Podcasts about predictive

How does an autonomous vehicle see? How does it sense the road? They are equipped of many sensors, of course. Are they all powerful enough? Small enough to hide them and make your car look beautiful?  In this episode I speak about LIDAR, high resolution cameras and some machine learning methods adapted to a minimal number of sensors.   Our Sponsors Ready to advance your career in data science? University of Cincinnati Online offers nationally recognized educational programs in business analytics and information systems. Predictive Analytics Today named UC as the No.1 MS Data Science school in the country and is nationally recognized with a proven track record of placing students at high-profile companies such as Google, Amazon and P&G.  Discover more about the University of Cincinnati's 100% online master's degree programs at online.uc.edu/obais    Amethix works to create and maximize the impact of the world's leading corporations and startups, so they can create a better future for everyone they serve. We provide solutions in AI/ML, Fintech, Healthcare/RWE, and Predictive maintenance.   References https://patents.google.com/patent/US20220043449A1/en?oq=20220043449    

Erik Jensen is the chief strategy officer at Predictive and a co-owner. Predictive helps clients build their authority positions in the niche(s) they want to serve — and then — monetize that position in the form of a sales pipeline filled with a steady stream of right-fit clients. But often — Erik has conversations with agency owners, business coaches, and consultants (that's our tribe here at Predictive ROI) — and he's asked questions like… “Hey — how do I get my list to buy more of my stuff.” Here's the reality — you can “get” your “list” to “buy your stuff.” What you CAN do is prove over and over again that you can help solve business issues and challenges they care about deeply. So let's reframe. Instead of the earlier question — we'd like you to consider three variables as the starting point for solving the equation of increasing sales. The first variable is WHO. The second variable is WHAT. And the third variable is HOW. Over the next three episodes of the podcast — Erik will join Stephen to work through the three variables of WHO — WHAT — and HOW they can work together to fill your sales pipeline. We're going to define each. We'll share specific examples in full transparency. And — we'll share any helpful frameworks along the way. Why? Because no matter how long you've been running your business — no matter how much experience you have — no matter how successful you've been — if we lose sight of the fundamentals…we lose our way, and we start showing up as “buy more of my stuff.” Whether you show up that way by accident or not — it feels like a whole lot of not awesome for your clients and prospects. Think of this episode as the “Starting Block” — it's time to get set…here we go. What you will learn about in this episode: The three ingredients in the right-fit client avatar recipe How your client stories can connect to your prospect's “psychographics” How to win the “heart” with the pain-pleasure principle followed by winning the “head” Why “hope” is the strongest decision-making force How to share your right-fit client avatar with a prospective client during an actual biz dev meeting

Craig Rodgers on LinkedIn:https://www.linkedin.com/in/craig-rodgers-891ba425/Double Irish, With a Dutch Sandwich:https://www.investopedia.com/terms/d/double-irish-with-a-dutch-sandwich.asp

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.25.509432v1?rss=1 Authors: Taha, A., Soni, B., Thakuri, D. S., Ritter, E., Bhattarai, P., Chand, G. Abstract: Amyloid-beta (A{beta}) and tau tangles are hallmarks of Alzheimer's disease. A{beta} distributions in the tau-defined Braak staging regions and their multivariate predictive relationships with mild cognitive impairment (MCI) are not known. In this study, we used PiB PET data from 60 participants (33 with MCI and 27 controls), quantified A{beta} as distribution volume ratio (DVR) in Braak regions, and compared between MCI and controls to test the hypothesis that DVR alters with declining cognition. We found elevated DVR in participants with MCI, especially in the spatial distribution of Braak stages III-IV and V-VII, while an alteration in Braak stage I-II was near the statistical significance. DVR markers correlated with cognitive status, especially in Braak stages III-IV and VI-V. To evaluate whether these markers are predictive of cognitive impairment, we designed support vector machine and artificial neural network models. These methods showed predictive multivariate relationships between A{beta} makers of Braak regions and cognitive impairment. Overall, these results highlight the importance of computer-aided research efforts for understanding AD pathophysiology. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.21.508808v1?rss=1 Authors: Terporten, R., Huizeling, E., Heidlmayr, K., Hagoort, P., Kosem, A. Abstract: Words are not processed in isolation, instead they are commonly embedded in phrases and sentences. The sentential context influences the perception and processing of a word. However, how this is achieved by brain processes and whether predictive mechanisms underlie this process remains a debated topic. To this end we employed an experimental paradigm in which we orthogonalized sentence context constraints and predictive validity, which was defined as the ratio of congruent to incongruent sentence endings within the experiment. While recording electroencephalography, participants read sentences with three levels of sentential context constraints (high, medium and low). Participants were also separated into two groups, which differed in their ratio of valid congruent to incongruent target words that could be predicted from the sentential context. For both groups we investigated modulations of alpha power before, and N400 amplitude modulations after target word onset. The results reveal that the N400 amplitude gradually decreases with higher context constraints. Contrary, alpha power is non-monotonically influenced, displaying the strongest decrease for high context constraints over frontal electrode sites, while alpha power between medium and low context constraints does not differ. This indicates that both neural correlates are influenced by the degree of context constraint but are not affected by changes in predictive validity. The results therefore suggest that both N400 and alpha power are not unequivocally linked to the predictability of a target word based on larger contextual information. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

We're doing something totally different for this episode of our podcast. As you know — Erik is one of the owners here at Predictive and has joined Stephen for several episodes on the podcast, which has been a fun opportunity to share Erik's smarts with our community in new and deeper ways. No doubt — Erik has a depth of expertise, and we're looking forward to him joining Stephen as his guest on the show again soon. That said — for today — we're going to do something different and share the full-length interview where Erik was interviewed by Deb Zahn, the fabulous host of the “Craft of Consulting” podcast. Deb did an exceptional job of orchestrating a deep and instructional conversation around a topic that, in our experience — agencies, business coaches, and strategic consultants — typically either run from — or — move toward in a somewhat committed way. And that topic is — niching down. When the episode aired — we listened to every word because what Erik shared was so helpful and awesome. You'll hear Erik map out in full transparency: How and where to build your position of authority (we call that planting your flag) Why it's important How to monetize it Overcoming your fear of opportunity costs Recognizing that everything gets easier when you niche down And much more, which is why we decided to share the full episode with you. Our guess is — you'll also love the food analogies Erik shared — so awesome. The one about the French pastry chef is fantastic! Okay — so without further ado — here are Deb Zahn and Predictive's own, Erik Jensen. Enjoy!

Sometimes applications crash. Some other times applications crash because memory is exhausted. Such issues exist because of bugs in the code, or heavy memory usage for reasons that were not expected during design and implementation. Can we use machine learning to predict and eventually detect out of memory kills from the operating system? Apparently, the Netflix app many of us use on a daily basis leverage ML and time series analysis to prevent OOM-kills. Enjoy the show! Our Sponsors Explore the Complex World of Regulations. Compliance can be overwhelming. Multiple frameworks. Overlapping requirements. Let Arctic Wolf be your guide. Check it out at https://arcticwolf.com/datascience   Amethix works to create and maximize the impact of the world's leading corporations and startups, so they can create a better future for everyone they serve. We provide solutions in AI/ML, Fintech, Healthcare/RWE, and Predictive maintenance.   Transcript 1 00:00:04,150 --> 00:00:09,034 And here we are again with the season four of the Data Science at Home podcast. 2 00:00:09,142 --> 00:00:19,170 This time we have something for you if you want to help us shape the data science leaders of the future, we have created the the Data Science at Home's Ambassador program. 3 00:00:19,340 --> 00:00:28,378 Ambassadors are volunteers who are passionate about data science and want to give back to our growing community of data science professionals and enthusiasts. 4 00:00:28,534 --> 00:00:37,558 You will be instrumental in helping us achieve our goal of raising awareness about the critical role of data science in cutting edge technologies. 5 00:00:37,714 --> 00:00:45,740 If you want to learn more about this program, visit the Ambassadors page on our website@datascienceathome.com. 6 00:00:46,430 --> 00:00:49,234 Welcome back to another episode of Data Science at Home podcast. 7 00:00:49,282 --> 00:00:55,426 I'm Francesco Podcasting from the Regular Office of Amethyx Technologies, based in Belgium. 8 00:00:55,618 --> 00:01:02,914 In this episode, I want to speak about a machine learning problem that has been formulated at Netflix. 9 00:01:03,022 --> 00:01:22,038 And for the record, Netflix is not sponsoring this episode, though I still believe that this problem is a very well known problem, a very common one across factors, which is how to predict out of memory kill in an application and formulate this problem as a machine learning problem. 10 00:01:22,184 --> 00:01:39,142 So this is something that, as I said, is very interesting, not just because of Netflix, but because it allows me to explain a few points that, as I said, are kind of invariance across sectors. 11 00:01:39,226 --> 00:01:56,218 Regardless of your application, is a video streaming application or any other communication type of application, or a fintech application, or energy, or whatever, this memory kill, out of memory kill still occurs. 12 00:01:56,314 --> 00:02:05,622 And what is an out of memory kill? Well, it's essentially the extreme event in which the machine doesn't have any more memory left. 13 00:02:05,756 --> 00:02:16,678 And so usually the operating system can start eventually swapping, which means using the SSD or the hard drive as a source of memory. 14 00:02:16,834 --> 00:02:19,100 But that, of course, will slow down a lot. 15 00:02:19,430 --> 00:02:45,210 And eventually when there is a bug or a memory leak, or if there are other applications running on the same machine, of course there is some kind of limiting factor that essentially kills the application, something that occurs from the operating system most of the time that kills the application in order to prevent the application from monopolizing the entire machine, the hardware of the machine. 16 00:02:45,710 --> 00:02:48,500 And so this is a very important problem. 17 00:02:49,070 --> 00:03:03,306 Also, it is important to have an episode about this because there are some strategies that I've used at Netflix that are pretty much in line with what I believe machine learning should be about. 18 00:03:03,368 --> 00:03:25,062 And usually people would go for the fancy solution there like this extremely accurate predictors or machine learning models, but you should have a massive number of parameters and that try to figure out whatever is happening on that machine that is running that application. 19 00:03:25,256 --> 00:03:29,466 While the solution at Netflix is pretty straightforward, it's pretty simple. 20 00:03:29,588 --> 00:03:33,654 And so one would say then why making an episode after this? Well. 21 00:03:33,692 --> 00:03:45,730 Because I think that we need more sobriety when it comes to machine learning and I believe we still need to spend a lot of time thinking about what data to collect. 22 00:03:45,910 --> 00:03:59,730 Reasoning about what is the problem at hand and what is the data that can actually tickle the particular machine learning model and then of course move to the actual prediction that is the actual model. 23 00:03:59,900 --> 00:04:15,910 That most of the time it doesn't need to be one of these super fancy things that you see on the news around chatbots or autonomous gaming agent or drivers and so on and so forth. 24 00:04:16,030 --> 00:04:28,518 So there are essentially two data sets that the people at Netflix focus on which are consistently different, dramatically different in fact. 25 00:04:28,604 --> 00:04:45,570 These are data about device characteristics and capabilities and of course data that are collected at Runtime and that give you a picture of what's going on in the memory of the device, right? So that's the so called runtime memory data and out of memory kills. 26 00:04:45,950 --> 00:05:03,562 So the first type of data is I would consider it very static because it considers for example, the device type ID, the version of the software development kit that application is running, cache capacities, buffer capacities and so on and so forth. 27 00:05:03,646 --> 00:05:11,190 So it's something that most of the time doesn't change across sessions and so that's why it's considered static. 28 00:05:12,050 --> 00:05:18,430 In contrast, the other type of data, the Runtime memory data, as the name says it's runtime. 29 00:05:18,490 --> 00:05:24,190 So it varies across the life of the session it's collected at Runtime. 30 00:05:24,250 --> 00:05:25,938 So it's very dynamic data. 31 00:05:26,084 --> 00:05:36,298 And example of these records are for example, profile, movie details, playback information, current memory usage, et cetera, et cetera. 32 00:05:36,334 --> 00:05:56,086 So this is the data that actually moves and moves in the sense that it changes depending on how the user is actually using the Netflix application, what movie or what profile description, what movie detail has been loaded for that particular movie and so on and so forth. 33 00:05:56,218 --> 00:06:15,094 So one thing that of course the first difficulty of the first challenge that the people at Netflix had to deal with was how would you combine these two things, very static and usually small tables versus very dynamic and usually large tables or views. 34 00:06:15,142 --> 00:06:36,702 Well, there is some sort of join on key that is performed by the people at Netflix in order to put together these different data resolutions, right, which is data of the same phenomenon but from different sources and with different carrying very different signals in there. 35 00:06:36,896 --> 00:06:48,620 So the device capabilities is captured usually by the static data and of course the other data, the Runtime memory and out of memory kill data. 36 00:06:48,950 --> 00:07:04,162 These are also, as I said, the data that will describe pretty accurately how is the user using that particular application on that particular hardware. 37 00:07:04,306 --> 00:07:17,566 Now of course, when it comes to data and deer, there is nothing new that people at Netflix have introduced dealing with missing data for example, or incorporating knowledge of devices. 38 00:07:17,698 --> 00:07:26,062 It's all stuff that it's part of the so called data cleaning and data collection strategy, right? Or data preparation. 39 00:07:26,146 --> 00:07:40,782 That is, whatever you're going to do in order to make that data or a combination of these data sources, let's say, compatible with the way your machine learning model will understand or will read that data. 40 00:07:40,916 --> 00:07:58,638 So if you think of a big data platform, the first step, the first challenge you have to deal, you have to deal with is how can I, first of all, collect the right amount of information, the right data, but also how to transform this data for my particular big data platform. 41 00:07:58,784 --> 00:08:12,798 And that's something that, again, nothing new, nothing fancy, just basics, what we have been used to, what we are used to seeing now for the last decade or more, that's exactly what they do. 42 00:08:12,944 --> 00:08:15,222 And now let me tell you something important. 43 00:08:15,416 --> 00:08:17,278 Cybercriminals are evolving. 44 00:08:17,374 --> 00:08:22,446 Their techniques and tactics are more advanced, intricate and dangerous than ever before. 45 00:08:22,628 --> 00:08:30,630 Industries and governments around the world are fighting back on dealing new regulations meant to better protect data against this rising threat. 46 00:08:30,950 --> 00:08:39,262 Today, the world of cybersecurity compliance is a complex one, and understanding the requirements your organization must adhere to can be a daunting task. 47 00:08:39,406 --> 00:08:42,178 But not when the pack has your best architect. 48 00:08:42,214 --> 00:08:53,840 Wolf, the leader in security operations, is on a mission to end cyber risk by giving organizations the protection, information and confidence they need to protect their people, technology and data. 49 00:08:54,170 --> 00:09:02,734 The new interactive compliance portal helps you discover the regulations in your region and industry and start the journey towards achieving and maintaining compliance. 50 00:09:02,902 --> 00:09:07,542 Visit Arcticwolves.com DataScience to take your first step. 51 00:09:07,676 --> 00:09:11,490 That's arcticwolf.com DataScience. 52 00:09:12,050 --> 00:09:18,378 I think that the most important part, though, I think are actually equally important. 53 00:09:18,464 --> 00:09:26,854 But the way they treat runtime memory data and out of memory kill data is by using sliding windows. 54 00:09:26,962 --> 00:09:38,718 So that's something that is really worth mentioning, because the way you would frame this problem is something is happening at some point in time and I have to kind of predict that event. 55 00:09:38,864 --> 00:09:49,326 That is usually an outlier in the sense that these events are quite rare, fortunately, because Netflix would not be as usable as we believe it is. 56 00:09:49,448 --> 00:10:04,110 So you would like to predict these weird events by looking at a historical view or an historical amount of records that you have before this particular event, which is the kill of the application. 57 00:10:04,220 --> 00:10:12,870 So the concept of the sliding window, the sliding window approach is something that comes as the most natural thing anyone would do. 58 00:10:13,040 --> 00:10:18,366 And that's exactly what the researchers and Netflix have done. 59 00:10:18,488 --> 00:10:25,494 So unexpectedly, in my opinion, they treated this problem as a time series, which is exactly what it is. 60 00:10:25,652 --> 00:10:26,190 Now. 61 00:10:26,300 --> 00:10:26,754 They. 62 00:10:26,852 --> 00:10:27,330 Of course. 63 00:10:27,380 --> 00:10:31,426 Use this sliding window with a different horizon. 64 00:10:31,558 --> 00:10:32,190 Five minutes. 65 00:10:32,240 --> 00:10:32,838 Four minutes. 66 00:10:32,924 --> 00:10:33,702 Two minutes. 67 00:10:33,836 --> 00:10:36,366 As close as possible to the event. 68 00:10:36,548 --> 00:10:38,886 Because maybe there are some. 69 00:10:39,008 --> 00:10:39,762 Let's say. 70 00:10:39,896 --> 00:10:45,678 Other dynamics that can raise when you are very close to the event or when you are very far from it. 71 00:10:45,704 --> 00:10:50,166 Like five minutes far from the out of memory kill. 72 00:10:50,348 --> 00:10:51,858 Might have some other. 73 00:10:51,944 --> 00:10:52,410 Let's say. 74 00:10:52,460 --> 00:10:55,986 Diagrams or shapes in the data. 75 00:10:56,168 --> 00:11:11,310 So for example, you might have a certain number of allocations that keep growing and growing, but eventually they grow with a certain curve or a certain rate that you can measure when you are five to ten minutes far from the out of memory kill. 76 00:11:11,420 --> 00:11:16,566 When you are two minutes far from the out of memory kill, probably this trend will change. 77 00:11:16,688 --> 00:11:30,800 And so probably what you would expect is that the memory is already half or more saturated and therefore, for example, the operating system starts swapping or other things are happening that you are going to measure in this. 78 00:11:31,550 --> 00:11:39,730 And that would give you a much better picture of what's going on in the, let's say, closest neighborhood of that event, the time window. 79 00:11:39,790 --> 00:11:51,042 The sliding window and time window approach is definitely worth mentioning because this is something that you can apply if you think pretty much anywhere right now. 80 00:11:51,116 --> 00:11:52,050 What they did. 81 00:11:52,160 --> 00:12:04,146 In addition to having a time window, a sliding window, they also assign different levels to memory readings that are closer to the out of memory kill. 82 00:12:04,208 --> 00:12:10,062 And usually these levels are higher and higher as we get closer and closer to the out of memory kill. 83 00:12:10,136 --> 00:12:15,402 So this means that, for example, we would have, for a five minute window, we would have a level one. 84 00:12:15,596 --> 00:12:22,230 Five minute means five minutes far from the out of memory kill, four minutes would be a level two. 85 00:12:22,280 --> 00:12:37,234 Three minutes it's much closer would be a level three, two minutes would be a level four, which means like kind of the severity of the event as we get closer and closer to the actual event when the application is actually killed. 86 00:12:37,342 --> 00:12:51,474 So by looking at this approach, nothing new there, even, I would say not even a seasoned data scientist would have understood that using a sliding window is the way to go. 87 00:12:51,632 --> 00:12:55,482 I'm not saying that Netflix engineers are not seasoned enough. 88 00:12:55,556 --> 00:13:04,350 Actually they do a great job every day to keep giving us video streaming platforms that actually never fail or almost never fail. 89 00:13:04,910 --> 00:13:07,460 So spot on there, guys, good job. 90 00:13:07,850 --> 00:13:27,738 But looking at this sliding window approach, the direct consequence of this is that they can plot, they can do some sort of graphical analysis of the out of memory kills versus the memory usage that can give the reader or the data scientist a very nice picture of what's going on there. 91 00:13:27,824 --> 00:13:39,330 And so you would have, for example, and I would definitely report some of the pictures, some of the diagrams and graphs in the show notes of this episode on the official website datascienceaton.com. 92 00:13:39,500 --> 00:13:48,238 But essentially what you can see there is that there might be premature peaks at, let's say, a lower memory reading. 93 00:13:48,334 --> 00:14:08,958 And usually these are some kind of false positives or anomalies that should not be there, then it's possible to set a threshold where the threshold to start lowering the memory usage because after that threshold something nasty can happen and usually happens according to your data. 94 00:14:09,104 --> 00:14:18,740 And then of course there is another graph about the Gaussian distribution or in fact no sharp peak at all. 95 00:14:19,250 --> 00:14:21,898 That is like kills or out of memory. 96 00:14:21,934 --> 00:14:33,754 Kills are more or less distributed in a normalized fashion and then of course there are the genuine peaks that indicate that kills near, let's say, the threshold. 97 00:14:33,802 --> 00:14:38,758 And so usually you would see that after that particular threshold of memory usage. 98 00:14:38,914 --> 00:14:42,142 You see most of the out of memory kills. 99 00:14:42,226 --> 00:14:45,570 Which makes sense because given a particular device. 100 00:14:45,890 --> 00:14:48,298 Which means certain amount of memories. 101 00:14:48,394 --> 00:14:50,338 Certain memory characteristics. 102 00:14:50,494 --> 00:14:53,074 Certain version of the SDK and so on and so forth. 103 00:14:53,182 --> 00:14:53,814 You can say. 104 00:14:53,852 --> 00:14:54,090 Okay. 105 00:14:54,140 --> 00:15:10,510 Well for this device type I have this memory memory usage threshold and after this I see that I have a relatively high number of out of memory kills immediately after this threshold. 106 00:15:10,570 --> 00:15:18,150 And this means that probably that is the threshold you would like to consider as the critical threshold you should never or almost never cross. 107 00:15:18,710 --> 00:15:38,758 So once you have this picture in front of you, you can start thinking of implementing some mechanisms that can monitor the memory usage and of course kind of preemptively dialocate things or keep that memory threshold as low as possible with respect to the critical threshold. 108 00:15:38,794 --> 00:15:53,446 So you can start implementing some logic that prevents the application from being killed by the operating system so that you would in fact reduce the rate of out of memory kills overall. 109 00:15:53,578 --> 00:16:11,410 Now, as always and as also the engineers state in their blog post, in the technical post, they say well, it's much more important for us to predict with a certain amount of false positive rather than false negatives. 110 00:16:11,590 --> 00:16:18,718 False negatives means missing an out of memory kill that actually occurred but got not predicted. 111 00:16:18,874 --> 00:16:40,462 If you are a regular listener of this podcast, that statement should resonate with you because this is exactly what happens, for example in healthcare applications, which means that doctors or algorithms that operate in healthcare would definitely prefer to have a bit more false positives rather than more false negatives. 112 00:16:40,486 --> 00:16:54,800 Because missing that someone is sick means that you are not providing a cure and you're just sending the patient home when he or she is sick, right? That's the false positive, it's the mess. 113 00:16:55,130 --> 00:16:57,618 So that's a false negative, it's the mess. 114 00:16:57,764 --> 00:17:09,486 But having a false positive, what can go wrong with having a false positive? Well, probably you will undergo another test to make sure that the first test is confirmed or not. 115 00:17:09,608 --> 00:17:16,018 So adding a false positive in this case is relatively okay with respect to having a false negative. 116 00:17:16,054 --> 00:17:19,398 And that's exactly what happens to the Netflix application. 117 00:17:19,484 --> 00:17:32,094 Now, I don't want to say that of course Netflix application is as critical as, for example, the application that predicts a cancer or an xray or something on an xray or disorder or disease of some sort. 118 00:17:32,252 --> 00:17:48,090 But what I'm saying is that there are some analogies when it comes to machine learning and artificial intelligence and especially data science, the old school data science, there are several things that kind of are, let's say, invariant across sectors. 119 00:17:48,410 --> 00:17:56,826 And so, you know, two worlds like the media streaming or video streaming and healthcare are of course very different from each other. 120 00:17:56,888 --> 00:18:05,274 But when it comes to machine learning and data science applications, well, there are a lot of analogies there. 121 00:18:05,372 --> 00:18:06,202 And indeed. 122 00:18:06,286 --> 00:18:10,234 In terms of the models that they use at Netflix to predict. 123 00:18:10,342 --> 00:18:24,322 Once they have the sliding window data and essentially they have the ground truth of where this out of memory kill happened and what happened before to the memory of the application or the machine. 124 00:18:24,466 --> 00:18:24,774 Well. 125 00:18:24,812 --> 00:18:30,514 Then the models they use to predict these things is these events is Artificial Neural Networks. 126 00:18:30,622 --> 00:18:31,714 Xg Boost. 127 00:18:31,822 --> 00:18:36,742 Ada Boost or Adaptive Boosting Elastic Net with Softmax and so on and so forth. 128 00:18:36,766 --> 00:18:39,226 So nothing fancy. 129 00:18:39,418 --> 00:18:45,046 As you can see, Xg Boost is probably one of the most used I would have expected even random forest. 130 00:18:45,178 --> 00:18:47,120 Probably they do, they've tried that. 131 00:18:47,810 --> 00:18:58,842 But XGBoost is probably one of the most used models on kaggle competitions for a reason, because it works and it leverages a lot. 132 00:18:58,916 --> 00:19:04,880 The data preparation step, that solves already more than half of the problem. 133 00:19:05,810 --> 00:19:07,270 Thank you so much for listening. 134 00:19:07,330 --> 00:19:11,910 I also invite you, as always, to join the Discord Channel. 135 00:19:12,020 --> 00:19:15,966 You will find a link on the official website datascience@home.com. 136 00:19:16,148 --> 00:19:17,600 Speak with you next time. 137 00:19:18,350 --> 00:19:21,382 You've been listening to Data Science at home podcast. 138 00:19:21,466 --> 00:19:26,050 Be sure to subscribe on itunes, Stitcher, or Pot Bean to get new, fresh episodes. 139 00:19:26,110 --> 00:19:31,066 For more, please follow us on Instagram, Twitter and Facebook or visit our website at datascienceathome.com   References https://netflixtechblog.com/formulating-out-of-memory-kill-prediction-on-the-netflix-app-as-a-machine-learning-problem-989599029109

Welcome to another episode of our podcast. Today we are talking about how to choose a process mining tool, to close the loop to the "How to select an architecture tool" episode (link in the full show notes on whatsyourbaseline.com/episode30). In this episode of the podcast we are talking about: • What are process mining tools and how they are different than other tools (BI tools, modeling tools) and how can they work together • Features to look at when choosing a process mining tool ◦ Process Explorer (listen to the details what tools today can and cannot do) ◦ Variant analysis ◦ Root/cause miner, ideally AI-driven ◦ Custom dashboards ◦ Calculate new fields ◦ Package (dashboards, transformations, calculations) and make it reusable ◦ Predictive analysis ◦ Trigger actions (as a quick fix for a process, not a redesign) • Integration requirements ◦ Integration with source tools ◦ Integration with other mining tools (task mining) ◦ Integration with architecture tool ◦ Integration with process execution • Cost and other considerations The full show notes, including graphics, further links, credits, and transcript, are available at whatsyourbaseline.com/episode30.

On today's show, we're answering YOUR questions about minor league baseball! We discuss the impressive debuts of Arizona Diamondbacks pitcher Drey Jameson and Minnesota Twins OF Matt Wallner before getting to the headline: What minor league hitting stats are (and are not) predictive of Major League success? We close with questions about Seattle Mariners C Harry Ford and Los Angeles Angels RP Eric Torres.  Find and follow LockedOn MLB Prospects on your favorite podcast platforms: Apple Podcasts: https://podcasts.apple.com/us/podcast/locked-on-mlb-prospects/id1525225214 Spotify: https://open.spotify.com/show/2wzJIf26tGgVbB7rsoKyLD Stitcher: https://www.stitcher.com/show/locked-on-mlb-prospects Follow along with LockedOn MLB Prospects host Lindsay Crosby as we follow 120+ affiliated teams throughout the 2022 season! From prospect call-ups to impactful trades to the ever evolving battle for minor league living and working conditions, Lindsay is covering it all on five days a week. Available exclusively on the Locked On Podcast Network. Follow the show on twitter @LockedOnFarm and email your Mailbag Monday questions to LockedOnMLBProspects@gmail.com Follow Lindsay for up to the minute details on all things Minor League Baseball: On Twitter: https://twitter.com/CrosbyBaseball Support Us By Supporting Our Sponsors! Nugenix Now get a complimentary bottle of Nugenix Total T when you text MLB to 231-231. Text now and get a bottle of Nugenix Thermo, their most powerful fat incinerator ever, with key ingredients to help you get back into shape fast. Built Bar Built Bar is a protein bar that tastes like a candy bar. Go to builtbar.com and use promo code “LOCKEDON15,” and you'll get 15% off your next order. BetOnline BetOnline.net has you covered this season with more props, odds and lines than ever before. BetOnline – Where The Game Starts! LinkedIn LinkedIn Jobs helps you find the candidates you want to talk to, faster. Did you know every week, nearly 40 million job seekers visit LinkedIn? Post your job for free at LinkedIn.com/LOCKEDONMLB. BlueChew Try BlueChew FREE when you use our promo code LOCKEDON at BlueChew.com,-just pay $5 shipping. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Companies and other business entities are actively involved in defining data products and applied research every year. Academia has always played a role in creating new methods and solutions/algorithms in the fields of machine learning and artificial intelligence. However, there is doubt about how powerful and effective such research efforts are. Is studying AI in academia a waste of time?   Our Sponsors Explore the Complex World of Regulations. Compliance can be overwhelming. Multiple frameworks. Overlapping requirements. Let Arctic Wolf be your guide. Check it out at https://arcticwolf.com/datascience   Amethix works to create and maximize the impact of the world's leading corporations and startups, so they can create a better future for everyone they serve. We provide solutions in AI/ML, Fintech, Healthcare/RWE, and Predictive maintenance.  

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.08.507204v1?rss=1 Authors: Miller, A. M. P., Jacob, A. D., Ramsaran, A. I., De Snoo, M. L., Josselyn, S. A., Frankland, P. W. Abstract: The brain organizes experiences into memories that can be used to guide future behavior. Hippocampal CA1 population activity may reflect the retrieval of predictive models that contain information about future events, but little is known about how these kinds of memories develop with experience. We trained mice on a series of tone discrimination problems with or without a common statistical structure to observe how memories are formed and updated during learning. Mice that learned structured problems integrated their experiences into a predictive model that contained the solutions to upcoming novel problems. Retrieving the model during learning improved discrimination accuracy and facilitated learning by decreasing the amount of new information that needed to be acquired. Using calcium imaging to track the activity of thousands of CA1 neurons during learning on this task, we observed the emergence of a persistent hippocampal ensemble at the same time that mice formed a predictive model of their environment. This ensemble was reactivated during training and incorporated new neuronal activity patterns from each training problem. Interestingly, the degree to which mice reactivated the ensemble was related to how well their model predicted the content of the current problem, ensuring that the model was only updated with congruent information. In contrast, mice trained on unstructured problems did not form a predictive model or engage a persistent ensemble. These results show how hippocampal activity supports building predictive models by organizing newly learned information according to its congruence with existing memories. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

In this interview we speak with Neal Bawa, CEO and Founder of Grocapitus, a commercial real estate investment company in the U.S. With a $1 billion portfolio, the Grocapitus team acquires and builds multifamily and commercial properties across the U.S. spanning over 10 states.   In this episode we dive deep into: Predictive analytics and how Neal uses them to make strategic decisions How things like population growth, income growth, job growth, home price growth and crime reduction can help you find places to invest in How analytics and interest rates can affect the multifamily space How you can prepare for a higher-cost capital in both the U.S. and Canada as an investor Neal's top market picks for multifamily investing in the U.S. for 2022   *Remember to leave us a rating and review on your favourite podcast platform!   Connect With Us: Neal Bawa - CEO and Founder of Grocapitus Website: www.grocapitus.com Facebook: Neal Bawa  LinkedIn: www.linkedin.com/in/neal-bawa   Peak Multifamily Investments Instagram: @peakmultifamily Facebook: @peakmultifamily Facebook Group: Apartment Building Investors Network LinkedIn: Peak Multifamily Investments Email: connect@peakmultifamily.ca Website: www.peakmultifamily.ca   Mark Baltazar - Co-Founder, Peak Multifamily Investments Instagram: @mark_baltazar Facebook: Mark Baltazar LinkedIn: www.linkedin.com/in/mark-baltazar Email: mark@peakmultifamily.ca   Mike Rockall - Co-Founder, Peak Multifamily Investments Instagram: @rockallrealestate Email: mike@peakmultifamily.ca   More Resources: We are bringing together a group of action-takers that are looking to scale to the next level by growing their apartment building portfolio and infusing a group of industry professionals and veterans to expedite growth. Interested in joining our mastermind? Apply at https://peakmultifamily.ca/mastermind/    Looking to learn the steps you should take to invest in multifamily assets? Our on-demand video masterclass is now available! Get access now by visiting https://peakmultifamily.ca/masterclass    We are currently buying 6-30 unit buildings all across Ontario. If you are looking to sell your building, visit https://peakmultifamily.ca/seller   Interested in learning more about our coaching and educational programs related to multifamily investing? Set up an introductory call with our hosts by visiting https://bit.ly/peak_coachingservices     Are you looking to purchase an apartment building? Join our V.I.P. buyers list by visiting https://peakmultifamily.ca/buyer   Sign up to stay up to date on the latest webinars, events, podcast episodes and other resources pertaining to multifamily investing in Canada and building generational wealth. Visit: http://bit.ly/signup_peak   Visit https://bit.ly/peak-duediligence to download our FREE ‘Apartment Building Purchase Due Diligence Checklist'.    Visit our website at www.peakmultifamily.ca for more information about our investment strategy and other FREE resources to help guide you along your apartment building investment journey.

Mark Stouse is the CEO of Proof Analytics. The company's Proof BusinessGPS™ is the world's best and fastest automated Marketing and Revenue Optimization platform. Proof is integrating seamless data management and automated, no-code modeling analytics with top-class planning and budgeting capabilities. Their tools are all delivered as an easy-to-use, easy-to-understand SaaS platform. 00:00 - 00:17 “Data is all about the past, and only about the past.” — Mark Stouse 00:18 - 00:36 Welcome to Peppershock Media's Marketing Expedition Podcast 00:37 – 01:29 Mark' Bio 01:30 - 05:22 Marketing Essentials Moment: Provide Value to Others 05:23 - 07:08 Welcome to the show, Mark! 07:09 - 11:09 Extreme risk levels around decision making 11:10 - 15:01 How Mark started his company, Proof Analytics 15:02 - 22:53 The process of understanding data analytics 22:54 - 25:53 Predictive and prescriptive analytics software 25:54 -26:42 Explore the world of podcasting with Kitcaster! 26:43 - 28:43 Keeping up with the changes 28:44 - 34:55 The process of building an analytical model for the customer 34:56 - 37:33 Affordable and approachable tool for marketing 37:34 – 38:20 Combining data sets with analytics 38:21 - 40:29 Best way to reach Mark: Proof Analytics 40:30 - 41:15 Thank you so much, Mark! Enjoy your Marketing journey! 41:16 - 42:02 Join The Marketing Expedition today! #analytics #data #marketinganalytics #dataanalyst #software #marketingtools #marketingstrategies #digitalmarketing #business #businesstechnology #advertising #branding

Title: ⏰ Synergy Traders #41.06: Learn How to Trade Institutional Levels Using The Predictive Power of Fibonacci with Anka Metcalf of TradeOutLoud.com   Recorded on September 6th, 2022 as part of the Synergy Traders #41, Day 1: "Fibonacci & Elliott Wave 2022 Conference" event, hosted by TradeOutLoud and TimingResearch.   The full event video/podcast series and presentation notes are available here: https://timingresearch.com/blog/2022/synergy-traders-41-day-1-fibonacci-elliott-wave-2022-conference/   Bonus... ⚡ eBook: 10 Recession Trading Strategies https://timingresearch.com/LR2POD   Terms and Policies: https://timingresearch.com/policies/  

How does the brain work at its deepest levels? And to what extent can we radically upgrade it, creating neuroplastic changes? Ruben Laukkonen, PhD is a cognitive neuroscientist, contemplative, speaker, and poet. His eclectic background includes competing semi-professionally in Muay Thai Kickboxing, founding two businesses (including the first online market for bitcoin in Australia), and intensive meditation training. Dr. Laukkonen is currently a principal investigator and lecturer at Southern Cross University and holds honorary fellowships at VU Amsterdam and The University of Queensland. He uses methods such as behavior, neuroimaging, machine learning, and phenomenology to empirically investigate some of the rarest states of human consciousness. This episode is a full tour of the mind, including a deep dive into some of those rare states of consciousness and what they reveal about achieving the highest levels of human happiness. FitMind Neuroscience-Based App: http://bit.ly/afitmind Website: www.fitmind.com   SHOW NOTES 0:00 | Introduction to Ruben Laukkonen, PhD 2:02 | Early Experience  10:45 | How the Mind Makes Itself 22:18 | Predictive Processing & Agitation 25:30 | Why There's Always Something Wrong 31:28 | Chain of Causality in the Mind 36:00 | The Mind Rebuilding After Deepest Levels of Meditation 38:25 | Meditation for Reconditioning the Mind 52:06 | Doing Nothing Very Well 53:14 | Stages of Meditation & Predictive Processing 1:08:28 | Cessation & Awakening Research 1:17:12 | Jhanas - Stages of Deconstruction 1:24:22 | Rapid Fire Questions   LINKS https://rubenlaukkonen.com https://fitmind.com/give 

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.05.506691v1?rss=1 Authors: Garcia-Saldivar, P., de Leon, C., Concha, L., Merchant, H. Abstract: We determined the intersubject association between rhythmic entrainment abilities of human subjects during a synchronization continuation tapping task (SCT) and the macro and microstructural properties of their superficial (SWM) and deep (dWM) white matter. Diffusion-weighted images were obtained from 32 subjects who also performed the SCT with auditory or visual metronomes and five tempos ranging from 550 to 950 ms. We developed a method to determine the fiber density of U-fibers running tangentially to the cortex. Notably, the right audiomotor system showed individual differences in the density of U-fibers that were correlated with the degree of predictive entrainment across subjects. These correlations were selective for the synchronization epoch with auditory metronomes and were specific for tempos around 1.5 Hz. In addition, there was a significant association between predictive rhythmic entrainment and the density and bundle diameter of the corpus callosum (CC), forming a chronotopic map where behavioural correlations of short and long intervals were found with the anterior and posterior portions of the CC. Finally, the fiber bundle cross-section of the arcuate fasciculus, the CC, and the Superior Longitudinal Fasciculus showed a significant correlation with the mean asynchronies of the auditory SCT. These findings suggest that the structural properties of the SWM and dWM in the audiomotor system support the predictive abilities of subjects during rhythmic tapping, where the density of cortical U-fibers are linked to the preferred tapping tempo, while the bundle properties of CC define an interval selective topography that has an anterior posterior gradient. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

Welcome to the Trend Detection podcast, powered by Senseye, an industry leader in using AI to drive scalable and sustainable asset performance and reliability. For this 3-part series, I'm joined by Burak Polat, CEO at Skysens, a company that is focused on creating user centric IoT infrastructures for every enterprise. In the third and final episode of this series, we discuss how to get the C-Level excited about agile predictive monitoring and the key trends in industrial IoT. Please subscribe via your favorite podcast provider if you'd like to be notified about future episodes and it would mean a lot if you could let us know your feedback by leaving us a review. You can find out more about how Senseye can reduce unplanned downtime and contribute towards improved sustainability within your manufacturing plants, by visiting Senseye.IO. Thanks a lot for listening.

When compared to healthy goal values, the results of these eight independent, primary, predictive tests are effective forecasters of individual health risk or resilience. You can function years – or even decades – younger than your birth age just by bringing (or keeping) each of these biomarkers at their predictive (healthy) goal value.

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.02.506366v1?rss=1 Authors: de Vries, I. E. J., Wurm, M. F. Abstract: Our capacity to interact with our dynamic world in a timely manner (e.g., catch a ball) suggests that our brain generates predictions of unfolding external dynamics. While theories assume such neural predictions, empirical evidence typically only captures a snapshot or indirect consequence of prediction, and uses simple static stimuli of which predictability is manipulated. However, the rich dynamics of predictive representations remain largely unexplored. We present a novel dynamic extension to representational similarity analysis (RSA) that uses temporally variable models to capture naturalistic dynamic stimuli, and demonstrate both lagged and predictive neural representations in source-reconstructed MEG data. Interestingly, the predictive representations show a hierarchical pattern, such that higher-level stimulus features are predicted earlier in time, while lower-level features are predicted closer in time to the actual sensory input. This promising new approach opens the door for addressing important outstanding questions on how our brain represents and predicts our dynamic world. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

Siempre en clave de proyectos (es la producción audiovisual "Agile" o "Predictive"..) el multipremiado Director Justin Webster nos habla del proceso creativo y del mundo audiovisual, las plataformas.... "Justin es un director y productor ejecutivo galardonado con 20 años de experiencia en contar historias reales En los últimos 5 años ha realizado cuatro "series cinematográficas de no ficción", un término que ha promovido para poner énfasis en lo que él cree que las hace especiales, la narrativa impulsada por los personajes, que las diferencia de las muchas otras formas de documentales. Cuatro series innovadoras: la aclamada por la crítica Muerte en León (2016, Movistar, relanzada en 2019, HBO España), la mundialmente ganadora del Emmy Six Dreams (2018, Amazon), la mejor valorada The Pioneer (2019, HBO Europe ) y la próxima The Prosecutor, The President and the Spy (coproducción española, estadounidense y alemana) que se estrenó en el festival de cine de San Sebastián en septiembre de 2019 y se estrenó en Netflix, ZDF y Movistar en enero de 2020. Sus largometrajes documentales para audiencias internacionales incluyen "Gabo, la creación de Gabriel García Márquez" nominado al Emmy y "I Will Be Murdered", ganador de múltiples premios. Justin desarrolló su oficio primero como escritor, mientras trabajaba como reportero para The Independent en Londres a principios de los 90, y se graduó en Literatura Clásica en la Universidad de Cambridge. Le impulsa una pasión por descubrir el significado de la historia a través de la experiencia de los personajes, y encontrar la narrativa convincente, original y verdadera con la que contarla. Todo se basa en la creencia de que las historias reales, contadas con creatividad pero sin inventiva, pueden ser tan dramáticas y, a menudo, más reveladoras que la ficción. https://www.linkedin.com/in/justin-webster-8b48a617/

PMP Exam - When to Choose Agile vs. Predictive (Stacey Model)

Kanye vaguely threatened his enemies with Instagram death wishes. Diana, Artemis, Girl in Room 13, Gorbachev 91, 1991, 9/1, Predictive programming indicates Nuke apocalypse is incoming. Red Witch, Game of Thrones Scarlet Witch, Dr. Strange Scarlet Woman, Thelema, Scarlet Lady, Virgin Galactic Ship"KANYE GOING OJ AND BROKEN ARROW 33 INCOMING" INFINITE PLANE RADIO 9/1/22 --- Send in a voice message: https://anchor.fm/infinite-plane-radio/message

Sarah Peck, Head of Talent Acquisition at AEVEX, joins host James Mackey to discuss best practices to navigate market contractions, salary transparency, the challenges with salary negotiations, talent acquisition costs, and much more!Episode Chapters:00:15 Who is Sarah Peck?01:25 What can talent acquisition leaders do to insulate themselves from recessions?04:23 LinkedIn content doesn't have to go viral to be impactful for your brand06:15 The value of working for a company that understands what you do7:21 Not every company sees talent acquisition as an equal partner at the table09:22 Predictive metrics for talent acquisition11:42 Talent acquisition has become very expensive17:36 Working with recruitment agencies19:34 Vendor experience is important22:13 The importance of being kind in business25:47 Salary transparency31:45 When to have the salary expectation conversation in the recruitment process33:32 Hiring managers time should be included in cost per hire34:35 Transparency is a two-way street38:45 How to think about salary negotiation40:14 Wrap up

Mike Noble joined Broomhead to discuss what his primary poll did and didn't get right and future methodology with a changing voter base.See omnystudio.com/listener for privacy information.

Lung cancer specialized testing in NSCLC: What do we do if we biopsy a suspected metastatic lesion?* Immunohistochemistry (IHC): **Confirm if it is metastatic NSCLC **Confirms the histology of the NSCLC (such as adenocarcinoma vs. squamous cell)**Used to determine the type of chemotherapy that can be administered for treatment *PDL1 testing: **PDL1 is a protein expressed by certain cancer cells allowing them to evade the immune system (“fake mustache analogy”).**Also confirmed by IHC**This protein is targetable!**Often measured as:***Total protein expression (TPS): The number of positive tumor cells divided by the total number of viable tumor cells multiplied by 100%***Composite protein expression (CPS): The number of positive tumor cells, lymphocytes and macrophages, divided by the total number of viable tumor cells multiplied by 100%*Molecular testing: **We discuss this in detail in Episode 005**Genetic information from the tissue sample**Always better to get sample from soft tissue than from bone**Why is this important?***To be able to identify “driver mutations”****What is it? Important mutations that may be “driving” oncogenesis****Many of these have drugs that directly target these mutationsPrognostic vs. predictive biomarkers:*Prognostic biomarkers: Mutations or changes that give information about the cancer's overall outcome regardless of therapy*Predictive biomarkers: Mutations that provide information about how a cancer may respond to a particular drug Cell-free DNA (AKA “liquid biopsy”):*Special tests that can detect microscopic amounts of cancer cell DNA within the patient's blood which may also be used to find prognostic/predictive biomarkers*Ongoing studies to see if this can be used to find relapse of diseasePlease visit our website (TheFellowOnCall.com) for more information Twitter: @TheFellowOnCallInstagram: @TheFellowOnCallListen in on: Apple Podcast, Spotify, and Google Podcast

Andy discusses criteria for "diagnosing" psychic kids, talks about being a psychic kid, and addresses misinformation around the Indigo and Crystal Child theory.  Let psychic kids be kids and encourage them to focus on achievements in other areas for a more well-rounded life experience. If the paranormal activity in a home becomes uncomfortable, the adults in the home need to look within. Andy also discusses Halloween and her scary movie list for this fall. Book a Session: https://www.andyraymedium.com/TikTok: https://www.tiktok.com/@andyraymedium?lang=enYouTube: https://www.youtube.com/channel/UCzfuUIS4TorX24gqNqPuCEA/videosInstagram: https://www.instagram.com/andyraymedium/?hl=en 

Our guest today is Rob Friday, HBA, Managing Principal at Predictive Success. Rob is an entrepreneur, best-selling author, international keynote speaker and Talent Optimization advisor based in Toronto Canada. As a Managing Principle with Predictive Success, Rob works with organizations to optimize hiring and team performance with the Predictive Index.Rob has published a book entitled, Talent Optimizer: Why some companies always get great people.Rob will also be speaking at the AGILITY REIMAGED summit on Sept 14th.Questions Include: Tell us about Predictive Index and the problems it tries to solve for HR pros and leadersWhat's needed from leadership to offer an environment that encourages and nurtures high-performing teams?What are you seeing your clients do differently to attract and hire quality talent in this era of low employment? How will this change if we enter an anticipated recession?How can companies use HR analytics to more effectively onboard talent?Talk about the role of AI and the potential for misuse of assessments in the hiring process, potentially introducing bias. How do you address this for your clients?Tell us about your book, Talent Optimizer: Why some companies always get great people, and some of the lessons in it for HR pros and leadersTell us about your session at the AGILITY REIMAGED summit on Sept 14th called Insights on hiring and inspiring talent in a competitive landscapeWhat are some new innovations in the HR tech space to watch out for in 2023?Enjoyed the conversation with Rob? Check out HRchat episode 356, in which Bill Banham talks with Matt Poepsel, Ph.D, over at Predictive Index. We do our best to ensure editorial objectivity. The views and ideas shared by our guests and sponsors are entirely independent of The HR Gazette, HRchat Podcast, and Iceni Media Inc.

With host Tom Appel out on vacation this week, co-hosts Damon Bell and Jill Ciminillo start the show by discussing Ford's just-unveiled Bronco Heritage Edition models and the recently announced NASCAR Chicago Street Race, which is scheduled for next summer. Collectible Automobile magazine Editor-in-Chief John Biel joins us to chat about the great features in the October 2022 issue, including histories of the 1977-1979 full-size Chevrolets and the pickup trucks of 1957. Damon has a quiz on boxy subcompacts for Jill and John, and Damon also runs down the latest articles on the Consumer Guide Daily Drive blog, including a test-drive review of the 2022 GMC Sierra 1500 AT4X.

Your entire life is an open book of information collected by tech companies. According to Tom Wheeler, former head of the Federal Communications Commission, the privacy problem is shockingly large, getting bigger, and has frightening consequences. What, if anything, can be done? Listen and find out.

Doogles revisits the discussion around the Fed raising interest rates all at once. Skippy raises the price to sales ratio of the recent purchase of the Denver Broncos. Michael Saylor is out as CEO of Microstrategy. Dollar stores are back in the conversations, quickly becoming the de facto grocery store for folks. Doogles discusses separating signal from noise, using a recent Farnam Street post and tips from Richard Feynman. Skippy is marveled by the UK's ability to precisely predict recessions. The episode wraps with Doogles pontification on what the next market surprise will be. Join the https://skippydoogles.supercast.com/ (Skippy and Doogles fan club). You can also get more details about the show at http://skippydoogles.com/ (skippydoogles.com), show notes on https://skippydoogles.substack.com/ (our Substack), and send comments or questions to skippydoogles@gmail.com.

S3 EP3: Talking Predictive Networks with JP Vasseur by Cisco

In this podcast we cover: The five predictive behaviors of failure. If you like the Elevate Construction podcast, please subscribe for free and you'll never miss an episode.  And if you really like the Elevate Construction podcast, I'd appreciate you telling a friend (Maybe even two 

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. In episode 3 of the podcast, the topic is: Re-imagining workforce training. Our guest is Sarah Boisvert, Founder and CEO Fab Lab Hub, LLC and the non-profit New Collar Network.In this conversation, we talk about re-imagining workforce training, industry 4.0., what do you mean by “New Collar” jobs? We discuss the mushrooming of Fab Labs. What skills are needed? How can they be taught? How can the credentials be recognized? .What has the impact been? Where do we go from here.After listening to this episode, check out Sarah Boisvert's online profile as well as the New Collar Network: Sarah Boisvert https://www.linkedin.com/in/sarah-boisvert-3a965031/ The New Collar Network (@NewCollarNetwrk): http://newcollarnetwork.com/Fab Lab Hub (@FabLabHub): http://fablabhub.org/Augmented is a podcast for leaders in the manufacturing industry hosted by futurist Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform, and associated with MFG.works, the open learning community launched at the World Economic Forum. Our intro and outro music is The Arrival by Evgeny Bardyuzha (@evgenybardyuzha), licensed by @Art_list_io. Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars on Apple Podcasts. To nominate guests, to suggest exciting episode topics or give feedback, follow us on LinkedIn, looking out for live episodes, message us on Twitter @augmentedpod or our website's contact form. If you liked this episode, you might also like episode 3: How to Train Augmented Workers. Augmented--the industry 4.0 podcast. Transcript: TROND: Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What's next in the digital factory? Who's leading the change, and what are the key skills to learn? How to stay up to date on manufacturing and industry 4.0. Augmented is a podcast for leaders in the manufacturing industry, hosted by futurist Trond Arne Undheim, presented by Tulip.co, the manufacturing app platform, and associated with MFG.works, that is M-F-G.works, the open learning community launched at the World Economic Forum. Each episode dives deep into a contemporary topic of concern across the industry and airs at 9:00 a.m. U.S. Eastern, every Wednesday. Augmented — the industry 4.0 podcast. In episode 3 of the podcast, the topic is Reimagining Workforce Training. Our guest is Sarah Boisvert, Founder and CEO of Fab Lab Hub and the non-profit New Collar Network. In this conversation, we talk about reimagining workforce training, industry 4.0, and what do you mean by new collar jobs? Fab Labs, what skills are needed? How can they be taught? How can the credentials be recognized? What has the impact been, and where do we go from here? Sarah, how are you doing today? SARAH: I'm doing well. How are you? TROND: I'm doing fine. I'm excited to talk about reimagining workforce training, which seems to be an issue on your mind, Sarah. You are a founder yourself. You have been actively involved in advanced manufacturing. I understand part of your story is that your company manufactured and sold the Lasik eye surgery back in 1999. So you've been involved in manufacturing for a while. We're here to talk about something very exciting. You say new-collar jobs is the big focus. I know you didn't invent the term. Can you give me a sense of what new-collar jobs refers to, first of all? SARAH: Sure. It is a term that was coined by Ginni Rometty, who was then the CEO of IBM. She's now the executive chair. And it refers to blue-collar jobs that have now become digital. And so many of our jobs...if you just think about your UPS man who now everything's not on paper, it's all in a handheld tool that he takes around on his deliveries. And all jobs are becoming digital. And so I thought that Ginny's term encapsulated exactly what's happening, and the technologies that we used to use just in manufacturing are now ubiquitous across industries. TROND: You have also been instrumental in the MIT spinout project called Fab Labs. Just give us a quick sense, Sarah; what are Fab Labs? Not everybody is aware of this. SARAH: Fab Labs are workshops and studios that incorporate many different kinds of digital fabrication. So we are taking the ones and zeros, the bits of CAD designs, and turning them into things that you can hold in your hand. And it covers topics like 3D printing, and laser cutting, and CNC machining. But Neil Gershenfeld, who founded the international Fab Lab Network, likes to say the power of digital fabrication is social, not technical. TROND: You know, this brings me to my next question, what skills are needed? So when we talk about new-collar jobs and the skills and the workforce training, what exact skills is it that we need to now be more aware of? So you talked about some of them. I guess digital fabrication, broadly, is another. Can you go a little bit more into what kind of skills you have been involved in training people for? SARAH: Well, when I first started this project, I had always been interested in workforce training, obviously, because I had a manufacturing company, and I needed to hire people. And we had worked with the community college near our factory to develop a two-year curriculum for digital manufacturing. But I had in mind exactly what I needed for my own company and the kinds of skills that I was looking for. And so a lot of Fab Labs, because we have about 2,000 Fab Labs around the world, heard about this program and started asking me, "Could you make a curriculum for us?" And there were so many of them that I thought I needed to come up with something that is going to fit most of the Fab Labs. And so I interviewed 200 manufacturers in all kinds of industries and from startups to Fortune 10 and so companies like GE, and Boeing, and Apple, and Ford, as well as companies in the medical device space. What they all told me they wanted was...the number one skill they were looking for was problem-solving. And that's even more important today because we're getting all these new technologies, and you haven't got some guy in the back of the machine shop who has done this before. And we're getting machines that are being built that have never been built before. And it's a whole new space. And the second thing they were looking for was hands-on skills. And I was particularly looking at operators and technicians. They were also looking for technical skills like CAD design, AI. Predictive analytics was probably the number one skill that the international manufacturers' CEOs were looking for. And I got done, and I thought, well, this is all the stuff we do in Fab Labs. This is exactly what we do. We teach people how to solve problems. And so many of our labs, particularly in places like Asia or Africa where there was tremendous need and not enough resources, necessity is the mother of invention. And so many of our Fab Labs invent amazing things to help their communities. And I thought, well, we don't need a two-year curriculum because the need for the employers was so extreme. I thought we need something more like what we do in Fab Labs. TROND: And how can these skills be taught? What are the methodologies that you're using to teach these skills that aren't necessarily, you know, you don't need to go to university, as you pointed out, for them? But they have to be taught somehow. What are the methods you're using? SARAH: Well, I did a lot of research trying to nail that down when I got done figuring out what it was people needed in the factories. And it seemed like digital badges were the fastest, easiest, most affordable way to certify the ability of a badge earner to work with a particular skill set. And they were developed by IBM and Mozilla probably decades ago now and are used by many organizations to verify skills. And it's a credential that is portable and that you can put on your digital resume and verify. There is an underlying standard that you have to adhere to; an international standards body monitors it. And there's a certain level of certainty that the person who says they have the skill actually has it. TROND: That's a good point because, in this modern day and age, a lot of people can say that they have gone through some sort of training, and it's hard to verify. So these things are also called micro certifications. How recent is this idea to certify a skill in that digital way? SARAH: I think that these particular badges have been around for decades, and people like Cisco, and IBM, and Autodesk have been using them for quite a long time, as well as many colleges, including Michigan State, is one that comes to mind that has a big program. And they can be stacked into a credential or into a higher-level course. So we stack our badges, for example, into a master badge. And that combines a number of skills into something that allows someone to have a job description kind of certification. So, for example, our badges will combine into a master badge for an operator. And so it's not just someone who knows CAD. They know CAD. They know how to run a machine. They know how to troubleshoot a machine. TROND: So we touched a little bit on how these things can be taught. But is this a very practical type of teaching that you are engaged in? I mean, Fab Labs, so they are physically present, or was that kind of in the old, pre-COVID era? SARAH: Well, yes, we were typically physically present with COVID. This past summer, I spent a lot of time piloting more online programs. And so, for our design classes, we can still have people online. And our interns 3D-print their designs, and then they can look at them via photography or video, if it's a functional design, and see how the design needs to be iterated to the next step. Because, as you know, it never comes out right the first time; it takes a number of iterations before it works. And we just recently, this week, actually completed an agreement with MatterHackers, who are a distributor of tabletop 3D printers, to bundle their 3D printers with our badges. And so someone can then have a printer at home. And so, if you have a family and you're trying to educate a number of children, it's actually a pretty economical proposition. And they offer two printers that are under $1,000 for people who are, for example, wanting to upskill and change careers. They also offer the Ultimaker 3D printer that we use pretty heavily in our lab. And it's a higher level with added expense. But if you're looking at a career change, it's certainly cheaper than going back to college [laughs] instead. TROND: So I'm curious about the impact. I know that you started out this endeavor interviewing some 200 U.S. manufacturers to see that there was...I think you told me there was like a paradigm shift needed really to bring back well-paying, engaging manufacturing careers back to middle-class Americans. And that's again, I guess, pointing to this new-collar workforce. What has the impact been? I mean, I'm sitting here, and I see you have the book, too, but you generously gave me this. So I've been browsing some of the impacts and some of the description of what you have been achieving over the past few years. What has the impact been? How many people have you been able to train? And what happened to the people who were trained? SARAH: We've only been doing it a couple of years. And in our pilot, we probably have trained 2,3,400 people, something on that. And it's been a mix of people who come to us. Because we teach project-based learning, we can have classes that have varying levels of experience. So we have people who are PhDs from the Los Alamos National Lab who drive the 45 minutes over to us, and they're typically upskilling. They're typically engineers who went to school before 3D printing was in the curriculum. And they are adding that to their existing work. But we get such a wide range of people from artists. We're an artist colony here. And we get jewelers, and sculptors, and a wide range of people who have never done anything technical but are looking to automate their processes. And so my necklace is the Taos Pueblo. And it was designed by a woman...and her story is in the book. So I should add that the book you're referring to has augmented reality links to the stories of people. And she just was determined. She, I think, has never graduated from high school and is an immigrant to the United States. And she just was determined to learn this. And she worked with us, and now she designs in CAD, and we 3D-print the molds. And her husband has a casting company, and then he has it cast in sterling. TROND: I find that fascinating, Sarah because you said...so it goes from people who haven't completed high school to kind of not so recent PhDs. That is a fascinating range. And it brings, I guess, this idea of the difficulty level of contemporary technologies isn't necessarily what it was years ago. It's not like these technologies take years to learn, necessarily at the level where you can actually apply them in your hobbies or in the workplace. Why is that, do you think? Have we gotten better at developing technologies? Or have companies gotten better to tweak them, or have we gotten faster at learning them? Or is the discrepancy...like, this could be surprising for a lot of people that it's not that hard to take a course and apply it right afterwards. SARAH: Learning anything comes down to are you interested? It comes down to your level of motivation and determination. A couple of things, I think the programs, the technical programs, and the machines have become much easier. When I started in the laser business, every time that I wanted to make a hole, I would have to redesign the optical train. And so I'd have to do all the math, so I'd have to do all the advanced math. I would have to put it together on my bench, and hopefully, it worked, and tweak it until I got the size hole I needed in the material I needed. Today, there's autofocus. It's just like your camera. You press a button; you dial in the size hole you want, and away you go. And it's interesting because many of the newer employees at our company Potomac Photonics really don't have the technical understanding that I developed because they just press the button. But it moves much faster, and we have more throughput; we have a greater consistency. So the machines have definitely improved tremendously in recent years. But I also think that people are more used to dealing with technology. It's very rare to run into somebody who doesn't have email or somebody who isn't surfing the web to find information. And for the young people, they're digital natives. So they don't even know what it's like not to have a digital option. I think that a number of things have come together to make that feasible. TROND: Sarah, let me ask you then this hard question. I mean, it's a big promise to say that you can save the middle class essentially. Is it that easy? Is it just taking one or two courses with this kind of Fab Lab-type approach, and you're all set? Can you literally take someone who feels...or maybe are laid off or feels at least not skilled really for the jobs they had, the jobs they want, and you can really turn them into highly employable in a matter of one course? Has that really happened? SARAH: In one course or one digital badge, it is possible to get some jobs, but it probably takes a combination of courses in order to have the right skill set because it's typically not one skill you need. It's typically a combination of skills. So to run the 3D printers, for example, you need CAD design. You need to understand design for 3D printing. And then you have to understand how to run the machines and fix them when they break. So it's probably still a more focused and condensed process. So you could do our master badge, which comprises five or six badges, and get a job in six months for about $2,000. With one class, you could get a job part-time and continue the other badges and be paying for school while you're working in a field that is paying a substantial increase over working at McDonald's. TROND: So give me a sense. So this is happening, in your case, in Santa Fe, New Mexico. Where do we go from here? Is this going on anywhere else? What are the numbers? How many people are being trained this way? How many people could be trained this way? How easy is the approach you're taking to integrate and scale up? And is it happening anywhere else? SARAH: Our non-profit, which is the organization that issues the badges, has, right now, I think, 12 or 13 members, and they were part of our pilot, and they are all over the country. So in my team, Lemelson, the Fab Lab in El Paso, the Fab Lab in Tulsa, MakerspaceCT in Hartford, Connecticut. And so we have a group that just started this year was when I started the scaling after, I was really pretty confident that it was going to work. If it worked in Santa Fe, which is a small town and in a very rural, very poor state, I really thought if I could make it work here, we could make it work anywhere because there are a lot of challenges in our state. So we started scaling this year, and each of our pilot sites is probably putting through their first cohort of 4, 5, or 6 badges, and they each have about 10 in that first cohort. We have a lot of requests for people to join our group and start issuing the badges. I've really come to see the success of our online program. And so, our online program is instructor-led at this point. And I'm working to create a self-directed program that people could do online with a tabletop printer at home. But we will still continue to scale the New Collar Network that actually disseminates the badges. And I really see enormous interest. As you know, college enrollment has been declining for the last ten years. There has been an 11% decline in college enrollment. And people are looking for alternatives. And I think that I've had requests from school systems. I had a request from a school system back East that has 45,000 students that they want to get badges. We have had a request from a school system in the Midwest where they get a lot of teachers who are getting 3D printers, and they don't know what to do with them. And they'd like for us to train the teachers. So I really see a huge opportunity. And these tools that we're using are not just being used in manufacturing. One of the people that we worked with on the HR side in research was Walmart. And their big worry is now they're putting in these janitorial robots. And their big dilemma is who's going to program them, and who is going to fix the robots when they're not working? And it's everywhere. It's not just am I going to get a job at that manufacturing company? It's also your local retail store. TROND: Fantastic. This is very inspiring. I thank you so much for sharing this with us. And I hope that others are listening to this and either join a course like that or get engaged in the Fab Lab type Network and start training others. So thanks again for sharing this. SARAH: Oh, it's a pleasure. It's a real mission, I think. [laughs] TROND: Sounds like it. Have a wonderful rest of your day. SARAH: Thank you. TROND: You have just listened to Episode 3 of the Augmented Podcast with host Trond Arne Undheim. The topic was Reimagining Workforce Training. Our guest was Sarah Boisvert, Founder, and CEO of Fab Lab Hub and the non-profit New Collar Network. In this conversation, we talked about reimagining workforce training, industry 4.0, and what you mean by new-collar jobs and Fab Labs; what skills are needed? How can they be taught, and how can the credentials be recognized? What has the impact been, and where do we go from here? My takeaway is that reimagining workforce training is more needed than ever before. The good news is that training new generations of workers might be simpler than it seems. Practical skills in robotics, 3D scanning, digital fabrication, even AR and VR can be taught through experiential learning in weeks and months, not in years. Micro certifications can be given out electronically, and the impact on workers' lives can be profound. Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. Augmented — the industry 4.0 podcast. Special Guest: Sarah Boisvert.

Welcome to Now in Android, your ongoing guide to what's new and notable in the world of Android development. Today, we're covering updates on Android 13 Beta 4, Predictive back gestures, Jetpack Compose, Wear OS, Text gradients, Large screens, System UI, and more! Now in Android podcast → https://goo.gle/2BDIo9y            Now in Android articles → https://goo.gle/2xtWmsu          Now in Android playlist → https://goo.gle/now-in-android            Subscribe to Android Developers → https://goo.gle/AndroidDevs  

Mike Noble joins the Broomhead show to discuss a new poll on the state of the GOP ahead of the primary.See omnystudio.com/listener for privacy information.

On this episode of the Hunting Gear Podcast, Dan talks with Matt Drury of Drury Outdoors about their predictive deer movement app, DeerCast. DeerCast is based off of an algorithm that includes several weather data points like temperature, barometric pressure, and precipitation that updates hourly throughout the day that inform you on the best days and times to hunt. That, mixed with several years of deer hunting experience and deer behavior observation from Mark & Terry Drury. This is a really great conversation we are sure you will enjoy!

On this episode of the Hunting Gear Podcast, Dan talks with Matt Drury of Drury Outdoors about their predictive deer movement app, DeerCast. DeerCast is based off of an algorithm that includes several weather data points like temperature, barometric pressure, and precipitation that updates hourly throughout the day that inform you on the best days and times to hunt. That, mixed with several years of deer hunting experience and deer behavior observation from Mark & Terry Drury. This is a really great conversation we are sure you will enjoy! Learn more about your ad choices. Visit megaphone.fm/adchoices

On this episode of the Hunting Gear Podcast, Dan talks with Matt Drury of Drury Outdoors about their predictive deer movement app, DeerCast. DeerCast is based off of an algorithm that includes several weather data points like temperature, barometric pressure, and precipitation that updates hourly throughout the day that inform you on the best days and times to hunt. That, mixed with several years of deer hunting experience and deer behavior observation from Mark & Terry Drury. This is a really great conversation we are sure you will enjoy!

This monthly installment of the Auto Remarketing Podcast focused on fraud with two experts from Point Predictive tackled the world of credit washing. Justin Davis and Frank McKenna recapped what credit washing is and how it can impact auto financing, as well as a recent case that triggered a major indictment stemming from an investigation that began within a bank's underwriting department.

Peter VermeulenCheck out this article on predictive coding:https://petervermeulen.be/2019/08/20/autism-absolute-thinking-in-a-relative-world/About Dr. Vermeulen in his own words.Peter got a Bachelor in Family Sciences (Brussels, 1985) and in the same year also a Master in Psychology and Pedagogical Sciences at the oldest university of Belgium, the University of Leuven. Because he got the taste of studying at the oldest universities, Peter started to study at the oldest university of The Netherlands, Leiden, while already working in the field of autism. There he obtained his PhD in 2002 with his research on late diagnosis in people on the autism spectrum with above average IQs.So, officially it is: Dr. Vermeulen Peter, MSc, PhD but Peter just prefers to be called PeterPeter started his career in the field of autism in 1987, working for the Vlaamse Vereniging Autisme (Flemish Autism Society) as diagnostician and home trainer for families on autistic children. He later become the director of the home training center and started gradually to share his experiences via presentations and books.Peter wrote more than 15 books, some of them translated into more than 10 languages. Peter works for ‘sterkmakers in autisme' (strongmakers in autism), a non-profit organization that makes people and organizations strong in their ambition towards full inclusion of autistic people.Peter created Autism in Context, where autism is seen in context. He is teaching, training and presenting all over the globe and in his work he focuses on two topics in particular: the autistic thinking (autism as context blindness) and happiness & well-being in autism.In 2019 Peter received the Passwerk Life Time Achievement Award for his more than 30 years contribution to the autism community in Flanders.When Peter is not presenting, writing, driving or flying around for his job, he can usually be found on one of his 4 bicycles, trying to imitate Tour de France winners, hereby being oblivious to his age and physical state. When tired from cycling, he can be found at home, with his wife and their dog (which actually is more a Gremlin than a dog). Or he is playing with his 3 grandchildren. Because the Gremlin loves the beach, Peter often spends his weekends at the seaside, savouring seafood and  – being a descendant of a brewing family –  enjoying a good beer.Website: www.petervermeulen.beDisclaimer:When we have guests on the ASR podcast they are recognized in their expertise on autism as an advocate, self-advocate, clinicians, parents, or other professionals in the field. They may or may not be part of the faith community; having a guest on the broader topic of autism does not reflect complete agreement with the guest just as many guests may not agree with our faith perspective. Guests are chosen by topic for the chosen podcast discussion and not necessarily full agreement of all beliefs from the chosen guest(s).

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Social scientists at the University of Chicago have developed an algorithm to predict crime weeks before it occurs. The system is intended to eliminate "biases in crime enforcement." What this implies is that certain crimes will be ignored while others are enforced based on some misappropriation of the concept of equality. Notice, they did not call it law enforcement! Two recent shootings also shine light on the direction of law enforcement, or the lack thereof: when a shooter with a rifle opened fire in an Indiana mall over the weekend, and a good samaritan fired back with a handgun to prevent further bloodshed, Newsweek blamed the law allowing the good guy to kill the bad guy; in the case of a man shooting into a black woman's home in Minneapolis, with her two children inside, police arrived and killed the shooter - this resulted in a group of white people demanding justice for the man who attempted to murder two kids and their mother, while telling the mother to shut her mouth in the name of social justice. This is malum in se, or evil in itself.

Welcome to the Grounded Sleep Podcast. In tonight's episode, I will guide you in a meditation. You don't have to do anything except get into bed, close your eyes, and allow yourself to find a deep, peaceful rest. Enjoy letting go of the day, quieting all the mental noise, and going back to that primordial place of stillness that is calling you back. Come sit live with David in Meditation School with a 30-day free trial here: https://www.meditationschool.us/ The 7 Energies of the Soul Book: https://amzn.to/3mAByGN QUIZ - The 7 Energies of the Soul: https://bit.ly/3pgSbbz Meditation School Podcast: https://bit.ly/2QsOxNC Instagram: https://www.instagram.com/david_gandelman Spotify: https://spoti.fi/32IP54J Energy Matters YouTube: https://bit.ly/3s45Zpl Grounded Sleep Podcast: https://bit.ly/2QUcevj Facebook: https://bit.ly/2OH0j6h Insight Timer: https://insighttimer.com/groundedmind FREE Meditation Trainings: https://bit.ly/3nkmprZ Download the Meditation School App Apple: https://apple.co/3Fr8p7Q Android: https://bit.ly/3oKmN4d --- Support this podcast: https://anchor.fm/grounded-sleep-podcast/support

Today's episode, with Chris Linsell, is on one of the most popular topics. I talk about it in so many classes, and people have so many questions. Chris is an expert reviewer of customer relationship management systems, or CRMs, the organizational tool we all need to have in some form. He breaks it down into how we use it, why we use it, and how we can benefit from having this system in our business. When he explains it, he makes these principles and ideas easy to understand, so I love his teacher heart that comes through. He and I discuss many tools for you. We talk about the broker-centric systems that many of you already have access to in your brokerage firm, we also then talk about some of our favorite independent systems for you to consider. Listen in for timely expertise on CRMs and more office tech.   Announcement: The Center for REALTOR® Development has migrated all of its educational tools to one website: Learning.REALTOR. [1:51] Chris Linsell is an expert and reviewer for customer relationship management systems. [4:09] Monica welcomes Chris Linsell to The Center for Real Estate Development Podcast. As a consultant, Chris stays up with tech and real estate trends; he needs to know a lot about a lot of things. [5:21] A CRM is a customer relationship management system. A spiral notebook can be a CRM. [8:18] We all must manage customers and relationships. Most agents think of a CRM as difficult technology and software. Technology provides the tools we use to create the business that we want. Those tools are often based on the internet. But don't overcomplicate things. [11:01] If you want to scale, you are going to need better technology. You cannot share a spiral notebook or an Excel sheet with your associates. Agents need to keep up with leads and current clients, their deals, and their client database. [14:21] Chris's top CRM recommendation for real estate professionals is LionDesk. Other CRMs he recommends are MarketLeader®, Elevate Social ProTM, IxactContact, TopProducer®, PropertyBase, and WiseAgent. Monica likes WiseAgent and Follow Up Boss. [23:56] Compared to general-purpose CRMs, these real-estate-based CRMs give you an amazing number of tools in one piece of software, Chris explains how CRMs substitute for tech stacks. [27:22] What are CRM best practices? Use it every single day. Take advantage of the shortcuts your CRM provides. For example, sync contacts with your CRM. [31:30] Chris advises Monica to commit to one consistent place to enter contacts, so she will have all her contacts together. Also, get tech support. Your brokerage may offer you a CRM, but have an exit plan in case you leave. [37:11] Chris always coaches agents considering a CRM to ask themselves three questions: Can they commit to it for at least one year of subscription? It takes time for a CRM to build value. Does this tool solve one major problem? Does this tool provide the opportunity to effectively exit it if needed? [39:53] The software is constantly upgrading. [42:38] More best practices for CRM use: Use the calendar for birthdays, “house-iversaries,” and reminders to send notes to and contacts. Manage your contacts in tiers. For example, people who know your name, people who know and like you, and your core group of 100 people or fewer who know, like, and trust you. Create a cadence of branded communications that suit your contacts of each tier. [50:35] Additional tasks: Set up an automated branded text messaging and email message combination for new contacts. Look at how your CRM integrates with other tools, like ringless voicemail drops. The CRM has a list of tools that can be used. [54:14] A CRM is only as valuable as the time you put in on it. In the beginning, you will put more time into it while you learn to use it. [55:17] What do agents need to know about predictive analytics? Predictive analytics is the use of big data to understand better and make better predictions about what's going to happen next. Chris shares an example of predictive analytics for lead generation for the next 12 months. [57:50] Big data sells predictive analytics to real estate companies; small companies can access the same insights as large corporations. Chris gives an example of how you have already used predictive analytics over small sets of data. Predictive analytics tools use larger data sets for greater accuracy. [59:44] Chris lists some predictive analytics tools for real estate agents. He predicts that predictive analytics will be pretty mainstream within a year. Monica has been watching the vendors for a while! [1:04:00] Chris's final word is, you are in control of your success! You have the opportunity to run exactly the business that you want to run and the tools exist to give you every chance to have the business, the career, and the professional life that you want. Get proactive and invest in your success! [1:05:21] Monica thanks Chris Linsell for bringing his wisdom, experience, and a willingness to call her out for the flaws in her systems! Who is helping you learn and level up in areas of your life? Every day is an opportunity to learn something new in business or life. Are you looking for those nuggets and the people who will share them with you? [1:05:58] If you have enjoyed this episode, press the Follow or Subscribe button to get the notice of new episodes each month!   Tweetables:   “[Chris] breaks down how we use [a CRM], why we use it, and how we can benefit from having this system in our business. When he explains it, he makes some of these principles and ideas so easy to understand, so I love his teacher heart that comes through!” — Monica   “[As a consultant,] you've got to be at least a voice in the room if not one of the smartest ones. So I do my best to stay up on everything that we're talking about.” — Chris   “A CRM … is a tool that you are using to create better conversations with the people who are moving your business forward. … The most basic one … is a little spiral-bound notebook. … You're managing that relationship by taking some good notes and recording the information that you need.” — Chris   “If you are a real estate professional who does more than five transactions a year, you are going to hit a critical mass in your short-term memory … where you won't be able to keep straight the conversations … and the priorities for the people for whom you are acting.” — Chris   “If you want to scale your business … from 20 transactions to 50 transactions, … this is a critical tool because you might be able to remember all the people that you talked to today, but you're going to have a really hard time prioritizing those conversations.” — Chris   “Anyone who's listening to this, you have a tech stack already, even if you don't realize it.” — Chris   “For my money, a real estate CRM is usually the way to go.” — Chris   “The real estate business has a very low bar to entry, … but it has a very high bar for success. You have so many things that you have to do in order to make it in this business. One of them is committing to a system that realizes that you're running your own business here.” — Chris   “When your contacts exceed 100 people, you need a bigger system to categorize, prioritize, and communicate. And that's where real estate CRMs really make you your money.” — Chris   “If you're not a prospector, … [then predictive analytics] isn't a tool for you!” — Chris   Guest Links:   https://theclose.com/real-estate-tech-companies/ https://theclose.com/predictive-analytics-overview/ https://theclose.com/real-estate-lead-management-strategy/ https://theclose.com/best-real-estate-crm/ https://theclose.com/market-leader-real-estate-lead-conversion/ ChrisLinsell.com TheClose.com The Matrix Excel “The Best Real Estate CRM for 2022: In-depth Reviews & Pricing” LionDesk® MarketLeader® Zillow® BoldLeads BoomTown Elevate Social ProTM AI Elevate Real Estate CRM Review + Video Walk-through Jasper IxactContact® TopProducer® PropertyBase WiseAgent Follow Up Boss Tech Stack (Solution Stack) Facebook MLS Gmail ReMax Coldwell Banker Real Estate One Keller Williams EXP “Compass acquires Contactually” Outlook MailChimp SlyBroadcast Slydial Offrs Revaluate NAR Annual   Additional Links:   Micro courses found at Learning.REALTOR   Crdpodcast.com   Learning.REALTOR for NAR Online Education Training4RE.com — List of Classroom Courses from NAR and its affiliates   crd@nar.realtor   Host Information: Monica Neubauer Speaker/Podcaster/REALTOR® Monica@MonicaNeubauer.com MonicaNeubauer.com FranklinTNBlog.com   Monica's Facebook Page Facebook.com/Monica.Neubauer Instagram Instagram.com/MonicaNeubauerSpeaks   Additional Bio:   Chris Linsell is a REALTOR®, real estate coach, writer, technology analyst, and content strategist based in the United States. He is a hands-on real estate professional with more than 10 years of experience buying and selling anything from modest starter homes to massive waterside compounds. As a digital and content strategist, he's worked with teams (real estate and non-real estate alike) to realize their lead generation and overall business goals by finding new ways to demonstrate their expertise and authority in their local markets.   Chris is a Writer and Technology Analyst for The Close, the internet's leading source of actionable, strategic insight for and by industry professionals. At The Close, his job is to be up-to-date on the latest and greatest technology platforms, real estate strategies, and best practices that the leaders of our industry are using to buy and sell more homes every year. He is a thought leader in the real estate space, regularly hosting webinars and group coaching calls, as well as being a featured speaker at events like the National Association of REALTORS® Annual Conference. Linsell is also a contributor to publications like Apartment Therapy, Bank Rate, The Simple Dollar, EOE Journal, Mashvisor, and Constant Contact. He is also a regular contributor to industry-leading media like the Keeping It Real Real Estate Podcast

Dr. Justin Abbatemarco discusses the Neurology: Neuroimmunology & Neuroinflammation article “Optic Nerve Lesion Length at the Acute Phase of Optic Neuritis is Predictive of Retinal Neuronal Loss.”  Show references: https://nn.neurology.org/content/9/2/e1135

Eleven states in the country are currently using child welfare tracking algorithms to better identify children at risk. According to research conducted by Carnegie Mellon University, the algorithms target a disproportionate number of Black and low-income families. We discuss the implementation of child welfare tracking algorithms with Anjana Samant, senior attorney at the ACLU and Nico'Lee Biddle, Senior Program Manager at the Center for the Study of Social Policy.

Only mathematics describes the infinite complexities of life within time. Our futures fixed points cannot be changed. The Simpson's knew Trump would concede to Lisa. Predictive analytics and the billions of choices and directions. When will the SCOTUS situation come to life? The terminal node and why it is fixed. Working now to influence future change. It does not matter what version they choose. Let's say it again, the CISA algorithm steals elections. Ramanjuan the genius. The 3x + 1 loop is back. Mendalbrot explains. Think patterns and numbers. All things are interconnected. Was Trump predicted long ago? 911 was orchestrated to delay. Portals, and a world within the world. Mars is the past, Venus is the future. Amazing Javier vids. VR future food. Plant coms. In fiction there is always a root of truth, so we must stick to the foundations that give us discernment. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, We have a conversation with Da Hood Professor Melle Mel about everything from the recent mass shooting to the idiots presently disguising themselves as content creators and the possibility that it all may be attributed to "Predictive Coding" Tap in with Melle Mel @dahoodpostman See omnystudio.com/listener for privacy information.