Podcasts about ML

Lightning Sessions #1 with Peeyush Agarwal, Scaling Real-time Machine Learning at Chime. // Abstract In this Lighting Talk, Peeyush Agarwal explains 2 key pieces of the ML infrastructure at Chime. Peeyush goes into detail about the current feature store design and feature monitoring process along with the ML monitoring setup. This Lighting Talk is brought to you by arize.com reach out to them for all of your ML monitoring needs. // Bio Peeyush Agarwal is the Lead Software Engineer, ML Platform at Chime. He leads the team which enables data science all the way from exploration, model development, and training to orchestrating batch and real-time models in shadow and production. Earlier, Peeyush was a founding engineer in Chime's DSML team and worked on both building models and getting them into production. Before Chime, Peeyush was a software engineer at Google where he developed unsupervised ML models that run on Google's data across search, Chrome, YouTube, and other properties to identify intent and use it for personalized ads and recommendations. At Google, he also worked on ML-powered Adaptive Brightness and Adaptive Battery which were launched into Android. Prior to joining Google, Peeyush was an entrepreneur who founded a customer engagement platform that counted Aurelia, Reebok, W, and Red Chief among its clients. // MLOps Jobs board https://mlops.pallet.xyz/jobs // Related Links arize.com --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Peeyush on LinkedIn: https://www.linkedin.com/in/apeeyush/ Timestamps: [00:00] Introduction to Peeyush Agarwal [01:08] Agenda [01:27] What Chime is and what Chime do [01:44] Chime's products [02:27] Data Science and Machine Learning at Chime [08:06] Chime's first real-time model [08:09] Preventing fraud on Pay Friends [11:01] Feature Store: Unblock real-time capability [12:40] Preventing fraud on Pay Friends: Monitoring [13:35] Preventing fraud on Pay Friends: Instrumentation [14:36] Monitoring: 4 diverse ways to triage [15:27] Examples of Metrics: Feature and Model Metrics [16:39] Scaling Real-time ML at Chime [17:09] Scaling Real-time ML: Monitoring and Alerting [18:28] Scaling Real-time ML: Build tools [20:13] Scaling Real-time ML: Infrastructure Orchestration [21:36] Scaling Real-time ML: Lessons

MLOps Coffee Sessions #100 with Matthijs Brouns, MLOps Critiques co-hosted by David Aponte. // Abstract MLOps is too tool-driven, don't let FOMO drive you to pick the latest feature/model/evaluation/ store but pay closer attention to what you actually need to release more safely and reliably. // Bio Matthijs is a Machine Learning Engineer, active in Amsterdam, The Netherlands. His current work involves training MLEs at Xccelerated.io. This means Matthijs divides his time between building new training materials and exercises, giving live trainings, and acting as a sparring partner for the Xccelerators at their partner firms, as well as doing some consulting work on the side. Matthijs spent a fair amount of time contributing to their open scientific computing ecosystem through various means. He maintains open source packages (scikit-lego, seers) as well as co-chairs the PyData Amsterdam conference and meetup. // MLOps Jobs board https://mlops.pallet.xyz/jobs // Related Links https://www.youtube.com/watch?v=appLxcMLT9Y https://www.youtube.com/watch?v=Z1Al4I4Os_A --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/ Connect with Matthijs on LinkedIn: https://www.linkedin.com/in/mbrouns/ Timestamps: [00:00] Introduction to Matthijs Brouns [00:28] Takeaways [03:09] Best of Slack Newsletter [03:38] AI MLFlow [04:43] Nanny ML [05:08] Best confinement buy over the last 2 years [06:35] Matthijs' day-to-day [08:24] What's hot right now? [09:36] ML space, orchestration, deployment [10:21] Scaling [13:20] Low-risk releases [15:27] Scale Limitations or Fundamental in API [16:33] MLOps maturity to a certain point [18:57] Interdisciplinary leverage need [21:11] PyScript [22:41] Next pipeline tools [24:02] General pattern to build your own tools [30:25] Technology recommendation to a chaotic space [33:46] Structured data vs tabular data [35:52] Big barriers in production [37:57] Standardization [39:20] Automation tension between the engineering side and data science side [41:50] Low-hanging fruit [42:30] Human check [43:43] Rapid fire questions [48:30] PyData Meetups

In this episode, Jon kicks off a new Five-Minute Friday series that explores the strategies for getting business value from machine learning. Part one sees him review several ways to identify a commercial problem before starting data collection or ML model development. Additional materials: www.superdatascience.com/578

Im Tech-Podcast nachgefragt: Expertengespräch mit HPE und Cancom am Beispiel von Scality ARTESCA, container-basierte „fast Object“ Storage Software Lösung Zum Hintergrund: Unternehmenswichtige Daten werden - von Core-to-Edge - in immer größeren Mengen erzeugt. Das betrifft Enterprise Anwendungen in Rechenzentren und in der Cloud / SaaS, Industrial Internet of Things mit Sensoren an Außenstellen, Video-Überwachung, Maschinendaten usw. Zusätzlich fordert der beschleunigte Einsatz von ML- und künstlicher Intelligenz auf Grund von Digitalisierungs-Initiativen die Speicher- und IT-Infrastruktur heraus. Stichworte: Datenmobilität, Agilität, Sicherheit, skalierbare Leistung, Kosten. Zum Podcast-Inhalt (Hörzeit: 28:21 min.) Die Gesprächspartner dieser Folge sind Dirk Hannemann, HPE Principal Solution Architect und Christian Hansmann, Solution Sales Architect bei Cancom SE. Die Fragen stellt Norbert Deuschle (Storage Consortium): Wo liegen für ihre Kunden derzeit die größten Herausforderungen im Bereich der Daten- und Speicherverwaltung ? ARTESCA ist ein verteiltes Objektspeichersystem, das mit Cloud-nativen Methoden als Set von containerisierten Mikroservices auf Basis einer Scale-out-Architektur aufgebaut ist. Warum soll ich mich als mittelständisches Unternehmen mit vielleicht nur 50 oder 100 TB überhaupt mit dieser Art von Lösungen beschäftigen? Mit Einsatzbeispielen (Use Cases) Kosteneffizienz, Performance und Hochverfügbarkeit sind im Bereich der Datenspeicherung wichtige Randbedingungen. Wie positioniert sich eine moderne Objekt Storage Lösung in diesem Umfeld? Hohe Datenverfügbarkeit und Sicherheit ist für Anwenderunternehmen überlebensnotwendig. Daneben spielen datenschutz-rechtliche Aspekte (Compliance, wer hat die Hoheit über die Daten), Schutz von Ransomware & Co sowie Backup- und Disaster Recovery Verfahren eine zentrale Rolle. Wie spielt das mit Object Storage zusammen und wo liegen betriebsseitig derzeit die größten Herausforderungen? Für welche Datenspeicherungs-Anforderungen ist ARTESCA primär konzipiert und was unterscheidet die Software von Scality RING, dem klassischen Objektspeicherangebot des Unternehmens? Das S3-Protokoll und Cloud-native gewinnen an Beliebtheit (Stichworte: Agilität, DevOps, Kubernetes & Co.). Allerdings besteht Kundenseitig der Bedarf, auch weiterhin bewährte File-Protokolle- und Daten einzusetzen-/zu verwalten. Wie lassen sich im Rahmen dieser Lösung beide Aspekte sinnvoll kombinieren? Weitere Information unter www.storageconsortium.de

Today's episode features an interview between Matt Trifiro and Rob Tiffany, Executive Director at the Moab Foundation, and Founder and Managing Director at Digital Insights. A bestselling author and frequent keynote speaker, Rob serves on multiple boards and is routinely ranked as one of the top IoT experts and influencers in the world by Inc Magazine, Onalytica and other outlets.In this episode we delve deep into how Rob went from a life of driving submarines, to being self taught in technology, and eventually becoming a leader in the world of IoT. Rob explains the value of IoT and the best ways to sell and explain it to the average person in real world terms, so they understand how embracing technology can save them time and money. Finally, Rob talks about how edge computing, IoT, and automation can be used to help with sustainability around the globe.---------Key Quotes:“All that R&D and the rise of arm based processors are making things smaller that we would not have ever built. The chips, the sensors, the technology at a low cost - if it wasn't for this mega trend of smartphones forcing us down that path. And so a side effect of all this work, and you know how things like the most expensive version of the thing you make is the first version. And then it gets cheaper and cheaper. Well, IOT,  the thing part of it, the device side of it, benefited from the whole planet going all in on smartphones.”“When I talk about IOT and value, I try to stay away from saying AI and things like that. And I say, there is so much value just doing the stupid stuff, the low hanging fruit. I think we oftentimes do our customers a disservice because I hear people say IOT and AI in the same sentence over and over again. And I go, you know what, you really need to get in your car and I need you to drive to Omaha, Nebraska. I need you to drive to St. Louis. I need you to go to Oklahoma City, and I need you to meet real people who are just trying to get their job done. And they have no idea about your neural nets and stuff like that. And they don't understand it. And I think we scare customers. It turns out what my experience, not only at building Azure IT, but even more importantly building Lumata industrial IoT at Hitachi; I'd say that first 10 to 20% of value, whatever that means, saving money, making money that comes from the easy stuff. It really does. Just being connected, just not having to visit simple KPIs, simple thresholding; like stuff that manufacturers have done for a million years. It turns out that's the most of the value.”"So my big recommendation to the world is start to crawl before you run. Do the basics, because it turns out you might get tons of value that you never realized just by doing the easy stuff first. Don't feel pressured to do something you don't even understand."“When you talk about poverty, it turns out most poverty and hunger are related to farming. They are all correlated. Most of the poorest people in the world are in farming, and they're also starving. So when you can start knowing, remotely knowing in real time and doing it low cost out there where it happens, and then combined with automation, what's the action I'm going to take to make this better for someone, right? There's so much you can do. It's mind boggling.”---------Show Timestamps:(01:12) Time in the Navy(03:25) Getting started in technology(04:06) Getting started in Visual Basic(04:29) Getting started in IoT Career(10:17) Defining the Internet of Things(15:77) Connection between IoT and Smartphones(17:33) Power of Wireless and IoT(19:09) Briefing others on IoT while at Microsoft(21:36) IoT and Value (25:05) Edge Computing(33:08) Alternatives to on premise equipment(34:49) Automation strategy (43:49) What makes edge harder than the cloud?(44:31) Edge, IoT, and Sustainability (47:50) Digital Twins--------Sponsor:Over the Edge is brought to you by Dell Technologies to unlock the potential of your infrastructure with edge solutions. From hardware and software to data and operations, across your entire multi-cloud environment, we're here to help you simplify your edge so you can generate more value. Learn more by visiting DellTechnologies.com/SimplifyYourEdge for more information or click on the link in the show notes.--------Links:Follow Matt on TwitterConnect with Rob on TwitterConnect with Rob on LinkedInwww.CaspianStudios.comRob's Website and Podcast

When you're applying AI from scratch, there are a few lessons to keep in mind. One that stands out is to ensure the machine learning solution is well suited to the problem. Here's the story of how we evolved our ML strategy at Upstart. Hear about how to apply AI in the lending environment from Leaders in Lending host Jeff Keltner, Senior Vice President of Business Development at Upstart: Challenges to overcome at the beginning stage of the ML journey Why feature engineering and first-party data build on each other The evolution of Upstart's underwriting model Shifting from manual to automatic identity verification How to attain more interesting predictions and apply them in the credit industry Mitigating risk with creativity More information about Jeff and today's topics: LinkedIn Profile: https://www.linkedin.com/in/jeffkeltner/  Company Website: https://www.upstart.com/  Ep. 41 - Back to Basics: AI Lending 101 w/ Jeff Keltner: https://podcasts.apple.com/us/podcast/back-to-basics-ai-lending-101/id1561389602?i=1000548218905 To hear more from Leaders in Lending, check us out on Apple Podcasts, Spotify, or on our website. Listening on a desktop & can't see the links? Just search for Leaders in Lending on your favorite podcast player.

Harry's guest this week is Rohit Nambisan, CEO of Lokavant, a company that helps drug developers get a better picture of how their clinical trials are progressing. He explains the need for the company's services with an interesting analogy: these days, Nambisan points out, you can use an app like GrubHub to order a pizza for $20 or $25, and the app will give you a real-time, minute by minute accounting of where the pizza is and when it's going to arrive at your door. But f you're a pharmaceutical company running a clinical trial for a new drug, you can spend anywhere from $3 million to $300 million—and still have absolutely no idea when the trial will finish or whether your drug will turn out to be effective. Because there's little infrastructure for analyzing clinical trial data in midstream or spotting trouble before it arrives, some studies continue long after they should have been canceled, and positive data sometimes gets thrown out because of minor procedural flaws; in the end, 20 to 30 percent of the money drug makers spend on clinical trials goes down the drain, Nambisan says. Lokavant's platform allows drug makers and clinical research organizations to harmonize the results coming in from study sites, compare it to data from other trials, and discover important signals in the data before it's too late. For example, a company might discover that it's not enrolling patients fast enough to complete a trial on schedule, or that the researchers administering the study aren't following the exact protocols laid out in advance. Such headaches might sound abstract and remote, but poor data management slows down the whole drug development process, which means fewer beneficial new drugs make it to market ever year; that's the ultimate problem Lokavant is trying to fix.Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:1. Open the Podcasts app on your iPhone, iPad, or Mac. 2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode.3. Scroll down to find the subhead titled "Ratings & Reviews."4. Under one of the highlighted reviews, select "Write a Review."5. Next, select a star rating at the top — you have the option of choosing between one and five stars. 6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.7. Once you've finished, select "Send" or "Save" in the top-right corner. 8. If you've never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out. 9. After selecting a nickname, tap OK. Your review may not be immediately visible.That's it! Thanks so much.TranscriptHarry Glorikian: Hello. I'm Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare.My guest Rohit Nimbasan comes from the worlds of biotech and data science. And during our interview he made an interesting point.These days you can use an app like GrubHub to order a pizza for twenty or twenty-five bucks, and the app will give you a real-time, minute by minute accounting of where the pizza is and when it's going to arrive at your door.But Nimbasan points out that if you're a pharmaceutical company running a clinical trial for a new drug, you can spend anywhere from $3 million to $300 million and still have absolutely no idea when the trial will finish or whether your drug will turn out to be effective.The problem is, there's just no infrastructure for analyzing clinical trial data in midstream or spotting trouble before it arrives.As a result, according to Nimbasan, twenty to thirty percent of the money drug makers spend on clinical trials goes down the drain, because of studies that continue long after they should have been canceled, or good data that gets thrown out because of some minor procedural flaw.Nimbasan is the CEO of a company called Lokavant that wants to change all that.The company is building a data platform that allows drug makers and clinical research organizations to harmonize the results coming in from study sites, compare it to data from other trials, and discover important signals in the data before it's too late.For example, a company might discover that it's not enrolling patients fast enough to complete a trial on schedule, or that the researchers administering the study aren't following the exact protocols laid out in advance.All of those problems can increase the cost of a trial.They can even lead regulators to deny approval for a drug that might have proved effective if it had been property tested.For an average healthcare consumer, these kinds of headaches might sound abstract and remote, like something only clinical trial managers would ever have to worry about. But the fact is poor data management slows down the whole drug development process, which means fewer beneficial new drugs make it to market ever year.So I think we should all be cheering companies like Lokavant who are trying to fix the process.Here's my full interview with Rohit.Harry Glorikian: Rohit, welcome to the show.Rohit Nambisan: Thanks, Harry, for having me.Harry Glorikian: You know, you and I sort of talk off and on all the time about the space and what's going on, but, you know, having it on the show, I have to step back and sort of forget everything I know about the company and start from scratch. So, you know, can you explain to people Lokavant's business in a way that would make sense to someone, say, outside of the pharmaceutical industry. In other words, you know, what are the big problems you're solving for organizations that, say, are running a clinical trial, and how are you solving them?Rohit Nambisan: Sure, I can do that. I think it bears noting that we should probably step back a little bit and talk about the industry as a whole and where it's been going, and then I can clarify where Lokavant comes in. So I think as many folks know and for those who don't, I'll fill in the blanks. I know you know this area, but in the last, I'd say 15 to 20 years, we've been moving in pharmaceutical development away from blockbuster medications, things like diabetes type 2. Right, developing therapies for that and getting each drug developer trying to find a smaller piece of market and larger pie to specialized, niche therapeutic indications. Right. So the way I could probably better started with the diabetes example is it's no longer diabetes type 2. It's let's develop the compounds or therapies for diabetes type 2 patients that are comorbid with that have also chronic kidney disease and are metformin naive, meaning they haven't taken a particular therapy known as metformin. Right. So it's a more complex filter criteria, so to speak. Right. And so what happens when the industry moves in that that direction is that when you get into these very niche therapeutic areas, you need to collect particular niche, commensurately niche types of data to validate your hypothesis whether or not this therapy is safe and efficacious through clinical trials. Right. Rohit Nambisan: And in doing that, you now increased the complexity of the trial greatly, not only in terms of the different types of data collecting, but the amount of different types of data you're collecting. So now each trial becomes a lot more specialized. Not just specialized therapeutics, but each trial becomes more specialized. Right? And so for that reason, we've seen a big challenge as we as we moved across that space. And actually, it's been really beneficial for patients because now we're going after, as an industry, we're going after really niche unmet clinical needs that previously there were no therapies for or being developed for. So it's a really good thing for a patient perspective, but from the perspective of development, it makes it that much harder. Not only is there a smaller market opportunity, there's less patients to treat, right, but the complexity, the actual costs of the trial and the complexity of trial has gotten exponentially that much greater. So what Lokavant came out of was we were actually a, shall we say, an internal initiative within Roivant Sciences, which is a company that launches a number of different biotechnology companies and tech companies as well. But better known for biotechnology companies. And we saw a great need to be able to develop therapies for niche indications much faster, much more efficient, much more cost effectively, and also meet the complexities of that trial better through novel data and tech.Rohit Nambisan: And so what Lokavant is essentially, is a data platform that allows drug developers, pharma, therapy developers, to be able to choose which data sources they need, data types they need for a trial. And we can ingest any of those data sources, we can analyze any of those data sources in a holistic manner and expose patterns or signals that could be beneficial or detrimental to the study on an ongoing basis. And when I say ongoing basis, I mean you're not waiting until the end of the study. And I guess the best way I can align this is just like my kids do sometimes. You're not waiting until the last day before your term paper is due, before the project's due to finish your work, you're actually assessing, doing bits of it along the way to assess where there may be challenges, which gives you, really, the time to correct issues to manage your trial better. And frankly, each one of these trials now, there are between, what, $2 million and $300 million we're investing in these single trials at this point. So it's egregious to me that we do not have the toolset to be able to even identify, pull in that data effectively on an ongoing basis to detect these signals so we can plan effectively to do something about it.Harry Glorikian: Anybody who's done a clinical trial knows that there's a lot of risk. Right. So, you know, can you talk about some of the types of risks you're trying to help make sure drug developers diminish, for the most part.Rohit Nambisan: Yeah. So I think the way we start with that is always at the highest level, time, cost and quality, right? So when we talk about time, it's really important to understand that you're going to be able to achieve less. For example, I'll give you a few instances. Target participant accrual, right? Obviously for you to run a trial effectively, you need to have particular types of participants or patients, if it's a sick population. In a vaccine population, they weren't necessarily sick. So that's why I use participants as the term. But you need to make sure that you have path to randomized screening and randomizing these patients for your trial in a given time period. Right. And if that's if your enrollment is is not on track for the countries and the sites you've decided to actually activate the study in, you could, your timeline for your study could be exceptionally extended. Right. So that's that's one type of one example of a thing we look at to understand how the timeline looking for the study. Another area on timeline for example and similarly is discontinuation. So you can you could enroll patients fine. But if you've high volumes of discontinuation of participants in your study, then what ends up happening is you actually don't have as many evaluable subjects in your study of some evaluable participants. So you have to recruit or enroll more subjects, right? So that could extend the timeline as well. One aspect of the timeline that really affects the overall market opportunity is oftentimes these therapies are only in under patient for a certain amount of time. So the faster you can get them to market, the faster you can get recoup your return on investment. But also on the patient side, the faster we can get these therapies out through the market to address unmet clinical needs. That's just one flavor.Rohit Nambisan: Then we have subsequent types of flavors. When we talk about data quality, making sure the data is actually collected in the way that you stated you wanted it to be collected in the plan and the protocol at the outset of the study, as well as cost implications. Right. So we look at cost implications as well, which is how, what will this, what will the extension of enrollment or bad data quality data do to the overall budget that you had planned for this study? But then when you drill down on the level further, you can get into risk categories, is something we look at quite a bit when we look at things like protocol, adherence, when you're when you're collecting this data, as I mentioned, it has to be done per a very prescriptive method that is specified a priori before starting the trial in a protocol. And if it's not collected in that manner, it can be discounted. So we are tracking the risk to protocol deviations and understanding trends and not only understanding trends within that study, but we're looking at similar types of studies in this particular therapy area, neurology or say, psychiatry or gastrointestinal type studies and saying, what has been the protocol adherence in studies like yours? And therefore, can we glean some insights about how you are doing in your study based on your comparators in the study as well? But that's just a small flavor. I could probably wax on for quite some time on this question.Harry Glorikian: Well, that that brings us to the question -- I mean, everything you just said, it brings to the question like, from what I know, the company sort of predicts how clinical trials will go by comparing it against a proprietary data set of, I think I was reading, 2000 past trials, right? So I guess the question becomes, so you're comparing one to the past of things that are similar, but you know, for everybody who's listening sort of, you know, where does that data come from in one sense, is it truly proprietary? I mean, that's what I'm you know, that's my set of questions at the moment.Rohit Nambisan: Sure. So I worked for a while, before coming to the life sciences, in the R&D space and the life sciences commercial space. And I think that data sets, are there are proprietary datasets in that space? Very much so. But there is a third party market for that data a little bit more. So then we find life sciences data. It's really hard to get access to R&D data and as you can imagine, that makes a lot of sense, right? If you're a drug developer or a pharmaceutical developer that successfully completed a trial, you never want to share that data. Thereafter, you spent millions of dollars investing in the study, if you want. If there are potentially unknown issues that you haven't identified, would you want to put that at risk? If you are similarly, if you are a therapeutics developer that didn't meet your endpoints, do you want that information to get out and maybe potentially things that issues that that you should have should not overlook, right, getting out in public, etc.? There's just a lot of business risk. There's also IP risk, right? There's a number of different risks associated with getting that data out. So it's been not a very straightforward journey to aggregate data in life sciences, R&D. That being said, I think how we approached this was we've developed models that are both used for benchmarking, as I mentioned before, comparing against similar trials for particular performance KPIs, so to speak, as well as predictive model generation and machine learning models that require a fair amount of data to train on to actually deliver value.Rohit Nambisan: And in that model, we've talked to a lot of our partners or let's say folks that leads them before their partners. And we talk to them. We say we have a growing dataset. There's precedent for this because we've done this with other partners, number one. Number two, we've worked with them to leverage their data combined with our data, write their enterprise data with our data, because it's a common, it's not just one entity's data that's going to provide that value. Your performance, your processes, the way you run trials is inherent in your data. And if we don't leverage that data to train our model to retrain some parts of our models against, we're not providing you the most value we could be with our predictive models or benchmarking. So with that approach, we've been able to do comparative analysis of our data set versus other people's datasets and then anonymize their data upon having a partnership with them to grow our data assets in a very risk-tolerant manner. Right. All the information about CROs or sponsors or other entities, people running trials is removed from the data and we only leverage that data for the purposes of analytics or generating a benchmark. So none of that data is ever shared. So through that process, over the last, I'd say two years, maybe a little two years and change since we started, we've been able to continuously grow this asset and provide greater and greater value with our descriptive diagnostic predictive analytics as well as our benchmarking.Harry Glorikian: How much money, if you had to guess just to give people like an idea, how much money do you think gets poured down the drain preventably every year, and you could save all this money if you just ran smarter, if you did smarter clinical trial management, if I had to frame it that way.Rohit Nambisan: Oh, at least I would say we've done some back calculations on this and happy to digress into the details of them if warranted, but at least somewhere between 20 to 30 percent of the trial costs right now and depending on the phase and depending on the therapeutic area, again, that could be anywhere from 20 to 30 percent of $3 million to $300 million per study.Harry Glorikian: Yeah. I mean it's you know, that's got to be, I don't know how many billions that is. I can't I don't know exactly how much is being spent annually off the top of my head.Rohit Nambisan: We believe we've done some back of the envelope calculations to show that it is in the billions for sure. Across the across the global pharmaceutical market, we're looking just just the value proposition and the signal detection we're bringing to bear is somewhere around $18 to $20 billion, in terms of market opportunity.Harry Glorikian: I mean, how would you guys run or help a team run a clinical trial in practice? Can you sort of give me a real-world example, maybe de-identified, where you helped the client avoid or mitigate some kind of risk, whether it has to do with patient enrollment or site compliance or safety issues during a trial, any one of those will do.Rohit Nambisan: Sure. So I think one example that I can bring to bear is working with a large CRO. And with this large CRO, they had a sizable data asset that was not harmonized, so to speak. It was still living in the transactional exports from the source data systems or CSPs. Et cetera. All around. So it was they had a bunch of different hypotheses about where they were proficient, where they were deficient, but nothing validated. So we spent some time with them trying to understand what all their data assets looked like. And we started collecting these different representations of former trials and ongoing trials, and we collected them and we harmonized them. In fact, as I mentioned before, one of our major differentiators is this is creation of a single source of truth. And we take that upon ourselves, too. It's not like a service, it's part of our offering, right? Our platform offering. And so what we did was we brought that data together and we it was about, I think 400 to 500 studies worth of data at that point. We harmonized it into what we call our local and canonical data format, which is a single representation for multiple different domains of data, scientific data, operational data, enrollment data, etc. And then we compared that against similar studies in our repository, our growing repository, and said, okay, we can tell you comparatively that you are deficient in these particular areas and you're very proficient at the various--for example, in this case they were very proficient in achieving first patient in on the timeline that they expected to actually, scratch that, that they were very they were very proficient in actually accruing the subjects by last patient in in the time they were expected to write so they could hit their accrual when they wanted to.Rohit Nambisan: But when we looked deeper into the data and looked at across like first patient in, the 50 percent enrollment mark for the study and then last patient in for the study, we were able to identify that there was actually a slowdown and a major overcorrection to make up for that. So they were actually hitting what they needed to hit. But as we all probably know, at least in the clinical research phase and any or any budgeting process, being over your budgeting process is bad. Being under your budgeting process is bad, right? So in this case, it's again the same. They were burning resource and cash and resources to rapidly overcorrect for for a milestone they were not hitting reliably earlier in their studies. And so we realized in that accrual situation we said, okay, what you need is, we've identified an error, you're potentially deficient. What you need is an enrollment forecasting application that brings in the data in real time from your study. Right. And it also combines historical data from our repository in your historical data to seed some prior knowledge about the study. So and it's automated, fully automated. So every day you can understand where you are in relation to where you need to be. Right? And it's not a naive straight line kind of curve. It's basically it's based on looking at thousands of historical studies in this space and understanding what the curvature of the actual model should look like.Rohit Nambisan: So we generated that and we were able to actually, in the proof of concept, and this is just one particular example of an application we've been able to generate from our clinical trial intelligence platform, we generated that and we were able to, on a study, predict two years out within one month when they would actually really hit the accrual and it was within one month accurate. Now while that was valuable in terms of understanding at the end state, what really the value was of this closed loop model, so to speak, right, is that it is closed loop. It allows them in silica to say, what happens if I open some sites here? What happens if I close some sites? So what happens if I close this country here? How will that affect my plan before I put that into action in the real world, which oftentimes is very, very, first of all, it's very risky. But second of all, it can yield a number of unknown consequences if you don't try it before in silico. So I think the approach here was we were able to not only predict these things better and also predict the impact of change orders on the study, that might actually affect the timeline of the study. But we were able to actually provide them an application, an interface by which they could test it all their hypotheses in a virtualized manner before they implemented them. And we're growing like crazy with that, with that partner right now at that point.Harry Glorikian: Yeah. And I mean, I mean, you know, in some ways it sounds like, you know, I didn't get it done and I'm pulling all nighters, like at some point so that I can get it done. Right. So there's a whole staffing model. And how do you bring this to the attention of everybody so that they don't drop the ball? Right, because there's a million other things that might be coming at them at that moment.Rohit Nambisan: That's exactly right. Actually, one thing I'll add to that, given you mentioned the staffing model around it, is that we were born within small biotech. Right. And small biotech is very resource-constrained in its ability to manage and oversee a study. That's fairly well known. So our approach has always been what I'd like to call machine-assisted human intelligence. We have experts that are human experts that know the space, but they need to be augmented. They need to be able to look at more complex streams of information and have a machine pick out particular salient insights, salient information, and provide that to them so they can process it, reducing degrees of freedom for them to process it.Harry Glorikian: So just I mean, there are a lot of statistical tools out there now that that for managing risks in clinical trials. So how is the approach that you guys are taking either different or better or both.Rohit Nambisan: It's a good question. One way we've been able to address this question is that statistical approaches generally require certain amounts of data points to be collected before you can warrant using statistical parameters or assumptions, etc. And so there's two things at play here. On top of that, I just mentioned, we're moving into more specialized therapeutic areas, right? So patients per study are smaller, right. And on top of that, when you're starting out a study which is usually the riskiest points in the study, when you're early in the study to mid-stage in a study, you cross them with the fact that you have less patients and there are more niche studies, it's hard to find those patients. Now, your early phase, your riskiest phase, is going to be extended as compared to when you were developing against blockbuster indications. So for a long time in the study, you can't really reliably use statistical parameters to identify an outlier or identify something as aberrant. And then you need to focus on so the way we've done it, we've done it in a slightly different way. There's two approaches. One is we've actually developed a pretty complex risk score system that's based on a set of very different metrics. Think of it as like an array of different KPIs, right? Those KPIs will affect risk differently depending on the type of study you're in. And they'll have different weights to those risks of time, cost and quality depending on the study you're in. So we look at the given study, we're going to deploy and we say, okay, what are the features that characterize the study? Let's look in our historical repository against those same features, pull similar, we call look alike studies and we'll understand how to set those weightings to say protocol deviations at this point in the study are going to impact the overall quality of time. That's much more for this type of study. So we can basically, for lack of a better term, I guess the simplistic way of saying is we can augment the data that we have coming in from a study, which is small at the outset of the study, with lookalike data to increase the power. Right? So that's another way to look at this. So we can actually, we have much better power to be able to detect these issues earlier on and reliably confer that to clinical operators and clinical developers who can do something about it.Harry Glorikian: It would be nice if you had enough data at some point to almost run the whole trial in silico, in a sense. But I think we need a lot more data get there. But, just for everybody that's listening, sort of as a philosophical point, the reason we put drugs through clinical trials in humans is we simply don't know whether they'll work or what the unexpected side effect they might have once you start them on a much larger population. So in that sense, it's expected, even normal for some drugs, maybe even a lot of drugs, to fail at some point in phase one, phase two or phase three. And as an investor, you know, you don't want it to fail in phase three. You want it to fail early. So is Lokavant's goal to reduce the failures or simply help drug developers get to yes or no faster, safer, more cheaply?Rohit Nambisan: Yeah. So our approach has been initially get yes or no faster, safer, more cheaply, more efficiently, right. As part of that process and actually related to some of the work we have done in the last few months on monitoring scientific risk. Right. You have to be careful about these efficacy analyses because they can unblind the study, especially when you have single or double blind blinded studies. So you have to be careful about this point. But in some circumstances we can actually leverage our analysis on blinded endpoint analysis and understand how particular endpoints are collaborating or not collaborating or trending, to understand if there is any effect whatsoever that's being generated in the study. So this is early days for us. But to your to your point about the first use case, we are starting to think about that as an opportunity as well, because we found a way to effectively blind the information and still assess the information content to understand if there is any form of efficacy signal being produced. So I think that that is a really valuable way for us to approach the market in the near future. I think the other point here is that if you are cleaning the data, if you are identifying those data quality issues on a more real time basis, you should be able to reduce the time to do an interim analysis. Right. We should be able to -- you mentioned fail fast. Right. Failing fast requires you to also assess the data, to understand if there's an efficacy signal, there's a safety issue. And if we have these long cycle times before we can actually do an interim analysis. And much of the data indicates that those long cycle times are due to not knowing where the issues are and finding those issues then cleansing them. If we can do that faster, we should be able to do interim analysis much more frequently. Therefore, being able to generate a fail fast scenario.Harry Glorikian: You could almost, you should be able to set up the system to almost be running it and sort of move the bar on where it is on, “Looks successful,” or “It's moving down towards failure.” There's got to be some sort of almost real-time indicator as data is coming in to. You just don't want humans to jump the gun on that. The interesting thing is, I was looking at one of the blogs you have and you sort of say that one of the main reasons clinical trials are so costly and inefficient is bad data management and a lack of interoperability across data repositories. And, you know, it's funny because anybody who listens to this show knows that just comes up over. And it doesn't matter who you are in health care. It is a recurrent theme that for some reason people are not willing to step up and solve. I mean, it has to be a party like yours that comes in and helps clean it up from the outside as opposed to it being cleaned from the inside the way that you would ideally like it to be.[musical interlude]Harry Glorikian: Let's pause the conversation for a minute to talk about one small but important thing you can do, to help keep the podcast going. And that's leave a rating and a review for the show on Apple Podcasts.All you have to do is open the Apple Podcasts app on your smartphone, search for The Harry Glorikian Show, and scroll down to the Ratings & Reviews section. Tap the stars to rate the show, and then tap the link that says Write a Review to leave your comments. It'll only take a minute, but you'll be doing a lot to help other listeners discover the show.And one more thing. If you like the interviews we do here on the show I know you'll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.It's a friendly and accessible tour of all the ways today's information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.The book is now available in print and ebook formats. Just go to Amazon or Barnes & Noble and search for The Future You by Harry Glorikian.And now, back to the show.[musical interlude]Harry Glorikian: So on this show we talk about, you know, how does analytics play into this? So, how do—and I've got to start finding new words—but AI and ML come into this picture. What types of tools in the AI toolbox is Lokavant using? What special powers does AI give you to extract predictions from your data set that other people don't?Rohit Nambisan: Yeah, I think I think the first piece is, and it's going to sound interesting in relation to what folks usually talk about in terms of AI and ML, but it's a harmonized data model, right? When I was working as a data scientist a number of years back, nobody told me all the work that you have to do with data governance and data harmonization. And then when you think about fast forward today where a lot of the actual models themselves are function calls, right? You realize that a lot of the work is actually making sure that data is ready to be analyzed for this particular use case. Right. So it's not to say that we don't do a number of different, try different approaches to gradient boosted descent or support vector machines or neural nets, which is actually my background in terms of grad school and research. But we spend a lot of time thinking through how we need to harmonize, create validated data pipelines to harmonize data for use. In this case. And even in that case, a lot of the work we do is a kind of intelligence or artificial intelligence. So when we're harmonizing the data, we're looking for views on leveraging multivariate clustering algorithms to actually figure out which particular types of data attributes should be mapped to one particular field.Rohit Nambisan: So it's not to say that the data harmonization is devoid of intelligent approaches, it is full of intelligent approaches, but it is an absolute necessity to have the integrity of the data that you need to run those sophisticated front end models, which we run a ton of. But I just I want to call attention to the fact that that is a core asset for Lokavant from the get-go, that Lokavant's canonical data model and the processes we use to harmonize data to get it into that state has been a core focus because if you can do that—and that is the same model you're providing to your data science and analytics teams, your product development teams—then you really have that flywheel that you can generate a number of different analyses. For example, I just mentioned that predictive enrollment forecast model that comes off of in our our Lokavant canonical data model. That is something that is a predictive model, leveraging historical data and ongoing study data in an automated model that indexes towards the historical data early in the trial, indexes towards prediction indexes towards ongoing study data as it comes in. And we have more confidence that input over the trial, that's like an emergent benefit of having the harmonized data harmonize.Harry Glorikian: So, you know, one has to ask in the age of the coronavirus, right, how has the business of running clinical trials changed since the pandemic? I mean. And how do you guys...is that an advantage or disadvantage? I'm trying to, you know, place where you guys are in the whole realm of how things have hopefully changed for the better.Rohit Nambisan: Yeah, it's been quite a tailwind for us actually. And I would say that, number one, it's been it's been beneficial to us because there's just been a lot more scrutiny and interest in clinical research. Not to say there wasn't before, especially for niche therapeutic areas, but and the fact that we were able to develop and get novel COVID vaccines out pretty rapidly. But there was also a lot of challenges along the way in getting to that point. And also delays and trials and challenges in therapeutics development to address COVID as well. So there's just been a lot of scrutiny in the last 24 to 30 months on how efficient and how fast and how effective clinical research can be. So just that alone has been beneficial. Now let's take the next step there and say that all associated with the pandemic, there's been a great impact to clinical trials across the board, not just COVID trials or therapeutic trials. Patients, participants couldn't get to sites for site data collection, right. Site staff couldn't get in there, too, for data entry or site management or site oversight activities. Right. So in general, it's been a huge boon to those technology groups that have developed, decentralized or direct-to-patient data capture methodologies, thereby lowering the patient burden and the site burden for clinical trials to continue in a pandemic fueled environment. What's interesting about that as well, when we think about ourselves as both a data type agnostic platform for clinical research as well as an analytics engine, a platform on top of that, you see this huge movement to another type of data, another data, for example, decentralized trial data as another data source.Rohit Nambisan: And what we've seen also is that while there's been a shift to a lot of decentralized trial collection on most studies, at least 90 percent of studies and above, they're hybrid, they're not fully decentralized. So you have to have some site data collection and you have some decentralized data collection. And that makes sense for those things that may make the most sense to lower patient and site burden to administer. Let the patient let the participant be at home. For those that require like biopsies, etc., that require a participant oftentimes to come into the site, let that be the site. The challenge there is now you have these two different complex data streams that are not necessarily harmonized and aggregated. So this is, again, I think that's been an area where we've been able to come in and say we'll just as a matter of course, you're doing business, this is another data set to us. We need to bring these two in and we have to also enable comparative analysis against decentralized and traditional site based data collection, because otherwise you're going to miss insights. You're going to miss information that are critical to your study.Harry Glorikian: Yeah, a part of me was just thinking, you know, you guys should buy somebody, like Unlearn AI and go at it together where you can have, you know virtualized patients that you can put into the trial, but that's… we won't go there. So let's step back for though, for a second. So let's talk about the company's origin story. Lokavant is one of many companies launch by Roivant, as you mentioned earlier. A Lot of the companies end up with the word “vant.” So can you explain so that people understand: What is Roivant, how it operates, what are vants and and why was Lokavant born. And how did you become president and CEO?Rohit Nambisan: Sure. So Roivant started about seven years ago. And I should mention Roivant is our parent company. We were founded out of Roivant and spun out as a technology company itself. So Roivant initially started as a company that launched "vants" -- nimble, entrepreneurial biotech companies and now health tech companies as well. When I joined Roivant three and a half years ago, Roivant had about 15 different biotech companies. And what was really interesting about their approach is it was therapy agnostic, so it was not that there was a strategic focus or oncology or strategic focus on immunology. There was a focus around identifying compounds that may have been deprioritized in larger pharma companies, which says pharma companies that had a lot of potential and had could address critically unmet clinical needs. And so Roivant would in-license those therapies and start a therapy therapeutically oriented vant. So at the time Axavant it was the new neurological oriented, neurological disease oriented vant. Myovant was the human reproductive oriented, disease oriented vant. Et Cetera. And so now when you think about somebody like myself who comes from the tech world and life sciences, health care technology world, brought into Roivant three and a half years ago, the premise behind Roivant at the time was we can more efficiently develop these therapeutics and have more favorable outcomes leveraging innovative ways of addressing human talent as well as technology. And that latter piece is where obviously I came in and we were starting to look at in my team what are some of the most significant challenges and frequent challenges amongst the vants themselves in running these clinical trials? And then does that map against some of the more significant frequent challenges we see outside in the market? And not surprisingly, there were quite a few particular areas of resonance.Rohit Nambisan: At that point in time, they're about 54, 45 programs being run by Roivant. And so it was across a variety of therapeutic areas. And I guess the thing that hit us in the face primarily was I guess the best way I could say it is you can order a pizza, right? You can understand what is it, a $25 investment, $20 investment. Maybe it's gone up since then, since I ordered a pizza. But the point is that you can understand what time it was ordered, when it was when they said they were going to deliver it to you, and you can track it. And most of these apps now [show it] along its destination to a chain of custody to get to you. We were we could spend $3 to $50 million on any given trial and we were at struggling with our partners to actually identify what is the current state of enrollment in the last week? What is the current state of discontinuation? Where are we at with our with these particular sites in this region? Why are we seeing high screen failure rates, etc.? Right. That's egregious to me. That's just that should not be the case.Rohit Nambisan: We are fairly frustrated with that. And then even when we when even at Roivant or even in my former experiences at Novartis or other pharma, when we brought in a source system to say, okay, well, we're going to have a representation of data ourselves, right? So that we can understand what's going on. Invariably what happened is you would have one source system here and then a duplicate version of that sort of system at the CRO or another vendor that's working with you. You spent your entire time trying to figure out which was the source of truth, because they're spending all your time doing data reconciliation, saying, is that really accurate? Is that really the signal? So that didn't work either. So we felt pretty frustrated about this. We initially tried not to build it ourselves. We worked with a few different collaborators outside of Roivant and tech vendors, etc., and we were fairly frustrated with what we came back with there. So at that point we started thinking, if we can't buy it, we need to take a lead user innovation approach to address this. So we started out with the data platform, as I mentioned to you, and we built that capability to connect, ingest and map from any source, deliver that within a canonical data model, one single canonical data model. And then initially we did a bunch of bespoke analysis on top of that for a few different biotech vants. Rohit Nambisan: That went really well. Some of the external collaborators looked to Roivant at that point we said we'd like to work with this technology outside of the Roivant family, and we realized we were on to something, and we externally launched the company in January of 2020, which was very interesting time and year to launch a company. That being said, we spent the first, I'd say, just under two years, really focused on externally subsidized R&D phase. We're pretty fortunate to have some partners that invested in us in that phase, and we focused on first one particular application in response and we talked a lot about risk. But then we also realized that the needs across different companies, different vendors, etc. for managing clinical trials are very varied. So we realized what we need to really build as generalized on that first application we built and create a highly configurable analytics platform on top of this data platform so that we could actually analyze many different things and configure it for use for any particular customer. And so now we built across, I'd say seven or six or seven different use cases now, and we've deployed most of them and we're continuing to aggregate information in a true product sense where the biggest pain points in the market and how do we build or configure a version of the platform and the platform to address that. And at the same time, we're delivering on global trials with a number of pharma studies as well as on the side of the vendors working through them to deploy on studies as well.Harry Glorikian: So in a perfect world, right, if you had access to all the relevant data, if every drug developer in the world was taking advantage of your services, how would it change the business of clinical trials? What would the outcomes look like? Would it be like you get more drugs approved every year, at a lower cost, fewer disaster failures, I mean. What changes for the industry and for patients?Rohit Nambisan: Yeah. I think the first piece is you would reduce—and this is a lofty question so I'm going to answer with a lofty response—the first thing to note is that, and we touched on this earlier, I think you'd see fewer bigger failures in the analytics phase. You'd be able to identify earlier on, both in terms of the lifecycle of a compound, right? So from phase one to phase three or even phase four, but especially within the study itself, you'd be able to identify that there would be an issue in the study earlier on and you could kill it early on. So that's one one aspect I think would be that's important to note. The other thing I think you would identify is less operational issues. So I think one in six trials across the globe failed just because of operational issues. And when I mean operational issues, I mean the protocol and the plans at the outset of a study say need to administer the study following these steps. And when those steps are not followed, there's compliance risk. And therefore, when there's enough compliance rates to throw out the data or you have to not submit the study.Rohit Nambisan: And so one in six is, it's not that small. And so if we're tracking, if we're more rigorously tracking both what is happening and what could happen, right, based on the indication, leading indicators of risk across time, cost and quality, we should basically never see -- that's a that's one of our major goals -- never see a trial fail just because of an operational reason. Not to mention, how can you go to the patients with unmet clinical needs in a particular indication in particular disease and say, “Oh, I'm sorry, while the drug probably was effective, we just couldn't get it out into the market this time. And it's going to take us another trial, potentially.” A lot of times folks don't actually resurrect the failed study, a failed therapy. So even if they resurrected it and said it was because of an operational issue, “Oh, you've got to wait another six years.” That's just not acceptable. So I think those are the two components that come top of mind. And I think early in our in our tenure, our mission was no trial should fail due to operational error.Harry Glorikian: What is the path to financial success for a company like Lokavant? Is it to just keep growing? To go public? To get acquired by a maybe by a big pharma. What's the path?Rohit Nambisan: It's a good question. I think folks that that know exactly what their exit strategy are probably, for lack of a better term, often deluded. But I will say that we've seen a lot of growth. Not only during, there's been a lot of interest in Lokavant during the pandemic, I mentioned we were in this externally subsidized R&D phase, we were actively trying not to do too much externally. We wanted to figure out how to set up the platform for success. Coming out of that phase, in the last six months, we've seen an incredible amount of traction externally. And so I think we are still in the path of doing it on a growth trajectory ourselves. What does that mean in terms of opportunities to collaborate both commercially and partner and strategically? Well, it means that we can only do as much as we can, even if we continue to grow. There's data out in the market and partners that have access to that data that we would love to collaborate with. If that means that we need to be more strategic in our approach to what Lokavant can do or how to structure Lokavant, we'll do that just because we need to actually achieve our mission, which is to have no trials fail due to operate operational error. Right. And so I think that requires more data. That requires more data science. We have a lean, very, very proficient data science team. So I think there will be opportunities for strategic collaboration, but it's all related to the mission of bringing this clinical trial intelligence platform to address operational and other risks in a study as effectively as possible.Harry Glorikian: You know, one of the things that crosses my mind is you could also use this from an investing perspective to analyze a trial that's going through its paces against historical information and determine, give it a weighting of probability of success versus failure from an investment perspective, that that seems attractive to me.Rohit Nambisan: Yeah. So that's an interesting point to bring up. There are folks now asking us in the market about what we've been informed firmly in trial execution stage. Folks are asking us to move into feasibility and effectively feasibility. Is that the planning of the study? Tell me with this particular configuration of sites, countries and for this indication, knowing the standard of care in different countries, knowing the approach to clinical care, not just clinical research, how successful would this study be? Right. And obviously, the success of a study, when you think about biotech, the success of a study is the success of the company. When you think when you go up the market, depending on the study, it can still have incredible impacts, the success of the company. So there is definitely an afferent towards the investing world and financial. I think at first we probably take a progressive step towards that by moving into trial planning analytics in this manner and then validating our approach against progress in space and seeing how we can continue to grow in that sector.Harry Glorikian: Well, Rohit, it was great having you on the show. I hope everybody enjoyed our discussion. You know, a lot of problems to solve in this industry. So there's there's no lack of opportunity from, you know, businesses that need to get started and the data that needs to be optimized to help move the process forward. But, you know, luckily, everybody I talk to on the show, that's the direction we're all moving. So hopefully we'll get there faster, because I'm not getting any younger. So, so good drugs are going to be needed at some point. So good to have you here. And I can't wish you and the team at Lokavant, you know, more success.Rohit Nambisan: Thanks, Harry, for having me on the show. It was wonderful to be here.Harry Glorikian: That's it for this week's episode. You can find a full transcript of this episode as well as the full archive of episodes of The Harry Glorikian Show and MoneyBall Medicine at our website. Just go to glorikian.com and click on the tab Podcasts.I'd like to thank our listeners for boosting The Harry Glorikian Show into the top three percent of global podcasts.If you want to be sure to get every new episode of the show automatically, be sure to open Apple Podcasts or your favorite podcast player and hit follow or subscribe. Don't forget to leave us a rating and review on Apple Podcasts. And we always love to hear from listeners on Twitter, where you can find me at hglorikian.Thanks for listening, stay healthy, and be sure to tune in two weeks from now for our next interview.

This week, we continue our conversations around the topic of Data-Centric AI joined by a friend of the show Adrien Gaidon, the head of ML research at the Toyota Research Institute (TRI). In our chat, Adrien expresses a fourth, somewhat contrarian, viewpoint to the three prominent schools of thought that organizations tend to fall into, as well as a great story about how the breakthrough came via an unlikely source. We explore his principle-centric approach to machine learning as well as the role of self-supervised machine learning and synthetic data in this and other research threads. Make sure you're following along with the entire DCAI series at twimlai.com/go/dcai. The complete show notes for this episode can be found at twimlai.com/go/575

Could probiotics restore microbiome imbalance linked to autoimmune disorder? UCLA and Oslo University Probiotics might help restore gut bacterial imbalance in patients with systemic sclerosis, says a new study looking at gastrointestinal bacterial compositions in two geographically-distinct populations suffering from the autoimmune disorder. Systemic sclerosis is an autoimmune disease which impacts the body's connective tissue. It is an uncommon condition that results in hard, thickened areas of skin and sometimes problems with internal organs and blood vessels.The study ran across the US and Norway and found that Norwegians and Americans with systemic sclerosis had higher levels of bacteria which can cause inflammation and lower levels of bacteria which are said to protect against inflammation compared to those not suffering from systemic sclerosis.The study found that those with systemic sclerosis had significantly lower levels of gut bacteria which is thought to protect against inflammation, such as Bacteroides.They were also found to have higher amounts of bacteria which promote inflammation, such as Fusobacterium, in comparison to those without systemic sclerosis.The study suggests that probiotics may aid restoring gut bacterial balance in those suffering from systemic sclerosis.   Caraway extract shows slimming potential for women University of Malaya (Malaysia),  An aqueous extract of caraway seeds may suppress appetite and help slim waistlines and thighs in physically active women, says a new study. Data published in Phytotherapy Research indicated that 90 days of supplementation with the caraway (Carum carvi L.) extract led to significant reductions in waist circumference of 6.2 cm and thigh circumference of 5.4 cm, compared to baseline levels. No significant waist reductions were recorded in the placebo group. “This study showed that the consumption of 30 mL/day CAE [caraway aqueous extract] may result in reasonable anti-obesity effects,” wrote the researchers. “Most likely, this occurs through a combination of four major bioactivities, including anti-microbial, anti-oxidant, and anti-inflammatory properties, together with the appetite-suppressing activity. Scientists from the University of Malaya (Malaysia), Shahid Beheshti University of Medical Sciences (Iran), and Natural Products Inc (USA) recruited 70 aerobically trained, overweight, and obese women to participate in their triple-blind, placebo-controlled, clinical study. The women – who were instructed to not change their diet or physical activity – were randomly assigned to receive either the caraway extract or placebo for 90 days. Results showed that women in the caraway group had significant reductions in both appetite levels and carbohydrate intake compared with the placebo group. Commenting on the potential bioactives compounds responsible for the effects, the researchers note that caraway seed extracts contain volatile compounds such as limonene, gamma-terpinene, trans-carveol, carvone, thymol, and carvacrol. Friends Provide Better Pain Relief Than Morphine, Oxford University Study Reveals Oxford University Recent studies have explored the science behind friendships and discovered that there are actually measurable differences between people who have strong, healthy social networks and those who don't. In particular, people with strong friend connections were found to experience significantly better states of physical and mental health. “People with social support have fewer cardiovascular problems and immune problems, and lower levels of cortisol — a stress hormone,” says Tasha R. Howe, PhD, associate professor of psychology at Humboldt State University. Adding to the growing research on the benefits of friendship, a recent study conducted by researchers at Oxford University established that people with more friends have higher pain tolerance.  The study was designed to use pain tolerance to test the brain's endorphin activity. The researchers theorised that people with larger social networks would, as a result, have higher pain tolerance. The findings of the study supported their theory in that it showed that indeed, strong social connections were correlated with higher pain tolerance. As mentioned in the final statement it is not just the size of our social network that is important to our wellbeing, but the quality of the friendships that matters as well. With the advent of the internet modern society is changing quickly, and our interactions are increasingly occurring online. Even though the internet can be a great way to connect with likeminded people, online friends just aren't the same as those we can actually sit with and look directly in the eye when we communicate–and a digital hug is just nowhere near as good as a real one!  Videos: 1. Pfizer CEO Albert Bourla explains Pfizer's new tech to Davos crowd (0:25) 2. You'll Never See This on Tell Lie Vision (2:19) 3. Jimmy Dore-  TV War “Experts” Revealed As Paid Shills For Weapons Manufacturers  (only first 6:00) 5. SHOCKING! Assad Spills Truth About Ukraine Conflict and NATO by Richard Medhurst (11:50)

In this podcast segment, The Forecast's editor Ken Kaplan talks to Tony Palmer, principal validation analyst at research firm ESG, who tested Nutanix Cloud Clusters on AWS, designed to reduce the operational complexity of migrating, extending or bursting business applications and data between on-premises and clouds. Perhaps at the top of IT's wish list is […]

Start using a digital workforce. It does the tedious work and allows your team to focus on the higher-value "human work". This improves profitability, efficiency and productivity while boosting job satisfaction. I host Jet Theurkauf, Chief Customer Strategy and Transformation Officer of Blue Prism, who shares how every leader can benefit from digital workers right now. If you've never heard of a digital worker (not remote worker), you may be surprised to learn that it's a category of software robot trained to perform a task or process in partnership with a human colleague. Blue Prism is the global leader in intelligent automation which combines the power of artificial intelligence (AI) and machine learning (ML) to deliver digital workers that take away the mundane tasks human workers are overloaded with and empowers them to focus on the profit driving initiatives only people can do. Jet has more than 26 years of experience leading global transformation initiatives that deliver positive results. Before coming to Blue Prism he was Head of Transformation at BNY Mellon. Jet has also served as the Chairman of the Diversity Committee for one of the largest banks in Europe, and he is constantly involved in philanthropic efforts. He has multiple degrees in Psychology and advanced training in business specialties from Harvard, London Business School, and Thunderbird University. LinkedIn Profile: https://www.linkedin.com/in/jontheuerkauf/ Company Link: https://www.blueprism.com/ What You'll Discover in this Episode: What feeding sharks teaches you about leadership. Why pirates can be leadership role models. What it was like being one of the first quality leaders at GE. How to use digital workers (not remote workers) to boost profitability. The urgency and opportunity for leaders to use digital workers. How to accelerate your career with courage and tenacity. Quotes: "We take the robot out of people." ----- Connect with the Host, #1 bestselling author Ben Fanning https://www.benfanning.com/speaker/ (Speaking and Training inquires) https://followbenonyoutube.com (Subscribe to my Youtube channel) https://www.linkedin.com/in/benfanning/ (LinkedIn) https://www.instagram.com/benfanning1/ (Instagram) https://twitter.com/BenFanning1 (Twitter)

Ve vršovickém Divadle Mana můžete Martina Dejdara vidět v detektivce Mléčné sklo. To je žánr na divadle velmi neobvyklý. „Splňuje to všechny atributy, které mám rád u divadla a u filmu, že divák je každých deset minut překvapený. Rozhodně nečekejte happy end,“ láká. V novém muzikálu Jana Svěráka a Tomáše Kluse Branický zázrak si zase vychutnává bezdomovce. „Takovou vstřícnost na zkouškách jsem za posledních 30 let nezažil,“ chválí spoluautory představení.Všechny díly podcastu Host Lucie Výborné můžete pohodlně poslouchat v mobilní aplikaci mujRozhlas pro Android a iOS nebo na webu mujRozhlas.cz.

Dave Chappelle triggered John Mulaney fans, Elon Musk pulls a Deshaun Watson, Monkeypox, Stagflation, George Carlin's American Dream, Drew Crime: The Cyclist Murder, and the Detroit Lions' DJ cancels Kid Rock.John Mulaney is in trouble for not issuing a trigger warning for fans before bringing comedian Dave Chappelle on stage at his Columbus comedy show.Bill Maher went off on the trendiness of transgendered youth.Dustin Diamond is making a comeback and trying to eclipse Drew in Twitter followers.Other John Mulaney Notes: Chip 'n Dale: Rescue Rangers is really insensitive to the original Peter Pan. People are mad that John's baby-mama needs baby formula too. Early Voting won the Preakness stakes and Maz owes us all money for his 2nd consecutive incorrect horse race prediction.The Detroit Lions DJ scrubs Kid Rock from Ford Field.Harry Styles is ripping off Paul McCartney.Dolly Parton has covered a lot more rock songs than you think.My Next Guest Needs No Introduction with David Letterman landed a Will Smith interview... before the Oscars slap.The biggest stars in the world are showing up for the Queen's month-long Platinum Jubilee.The Detroit Tigers have many injuries.Congratulations to Travis and Kourtney on their THIRD wedding... this time in Italy.Stagflation is the newest thing we're told to fear.McDonald's in Royal Oak closes at 7pm despite the internet saying it's 24/7.A bunch of German baby formula has landed, but will take weeks to get on the shelves. Marc's worried there might be a peanut butter shortage soon.Monkeypox is the second newest thing we're told to fear.Elon Musk is #MeToo'd by a former flight attendant/masseuse that he has sign an NDA.Britney Spears is still CRAZY.Rebel Wilson was sexually assaulted years ago by a mystery celebrity (before she was sizzling). She wants you to know she handled it perfectly because she was a lawyer.Johnny Depp vs Amber Heard rolls into its 230th week. The Daily Wire spends thousands to somehow make this case political.A Bhad Bhabie look-a-like/sound-a-like battled with a Florida McDonald's and the McPuns were McPlentiful.George Carlin's American Dream doc on HBO is worth your time.Gaylord was hit hard by a tornado over the weekend leaving two dead.Studio Woes: The No BS News Hour can leave the DealsintheD.com studio in slight disarray every Friday. Check out ML's latest piece on Sam Riddle who will be in this basement on Tuesday.Mitch Albom has a lukewarm take on Nick Saban vs Jimbo Fisher.Juwan Howard told the LA Lakers to pound sand/salt.Drew Crime: Another nude pic suicide. Stop sending photos of your weiner. Player Dave 2.0 leads to another murder and a smoking hot fugitive. One of the Big 3 (Dateline, 48 Hours & 20/20) covered the murder surrounding ex-Chicago Bear Shaun Gayle.Jay Cutler allegedly nails his friend's wives.Matthew Stafford is in Top Gun commercials now. He's complicit with the atrocities of Scientology.Social media is dumb, but we're on Facebook, Instagram and Twitter (Drew and Mike Show, Marc Fellhauer, Trudi Daniels and BranDon).

Libretos e investigación periodística: Leticia Heguy, Sandro Paredes, y David Alfonso. Ganadora de Eurovisión 2022.Impone 23 de sus nuevas canciones en el top 50 global de Spotify.A sus 18 años de edad es # 2 mundial, según t40ch.# 1 ML, Perú, Paraguay y Panamá.Llega al # 2 global digital Fuerte candidata a ser la canción del verano.Otra canción del amargue.Durante la pandemia se hace conocido. El himno femenino latinoamericano del año.Recibe 11 certificados de platino en USA hispano. En México es un severo hit.Inspirado en un cóctel de vodka, cerveza de jengibre y jugo de lima. Top de GH 386

In this video learn one way to make a 1% povidone iodine solution for the povidone iodone nose wash if you have had an exposure to Covid-19. What nasal spray or rinse? Use 1% povidone-iodine commercial nasal spray as per instructions 2-3 x daily. If 1% product is not available, dilute the more widely available 10% solution and apply 4-5 drops to each nostril 4-5x daily for post-exposure prevention and the early symptomatic period. To make 1% povidone/iodine concentrated solution from 10% povidone/iodine solution, IT MUST BE DILUTED FIRST. One dilution method is as follows: First pour 1½ tablespoons (25 ml) of 10% povidone/iodine solution into a nasal irrigation bottle of 250 mL. Then fill to top with distilled, sterile or previously boiled water. Tilt head back, apply 4-5 drops to each nostril. Keep tilted for a few minutes, let drain. No more than 5 days in pregnancy. Please note: this is not a sterile solution. The comment about making a new solution every 24 hours is just a suggestion when you have multiple people using the solution. When putting a dropper in used by more than one person the solution can be contaminated. Please wash your hands before and after making or using the solution. Also keep the bottle/container and dropper clean and disinfected when not in use. Please visit our FAQ page for more answers to your questions. Donate to the Front Line Covid-19 Critical Care Alliance, Inc To educate medical professionals and the public in safe and effective ways to prevent and treat COVID-19. Click here to make a donation: https://covid19criticalcare.com/network-support/support-our-work/ GoFundMe: https://charity.gofundme.com/donate/project/front-line-covid-19-critical-care-alliance/joyce-kamen Buy FLCCC gear at: https://theflcccstore.org/ Subscribe to our mailing list on our website: http://flccc.net/signup Follow us on Telegram: https://t.me/FLCCC_Alliance

In the 10 a.m. hour of the K&C Masterpiece, the guys overreact to game 1 of the WCF, talk with Audacy NBA insider Brian Scalabrine, and get into some Rangers in Baseball Nuggets

MLB: Thursday's Best Bets: Run Lines, Player Props, NRFIs for May 19th Games Talked About: White Sox vs. Royals Orioles vs. Yankees Diamondbacks vs. Cubs Join Our Chalkboard here!: https://links.chalkboard.io/join-board/MTI0/SFE4OU1NMm1iM2VsaU1pekdWNlJuMGxYbDQ3Mw== Parlay of the Day: https://oddsjam.com/mlb/calling-our-shot-mlb-best-parlay-picks-today

AlloyDB for PostgreSQL has launched and hosts Mark Mirchandani and Gabe Weiss are here this week to talk about it with guests Sandy Ghai and Gurmeet Goindi. This fully managed, Postgres compatible database for enterprise use combines the power of Google Cloud and the best features of Postgres for superior data management. AlloyDB began years ago as a solution to help manage huge data migrations to the cloud. Customers needed a way to take advantage of the benefits of cloud, modernizing their databases as they migrated in an easy, flexible, and scalable way. Databases had to maintain performance and availability while offering enterprise customers optimal security features and more. We learn why PostgreSQL is important in the equation and how AlloyDB is built with Google scaling abilities and ML while supporting open source compatibility. We talk about data analytics workloads and how AlloyDB handles in-the-moment analytics needs. Our guests describe and compare different database offerings at Google, emphasizing the solutions that set AlloyDB apart. We chat about the types of projects each database is best suited for and how AlloyDB fits into the Google database portfolio. We hear examples of customers moving to AlloyDB and how they're using this new service. Clients have been leveraging the embedded ML features for better data management. Sandy Ghai Sandy is a product manager on GCP Databases and has been working on the AlloyDB team since the beginning. Gurmeet “GG” Goindi GG is a product manager at Google, where he focuses on databases and attends meetings. Prior to joining Google, GG led product management for Exadata at Oracle, where he also worked on databases and attended meetings. GG has had various product management, management, and engineering roles for the last 20 years in Silicon Valley, but his favorite meetings have been at Google. He holds an MBA from the University of Chicago Booth School of Business. Cool things of the week Google I/O site Introducing “Visualizing Google Cloud: 101 Illustrated References for Cloud Engineers and Architects” blog Meet the people of Google Cloud: Priyanka Vergadia, bringing Google Cloud to life in illustrations blog Working with Remote Functions docs Interview AlloyDB for PostgreSQL site AlloyDB Documentation docs AlloyDB for PostgreSQL under the hood: Intelligent, database-aware storage blog PostgreSQL site Introducing AlloyDB for PostgreSQL video Introducing AlloyDB, a PostgreSQL-compatible cloud database service video BigQuery site Spanner site CloudSQL site What's something cool you're working on? Gabe is working on some exciting content to support landing the AlloyDB launch. Hosts Mark Mirchandani and Gabe Weiss

There's a website called thispersondoesnotexist.com. When you visit it, you're confronted by a high-resolution, photorealistic AI-generated picture of a human face. As the website's name suggests, there's no human being on the face of the earth who looks quite like the person staring back at you on the page. Each of those generated pictures are a piece of data that captures so much of the essence of what it means to look like a human being. And yet they do so without telling you anything whatsoever about any particular person. In that sense, it's fully anonymous human face data. That's impressive enough, and it speaks to how far generative image models have come over the last decade. But what if we could do the same for any kind of data? What if I could generate an anonymized set of medical records or financial transaction data that captures all of the latent relationships buried in a private dataset, without the risk of leaking sensitive information about real people? That's the mission of Alex Watson, the Chief Product Officer and co-founder of Gretel AI, where he works on unlocking value hidden in sensitive datasets in ways that preserve privacy. What I realized talking to Alex was that synthetic data is about much more than ensuring privacy. As you'll see over the course of the conversation, we may well be heading for a world where most data can benefit from augmentation via data synthesis — where synthetic data brings privacy value almost as a side-effect of enriching ground truth data with context imported from the wider world. Alex joined me to talk about data privacy, data synthesis, and what could be the very strange future of the data lifecycle on this episode of the TDS podcast. *** Intro music: - Artist: Ron Gelinas - Track Title: Daybreak Chill Blend (original mix) - Link to Track: https://youtu.be/d8Y2sKIgFWc *** Chapters: 2:40 What is synthetic data? 6:45 Large language models 11:30 Preventing data leakage 18:00 Generative versus downstream models 24:10 De-biasing and fairness 30:45 Using synthetic data 35:00 People consuming the data 41:00 Spotting correlations in the data 47:45 Generalization of different ML algorithms 51:15 Wrap-up

5 Straight Winning Days! 10-4 Run! Let's keep moving! MLB Wednesday's Best Bets: Run Lines, Player Props, NRFIs for May 18th Games Talked About: Braves vs. Brewers Phillies vs. Padres Orioles vs. Yankees Parlay of the Day: https://oddsjam.com/mlb/calling-our-shot-mlb-best-parlay-picks-today

Joe Schurman teaches from his deep experience in the software, machine learning, AI, and processes that organizations need today as they transition to data-driven technology companies. He names some of the cloud services and tech tools he uses to lead clients to start with a user case, break it into stories,  build a team led by the solution owner, assign the stories to developers to build, and iterate product demos until the Minimum Loved Project (MLP) is achieved. Joe offers observations on investing the “right” amount of time in projects, and wisdom on developing a learner mindset.   Key Takeaways [2:06] Joe Schurman is a 2nd-degree black belt in Kung Fu. He once judged a competition in Las Vegas. He has four children; two daughters and two sons. [2:57] Joe is an expert on the fringes of what we can do with computing technology. What we can do changes every day. In the past couple of years, from an AI perspective, with data and automation, it's taken leaps and bounds. [4:30] We're still pretty far away from general AI, despite Sophia, an AI robot that was granted Saudi Arabian citizenship in 2017. Today's AI depends on the programming we give a machine and its interpretation and output. Joe's focus is narrow or weak AI. His business colleagues call it magic. Computer vision is an area he loves. [5:45] Joe uses a lab environment across Google Cloud Platform, Microsoft Azure, and Amazon Web Services. The capabilities that have come up in the last year are “just insane” with what you can do with computer vision and building libraries of what the machine can see. [6:06] Joe loved seeing a computer vision capability demonstration at AWS re:Invent of tracking every NFL player on the field and predicting injuries and other types of output and insights in real-time. The machine used narrow AI to access a library seeded with “a ton” of data to interpret the action. [7:15] What you can do with this technology comes down to the data that you feed the engine. Think about the amounts of data that organizations have to sift through to generate reflective or predictive insights. Auto machine learning helps organize the data into useful information such as anomaly detection in software engineering. The data can also come from tools like GitHub and Jira. [8:25] Joe did a fun computer vision project on UAPs for the History Channel, working with some of the nation's top military leaders, building a library of video and audio data to be able to detect unidentified aerial phenomena that were not supposed to be entering our airspace, and curating that library. [10:06] AI started with the idea of speeding up processes, such as getting an app to market faster or gathering insights quicker to make business decisions more timely. [11:28] AI can enhance human performance. Joe starts by finding people who know how to fail fast; to get a Minimum Viable Product (MVP) out the door. Solutions such as quality engineering automation, test automation, and monitoring services for DevOps detect bugs and performance issues quickly and ensure that the quality of the team is sound.[12:47] Joe notes the importance of individuals performing, contributing to, and collaborating as a team. Set your organization and standards governance up first. Look for a platform of technology to leverage that enables you to build and tinker. Finding the latest and greatest tool is no good unless it provides the right level of collaboration with their platform and connection to different processes. [14:53] When introducing ML to an organization, start with discovery, to understand the culture and talent within the organization. How are they communicating today? Joe sees the biggest gap between data scientists and data engineers. Projects tend to fail without collaboration, regardless of the tech. If the data scientists don't understand the domain, then the platform is irrelevant,[17:28] Joe stresses the need for a methodology in place to make any of these aspirations work for your organization. After discovery, there's an align phase. Focus on the outcome and the use case. The solution owner is crucial. The solution owner leads the technology team and brings them together around the client's outcome to develop that use case.[18:12] If you can't take an actual use case and break it down into bite-sized chunks or user stories, then the project will never be on the right track. Start with the use case to mitigate risks. Break the use case into user stories. Match the user stories with the number of engineers that can develop a number of user stories within a given time frame. [18:38] Those user stories given to the engineers are deducted into Story Points, in the Agile Process of engineering software. Price Waterhouse Coopers (PcW) has taken it to the next level, being able to do Engineering as a Service, being able to do it at scale, and being able to pivot quickly.[18:58] Joe explains what can happen if you have a great idea, take three to six months to break down the use case, and fill all the requirements, but hand it off to the Dev team that has no idea what the use case is: you get irrelevant software that doesn't tie back to the outcome! [19:22] Keep the solution team engaged in building the bridge between the subject matter expert stakeholders and the engineers. Every two weeks, demonstrate the iteration or program increment you have built. Does it match the outcome? Does it provide any relevance? Then take the feedback and figure out what happened in that iteration. Fix errors. You will build a product that has value to launch. [20:45] Communicate a lot, so all the people are on the same page! When you have stovepiped organizations where the departments don't talk to one another, you waste time, effort, and money building a product no one will use. One of Joe's colleagues, José Reyes, uses the term Minimum Lovable Project (MLP), where people rally around the outcome, not just the tech. [22:33] What skills and knowledge will the leaders of PwC need to endure for the next five years? Joe says first are character and attitude; people that have a hunger to build something, with a fail-fast mentality, and that are excited to learn constantly, that read every day and learn new technology. [24:27] Then know the tools. Documents exist on the internet for every solution and there is access to services like GitHub to download projects and starter templates without being an expert but just reading the README file and installing the base-level template, learning as you go, and as you tinker. That's way more valuable than coming in as a book-smart expert in a specific product or technology. [24:57] When it comes to tooling, there are products like the Atlassian platform with Confluence and Jira. For an AI stack, Joe typically works with AWS, GPC, and Microsoft, more so on the Amazon side with AWS AI tools, like Rekognition, Glue DataBrew, Redshift ML, Comprehend, and more. Amazon, Microsoft, and Google produce so much documentation and certification to get you up to speed. [26:30] Judgment, wisdom, and character will not be replaced by AI anytime soon. There's still room for philosophy in leadership. There are tools and technologies to speed up the processes, but not the individuals. There are no general AI solutions out yet to replace a pod of application developers, designers, and solution owners to execute a successful MVP or MLP out the door for a client. [27:55] Advice to CEOs: Be patient and understanding. Be willing to fail fast. Support tinkering and R&D, even if the project doesn't work out. Organizations are generally realizing that today they need to be data-driven, technology companies but there is still hesitance over the risk that needs to be taken. [30:03] Why would an insurance company or other traditional company need R&D? Look at Loonshots, by Safi Bahcall for some ideas about R&D. [30:56] Joe shares how he got to this point in his career. He wanted to play baseball but started at Compaq (now HP) when he was 18, writing scripts in Unix and other environments. Just being able to make certain changes to help clients get products faster and seeing the quick response from the outcomes felt like a home run to him! [31:49] Years later, Joe went on his own, with a vision to create telehealth before telemedicine was a thing, using Skype for Business and Microsoft Lync, enabling an API for that. Seeing people connect through a technology he had built, replaced the need to be a baseball star! Joe is grateful for the break he got at a young age and enjoys his work. [33:22] When Joe first started, he was trying to be the smartest person in the room, seeing the instant gratification of making code snippets that tested successfully. Eventually just building the app wasn't enough for him. He got the dopamine hit from seeing users interacting with his code and seeing its value. [34:58] Joe's mentors include many people he worked with. X. D. Wang at Microsoft Research inspired him to tinker, build, and focus on the short-run more than the long-run. Randeep Sing Pal at Microsoft Unified Communications was another great mentor. Also Steve Justice and Chris Mellon, in terms of character and collaboration. Joe shares how they mentored him. [37:23] Jan says something we forget about technology is that there are a lot of failures and attempts before the success hits. We have to be mindful of that as leaders to give people time and space to do really creative, cool things. [38:01] Joe appreciates the opportunity to discuss these things. Joe spent a lot of his career building software solutions that were way ahead of their time. It's frustrating to see telemedicine so successful now, but not when he attempted it. He had to learn to let go. It's not just about releasing bleeding-edge tech; you've got to find some value associated with it to resonate with the end-user. [39:31] Always think about the outcome and understand your audience first. And then be able to supplement the back end of that with bleeding-edge technology, development, tinkering, failing fast, and all the things that go with software engineering. Also, be humble! Get perspective from outside your bubble to build a better solution and be a better person. [40:49] WHenever you're setting out to build anything, start with a press release! Write a story of what it would look like if it were released today. Then just work back from there!   Quotable Quotes “There are so many new and cool technologies and innovations that are coming out at the speed of thought, which are pretty fascinating.” “I've been in real cloud engineering for about a decade, and from an AI perspective, with data and automation, over the past five to 10 years, in terms of running on a cloud environment, and it's just taken leaps and bounds.” “You've got to be able to connect that [data] environment to a use case or an outcome. If you can't do that and you can't enable a data scientist to understand the domain, then the data platform is irrelevant. I see a lot of performance issues occur because of that disconnect.” “If you can't take an actual use case and break it down into bite-sized chunks or user stories, then the project will never be on the right track.” “In this industry, you're constantly learning; constantly reading. I'm reading every day and learning about new technology every day and how to apply it and how to tinker with it. I need people on the team … that have that ability or that hunger to tinker and learn.” “Transitioning from a ‘knower' mindset to a ‘learner' mindset was the biggest shift for me.” “Always think about the outcome and understand your audience first. And then be able to supplement the back end of that with bleeding-edge technology, development, tinkering, failing fast, and all the things that go with software engineering.”   Resources Mentioned Joe Schurman, PwC Joe Schurman on LinkedIn PwC Sophia robot granted citizenship I, Robot film Weak AI Google Cloud Platform Microsoft Azure Amazon Web Services AWS re:Invent GitHub Atlassian Jira Unidentified, The History Channel José Reyes, PwC The Shackleton Journey Atlassian Confluence AWS Rekognition AWS Glue DataBrew AWS Redshift ML AWS Comprehend Steve Justice on LinkedIn Chris Mellon Loonshots: How to Nurture the Crazy Ideas That Win Wars, Cure Diseases, and Transform Industries, by Safi Bahcall  

As we all re-entered society over the last year, after months of WFH sweatpants and t-shirts, a personal stylist would have been helpful. Well, now there's AI for that - THE YES. By taking a quick style quiz, you'll have daily product and brand recommendations and a personalized feed displaying exactly what you're looking for — in your size, budget and preferences. The more you say “yes”, the more personalized your feed gets. Right now, the company only offers women clothes but may expand in the future. The CTO of THE YES, Amit Aggarwal, joins the show to share how the company uses ML to create millions of unique stores daily for each one of their users.SUBSCRIBE TO THE ROBOT BRAINS PODCAST TODAY | Visit therobotbrains.ai and follow us on YouTube at TheRobotBrainsPodcast, Twitter @therobotbrains, and Instagram @therobotbrains. See acast.com/privacy for privacy and opt-out information.

A man who fishes guns out of the Detroit River tells ML, Marc and Patrick us what else his magnet […]

Technology has come a long way in the last decade and machine learning and artificial intelligence are perfect examples of how a machines can help us get the job done. But before you buy a piece of technology for your business, you need to take a step back, pinpoint your core problem, and decide if you actually need AI or ML to solve it. In this episode of the Dr. Dark Web podcast, host Chris Roberts answers vital questions regarding AI and ML, such as, “Do you actually need AI to solve your problems?“; “How can you find a suitable vendor for your solution?“;”Do you have enough data to train an AI model?” and more.

MLB Tuesday's Best Bets: Run Lines, Player Props, NRFIs for May 17th Games Talked About: Braves vs. Brewers Giants vs. Rockies Mariners vs. Blue Jays Parlay of the Day: https://oddsjam.com/mlb/calling-our-shot-mlb-best-parlay-picks-today

In this episode, the Director of Architecture at NVIDIA, Dr. Magnus Ekman, joins Jon Krohn to discuss how machine learning, including deep learning, can optimize computer hardware design. The pair also review his exceptional book 'Learning Deep Learning.' In this episode you will learn: • What hardware architects do [10:15] • How ML can optimize hardware speed [ 13:19] • Magnus's Deep Learning Book [21:14] • Is understanding how ML models work important? [36:16] • Algorithms inspired by biological evolution [41:25] • How artificial general intelligence won't be obtained by increasing model parameters alone [51:24] • Why there will always be a place for CNNs and RNNs [54:51] • How people can "transition" realistically into ML [1:09:15] Additional materials: www.superdatascience.com/575

Traditional hiring processes often place a lot of undue emphasis on GPA and alma mater, data points that, generally, aren't very predictive of future performance.  Aaron Myers, Ph.D., Chief Technology Officer, and the team at Suited have built a company around a better way to hire—using AI to measure personality traits, experiences, cognitive data, and other metadata so that companies and job candidates find the most suitable match.  Aaron discusses how Suited works, how the company helps customers implement better hiring data practices, and what the future holds for technology like AI and ML.  We discuss: Leveraging AI to deliver better hiring outcomes Onboarding, setting expectations, and defining KPIs Explainable AI and the future of hiring and performance management  Want to hear more stories from high growth software companies? Follow Application Modernization on Apple Podcasts, Spotify, or check out our website.  Listening on a desktop & can't see the links? Just search for Application Modernization in your favorite podcast player.

At CES this year, Peter Virk, Vice President, IVY Product & Ecosystem at BlackBerry on #1839, shared how over 195 million cars worldwide currently use BlackBerry software. In the light of current geopolitical events and the consequent necessity for extra cyber security vigilance, I wanted to bust a few more myths and talk about the role of BlackBerry in Cybersecurity. After 14 years at McAfee, where he was vice president of central and north/eastern Europe, Hans-Peter Bauer moved to BlackBerry to start his new role as senior vice president of EMEA. But I wanted to learn more about BlackBerry's Prevention First approach, which involves using AI and machine learning ML to prevent attacks from the outset. We discuss why a four-day workweek is possible for the always-on cybersecurity industry and what it looks like for a small business to be fully protected against cyber-attacks and explore if it's sustainable. We also talk about why a prevention first cybersecurity approach is much more than just a perimeter defense – at its best; it's an active effort to neutralize malware before the exploitation stage of the kill-chain Finally, we talk about how not to become a victim, the shortage of IT workers, why security specialists are critical, and what organizations can do to attack new cyber specialists.

Noite de mistérios: mistério da fé, mistério de Rendeiro. Rui Zink enterra-se pela verdade e Júlia Pinheiro recorda a noite em que esteve na Assembleia. Manuel Serrão é assado e Rita Blanco lembra uma noite tórrida. É no centro que está a virtude (e o CDS) e é na Noite da Má Língua que se descobrem as verdades. É só perguntar ao chefe dos pirilampos. See omnystudio.com/listener for privacy information.

Today we're joined by Rob Walker, VP of decisioning & analytics and gm of one-to-one customer engagement at Pegasystems. Rob, who you might know from his previous appearances on the podcast, joins us to discuss his work on AI and ML in the context of customer engagement and decisioning, the various problems that need to be solved, including solving the “next best” problem. We explore the distinction between the idea of the next best action and determining it from a recommender system, how the combination of machine learning and heuristics are currently co-existing in engagements, scaling model evaluation, and some of the challenges they're facing when dealing with problems of responsible AI and how they're managed. Finally, we spend a few minutes digging into the upcoming PegaWorld conference, and what attendees should anticipate at the event. The complete show notes for this episode can be found at twimlai.com/go/573

MLB: 3 STRAIGHT WINNING DAYS! Monday's Best Bets: Run Lines, Player Props, NRFIs for May 16th Games Talked About: Rockies vs. Giants Rangers vs. Angels Astros vs. Red Sox Parlay of the Day: https://oddsjam.com/mlb/calling-our-shot-mlb-best-parlay-picks-today

Frank had his first PR merged into iOS and macOS for .NET! We explore his journey to getting it accepted Follow Us Frank: Twitter, Blog, GitHub James: Twitter, Blog, GitHub Merge Conflict: Twitter, Facebook, Website, Chat on Discord Music : Amethyst Seer - Citrine by Adventureface ⭐⭐ Review Us (https://itunes.apple.com/us/podcast/merge-conflict/id1133064277?mt=2&ls=1) ⭐⭐ Machine transcription available on http://mergeconflict.fm

In our previous episode, we talked to Dr. Seth Benzell about How the Pandemic Changed Global Labor Markets and in this episode, we talk about the work he's done to find a solution to the age-old issue of rising automation. The automation scare is not new.  Since the luddites in the 19th century, we've heard the laborers complain about automation. In fact, it is the new standard in todays world. We all hear about the benefits of automation but we are still trying to understand and remediate the negative externalities.If you've listened to Things Have Changed before, you know Dr. Seth Benzell spends his time studying the digital economy, the platforms that have succeeded in it, and how it's affected our economy as a whole. In this episode, he helps us understand how he's gotten to the two solutions that he mentions in his paper on "Simulating Endogenous Global Automation". Seth walks us through two of his considerations for policy that could possibly change the direction away from increased wage inequality:Mandating automation for regions that would otherwise not automateUniversal Basic Income Check out our previous episodes with Dr. Seth Benzell on the links below:How Tech Created a Winner-Take-All Economy – with Seth BenzellHow the Pandemic Has Changed Global Labor Markets with Dr. Seth BenzellHere is a link to Dr. Seth Benzells work:Dr. Seth Benzell WebsiteEfficiency of U.S. Public Space UtilizationSupport the show

MLB Saturday's Best Bets & Picks Run Lines, Player Props, NRFIs for May 14th Games Talked About: Angels vs. Athletics Rockies vs. Royals Orioles vs. Tigers PRIZEPICKS! Take advantage of the Luka Doncic OVER 0.5 Points Free Square that is LIVE. Use code 'COS' for a 100% Deposit Match up to $100. OR use this link!: https://app.prizepicks.com/sign-up?invite_code=COS Parlay of the Day is back on Monday!

We talked about: Jeff's background Getting feedback to become a better teacher Going from engineering to teaching Jeff on becoming a curriculum writer Creating a curriculum that reinforces learning Jeff on starting his own data engineering bootcamp Shifting from teaching ML and data science to teaching data engineering Making sure that students get hired Screening bootcamp applicants Knowing when it's time to apply for jobs The curriculum of JigsawLabs.io The market demand of Spark, Kafka, and Kubernetes (or lack thereof) Advice for data analysts that want to move into data engineering The market demand of ETL/ELT and DBT (or lack thereof) The importance of Python, SQL, and data modeling for data engineering roles Interview expectations How to get started in teaching The challenges of being a one-person company Teaching fundamentals vs the “shiny new stuff” JigsawLabs.io Finding Jeff online Links: Jigsaw Labs: https://www.jigsawlabs.io/free Teaching my mom to code: https://www.youtube.com/watch?v=OfWwfTXGjBM Getting a Data Engineering Job Webinar with Jeff Katz: https://www.eventbrite.de/e/getting-a-data-engineering-job-tickets-310270877547 MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Episode 93: Hyponatremia treatment.    Catherine and Dr. Saito discuss how to treat hyponatremia in an effective and safe way, especially when the hyponatremia is severe.Introduction: What is sodium?By Hector Arreaza, MD. Read by Alyssa Der Mugrdechian, MD; and Gina Cha, MD.  Sodium is a white metal that does not exist in nature in its free form. In its solid form, it's so soft that you could cut it like butter with a knife. It is the sixth most common element in the earth's crust. Even though sodium only makes up to 0.2% of our body weight, it plays a key role in nerve conduction, muscle contraction, and most importantly regulating water balance. Today we will be talking about low sodium, known as hyponatremia. We will focus on how to treat hyponatremia and will mention some common causes and symptoms. We hope you can learn something from us today.This is the Rio Bravo qWeek Podcast, your weekly dose of knowledge brought to you by the Rio Bravo Family Medicine Residency Program from Bakersfield, California. Our program is affiliated with UCLA, and it's sponsored by Clinica Sierra Vista, Let Us Be Your Healthcare Home. This podcast was created for educational purposes only. Visit your primary care physician for additional medical advice.___________________________Hyponatremia treatment.   By Catherine Nguyen, MS4, Ross University School of Medicine. Comments by Steven Saito, MD; and Hector Arreaza, MD.  DEFINITION: Serum sodium concentration impairment in free water excretion > hypoosmolality of serum-Diuretics (thiazides first 1-2 weeks)  -SIADH (Syndrome of inappropriate ADH, I call it the syndrome of EXCESSIVE ADH to help me remember it), caused by common meds.-Heart failure (low cardiac output) & cirrhosis (arterial vasodilation impairment) > decreased tissue perfusion (baroreceptors in carotid sinus senses reduction in pressure) > stimulus of ADH-GI fluid loss (diarrhea, vomiting)-CNS disturbances (stroke, hemorrhage, infections, psychosis, trauma) > increases ADH release-Malignancies > ectopic production of ADH (small cell carcinoma)-Drugs > SSRI, carbamazepine, cyclophosphamide -Potomania > patient drinks large amounts of beer and decreased intake of foods (solids). PRESENTATION:-Asymptomatic-Nausea & malaise earliest findings (125-130)-Headache, lethargy, muscle cramps, confusion/AMS, and eventually seizures, coma, and respiratory arrest (115-120)-Acute hyponatremia encephalopathy may be reversible, but permanent neurologic damage or death can occur. TREATMENT: Clinic: Chronic cases of hyponatremia may require spread-out treatment. Hyponatremia is never normal. -Mild hyponatremia > concentration of 130 to 134 mEq/L: NO treatment with hypertonic saline. Rather, the initial approach includes general measures that are applicable to all hyponatremic patients (i.e., identify and discontinue drugs that could be contributing to hyponatremia; identify and, if possible, reverse the cause of hyponatremia; and limit further intake of water [e.g., fluid restriction, discontinue hypotonic intravenous infusions]. -Moderate hyponatremia > concentration of 120 to 129 mEq/L ASYMPTOMATIC - 50 mL bolus of 3 percent saline (ie, hypertonic saline) to prevent the serum sodium from falling further.SYMPTOMATIC – (call ICU) 100 mL bolus of 3 percent saline, followed, if symptoms persist, with up to two additional 100 mL doses (to a total dose of 300 mL); each bolus is infused over 10 minutes. -Severe hyponatremia > concentration of

MLB: THE CRON ZONE! | Friday's Best Bets & Picks: Run Lines, Player Props, NRFIs for May 13th Games Talked About: Braves vs. Padres Rockies vs. Royals Blue Jays vs. Rays Our Parlay of the Day is HERE!: https://oddsjam.com/mlb/calling-our-shot-mlb-best-parlay-picks-today

This is the 100th Episode of the Total Knee Tips & Pearls PodcastSome techy stuff on TKARecommended Distal Femoral Resections8mm - Stryker Triathlon9mm - DePuy Attune9.5mm - Smith & Nephew10mm - Zimmer Persona, DJO, MicroportAnterior Flange Angle to Prevent Notching3 degrees - S&N, Zimmer5 degrees - DJO, DePuy6 degrees - Microport7 degrees - StrykerRecommended Tibial Slope0 degrees - Stryker PS, Aesculap3 degrees - Stryker CR, Aesculap, Persona PS, Attune PS, Microport, S&N5 degrees - Attune CR7 degrees - Attune CR, Persona CR1 mm Poly OptionsStryker, Zimmer, Depuy, S&NMetal Sensitive OptionS&N OxiniumZimmer Ti-NidiumMicroport NitrXDJO ArmourCoatAesculap Advanced Surface TechnologyTJO AurumNarrow OptionsZimmer, DePuy, S&N, AesculapSmallest - Zimmer 1 Narrow (55.5 mm M/L, 48.1 mm AP)Biggest - Aesculap F8 (82 MM M/L, 80.5 mm AP)Lots of stuff! Check with your reps and always refer to the technique manual, this is just a brief review but does not take the place of training and education. Support the show