obviously, I can't say I blame her 100%. I was also working out a ton because I'm like, “I don't want this pregnancy to change my body. I'm going to be skinny.” That's all I cared about so I'm sure I was holding my abdominal muscles way too tight too. I'm sure I contributed to it as well, but just knowing that probably was a major contributor to what ended up happening to this day irritates me. But anyway, he never flipped. He was solidly in my ribcage. He never moved. I would push on his head and he would not even budge an inch. My doctor was like, “You know, I would normally recommend an ECV, but he seems very wedged in your rib cage. He's stargazing,” which means his head is tilted up. His chin is pointed up. She said, “You are on the low end of normal for amniotic fluid.” She was like, “You have these three strikes against you basically. We can try it if you want to try it, but I'm going to say it's probably not going to work.” I had to wrestle with that. I ended up calling my husband's aunt who is a labor and delivery nurse for 30 years. I asked her for her opinion. I'm like, “Have you ever been in on an ECV? Tell me about it.” Naively, I went with her advice. She said, “If your doctor is not confident, then that means it's not going to work.” She's like, “I've seen so many births and I believe that every baby should be delivered via C-section because birth is dangerous and it's scary.” I'm like, “Okay, okay. I'm just going to move forward with the C-section. I'm so glad I talked to you.” Meagan: Whoa. Lauren: We scheduled the C-section and you know what? It really wasn't that big of a deal. My friend's husband was actually my anesthesiologist. My doctor was there. It was very happy. It was very pleasant. I had gone out to dinner with my friends the night before. If you could plan the perfect C-section, it was the perfect C-section. I just talked to my friend's husband the whole time. Again, not connected to this pregnancy at all. It was very much like, “Okay, a baby is going to come out. What is this going to be like?” I remember the doctor held him up over the curtain. I made eye contact with him and I was like, “Oh my gosh. I'm a mom.” The nurse was like, “Do you want to do skin-to-skin?” I was like, “What's that? Sure.” “Do you want to breastfeed?” “I think so. Sure.” Very naive. What ended up happening was that the recovery was just really tough. The surgery was great, but I did not expect the recovery to be so tough. I feel like the way people speak of C-sections is so casual. “Oh, just have a C-section. I had C-sections for all my babies. It's no big deal. It's a cakewalk.” That's the mindset I went into it with. Same with my husband because I reassured him, “It's no big deal. We're just going with the flow.” No. It's awful. It's major surgery. I'm allergic to– I think a lot of people are– the duramorph that they put in the spinal so I had the most severe, horrible itching for 24 hours to the point that they basically overdosed me on Benadryl because I could not cope and my vitals were crashing. I was barely having any respiration. They had to shake me awake and put cold washcloths on my head. They were like, “Hello,” because I was having such a hard time with the itching. Not only that, but the pain. It's painful. In my surgery, backing up a little bit, the doctor said, “Wow. He's really wedged in there and he's a lot bigger than I expected. I thought he was going to be maybe 7.5-7.25 pounds.” She goes, “He tore your incision coming out because he was so big.” She was like, “You have a J incision now so your incision goes horizontal and then vertically up.” She said, “Unfortunately, that means you'll never be able to have a VBAC. You're just going to be a C-section mama.” I was just lying there like, “Whatever. You're asking me what skin-to-skin is and breastfeeding and no vaginal births.” It was just a lot of information to process and take in and make decisions about. He ended up being 9 pounds. He was a good-sized baby. Anyway, that was my c-section experience. I know I'm probably one of the lucky few who could say that their C-section was so peaceful, really no trauma from it. I just thought, “I'm fine with that.” I watched my sister have a failed TOLAC and it looked kind of traumatizing and she was still traumatized from it just a couple months before my C-section so I'm like, “It's fine. I'll just be a C-section mom, but that recovery was terrible so I'll have one more baby and that's it.” I'm not going to have any more kids. I don't want to experience that again. That was May 2019. Fast forward to COVID times. We were thinking about getting pregnant before my son turned one but COVID hit so we were like, “Let's just give it a couple of months and see what shakes up with this pandemic.” The world stopped. I'm in real estate so for a while, we weren't allowed to show any property or do anything so I just was sitting at home doing nothing. I remember one night, I was just sitting there doing a puzzle bored as heck and I'm like, “I'm going to go listen to a podcast while I do this.” My phone suggested The Birth Hour. I hope I'm allowed to say that. Meagan: I love The Birth Hour, yes. Lauren: I was scrolling through the episodes and there was one on VBAC. I'm like, “Okay, I'm going to listen to this.” The interviewee mentioned The VBAC Link so I was like, Okay, I should check that podcast out. I was like, Why am I even listening to this? This is so not my wheelhouse, childbirth. I still didn't care about it, but listening to these podcasts opened up a whole new world for me. I'm so glad I found it all before I got pregnant. I started listening to all of those podcasts then I think I found through your podcast. I don't think it was The Birth Hour. Someone mentioned Dr. Stu so I started listening to his podcast and man, that guy set fire. He had so much great information. I listened to every podcast pretty much that he had done, especially the ones on VBAC because he talks about VBAC a lot and just how it really shouldn't be a big deal or shouldn't make you high risk and all of that. At the time, he was still graciously reviewing people's op-reports for them and now he doesn't do that. I think you have to pay for it, but I emailed him. I reached out to him and I emailed him my op report and I just said, “If you could look at this, my provider told me I wasn't a VBAC candidate but I want your opinion.” He got right back to me and he was like, “There's no reason you can't have a VBAC. This scar is really not that big of a deal. Yes, it's a special scar, but it shouldn't take away from your opportunity to TOLAC.” I ended up getting pregnant in the fall of 2020 and I went to my first appointment and my OB was like, “What do you want to do for your birth this time?” I'm like, “Did she forget what she told me? She must have forgotten.” I was like, “I want a VBAC.” She was like, “Okay, I'll give you my VBAC consent form and we can talk about it as your pregnancy progresses.” I'm like, “Okay, cool.” I saw her again at 12 weeks and she was like, “I'm having some hesitations because you had such a big baby and your scar is not normal. I think we need to talk about this a little bit more but let's not worry abou tit now. We can put it off and worry about it later.” I was like, “Okay.” I was so bummed because I love my OB. Funny story, I met my OB when I was worked for a home design company called Pottery Barn and I met her one day just helping her buy pillows. I'm like, “What do you do for work?” She was like, “I'm an OB.” I'm like, “Cool. I need an OB.” I had just moved to the area so I just started seeing her. I think I was one of her first patients so she knew me. It wasn't like she was a friend and a provider I only saw once a year, but we always picked up where we left off. We had a good relationship. I really did not want to change providers. I don't want this to sound like I was being manipulative, but I was like, I'm just going to really lean into this good relationship we have and just try to win her over. As the pregnancy progressed, at the next appointment I think I saw a midwife. I talked to the midwife about the VBAC and my OB's opinion and she was like, “I've seen a lot of women VBAC with a J scar at my old practice. I don't think it's a big deal, but I'll talk to the doctor for you and hopefully, we can figure this out.” I was like, “Okay.” Then I want to say I went to my 20-week appointment and they told me, “Okay, your baby is gigantic.” They said, “He is going to be between 9 and 10 pounds,” because he was measuring two weeks ahead. They said, “But the other concern we have is that you have marginal cord insertion and that could make for a small baby.” I'm like, “Okay, so is he big, or is he small?” Clearly that marginal cord insertion is helping him not being 12 pounds? What are you trying to tell me? They're like, “Either way, we suggest that you come back at 32 weeks. We have concerns about his size. He might be a tiny peanut. He might be enormous.” I'm like, “I think I'm good. Thanks, but no thanks.” Thanks to you guys, you push advocation so much that I'm like, “This doesn't add up. You can't tell me that he's too big and too small. I'm just going to go with fundal height and palpation if my doctor has a concern, we'll come back.” I never scheduled that growth scan. I was very protective of this pregnancy. I didn't want any outside opinions. I was so afraid that if I went and had this growth scan, I would be pushed to do a C-section. I wanted an unmedicated birth. I was terrified of the hospital. I was listening to so many podcasts all day every day. It was like an obsession so then I told Meagan before we were recording is that I felt like I was almost idolizing the VBAC. It was all I could think about. It was all I could talk about and it became this unhealthy obsession. Right around 25-26 weeks, I decided to hire a doula and move forward with the VBAC. It didn't matter to me what the doctor said. Right around that time, I was having some hesitations. Just getting that pushback from my doctor and knowing he was big, I started to let the fear creep in. I told my husband, “You know what? Maybe we should just do a C-section. I think I'm overanalyzing this so much. I'm just going to push aside this research I have done because clearly I'm obsessed and it's consuming me.” Meagan: Yeah, which is easy to do. Just to let you know, it really is easy to let it consume you. Lauren: It totally is. I think that we have to take a step back sometimes, come back to reality, and if you let the information override your instincts which I think is really easy to do, I think you can get too wound up or too set on something that might not be meant for you. Speaking of instincts, that night, I still remember. I had told my husband, “I'm just going to have a C-section.” I went to bed and I had a dream. I was in the hospital in the dream and I was holding my baby and my dad walked in. I have a really great relationship with my parents but especially my dad. I love my dad. He comes in the room and he's like, “How did it go?” He was meeting the baby for the first time and I burst into tears in the dream. I said, “Dad, I didn't even give myself the opportunity to VBAC. I just went in for a C-section. I just have so much regret about it and what could have happened if I had tried to have a VBAC.” Meagan: That just gave me the chills. Lauren: Yes. It was so weird. I have never really had a dream like that before. I woke up and I was like, “There's my answer. I have to move forward with this.” Having that dream gave me this peace that there is the instinct I need to follow. Yes, I have all of this information that is consuming me, but it was like, Keep going. I hired a doula which I found through The VBAC Link Facebook page. I put it out there, “Does anyone know a doula in my area?” Julie commented and it happened to be her really good friend who had just moved back to my area. I called her and it turned out that we had mutual friends. We connected really fast. I think, like I said, it was about 26 weeks. I go to my OB again and we had more of a pow-wow like a back-and-forth on the VBAC option. She was like, “I'm just worried about it. A C-section is not that big of a deal. We could just tie your tubes and then you won't have pelvic floor issues.” False. I said, “I got a second opinion from another doctor.” I didn't say it was Dr. Stu. I didn't say it was some guy with a podcast in LA. I said, “I got a second opinion and I feel like I just want the opportunity.” We didn't really land on anything solid, but she got up to leave the room and she got to the door and she turns around. She came back over to me and she gave me this big hug. She said, “I don't want to disappoint you. I want you to be happy, but let's keep talking about this.” I was like, “Okay.” That gave me a little bit of reassurance that I was leaning into that relationship I had built with her over the years because it had been 6 or 7 years of seeing her. I would also bring her flowers. I would always try to talk to her about her life and making a social connection with someone. If you let your doctor intimidate you just from the standpoint of being a stranger, I feel like that can really change the course of your care. But if you try to get to know people, and that's not necessarily a manipulative thing, but I think it's important. It should be important in your relationship with your doctor. If you don't feel like you can connect with them, there is issue number one, but I really felt like I could connect with her. I leaned into that. I have a cookie business on the side. She loved my cookies. We just had some other things to talk about other than my healthcare and I feel like it set this foundation of mutual respect. What doctor comes over, gives you a hug, and tells you, “I want you to love your birth”? So fast forward again, I see her again the next time and she said, “Look. I brought your case to my team and because we support moms who have had two C-sections, we felt like your risk is similar to theirs and that it shouldn't risk you out of a TOLAC so I'm going to support you if this is what you want.” I had given her this analogy that I think was Julie's analogy. She said, “If you needed heart surgery and you were told that you had a 98% chance of success–” because I think my risk of rupture was 2% or maybe a little bit lower, maybe 1.5. I told her this. I'm like, “If you told me I needed heart surgery and I had a 98 or 99% chance of success, we would do it. There would be no question. I have this 1% risk of rupture. I'm coming to the hospital. What gives? I should at least be able to try.” The problem is, I'm sure some people are like, “Why didn't you just switch providers?” We have three hospitals in my area. One is 20 minutes from me and two are one hour away. One of them which is an hour away is the only place where I can VBAC and there isn't a VBAC ban. There is maybe a handful of providers who deliver there. I knew my provider was VBAC-supportive sort of. She had the most experience of a lot of the providers around me so that's why I didn't switch. I had very minimal options for care. I couldn't go to LA or I couldn't go somewhere further away. It would be a four-hour drive either way. We are in an isolated area. I felt like that was a huge win. We are set to go. I remember I told Katrina. Katrina was so happy for me, my doula. I just soldiered on. I started taking Dr. Christopher's Birth Prep at 36 weeks. I was doing my dates and I was really busy in real estate. That's part of my story. I was so busy working super hard and I was getting to the end of my pregnancy. At 38 weeks, I went in and I had clients lined up showings coming up. I was like, “I can't have a baby anytime soon.” I was talking to my provider about it. “Maybe at 40 weeks, we can talk about a membrane sweep or something. I have so much on my plate. I can't have a baby this week.” My husband is a firefighter and his shift that he was going to be taking off was starting maybe the following week. I'm like, “He's not even going to be home. He's going to be gone most of this week. This is a horrible week to have a baby.” I let her check my cervix because I'm like, “I want to see if my birth prep or my dates are doing anything.” At the same time, I still had this fear of, What if I do all of this work and I don't even dilate? That was kind of what happened with my sister so I had that fear in the back of my mind. She checks me and she was like, “You are 2 centimeters dilated, 50% effaced. You're going to make it to your due date no problem. We're not even going to talk about an induction until 41 weeks.” She was like, “I'm just not worried about it. He doesn't feel that big to me. He doesn't feel small. He doesn't feel too big. He feels like a great size.” I said, “I know. I feel really confident that he's going to be 8 pounds, 2 ounces.” I spoke that out. I said, “That's my gut feeling. I just have so much confidence and peace about this birth. I just know it's going to work out.” I go on my merry little way from that appointment. I'm walking around. We had gone down to the beach. We were walking around and I'm like, “Man, I'm so crampy. For some reason, that check made me so, so crampy.” This was 38 weeks exactly. We go back home and I have prodromal labor that night. I'm telling Katrina about it. She goes, “You know, I bet the check irritated your uterus.” The next day, I start having some bloody discharge. I'm like, “What is this? What does this mean?” I told Katrina and she said, “It could mean nothing. It could mean labor is coming soon. We'll just have to see.” I hadn't slept the whole night before. She was like, “You need to get a good night's sleep.” I had to show property all day. I met these clients for the first time. I showed four or five houses to them and meanwhile, I'm like, “Gosh, I'm so sore and tired and crampy.” I told them, “I'm very obviously pregnant, but my due date is not until the end of the month.” This was June 10th and my due date was June 23rd. I said, “We have time. If you need to see houses, it shouldn't be a big deal. I don't want my pregnancy to scare you away.”That night, I get home and I'm like, “I'm going to bed. It's 8:00. I'm going to bed. I'm going to take Benadryl and I'm going to get the best night's sleep.” They call me at 9:00 PM and they're like, “Lauren, we saw this house online. It's brand new on the market. We have to see it.” They lived a couple of hours away so I'm like, “I'll go and I'll Facetime you from the house. I'll go tomorrow.” Tomorrow being June 11th. I'm like, “We'll make it happen. I promise I will get you a showing on this house.”I texted Katrina and I'm like, “Oh my gosh. I feel so crampy and so sore. Something might be going on, but I have to work tomorrow. I'll keep you posted.” I wake up the next morning. It's now June 11th and I lose my mucus plug immediately first thing. There was some blood. It was basically bloody show. I told Katrina and she's like, “Okay, just keep me posted. I have a feeling he's going to come this weekend. It was a Friday. I'm like, “Well, he can't because my husband works Saturday, Sunday, Monday. I don't have time to have a baby.” We go to the showing. I'm finally alone without my toddler and my husband. I'm in the car and I'm like, “Man, my lower back hurts. It's just coming and going but nothing to write home about, just a little bit of cramping.” Of course, I never went into labor with my first so I did not know what to expect. I get to the showing and this house had a really steep staircase. I'm Facetiming my clients and I'm going up the stairs. It was probably at noon and I'm thinking to myself, Man, it's really hard to go up these stairs. Why do I feel so funny? I finish up the showing and they're like, “We want the house. This is the house for us.” I get back in the car. I'm getting all of their information. I'm talking to the other agent. I start the offer and I'm like, “I'm just going to drive home and get in my bed because I don't feel good. I'm just going to write this offer from my bed and everything will be fine.” I get home and I tell my husband at 2:30, “I'm just going to sit in our bed and get this offer sent off.” Mind you, I had a work event, a big awards event that night for my whole office and we were going to have to leave at 4:00 PM. My in-laws were going to come get my son and take him to sleep over. It's 2:30. I'm writing this offer and I'm like, “I don't feel good.” My partner calls me. I tell her, “Listen, I don't know if I'm in labor, but I don't feel well. Maybe I have a stomach bug. I'm going to write this offer. I'm going to give you my clients' information and I want you to take over for me a little bit. They know I'm really pregnant, but this could just be a sickness but either way if something happens, I want them to have the best care and be taken care of if we are going to send this offer off.” I send the offer off. It's 3:30 at this point. I close my computer and I'm waiting for them to DocuSign. I text my husband, “There's no way I'm going tonight. I don't feel well. Something is up. I'm not sure what.” He didn't see my text for a little while. He comes in the room at 4:00 and he starts to talk to me. I literally fall to the ground with my first contraction. I'm in active labor.I don't know it yet, but I'm in active labor. I'm just like, “It feels like there's a wave crashing in my body.” That was the best way I could describe it. I'm like, “I feel this building. It's an ebb and flow,” but it reminded me of playing in the waves as a kid because I grew up in Orange County at the beach and just that feeling of the waves hitting you when you are playing in the surf. I'm like, “This is really intense. What is going on?” I'm like, “I'm certain it's a stomach bug.” I told him, “I have gas or something.” I was just like, “I'm going to give myself an enema and this will all go away.” I did that and sitting down on the toilet, I was like, “Oh my gosh.” It made everything so much more intense. I texted Katrina, “Something is going on. I'm not really sure it is.” She's like, “Well, why don't you try timing some contractions for me and let me know?” I crawl into my closet. I can hear my son and my husband getting ready. My son was 2 so of course, 2-year-olds are not always behaving. I can hear them interacting. I crawl into my closet and I'm lying on the floor in the dark. The contractions are 3.5-4 minutes apart lasting a minute. I was like, “I'm still pretty sure this is a stomach thing that is happening every 3-4 minutes.” I call Katrina and I'm like, “I don't know. I think I'm in labor. This is the length of my contractions. It's probably just prodromal.” I had so much prodromal.She was like, “Um, it doesn't really sound like prodromal labor, but I'll let you just figure it out. You let me know when you are ready for support. Make sure you are eating anything. Have you eaten anything today?” “No.” “Have you had any water?” “Not really.” “Okay. Please eat something. Please drink some water and keep me posted.” She goes, “Can you talk through the contractions?” I said, “I can cry.” She's like, “Okay. I'm ready to go as soon as you tell me.” Then the next thing I know, literally, this is probably an hour later so at 4:00 I had my first contraction. Now it's 5:00 and I'm like, “The contractions are 3 minutes apart and lasting a minute.” I said, “Maybe you should come over. I think Sean (my husband) is getting a little nervous.” We were still so naive. We didn't know what labor looked like and what was going on. We were like, “If we're not going to the event, why don't we just keep August (my son) at home? I'll just make him dinner and I'm going to make you dinner.” He starts prepping dinner and I'm like, “I don't think either of us really know what's going on.” Of course, Katrina knew what was going on and probably thought I was a crazy person but I was very much in denial. We texted her to come over and she gets there. I'm lying in my bed and she's like, “Okay, yeah. They're coming 2.5-3 minutes apart. If you're ready to go to the hospital, I'm ready to go with you.” I'm mooing through these contractions, vocalizing everything. I'm like, “It just feels good to vocalize and I just really keep having to use the bathroom. It's probably just my stomach.” She's like, “No.” I can hear her outside my bathroom telling my husband, “I think we should go. She's really vocalizing a lot and that usually means it's pretty substantial, active labor.” Meanwhile, all I can think about is, “I've got to get this offer in for my clients.” I'm waiting on DocuSign, checking my email. Finally, it comes through. This is 6:00, maybe 6:30. I see it come in. I send it off and I'm standing at my kitchen counter with my computer on, mooing, doing this freaking offer. I go to cross my legs as I'm leaning over and I'm like, “I can't cross my legs, Katrina. I feel like my bones are separating.” She's like, “Yeah, baby is probably descending into your pelvis. I think we should get going if you're okay with going.” We have a 45 to an hour drive depending on traffic and the time of day. It's a Friday night so basically where I live, there's not a ton of traffic but we get in the car. She's following us and we get to the hospital. It's probably 7:15-7:30 or something like that. I'm telling my husband as I'm mooing through these contractions, “This really isn't that bad. If this is labor, it's intense and it feels like there's an earthquake in my body, but I would not tell you that I'm in any pain right now.” He's like, “Okay, whatever you say lady.” We ended up having to walk across the whole hospital parking lot to the ER because the regular hospital entrance was closed. As soon as we walked in the hospital, the hormones changed. The adrenaline kicks in. I start feeling pain. I start feeling a little bit panicky and it starts getting harder to cope through these contractions. I'm on the floor of the triage room crying into a trash can and everyone is staring at me. Katrina's like, “They need to stop staring!” She was trying to defend me while my husband is answering all of their dumb questions like, “What's your favorite color? What city is your mom born in?” They're like, “Let's just put you in a wheelchair and get you up there.” I'm like, “I can't sit.” Anytime I tried to sit, the contractions were a minute apart and they were so intense. I get there and I was so protective of this birth and outside interventions, I just was like, “Everything is evil. Cervical checks are evil. The epidural is evil. Everything is going to make me have a C-section.” I was like, “I don't want to know how dilated I am. I don't want anyone in this room to know except the nurse. That's who is allowed to know how dilated I am.” She checks me and the doctor comes in. It was the hospitalist and of all the providers in my area, it was miraculous that I got this hospitalist because he has so much experience. He is so calm, so kind, so supportive. He just said, “Hi, Lauren. I'm Dr. so-and-so and you're in labor. Happy laboring.” No concerns about my TOLAC, nothing. He didn't even bring it up. He didn't ask to check, nothing. Just, “Happy laboring,” and he left the room. I'm like, “Okay. Clearly I'm in active labor.” So then they were getting the tub ready because my room had a tub and as we were waiting for it to warm up, I'm sitting on the ball. I'm having all this bloody show. The nurse asked to check me again before I get in the tub. Unknowingly, I had been 5 centimeters when we arrived. I was 7 now when we got in the tub an hour later. I get in the tub and I wouldn't say it provided me any relief. Honestly, I was so in my head and not necessarily in pain, just so mentally unaware of everything going on, in labor land, but also very overwhelmed by the intensity of it. I told Katrina, “George Washington could have been sitting in the corner watching me labor. I would not have known.” I barely opened my eyes. I had a nurse who was there sitting with us because I had to have a one-on-one nurse for being high-risk and I had to have continuous fetal monitoring. Because I was in the water, she needed to sit there and make sure the monitors didn't move. I couldn't have told you what she looked like, nothing. I didn't speak to her. I was in another world. I think I maybe was in the tub for 30 minutes to an hour. It's probably 9:00 or 10:00. I can't even remember the timeline of it but it wasn't that long of a labor. My water breaks and I start grunting. They're like, “Let's get you out of the tub. Let's get you out of the tub.” I think I was 9 centimeters at this point. We arrived at 7:30. This is probably 10:00 PM or something like that. I'm like, “Okay. I'm just going to lean over the back of this bed and just moo and make noises.” Me being who I am and not super emotional, I'm making jokes about how I sound. I'm like, “You guys, I sound like Dory in Finding Nemo. I'm so embarrassed. Please don't look at my butthole.” I was naked. I'm making all these jokes and coping, I would say pretty well in terms of pain but just very overwhelmed by the intensity of it. They come in and check me and they're like, “Okay, you're complete.” This is at 11:00 PM maybe or 10:30, something like that. But she was like, “You have a little bit of a cervical lip.” It was a provider I hadn't met before at my OB's office but they were like, “We will just let you do your thing. You sound pushy but please don't push because you have a lip. Let's just let him descend.” I could feel his head inside of myself. I could feel his head coming down. I was like, “I want it to be over. I want it to be over.” I'm still in denial of this whole thing this entire time. Are we sure it's not poop? I know there's a baby coming out. Once my water broke, I'm like, “Okay, I guess I'm having a baby.” That was really, truly the first time that I was like, “Okay, this is really happening.”Maybe 30 minutes later, the hospitalist peeks his head in the room and he's like, “Lauren, why don't you try laying on your side?” I tried and it was too painful. I flip over on my back and three pushes later, he comes flopping out. I screamed him out and it was super painful. I was so overwhelmed by how painful it was. I just screamed like a crazy, wild woman. He's on my chest and he's screaming and I'm in all this pain and then she's like, “I've got to give you lidocaine. You tore a little bit. I'm going to stitch you up.” It was just all this pain happening at once, but I was like, “I got my VBAC. That's all that matters. No one touched me and I got my VBAC. I don't care about anything else.” Anyway, it was great. I would not change it for the world because I never had a ton of pain. I never really thought I needed an epidural, but it was a little bit mentally overwhelming. Meagan: Mhmm, sure. Lauren: Anyway, that was my first VBAC. The doctor said, “You pushed so primally. That was the most amazing thing I've ever seen.” The hospitalist was like, “That was incredible to watch. You are a badass.” I was like, “That was such a compliment because I didn't know what I was doing and you're this doctor with all the experience.” Anyway, fast forward to my third pregnancy. This is now the summer of 2023. We decide we're going to have one more baby. I of course had no issues with the VBAC this time because I had a successful TOLAC with my second. I made it to 20 weeks. I had COVID, RSV, and the flu all right around then so they were telling me, “Your baby is measuring totally normal.” I'm like, “Yeah, because I've been sick as a dog for 6 weeks.” I'm like, “Maybe I'm going to get this newborn who is a normal size,” because my son was born at 38 and 2, the second one, and he was 8 pounds, 3 ounces. I had told my doctor 8 pounds, 2 ounces. I was one ounce off. I was like, “Maybe I'll get this little peanut baby and it's going to be so great. I'll finally have a newborn who fits in a diaper for more than two days.” Then I hit 33 weeks and I got huge. I just exploded inside. I go to my OB and I'm like, “I don't feel good. I'm too big. This baby is too big. Something is wrong.” She's like, “No, Lauren. I really just think you make big babies and he just went through a growth spurt. Let's not worry. I'm not going to have you do an ultrasound or anything like that. If he continues to measure 2-3 weeks ahead,” because I was measuring 36 weeks at 33 weeks, “then we can talk about it, but I don't want to worry about it.” I was like, “Okay.” I was having all of this round ligament pain more than I had with my others and prodromal labor was so painful. I remember telling Katrina who I hired again, “I feel like something is wrong with my muscles. I just am so uncomfortable. But I don't want to make any rash decisions based on it. I might get an epidural if this keeps up because this doesn't feel normal. “She was like, “Okay, whatever works.” So I get to my 38-week appointment and I'm thinking, I'm going to have this baby at 38 weeks just like I had my second baby. I had everything ready. Everything was good to go at my house and then day by day, it ticks on. Baby is not coming. Baby is not coming. I was due April 6th. This was just this year, 2024. I get to 38 weeks. I tell my doctor, “Just strip my membranes. I don't even care.” She was like, “Okay, I guess if that's what you want.” She did. Nothing happened. 39 weeks rolls around. She strips my membranes again. Nothing really happens and then the night of Easter, I had this strange experience where I woke up in the middle of the night and I had this contraction that wouldn't end. I couldn't feel the baby move and it freaked me out. I did everything I could to get him to move. I was in the shower. I was eating. I was drinking and doing all of these things. Finally, I called Katrina at 2:00 in the morning. I'm like, “My baby's dead. I'm 100% sure he's gone. What do I do?” She's like, “Lauren, just relax. Lie on your side and drink something sweet.” We were ready to go to the hospital. I remember we had a stethoscope. I got the stethoscope and I put it right where I knew his heartbeat was and I heard a heartbeat. I burst into tears. It was the first time I've ever cried with any of my babies even being put on my chest. I just felt this relief because I had so much anxiety about him with my size being so big and the pain I was having. I was like, “I just want this baby out.” I never really felt that way, but it was this desperate anxiety. A couple of days passed and I'm now in week 39. I'm like, “My uterus is silent like a little church mouse. She's not doing a thing. She's not cramping. She's not contracting. No discharge, nothing.” I'm like, “This baby is never going to come.” I tell my doctor at my 39-week appointment, “If this baby hasn't come by Friday, I'm back here and I want another membrane sweep.” I felt kind of crazy because I'm like, “This is technically an induction, like a natural and I'm intervening.” Me who never wanted anyone to touch me and now I'm like, “Please touch me and pull this baby out of my body.” She goes to check me and she's like, “Lauren, I think he's coming tonight. Your body contracted around my hand when I tried to sweep you. I just wouldn't be surprised. Don't worry.” I'm like, “Okay, well you're breaking my water on Monday.” I was 3 or 4 centimeters dilated and I'm like, “We're waiting until Monday but I want you to break my water because I'm over it.” She's like, “That's a good idea. Let's threaten this baby and he'll come right out.” This was early in the morning on Friday, the 5th. Anyway, I had all of this anxiety and I just felt like he needed to come out. I couldn't get any peace until I knew he was alive and happy and healthy and on my chest. Friday afternoon, I felt crampy just a little bit the whole day and then at 4:30 PM, I feel this gush and I'm like, “Okay. Is that my water or is it my pee?” because his head felt like it was on my bladder. I didn't say anything to anyone. Then 6:00 rolls around. I text Katrina. I'm like, “Listen, I felt a little gush and I keep feeling it. I put a pad on and it doesn't seem to be urine. I'm not really sure what's happening. I'm just going to do some Miles Circuit and I'll update you.”At 7:30, I'm cleaning my kitchen and all of a sudden, I'm hit with an active labor contraction. I'm like, “Not again. I want labor to start normally so I know what's happening.” No. Baby's like, “I'm ready.” At 7:30, I tell her, “Okay, I'm feeling contractions. I'm getting in the shower to see if it will stop. It might be prodromal. Let's give it an hour. I'm going to text you, but they are 2.5 minutes apart.” She's like, “I'm at dinner. I'm getting boxes. Just let me know.” I was like, “Okay. It might stop though so I wouldn't worry about it.” No, it did not stop. She gets to my house at 9:00 and my car is already running. I'm like, “We're going.” I am mooing through these contractions. I'm going to pop this baby out right now. I had thankfully put some chux pads in the back of my car. I'm on all fours in the back of my car. Mind you, we have to drive an hour to the hospital. I peed all over the chux pad. I just was like, “He's on my bladder. He's on my bladder.” It was so painful and I couldn't control anything. I'm like, “Is this water? Is this pee? I don't even know what's happening.” We get to the hospital. He did not come in the car, thank God, but we did have to go to the ER again and the ER was taking forever. It took a half hour to get me up to labor and delivery as I'm actively mooing in front of the hospital. I was like, “I'm not going in,” because there was a little girl sitting in the waiting room and some convict sitting with a police officer. I'm like, “I'm not having my labor in front of these people!” Even the police officer came out and he was like, “I don't understand what is taking so long. You are clearly about to have this baby. I will bust open these doors for you and walk you up to L&D myself if that's what it takes.” Finally, they got me up there. I arrived. I told Katrina and my husband, “You guys, I'm getting an epidural.” I said, “I have had so much anxiety and so much pain. This does not feel like my previous labor. This feels like I'm suffering.” I said, “I just want to smile. I just want to smile. I want to smile this baby out.” We get up there. I'm 8 centimters dilated. This was the part of the story that I feel like it comes back to advocating for myself. I go in there and I'm like, “I don't care what you need from me. I just need the epidural and stat.” The nurses are scrambling and this doctor walks in. I am on all fours on the bed just staring at the ground, actively transitioning. I see this doctor walk in. I see his feet and he had his shoelaces untied. Immediately, I'm just like, “No. It's a no.” I don't know why. I just was like, “Your shoes are dirty and they are untied. You seem like a hot mess. I'm already a hot mess. I want someone to come in and just be like clean-cut and normal.” He starts asking me all these questions. He's asking me my whole health history, everything about my grandparents, my parents, all of this stuff. I'm in transition then he goes, “You're aware of the risk of TOLAC, right?” I said, “Yes.” He goes, “That your uterus could burst wide open?” I literally saw red. I'm in a contraction and I just screamed like a wild lady. I was like, “Get out.” I wanted to add on some expletives and tell him to get out of the room. I just said, “Get food.” He was like, “I'm  just saying.” He ended up leaving and my nurse peeks her head under. I look over and I see this nurse peeking her head right into my face and it's the same nurse who was there with my first VBAC. She goes, “You don't have to accept care from him.” She goes, “Your doctor is actually the backup on-call doctor tonight.” She goes, “If you refuse care, we can call her and she can come in.” I was like, “Oh my gosh. This is a miracle.” We get the epidural. I'm like, “We've got to slow this thing down. I don't want to have this baby and have this crazy man who I cannot stand anywhere near my body parts, anywhere in this room.” We get the epidural and everything slowed down. I labored down. My doctor ended up coming in and she checked me. She was like, “Your bag is bulging. It feels like rubber. It's so thick.” She was like, “I think that's why he's not coming out.” We got to the hospital at 9:30-9:45. By the time we got in the room, 11:00 by the time I got the epidural, and the anesthesiologist was like, “You're going to have this baby in 30 minutes. I'm certain of it.” To slow it down, I'm closing my legs and doing all of these things to slow it down.My doctor comes in. She breaks my water and fluid goes everywhere. It floods the floor. She goes, “I don't remember any time I've ever seen this much water come out of someone without polyhydramnios. Maybe you had it. I don't know but this is an insane amount of water.” She breaks my water and then my epidural was a pretty low dose because he thought I was having the baby in 30 minutes. It's now 2:30 in the morning and I haven't had the baby yet. I'm getting up on my knees. I'm leaning over the back of the bed and I feel him descending. Then my doctor comes in an hour later and she's like, “Let's get this baby out.” It was 3:30 in the morning and she's like, “Let's go.” She feels me. She's like, “You're complete. I feel his head right here. You just need to push and you can't feel that his head is right here.” So I just get on my back, in lithotomy with the freaking stirrups like I said I would never do with the epidural I said I would never get and I pushed him out in three pushes. He was 9 pounds, 7 ounces. I am so glad I got that epidural. No regrets there because that's a really freaking huge baby. His head was in the 100th percentile or something like gigantic. I tore a little bit again, but I feel like the tradeoff was this peaceful, happy birth. I was making jokes. I had this nurse that I loved and knew. I had my doctor I loved and knew. I had Katrina and I had my husband who were the only people in the room and we laughed our way into this birth. I laughed my baby out basically. I was making jokes the whole time and I just had this peaceful experience. I told my husband, “I know I railed on the epidural my whole pregnancy and I said I would never get it,” but it's a tool ultimately. It's a tool. If you use it wisely, I was very far along. I said, “I don't think it's going to stop my labor.” I felt really confident in my decision. I didn't feel like anything was pushed on me. I made the decision. I'm happy I did it that way. Would I do it again that way? I don't know. I think with every birth, you should be open-minded to the possibilities and your needs. I hear so many stories where women are like, “And then I got the epidural. I had to.” I'm like, “It's okay. Own that decision. You're no worse off for getting it and it doesn't make you any less of a mom or any less of a good person for getting it. It's okay to not feel every single pain of labor if it's overclouding your ability to be in the moment.” Meagan: Yeah.Lauren: So anyway, that was my second VBAC story. Honestly, it was so redemptive because there was no trauma from the pain of having this wild, chaotic, primal birth. It was just peaceful and happy with all of the people. If I could have dreamt up a list of people who could have been with me, that's who it would have been. Meagan: Good. Oh, I love that you pointed that out. Well, I am so happy for you. Congrats again, 11 days ago and right now I want to thank you again so much for sharing your story. Lauren: Thank you for having me. ClosingWould you like to be a guest on the podcast? Tell us about your experience at thevbaclink.com/share. For more information on all things VBAC including online and in-person VBAC classes, The VBAC Link blog, and Meagan's bio, head over to thevbaclink.com. Congratulations on starting your journey of learning and discovery with The VBAC Link.Support this podcast at — https://redcircle.com/the-vbac-link/donationsAdvertising Inquiries: https://redcircle.com/brands

INSIDER TRADING UNDEFINED: 1/4:  Uneven Justice: The Plot to Sink Galleon, by Raj Rajaratnam https://www.amazon.com/Uneven-Justice-Plot-Sink-Galleon/dp/1637582811/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= Raj Rajaratnam, the respected founder of the iconic hedge fund Galleon Group, which managed $7 billion and employed 180 people in its heyday, chose to go to trial rather than concede to a false narrative concocted by ambitious prosecutors looking for a scapegoat for the 2008 financial crisis. Naively, perhaps, Rajaratnam had expected to get a fair hearing in court. As an immigrant who had achieved tremendous success in his adopted country, he trusted the system. He had not anticipated prosecutorial overreach—inspired by political ambition—FBI fabrications, judicial compliance, and lies told under oath by cooperating witnesses. In the end, Rajaratnam was convicted and sentenced to eleven years in prison. He served seven and a half. Meanwhile, not a single senior bank executive responsible for the financial crisis was even charged. Uneven Justice is the story of his bewildering and confounding prosecution by forces who, quite frankly, were looking for bigger game. When Rajaratnam refused to support the narrative that would make that happen, he and the Galleon Group became collateral damage. A cautionary tale with implications for us all, Uneven Justice is both a riveting page-turner and an eye-opening lesson in the vagaries of justice when an unscrupulous prosecutor is calling the shots 1929 WALL STREET

INSIDER TRADING UNDEFINED: 2/4:  Uneven Justice: The Plot to Sink Galleon, by Raj Rajaratnam https://www.amazon.com/Uneven-Justice-Plot-Sink-Galleon/dp/1637582811/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= Raj Rajaratnam, the respected founder of the iconic hedge fund Galleon Group, which managed $7 billion and employed 180 people in its heyday, chose to go to trial rather than concede to a false narrative concocted by ambitious prosecutors looking for a scapegoat for the 2008 financial crisis. Naively, perhaps, Rajaratnam had expected to get a fair hearing in court. As an immigrant who had achieved tremendous success in his adopted country, he trusted the system. He had not anticipated prosecutorial overreach—inspired by political ambition—FBI fabrications, judicial compliance, and lies told under oath by cooperating witnesses. In the end, Rajaratnam was convicted and sentenced to eleven years in prison. He served seven and a half. Meanwhile, not a single senior bank executive responsible for the financial crisis was even charged. Uneven Justice is the story of his bewildering and confounding prosecution by forces who, quite frankly, were looking for bigger game. When Rajaratnam refused to support the narrative that would make that happen, he and the Galleon Group became collateral damage. A cautionary tale with implications for us all, Uneven Justice is both a riveting page-turner and an eye-opening lesson in the vagaries of justice when an unscrupulous prosecutor is calling the shots. 1910 WALL STREET

INSIDER TRADING UNDEFINED: 3/4:  Uneven Justice: The Plot to Sink Galleon, by Raj Rajaratnam https://www.amazon.com/Uneven-Justice-Plot-Sink-Galleon/dp/1637582811/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= Raj Rajaratnam, the respected founder of the iconic hedge fund Galleon Group, which managed $7 billion and employed 180 people in its heyday, chose to go to trial rather than concede to a false narrative concocted by ambitious prosecutors looking for a scapegoat for the 2008 financial crisis. Naively, perhaps, Rajaratnam had expected to get a fair hearing in court. As an immigrant who had achieved tremendous success in his adopted country, he trusted the system. He had not anticipated prosecutorial overreach—inspired by political ambition—FBI fabrications, judicial compliance, and lies told under oath by cooperating witnesses. In the end, Rajaratnam was convicted and sentenced to eleven years in prison. He served seven and a half. Meanwhile, not a single senior bank executive responsible for the financial crisis was even charged. Uneven Justice is the story of his bewildering and confounding prosecution by forces who, quite frankly, were looking for bigger game. When Rajaratnam refused to support the narrative that would make that happen, he and the Galleon Group became collateral damage. A cautionary tale with implications for us all, Uneven Justice is both a riveting page-turner and an eye-opening lesson in the vagaries of justice when an unscrupulous prosecutor is calling the shots. 1918 WALL STREET

INSIDER TRADING UNDEFINED: 4/4:  Uneven Justice: The Plot to Sink Galleon, by Raj Rajaratnam https://www.amazon.com/Uneven-Justice-Plot-Sink-Galleon/dp/1637582811/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= Raj Rajaratnam, the respected founder of the iconic hedge fund Galleon Group, which managed $7 billion and employed 180 people in its heyday, chose to go to trial rather than concede to a false narrative concocted by ambitious prosecutors looking for a scapegoat for the 2008 financial crisis. Naively, perhaps, Rajaratnam had expected to get a fair hearing in court. As an immigrant who had achieved tremendous success in his adopted country, he trusted the system. He had not anticipated prosecutorial overreach—inspired by political ambition—FBI fabrications, judicial compliance, and lies told under oath by cooperating witnesses. In the end, Rajaratnam was convicted and sentenced to eleven years in prison. He served seven and a half. Meanwhile, not a single senior bank executive responsible for the financial crisis was even charged. Uneven Justice is the story of his bewildering and confounding prosecution by forces who, quite frankly, were looking for bigger game. When Rajaratnam refused to support the narrative that would make that happen, he and the Galleon Group became collateral damage. A cautionary tale with implications for us all, Uneven Justice is both a riveting page-turner and an eye-opening lesson in the vagaries of justice when an unscrupulous prosecutor is calling the shots. 1918 WALL STREET

PREVIEW: #WALLSTREET: Excerpt from a conversation with Wall Street executive Raj Rajaratnam, who served many years in Federal prison for insider trading and here comments on why he wrote this book after leaving jail, and what he looks to achieve -- clairty about insider trading rules.  More later. 1929 Wall Street during the crash.  Uneven Justice: The Plot to Sink Galleon, by Raj Rajaratnam https://www.amazon.com/Uneven-Justice-Plot-Sink-Galleon/dp/1637582811/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= Raj Rajaratnam, the respected founder of the iconic hedge fund Galleon Group, which managed $7 billion and employed 180 people in its heyday, chose to go to trial rather than concede to a false narrative concocted by ambitious prosecutors looking for a scapegoat for the 2008 financial crisis. Naively, perhaps, Rajaratnam had expected to get a fair hearing in court. As an immigrant who had achieved tremendous success in his adopted country, he trusted the system. He had not anticipated prosecutorial overreach—inspired by political ambition—FBI fabrications, judicial compliance, and lies told under oath by cooperating witnesses. In the end, Rajaratnam was convicted and sentenced to eleven years in prison. He served seven and a half.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: SAE reconstruction errors are (empirically) pathological, published by Wes Gurnee on March 29, 2024 on The AI Alignment Forum. Summary Sparse Autoencoder (SAE) errors are empirically pathological: when a reconstructed activation vector is distance ϵ from the original activation vector, substituting a randomly chosen point at the same distance changes the next token prediction probabilities significantly less than substituting the SAE reconstruction[1] (measured by both KL and loss). This is true for all layers of the model (~2x to ~4.5x increase in KL and loss over baseline) and is not caused by feature suppression/shrinkage. Assuming others replicate, these results suggest the proxy reconstruction objective is behaving pathologically. I am not sure why these errors occur but expect understanding this gap will give us deeper insight into SAEs while also providing an additional metric to guide methodological progress. Introduction As the interpretability community allocates more resources and increases reliance on SAEs, it is important to understand the limitation and potential flaws of this method. SAEs are designed to find a sparse overcomplete feature basis for a model's latent space. This is done by minimizing the joint reconstruction error of the input data and the L1 norm of the intermediate activations (to promote sparsity): However, the true goal is to find a faithful feature decomposition that accurately captures the true causal variables in the model, and reconstruction error and sparsity are only easy-to-optimize proxy objectives. This begs the questions: how good of a proxy objective is this? Do the reconstructed representations faithfully preserve other model behavior? How much are we proxy gaming? Naively, this training objective defines faithfulness as L2. But, another natural property of a "faithful" reconstruction is that substituting the original activation with the reconstruction should approximately preserve the next-token prediction probabilities. More formally, for a set of tokens T and a model M, let P=M(T) be the model's true next token probabilities. Then let QSAE=M(T|do(xSAE(x))) be the next token probabilities after intervening on the model by replacing a particular activation x (e.g. a residual stream state or a layer of MLP activations) with the SAE reconstruction of x. The more faithful the reconstruction, the lower the KL divergence between P and Q (denoted as DKL(P||QSAE)) should be. In this post, I study how DKL(P||QSAE) compares to several natural baselines based on random perturbations of the activation vectors x which preserve some error property of the SAE construction (e.g., having the same l2 reconstruction error or cosine similarity). I find that the KL divergence is significantly higher (2.2x - 4.5x) for the residual stream SAE reconstruction compared to the random perturbations and moderately higher (0.9x-1.7x) for attention out SAEs. This suggests that the SAE reconstruction is not faithful by our definition, as it does not preserve the next token prediction probabilities. This observation is important because it suggests that SAEs make systematic, rather than random, errors and that continuing to drive down reconstruction error may not actually increase SAE faithfulness. This potentially indicates that current SAEs are missing out on important parts of the learned representations of the model. The good news is that this KL-gap presents a clear target for methodological improvement and a new metric for evaluating SAEs. I intend to explore this in future work. Intuition: how big a deal is this (KL) difference? For some intuition, here are several real examples of the top-25 output token probabilities at the end of a prompt when patching in SAE and ϵ-random reconstructions compared to the original model's next-token distributio...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: SAE reconstruction errors are (empirically) pathological, published by wesg on March 29, 2024 on LessWrong. Summary Sparse Autoencoder (SAE) errors are empirically pathological: when a reconstructed activation vector is distance ϵ from the original activation vector, substituting a randomly chosen point at the same distance changes the next token prediction probabilities significantly less than substituting the SAE reconstruction[1] (measured by both KL and loss). This is true for all layers of the model (~2x to ~4.5x increase in KL and loss over baseline) and is not caused by feature suppression/shrinkage. Assuming others replicate, these results suggest the proxy reconstruction objective is behaving pathologically. I am not sure why these errors occur but expect understanding this gap will give us deeper insight into SAEs while also providing an additional metric to guide methodological progress. Introduction As the interpretability community allocates more resources and increases reliance on SAEs, it is important to understand the limitation and potential flaws of this method. SAEs are designed to find a sparse overcomplete feature basis for a model's latent space. This is done by minimizing the joint reconstruction error of the input data and the L1 norm of the intermediate activations (to promote sparsity): However, the true goal is to find a faithful feature decomposition that accurately captures the true causal variables in the model, and reconstruction error and sparsity are only easy-to-optimize proxy objectives. This begs the questions: how good of a proxy objective is this? Do the reconstructed representations faithfully preserve other model behavior? How much are we proxy gaming? Naively, this training objective defines faithfulness as L2. But, another natural property of a "faithful" reconstruction is that substituting the original activation with the reconstruction should approximately preserve the next-token prediction probabilities. More formally, for a set of tokens T and a model M, let P=M(T) be the model's true next token probabilities. Then let QSAE=M(T|do(xSAE(x))) be the next token probabilities after intervening on the model by replacing a particular activation x (e.g. a residual stream state or a layer of MLP activations) with the SAE reconstruction of x. The more faithful the reconstruction, the lower the KL divergence between P and Q (denoted as DKL(P||QSAE)) should be. In this post, I study how DKL(P||QSAE) compares to several natural baselines based on random perturbations of the activation vectors x which preserve some error property of the SAE construction (e.g., having the same l2 reconstruction error or cosine similarity). I find that the KL divergence is significantly higher (2.2x - 4.5x) for the residual stream SAE reconstruction compared to the random perturbations and moderately higher (0.9x-1.7x) for attention out SAEs. This suggests that the SAE reconstruction is not faithful by our definition, as it does not preserve the next token prediction probabilities. This observation is important because it suggests that SAEs make systematic, rather than random, errors and that continuing to drive down reconstruction error may not actually increase SAE faithfulness. This potentially indicates that current SAEs are missing out on important parts of the learned representations of the model. The good news is that this KL-gap presents a clear target for methodological improvement and a new metric for evaluating SAEs. I intend to explore this in future work. Intuition: how big a deal is this (KL) difference? For some intuition, here are several real examples of the top-25 output token probabilities at the end of a prompt when patching in SAE and ϵ-random reconstructions compared to the original model's next-token distribution (note the use of ...

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: SAE reconstruction errors are (empirically) pathological, published by wesg on March 29, 2024 on LessWrong. Summary Sparse Autoencoder (SAE) errors are empirically pathological: when a reconstructed activation vector is distance ϵ from the original activation vector, substituting a randomly chosen point at the same distance changes the next token prediction probabilities significantly less than substituting the SAE reconstruction[1] (measured by both KL and loss). This is true for all layers of the model (~2x to ~4.5x increase in KL and loss over baseline) and is not caused by feature suppression/shrinkage. Assuming others replicate, these results suggest the proxy reconstruction objective is behaving pathologically. I am not sure why these errors occur but expect understanding this gap will give us deeper insight into SAEs while also providing an additional metric to guide methodological progress. Introduction As the interpretability community allocates more resources and increases reliance on SAEs, it is important to understand the limitation and potential flaws of this method. SAEs are designed to find a sparse overcomplete feature basis for a model's latent space. This is done by minimizing the joint reconstruction error of the input data and the L1 norm of the intermediate activations (to promote sparsity): However, the true goal is to find a faithful feature decomposition that accurately captures the true causal variables in the model, and reconstruction error and sparsity are only easy-to-optimize proxy objectives. This begs the questions: how good of a proxy objective is this? Do the reconstructed representations faithfully preserve other model behavior? How much are we proxy gaming? Naively, this training objective defines faithfulness as L2. But, another natural property of a "faithful" reconstruction is that substituting the original activation with the reconstruction should approximately preserve the next-token prediction probabilities. More formally, for a set of tokens T and a model M, let P=M(T) be the model's true next token probabilities. Then let QSAE=M(T|do(xSAE(x))) be the next token probabilities after intervening on the model by replacing a particular activation x (e.g. a residual stream state or a layer of MLP activations) with the SAE reconstruction of x. The more faithful the reconstruction, the lower the KL divergence between P and Q (denoted as DKL(P||QSAE)) should be. In this post, I study how DKL(P||QSAE) compares to several natural baselines based on random perturbations of the activation vectors x which preserve some error property of the SAE construction (e.g., having the same l2 reconstruction error or cosine similarity). I find that the KL divergence is significantly higher (2.2x - 4.5x) for the residual stream SAE reconstruction compared to the random perturbations and moderately higher (0.9x-1.7x) for attention out SAEs. This suggests that the SAE reconstruction is not faithful by our definition, as it does not preserve the next token prediction probabilities. This observation is important because it suggests that SAEs make systematic, rather than random, errors and that continuing to drive down reconstruction error may not actually increase SAE faithfulness. This potentially indicates that current SAEs are missing out on important parts of the learned representations of the model. The good news is that this KL-gap presents a clear target for methodological improvement and a new metric for evaluating SAEs. I intend to explore this in future work. Intuition: how big a deal is this (KL) difference? For some intuition, here are several real examples of the top-25 output token probabilities at the end of a prompt when patching in SAE and ϵ-random reconstructions compared to the original model's next-token distribution (note the use of ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: the subreddit size threshold, published by bhauth on January 24, 2024 on LessWrong. Nobody goes there anymore. It's too crowded. Yogi Berra In the early days of the internet, people on Usenet complained about the influx of new users from AOL making it worse. I always thought the evolution of online communities with growth was an interesting and important topic. Do they really get worse with size? According to who? Why would that happen? What can be done about it? Today, Reddit has over 1 billion monthly active users. It's divided into smaller communities called subreddits, all using the same software. This provides an unprecedented amount of data on the dynamics of online communities. I haven't done a systematic study of every subreddit, but sometimes I read things on Reddit myself. I mainly do that by using a browser shortcut to see the weekly top posts of a particular subreddit, using the old site version. In doing that, I've gotten a decent idea of how particular subreddits differ, and I've noticed that very large subreddits tend to have lower quality than smaller ones. I'm not the only one; this has been widely noted. Naively, one might expect that the week's best posts from a larger group of people would be better, and that does seem to be the case up to a point - and then the trend reverses. At 100k users, the derivative of quality vs size is clearly negative. That raises the obvious question: why? Why would large subreddits be worse? Here are the possible reasons I've thought of. reasons for decline selection bias Maybe I'm selecting high-quality subreddits to read, and there are more small subreddits, so some of them will randomly be better. I certainly do select what subreddits I look at, but I don't think that's the reason here, because: I've seen changes in quality over time as subreddits grow. The variation seems mostly consistent across different ways of selecting subreddits to read. memes A common thing that relatively high-quality larger subreddits do is remove meme posts, which are mostly popular images with a few words added on them. I think the problem with those meme posts is that time spent on posts varies but every upvote is worth the same. Most people who see posts don't even vote on them, and there's some fraction of people who will see a meme, look at it for 2 seconds, upvote, and move on. That upvote is worth the same as an upvote from someone who spent 10 minutes reading an insightful essay. A similar problem happens with titles that confirm people's preconceptions. For example, if someone really hates Trump, and sees a title that implies "this shows Trump is bad", they might upvote without actually looking at the linked post. There have been a few attempts at mitigating this by making vote strength variable. Some sites have "claps" instead of "likes", which can be clicked multiple times. There are sites like LessWrong where users can make stronger votes by pressing the vote for a couple seconds. The problem I have with such systems is, while individual votes more accurately represent the voter's opinion, the result is a worse average of overall user views. For example, there might be a thread of 2 people arguing, and then 1 person strong-downvotes every post of the other person to make their argument look relatively better, and then the other person gets mad and does the same, and then those strong votes can outweigh votes from other people. new post visibility When you make a new post on a smaller subreddit, it goes directly to the front page, where ordinary users see it and vote on it. On a larger subreddit, new posts are only visible on a special "new" page, which only a small fraction of users visit. One uncommon thing TikTok did was showing new videos from creators with few followers to a hundred or so people. Videos that got some like...

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: the subreddit size threshold, published by bhauth on January 24, 2024 on LessWrong. Nobody goes there anymore. It's too crowded. Yogi Berra In the early days of the internet, people on Usenet complained about the influx of new users from AOL making it worse. I always thought the evolution of online communities with growth was an interesting and important topic. Do they really get worse with size? According to who? Why would that happen? What can be done about it? Today, Reddit has over 1 billion monthly active users. It's divided into smaller communities called subreddits, all using the same software. This provides an unprecedented amount of data on the dynamics of online communities. I haven't done a systematic study of every subreddit, but sometimes I read things on Reddit myself. I mainly do that by using a browser shortcut to see the weekly top posts of a particular subreddit, using the old site version. In doing that, I've gotten a decent idea of how particular subreddits differ, and I've noticed that very large subreddits tend to have lower quality than smaller ones. I'm not the only one; this has been widely noted. Naively, one might expect that the week's best posts from a larger group of people would be better, and that does seem to be the case up to a point - and then the trend reverses. At 100k users, the derivative of quality vs size is clearly negative. That raises the obvious question: why? Why would large subreddits be worse? Here are the possible reasons I've thought of. reasons for decline selection bias Maybe I'm selecting high-quality subreddits to read, and there are more small subreddits, so some of them will randomly be better. I certainly do select what subreddits I look at, but I don't think that's the reason here, because: I've seen changes in quality over time as subreddits grow. The variation seems mostly consistent across different ways of selecting subreddits to read. memes A common thing that relatively high-quality larger subreddits do is remove meme posts, which are mostly popular images with a few words added on them. I think the problem with those meme posts is that time spent on posts varies but every upvote is worth the same. Most people who see posts don't even vote on them, and there's some fraction of people who will see a meme, look at it for 2 seconds, upvote, and move on. That upvote is worth the same as an upvote from someone who spent 10 minutes reading an insightful essay. A similar problem happens with titles that confirm people's preconceptions. For example, if someone really hates Trump, and sees a title that implies "this shows Trump is bad", they might upvote without actually looking at the linked post. There have been a few attempts at mitigating this by making vote strength variable. Some sites have "claps" instead of "likes", which can be clicked multiple times. There are sites like LessWrong where users can make stronger votes by pressing the vote for a couple seconds. The problem I have with such systems is, while individual votes more accurately represent the voter's opinion, the result is a worse average of overall user views. For example, there might be a thread of 2 people arguing, and then 1 person strong-downvotes every post of the other person to make their argument look relatively better, and then the other person gets mad and does the same, and then those strong votes can outweigh votes from other people. new post visibility When you make a new post on a smaller subreddit, it goes directly to the front page, where ordinary users see it and vote on it. On a larger subreddit, new posts are only visible on a special "new" page, which only a small fraction of users visit. One uncommon thing TikTok did was showing new videos from creators with few followers to a hundred or so people. Videos that got some like...

Fluent Fiction - Mandarin Chinese: Lost in the Alleyways: A Journey of Love and Discovery Find the full episode transcript, vocabulary words, and more:fluentfiction.org/lost-in-the-alleyways-a-journey-of-love-and-discovery Story Transcript:Zh: 岁月的琴弦在北京的小巷里独奏，正所谓，大城市的生活也有大城市的困惑。在这样的一天，小巷子里出现了两位特殊的游客——张伟和李静。En: The strings of time played a solo in the alleyways of Beijing. It is true that living in a big city comes with its own perplexities. On such a day, two special tourists appeared in the alleyway – Zhang Wei and Li Jing.Zh: 换了个城市，他们起初犹如丢了水平仪的探险家，摸索着前行。无拘无束的寻找，在近乎大海捞针的巷子里，张伟和李静像是走进了魔法森林，分不清路。他们尝试了解每个巷口，每个转角，结果却是他们和北京的小巷子玩起了你追我赶的游戏。En: In this new city, they initially felt like explorers who had lost their compass, groping their way forward. In their unrestricted search, in the alleyways where finding a needle in a haystack seemed almost impossible, Zhang Wei and Li Jing found themselves walking into a magical forest, unable to distinguish the path. They tried to familiarize themselves with each lane, every corner, but instead ended up playing a game of cat and mouse with the alleyways of Beijing.Zh: 笑闹后，张伟突发奇想，他们决定交换身上的衣服，用亮瞎眼的撞色大衣和酷炫的皮夹克进行伪装。李静笑呵呵地看着这副模样：“张伟，你倒是挺适合穿裙子的。”En: After some laughter and teasing, Zhang Wei had a sudden idea. They decided to exchange their clothes, dressing up in eye-catching coats and cool leather jackets. Li Jing laughed, saying, "Zhang Wei, you actually look good in a skirt."Zh: 这城市的街头小吃摊 ubiquitous像星星点点的装点着夜晚的北京城。他们闻到了的烤鸭的香味，炸酱面的滋味，然后它们的注意力被一个小吃摊起的名字引了过去——那店叫“包子西”，戏谑的名头下竟然跟着一行小字写着“著名人物常来”。En: The street food stalls in the city adorned the night sky of Beijing like scattered stars. They caught a whiff of the aroma of roast duck, the flavor of fried sauce noodles, and their attention was drawn to a stall with an intriguing name – "Baozi West". Beneath the playful name, a small line of text read "frequented by famous people".Zh: “嘿，你看，咱们找到了名人！”张伟大笑。他们单纯地以为那是真的，便在摊子前喧闹起来，想像自己正在与名人互动，恬不知耻地引起了周围人疑惑的目光。En: "Hey, look, we've found celebrities!" Zhang Wei burst into laughter. Naively believing it to be true, they started making a commotion in front of the stall, imagining themselves interacting with famous people, shamelessly attracting the curious gazes of those around them.Zh: 经过这一通乌龙，他们明白，失去的不仅仅是方向，还有那种在熟悉环境中的安逸。在这即兴的小行当里被北京的复杂所戏弄，他们发现了更多生活的精彩。En: After this misunderstanding, they realized that they had lost not only their way but also the comfort of familiarity. Amidst the improvisation and complexity of Beijing, they discovered more of life's wonders.Zh: 最后，悄然回归的是两个人变的成熟，他们明白了一个道理，人走得再远，地荡得再早，只要心中有个热爱，身边有个陪伴，便可笑对人生。就像当时他们神气活现地说：“即使迷失在知识的海洋中，我们也可以幸福地挥舞双臂。”En: In the end, they quietly returned as two mature individuals. They understood one truth: no matter how far one travels or how early one explores, as long as there is love in their hearts and someone by their side, they can laugh at life. Just like they confidently said at that time, "Even if we get lost in the ocean of knowledge, we can happily wave our arms."Zh: 阳光下，两个动静分明的生动形象便这样存在，存在于这个豪宅丛生的大都市，存在于他们的生命中，用最简单的方式感受着大世界的温暖。En: Under the sunlight, these two distinct and lively figures existed – existing in this city filled with mansions and in their own lives, experiencing the warmth of the world in the simplest way. Vocabulary Words:The strings: 岁月的琴弦time: 时间solo: 独奏alleyways: 小巷Beijing: 北京living: 生活big city: 大城市perplexities: 困惑special tourists: 特殊的游客Zhang Wei: 张伟Li Jing: 李静new city: 换了个城市explorers: 探险家lost their compass: 丢了水平仪groping their way forward: 摸索着前行unrestricted search: 无拘无束的寻找needle in a haystack: 大海捞针magical forest: 魔法森林distinguish the path: 分不清路familiarize themselves: 了解cat and mouse: 你追我赶laughter and teasing: 笑闹exchange their clothes: 交换身上的衣服eye-catching coats: 撞色大衣cool leather jackets: 酷炫的皮夹克skirt: 裙子street food stalls: 街头小吃摊adorned the night sky: 装点着夜晚aroma of roast duck: 烤鸭的香味flavor of fried sauce noodles: 炸酱面的滋味

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PHOTO: 1940 Lithuania NO KNOWN RESTRICTIONS ON PUBLICATION. @BATCHELORSHOW BEAR MARKET: 1/4: Mysteries of the Early 21st Century Bull Market: 4/4:  Uneven Justice: The Plot to Sink Galleon, by Raj Rajaratnam https://www.amazon.com/Uneven-Justice-Plot-Sink-Galleon/dp/1637582811/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= Raj Rajaratnam, the respected founder of the iconic hedge fund Galleon Group, which managed $7 billion and employed 180 people in its heyday, chose to go to trial rather than concede to a false narrative concocted by ambitious prosecutors looking for a scapegoat for the 2008 financial crisis. Naively, perhaps, Rajaratnam had expected to get a fair hearing in court. As an immigrant who had achieved tremendous success in his adopted country, he trusted the system. He had not anticipated prosecutorial overreach—inspired by political ambition—FBI fabrications, judicial compliance, and lies told under oath by cooperating witnesses. In the end, Rajaratnam was convicted and sentenced to eleven years in prison. He served seven and a half. Meanwhile, not a single senior bank executive responsible for the financial crisis was even charged. Uneven Justice is the story of his bewildering and confounding prosecution by forces who, quite frankly, were looking for bigger game. When Rajaratnam refused to support the narrative that would make that happen, he and the Galleon Group became collateral damage. A cautionary tale with implications for us all, Uneven Justice is both a riveting page-turner and an eye-opening lesson in the vagaries of justice when an unscrupulous prosecutor is calling the shots.

PHOTO: 1949 Lithuania NO KNOWN RESTRICTIONS ON PUBLICATION. @BATCHELORSHOW BEAR MARKET: 2/4: Mysteries of the Early 21st Century Bull Market: 4/4:  Uneven Justice: The Plot to Sink Galleon, by Raj Rajaratnam https://www.amazon.com/Uneven-Justice-Plot-Sink-Galleon/dp/1637582811/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= Raj Rajaratnam, the respected founder of the iconic hedge fund Galleon Group, which managed $7 billion and employed 180 people in its heyday, chose to go to trial rather than concede to a false narrative concocted by ambitious prosecutors looking for a scapegoat for the 2008 financial crisis. Naively, perhaps, Rajaratnam had expected to get a fair hearing in court. As an immigrant who had achieved tremendous success in his adopted country, he trusted the system. He had not anticipated prosecutorial overreach—inspired by political ambition—FBI fabrications, judicial compliance, and lies told under oath by cooperating witnesses. In the end, Rajaratnam was convicted and sentenced to eleven years in prison. He served seven and a half. Meanwhile, not a single senior bank executive responsible for the financial crisis was even charged. Uneven Justice is the story of his bewildering and confounding prosecution by forces who, quite frankly, were looking for bigger game. When Rajaratnam refused to support the narrative that would make that happen, he and the Galleon Group became collateral damage. A cautionary tale with implications for us all, Uneven Justice is both a riveting page-turner and an eye-opening lesson in the vagaries of justice when an unscrupulous prosecutor is calling the shots.

PHOTO: 1949 Lithuania NO KNOWN RESTRICTIONS ON PUBLICATION. @BATCHELORSHOW BEAR MARKET: 3/4: Mysteries of the Early 21st Century Bull Market: 4/4:  Uneven Justice: The Plot to Sink Galleon, by Raj Rajaratnam https://www.amazon.com/Uneven-Justice-Plot-Sink-Galleon/dp/1637582811/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= Raj Rajaratnam, the respected founder of the iconic hedge fund Galleon Group, which managed $7 billion and employed 180 people in its heyday, chose to go to trial rather than concede to a false narrative concocted by ambitious prosecutors looking for a scapegoat for the 2008 financial crisis. Naively, perhaps, Rajaratnam had expected to get a fair hearing in court. As an immigrant who had achieved tremendous success in his adopted country, he trusted the system. He had not anticipated prosecutorial overreach—inspired by political ambition—FBI fabrications, judicial compliance, and lies told under oath by cooperating witnesses. In the end, Rajaratnam was convicted and sentenced to eleven years in prison. He served seven and a half. Meanwhile, not a single senior bank executive responsible for the financial crisis was even charged. Uneven Justice is the story of his bewildering and confounding prosecution by forces who, quite frankly, were looking for bigger game. When Rajaratnam refused to support the narrative that would make that happen, he and the Galleon Group became collateral damage. A cautionary tale with implications for us all, Uneven Justice is both a riveting page-turner and an eye-opening lesson in the vagaries of justice when an unscrupulous prosecutor is calling the shots.

PHOTO: 1947 Lithuania NO KNOWN RESTRICTIONS ON PUBLICATION. @BATCHELORSHOW BEAR MARKET: 4/4: Mysteries of the Early 21st Century Bull Market: 4/4:  Uneven Justice: The Plot to Sink Galleon, by Raj Rajaratnam https://www.amazon.com/Uneven-Justice-Plot-Sink-Galleon/dp/1637582811/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= Raj Rajaratnam, the respected founder of the iconic hedge fund Galleon Group, which managed $7 billion and employed 180 people in its heyday, chose to go to trial rather than concede to a false narrative concocted by ambitious prosecutors looking for a scapegoat for the 2008 financial crisis. Naively, perhaps, Rajaratnam had expected to get a fair hearing in court. As an immigrant who had achieved tremendous success in his adopted country, he trusted the system. He had not anticipated prosecutorial overreach—inspired by political ambition—FBI fabrications, judicial compliance, and lies told under oath by cooperating witnesses. In the end, Rajaratnam was convicted and sentenced to eleven years in prison. He served seven and a half. Meanwhile, not a single senior bank executive responsible for the financial crisis was even charged. Uneven Justice is the story of his bewildering and confounding prosecution by forces who, quite frankly, were looking for bigger game. When Rajaratnam refused to support the narrative that would make that happen, he and the Galleon Group became collateral damage. A cautionary tale with implications for us all, Uneven Justice is both a riveting page-turner and an eye-opening lesson in the vagaries of justice when an unscrupulous prosecutor is calling the shots.

The saguaro cactus is perhaps the most iconic symbol of our state. You can find it everywhere from coffee cups to murals to tattoos. People love saguaros but we know surprisingly little about them. The Desert Botanical Gardens in Phoenix and other researchers are trying to fix that. The first step is to count as many of the saguaros growing in the Valley as possible. "Naively, we call this the Saguaro Census because we thought we were going to be capable of counting every single saguaro in the city. We clearly overestimated our capabilities because the Phoenix Valley is huge," Tania Hernandez says. She works as a research scientist at the Desert Botanical Gardens. For help with this task, the Gardens are turning to the public for help. In this episode of Valley 101, in conjunction with The Lab, we're exploring two questions: why does the saguaro cactus only grow in the Sonoran Desert? And how many are there? Learn more about your ad choices. Visit megaphone.fm/adchoices

The saguaro cactus is perhaps the most iconic symbol of our state. You can find it everywhere from coffee cups to murals to tattoos. People love saguaros but we know surprisingly little about them. The Desert Botanical Gardens in Phoenix and other researchers are trying to fix that. The first step is to count as many of the saguaros growing in the Valley as possible. "Naively, we call this the Saguaro Census because we thought we were going to be capable of counting every single saguaro in the city. We clearly overestimated our capabilities because the Phoenix Valley is huge," Tania Hernandez says. She works as a research scientist at the Desert Botanical Gardens. For help with this task, the Gardens are turning to the public for help. In this episode of The Lab, in conjunction with Valley 101, we're exploring two questions: why does the saguaro cactus only grow in the Sonoran Desert? And how many are there? Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Why no major LLMs with memory?, published by Kaj Sotala on March 28, 2023 on LessWrong. One thing that I'm slightly puzzled by is that an obvious improvement to LLMs would be adding some kind of long-term memory that would allow them to retain more information than fits their context window. Naively, I would imagine that even just throwing some recurrent neural net layers in there would be better than nothing? But while I've seen LLM papers that talk about how they're multimodal or smarter than before, I don't recall seeing any widely-publicized model that would have extended the memory beyond the immediate context window, and that confuses me. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Fr. Ryan preached this homily on March 26, 2023 at St Basil the Great Catholic Church in Brecksville, OH. The Sunday readings are from Ez 37:12-14, 130:1-2, 3-4, 5-6, 7-8, Rom 8:8-11 & Jn 11:1-45.

What makes Jesus weep?

This podcast goes over the basics of unbacked SymInts. You might want to listen to this one before listening to https://pytorch-dev-podcast.simplecast.com/episodes/zero-one-specialization Some questions we answer (h/t from Gregory Chanan): - Are unbacked symints only for export?  Because otherwise I could just break / wait for the actual size.  But maybe I can save some retracing / graph breaks perf if I have them too?  So the correct statement is "primarily" for export?- Why am I looking into the broadcasting code at all?  Naively, I would expect the export graph to be just a list of ATen ops strung together.  Why do I recurse that far down?  Why can't I annotate DONT_TRACE_ME_BRO?- How does 0/1 specialization fit into this?  I understand we may want to 0/1 specialize in a dynamic shape regime in "eager" mode (is there a better term?), but that doesn't seem to matter for export?- So far we've mainly been talking about how to handle our own library code.  There is a worry about pushing complicated constraints downstream, similar to torchscript.  What constraints does this actually push?

Transcript: https://wearenotsaved.com/2023/01/28/the-optimal-dosage-of-war/ Previous to the invasion of Ukraine, a sense of pessimism seemed to be ubiquitous with respect to Europe. Since the invasion things seem far more optimistic. One might even say that there's a new vitality and unity. Naively one might expect war to do the opposite, but we have a funny way of stepping up to challenges and war is the biggest challenge of all. This unity is not limited to Europe, it's an issue that even Republicans and Democrats seem to agree on. The question is, can we get these benefits in the absence of war. If not, are we doomed to descend into an increasingly fractious political environment?

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: 200 COP in MI: Exploring Polysemanticity and Superposition, published by Neel Nanda on January 3, 2023 on The AI Alignment Forum. This is the fifth post in a sequence called 200 Concrete Open Problems in Mechanistic Interpretability. Start here, then read in any order. If you want to learn the basics before you think about open problems, check out my post on getting started. I'll make another post every 1-2 days, giving a new category of open problems. If you want to read ahead, check out the draft sequence here! Motivating paper: Toy Models of Superposition, Softmax Linear Units Background If you're familiar with polysemanticity and superposition, skip to Motivation or Problems. Neural networks are very high dimensional objects, in both their parameters and their activations. One of the key challenges in Mechanistic Interpretability is to somehow resolve the curse of dimensionality, and to break them down into lower dimensional objects that can be understood (semi-)independently. Our current best understanding of models is that, internally, they compute features: specific properties of the input, like "this token is a verb" or "this is a number that describes a group of people" or "this part of the image represents a car wheel". That early in the model there are simpler features, are later used to compute more complex features by being connected up in a circuit (example shown above (source)). Further, our guess is that features correspond to directions in activation space. That is, for any feature that the model represents, there is some vector corresponding to it. And if we dot product the model's activations with that vector, we get out a number representing whether that feature is present.(these are known as decomposable, linear representations) This is an extremely useful thing to be true about a model! An even more helpful thing to be true would be if neurons correspond to features (ie the output of an activation function like ReLU). Naively, this is natural for the model to do, because a non-linearity like ReLU acts element-wise - each neuron's activation is computed independently (this is an example of a privileged basis). Concretely, if a neuron can represent feature A or feature B, then that neuron will fire differently for feature A and NOT feature B, vs feature A and feature B, meaning that the presence of B interferes with the ability to compute A. But if each feature is its own neuron we're fine! If features correspond to neurons, we're playing interpretability on easy mode - we can focus on just figuring out which feature corresponds to each neuron. In theory we could even show that a feature is not present by verifying that it's not present in each neuron! However, reality is not as nice as this convenient story. A countervailing force is the phenomena of superposition. Superposition is when a network represents more features than it has dimensions, and squashes them all into a lower dimensional space. You can think of superposition as the model simulating a larger model. Anthropic's Toy Models of Superposition paper is a great exploration of this. They build a toy model that learns to use superposition (notably different from a toy language model!). The model starts with a bunch of independently varying features, needs to compress these to a low dimensional space, and then is trained to recover each feature from the compressed mess. And it turns out that it does learn to use superposition! Specifically, it makes sense to use superposition for sufficiently rare (sparse) features, if we give it non-linearities to clean up interference. Further, the use of superposition can be modelled as a trade-off between the costs of interference, and the benefits of representing more features. And digging further into their toy models, they find all kinds of fascin...

Photo: No known restrictions on publication. @Batchelorshow 1/4: Mysteries of the Early 21st Century Bull Market: 1/4:  Uneven Justice: The Plot to Sink Galleon, by Raj Rajaratnam https://www.amazon.com/Uneven-Justice-Plot-Sink-Galleon/dp/1637582811/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= Raj Rajaratnam, the respected founder of the iconic hedge fund Galleon Group, which managed $7 billion and employed 180 people in its heyday, chose to go to trial rather than concede to a false narrative concocted by ambitious prosecutors looking for a scapegoat for the 2008 financial crisis. Naively, perhaps, Rajaratnam had expected to get a fair hearing in court. As an immigrant who had achieved tremendous success in his adopted country, he trusted the system. He had not anticipated prosecutorial overreach—inspired by political ambition—FBI fabrications, judicial compliance, and lies told under oath by cooperating witnesses. In the end, Rajaratnam was convicted and sentenced to eleven years in prison. He served seven and a half. Meanwhile, not a single senior bank executive responsible for the financial crisis was even charged. Uneven Justice is the story of his bewildering and confounding prosecution by forces who, quite frankly, were looking for bigger game. When Rajaratnam refused to support the narrative that would make that happen, he and the Galleon Group became collateral damage. A cautionary tale with implications for us all, Uneven Justice is both a riveting page-turner and an eye-opening lesson in the vagaries of justice when an unscrupulous prosecutor is calling the shots.

Photo: No known restrictions on publication. @Batchelorshow 2/4: Mysteries of the Early 21st Century Bull Market: 2/4:  Uneven Justice: The Plot to Sink Galleon, by Raj Rajaratnam https://www.amazon.com/Uneven-Justice-Plot-Sink-Galleon/dp/1637582811/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= Raj Rajaratnam, the respected founder of the iconic hedge fund Galleon Group, which managed $7 billion and employed 180 people in its heyday, chose to go to trial rather than concede to a false narrative concocted by ambitious prosecutors looking for a scapegoat for the 2008 financial crisis. Naively, perhaps, Rajaratnam had expected to get a fair hearing in court. As an immigrant who had achieved tremendous success in his adopted country, he trusted the system. He had not anticipated prosecutorial overreach—inspired by political ambition—FBI fabrications, judicial compliance, and lies told under oath by cooperating witnesses. In the end, Rajaratnam was convicted and sentenced to eleven years in prison. He served seven and a half. Meanwhile, not a single senior bank executive responsible for the financial crisis was even charged. Uneven Justice is the story of his bewildering and confounding prosecution by forces who, quite frankly, were looking for bigger game. When Rajaratnam refused to support the narrative that would make that happen, he and the Galleon Group became collateral damage. A cautionary tale with implications for us all, Uneven Justice is both a riveting page-turner and an eye-opening lesson in the vagaries of justice when an unscrupulous prosecutor is calling the shots.

Photo: No known restrictions on publication. @Batchelorshow 3/4: Mysteries of the Early 21st Century Bull Market: 3/4:  Uneven Justice: The Plot to Sink Galleon, by Raj Rajaratnam https://www.amazon.com/Uneven-Justice-Plot-Sink-Galleon/dp/1637582811/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= Raj Rajaratnam, the respected founder of the iconic hedge fund Galleon Group, which managed $7 billion and employed 180 people in its heyday, chose to go to trial rather than concede to a false narrative concocted by ambitious prosecutors looking for a scapegoat for the 2008 financial crisis. Naively, perhaps, Rajaratnam had expected to get a fair hearing in court. As an immigrant who had achieved tremendous success in his adopted country, he trusted the system. He had not anticipated prosecutorial overreach—inspired by political ambition—FBI fabrications, judicial compliance, and lies told under oath by cooperating witnesses. In the end, Rajaratnam was convicted and sentenced to eleven years in prison. He served seven and a half. Meanwhile, not a single senior bank executive responsible for the financial crisis was even charged. Uneven Justice is the story of his bewildering and confounding prosecution by forces who, quite frankly, were looking for bigger game. When Rajaratnam refused to support the narrative that would make that happen, he and the Galleon Group became collateral damage. A cautionary tale with implications for us all, Uneven Justice is both a riveting page-turner and an eye-opening lesson in the vagaries of justice when an unscrupulous prosecutor is calling the shots.

Photo: No known restrictions on publication. @Batchelorshow 4/4: Mysteries of the Early 21st Century Bull Market: 4/4:  Uneven Justice: The Plot to Sink Galleon, by Raj Rajaratnam https://www.amazon.com/Uneven-Justice-Plot-Sink-Galleon/dp/1637582811/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= Raj Rajaratnam, the respected founder of the iconic hedge fund Galleon Group, which managed $7 billion and employed 180 people in its heyday, chose to go to trial rather than concede to a false narrative concocted by ambitious prosecutors looking for a scapegoat for the 2008 financial crisis. Naively, perhaps, Rajaratnam had expected to get a fair hearing in court. As an immigrant who had achieved tremendous success in his adopted country, he trusted the system. He had not anticipated prosecutorial overreach—inspired by political ambition—FBI fabrications, judicial compliance, and lies told under oath by cooperating witnesses. In the end, Rajaratnam was convicted and sentenced to eleven years in prison. He served seven and a half. Meanwhile, not a single senior bank executive responsible for the financial crisis was even charged. Uneven Justice is the story of his bewildering and confounding prosecution by forces who, quite frankly, were looking for bigger game. When Rajaratnam refused to support the narrative that would make that happen, he and the Galleon Group became collateral damage. A cautionary tale with implications for us all, Uneven Justice is both a riveting page-turner and an eye-opening lesson in the vagaries of justice when an unscrupulous prosecutor is calling the shots.

We took a trip to visit Jamie and Lucy who run Banton Frameworks to have a long-form conversation about their business and what it's like to be a small business. They handmake spectacles and sunglasses frames from their workshop by Banton Loch, just outside Kilsyth, near Glasgow.  Extract from Edition Three, We Are Makers.Why do what you do?"It stems from Lucy's project. Behind every business there's some degree of a problem being solved – in Lucy's case, a dissatisfaction with what was available from her optician at the time. Naively, we thought that we could do it better. We can now, but it took us almost a decade to get here. The result is better and, to us, much more personal to the wearer than mass manufactured frames made in their thousands. Eyewear is almost like perfume or aftershave: apply the same perfume to 10 different people and it will smell 10 different ways. I think glasses are very much like that. Glasses are such a crucial implement – they help people see – but they are also a very personal and intimate item, as eyewear is a huge part of the wearer's identity. Glasses are quite simple objects – figures of eight with sticks on either side – but when you wear that frame for up to 16 hours a day, it represents you. I think it's important to have eyewear that is made well. I don't see it as any different to having a tailored suit, a smart pair of brogues or a nice handbag.Frames can be very mass produced and almost loveless: you go into an optician and see rows on the wall and they're basically just lens holders. Some manufacturers who we admire make very nice frames, but generally, mainstream optical brands lack the magic that we strive to produce. So why do we do it? We do it because eyewear is a balance of fashion, identity and medical aid. We think that's a pretty important range of roles for a single item." - Jamie BartlettThis video podcast was filmed in Banton Frameworks workspace in Kilsyth, Glasgow.Useful links:We Are Makers Insta: @weare_makersWebsite: https://wearemakers.shop/Banton Frameworks Insta: @bantonframeworksWebsite: https://www.bantonframeworks.co.uk/The discussions and opinions in the podcast are not necessarily the opinions of us (We Are Makers).Like this podcast and want to watch it? Subscribe to our YouTube Channel! Or, Like to read? Discover our biannual publication that includes stories of makers worldwide! (We ship worldwide too!)

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: We may be able to see sharp left turns coming, published by Ethan Perez on September 3, 2022 on The AI Alignment Forum. There's a lot of discourse around abrupt generalization in models, most notably the "sharp left turn." Most recently, Wei et al. 2022 claim that many abilities suddenly emerge at certain model sizes. These findings are obviously relevant for alignment; models may suddenly develop the capacity for e.g. deception, situational awareness, or power-seeking, in which case we won't get warning shots or a chance to practice alignment. In contrast, prior work has also found "scaling laws" or predictable improvements in performance via scaling model size, data size, and compute, on a wide variety of domains. Such domains include transfer learning to generative modeling (on images, video, multimodal, and math) and reinforcement learning. What's with the discrepancy? One important point is the metric that people are using to measure capabilities. In the BIG Bench paper (Figure 7b), the authors find 7 tasks that exhibit "sharp upwards turn" at a certain model size. Naively, the above results are evidence for sharp left turns, and the above tasks seem like some of the best evidence we have for sharp left turns. However, the authors plot the results on the above tasks in terms of per-character log-likelihood of answer: The authors actually observe smooth increases in answer log-likelihood, even for tasks which showed emergent behavior according to the natural performance metric for the task (e.g. accuracy). These results are evidence that we can predict that emergent behaviors will occur in the future before models are actually "capable" of those behaviors. In particular, these results suggest that we may be able to predict power-seeking, situational awareness, etc. in future models by evaluating those behaviors in terms of log-likelihood. We may even be able to experiment on interventions to mitigate power-seeking, situational awareness, etc. before they become real problems that show up in language model -generated text. Clarification: I think we can predict whether or not a sharp left turn towards deception/misalignment will occur rather than exactly when. In particular, I think we should look at the direction of the trend (increases vs. decreases in log-likelihood) as signal about whether or not some scary behavior will eventually emerge. If the log likelihood of some specific scary behavior increases, that's a bad sign and gives us some evidence it will be a problem in the future. I mainly see scaling laws here as a tool for understanding and evaluating which of the hypothesized misalignment-relevant behaviors will show up in the future. The scaling laws are useful signal for (1) convincing ML researchers to worry about scaling up further because of alignment concerns (before we see them in model behaviors/outputs) and (2) guiding alignment researchers with some empirical evidence about which alignment failures are likely/unlikely to show up after scaling at some point. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

The Field Guide to Particle Physics : Season 3https://pasayten.org/the-field-guide-to-particle-physics©2022 The Pasayten Institute cc by-sa-4.0The definitive resource for all data in particle physics is the Particle Data Group: https://pdg.lbl.gov.Also check out the links embedded this description. Or also check out those same links at:https://pasayten.org/the-field-guide-to-particle-physics/antineutrinoThe Pasayten Institute is on a mission to build and share physics knowledge, without barriers! Get in touch.The AntineutrinoThe neutrino is a curious particle. As fundamental as the electron or the muon, but rarely interact with other particles. This makes the study of these neutrini quite challenging. But also quite interesting.Are there antineutrini? Yes, surely. But, a better question is what are antineutrini?Antiparticles with an electric charge are easier to identify. Positrons and electrons have opposite charges and behave oppositely in most respects. Photons and neutral pions do not have any electric charge. They are their own antiparticle partners! But this isn't always the case with neutral particles.  As we have antineutrons and two distinct kinds of neutral kaons: the K0 and K0bar which are antiparticles of each other.Neutrini - those smallest of massive matter particles in the Standard Model - are electrically neutral. So it is natural to ask: are they their own antiparticle? Or are there distinct antineutrini? And importantly, how can we tell the difference?The short answer is, we don't know yet. End of story. But the short answer is boring.Neutrini are famously shy and interact only via the weak nuclear force - and gravity - so detecting them so detecting them is no small task.So without further ado, let's go ahead with the long answer.Beta DecayNeutrons decay to protons by emitting an electron. This is usually called beta decay, and is mediated by the W- boson. Other nuclei experience it as well.  Detailed studies of beta decay suggest that the neutron should decay into two particles rather than one. That second particle was need to make sure that energy, momentum and spin angular momentum was conserved. As it should be.The neutrino - the small neutral one - was discovered nearly 26 years after their proposal.Now, electric charge is conserved in beta decay. The uncharged neutron decays to a positively charged proton and a negatively charged electron and a neutrino. The neutrino also has no electric charge, but carries away some of the energy and some of the momentum.So far as we can tell, energy, momentum and spin like electric charge, is always conserved. Such conservation laws are useful organizing principles for understanding the laws of particle physics. Some might argue they are foundational.Another thing that seems to be conserved in nature - usually anyway - is the number of leptons in the universe. There are actually quantum effects that can change the number of leptons, but in ordinary decays - like beta decay -  they seem to conserve the number of leptons.Neutrini - like electrons, muons and taus - are leptons. Naively you might think that beta decay creates two leptons: a neutrino and an electron. The thing is, the neutron actually emits an electron and an antielectron neutrino. Like electric charge, antineutrinos count as minus one lepton.The math also works in reverse. If a nucleus absorbs an electron - which sometimes happens in certain isotopes of Vanadium, Nickel and Aluminum - it will convert a proton to a neutron, and spit out a regular neutrino. Conserving the number of leptons.Now, before your eyes glaze over, I know. Talking about weird conservation rules like lepton number is tricky, because it seems like a bunch of silly rules the details quickly spiral out of control. Neutrino physics is nothing if not complicated.So let's talk more about some of the reactions.Flavors of AntineutriniEach electrically charged lepton: the electron, the muon and the tau, has it's own flavor of neutrino. There's an electron neutrino. A muon neutrino and a tau neutrino. Each electrically charged antilepton also has its antineutrino partner: antielectron neutrino. anti muon neutrino. Anti tau neutrino.When a muon decays into an electron, it actually emits three particles: the electron, the antielectron neutrino and a regular muon neutrino.Given that there are so many cosmogenic muons around us, muon neutrinos  - and anti electron neutrinos - are also fairly ubiquitous here on Earth.And of course you might remember the famous experimental result that neutrinos can change their flavor as they move. So neutrinos flavors can get all mixed up, just like antineutrino flavors can get all mixed up. But do neutrini get mixed up with antineutrini?They would if they were the same particle, wouldn't they? Let's think about it another way. In terms of annihilation. Do Neutrini and Antineutrini annihilate each other?When an electron and positron collide, a pair of photons usually comes out. The antiparticle partners annihilate into pure electromagnetic energy. What do you suppose happens when a neutrino collides with an antineutrino?A neutrino and an antineutrino - assuming it exists - would not annihilate to form photons. They have no electromagnetic charge and therefore no chance. They could potentially exchange a Z-boson, or even a Higgs Boson! Although the likelihood of the latter is proportional to the mass of the neutrini involved - and so very, very small.If a neutrino-antineutrino pair of the same flavor smashed against each other violently enough, a pair of Z-bosons could come out. And.. if the neutrino were its own antiparticle partner, well, then any two neutrini of the same flavor could do this!Such an annihilation of two regular electron neutrini would be strong evidence that the neutrino is its own antiparticle. But what a challenging experiment that would be! Where would you get dedicated, high energy neutrino beams?Instead, physicists are looking for a slightly easier measurement with a clear signature: neutrinoless double beta decay.Rarely, nuclei emit two electrons at time, converting their atomic number by changing two neutrons into two protons simultaneously. Germanium-76 and Xenon-136 are just a few of the many nuclei that undergo double beta decay.If neutrini are their own antiparticle partners, it's possible that those two electrons could come out, and the pair of neutrini would annihilate each other just as the decay happens.If no neutrini are produced, conservation of momentum suggests that the electrons will be emitted in opposite directions, and conservation of energy suggests that their energy should sum exactly to difference in atomic mass of the parent and child nucleus.To date, all double beta decays observed have been consistent with the emission of neutrini. Studies from experiments like EXO, NEMO, GERDA have shown that it takes nuclei over 10,000 times longer to decay without neutrini. But of course if it cannot happen  - if the neutrino is NOT its own antiparticle in any capacity - then it never will.But the search is one. The CUORE and KamLand-Zen experiments are still taking data and nEXO is still be planned.Neutrino Masses and the SeesawFinally, we know that neutrini have tiny masses. Super tiny. A million times smaller than the electron, at least.If neutrini are their own antiparticle partners, they have a special kind of mass called the “Majorana” mass. If the antineutrini are distinct particles, then their mass might well be a “Dirac” mass - which is the usual kind mass that leptons pick up in the standard model.  This distinction is of course reductive. There is no reason why they couldn't have both a Majorana mass and a Dirac mass.In fact, if they do have both, then there is a very natural explanation for why the neutrino mass is so small compared with all the other fundamental particles.If the Majorana mass is really, really big, say associated to some complicated physics we don't yet understand, and the Dirac mass is “normal” by comparison to other particles, like a few thousand electron volts, the combination of those two masses we experience actually appears as a ratio of the two, rather than the sum. This is the famous seesaw mechanism. Neutrini are the only electrically neutral, elementary fermions known to science. Quarks all have electric charges. Electrons, muons and taus all do too. It is perhaps no surprise that neutrino physics is uniquely complicated. And if there's one thing particle physics enjoys, it's being complicated.©2022 The Pasayten Institute cc by-sa-4.0

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Quantilizers and Generative Models, published by Adam Jermyn on July 18, 2022 on The AI Alignment Forum. Thanks to Evan Hubinger for discussions about quantilizers, and to James Lucassen for discussions about conditioned generative models. Many of these ideas are discussed in Jessica Taylor's Quantilizers: A Safer Alternative to Maximizers for Limited Optimization: this post just expands on a particular thread of ideas in that paper. Throughout I'll refer to sections of the paper. I have some remaining confusion about the “targeted impact” section, and would appreciate clarifications/corrections! Abstract This post explores the relationship between quantilizers and generative models. My main takeaways are: A natural way to build a quantilizer is by sampling from an appropriately-conditioned generative model. Unfortunately quantilizing doesn't seem to confer much advantage over the underlying generative model: to the extent that a quantilizer is more powerful than a generative model, it's more dangerous, and vice versa. Quantilizing is pretty computationally expensive relative to the advantage it brings, making it unclear if this is a competitive approach even if it conferred a net safety advantage at fixed power. Definitions I'll follow the setup in “Quantilizers: A Safer Alternative to Maximizers for Limited Optimization” (Section 1, Definition 1) and say that a quantilizer is a model with: A base distribution γ, which describes a set of outputs we think are “normal” or “unlikely to lead to doom”. A quantile q∈[0,1]. An ordering over outputs (e.g. a utility or loss function). A quantilizer returns a random output from the top q fraction of outputs from γ according to the ordering. So for instance a 0-quantilizer is a utility maximizer, a 0.1-quantilizer returns outputs in the best 10%, etc. Where does the base distribution come from? One challenge with quantilizers is that they require a base distribution γ that we trust. That is, we want to be able to sample from γ many times and never hit bad outputs. Unfortunately, we don't know how to explicitly write this distribution down. A natural alternative to explicit construction is to train a generative model on the distribution of actions a humans takes (Section 3.1). Then for any given task γ can be written as This seems quite doable. Train GPT-N on text written by humans, then prompt it with the task: Prompt: You are a good person who wants to accomplish . What follows is your description of how you intend to do this. The output will probably be text describing how a good person would accomplish that task! Unfortunately we might get unlucky and sample a person who isn't so good (but thinks of themselves as good). That still satisfies the prompt. Robustly getting the agent we want seems hard, but might be possible in the limit where we ask for agents that are generally plausible in the training distribution of the generative model. Power vs Safety Armed with our base distribution, we can start quantilizing. Naively we can do this by sampling q−1 times from γtask, then returning the top output. This produces a tradeoff between capabilities and safety. Single Sampler In the limit q→1 we're just returning actions a good human would take. That is, our quantilizer just samples once and returns that sample as output, so it's no more powerful than a random good person. Such a model is really unlikely to take over the world, but it's also really unlikely to solve quantum chemistry problems. This is because both of these outcomes unlikely according to the base distribution. We can improve things a bit by conditioning on being an unusually capable person, e.g. Prompt: You are a good and extremely capable person who wants to accomplish . What follows is your description of how you intend to do this. Now we can get top-end human ...

Dr. Neil Ray is the Founder and CEO of Raydiant Oximetry, a medical device company that has developed a safe and non-invasive technology that directly monitors babies' oxygenation during childbirth. This prevents unnecessary C-sections for women during childbirth, while keeping the newborn safe. Neil is a clinical anaesthesiologist by training, who gave up his medical practice to build this startup. We talk about the underlying technology, the challenges Nail had to face while transitioning to the role of a founder; naive assumptions he made; innovation in women's health; Interactions with investors and fundraising; challenges that entrepreneurs in the medical device space face trying to get pilots at hospitals; go to market strategy and lessons he learnt being a physician entrepreneur. Shownotes: - https://www.raydiantoximetry.com - Raydiant Oximetry uses the principles of pulse oximetry to measure fetal oxygen levels - Uses light to measure the differences in the color of oxygenated vs deoxygenated blood - Built the solution based on his experience being a clinical anaesthesiologist - Very little innovation in women's healthcare - Naively (at the beginning) focused on the patients rather than thinking about the market size - Concept of market size is not hard. It just needs to be taught - Realize that selling is a big part of becoming an entrepreneur (always selling) - Making decisions with incomplete information - Colleagues were not too supportive during the transition to becoming an entrepreneur - Giving up a full time paying job as a doctor and becoming a founder is hard - Breaking down silos in medical practices and bringing other experts to the table - Investors want to see how you're putting your skin in the game before they invest- Neil gave up his job and invested from his savings to build the prototype! - Lessons learnt from interacting with investors as a physician - Angel MD, MD Angels, Mass Medical Angels, D Capital, Avestria Ventures, etc. - CITRIS Foundry helped tell a good story that helped them get into Fogarty Institute - 90% of the companies from Fogarty institute have been successful - Network effects at play - Working with hospitals to test prototypes is hard- Need a champion - Work on something transformative- not on incremental innovations - Sales cycle of medical devices are long at hospitals- need big, established players - As a small player, focus on clinical data and find someone else to sell - NSF I-Corps helped identify/understand stakeholders - It's easy to get into echo chambers. You need to become intellectually honest - Investors tend to say that there is no need for a product like this, but those in the space realize how important the innovation was. - Advice to physicians planning to launch a startup

Photo: Logo for Galleon Group Rajakumaran Rajaratnam is a Sri Lankan-American former hedge fund manager and founder of the Galleon Group, a New York-based hedge fund management firm. On October 16, 2009, he was arrested by the FBI for insider trading, which also caused the Galleon Group to fold. 1/4: Uneven Justice: The Plot to Sink Galleon, by Raj Rajaratnam  Hardcover – December 14, 2021 https://www.amazon.com/Uneven-Justice-Plot-Sink-Galleon/dp/1637582811/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= Raj Rajaratnam, the respected founder of the iconic hedge fund Galleon Group, which managed $7 billion and employed 180 people in its heyday, chose to go to trial rather than concede to a false narrative concocted by ambitious prosecutors looking for a scapegoat for the 2008 financial crisis. Naively, perhaps, Rajaratnam had expected to get a fair hearing in court. As an immigrant who had achieved tremendous success in his adopted country, he trusted the system. He had not anticipated prosecutorial overreach—inspired by political ambition—FBI fabrications, judicial compliance, and lies told under oath by cooperating witnesses. In the end, Rajaratnam was convicted and sentenced to eleven years in prison. He served seven and a half.

Photo: 4/4: Uneven Justice: The Plot to Sink Galleon, by Raj Rajaratnam  Hardcover – December 14, 2021 https://www.amazon.com/Uneven-Justice-Plot-Sink-Galleon/dp/1637582811/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= Raj Rajaratnam, the respected founder of the iconic hedge fund Galleon Group, which managed $7 billion and employed 180 people in its heyday, chose to go to trial rather than concede to a false narrative concocted by ambitious prosecutors looking for a scapegoat for the 2008 financial crisis. Naively, perhaps, Rajaratnam had expected to get a fair hearing in court. As an immigrant who had achieved tremendous success in his adopted country, he trusted the system. He had not anticipated prosecutorial overreach—inspired by political ambition—FBI fabrications, judicial compliance, and lies told under oath by cooperating witnesses. In the end, Rajaratnam 

Photo:  Fort Hammenhiel (Ceylon) 3/4: Uneven Justice: The Plot to Sink Galleon, by Raj Rajaratnam  Hardcover – December 14, 2021 https://www.amazon.com/Uneven-Justice-Plot-Sink-Galleon/dp/1637582811/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= Raj Rajaratnam, the respected founder of the iconic hedge fund Galleon Group, which managed $7 billion and employed 180 people in its heyday, chose to go to trial rather than concede to a false narrative concocted by ambitious prosecutors looking for a scapegoat for the 2008 financial crisis. Naively, perhaps, Rajaratnam had expected to get a fair hearing in court. As an immigrant who had achieved tremendous success in his adopted country, he trusted the system. He had not anticipated prosecutorial overreach—inspired by political ambition—FBI fabrications, judicial compliance, and lies told under oath by cooperating witnesses. In the end, Rajaratnam was convicted and sentenced to eleven years in prison. He served seven and a half.

Photo: 2/4: Uneven Justice: The Plot to Sink Galleon, by Raj Rajaratnam  Hardcover – December 14, 2021 https://www.amazon.com/Uneven-Justice-Plot-Sink-Galleon/dp/1637582811/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr= Raj Rajaratnam, the respected founder of the iconic hedge fund Galleon Group, which managed $7 billion and employed 180 people in its heyday, chose to go to trial rather than concede to a false narrative concocted by ambitious prosecutors looking for a scapegoat for the 2008 financial crisis. Naively, perhaps, Rajaratnam had expected to get a fair hearing in court. As an immigrant who had achieved tremendous success in his adopted country, he trusted the system. He had not anticipated prosecutorial overreach—inspired by political ambition—FBI fabrications, judicial compliance, and lies told under oath by cooperating witnesses. In the end, Rajaratnam was convicted and sentenced to eleven years in prison. He served seven and a half.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: The biggest risk of free-spending EA is not optics or epistemics, but grift, published by Ben Kuhn on May 14, 2022 on The Effective Altruism Forum. In EA and the current funding situation, Will MacAskill tried to enumerate the "risks of commission" that large amounts of EA funding exposed the community to (i.e., ways that extra funding could actually harm EA's impact). Free-spending EA might be a big problem for optics and epistemics raised similar concerns. The risks described in these posts largely involve either money looking bad to outsiders, or money causing well-intentioned people to think poorly despite their best effort. I think this misses what I'd guess is the biggest risk: the risk that large amounts of funding will attract people who aren't making an effort at all because they don't share EA values, but instead see it as a source of easy money or a target of grift. Naively, you might think that it's not that much of a problem if (say) 50% of EA funding is eaten by grift—that's only a factor of 2 decrease in effectiveness, which isn't that big in a world of power-law distributions. But in reality, grifters are incentivized to accumulate power and sabotage the movement's overall ability to process information, and many non-grifters find participating in high-grift environments unpleasant and leave. So the stable equilibrium (absent countermeasures) is closer to 100% grift. The basic mental model This is something I've thought about, and talked to people about, a fair amount because an analogous grift problem exists in successful organizations, and I would like to help the one I work at avoid this fate. In addition to those conversations, a lot of what I go over here is based on the book Moral Mazes, and I'd recommend reading it (or Zvi Mowshowitz's review/elaboration, which IMO is hyperbolic but directionally correct) for elaboration. At some point in their growth, most large organizations become extremely ineffective at achieving their goals. If you look for the root cause of individual instances of inefficiency and sclerosis in these orgs, it's very frequently that some manager, or group of managers, was "misaligned" from the overall organization, in that they were trying to do what was best for themselves rather than for the org as a whole, and in fact often actively sabotaging the org to improve their own prospects. The stable equilibrium for these orgs is to be composed almost entirely of misaligned managers, because: Well-aligned managers prioritize the org's values over their own ascent up the hierarchy (by definition), so will be out-advanced by misaligned managers who prioritize their own ascent above all. Misaligned managers will attempt to sabotage and oust well-aligned managers because their values are harder to predict, so they're more likely to do surprising or dangerous things. Most managers get most of their information from their direct reports, who can sabotage info flows if it would make them look bad. So even if a well-aligned manager has the power to oust a misaligned (e.g.) direct report, they may not realize there's a problem. For example, a friend described a group inside a name-brand company he worked at that was considered by almost every individual contributor to be extremely incompetent and impossible to collaborate with, largely as a result of poor leadership by the manager. The problem was so bad that when the manager was up for promotion, a number of senior people from outside the group signed a memo to the decision-maker saying that approving the promotion would be a disaster for the company. The manager's promotion was denied that cycle, but approved in the next promotion cycle. In this case, even despite the warning sign of strong opposition from people elsewhere in the company, the promotion decision-maker was fed enough b...

https://www.metaculus.com/notebooks/9885/action-ontologies-computer-ontologies/ The following essay is by Jacob Falkovich who writes at Putanumonit.com The mystery of perception Out in the universe, there are merely atoms¹ and the void. On the table in front of you, there's a ripe tomato. Inside your skull is a brain, a collection of neurons that have no direct access to either atoms or tomatoes — only the electrochemical state of some other neurons. And yet your brain is able to perceive a tomato and various qualities of it: red, round, three-dimensional, real. On the common how-it-seems view of perception, there is no particular mystery to this. In this view, light from the tomato hits your eyes and is decoded “bottom-up” in your brain into simple features such as color, shape, and size, which are then combined into complex perceptions such as “tomato.” This view is intuitively appealing: Whenever we perceive a tomato we find the actual tomato there; thus we believe the tomato to be the sole and sufficient cause of the perception. A closer look begins to challenge this intuition. You may see a tomato up close or far away, at different angles, partially obscured, in dim light, etc. The perception of it as being red, round, and a few inches across doesn't change even though the light hitting your retina is completely different in each case: different angles of your visual field, different wavelengths, etc.  Take color for example. Naively, the perception of color is the detection of wavelengths of light, and yet you perceive the same color from green light (530 nm) as you do from a mix of blue (470 nm) and yellow (570 nm). A white piece of paper will appear white in your perception even though it actually reflects the wavelengths of the light around it: blue under a clear sky, green if held close to grass, orange by candle light. The strawberries in the image below appear red even though there isn't a single red-hued pixel in it. Wherever the perception of color is coming from, it is certainly not the mere bottom-up decoding of light wavelengths.

In this week's Failure Friday segment, we hear from an Australian who owns an ecommerce business offering mindful toys and gifts. Following a rebrand, she pays Instagram influencers to create interest—but mostly they just took her money.   Side Hustle School features a new episode EVERY DAY, featuring detailed case studies of people who earn extra money without quitting their job. This year, the show includes free guided lessons and listener Q&A several days each week. Show notes: SideHustleSchool.com Email: team@sidehustleschool.com Be on the show: SideHustleSchool.com/questions Connect on Twitter: @chrisguillebeau Connect on Instagram: @193countries Visit Chris's main site: ChrisGuillebeau.com If you're enjoying the show, please pass it along! It's free and has been published every single day since January 1, 2017. We're also very grateful for your five-star ratings—it shows that people are listening and looking forward to new episodes.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Towards a better circuit prior: Improving on ELK state-of-the-art, published by Evan Hubinger on March 29, 2022 on The AI Alignment Forum. This post is the result of joint work with Kate Woolverton. Thanks to Paul Christiano for useful comments and feedback. The basic circuit prior setup We'll start with the basic setup that we're trying to improve upon, which is trying to solve ELK via the use of a Boolean circuit size prior. Previously, I summarized Paul, Mark, and Ajeya's argument for why this might work as follows: As in the ELK report, there is a plausible argument for why there exists a speed prior that would prefer the direct translator to the human imitator. Naively, the problem with the speed prior here is that the computation required for the human imitator is proportional to the size of the human's Bayes net, whereas the computation required for the direct translator is proportional to the size of the model's Bayes net—and in the superhuman limit we should expect the latter to be substantially larger than the former. The argument in the ELK report, however, is that while this argument is valid in the limit, there's reason to believe it might be invalid for all the finite cases that we care about. That's because perfect inference in either Bayes net, and thus perfect loss, shouldn't be possible in any finite case. Thus, the performance of the ontology mapping function, and thus its loss, should be proportional to how much computation it puts into its inference task—for which the direct translator has a big advantage, since it gets to reuse the computation performed by the model. The obvious response here, and the response that is given in the ELK report, is that the above argument is very fragile—it relies on inference in the human's Bayes net being too hard to always get right on the training distribution, which is a strong assumption both about the difficulty of inference and the complexity of the training data. Furthermore, as the ELK report also notes, it's not enough for the direct translator to just be more efficient than the human imitator: the direct translator has to be a cost-effective improvement (in terms of how much loss/computation it saves per increase in description complexity) compared to all other possible mappings. Overall, however, despite the issues with this approach, I agree with Paul, Mark, and Ajeya's conclusion that it is the most promising approach currently on offer:[1] In order to ensure we learned the direct translator, we would need to change the training strategy to ensure that it contains sufficiently challenging inference problems, and that doing direct translation was a cost-effective way to improve speed (i.e. that there aren't other changes to the human simulator that would save even more time). Compared to all our previous counterexamples, this one offers much more hope. We can't rule out the possibility of a clever dataset where the direct translator has a large enough computational advantage to be preferred, and we leave it as an avenue for further research. In Section: penalizing inconsistencies we discuss an additional ingredient that we think makes computation-complexity-based approaches more plausible. Furthermore, I agree that, as Paul, Mark, and Ajeya argue, adding consistency might improve this approach as well: If our training process looks at the relationship between different inputs, then a bad reporter might also need to consider lots of alternative inputs before making its decision. Moreover, if we choose sets of inputs randomly, then a bad reporter might not know which other inputs it needs to think about, and there is an exponentially large space of possible situations it potentially needs to worry about. So we could imagine getting to a situation where “just answer honestly” is the computationally easie...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Musings on the Speed Prior, published by Evan Hubinger on March 2, 2022 on The AI Alignment Forum. Thanks to Paul Christiano, Mark Xu, Abram Demski, Kate Woolverton, and Beth Barnes for some discussions which informed this post. In the ELK report, Paul, Mark, and Ajeya express optimism about penalizing computation time as a potentially viable way to select the direct translator over the human imitator: Human imitation requires doing inference in the entire human Bayes net to answer even a single question. Intuitively, that seems like much more work than using the direct translator to simply “look up” the answer. Compared to all our previous counterexamples, this one offers much more hope. We can't rule out the possibility of a clever dataset where the direct translator has a large enough computational advantage to be preferred, and we leave it as an avenue for further research. I am more skeptical—primarily because I am more skeptical of the speed prior's ability to do reasonable things in general. That being said, the speed prior definitely has a lot of nice things going for it, and I do think it's worth taking a careful look at both the good and the bad that the speed prior has to offer. Conceptually, what we want to pay attention to when evaluating a prior from an AI safety perspective is threefold: it needs to favor good models over bad models (e.g. the direct translator over the human imitator), it needs to be competitive to implement, and it needs to favor models with good generalization over models with bad generalization (e.g. the resulting models need to themselves be performance competitive). Before I do that, however, an important preliminary: there are multiple different forms/types of speed priors, so when I say “the speed prior,” I really mean a class of priors including: various combinations of circuit-size and circuit-depth complexity and various combinations of Turing machine time and description complexity, where Turing machine time complexity can be measured either by taking the max number of steps taken across all inputs or the average number of steps taken on a particular distribution of inputs. The basic structure of this post will be a dialog of sorts between a pro-speed-prior and an anti-speed-prior perspective. I'll start with some of the arguments in favor of the speed prior and how the anti-speed-prior perspective might respond, then give some arguments against the speed prior and how the pro-speed-prior perspective might respond. Why you should love the speed prior As in the ELK report, there is a plausible argument for why there exists a speed prior that would prefer the direct translator to the human imitator. Naively, the problem with the speed prior here is that the computation required for the human imitator is proportional to the size of the human's Bayes net, whereas the computation required for the direct translator is proportional to the size of the model's Bayes net—and in the superhuman limit we should expect the latter to be substantially larger than the former. The argument in the ELK report, however, is that while this argument is valid in the limit, there's reason to believe it might be invalid for all the finite cases that we care about. That's because perfect inference in either Bayes net, and thus perfect loss, shouldn't be possible in any finite case. Thus, the performance of the ontology mapping function, and thus its loss, should be proportional to how much computation it puts into its inference task—for which the direct translator has a big advantage, since it gets to reuse the computation performed by the model. Response: The obvious response here, and the response that is given in the ELK report, is that the above argument is very fragile—it relies on inference in the human's Bayes net being too hard to always get r...

In this deep, open and rich conversation, Deri Hughes shares how he learnt to take conscious decisions instead of being led by unconscious drivers. He shares some practical tools and techniques. This has deep implications for our careers and our lives. Deri Hughes has had a rich and diverse career with a focus on strategic consulting. He graduated from Oxford University with a first class degree in organic chemistry. He even went all the way to doing a PhD there. He then started his career at Bain, founded a short-lived company and continued with a few free-lance consulting roles. He then moved on to become the CFO and COO in a strategic consultancy. After this he founded his two companies HoneyComb PS and Explore Consulting where he helps leaders of consulting firms to recruit, train, and develop their teams. A few months ago he wrote a post on LinkedIn where he very openly talked about his personal difficulties and how he has been working through them. This was a beautiful example of daring to be, daring to show up, with his strengths and vulnerabilities. SHOW NOTESDeri's LinkedIn profile.03'46 - Doing a PhD in organic chemistry at Oxford because his father also had a PhD… but actually not liking it.05'01 - Had the option to stop his PhD despite, but he decides otherwise because of his self-judgement.06'09 - Key learning: fear of judgement unconsciously driving decisions in himself and observing this pattern in many people.07'30 - Key learning: Not just sticking with something because you've started it, but having that conscious decision that it's the thing you want to do to continue. Tool used by Bain Partner: every year goes to ⅔ interviews externally to always make a positive decision to stay at Bain. 08'35 - The signs that his PhD was not for him: difficulty focusing, booms and busts of energy, dragging himself to the lab. Not getting enjoyment or motivation from his tasks. 10'30 - At the time, he did not accept that he did not like his PhD. Probably had undiagnosed depression at that time in an unsupportive environment. 12'27 - Becoming the President of the university Sports Club, running it as a small business. Getting his energy from it. 13'33 - Joined Bain and did very well there thanks to a strong strengths and cultural fit. 14'37 - His technique to get into a mindset to be fully present and ready to perform at his best in critical situations - using visualisation to know what he needs to do and how he needs to feel to focus on the effort and avoid being distracted. 18'17 - Getting into the right emotional state to perform to his highest possible level in key moments by focusing on the importance of the event for him. 20'42 - His sense of mission, aligning his business activities with it enables him to stay focused and motivated and to do the things that are difficult. 21'51 - Focusing your personal mission on what is difficult, because it gives you a constant reminder to focus on what you want to grow into. 21'56 - Explains his personal mission statement: “I am here now creating a world of golden connections by shouting my joy and hearing its echo.”26'29 - The Mankind Project has been hugely helpful to him - groups of men to help bring about positive masculinity and helping men understand themselves better. 27'00 - Developed his mission based on what he wanted and needed as a child and did not get. These are typically the things that you want to give out the most but are difficult to give.28'23 - An example of the way he expresses his mission statement in business situations, as a trainer.29'26 - The prospect of becoming a father as a consultant and an honest conversation with his wife made him leave his job at Bain. 31'19 - Being surprised by the number of people who were jealous of him leaving, but in the end all of them stayed. 33'20 - Differences in people's risk tolerance may make them more or less prone to change jobs, industry or careers. 35'07 - In SAAS, people say you need to see a 5-10 times difference in the value or ROI that you get to switch to a new solution. Maybe that also applies to how we make our career decisions. See this post on the change equation. 36'22 - He switched lifestyle before he got to the point of being trapped by high run rate costs commitments.36'54 - “Naively” started his own business. Failed and stopped after 6 months. 38'34 - Solving the wrong problem (feeling the need to be in teams) by being employed again. Then realised and accepted that he did not need to be part of a team. 42'08 - How to know what you truly want fast? By building your self-awareness, particularly with someone holding up the mirror. 44'16 - Possible techniques to reduce or eliminate our addictive behaviours (don't take this for mental health advice!). Give yourself compassion and sit with the discomfort of the emotional need. 50'17 - His perspectives on the world of work that is likely to emerge in the next few years that could give more freedom, opportunities and choices for people in their work.

In 2012, Woodie Neiss was a CFO and entrepreneur who was seeking to change the rules of the investment game. When President Obama signed his crowdfunding bill later that year, it opened a whole new world for individuals to participate in the private capital markets. Today, the crowdfunding industry has surpassed $1 billion in funding and takes place almost entirely online.Woodie continues to empower entrepreneurs through his latest venture, GUARDD, where he automates the process of ensuring audited financials with the goal of creating a more liquid environment for private company founders and shareholders. He is a vanguard of empowering founders throughout the lifecycle of their businesses from fundraising to exit.On this episode of The Modern CFO, Woodie shares how he architected the roadmap of Regulation Crowdfunding (Reg CF), what is so powerful about crowdfunding, and why one-size regulation does not fit all.Show Links Check out GUARDD Connect with Sherwood (Woodie) Neiss on LinkedIn or Twitter Check out Nth Round Connect with Andrew Seski on LinkedIn Key Takeaways2:30 - Tackle outdated investing lawsIn his early days as an entrepreneur, Woodie was frustrated by the inability to tap into individuals as investors, like his large customer base.“I had started a company with my brother-in-law called Flavor RX. We flavored medicine for children so they're more compliant. The coolest thing about our company was a mother got her kid to take her medicine by going into the pharmacy and asking pharmacists to flavor it. The kid took the medicine and she's like, ‘Oh my God, you just saved me countless hours of struggling.' I would get a phone call the next day: ‘How do I invest in your business?' I thought, well, you can't because we can only raise money from accredited investors. When my lawyers told me this, I knew this was a complete missed opportunity. I have hundreds of mothers calling me saying, ‘How can I become an investor in your business?' They can be a marketing agent for my company. Why can't I take money from them? These laws were written 80 years ago to protect retail investors, and you have to live by them. I thought was stupid.”3:53 - How the crowdfunding bill was signedWoodie and two friends from business school ultimately drafted a new framework for Reg CF. President Obama signed it into the Jumpstart Our Business Startups (JOBS) Act in 2012.“We wrote this eight bullet-point framework for investment crowdfunding. We went to the SEC, they said it was cute. Then they said, You should head over to that building with the white dome on it.' I kid you not. Naively enough, we just walked over there, because we had a few days in Washington. We started knocking on the doors of both Republicans and Democrats. People were shocked that entrepreneurs were there, so they listened.”6:04 - Connecting retail investors to the private capital marketsToday, crowdfunding doesn't just live on websites—it's been used on social media as well. Woodie never anticipated the breadth and reach of his bill, which he sees as bringing positive transparency to the investment space.“The industry launched in May 2016. Just this past month it surpassed $1 billion in funding. When we put this together, I was thinking, ‘Wouldn't it be great to use a website to be able to allow people that have a customer list, or their own friends and family, to invest in their business the same way that a campaign on Kickstarter or Indiegogo can?' To see people using Twitter, Instagram, YouTube Live as the outreach, the public solicitation, I think is awesome. It really connects the people to the entrepreneur in a way that a website just doesn't do by itself. I think the advances in technology are really benefiting the industry because it ties you closer to the people that are raising capital. I think that's all good, too, because I think it brings this level of transparency that you otherwise don't have in the private capital markets.”8:43 - Deciding which constraints would minimize retail investor riskOne major fear was that crowdfunding would end up like gambling, leading some investors to lose everything. That's why Woodie and his team wrote in limits based on annual income.“Any accredited investor can risk everything that they have in one company. Do they? No. They're smart enough to diversify their assets. Now, when we built this framework we were concerned that people might be risking more than they can afford to lose. This is the only segment in the private capital markets where investors are capped on how much exposure they can have. We built into the framework, based on net income, or annual salary I should say, as an individual investor, how much you make or how much you have saved thresholds as to how much you can invest. That doesn't exist anywhere else. People will tell us that's pedantic, but quite frankly that was an investor protection mechanism we put in there. So, the investors that are saying, ‘I think it's too risky for certain investors to put in there,' my response to that is well, there are caps and limits on how much people can risk.”11:11 - Tap into the power of the crowdSome assumed crowdfunding would operate only within the San Francisco and New York venture communities. That hasn't been the case.“The deals are the same deals that are being seen in the Bay Area and New York City, with the same investors. What we've seen is the evolution of this industry, where instead of those people saying, ‘You know what? You shouldn't go to crowdfunding.' They're saying, ‘Well, we should use crowdfunding and we should syndicate our deals to the crowd because the crowd brings something that we don't bring.' We can bring deep pockets, but they can bring marketing power because they've got a vested interest in the outcome of the business. They can bring connections. They can bring their own brainpower to how we can help scale this business. VCs are great for that Rolodex of people that they might be able to connect you to, but the reality is you can get so much more out of a crowd.”12:44 - Make transformative moves at the right timeWoodie credits the success of the crowdfunding initiative to timing. His team pitched the idea to Capitol Hill on the back of the recession when political leaders were hungry for job creation.“We were in the right place at the right time. In 2008, we had a recession. Washington was looking for solutions. We went and showed up in 2010 with this [pitch]. People were looking for an answer to the question, ‘How do we create jobs in local communities?' The whole point of investment-based crowdfunding is you are, essentially, helping people that have great ideas all over the country create businesses that hire people. The government can't do that on a macro level and so they need to look at innovative solutions like this that can actually solve what they need to solve at the most basic zip code level. That's what we delivered to them. That's why we were able to build support for this.”16:34 - One size regulation does not fit allRaising capital in public markets requires intense structure, while in private markets it takes place behind closed doors. Woodie knew that crowdfunding deserved structure, but scaled down.“In the public markets, we have a structure under which you raise capital, and you have to do filings with the SEC. They have to be reviewed and approved, and then your offering can go public. But you also [need to] use a broker-dealer. There's a tremendous amount of time, effort, and money that goes into these IPOs that take place. In the private capital markets, that structure doesn't exist because when your company goes out and raises money, they do it behind closed doors. What we tried to do was create a structure and a framework. I believe we did that. With the private capital markets they say, ‘If you want to raise money from retail investors, you actually have to provide them a certain level of disclosure that they're typically used to in a public setting. But let's scope it down.' And I think that's one of the big things that I, as an entrepreneur, and my experience over time, has taught me--that one size regulation doesn't fit all.”20:59 - The importance of a holding period for investor protectionTo weed out fraud and get-rich-quick scams, Woodie is a fan of a required holding period. This was another element built into the crowdfunding bill.“People take advantage of people where there's efficiency in the market. The fact that you don't have a holding period on something means that someone can come in there, buy a bunch of it, and then push hype out there, and then push up the price of it. They sell. And then everyone's left holding these points that are worthless. I think if you had the holding period attached to it, it would keep the people out that are like, ‘Well, I have to wait until I can make my money off of this.' They're looking for quick get-rich schemes. A holding period on anything pushes the frauds away. Not that it doesn't happen. There will be fraud, and there is fraud everywhere. I'm just saying that when you can put triggers like that in there that keep people out of the marketplace, I don't think it's such a bad thing. So we put that in there for regulation crowdfunding.”26:00 - Value your company based on supply and demandAlternative trading systems help provide proof of a company's worth. Woodie thinks there is no better way to accurately value a company.“The other thing I tell these issuers is, if you have a desire to raise a future round, if you have a desire to go public, there is no better way to value your company than a supply and demand decision. To let the market decide what an investor is willing to pay for that security. Because then you don't have an argument. VCs will sit behind a closed door. Investment banks will sit behind a closed door and price your IPO based on what they think the market will pay for it. But what these alternative trading systems provide now is an actual proof point that says: these are what our shares traded for in the past. I think that's a baseline for where we are. I think we can go up from there, because we've accomplished X, Y, and Z.”28:49 - The future of secondary marketsWoodie sees a bright future for secondary markets, where VCs will be able to get real ROI to reinvest by selling part of their cap table.“This is where this market's going to go, mark my words. VCs make money in private equity by returning money to their investors. There's no money in without money out. That's our mantra. The way in which we have these alternative trading systems, these secondary markets now have evolved to the point where VCs can say, you know what, let's take 10% of this hot company that's doing really well. We'll still have the 90% of it on our cap table. But we'll get rid of 10% that people pay for that. We'll get the money for that. We'll return that to our investors. Let's go out for our next fund. They're going to be thrilled at how we're doing because they can see actual—it's not unrealized ROI at that point. There's a way in which they can say, this is what we got for this. So we know it's proof of what it's valued at. And it allows them to actually raise more money. So they're going to be using these secondary markets to liquidate some of their investments so that they can get more investments down the road.”

In the official first episode of The Blerd Dad Podcast we talk with Comedian Alex Brown about CoVid, coaching and raising the youngins up in the current world. Blerding Question: What do you think of the new Captain America being black? --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app

In this episode Corine shares her journey toward the Cirque du Soleil stage via l'Ecole nationale de cirque. Let's talk about pursuing goals from step one instead of figuring it all out in advance.  If you want to connect with Corine directly you can email her at hello@corinemoments.com or hang out with her on IG The fresh Corine Moments website is under construction, stay tuned! #letsgetenergized 

For the next few weeks I will be publishing some of my "greatest hits" from this podcast, starting with this episode! I went traveling… and came home to zero clients. Naively, I had just “hoped” clients would find me without me having to do any real marketing while I traveled. They didn't, so I had to put my marketing cap on and find clients - fast! Tune in to learn four things I did to go from zero clients to fully booked for six months! SHOW NOTES: https://neshawoolery.com/blog/019 FREE STARTER KIT: 4 Simple Steps to Book Clients Consistently https://neshawoolery.com/starterkit SPONSOR: Dubsado  https://www.dubsado.com/?c=nesha (Affiliate link) NESHA'S INSTAGRAM: https://www.instagram.com/neshawoolery/ NESHA'S BLOG: https://neshawoolery.com/podcast RESOURCES MENTIONED: Free Starter Kit for Booking Consistent Clients: https://neshawoolery.com/starterkit Thanks for listening! If you enjoyed this episode, please subscribe, rate and review this podcast on iTunes. Ratings and reviews are incredibly helpful and really appreciated.

Adam McCalvy, Brewers.com/MLB.com writer/Author of “The Milwaukee Brewers at 50”, joins the show to recap the MLB draft, the latest proposals from the MLB and MLBPA, and more. How has his book been doing? Does he expect the owners and the MLBPA to reach a deal? Is this all about money? What are some of the things he's heard from fans? What does the timeline look like if Rob Manfred institutes a Back-To-Work order? Did he like the Brewers draft picks this season?

I went traveling… and came home to zero clients. Naively, I had just “hoped” clients would find me without me having to do any real marketing while I traveled. They didn't, so I had to put my marketing cap on and find clients - fast! Tune in to learn four things I did to go from zero clients to fully booked for six months!   SHOW NOTES: https://neshawoolery.com/blog/019   FREE STARTER KIT: 4 Simple Steps to Book Clients Consistently https://neshawoolery.com/starterkit   SPONSOR: Dubsado  https://www.dubsado.com/?c=nesha   NESHA'S INSTAGRAM: https://www.instagram.com/neshawoolery/   NESHA'S BLOG: https://neshawoolery.com/podcast   RESOURCES MENTIONED: Free Starter Kit for Booking Consistent Clients: https://neshawoolery.com/starterkit   Thanks for listening! If you enjoyed this episode, please subscribe, rate and review this podcast on iTunes. Ratings and reviews are incredibly helpful and really appreciated.