Steven Banerjee: How Machine Intelligence, NLP and AI is changing Health Care  

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Steven Banerjee is the CEO of NExTNet Inc. NExTNet is a Silicon Valley based technology startup pioneering natural language based Explainable AI platform to accelerate drug discovery and development. Steven is also the founder of Mekonos, a Silicon Valley based biotechnology company backed by world-class Institutional investors (pre-Series B) — pioneering proprietary cell and gene-engineering platforms to advance personalized medicine. He also advises Lumen Energy, a company that uses a radically simplified approach to deploy commercial solar. Lumen Energy makes it easy for building owners to get clean energy.  

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Steven Banerjee LinkedIn: https://www.linkedin.com/in/steven-banerjee/ 

Steven Banerjee Website: https://www.nextnetinc.com/ 

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Outline: 

Here’s the timestamps for the episode: 

(05:20)- So I am a mechanical engineer by training. And I started my graduate research in semiconductor technologies with applications in biotech almost more than a decade ago, in the early 2010s. I was a Doctoral Fellow at IBM labs here in San Jose, California. And then I also ended up writing some successful federal grants with a gene sequencing pioneer at Stanford, and Ron Davis, before I went, ended up going to UC Berkeley for grad school research, and then I became a visiting researcher.  

(09:28)- An average cost of bringing a drug to market is around $2.6 billion. It takes around 10 to 15 years, like from the earliest days of discovery, to launching into the market. And unfortunately, more than 96% of all drug R&D actually fails . This is a really bad social model. This creates this enormous burden on our society and our healthcare spending as well. One of the reasons I started NextNet was when I was running Mekonos, I kept on seeing a lot of our customers had this tremendous pain point of, where you go, there's all this demand and subject matter experts, as scientists, they're actually working with very little of the available biomedical evidence out there. And a lot of the times that actually leads to false discoveries. 

(13:40)- And so there are tools, they're all this plethora of bioinformatics tools and software and databases out there that are plagued with program bugs. They mostly lack documentation or have very complicated documentation and best, very technical UI’s. And for an average scientist or an average person in this industry, you really need to have a fairly deep grasp or a sophisticated understanding of database schemas and SQL querying and statistical modeling and coding and data science.  

(22:36)- So, a transformer is potentially one of the greatest breakthroughs that has happened in NLP recently. It's basically a neural net architecture that was incorporated into NLP models by Google Brain researchers that came along in 2017 and 2018. And before transformers, your state of the art models and NLP basically were like, LSTM, like long term memories are the widely used architecture. 

(27:24)- So Sapiens is, our goal here is to really make biomedical data accessible and useful for scientific inquiry, using this platform, so that, your average person and industry, let's say a wet lab or dry lab scientist, or a VP of R&D or CSO, or let's say a director of research can ask and answer complex biological questions. And a better frame hypothesis to understand is very complex, multifactorial diseases. And a lot of the insights that Sapiens is extracting from all this, with publicly available data sources are proprietary to the company. And then you can map and upload your own internal data, and begin to really contextualize all that information, by uploading onto the Sapiens.  

(31:34)- We are definitely looking for early adopters. This includes biotech companies, pharma, academic research labs, that would like to test out Sapiens and like this to be a part of their journey of their biomedical R&D. We're definitely, as I said, looking for investors who would like to partner with us, as we continue on this journey of building this probably one of the most sophisticated natural language based platforms, or as we call it, an excellent AI platform.  

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Steve received his PhD from Johns Hopkins University in Cognitive Science where he began his AI research and also taught Statistics at Towson State University. After receiving his PhD in 1979, AI pioneer Roger Schank invited Steve to join the Yale University faculty as a postdoctoral researcher in Computer Science. In 1981, Roger asked Steve to help him start one of the first AI companies, Cognitive Systems, which progressed to a public offering in 1986.  

Steve then started Esperant, which produced one of the leading Business Intelligence products of the 1990s. During the 1980s, Steve published 35 articles and a book on AI, spoke at many AI conferences, and received two commercial patents on AI. As the AI Winter of the 1990s set in, Steve transitioned into a career as a successful serial software entrepreneur and investor and created several companies that were either acquired or had a public offering.  

He tries to use his unique perspective as an early AI researcher and statistician to both explain how AI works in simple terms, to explain why people should not worry about intelligent robots taking over the world, and to explain the steps we need to take as a society to minimize the negative impacts of AI and maximize the positive impacts. 

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Steven Shwartz LinkedIn: https://www.linkedin.com/in/steveshwartz/ 

Steven Shwartz Twitter: https://twitter.com/sshwartz 

Steven Shwartz Website: https://www.device42.com 

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Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(09:42) – So most of the things that are taking jobs for example, is conventional software, not AI software.

(10:57)- Exactly. And that's automated but it's conventional software. It's not AI. And most of the examples of where computers are replacing people, it's conventional software. It's not AI software.

(14:49)- How you get data quality into your AI models and it's what they do that's really interesting. And I hadn't actually focused on it until I talked to this company. There's a big industry to clean data for tools like business intelligence that have been around for a long time. And there are, there are companies that are multi-billion dollar companies that provide data, cleaning tools, data extraction, and so forth.

(17:13)- Everybody thought that with AI, you could diagnose illnesses from medical images better than the radiologists. And it's never actually worked out that way. I have friends who are radiologists, who use those AI tools and they say yes, sometimes they find things that I might've missed. But at the same time, they miss things that we would have found.

(22:17)- I think we're seeing a lot of the rollout of a specific type of AI supervised learning, which is a type of machine learning. We're seeing it applied in many different areas. I actually have a database I keep before every time I see a new application of supervised learning and it's fascinating. It's being used in almost every area of business, of government, of the nonprofit world. It is fascinating how much application there is.  

(27:06)- And they're not really going to make sense if you drill down into them. So what's going to be the implication of that. Is it only going to be useful if there's all kinds of search engine optimization where you don't really care If what you're right makes sense. We're going to generate a lot of crap using GPT three and put it out there for search engine optimization purposes.

(31:19)- And I think there's a lot of opportunity for companies that are helping develop software and services to help companies build non-biased explainable systems. And then you have a whole issue around when you build a machine learning system, it deteriorates over time. So it might only work for a couple of days and then start to go downhill. It might work for weeks, but you have to monitor those systems and go back and retrain them when the performance goes down. And all of that is a lot of effort. 

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Gianluca Mauro is the CEO of AI Academy, which he founded with the mission of helping people understand what artificial intelligence is and its place in their organizations and their career. Gianluca is the author of the book "Zero to AI - A nontechnical, hype-free guide to prospering in AI era" 

Over the years, Gianluca and his team have done both technical consulting and training workshops, working with companies like P&G, Merck, Brunello Cucinelli, Daikin, Fater, Bayer, and EIT Innoenergy 

Gianluca teaches Artificial Intelligence to people without a tech background, without any code or math. Why? Because he believes, the future of artificial intelligence is in the hands of people who can find use cases in their organizations, and then define and run AI projects. 

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Gianluca Mauro LinkedIn: https://www.linkedin.com/in/gianlucamauro/ 

Gianluca Mauro Twitter: https://twitter.com/gianlucahmd 

Gianluca Mauro Website: academy.com">https://ai-academy.com 

Podcast Details: 

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Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

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Support and Social Media:  

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Outline: 

Here’s the timestamps for the episode: 

(04:15)-Sometimes it's not a concept that people are familiar with. It sounds weird to anybody who works in tech. But, a lot of companies, in these industries, are still struggling with the cloud. So, when you go to these companies and start talking about this technology, they are excited. They're like, this sounds amazing, but you have to keep into account the reality of where they are, they're not in a place where they can invest in hiring a full-blown data science team, because then nobody knows how to interact with them. 

(09:29)- So, having the right governance for how to use the data, how to keep it in the right shape, and making sure that the quality is what we need, and then actually bring into the laptops of the data scientists that they can make tests and run experiments and make graphs. So, I always like to say it doesn't really matter how good your technology is. How good is your data warehouse or whatever kind of stock you use if using that data is not easy. If using that data it's not straightforward for a data scientist. 

(17:32)- And in the same way, if we want to use AI for marketing, you need to give tools to the marketers that understand the problem to use AI on their data for their problems. When I talk about sales, well, I understand sales data set and takes me a lot of time to understand the logics of sales, have a sales team of the data that its Sales team works with to a sales team who really understands this data, the right tools to, they don't have to be able to do everything but the list to get started, well, then they know much better than me the data.  

(18:17)- So, it's kind of a paradox, because the most important thing of the app is the recommender system. But the reason why that works is not because of the tech, but because of how the UX feeds the tech. And if you think about this, think about this concept, well, then your UX designers, they need to understand this, they need to understand what it means to feed an algorithm with the right data.  

(23:40)- And so we have seen cases where these things went wrong. And I may start from the stuff that everybody knows about, the elections in 2016, fake news and all this stuff up until more niche, let's say topics that maybe not a lot of people aren't aware of. But that actually had a strong impact on people. An example is AI in hiring. There was a very interesting research made by MIT Technology Review about how a lot of companies that sell software for hiring and leverage AI are actually biased. 

(31:01)- And it has been amazing, honestly, because then you'll have people coming from all sorts of backgrounds. I give them the tools and the foundational knowledge that they need to talk about these topics in a way that is productive and they bring the wrong perspectives. They bring their own experience. And I had to say, I've been amazed by the insights that we were able to get from these conversations. 

Ben Zweig: How Data Science and Labor Economics Connects to Workforce Intelligence  

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Ben Zweig is the CEO of Revelio Labs, a workforce intelligence company. Revelio Labs indexes hundreds of millions of public employment records to create the world’s first universal HR database. This allows Revelio Labs to understand the workforce dynamics of any company. Revelio customers include investors, corporate strategists, HR teams, and governments.

Ben worked as a data scientist at IBM where he led analytic teams. He is an economist and entrepreneur and also an adjunct professor at Columbia Business School and NYU Stern School of Business respectively. He teaches courses currently at NYU Stern School of Business including future of work, data boot camp and econometrics.

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Ben Zweig Website: https://www.reveliolabs.com 

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Outline: 

Here’s the timestamps for the episode: 

(02:56)- So, I started my career in academia, I was doing a Ph.D. in economics and specialized in labor economics. So I was always very interested in labor data, and understanding occupational dynamics, social mobility, things like that. My first job was a data scientist, this was very early on at a hedge fund in New York. It was an emerging market hedge fund. I started that in 2012. That was kind of interesting. I was like the lone data scientist on the desk. So that was kind of interesting. And then went to work at IBM, in their internal data science team was called the Chief Analytics Office. 

(08:13)- The workers that were really hardest hit from remote work are really junior employees. They're just getting started and they need that mentorship. And it's much harder to feel like you're developing and learning from others in a remote environment. But as we're sort of going back, the more senior positions, will probably not have that same benefit as junior employees. 

(15:53)- One phenomenon that we see quite a lot is that companies have a huge contingent workforce that is not reported on their financial statements. So, for example, I mentioned I used to run this workforce analytics team at IBM. And at IBM, we had 330,000 employees, that was like the number that's in their HR database, but you go to their LinkedIn page, and it looks like 550,000 people say that they work at IBM. So, what's going on here? Why are there so many more people that claim to work at a company, then the company claims to work there? And that, of course, is just a sample; only a sample of people actually have online profiles.  

(29:33)- But when it comes to human capital data, and employment data, that really does not exist, it's not even really close to that. There's so much data that's siloed in internal HR databases, which like I mentioned before, really only include a fraction of the overall workforce of a company. But what's cool about this is that when an employee is stored in an HR database, that information is mirrored in the public domain. 

(21:22)- So, we really have to create a taxonomy that updates that changes with an evolving occupational landscape and the changing economy. We also really need to infer the activities that people do, because those are the building blocks of a job, or the job is a bundle of activities. So, we really need to understand that when one person says lawyer and another person says, attorney, those are probably the same occupation, but when one person says Product Manager in Facebook versus a Product Manager at JPMorgan, those might be totally different occupations. 

(30:21)- So, what are the HR tech companies that are really dominating, and then it gets even specific, who's dominating the self-driving car market, how benefits help retention of women in the workforce, that's something that we've seen some changes in the past couple of years. We did a piece that I really liked, which was tracking the rise and fall of hustle culture. 

Edo Liberty: How Vector Data Is Changing The Way We Recommend Everything  

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Edo Liberty is the CEO of Pinecone, a company hiring exceptional scientists and engineers to solve some of the hardest and most impactful machine learning challenges of our times. Edo also worked at Amazon Web Services where he managed the algorithms group at Amazon AI. 

As Senior Manager of Research, Amazon SageMaker, Edo and his team built scalable machine learning systems and algorithms used both internally and externally by customers of SageMaker, AWS's flagship machine learning platform. 

Edo served as Senior Research Director at Yahoo where he was the head of Yahoo's Independent Research in New York with focus on scalable machine learning and data mining for Yahoo critical applications.

Edo is a Post Doctoral Research fellow in Applied Mathematics from Yale University. His research focused on randomized algorithms for data mining. In particular: dimensionality reduction, numerical linear algebra, and clustering. He is also interested in the concentration of measure phenomenon. 

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Outline: 

Here’s the timestamps for the episode: 

(06:02)- It's funny how being a scientist and building applications and building platforms are so different. It's kind of like for me it's just by analogy, I mean, kind of a scientist, if you're looking at some achievement, like technical achievement as being a top of a mountain and a scientist is trying to like hike, they're trying to be the first person to the summit. 

(06:28)- When you build an application, you kind of have to build a road, you have to be able to drive them with a car. And when you're building a platform on AWS or at Pinecone, you have to like build a city there. You have to really like, completely like to cover it. For me, the experience of building platforms and AWS was transformational because the way we think about problems is completely different. It's not about proving that something is possible, it is building the mechanisms that make it possible always for, in any circumstance. 

(13:43)- And so on and today with machine learning, you don't really have to do any of that. You have pre-trained NLP models that convert a string, like a, take a sentence in English to an embedding, to a high dimensional vector, such that the similarity or either the distance or the angle between them is analogous to the similarity between them in terms of like conceptual smelts semantic similarity.

(18:17)- Almost always Pinecone ends up being a lot easier, a lot faster and a lot more production ready than what they would build in house. A lot more functional. We've spent two and a half years now baking a lot of really great features into Pinecone. And we're, we've just launched a version 2.0 that contains all sorts of filtering capabilities and cost reduction measures and you name it.    

(21:22)- And so I'm a great believer in knowing your own data and knowing your own customers and training your own models. It doesn't mean that you have to train them from scratch. It doesn't mean you don't have to use the right tools. You don't have to reinvent the wheel, but I'm not a big believer in completely pre-trained, plucked off of a random place in the internet models. I do want to say that there are great models for just feature engineering for objects that don't change so much. So we have language models like BERT that transform text and create great embeddings and they're a good starting point. 

(31:01)- So I think you'll see two things. First of all, with Pinecone specifically, we're focused on really only two things; making it easy to use and get value out of Pinecone and making it cheaper. That's it! I mean that, those are the only two things we care about. Like if you can get a ton of value out of it and it doesn't cost you too much, that's it, you're a happy customer and we're happy to get you there. So that pretty much sums up all of our focus. 

Thor Ernstsson: How To Use Data Science for Stronger Relationships  

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Thor Ernstsson is the CEO of Strata, a company that helps customers invest in their networks, no matter how busy they are. Strata enables intelligent outreach recommendations that strengthen professional relationships. With their easy to use platform, clients become more thoughtful and helpful to the most important people in their network.

Thor is also the founder of Feedback Loop, which companies use to build real time feedback loops with their target markets. Basically customer development delivered at scale. Used by half of the F100 as well as some of the best tech companies around. Thor previously served as CTO of Audax Health and lead architect at Zynga where helped build up Zynga's first remote studio. Thor and the team at Zynga created and released Frontierville as the company's most successful product launch at the time. 

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Thor Ernstsson´s LinkedIn: https://www.linkedin.com/in/thorernstsson/

Thor Ernstsson´s Twitter: https://twitter.com/ThorErnstsson

Thor Ernstsson´s Website: https://www.strata.cc/

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Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:24) – It starts in the very beginning in rural Iceland. I grew up on the Northern coast of Iceland, in a little fishing village. We're about 450 people in technology there, which is a little bit different than how we think of it today. But, in a roundabout way, we ended up in New York, 20 years in the US and 10 in New York and absolutely love it here. And the reason is primarily that there's so much creative energy around, exactly your topic.

(03:34) – So what we were doing at Feedback Loop, the core of it is really you take a business question: Is this going to work, for example. Which is not a well-formed research question. So we have to translate it into the intent of the question. What you're intending to do is assess functionality or competitors features or price point or messaging or whatever it is.

(07:13) – Because, even though you can only juggle in your mind, let's just say 150, and the number is a bit fuzzy, but let's say that it is 150. You interact with thousands of people throughout your career, and you go to a conference and you meet a bunch of great, interesting people that you want to stay in touch with. You have coworkers that you may have worked with five years ago, 10 years ago, doing either something really fascinating and you want to stay in touch, or they're just friends and you liked interacting with them and you want to stay in touch.

(10:10) – Most people, when they first think about it, they're like: I want more out of my network. But when we interview, especially the more senior, and we interview people, what we learn is the same thing over and over. It's not that they want to get something out of their network. It's not that they want to know who they should reach out to for sale or for deal or for VC. You need to stay in touch with their LPs and stuff like that, but it's really more about giving back.

(13:31) –You just highlight a perfect example, people can't actually track all the communication again. There are so many things that fall through. So what we do first is we start with a bunch of rules. So there's heuristics around what might be important. It's this sort of static analysis of your communication and your calendar of your stuff like that. And then what we learn over time is who's important to you.

(17:30) – The COVID and just in general, digitization of everything and making everything Zoom makes this problem much worse, because before you would get a coffee, you would see somebody in person, you have all these nonverbal cues, you have all these triggers and all those memories that are way more than what you have when it's just pixels on a screen. 

(21:22) – We're helping you uncover the things you should be doing, even if you don't know what you should be doing. That's kind of the key here is that it's doing the thinking and the heavy lifting for you. You click to accept it. You can reach out. You can action it. You can say like create a task out of it, basically. So that if I say to you in an email, or if you just send many emails ago, like that you used to introduce me to other speakers or podcasts.

(24:53) – There's a lot of really interesting work that has been done that we can leverage in your right, that like building this from scratch even 10 years ago would not be possible. It's everything from memory constraints on the actual servers. The fact that I can spin up a 90, it was a 96 or 92 core Amazon instance and just at the click of a button and trained a model. I couldn't have done that before. So it would have been prohibitively expensive and improvely hard, actually, it's just not wasn't there. 

(25:53) – So there's lots of ways that email threads end, then we're trying to figure out. Can we tell which ones are natural and which ones are effectively errors, where you were when you dropped the ball on something. It's a fascinating problem. We have millions of messages to train on where you can see this. This ended and this didn't, and then we've got to figure out, how do you know if it was intentional or not.

(28:55) – It's a combination of things. So, it's definitely the chief of staff in that way, but, arguably, it's more like a social secretary. So it's like helping organize the most important relationships you have. So for example, if you're traveling to Chicago, who should you reach out to? Because I've started heuristics, so obviously people that live there, fine. Second, people you met last time you were there, fine. Third, people you've talked about meeting up with in Chicago. Maybe you will remember that maybe you have a super memory where you're not limited by only 150 relationships and you can actually classify all minus like 30,000 people.

(32:37) – We have a few products that we launched: the recommendations where you get three recommendations every week, plus memes and so corporate communication seems to be working. So that's live now called Reconnect. So definitely go to Straddled that CC and sign up for that. Then we're going to be launching the broader platform that I'm talking about that has all these integrated triggers, and nudges, and juristics, and patterns like travel, list building, list sharing, all those things that I suspect just about everybody who's listening to this does right now, and it'd be great to hear feedback.

Stephen Miller: How To Leverage Mobile Phones And 3D Data To Build Robust Computer Vision Systems

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Stephen Miller is the Cofounder and SVP Engineering at Fyusion Inc. He has conducted research in 3D Perception and Computer Vision with Profs Sebastian Thrun and Vladlen Koltun while at Stanford University. His area of specialization is AI and Robotics, which included 2 years of undergraduate research with Prof Pieter Abbeel. 

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Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:42) – Started in robotics around 2010, training them to perform human tasks (surgical suturing, laundry folding). Clearest bottleneck was not “How do we get the robot to move properly” but “How do we get the robot to understand the 3D space it operates in?”   

(04:05) – The Deep Learning revolution around that era was very focused on 2D images. But it wasn’t always easy to translate those successes into real world systems: the world is not made up of pixels; it’s made up of physical objects in space.

(06:57) – When the Microsoft Kinect came out; I became excited about the democratization of 3D, and the possibility that better data was available to the masses. Intuitive data can help us more confidently build solutions. Easier to validate when something fails, easier to give more consistent results. 

(09:20) – Academia is a vital engine for moving technology forward. In hindsight, for instance, those early days of Deep Learning -- one or two layers, evaluating on simple datasets -- were crucial to ultimately advancing the state of the art we see today. 

(14:48) – Now that Machine Learning is becoming increasingly commodified, we are starting to see a growing demand for people who can bridge that gap on both sides: conferences requiring code submissions alongside a paper, companies encouraging their engineers to take online ML courses, etc.

(17:41) – As we do finally start to see real-time computer vision productized for mobile phones, it does beg the question: won’t this exacerbate the digital divide? Flagship devices, always-on network connectivity: whether computing on the edge or in the cloud, there is going to be a disparity. 

(20:33) – Because of this, I think the ideal model is to treat AI as one tool among many in a hybrid system. Think smart autocomplete, as opposed to automatic novel writing. AI as an assistant to a human expert: freeing them from the minutia so they can focus on high-level questions; aggregating noise so they can be more consistent and efficient. 

(23:08) – Computer Vision has gone through a number of hype cycles in the last decade –real-time recognition, real-time reconstruction, etc. But the showiest of these ideas seem to rarely leave the realm of gaming, or tech demonstrator. I suspect this is because many of these ideas require a certain level of perfection to be valuable. It’s easy to imagine replacing my eyes with something that works 100% of the time. But what about 90%? At what point is the hassle of figuring out whether I’m in the 10% bucket or the 90% bucket, outweighing the convenience?

Nell Watson: How To Teach AI Human Values   

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Nell Watson is an interdisciplinary researcher in emerging technologies such as machine vision and A.I. ethics. Her work primarily focuses on protecting human rights and putting ethics, safety, and the values of the human spirit into technologies such as Artificial Intelligence. Nell serves as Chair & Vice-Chair respectively of the IEEE’s ECPAIS Transparency Experts Focus Group, and P7001 Transparency of Autonomous Systems committee on A.I. Ethics & Safety, engineering credit score-like mechanisms into A.I. to help safeguard algorithmic trust.

She serves as an Executive Consultant on philosophical matters for Apple, as well as serving as Senior Scientific Advisor to The Future Society, and Senior Fellow to The Atlantic Council. She also holds Fellowships with the British Computing Society and Royal Statistical Society, among others. Her public speaking has inspired audiences to work towards a brighter future at venues such as The World Bank, The United Nations General Assembly, and The Royal Society.

Episode Links:  

Nell Watson’s LinkedIn: https://www.linkedin.com/in/nellwatson/ 

Nell Watson’s Twitter: https://twitter.com/NellWatson 

Nell Watson’s Website: https://www.nellwatson.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

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– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(2:57)- Even though the science of forensics and police work has changed so much in those last two centuries, principles are great, but it's very important that we create something actionable out of that. We create criteria with defined metrics that we can know whether we are achieving those principles and to what degree.

(3:25)- With that in mind, I’ve been working with teams at the IEEE Standards Association to create standards for transparency, which are a little bit traditional big document upfront very deep working on many different levels for many different use cases and different people for example, investigators or managers of organizations, etcetera.

(9:04)- Transparency is really the foundation of all other aspects of AI and Ethics. We need to understand how an incident occurred, or we need to understand how a system performs a function in order to. I analyze how it might be biased or where there might be some malfunction or what might occur in a certain situation or a certain scenario, or indeed who might be responsible for something having gone through it is really the most basic element of protecting ourselves, protecting our privacy, our autonomy from these kinds of advanced algorithmic systems, there are many different elements that might influence these kinds of systems.

(26:35)- We're really coming to a Sputnik moment and AI. We've gotten used to the idea of talking to our embodied smart speakers and asking them about sports results or what tomorrow's weather is going to be. But they're not truly conversational.

(32:43)- Fundamentally technologies and a humane society is about putting the human first, putting human needs first and adapting systems to serve those needs and to truly and better the human condition to not sacrifice everything for the sake of efficiency to leave a bit of slack and to ensure that the costs to society of a new innovation or the costs to the environment are properly taken into effect.

Ryan McDonald: How To Position People at the Center of AI Native Solutions 

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Ryan McDonald is the Chief Scientist at ASAPP working on NLP and ML research focusing on CX and enterprise. He is also an Associate researcher in the NLP group at Athens University of Economics and Business. Ryan was a Research Scientist in the Language Team at Google for 15 years where he helped build state-of-the-art NLP and ML technologies and pushed them to production. 

He managed research and production teams in New York and London that were responsible for a number of innovations used in Translate, Assistant, Cloud and Search. He was the first NLP research scientist in both New York and London, and helped grow those groups into world-class research organizations. Prior to that, he did his Ph.D. in NLP at the University of Pennsylvania. 

Episode Links:  

Ryan McDonald’s LinkedIn: https://www.linkedin.com/in/ryanmcd/ 

Ryan McDonald’s Twitter: https://twitter.com/asapp 

Ryan McDonald’s Website: http://www.ryanmcd.com 

CX: The Human Factor Report: Page.html">https://ai.asapp.com/LP-2021-09-CX-The-Human-Factor_Landing-Page.html

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

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YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

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– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

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– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(3:00)- The kinds of problems that deploying AI runs into for enterprise is more about scalability. Instead of having a single user of the technology, we have hundreds of users of the technology and how can we deliver a unique experience and an excellent experience for each of those users and this necessitates questions around adopting machine learning and natural language processing models to new domains. 

(10:49)- And this is exactly the technology we're building out. How can we sort of regularize that? How can we look at the conversation and the issue that the customer's happening? That's sort of embodied in the dialogue, up to a point in time and then allow AI to make recommendations to the agent; Here is a workflow that we think you should use and all the steps you need to follow in order to solve this issue

(28:33)- So we design everything and that's why it's critical to design these things from the bottom up with AI in mind. All of our artificial intelligence has been designed to serve those latency needs. So to kind of give you a couple of examples, the first is automatic speech recognition. So a huge number of calls that come into call centers are still voice, they're not digital. It's not people call contacting over chat. It's people calling in on their phone. 

(30:41)- So we've focused on building out something called SRU, which is an architecture where we can take super high, accurate AI models and then distill them into these faster architectures, which allows us to get into these millisecond range. So we can get responses back to agents and milliseconds, and that really is going to affect how much they use those suggestions at the end of the day.

(32:38)- Beyond what's happening in the conversation and see everything, all the information and all the actions that the agent can possibly do on their computer. And so agent journey is a product where we, you know, put a piece of software on the agent's computer and it allows us to access into all the tools they're using, how they're using them, how that interacts with the conversation.

(33:49)- Agent journey is our efforts in that space to understand everything holistically that the agent is doing to really make headway in task-oriented dialogue.

Humphrey Chen: How AI Can Revolutionize the Way We Consume Video 

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Humphrey Chen is the CEO and Co-Founder of CLIPr. He has a BS in Management Science from MIT. His work in tech specializes in the use of technology to make people and companies more productive.    

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Humphrey Chen’s LinkedIn: https://www.linkedin.com/in/humphreychen/ 

Humphrey Chen’s Twitter: https://twitter.com/humphreyc?s=20 

Humphrey Chen’s Website: cards.sort-by=item.additionalFields.createdDate&blog-cards.sort-order=desc">https://aws.amazon.com/es/rekognition/?blog-cards.sort-by=item.additionalFields.createdDate&blog-cards.sort-order=desc 

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

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YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

– Twitter:  https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:36) – CLIPr operating premise is that not all minutes of video content are equally relevant to everyone. So it uses machine learning to fully index that video and make it fully searchable.

(05:02) – Watching a whole video can be inefficient when a participant only wants to watch specific sections. CLIPr team's speeds up and accelerates more efficient automations to be helpful for both consumers and enterprises. 

(06:42) – The tools that CLIPr provides are a way to guarantee target audience engagement rates to be really informative. CLIPr focuses on this video insight when it comes to engagement and interaction around the video itself in a category called video analysis and management.

(08:04) – CLIPr aims to hand out the tools to efficiently find content that matters, bookmark it, share it, react to it, comment on it. 

(08:27) – The tools and the skills required to edit a video are completely opposite from the skills and tools required for editing inside of a document. CLIPr bridges the two effectively, by building a video-based document type.

(11:57) – There has not been as much disruption around video. Some use cases that have been thought out include recording customer meetings; customers’ feedback, integrations with a CRM record, and also, provide a score over time around the actual probability of closing a sale based on the relative perception for the customer reaction.

(14:20) – AI, additionally with the hospitals and the medical universities and researchers alike are still using antiquated technology and they're not extracting insights from these video moments. CLIPr is also useful in telemedicine. For surgeons, CLIPr means high value, highly visual, high-impact in a short time.

(24:26) – Machine learning, in general, it's all about the data and about engagement and interaction and training new models around the data. So, machine learning allows people to create things and bring solutions. Technology is actually going to find meaningful problems to solve more effectively and more efficiently. 

(28:21) – The purpose of services is to build businesses and to augment either with the stable technology or the experimental technology for what will be the future of AI, of natural language processing of emotion, detection of different technologies. Additional progress still needs to happen beyond the data in telemedicine, EMRs or courtrooms.

(31:49) – As new features get uncovered with specific use cases, anyone can benefit from CLIPr video analytics and management platform. There is continued acceleration for product led growth, closing a 5 million seed round with a strategic partner and keeping focus on machine learning and cloud-based services. Rather than just being an endpoint, it analyzes the data, allows for referential utility, allows for collaboration and allows for monthly recurring revenue.