Podcasts about probes

The Uni-T UT892 High Voltage Multimeter is unusually rated to 2000V! Teardown and testing time, is it any good? 00:00 – Uni-T UT892 2000V High Voltage Multimeter 02:39 – Unboxing 03:40 – FAIL right off the bat! Probes matter. 05:53 – Teardown 08:06 – Where's the MOV's? 09:07 – The case design leaves a lot …

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Ripple Dropped a Major XRPL Upgrade — UK Backlash Grows | US Fraud Probes Intensify Ripple just dropped a major XRPL upgrade — and almost nobody is talking about what it really means. Token Escrow (XLS-85) is now LIVE on the XRP Ledger, extending native escrow functionality beyond XRP to issued assets, trustline-based tokens, Multi-Purpose Tokens, stablecoins like RLUSD, and Real World Assets. This isn't hype. This is institutional-grade infrastructure quietly expanding on-chain settlement power. At the same time: • Political backlash is intensifying in the UK • Governance debates are heating up • Fraud investigations in the United States are gaining momentum As decentralized financial rails strengthen, public trust in traditional institutions is being tested. Is this just coincidence — or are we watching a larger shift unfold? In today's episode of **On The Chain**, we break down:

Improper DNA analyses by a former employee at the police department of Saga Prefecture, southwestern Japan, may have influenced investigations in 19 cases, the National Police Agency said in a report on its special re-examination Thursday.

Allen, Rosemary, and Yolanda, joined by Matthew Stead, discuss Vestas’ Q4 earnings beating competitors but disappointing investors, and the latest on the Wind Energy O&M Australia 2026 conference in Melbourne. Plus the European Commission opens a subsidy investigation into Goldwind, Texas sues over 3,000 dumped wind turbine blades, and Muehlhan Wind Service acquires Canadian AC883. Sign up now for Uptime Tech News, our weekly newsletter on all things wind technology. This episode is sponsored by Weather Guard Lightning Tech. Learn more about Weather Guard’s StrikeTape Wind Turbine LPS retrofit. Follow the show on YouTube, Linkedin and visit Weather Guard on the web. And subscribe to Rosemary’s “Engineering with Rosie” YouTube channel here. Have a question we can answer on the show? Email us! The Uptime Wind Energy Podcast brought to you by StrikeTape, protecting thousands of wind turbines from lightning damage worldwide. Visit strike tape.com. And now your hosts.  Allen Hall: Welcome to the Uptime Wind Energy Podcast. I’m your host Alan Hall, and I’m here with Rosemary Barnes, Yolanda Padron. Matthew Stead down in Australia. So welcome Matthew.  Matthew Stead: Great to be here. Thank you, Alan.  Allen Hall: We have a number of articles and interesting topics this week. Top of the list is Vestus. Vestus announced their Q4 numbers, and although the the revenue is great, uh, they, they had a profit of about 580 million euros. It was below what analysts expected, so the shares dropped about 6% on the news. But the CEO of Vestus is saying, uh, full speed ahead. They’re, they’re willing to make some concessions. Vestus, as it sounds like, in terms [00:01:00] of thinning out the company a little bit, which I, that’s been a, a, a complaint from investors for a little while. But in, in terms of, uh, going forward in renewable energy, Vestus is still going to pursue that. The offshore wind business looks like it’s gonna be profitable in 2027. And as we all know, and we, we see wind turbine prices, uh, quite a bit in each of our positions. Vestas is the most expensive one on the block, but they’re still winning a whole bunch of orders. And, and Matthew, uh, Vestas globally. I would say is the leader right now, if you look at Siemens GAA and GE Vestas is really winning a lot of the orders. Matthew Stead: Yeah, I think a very strong reputation for quality. Um, I have to say, I’ve got some Vestas turbines behind me, so, um, all paid for by myself. They’ve always been well regarded for their, um, you know, quality of [00:02:00] product. And when I first got into wind, um, you know, probably 15 years ago, you know, they were, they were the leaders at that point in time. And so, you know, quality. Reduces future o and m cost. I think  Rosemary Barnes: it’s not just about like the simple o and m, either it’s the risk that something really bad goes wrong and you’re just stuck with, you know, like a, a whole a hundred turbines that can’t be fixed or, you know, at least a large, a large chunk of them. The more that I work in, in o and m, the more you see, like on occasion when you do have those serial issues that mean, you know, like. Sometimes all the blades in the wind farm have to be replaced or sometimes all the generators or you know, even if it’s not replaced, if you’ve gotta take them all out and do something and put ’em back in, it is just such a massive cost. And, um, reducing the chance that that’s gonna happen is actually really valuable for insurance. And yeah, all sorts of other financial reasons.  Yolanda Padron: And even as an FSA customer, I feel like Vestus has a lot more transparency as to what actually is going on, [00:03:00] on site and more able to, to collaborate on, on like a site to site basis, which is very obviously helping them in getting a lot of return customers. Allen Hall: Yeah. One of the key revenues for Vestus has been the FSA, where almost every project I’ve seen over the last couple of years has had a 2030 year FSA attached to it. Rarely do you see. Order without that, and that’s a long-term revenue stream. The, the thing about Vestus and the complaints that are happening, uh, around vestus are odd because if you look at Siemens Cab Mesa, they’re really struggling to be profitable. And then GE Renova, which is really, really struggling to be profitable and they’re losing several hundred millions of dollars a year. Vestas is bringing in a profit, and, and yet the investors are wanting even more. I, I guess, is, is this just a relationship to the. Where you can invest money today. The stock market going up so high, gold and silver prices are at record highs. Rosemary Barnes: Haven’t they just [00:04:00] crushed?  Allen Hall: They have a little bit. They’ve, they’ve rescinded some, but they’re still at really high numbers, right? So Gold Cross, what? $5,000 and ounce and then, uh, it was it 2000 a year ago? So the, the rise in the value of, of, uh, rear metals is crazy. Is there a plan you think Vestas is changing the way they’re gonna operate? ’cause uh, they’re talking about thinning out the ranks and they do seem to be becoming more vertically integrated with the acquisition of the TPI factories down in Mexico. GPI in India  Rosemary Barnes: before we make it sound too much like a paid segment from investors, I have to say I disagree that they’re like just crushing it with the, the FSAs. I think that the full service agreements are across the board. Perform badly in Australia, at least I think it’s different elsewhere. Um, maybe it’s a good segue into, uh, talk about our event that we’ve got coming up to talk [00:05:00] about, um, the difficult operating conditions in Australia. But I, I think that best as, like everybody else has been surprised at how many things can go wrong in an Australia and wind farm. And, um, I don’t, I I would’ve put them up on a pedestal for. Particularly noteworthy, um, brilliant service with the FSAs. I think, yeah, across the board everyone’s doing a little bit less than they should be, and I have no doubt that they’re also making a whole lot less money on those agreements than what they spent or spending a lot more than what they’re expecting. So I don’t wanna be too harsh in my judgment.  Yolanda Padron: That’s fair. The bar is very low.  Rosemary Barnes: But what I do notice when I go to international events, um, and I, you know, I talk to, I’ve got a lot of ex-colleagues that’s still working in the industry and vest. Stands out as still investing a lot in r and d. And that doesn’t mean like crushing out a new platform every single year or every two years. It’s not that. But they are investing in a lot of new technologies that are more incremental. They’re [00:06:00] looking at bigger technology leaps and um, you know, still investigating stuff like that. Like I think if I was to go back working for an OEM, that’s the kind of work I’d like to do. And investors does seem like it’s the main company that’s still doing a whole lot of that. With the exception of, of the Chinese manufacturers, which are obviously doing like tons and tons of new development. But, um, I don’t have the insight into them like I do with the European ones.  Allen Hall: As you’re listening to this podcast, most of the people on this podcast are traveling to Melbourne, Australia for Woma 26. That’s Wind Energy and M Australia. Big event. Matthew, the numbers are impressive. I’m getting a little bit scared. Run out of food and uh, seats because there is a massive influx in the last 24, 48 hours, which is great to see, but wind energy in Australia. Is huge, and the o and m aspect is one of those key pain points. Matthew Stead: Yeah. I think, uh, thanks to Rosie and Alan, your argument, [00:07:00] um, a little while ago, your argument, which spurred the whole, um, the reason for the conference. Um, you know, the, the lack of, uh, Australian content, the lack of, um, poor. Conferences in Australia. I think unless you’d have that argument, um, this event wouldn’t, wouldn’t be there. Allen Hall: Rosie did bring up that she had been to a number of conferences and so had I that were pretty much useless in terms of take home. What could we be able to use in the world and, and make the world just slightly better from our knowledge and. With all the policy talk and uh, discussion about sort of global warming things that it’s not really useful necessarily in making your operations run more efficiently. And this was what Woma is all about is. Sharing information. Not everybody runs their operations the same. And you can learn from that of the way, uh, others do it. And at the same time, we’re bringing in experts from around the world to talk about some of [00:08:00] those really critical issues. One of them being leading edge erosion. And Rosie’s been doing a lot of work in Australia on leading edge erosion and the complexities around that. Rosie, the leading edge erosion discussion and the panel involved in the people are gonna be on the panel are impressive. What are you looking forward to?  Rosemary Barnes: I’m looking forward to, um, getting the international perspective because leading edge erosion, I mean, there’s heaps of aspects of wind turbine operation that I think are just dramatically different in Australia, but I think leading edge erosion is the one that like really, really jumped out at me. When I was, um, when I moved back to Australia and started looking at inspection reports for wind farms that were like one or two years old, and you see 90, 99% of turbines that have significant erosion like within a couple of years. It’s like, this is, this is not. Like, I’ve never, I’ve never seen this before. It’s clear that no one is designing these products that are gonna peel off [00:09:00] within a couple of years. Um, and so that was what kind of got me thinking, you know what, like Australia is really different. Climatically and in terms of the weather. Um, and so we need to start not just getting our information from overseas, but also relating it back to Australia. So I think that that’s what we’re trying really hard with the conference to do, is to like really ground it on Australian problems and solutions that have worked in Australia, but then draw on, you know, we don’t need to invent every single new product ourselves. Although there will also be. I, I’m very confident that, that we do need new products developed specifically for Australia. Um, but you know, there are a lot of things out there we can really accelerate how quickly we can solve our Australian problems if we know what’s worked overseas in, you know, different places and just get ideas about how things work. So I think that’s a really good mix of, of local and international. Matthew Stead: Yeah, as [00:10:00] we were talking before about, um, registrations, so we had. Definitely over 200 now. Um, and, um, I, I think we just need to warn people that we might need to cap it out. Um, so the venue’s told us two 50 maximum, so getting in quick  Allen Hall: and if you haven’t registered, you need to do so today. Go to WMA 2020 six.com. It’s very easy to do. It’s an inexpensive conference and full of great information. And the one thing you wanna register for also when you’re there is the free Lightning workshop. On the Monday, so this, it will be February 16th. It’s a lightning workshop in the afternoon, and then the, the full event begins Tuesday the 17th, and running through Wednesday the 18th. So you have two and a half full days of o and m. Knowledge sharing.  Matthew Stead: Don’t, don’t forget the workshops. There are two sessions of workshops with three, um, parallel sessions. And also don’t forget the chance to catch up with your buddies. So, uh, on the Monday [00:11:00] night, um, after the Lightning Masterclass, there’s, um, an event, you know, food and wine and drinks, et cetera. And then also on the, the Tuesday after the first day, there’s also a chance to catch up  Allen Hall: and you’ll go to Wilma 2026. Com and register. Now.  Speaker: Australia’s wind farms are growing fast, but are your operations keeping up? Join us February 17th and 18th at Melbourne’s Pullman on the park for Wind energy o and m Australia 2026, where you’ll connect with the experts solving real problems in maintenance asset management and OEM relations. Walk away with practical strategies to cut costs and boost uptime that you can use the moment you’re back on site. Register now at WM a 2020 six.com. Wind Energy o and m Australia is created by Wind professionals for wind professionals. Because this industry needs solutions, not speeches,  Allen Hall: the European Commission [00:12:00] has a message for Chinese wind turbine manufacturers. We are watching. Uh, Brussels just opened an in-depth investigation into Goldwind, that’s one of China’s biggest turbine makers. The concern is really straightforward. European regulators believe Goldwin may have received government subsidies that given it unfair advantage. Over European competitors such as Vestus and Siemens, GOMESA, Nordics, and others, grants preferential tax treatment and below market loans are all on the table. And if confirmed, the EU could impose corrective measures under its foreign subsidies regulation, which is a tool designed to keep the playing field level for everyone doing business in Europe. This has led to a number of heated exchanges in the press between China and the eu. China has, uh, said, Hey, eu, calm down. It’s not that big of a deal. We, and we don’t really do this. And if you wanna point [00:13:00] fingers, uh, the EU has given a lot of money and resources to the wind turbine operations in the eu. So it’s a, a, a bunch of back and forth, which is an odd thing at the moment because China is really trying to penetrate the EU market and the UK market for that matter, offshore in particular. Uh, Matthew, when you watch this go on and, and China obviously being the largest player in wind turbines, uh, there is some. Protection isn’t going into this. China has protected themselves from European manufactured turbines for the most part. Uh, it does seem like the EU has a leg to stand on and saying, Hey, if you’re gonna protect your borders, we’re gonna protect our borders. How does this end up? Does this end up with, uh, China making turbines or getting turbines shipped into EU or. There’s just gonna be a prohibition.  Matthew Stead: Uh, actually, I’m a little bit surprised that this hasn’t happened already. [00:14:00] I mean, there’s obviously plenty of European investigations and I’m a little bit surprised it didn’t happen earlier. Um, I, I guess my expectation is that, you know, this will be done and dusted and we can just move, move forward. Um, you know, my, my guesstimate is that it’ll be showing that, you know, this is all fine and, uh, yeah, just continue as per normal. Um, yep. Maybe, maybe critically. Um, I actually think a bit more competition in the industry is a good thing. Um, and so I think the whole, you know, global industry can, can, can benefit.  Allen Hall: And when we’re talking about, uh, the construction of wind farms in the eu, the Chinese manufacturers always come up because they tend to be somewhere between 30 and 40% less expensive than the European counterparts for basically the same turbine. What is the, the real linchpin there, because it does seem like operators and sted uh, evidently had a project going on where they’re looking at Chinese [00:15:00] turbines, but hasn’t made any decisions about it. There’s not a lot of history on the Chinese turbines. You can’t go back and pull, uh, o and m records. You can’t see reliability rates. You can’t see what their insurance rates have been. And Rosie, I think you’ve talked about this quite a bit. It does seem like the manufacturing capability in China is quite good, but then we see things on LinkedIn quite often. We’re uh, there has been some really massive failures there. How is the EU thinking about this? Is it really a competitive issue at this point, or is it a technology issue? What is the real. Uh, linchpin that it, it is, it everybody is trying to get at.  Rosemary Barnes: Yeah. Well I think Europe would be crazy to not support their wind industry because China is so big and has, um, you know, so many wind turbine manufacturers now that if Europe doesn’t specifically try to, you know, compete and survive, then I can [00:16:00] imagine no. non-Chinese manufacturers in 10 years time, um, or you know, at least 20, which I think would be a shame because there is a huge, long history of really good engineering, um, in Europe. Yes. Uh, every country supports their manufacturers. China do it in many, maybe most of their export industries. Everybody knows that. Chinese solar panels are subsidized most countries and regions, except that steel is heavily subsidized in, um, in China. And so there are in many countries restrictions on Chinese made wind turbine towers or tariffs on them. Because of that reason, it’s like pretty. It is pretty uncontroversial. Like it’s pretty obvious, right? That um, if you don’t fight, then um, you say, yeah, we’ll accept all these cheap products then, um, you know, because that’s beneficial for our economy to have them cheap. That’s like a short term thing. It’s [00:17:00] a lot easier in a country like Australia where we don’t have competing industries for many of these, um, many of these products, it’s a bit easier to say, yes, we would love cheap solar panels and cheap wind turbines and cheap electric vehicles and cheap batteries. But I mean, even Australia is trying to regain some of some of that, um, manufacturing capability.  Matthew Stead: But Rosie to, I guess Rosie to challenge you there. I mean, it won’t, it to improve the world’s, you know, position if we, you know, continue to drive prices down and drive a bit of innovation.  Rosemary Barnes: Yeah. If we drive prices down, but not if we drive, um, all competition out of business. And then you’re left with just one country that controls the supply chain for absolutely everything, which they’re already very largely. Do in terms of, you know, like, yeah, batteries, EVs, uh, solar panels, um, heaps of the raw materials, you know, like rare earths and a lot of other critical, um, critical [00:18:00] minerals. But I do think it’s a little bit different for Europe with wind because, um, if that, if that dies, it’s a big chunk of, um, just engineering knowledge that will just. Die with it. I would definitely, especially the countries like Denmark, where it is a, a significant industry for them, I have been a little bit surprised that they haven’t been supporting more the industry through some hard patches. But yeah, let’s, um. It’ll be an interesting next few years. Speaker 6: Delamination and bottomline failures and blades are difficult problems to detect early. These hidden issues can cost you millions in repairs and lost energy production. C-I-C-N-D-T are specialists to detect these critical flaws before they become expensive burdens. Their non-destructive test technology penetrates deep to blade materials to find voids [00:19:00] and cracks. Traditional inspections completely. Miss C-I-C-N-D-T Maps. Every critical defect delivers actionable reports and provides support to get your blades back in service. So visit cic ndt.com because catching blade problems early will save you millions. Allen Hall: Well, occasionally the wind industry has a recycling problem and down in Texas this has come to a head, uh, an Attorney General Ken Paxton. We as the Attorney General of Texas has sued global fiberglass solutions and affiliated companies for illegally dumping more than 3000 wind turbine blades in Sweetwater, Texas. Uh, the company was hired to break down and recycle the blades many years ago. Instead, it stockpiled them at two unpermitted disposal sites. The attorney General is seeking civil [00:20:00] penalties, complete removal of the waste and full cleanup costs paid to the state. And Yolanda, you have seen this facility, I’ve seen this facility down by Sweetwater. It is not a small site. It is massively large and has been there for a number of years. I, I guess there hasn’t been anybody willing to do it, and Global Fiberglass Solutions hasn’t stepped up to even start from what I understand. To take care of the problem. Is there a happy outcome of this? Does anybody else step into the, the fray and, and try to clean up these 3000 blades? Yolanda Padron: We were talking a little bit about this offline, but Rosie you mentioned there’s so many companies that can recycle in general, right? We know just in Texas, there’s a lot of smaller companies. That could take on at least part of, of what’s going on here. And I think, I mean, it’s, it’s something that is [00:21:00] affecting the people that are living there. It’s not just an eyesore. I mean, it’s just, I mean, nobody wants their home to be just this big dumping ground. It’s like a graveyard for blades. And it’s so sad to see that this is really affecting people and just their, how they view wind in the area because. Texas does really, really well with wind in general and that area gets a lot of money in. It’s very oftentimes rural areas that don’t get a lot of funding that are getting a lot of funding for schools are getting a lot of funding for hospitals are, are making sure that their roads are paved. Just in general, a lot of jobs are coming into town and it’s, it should be a really great win-win and it’s just really sad to know that it’s come to this point after years and years where it just, all of the pros are outweighed by a huge calm that is a [00:22:00] huge dumping site in the middle of people. General homes,  Rosemary Barnes: are they saying that it’s they’re storing the blades or did they just pretend that they recycled them and actually landfill them? What’s the Or? It’s unclear.  Allen Hall: They didn’t landfill them. I mean, in a sense, they didn’t bury them. They’re just sitting on the surface.  Yolanda Padron: Piled up.  Rosemary Barnes: I think a lot of this comes down to what, what does recycling mean? What’s your definition of it? Um, and it, depending on what your definition is, there absolutely are plenty of, um, companies, you know, like all over. And I’m sure that there are many more in Texas than there would be in, um, yeah, in the Australian regions I’ve looked at. But there’ll be companies that. Um, already a shredding waste of, from multiple sources and putting it into products like concrete for non-structural applications like, um, footpaths or sidewalks, stuff like that. Um, asphalt is another one. And then a little bit more high tech. You get, um, plastic products that [00:23:00] again, aren’t super duper structurally, um, demanding. So like, um. Decking materials or outdoor furniture, or even I saw one company who’s using recycled material in, um, rainwater tanks. I just really feel like any decent project manager could actually given enough money, like I’m, I’m not saying it’s an economic thing to do, like it’ll always be cheaper to landfill them, um, than to do something with them. But if you’ve been given money to recycle them enough money. Any decent project manager could make that happen?  Allen Hall: Well, just down the road is ever Point Services. And Rosemary, I don’t know if I’ve introduced you to ever Point Services, Tyler Goodell, Candace Woods, uh, they are recycling blades in a totally different way. They’re, they’re grinding them down, but they’re end use product is totally different than anything you have seen and all, although that is just getting ramped up from what I understand so far. The product they’re delivering has a [00:24:00] decent commercial value. It’s helping out in other industries. So it’s not just getting mixed with asphalt necessarily. Those 3000 turbine blades have value. They really do. And ever point, I think if they were involved, would turn them into something really useful. So there is the opportunity to recycle these blades by grinding them down in different, in different ways. But there are new markets. For this product and I’m, I’m just a little shocked that no one’s really stepped forward to say, Hey, I, I’ll take those blazes, but because it’s in a lawsuit, I assume that’s the problem. No wants to walk into there and say. Take responsibility for this thing that’s been hanging around for several years at this point.  Rosemary Barnes: I don’t know. I think I would disagree when, when you say those blades have value, I would be highly surprised if someone would just take them and make a profit from them. I would expect if I had 3000 blades in my backyard, I would expect to pay somebody to take them off my hands. Um. That should have been covered by the fee that they were paid for this [00:25:00] recycling, right? So if that money’s gone now, then there is gonna be a challenge in, um, doing something with it. Because I just want to you reiterate that like recycling is not the economic thing to do with wind turbine blades. Now it’s not even the best thing to do in terms of an energy or environmental or climate change, um, consideration. But if you are sure that you don’t want, um, to deal with the physicality of 3000 blades, um, then. You know, you and you’re prepared to pay to get rid of them, then there are definitely things that you can do.  Matthew Stead: Uh, I think this makes me like super angry because really if we look at it more from a social perspective, um, this is. These pictures are shown all over the world, and whenever I talk to someone and say, Hey, yeah, I’m in the wind industry, they say, oh yeah, what about all those blades in Yeah, and the, the stockpile, blah, blah, blah. So really this, this incident has really screwed up the whole global industry. So it may have destroyed parts of Texas, but it’s also destroyed part of [00:26:00] the global industry. Rosemary Barnes: I agree and it’s, it’s crazy because wind turbine blade waste is five to 10% of global composite waste. So the boats and cars and airplanes, um, and other composites are. They’re not piled up in a recognizable form. And so nobody is absolutely outraged that people are, you know, um, disposing of fiberglass boats every year. Um, so yeah, I mean, that, that, that es me too. I have, um, I’ve spent a long time being annoyed about that fact, and I’ve kind of come around to the, the fact that universally people absolutely hate. Wind turbine blades to be wasted and it just needs to be solved. For that reason, it’s not, it doesn’t need to be solved because of the economics. It doesn’t need to be solved because of the environment. It doesn’t need to be solved because of climate change, but it does really need to be solved because of the social perception.  Allen Hall: Well, as North American Wind Farms age, the companies that keep them running. Keep getting bigger. [00:27:00] And Mohan Wind Service, which if you haven’t worked with them, is a Danish turbine service provider. Uh, and they’ve acquired the operating assets of Canada based AC 8 83. And our friends at AC 8 83 have been evidently working behind the scenes to make that deal go through, which is. Awesome. Actually, uh, the deal gives Mulan a local platform for blade repair and turbine services across Canada and the United States, uh, with more than three. Thousand certified technicians in over 35 countries. Muhan says it is confident the long-term growth in North American market will, uh, continue to prosper. So Muhan come in and saying to AC 83 and others, uh, that they’re, uh, gonna be a, a real powerhouse in terms of a service provider in Canada and the United States and acquiring AC 83 is, is one of the good moves. And we know Lars Benson, [00:28:00] who’s run that business, and Yannick Benson who operates that business today. This is a big deal for both of them and the company.  Matthew Stead: Yeah, I mean, uh, Lars is a great guy and I, I think this is wonderful that you get more economies of scale by, you know, these companies growing and it has to be, has to be great for the industry. O obviously, you know, it’s a good thing for, for Lars and, um, Yanick. Um, but yeah. Yeah. Good on them for, for doing this. And you, we need more companies that are larger and able to operate across different industries. I know the seasonality might, might play into it. I don’t know. Maybe not. Um, but, and the more that companies can work across different regions, the better. Allen Hall: Well, it just gives a C 83 a lot of operating power. So as a sort of a small, medium sized business, that’s one of the problems that you try to scale is just a lot of detail. Human resources, all the legal aspects, and. Uh, international travel people coming back and forth all the time. It is just a lot to operate. Muhan gives them all that infrastructure support. So, [00:29:00] uh, the brain powers that lie at AC 8 83 to do great work can do that work. And they have the muhan to come underneath and provide the support and the, the financial stability. Matthew, as you point out, the season is pretty short up in Canada, uh, to make this thing go. So this is really great news and we’re, I think we’re gonna see more. Of this type of structure happen where the companies that have grown and have shown value to the wind industry, regardless of where they’re located at, are gonna become prized possessions and, and larger companies are gonna want to come in and, and acquire them to expand their portfolio at the same time. And there’s value there. I, I think a lot of ISPs around the world have shown themselves to be profitable, even in some really tough economic times. Uh, they’ve had. Done a good job. And it does seem like the industry is rewarding. Those companies that have put the effort in and have shown themselves to be the professionals that AC 83 is. So this, [00:30:00] this is a really great development. And do we see this happening, uh, through 26 and 27? Because I think, I think that’s where the industry’s headed. But I talk to a lot of my counterparts who say, oh, there is no. Everything’s gloomy and doomy, and none of this is gonna happen, and these companies are gonna just fade away. Where do you think this is headed at Matthew?  Matthew Stead: I think, um, we, we’ve done a little bit of work and we’ve been looking at the industry and I think, uh, if you compare it to, you know, construction or, you know, automotive or whatever, I, I think the, there is a, a strong opportunity for the industry to have some consolidation amongst companies. So I think, um, you know, the industry is still a bit of a baby. You know, maybe whatever, 30 years there is still opportunity, um, for consolidation. You know, much like a few of the other more mature industries, like I said. Um, so I, I, I think there’ll be more of this, um, going on the next few years.  Allen Hall: That wraps up another episode of the Uptime Wind Energy Podcast. If today’s [00:31:00] discussion sparked any questions or ideas. We’d love to hear from you. Reach out to us on LinkedIn and don’t forget to subscribe so you never miss an episode. And if you found value in today’s conversation, please leave us a review. It really helps other wind energy professionals discover the show for Rosie, Yolanda and Matthew. I’m Alan Hall, and we’ll see you here next week on the Uptime Wind Energy Podcast.

TODAY ON THE ROBERT SCOTT BELL SHOW: DOJ Probes Religious Exemptions, RFK Vaccine Freedom Choice, Kim Mack Rosenberg, AAP Lawsuit, Political Divide Deepens, Trump Signs Pandemic Bill, FDA Autism Page Removed, Ultra processed Food and Cigarettes, Costco Chicken Labels, Abies Canadensis - Pinus Canadensis, and MORE! https://robertscottbell.com/congress-demands-doj-probe-kennedys-vaccine-freedom-choice-kim-mack-rosenberg-political-divide-deepens-trump-signs-pandemic-bill-fda-autism-page-removed-food-or-cigarettes-costco-chicken-lab/ Purpose and Character The use of copyrighted material on the website is for non-commercial, educational purposes, and is intended to provide benefit to the public through information, critique, teaching, scholarship, or research. Nature of Copyrighted Material Weensure that the copyrighted material used is for supplementary and illustrative purposes and that it contributes significantly to the user's understanding of the content in a non-detrimental way to the commercial value of the original content. Amount and Substantiality Our website uses only the necessary amount of copyrighted material to achieve the intended purpose and does not substitute for the original market of the copyrighted works. Effect on Market Value The use of copyrighted material on our website does not in any way diminish or affect the market value of the original work. We believe that our use constitutes a 'fair use' of any such copyrighted material as provided for in section 107 of the U.S. Copyright Law. If you believe that any content on the website violates your copyright, please contact us providing the necessary information, and we will take appropriate action to address your concern.

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

On this episode of the Self-Publishing with ALLi podcast, Dan Holloway reports on the Selfies Awards opening to indie authors worldwide, marking a major shift for one of the most respected indie-only prizes. He also highlights a new Publishing Perspectives survey asking authors, readers, and publishers what they really want from book prizes, looks at Libro.fm's new annual audiobook subscription model supporting indie bookstores, and notes developments in artist basic income schemes and fresh details emerging from the Anthropic lawsuit. Sponsor Self-Publishing News is proudly sponsored by PublishMe—helping indie authors succeed globally with expert translation, tailored marketing, and publishing support. From first draft to international launch, PublishMe ensures your book reaches readers everywhere. Visit publishme.me. Find more author advice, tips, and tools at our Self-publishing Author Advice Center, with a huge archive of nearly 2,000 blog posts and a handy search box to find key info on the topic you need. And, if you haven't already, we invite you to join our organization and become a self-publishing ally. About the Host Dan Holloway is a novelist, poet, and spoken word artist. He is the MC of the performance arts show The New Libertines, He competed at the National Poetry Slam final at the Royal Albert Hall. His latest collection, The Transparency of Sutures, is available on Kindle.

Federal authorities are investigating vile trolls who sent cruel, taunting letters to SAVANNAH GUTHRIE amid the agonizing search for her missing mother, while Hollywood keeps spinning—insiders insist MARGOT ROBBIE and JACOB ELORDI are not a couple, just executing a perfectly timed PR play. Meanwhile, TIMOTHÉE CHALAMEThas quietly ditched the clown-core chaos and stepped into full awards-season polish. SAME DAY. VERY DIFFERENT DRAMAS. Rob’s latest exclusives and insider reporting can be found at robshuter.substack.com His forthcoming novel, It Started With A Whisper, is now available for pre-orderSee omnystudio.com/listener for privacy information.

In this episode of the HVAC Know It All Podcast, host Gary McCreadie talks with John Anderson, Senior Regional HVAC Technical Trainer at Sila Services and former Service Manager and Technician at Burns & McBride Home Comfort. They discuss the shift away from using manifold gauge sets in favor of digital probes and low loss fittings. John explains how modern tools can reduce system contamination, improve accuracy, and speed up processes like charging and evacuation. The conversation also explores the benefits of apps like MeasureQuick for diagnostics and training. Gary and John highlight how smart tools and good habits lead to better HVAC service and fewer callbacks. Gary and John talk about working without manifold gauge sets and how using digital probes can make HVAC work faster, safer, and more accurate. John shares how most residential jobs can be done without a manifold and explains why probes, low loss fittings, and proper charging tools are more efficient. They discuss best practices for recovery, evacuation, and charging while avoiding leaks and damage. John also explains how apps like MeasureQuick help techs understand system issues faster and more clearly. They wrap up by stressing the value of training, good habits, and using the right tools to reduce callbacks and improve system performance.   Expect to Learn: Why digital probes can replace manifold gauge sets for most HVAC jobs. How to charge systems using tees, ball valves, and liquid charging adapters. The risks of overtightening service valves and how to avoid damage. Why MeasureQuick helps techs find system issues faster and more clearly. How smart tools and better habits reduce callbacks and boost performance.   Episode Highlights:  [00:00] - Intro to John Anderson in Part 02 [01:16] - Topic intro: Not always gauging up [03:44] - Probes vs. manifolds debate [05:09] - Digital manifolds & modern tool preferences [08:27] - Future of HVAC tools: probes with low-loss fittings [10:42] - Real example: Bluetooth probes catching tech error [13:48] - Using Measure Quick for deeper diagnostics [18:54] - Time efficiency & preventing callbacks [21:35] - Wrap-up & plans for future talk   This Episode is Kindly Sponsored by: Cintas: https://www.cintas.com/ Cool Air Products: https://www.coolairproducts.net/ SupplyHouse: https://www.supplyhouse.com/tm Use promo code HKIA5 to get 5% off your first order at Supplyhouse!   Follow the Guest John Anderson on:  LinkedIn: https://www.linkedin.com/in/john-anderson-188093251/   Sila Services: https://www.linkedin.com/company/silaservices/   Burns & McBride Home Comfort: https://www.linkedin.com/company/burns-&-mcbride-home-comfort/    Website:  Sila Services: https://www.silaservices.com   Follow the Host: LinkedIn: https://www.linkedin.com/in/gary-mccreadie-38217a77/  Website: https://www.hvacknowitall.com Facebook: https://www.facebook.com/people/HVAC-Know-It-All-2/61569643061429/   Instagram: https://www.instagram.com/hvacknowitall1/

In this episode, we break down how Uber Freight posted flat Q4 results but finally achieved breakeven profitability through disciplined cost measures. We also discuss the company's pivot toward autonomous trucking as a long-term strategy to drive higher asset utilization. On the asset-based side, we analyze why losses continue at Heartland Express as the carrier navigates costly fleet integrations and a soft market. Despite recording its tenth consecutive net loss, the company's improving operating margins offer a glimmer of hope for a turnaround. Regulatory news takes center stage as the EPA targets truck engine makers to investigate widespread DEF system failures causing "limp mode" incidents. This major shift aims to treat equipment reliability as a manufacturer quality issue rather than a driver compliance problem. Global instability forces the Gemini Alliance to rely on military forces to secure Red Sea voyages, even as other carriers continue to divert around Africa. Back home, the industry faces a bureaucratic paradox where 65,000 new visas are available but remain inaccessible due to a State Department freeze. Finally, we examine the broader economic fallout as weak freight demand triggers facility closures and layoffs across the logistics and manufacturing sectors. With over 3,000 jobs cut since mid-January, the industry is questioning how much leaner operations can get. Follow the FreightWaves NOW Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, we break down how Uber Freight posted flat Q4 results but finally achieved breakeven profitability through disciplined cost measures. We also discuss the company's pivot toward autonomous trucking as a long-term strategy to drive higher asset utilization. On the asset-based side, we analyze why losses continue at Heartland Express as the carrier navigates costly fleet integrations and a soft market. Despite recording its tenth consecutive net loss, the company's improving operating margins offer a glimmer of hope for a turnaround. Regulatory news takes center stage as the EPA targets truck engine makers to investigate widespread DEF system failures causing "limp mode" incidents. This major shift aims to treat equipment reliability as a manufacturer quality issue rather than a driver compliance problem. Global instability forces the Gemini Alliance to rely on military forces to secure Red Sea voyages, even as other carriers continue to divert around Africa. Back home, the industry faces a bureaucratic paradox where 65,000 new visas are available but remain inaccessible due to a State Department freeze. Finally, we examine the broader economic fallout as weak freight demand triggers facility closures and layoffs across the logistics and manufacturing sectors. With over 3,000 jobs cut since mid-January, the industry is questioning how much leaner operations can get. Follow the FreightWaves NOW Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

This Day in Legal History: Fifteenth Amendment RatifiedOn February 3, 1870, the Fifteenth Amendment to the United States Constitution was ratified, marking a pivotal moment in American legal history. The amendment prohibits federal and state governments from denying a citizen the right to vote based on “race, color, or previous condition of servitude.” Its ratification was the third and final of the Reconstruction Amendments, following the Thirteenth (abolishing slavery) and Fourteenth (guaranteeing equal protection and due process) Amendments.The Fifteenth Amendment was a direct response to the systemic disenfranchisement of Black Americans in the post-Civil War South. While it granted a legal foundation for Black men's suffrage, implementation faced immediate resistance. Southern states adopted literacy tests, poll taxes, grandfather clauses, and other discriminatory practices to circumvent the amendment and suppress Black political participation.Despite its passage, the amendment's guarantees would not be meaningfully enforced until the passage of the Voting Rights Act of 1965, nearly a century later. The legal battles stemming from the Fifteenth Amendment's promise have shaped much of the country's voting rights jurisprudence and continue to echo in current debates about voter ID laws, redistricting, and access to the ballot box.A U.S. federal judge is set to hear arguments on February 5 regarding Danish company Ørsted's request to lift the Trump administration's pause on its offshore Sunrise Wind project near Long Island, New York. Ørsted has asked for a preliminary injunction, warning that without a decision by February 6, it could lose access to a specialized vessel crucial for cable installation, putting the project's timeline, financial viability, and even survival at risk. The Interior Department halted five offshore wind projects in December, citing newly obtained, classified national security concerns, particularly radar interference. Ørsted's filing states the company has already committed over $7 billion to the Sunrise Wind project, which is about 45% complete and projected to power nearly 600,000 homes by October.Judge Royce Lamberth, who previously granted an injunction for Ørsted's Revolution Wind project off Rhode Island, will preside over the case. Four similar wind developments have already won legal relief allowing construction to continue during litigation. The ongoing delays reflect broader tensions between offshore wind expansion and the Trump administration's skepticism of the technology, as well as evolving security concerns.US judge to consider last project challenge to Trump offshore wind pause | ReutersThe U.S. Department of Justice has launched a civil rights investigation into the fatal shooting of Alex Pretti, a 37-year-old ICU nurse, by federal immigration agents in Minneapolis. Pretti was killed during an enforcement operation that has since drawn national outrage and led the Trump administration to alter its tactics in Minnesota. Deputy Attorney General Todd Blanche said the FBI is conducting a preliminary review, with potential involvement from the DOJ's Civil Rights Division, though he emphasized that the investigation is still in early stages.Video footage verified by Reuters shows Pretti being tackled by agents while holding a phone, and an officer retrieving a firearm from his body just before shots were fired. The Justice Department said a formal criminal civil rights probe would only proceed if the evidence supports it. Local officials have voiced distrust of the federal response and are conducting their own inquiry. Pretti is the second protester killed by federal agents in Minneapolis this month, and his family, represented by attorney Steve Schleicher, is demanding a transparent and impartial investigation. So far, no similar federal probe has been opened into the earlier shooting of Renee Good by an ICE officer.US Justice Dept opens civil rights probe into Alex Pretti shooting, official says | ReutersIn this week's column for Bloomberg Tax, I argue that Volkswagen's decision to cancel plans for a new Audi plant in the U.S. highlights the limitations of using tariffs as a cornerstone of industrial policy. The assumption underpinning tariff-heavy strategies is that the U.S. market is irresistible enough to force global firms to onshore production, even as tariffs erode that market's size and appeal. Tariffs have come to function like sin taxes—meant to discourage consumption—but unlike cigarettes or soda, the goal with trade policy is not abstention, but investment and economic engagement. Instead, firms like VW are responding by pulling back, as higher costs reduce consumer demand and make U.S. market share too small to justify large-scale investment. The belief that global manufacturers can swiftly build U.S. capacity ignores the time, cost, and uncertainty involved, especially in capital-intensive sectors. VW's exit is rational: it doesn't make financial sense to break ground on a multibillion-dollar plant when the target market is shrinking and returns are questionable.Policymakers need to move beyond blunt tools and design trade incentives based on real market data, such as U.S. demand and potential return on investment. That means requiring ROI modeling before tariffs are imposed, and asking whether the targeted company has enough exposure to be moved by them. If the answer is no, we risk losing access to competitive products, jobs, and consumer choice—not gaining them. Trade policy should be surgical, not punitive, and should acknowledge that capital follows incentives, not threats.In a piece I wrote for Forbes late last week, and with apologies for a double dose of me today: I examined California's long-running flirtation with a mileage-based tax to replace its declining gas tax revenues—and how what began as a test program has quietly become a form of policymaking through delay. In 2014, the state authorized a pilot program to study a “road usage charge,” a per-mile fee designed to keep transportation funding solvent as gas consumption drops. That pilot wrapped up in 2017 and showed the system works: vehicles can be tracked, billing can be simulated, and the technical challenges are manageable. But nearly a decade later, no mileage tax has been implemented, and new legislation—AB 1421—would extend the advisory committee until 2035.The real issue now isn't feasibility but political avoidance. The state has drifted into a passive strategy where permanent pilots and advisory boards take the place of real decisions. This kind of inertia has a name: policy drift—when the law remains formally unchanged, but materially obsolete. California's ongoing study phase has become a way to defer a difficult conversation about revenue and equity in a post-gasoline economy. The technology exists, and other states have already tested it. What's missing is political will and public engagement.AB 1421 doesn't collect revenue or educate voters—it simply extends the status quo under the guise of preparation. From the outside, it looks like planning. In practice, it's a weather balloon designed to measure political tolerance, not policy readiness.California Mileage Tax—Pilot Programs And Permanent Policy Inertia This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.minimumcomp.com/subscribe

Months after families raised alarms over substandard care at Kimberley Mental Hospital following a damning Ombudsman report, the Northern Cape Health Department confirms investigations are underway into alleged neglect. One patient is currently bedridden after she was discharged, and another allegedly died after being assaulted by a male nurse. Lebogang Majaha, spokesperson for the Northern Cape Department of Health, explains

1.28.2026 #RolandMartinUnfiltered: FBI Probes Georgia Election Office. GOP Cuts HBCU Voting. Judge Blocks Va. Map. AOC Takes on CVS. The FBI has searched a Georgia election office for evidence to support former President Trump's false claims that his 2020 election defeat was due to widespread voting fraud. In North Carolina, Republicans have reduced the number of voting sites at the nation's largest historically Black college and university, prompting students to file a lawsuit. In Virginia, a judge has blocked lawmakers' pro-Democratic voting map. We'll also bring you the results of a new poll revealing what issues matter most to Virginians. It seems that local and state police agencies are on a collision course with ICE agents. The President of the National Organization of Black Law Enforcement Executives will explain the complexities of this power struggle. New York Congresswoman Alexandria Ocasio-Cortez has called out CVS Health's corporate strategy to monopolize patient care. And a white city councilman in Florida is facing backlash for how he addressed white supremacy. Many people focused on his choice of words in the example and overlooked his core message, prompting calls for his resignation. Download the Black Star Network app at http://www.blackstarnetwork.com! We're on iOS, AppleTV, Android, AndroidTV, Roku, FireTV, XBox and SamsungTV. The #BlackStarNetwork is a news reporting platform covered under Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research.See omnystudio.com/listener for privacy information.

Turning Point USA and Erika Kirk are issuing cease and desist orders against people questioning the uncomfortable inconsistencies surrounding Charlie Kirk's assassination — no transparency, just intimidation. Instead of answering questions, they're trying to shut us down. Turning Point USA, under Erika Kirk's regime, is in meltdown—firing 30-40 insiders in a desperate hunt for the mole leaking info about shady embezzlements, Fort Huachuca plots, and the DOGE audit fallout. John Jubilee joins Stew to expose how Energized Health's inner cellular hydration protocol turned his dad bod into peak strength stronger than his 20s.

NEWS: DoJ probes 14 infra scam cases in batches | Jan. 26, 2026Subscribe to The Manila Times Channel - https://tmt.ph/YTSubscribeVisit our website at https://www.manilatimes.net Follow us: Facebook - https://tmt.ph/facebook Instagram - https://tmt.ph/instagram Twitter - https://tmt.ph/twitter DailyMotion - https://tmt.ph/dailymotion Subscribe to our Digital Edition - https://tmt.ph/digital Check out our Podcasts: Spotify - https://tmt.ph/spotify Apple Podcasts - https://tmt.ph/applepodcasts Amazon Music - https://tmt.ph/amazonmusic Deezer: https://tmt.ph/deezer Stitcher: https://tmt.ph/stitcherTune In: https://tmt.ph/tunein#TheManilaTimes#KeepUpWithTheTimes Hosted on Acast. See acast.com/privacy for more information.

HEADLINES: DoJ probes 14 infra scam cases in batches | Jan. 26, 2026Subscribe to The Manila Times Channel - https://tmt.ph/YTSubscribe Visit our website at https://www.manilatimes.net Follow us: Facebook - https://tmt.ph/facebook Instagram - https://tmt.ph/instagram Twitter - https://tmt.ph/twitter DailyMotion - https://tmt.ph/dailymotion Subscribe to our Digital Edition - https://tmt.ph/digital Check out our Podcasts: Spotify - https://tmt.ph/spotify Apple Podcasts - https://tmt.ph/applepodcasts Amazon Music - https://tmt.ph/amazonmusic Deezer: https://tmt.ph/deezer Stitcher: https://tmt.ph/stitcherTune In: https://tmt.ph/tunein#TheManilaTimes#KeepUpWithTheTimes Hosted on Acast. See acast.com/privacy for more information.

SEGMENT 8: SPACE TUG AND OUTER PLANET PROBE DISCOVERIES Guest: Bob ZimmermanZimmerman discusses a new space tug designed to deorbit Pentagon satellites addressing orbital debris concerns. Discussion turns to Jupiter and Saturn probes returning surprising scientific results, expanding understanding of the outer solar system, and how commercial and government space programs increasingly collaborate on solving both practical and exploratory challenges.1959

More Minnesota protestors face federal investigation after disrupting a church service in St. Paul, the effects of Virginia Democrats' election sweep begin to take shape, and the Pentagon purchases a curious new device as evidence has come to light about the so-called “Havana Syndrome.” Get the facts first with Morning Wire. - - - Ep. 2589 - - - Wake up with new Morning Wire merch: https://bit.ly/4lIubt3 - - - Today's Sponsor: NetSuite - Get the free business guide, Demystifying AI, at https://Netsuite.com/MORNINGWIRE - - - Privacy Policy: https://www.dailywire.com/privacy morning wire,morning wire podcast,the morning wire podcast,Georgia Howe,John Bickley,daily wire podcast,podcast,news podcast Learn more about your ad choices. Visit podcastchoices.com/adchoices

TRENDING - Don Lemon livestreams anti-ICE church protest, Josh Shapiro says Kamala Harris' team asked him if he was an Israeli spy, IG probe finds Labor Secretary Lori Chavez-DeRemer's office booze stash and strip club trip with subordinates, and a Florida woman tried to avoid arrest by defecating towards officers.See omnystudio.com/listener for privacy information.

TRENDING - Don Lemon livestreams anti-ICE church protest, Josh Shapiro says Kamala Harris' team asked him if he was an Israeli spy, IG probe finds Labor Secretary Lori Chavez-DeRemer's office booze stash and strip club trip with subordinates, and a Florida woman tried to avoid arrest by defecating towards officers.

First, we talk to The Indian Express' Girish Kuber about the BJP-Shiv Sena's sweeping municipal poll win in Maharashtra and what it reveals about the shifting contours of urban politics in the state.Next, we speak to The Indian Express' Vineet Bhalla about a split Supreme Court verdict on Section 17A of the Prevention of Corruption Act and how it reopens the long-standing debate between shielding honest officers and enabling timely probes. (11:45)Lastly, we discuss the DGCA's record penalty on IndiGo for widespread flight disruptions in December, and what it tells us about accountability in the aviation sector. ((21:40)Hosted by Ichha SharmaProduced by Shashank Bhargava and Ichha SharmaEdited and mixed by Suresh Pawar

A federal investigation is underway after anti-ICE protesters disrupted a Sunday church service in St. Paul, Minnesota. Demonstrators stormed a Baptist church during worship, chanting “Justice for Renee Good,” and alleging a pastor has ties to ICE. The incident comes as immigration tensions escalate, with reports the Pentagon has ordered 1,500 troops to prepare for possible deployment to Minnesota.Tensions over Greenland are escalating, with President Trump now threatening new tariffs on European countries fighting U.S. efforts to acquire the territory. Treasury Secretary Scott Bessent called Greenland essential to U.S. national security, while European leaders held emergency talks.A winter storm brought brief snow to parts of Florida's Panhandle on Sunday, for the second year in a row. That system is tied to a powerful Arctic blast now spreading across much of the country, bringing heavy snow, strong winds, and some of the coldest temperatures of the season. Hundreds of flights are already cancelled across New York and Jersey.

NHTSA extended Tesla's deadline to respond to an FSD investigation covering 8,313 potential traffic violations. The new February 23 deadline arrives just after Musk announced FSD will become subscription-only on February 14, the same day California's DMV gave Tesla to fix misleading marketing or face a sales ban. We break down what federal regulators are actually investigating, why Tesla is juggling three major probes simultaneously, and how the subscription pivot may be a legal hedge as regulators close in.

6pm: Video Guest – Jim Walsh – State Rep and Chairman of The WA GOP // WA Democrats criticize reporter probes into potential daycare fraud // Audit flags $415M as WA department lacked data to track daycare funds // What else Jim is focusing on in Olympia right now: - The Anti-Elected Sheriff Proposal - Ferguson’s “cockeyed and constitutionally dubious West Coast Health Alliance” // This Day in History: 1972 - “American Pie” hits #1 on the pop charts // 2009 - Pilot Sully Sullenberger performs “Miracle on the Hudson” // Ozempic for cats is on the way

Today on Tech and Science Daily from The Standard, Alan Leer covers a London breakthrough from Moorfields and UCL using a routine eye-surgery gel injection to restore sight in rare hypotony cases, plus new UCL Alzheimer's research on APOE gene risk, Brazil's probe into WhatsApp Business terms, Hytale's early access launch and Minecraft's “cutest drop” tease. Plus a little bit for Genshin fans tooYou'll find all your latest news at Standard.co.uk Hosted on Acast. See acast.com/privacy for more information.

-- On the Show -- Josh Shapiro, Governor of Pennsylvania, joins us to discuss the killing of Renee Good, and how to resist Trump and his ICE agents -- Donald Trump and his allies respond to scandals involving Nicolás Maduro and the killing of Renee Good by rejecting investigation and demanding absolute loyalty -- Donald Trump pressures the Justice Department to open a criminal probe into Jerome Powell after the Federal Reserve refuses to cut interest rates, and Powell publicly resists -- Newly released video of ICE agents killing Renee Good contradicts official claims of self defense and raises serious questions about the justification for deadly force -- Donald Trump abruptly leaves a high level meeting with oil executives to admire the White House ballroom construction project -- Kristi Noem repeatedly defends Donald Trump and law enforcement actions on television by rejecting video evidence and reframing any challenge to Trump as illegitimate -- Donald Trump delivers incoherent and contradictory answers to reporters on immunity, policing, health care, and foreign policy -- Congressman Jake Auchincloss confronts Fox News host Peter Doocy with the implications of defending the ICE shooting of Renee Good -- On the Bonus Show: Vivek Ramaswamy leaves social media after receiving racist messages, ICE shortens academy training to 47 days, the Pentagon moves to cut Mark Kelly's pension, and much more... ⚠️ Ground News: Get 40% OFF their unlimited access Vantage plan at https://ground.news/pakman

Taking on the Fed. Chair Jerome Powell confirms the central bank has received grand jury subpoenas from the Justice Department — a dramatic escalation in President Trump's fight with the Federal Reserve. The president says he's unaware of the probe. Plus, Wall Street reacting fast, futures sinking and gold jumping to new highs. And later, the president's push to cap credit card rates and home prices. We speak with the CEOs of Klarna and Better Finance. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

A scandal so massive it could shatter the political status quo is finally coming into focus—and the numbers are staggering. Federal investigators are uncovering hundreds of millions of dollars in cash funneled out of the U.S. through Minneapolis–St. Paul Airport alone, with tens of billions more nationwide potentially tied to fraud, remittances, and illegal networks. Why did it stop under one administration—and restart under another? Who knew? Who looked the other way? And why are arrests suddenly happening now? This episode connects the dots between cash couriers, welfare fraud, illegal immigration, and political protection. ⚠️

In this episode, we take a deep dive into the cosmic events and scientific discoveries that await us in 2026. We kick off with an exciting preview of lunar exploration, as NASA's Artemis program prepares to send astronauts on a historic flyby of the Moon, alongside a fleet of robotic landers from various commercial companies, including Jeff Bezos's Blue Moon. Next, we highlight the total solar eclipse on August 12, which will cross the Arctic, as well as a ring of fire eclipse in Antarctica, making 2026 a year for eclipse chasers.Shifting our focus to the edge of our solar system, we discuss the latest findings from the Voyager probes, which have uncovered a "wall of fire" at the boundary of the heliosphere, challenging our understanding of solar and interstellar interactions. We also explore Russia's recent launch of the Abzor R1, a radar Earth observation satellite that enhances their surveillance capabilities, marking a significant step in their sovereign space program.In a discovery that feels like science fiction, scientists have detected interstellar tunnels—narrow structures of hot plasma extending from our solar bubble into the galaxy, possibly formed by ancient supernovae. This revelation adds a new layer of complexity to our understanding of galactic structure.Finally, we examine the rapidly evolving commercial space race, with updates on China's reusable rocket initiatives and how companies like Stokespace and Relativity Space are transforming Florida's historic Space Coast into a hub for future launches. Join us as we explore these captivating stories and much more in this episode of Astronomy Daily!00:00 – **Astronomy Daily brings you the latest news from across the cosmos00:43 – **2026 is shaping up to be a monumental year for lunar exploration01:41 – **A total solar eclipse will cross over the Arctic on August 12th02:36 – **NASA's Voyager probes have detected a searingly hot region of space04:16 – **Russia launches new radar Earth observation satellite with huge strategic importance05:24 – **Scientists have detected narrow structures of hot plasma extending into the wider galaxy06:58 – **The reusable rocket race is heating up, and it's not just SpaceX08:22 – **Stokespace and Relativity Space are building out launch sites at Cape Canaveral09:40 – **This is the end of today's Astronomy Daily show### Sources & Further Reading1. NASA2. Roscosmos3. Space.com### Follow & ContactX/Twitter: @AstroDailyPodInstagram: @astrodailypodEmail: hello@astronomydaily.ioWebsite: astronomydaily.ioClear skies and see you next time!

The latest in business, financial, and markets news and how it impacts your money, reported by CNBC's Peter Schacknow Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

First, we talk to The Indian Express' Jay Mazoomdaar about the new definition of the Aravalli hills proposed by the Environment Secretary and approved by the Supreme Court. He shares how this new definition will impact the hills and the activities that are taking place there, like mining and construction. Next, we talk to The Indian Express' Anonna Dutt about the concept of data exclusivity, which if implemented in India, can impact the future of the Indian pharmaceutical industry. She explains what it is, why the Indian government is making efforts towards it and how it will impact the industry. (15:04)Lastly, we speak about the Enforcement Directorate and its efforts towards speedy trials and completing open investigations after receiving criticism. (26:37)Hosted by Niharika Nanda Produced and written by Shashank Bhargava and Niharika Nanda Edited and mixed by Suresh PawarThis episode includes AI-generated content.

The latest North State and California news on our airwaves for Thursday, December 18, 2025.

Back in episode 112, Phil and JF devised a gimmick for a show: randomly select one of the many aphorisms in The Book of Probes, a compendium of Marshall McLuhan's prophetic quips designed by David Carson, and see what happens. It proved lively enough that they're trying it again nearly a hundred episodes later. The resulting conversation touches the weird across a range of themes: tourism, the two kinds of truth, advertising, Kubrick's marketing savvy, technology, orality versus literacy, and much more. A fitting feast for the mind as the year draws to a close. From all of us at Weird Studies, happy holidays. • Sign up for JF Martel and Erik Davis's upcoming course on Moby-Dick. • Join Phil, JF, and composer Pierre-Yves Martel for Weirdosphere's Solstice Story Hour on December 21. • For dates, venues, and the full slate of Weird Academia events in Bloomington this January, visit the Centre for Possible Minds website. • To participate in the Weird Academia Colloquium, email organizers Emma Stamm and Michael Garfield at elfthoughts@gmail.com Header Image: NASA. REFERENCES Marshall McLuhan, Distant Early Warning Deck Thomas Mann, The Magic Mountain Plato, The Seventh Letter Marshall McLuhan, The Book of Probes Toronto School of Communication Theory Walter Ong, Orality and Literacy Paul Kingsnorth, Against the Machine Charles Taylor, A Secular Age Plato, The Republic  Marshall McLuhan, Understanding Media Jonathan Crary, 24/7 H. P. Lovecraft, The Color out of Space Learn more about your ad choices. Visit megaphone.fm/adchoices

⭐ Typical Skeptic Podcast #2335Psychic Liz Cross – Live Readings, Mind Probes & Remote Viewing

The Trump administration announced on Tuesday that it has suspended the processing of all immigration applications from 19 countries, including Afghanistan and Somalia, citing national security and public safety concerns. The action comes a week after an Afghan national was arrested for shooting two National Guard members near the White House, killing one and critically wounding the other. The suspension includes green card and U.S. citizenship processing, according to a memorandum.The House Homeland Security Committee will hear testimonies from leaders of law enforcement associations regarding how anti-law enforcement rhetoric is fueling violence against officers nationwide.

The FBI launches an investigation into six Democratic lawmakers over a video urging military members to ignore illegal orders. A Tennessee Democrat's congressional campaign falters as resurfaced comments show her mocking Nashville and pushing far-left positions. A male competitor is disqualified from the Official Strongman Games after secretly entering and winning the women's division. President Trump pardons four turkeys ahead of Thanksgiving, including two from last year whose pardons he declared “invalid.” Riverbend Ranch: Visit https://riverbendranch.com/ | Use promo code MEGYN for $20 off your first order. Walmart: Learn how Walmart is fueling the future of U.S. manufacturing at https://Walmart.com/America-at-work Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Donate (no account necessary) | Subscribe (account required) Join Bryan Dean Wright, former CIA Operations Officer, as he dives into today's top stories shaping America and the world. In this episode of The Wright Report, Bryan breaks down the fierce political battles inside Washington over healthcare, immigration, and the courts. He then turns to global flashpoints involving the Muslim Brotherhood, Ukraine, foreign propaganda campaigns, and a surprising development involving Italian pasta. Healthcare Fight Intensifies: President Trump is preparing to release his updated plan for America's health insurance marketplace. Early details include extending Obamacare subsidies for two more years, with tighter income eligibility rules and minimum premium requirements. The White House will also expand Health Savings Accounts and allow federal assistance to be used for faith-based HealthShare programs. Republicans fear voter backlash if a fix is not delivered before the midterms. At the same time, critics warn that the extension will add around fifty billion dollars per year to the national debt. Bryan notes the frustration felt by many listeners facing soaring premiums, including his own fifty-four percent increase. Immigration Battle Escalates: DHS is recruiting "deportation judges" with salaries up to $200,000 and significant bonuses. The administration hopes to replace immigration judges with high asylum approval rates, particularly in cities like San Francisco, where twelve Democrat appointed judges have already been removed. Trump is prioritizing faster removals for millions of pending asylum cases. Meanwhile, the fight over Somali welfare fraud has led the White House to rescind long-standing protections for Somali migrants, prompting criticism from Democrats and activist groups. Representative Ilhan Omar mocked the policy shift and insisted, "We are here to stay." Courts Block Key Enforcement Tools: A Clinton-appointed judge ruled that the IRS cannot share data with DHS to identify illegal aliens, blocking access to more than one million records. Other Democrat appointed judges halted Trump's attempt to expand rapid deportations inside the United States for migrants who have been here for fewer than two years. Bryan explains why these rulings highlight a deeper partisan divide inside the judiciary and why Supreme Court control has become a central battleground for both parties. Sedition Charges and Military Discipline: Senator Mark Kelly and other members of the "Seditious Six" face investigations after urging military personnel to resist hypothetical unlawful orders from President Trump. Kelly insists he is exercising free speech, but Pentagon officials say retired officers remain bound by military law. Bryan argues that these calls to resist the President are politically motivated and undermine public trust in the armed forces. Representative Eugene Vindman is also under investigation for unapproved foreign consulting work in Ukraine after leaving military service. Comey and Letitia James Win a Round in Court: Charges against former FBI Director James Comey and New York Attorney General Letitia James were dismissed after a judge ruled that the Trump appointed prosecutor had been improperly selected. The Department of Justice says it will refile the charges and insists the statute of limitations has not expired. Bryan describes the moment as a tactical win for the defendants but not the end of the fight. Trump Targets the Muslim Brotherhood: The President ordered the State Department to determine which branches of the Muslim Brotherhood should be labeled as terrorist organizations. The group's history stretches back to its founding in Egypt in the 1920s, inspiring violent movements including Hamas and al Qaeda. Bryan notes that some Middle Eastern governments, particularly Turkey and Qatar, still support parts of the organization, and that groups like CAIR in the United States have roots in Brotherhood networks. Foreign Troll Farms Exposed on X: A new platform update revealed that many accounts posing as American conservatives or pro-Palestine activists are actually operated from Africa, Asia, and the Middle East. These users post inflammatory political content to generate clicks and payouts under Elon Musk's monetization system. Bryan urges listeners to be skeptical of viral accounts and to scrutinize sources. Ukraine Peace Plan Revised: Trump's proposed peace plan has been reduced from 28 points to 19 and now leans more toward Ukraine's favor. European leaders insist Ukraine must maintain a one-million-strong force, even as countries like Germany admit it will take a decade to reach 260,000 troops. Bryan argues that Europe's rhetoric far exceeds its ability to act and that Trump is correct to dismiss their objections. Italian Pasta Tariff Coming: The White House is preparing a tariff on imported Italian pasta to protect U.S. producers. Bryan jokes that listeners may want to stock up now.   "And you shall know the truth, and the truth shall make you free." - John 8:32   Put a smile on your face and give joy to your taste buds… Give Masa and Vandy beef tallow chips a try today! Use code WRIGHT for 25% off your first order… at MASAchips.com or VandyCrisps.com. So incredibly delicious! I promise, you won't be disappointed.     Keywords: Trump healthcare plan Obamacare subsidies, DHS deportation judges hiring, Somali welfare fraud Minnesota Omar quote, IRS DHS data sharing blocked, rapid deportation two year rule, Mark Kelly sedition investigation, Eugene Vindman ethics probe, James Comey Letitia James charges dismissed, Muslim Brotherhood terror designation review, foreign troll accounts X social media, Ukraine peace plan nineteen points, Italian pasta tariff

We tell you why a judge has dismissed the federal cases against a former FBI director and New York's Attorney General. Ukraine and Russia are at odds over the latest version of President Trump's peace plan. The Pentagon could court martial a Democratic senator for what he said in a recent video post. An NBA coach had his day in court in the alleged gambling scheme case. Plus, a new clinical trial shows the limits of popular weight loss drugs. Learn more about your ad choices. Visit podcastchoices.com/adchoices

President Trump calls for investigations into Democrats' ties to Epstein, War Secretary Hegseth announces “Operation Southern Spear,” and yet another illegal sports gambling case drops. Get the facts first with Evening Wire. - - - Wake up with new Morning Wire merch: https://bit.ly/4lIubt3 - - - Privacy Policy: https://www.dailywire.com/privacy morning wire,morning wire podcast,the morning wire podcast,Georgia Howe,John Bickley,daily wire podcast,podcast,news podcast Learn more about your ad choices. Visit podcastchoices.com/adchoices

The deal to reopen the government drives a wedge between Democrats, Florida probes JPMorgan Chase over connections to Operation Arctic Frost, and, on this Veterans Day, we discuss the effects of the shutdown on veterans and service members. Get the facts first with Morning Wire. - - - Wake up with new Morning Wire merch: https://bit.ly/4lIubt3 - - - Today's Sponsors: DeleteMe - Get 20% off your DeleteMe plan when you go to https://joindeleteme.com/WIRE and use promo code WIRE at checkout. Lumen - Head to http://lumen.me/WIRE for 10% off your purchase. Shopify - Go to https://Shopify.com/morningwire to sign up for your $1-per-month trial period and upgrade your selling today. - - - Privacy Policy: https://www.dailywire.com/privacy morning wire,morning wire podcast,the morning wire podcast,Georgia Howe,John Bickley,daily wire podcast,podcast,news podcast Learn more about your ad choices. Visit podcastchoices.com/adchoices

In today's episode, we engage with Congresswoman Mariannette Miller-Meeks from Iowa, who shares insights on the current political landscape, including updates on Obamacare and corporate welfare. We also hear from Bud Cummings, a former prosecutor who recounts his alarming experience with the U.S. attorney's office regarding Hunter Biden's tax issues. Finally, Lauren Zelt sheds light on a new trend in legal practices involving billboard lawyers and their financial ties to political donations. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Today we're talking about a breakthrough in Congress's government shutdown gridlock; the Supreme Court weighing in on SNAP and passports; the scandals and tragedies dominating sporting headlines; and other top news for Monday, November 10th. Stay informed while remaining focused on Christ with The Pour Over. Join over 1.5 million readers with our free newsletter here Looking to support us? You can choose to pay here Check out our sponsors! We actually use and enjoy every single one. Cru Surfshark Holy Post CCCU Upside Mosh LMNT Theology in the Raw Safe House Project Not Just Sunday Podcast Quince Life Application Study Bible She Reads Truth

The consequences of the government shutdown rear their ugly heads, a congressional hearing today examines the origins of political violence, and Trump continues his Asia tour as the Left melts down over his East Wing remodel. Get the facts first with Morning Wire. - - - Wake up with new Morning Wire merch: https://bit.ly/4lIubt3 - - - Today's Sponsors: Lean - Get 20% off when you enter MORNINGWIRE at https://TakeLean.com  Vanta - Visit https://vanta.com/MORNINGWIRE to sign up for a free demo today! Balance of Nature - Go to https://balanceofnature.com/pages/podcasters and use promo code WIRE for 35% off your first order as a preferred customer PLUS get a free bottle of Fiber and Spice. - - - Privacy Policy: https://www.dailywire.com/privacy morning wire,morning wire podcast,the morning wire podcast,Georgia Howe,John Bickley,daily wire podcast,podcast,news podcast Learn more about your ad choices. Visit megaphone.fm/adchoices