HumAIn Podcast

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSFernando Gomez Baquero is the Director of Runway and Spinouts at Jacobs Technion-Cornell Institute and Founder at Besstech LLC. He’s an innovation economist, nanomaterials engineer and entrepreneur who mentors companies on diverse topics such as IoT, digital innovations, new materials for transportation, creating better electric vehicles, improving wind and solar power, using social networks for gratefulness, and and more.Episode Links:  Fernando Gomez Baquero’s LinkedIn: https://www.linkedin.com/in/fernandogomezbaquero/ Fernando Gomez Baquero’s Twitter:  @FerGomezBaqueroFernando Gomez Baquero’s Website: http://www.fernandogomezbaquero.com/index.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 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– 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(01:37) – The most reasonable thing to do initially was to fairly quickly move every single class to online, which we did pretty fast andthe good thing is that we were already prepared for that. Most of our classes were already streaming and we already had a lot of experience doing that. (04:17) – We live in a good time that we definitely can move a lot of things to virtual and we are able to shift to that pretty fast. And I hope that everybody knows that by doing that, we can deliver not exactly the same content and continue to work that way. So, this is really a test of the future of work.(05:48) – Cornell Tech was created as an economic development story or as an economic development driver for the city of New York. Why don't we basically get the best of both worlds? revitalize an area that hasn't been used for a while, which is the Southern side of Roosevelt Island. And then we use that space to bring a campus of a university that is going to focus a lot of engineering and scientific resources to create the companies in the future. And that's the purpose of the campus, focusing on entrepreneurship and creating new companies.(08:09) – We no longer see entrepreneurship and academia as a binary thing. We don't see it as, you need to do your masters program. And then when you finish, you do entrepreneurship and you build a company in the country. What we see is while you're in the academic environment, you can be doing your degree. You can be working towards your degree, but at the same time, you should be creating a company. And we are more than capable of not only giving you the space to do that, but training you to do that with the people that have done that. So the people that come to Cornell Tech are people that want to have that academic and entrepreneurship experience at the same time, which is a lot of work.(09:43) – If you take a look at that set of degrees, it is  just the right combination of skills to build the company. And so once you take those people that are, one of them is a computer scientist, one of them is an engineer, one of them is an MBA, one of them is a lawyer and you put them together in teams, you build a very early stage, very good company.(10:58) – It really depends on where you are in your life right now, what you want to do. If you want to be an entrepreneur our goal is that we will have a program for you. If you are working in a company right now, you'd be working as a program manager or a project manager for a while, and you really want to have that experience of saying, I can give myself a year to improve my skills, know something better. And at the same time, have that experience of building an early stage company.(13:46) – We give them all of the support that they can get. And as Nanit would really focus on computer vision, we have companies working on genomics on computational biology, on computer vision for construction and infrastructure on a better simulation technologies for spaces. On big data on other types of devices. It's really a wide range of applications. (17:01) – Tech transfer is something that has been done in universities for many years. And that the dynamic of tech transfer has really changed for decades. And that dynamic is, you are a researcher, inside of a university system, creating knowledge, that knowledge belongs to the university. And then the university is trying to find on the outside ways of commercializing that research. (18:29) – People who are creating the knowledge are the best vehicles for commercializing that knowledge. We trust that you're the one that can make this into a billion dollar company. And what you need is for us to help you succeed, to give you the training that you need, to give you the tools that you need, to give you the resources, to give you the connections, to give you the environment that you need.(21:11) – We have a couple of our postdocs that immediately switched their companies to say, we can develop better financing strategies for what needs to be done with COVID. We have some other ones that are saying we definitely need to work a lot on finding a test for immune response to COVID. So now we have all of these people working on the health tech side.(23:15) – We're enabling communication in a different way, but we're also enabling leadership in a different way. We have people working on the future of work this way. We have people that are really building interesting tools for the gig economy.(25:01) –  There's very few segments of the population that are actually doing artificial intelligence. There's some that are, for the most part, who we're trying to teach our companies. And most of them are either doing some type of some interesting application of machine learning. Perhaps it could be some interesting signal processing or hubs or data mining in a particular way, or using tools like natural language processing and computer vision.(27:15) – We're still in a very primitive way on how we see machines and interact with them. We have just scratched the surface of how it is that we can improve our interaction with robots. (30:43) – We have many tools right now. These technologies, these tools, and just are  great opportunities to use all of that toolset for a very big problem. (33:07) – We have people that were product managers and they definitely don't want to be product managers anymore. They want to be entrepreneurs. They want to be CEOs, so this is just a segment of people. We have some that have been product managers and they want to continue to be product managers, but they want to raise their skill level. Now you can be an entrepreneur, you can be a product manager, you can be a CTO, you can be other things. And this really what we want, to open up possibilities for a career. (36:23) – Studio is really, the most innovative part of Cornell Tech. The core idea is that you can practice entrepreneurship while you are in academia, but practicing the real way, meaning that you could be driven to entrepreneurship and you can have that experience of being an entrepreneur at the same time that you are in academia.(39:15) – There are a lot of tools out there that you could use. Figma, Trello, Slack. There's a lot of inducing communication. WhatsApp actually is huge in a lot of parts of the world for communicating with businesses too. So for sure use the tool that makes more sense for the community that you're trying to get to.(42:25) – We have amazingly smart people oriented towards the common good that are putting a lot of effort into finding solutions. So that is some positive news. We don't want to downplay how complicated the situation is. It is an opportunity for all of us to to create things that are important for society, things that are good for society. And we are shifting a lot of resources to solve this problem.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSSaleema Vellani is an award-winning serial entrepreneur, keynote speaker, a professor, and the author of Innovation Starts With “I”. At the age of 21, Saleema co-founded and launched Brazil’s largest and #1 language school to finance an orphanage and social development programs, which has taught several thousands of students to date. Shortly after, she co-founded and ran a leading online translation agency in Italy to help companies expand their digital presence globally, while generating hundreds of jobs in the gig economy. The business was acquired in 2012. For over 12 years, Saleema has led 100+ international organizations, nonprofits, and Fortune 500 companies to their next stage of growth and innovation. As an intrapreneur, Saleema has been co-leading award-winning, groundbreaking research with the World Bank on solving food insecurity in conflict-affected countries through climate-smart technologies since 2016. Given her experience with running businesses online, in 2013, Saleema led startup education programs for Upwork (formerly Elance) to train Washington DC-based business owners on how to hire and manage remote teams.Currently, Saleema is the Founder and CEO of Ripple Impact, which helps entrepreneurs increase their influence and impact through accelerating the growth of their platforms and businesses. She also teaches Design Thinking and Entrepreneurship at Johns Hopkins University and is a frequent guest lecturer at business schools.Episode Links:  Saleema Vellani’s LinkedIn: https://www.linkedin.com/in/saleemavellani/ Saleema Vellani’s Twitter: @InnovazingSaleema Vellani’s Website: https://saleemavellani.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– 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(01:53) –  I was embracing a lot of the principles and the actual design thinking process. It is about iterations and cycles, but it was really about understanding a problem and that's something that we talk about customer development, understanding who are potential customers and what's the problem we need to solve. We realized we we needed to really carve our own niche and focus on what was working (05:16) – The skill of being able to think like a designer doesn't mean everyone needs to becomes a design thinking expert or an innovation expert, but just the skill of being able to connect dots that seem unrelated and that's also referred to as associative thinking, there's different theories around this, but really trying to connect things I that already exist in new ways, that ability to think that way is one of the skills that's going to be really important for the future of work.(05:51) – We're in the middle of this re-skilling revolution right now as stated by the world economic forum. Embedding that in the culture of an organization is becoming increasingly important.(06:35) – Innovation starts with I, and the mindset and developing that innovative way of thinking and being able to only share creativity and really just knowing yourself, know your sweet spot, what do you do? Or what can you offer to the world? Understanding who are the stakeholders that you need to really understand when you're solving a problem and then making your impact on the world through that and so that with more and more with technical fields.(07:03) – Showing empathy. Understanding yourself and who you are, and that ability to make things more humane. Understanding humans really starts by understanding yourself.(09:17) – Thinking about what data is available, but the design thinking mindset can be applied and data scientists, being able to question that and using design thinking principles, whether it's starting from empathy to really framing the problem and that's one of the hardest parts of design thinking is being able to frame the problem correctly, because oftentimes we're thinking about the solutions without really understanding the problem.(11:26) – We're entering the fourth industrial revolution as we talked about we're in this re-skilling revolution and a lot of businesses are stalling and they're falling behind, or sometimes it's hard to even see that you're stalling when you're so focused inside of the business and not on the business to develop that awareness until it's too late and you've been replaced or you've been automated.(18:15) – Resilience is really important for everyone to have and when it comes to innovation, entrepreneurship, design thinking. The time where you're hitting a dip, you're going rock bottom and you're not sure whether you should go, you should keep working at it, what to do and it's almost like crisis mode and that's happened to me. Resilience is important because you have to be okay with failure and more and more companies are trying to adopt this culture where failure is and it starts by having a psychologically safe environment.(22:48) – The Coronavirus has actually enabled us to be more human and really understand what's going on in the world and developing that global awareness, which is another insight that I got through my book interviews is really understanding what's going on with different cultures.(26:12) – With design thinking, it's important to understand the experience that humans or your customers go through and on the backend there is lot of the coding, a lot of that's already being automated a lot of things are being replaced,(28:04) – That ability to think in that way, like a designer, even just enough so that you can humanize the code or humanized data science, that's going to be increasingly important. (29:46) – Constant learning, the ability to just constantly be in learning mode and going to conferences, absorbing content. Try to get at least one nugget per day and learn something new and make that part of your routine that's really important to stay up to date with the trends cause it's so easy to just become obsolete in today's economy. (32:12) – This rise of entrepreneurship is like everyone wants to be an entrepreneur, a lot of people are trying to participate in the gig economy, being entrepreneurs and even the concept of an entrepreneur has evolved so much. There's Instagram influencers, social entrepreneurs, focusing on the feeling and the impact that's important, as well as figuring out how to collaborate with other people.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSHefin Rhys is a Senior Scientist (flow cytometry) at UCB. He completed his PhD at the William Harvey Research Institute in Queen Mary University of London in 2017, and graduated from my MPharmacol degree from the University of Bath in 2013. His main academic interests are conventional, imaging and small particle flow cytometry, data science and machine learning. Episode Links:  Hefin Rhys’ LinkedIn: https://www.linkedin.com/in/hefin-rhys/ Hefin Rhys’ Twitter:  @HRJ21Hefin Rhys’ Website: https://www.manning.com/books/machine-learning-with-r-the-tidyverse-and-mlr 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– 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(01:44) – My view is not that of someone who is an expert on this virus, but it's clearly something that's very serious and that we need to take seriously and treat with respect. So as much as the virulence of the virus itself is concerning, I particularly consider how viral misinformation and misinformed practices have gone along with it.(08:24) – As a pharmacologist, my PhD was in immunology. The traditional analysis methods that we had been using and that other people in biological fields were using started to not quite suit our needs, not quite answer our questions. In biological life sciences the level of maths left them. I started to teach statistics, R and machine learning during my PhD. Manning wanted a book that was not for computer scientists necessarily, but more for people who were an expert in their own area but who could use and benefit from machine learning, who could benefit from understanding and learning machine learning to make predictions and extract meaningful insights from the data that they have.(14:57) – The answer to the question of whether somebody should learn R or Python is yes, people should use either or both. Python would probably have been a more convenient choice for a lot of people for machine learning. Carat or MLR in R, which were kind of an answer to scikit-learn and create this common interface so that you learn how to use that package and then substituting in a variety of different machine learning techniques and algorithms is extremely simple. Tidyverse is a collection of data science packages, a set of packages that are designed to make common data science tasks extremely easy, clean and reproducible.(22:21) – There's basically no reason for Python and R to compete, we can incorporate code from both languages.(24:11) – R has a phenomenal community of people. You need only to tweet a question or ask for opinions, and hashtag our stats and you get a ton of really nice supportive answers back and a huge amount of support on github or stackoverflow. (25:41) – Submitting a package to CRAN, the Comprehensive R Archive Network, is not a difficult process at all, if you write your package well. But writing a package for it to be submitted on to CRAN has to meet certain criteria. The documentation has to be of a certain quality in data in a certain way. The script files have to be laid out and documented in a certain way. So the whole CRAN submission process selects for good quality packages. (27:30) – People that are asking the really important questions, whether to do with business or science or health or whatever, the people that know how to ask and are asking those important questions are the ones that should be able to harness and implement statistics, data science, and machine learning to get those answers. I don't think that machine learning should be the purview only of mathematicians and computer scientists.(28:13) – As long as you teach people how to do things properly, that they have enough of an understanding of how the techniques work and what they do and what they don't do, then, absolutely, we can democratize machine learning. We can absolutely teach people to be able to use these techniques, to extract the answers or make the predictions that they're looking for in their field of expertise.(29:18) – The MLR package, which stands for machine learning in R. It provides a unified interface to a huge number of, not only actual machine learning algorithms, but also processes and functions like missing value, imputation, hyperparameter tuning, validation techniques. Where MLR particularly shines is, It makes it extremely simple to validate your models, MLR works very nicely with parallelization. MLR helps achieve that because you can do some extremely complicated validation pre-processing with very small amounts of code. (34:49) – Caret has functions that you can use to split your data into train test validation sets. And it has the ability for you to perform data pre-processing steps like missing value, imputation and things like that. MLR has become more popular recently. Caret has been the mainstay.(38:15) – Tidy Models are a set of packages that come from the Tidyverse. And in a similar way in which MLR is trying to create a uniform interface to machine learning, Tidy models are packages that are trying to create a unified approach to modeling in general. So that includes, and it's probably more widely used, as linear modeling. (41:53) – I really do think that Machine Learning with R, the tidyverse, and mlr is an excellent book. And it sounds very braggy of me and I don't mean to be, because although I wrote the content, a huge number of people other than me have made the book very good. So I do think that people will learn a lot and get a lot from it. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSChristoph Lütge studied business informatics and philosophy in Braunschweig, Paris, Göttingen and Berlin. He was a visiting scholar at the University of Pittsburgh (1997) and research fellow at the University of California, San Diego (1998). After taking his PhD in philosophy in 1999, Lütge held a position as assistant professor at the Chair for Philosophy and Economics of the University of Munich (LMU) from 1999 to 2007, where he also took his habilitation in 2005. He was acting professor at Witten/Herdecke University (2007-2008) and at Braunschweig University of Technology (2008-2010). Since 2010, he holds the Peter Löscher Chair in Business Ethics at the Technical University of Munich. In 2019, Lütge was appointed director of the new TUM Institute for Ethics in Artificial Intelligence. He has held visiting positions in Venice (2003), Kyoto (2015), Taipei (2015), at Harvard (2019) and the University of Stockholm (2020). In 2020, he was appointed Distinguished Visiting Professor of the University of Tokyo. His main areas of interest are ethics of AI, ethics of digitization, business ethics, foundations of ethics as well as philosophy of the social sciences and economics. His major publications include "Business Ethics: An Economically Informed Perspective" (Oxford University Press, 2021, with Matthias Uhl), "An Introduction to Ethics in Robotics and AI“ (Springer, 2021, with coauthors) and "The Ethics of Competition” (Elgar, 2019; Japanese edition with Keio University Press, 2020).He has been a member of the Ethics Commission on Automated and Connected Driving of the German Federal Ministry of Transport and Digital Infrastructure (2016-17), as well as of the European AI Ethics initiative AI4People (2018-). He has also done consulting work for the Singapore Economic Development Board and the Canadian Transport Commission.Episode Links:  Christoph Lütge’s LinkedIn: https://www.linkedin.com/in/christophluetge/ Christoph Lütge’s Twitter: @chluetge Christoph Lütge’s Website: https://www.gov.tum.de/en/wirtschaftsethik/start/ 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– 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(02:25) –  On the Future Forum we developed the idea of forming a kind of global network of centers for AI ethics. And at the end of this forum, we launched a concrete project, the global AI Consortium, which we are now taking forward in order to form a kind of global alliance of centers working in this field.(04:06) – It's not just an academic thing. It's not just a traditional research Institute where you do research behind closed doors, basically intimate. You have to work with both industries, with civil society and with politics, and that's the only way to take these issues forward. (06:54) – More of these systems are more visible to the public, and that's why there's also this discussion about AI and the ethical as well as governance aspects of it.  Certainly the trend is now, and has been already for years, obviously, the machine learning and deep learning aspect of AI, which some of the more conservative countries still refuse to call real AI. So for a long time, the idea has been that there will be something more robot-like systems that are out there in the world and doing certain things. But  this is the major trend. And of course, the implementation into special vehicles, and probably also in the field of health. I would say these are the most important trends for the near future.(09:33) – AI systems can both speed up a lot of processes, as well as create entirely new ones, or let's say connect data. They will provide a lot of new input for doctors. And so we are, and will be more and more, at a point where we can say, it's not responsible anymore not to use AI.(12:10) – We have these different levels of autonomous, striving automated, highly automated driving and fully automated driving. So what we are witnessing now is a progression on these levels. We need to get beyond that level where it's actually where the company is liable during the time that the car was in control, but not the driver.(15:33) – We need to have robust software which must be able to drive on the difficult, maybe not most extreme conditions, that's if we want to drive under any conditions that will be difficult. And of course, that car must be able to deal with, let's say, rain, with hale, with snow, at least light snow, maybe. And that can pose a number of difficulties, also different ones around the globe.(17:05) – We presented our first guidelines for ethics of AI in late 2018 in the European parliament. And we came up with these five ethical principles for AI. So, which are beneficence-maleficence, justice-autonomy. And while these four are quite standard for ethics, the fifth one is quite interesting: the explainability criteria. Then we presented another paper on AI governance issues just recently last November, this was about how companies and States can interact on deriving rules and governance rules for these systems.(20:48) – There are a few people who have the expertise in ethics actually. I'm one of the few ones in there and it will be quite interesting to see how this process works out, because, ultimately, we will need to develop international standards for these AVs.(23:03) – Ethics is quite a fuzzy term. It has lots of connotations and, for some people, it's about personal morality and that's not really what we mean. We are aiming at standards or guidelines, rules which are not always legal ones, which might be so. So we found it also better to use the term responsible AI. Not just the typical research academic conference, but one where we plan to interact with other stakeholders from industry, from civil society, from politics as well.(24:37) – We invite the abstracts on many areas of AI and ethics in a general sense to visit our webpage to find a lot of potential topics, whether it will be AI in the healthcare sector, AI and the STGs, AI policy, AI and diversity and education, and many others.(25:47) – Engineering curriculum should be enriched with elements from humanities and social sciences, not least of which it will be ethics. But now with a focus on AI, it becomes clearer that working on AI will not be enough to just look at it from a purely technical point of view. It needs to generate the necessary trust. Otherwise people would just not use these systems. And this is something that engineers should be familiar with, engineers and computer scientists, and people from technology.(28:23) – One of the key challenges will be how we manage to some extent, standardize explainability. Every step within the system must be transparent and it must be clear, you must be able to track it down. Of course, there's no way to do that, if you are familiar with the technology. So we need to find some kind of middle way. And there is this research field of explainable AI in computer science, and the challenge will be to implement systems. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Transform your Data Science Projects with these 5 Steps of Design Thinking with David Yakobovitch.Available for reading on Medium: https://towardsdatascience.com/data-science-design-thinking-658a4f293a1c .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.About HumAIn Podcast:The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

The Top 10 Data Science & AI Books of 2020 with David Yakobovitch.Available for reading on Medium: https://towardsdatascience.com/what-are-the-10-must-read-data-science-and-ai-books-of-2020-36e2c5f0d72f .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.About HumAIn Podcast:The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSNick Walton is the founder and CEO at Latitude, a software company which develops AI-powered games designed for player freedom and self-expression. Latitude is the creator of AI Dungeon.Episode Links:  Nick Walton’s LinkedIn: https://www.linkedin.com/in/waltonnick/ Nick Walton’s Twitter:  @nickwalton00Nick Walton’s Website: https://github.com/nickwalton 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– 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(03:23) – I was able to make a decent little like AI Dungeon master with it and,  I didn't win anything at that hackathon, but I thought it was cool enough that over the next couple months I continued to work on it and worked on how to deploy it and I made a little web app.(04:53) – GPT-2 released the largest model and I also found a dataset of text adventures so I trained the largest model on text adventures, and that's where it got like much better and so released AI Dungeon II  in December and it was initially just released as a Python code and a Google CoLab notebook, which is just a way to run.(05:45) – We've made a mobile and web app version that you can play and now we just passed 750,000 registered users and so it's been growing pretty fast.(07:54) – The thing I love about hackathons is that you can build things completely in an explorative way, you don't have any pressure or time crunch. If it doesn't work out, you can just say I tried that for a day but it didn't work out and I don't have to keep going with it and you don't really feel bad about not continuing this project.(09:32) – In terms of the competition I didn't have a web playable version by the end of the hackathon cause that took quite a bit more work, especially on the machine learning side. Seeing how much fun people had playing it just around me sparked the inclination to take this to the next step, make a web app and I spent like a hundred hours over the next several weeks doing that and then it went from there.(11:21) – Google CoLab servers, a lot of them were in Asia and Europe and our model was hosted in the U.S. so we were getting international egress bandwidth fees. We were afraid of spending all the money on GPU compute but now actually our GPU compute infrastructure is much cheaper than the cost of downloading all those models for the initial Google CoLab version.(13:44) –  Since we released the co-lab version, a team  started to come together and a guy volunteered to build up the mobile apps. Now we have a team together that does the mobile and the web and the model serving infrastructure on the backend and we're looking at growing that team, but what we really want to do is explore all the awesome directions.(14:49) – With AI Dungeon, you literally have an infinite set of possibilities because anything you can express in text, you can do and that's a completely new idea for a game. There are also technical challenges and we have a really strong team to solve those, but we need to explore and figure out how to resolve those technical challenges. Being able to merge those two in a video game format would be really powerful and that's one of the main things we're working on.(16:48) – In the long term, we're thinking much broader than just like fantasy RPG type genre.  AI Dungeon has this vast knowledge from the vanilla GPT-2, which was trained off 40 gigabytes of text data. We're definitely interested in exploring that broad set for the initial game, the fantasy theme has been really powerful because it taps into getting closer to that D&D field that people are hungry for, but we definitely have larger long-term vision.(19:47) – There's two things that this AI generated content makes really powerful: one is this player freedom where you could potentially, rather than having this preset list of possible options you can make it much more expansive; the other thing is much more dynamic and interesting content. With AI generated content, you can have less developers and less creators and maybe the creators are creating more of the longterm and the overarching themes and then the AI is filling in all these details and helping create this super expansive world.(23:05) – There's a lot of potential in that area and in terms of AI generated content for games, NLP is going to be one of the first ones, just because so there are a couple of things that are powerful about NLP. (25:31) –  You can make surprisingly life-like and Dynamic NPCs. You can create a little bit more of that, but  you can do this with every NPCs and that creates a lot of really interesting  individual emotional connections.(29:03) – I transitioned to more of the robotics and machine learning side of things but in terms of doing things, so one of the issues with AI and Dungeon is it actually has the highest minimum required GPU spec of any game we know of. (31:39) – We have good prototypes that we're working on implementing things like multiplayer where it's turn-based. We've got a lot of interesting ways you can modify the game than some of the next steps. Text to speech could be really interesting.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSEdward Scott is the CEO of ElectrifAi, one of the oldest machine learning product companies in the US serving the Fortune 500 as well as the federal and state sectors. Ed has over 25 years of experience in the technology and private equity sectors building, managing and investing in dozens of high-growth enterprises globally.Ed started his career in the LBO group of Drexel Burnham Lambert and joined the Apollo Investment Fund in 1990. While at Apollo, Ed invested in dozens of companies across multiple industries focusing primarily on the TMT sector, chemicals, transportation and financial services sectors and was on the board of directors for numerous Apollo portfolio companies. Ed was also a partner at the Baker Communications Fund, originating and managing the firm’s two most successful portfolio company investments, both of which have become multi-billion dollar enterprises: Akamai Technologies (NASDAQ:AKAM) and Interxion Holding NV (NASDAQ: INXN). Akamai is the global leader in content distribution and edge computing and Interxion is the largest data center and managed services business in Europe. Ed has held senior-level positions at Napier Park Global Capital and White Oak Global Advisors. Ed graduated from Columbia University with a B.A. in history and earned an MBA from the Harvard Business School with second year honors.Episode Links:  Ed Scott’s LinkedIn: https://www.linkedin.com/in/edward-scott-74354923/ Ed Scott’s Twitter:  @ElectrifaiEd Scott’s Website: https://electrifai.net/ Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport 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(02:30) – ElectrifAI is the United States oldest machine learning company that started off in the procurement area and then pivoted to create the first, fully integrated closed proprietary machine learning platform, everything from all the data ingestion and the transformation to the DQM, to the preparation for the models to the scoring, to the insights and so forth.(02:57) – We transitioned that closed proprietary platform into a fully open platform built on the cloud, built on a common spark computational engine with the use of Kubernetes Docker containers and of course, notebooks.(03:29) – We not only change the entire re-architecting to reengineer the entire technology stack, for our customers, to make it more modern and open and agile. We also shifted from being more of a data science consulting type of company to a fledged world-class machine learning products company. (04:43) – We focus on a certain number of verticals and a certain number of products. Our products focus on Procurement AI, Contracts AI, hidden risks, image AI, customer attention, customer acquisition, retention, and development, which is very important in the healthcare area with regard to patient steerage.(06:17) – Everybody's data is disparate and it's disconnected and it's all over the place. It's on SAP system, Oracle systems, IBM systems, Cerner's systems, Epic systems, Allscripts systems. And there's no way really to get at that data until now. And that truly is one of the core competencies of ElectrifAi.(07:10) – Without  clean data, there's no AI, that's simply the case. And we are seeing it across the world in the most sophisticated enterprise customers. And of course in the hospital and the payer space. (08:57) – If we're going to drive AI and ML into every single part of this business, it has to be done by leadership from the top in the digital world. If you are not embracing digitization in this world, your company's dead. (09:55) – When you look at comprehensive AI or a machine learning program, you really have to understand what your objectives are. What the objectives of the C-suite are. You need leadership and you need definition, clear scoping, project definition. The success of AI and ML really is contingent upon your capability and your competency in the data pipeline.(12:58) –  If you, as the CEO or the CFO of your firm, cannot express a return on investment or return on invested capital from all the money you spent on data lakes and data marks and all the tools companies, you're going to be out of a job. (14:22) – Our areas of focus, our verticals are TMT, healthcare, financial services and the federal space. Principally because we have the machine learning products that dial up the revenue, dial down the cost and dial down the risk. (15:14) – The power of machine learning is using AI and NLP to extract key terms, words, and conditions from contracts to show risks, opportunities, how can you can reduce the number of suppliers that gain leverage with the ones that you actually annoyed, how can you can reduce the suppliers who are not focused on social issues.(17:59) – It's a team effort at ElectrifAi. We talk about our culture, our culture of urgency, our culture of transparency, our culture of disruption, re-invention and self-examination and our culture of teamwork.(18:51) – Data is in our blood, but it's practical data and practical ML, and that's why we go back to getting the data prepared and so forth. We are going to change the way the world works in machine learning. They believe that our suite of practical machine learning products will help that C-suite in a very differentiated way. So it's all done with that team.(21:23) – The world is facing a massive demand and supply shock. And that's going to hurt the technology business and the small companies. And it's going to hurt companies that have tremendous fixed costs and cannot adjust those fixed costs or that risk quick enough.(25:14) – We have an image analytics department that automates annotations and then turns all those pixels into ones and zeros, and in a sense, mimic SQL and is able to search a database to say over the last 50 years, and give all the liver tumors. That is real power for ML and it's spreading into how we do with COVID. We can get that person segregated quickly into care versus them going into the cities and spreading it more. That's a game changer. Our technology is three years out ahead of the market.(30:49) – We haven't seen in a while the collaboration of the world together to attack an issue. We are citizens of the world and we have to solve this problem together and we have to solve it now. And it's a very exciting time.(33:46) – Businesses will adapt and will adjust to the new world of not necessarily conducting business by congregating in the office. But, those that are very adaptable and flexible and purposeful and very customer driven.(37:01) – Our mission is to change the way the world and our customers work in machine learning. Our culture is a culture of urgency, transparency, disruption, re-invention, self-examination. We tell our customers, we'll serve you through ML today. But tomorrow there might be a completely new technology, and we'll have to adapt. And that adaptability is at the heart of who we are. (43:13) – I'm going to say that the Time’s 2020 person of the year is humanity, because we're going to come together as a global family and solve this. AI for the good.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSCyrus Radfar is a long-time programmer and serial entrepreneur. Radfar initially studied computer science and psychology at Georgia Tech. His first entrepreneurial endeavor was with AddThis, where he was the founding engineer, led their analytics products, and managed the creation of the monetization offerings. AddThis pioneered the sharing movement and grew to become the largest sharing platform. It was sold to Oracle in 2016. Since leaving AddThis, Radfar has been testing new products and formally advises entrepreneurs building new companies. He founded V1 to share and scale his existing learning with companies who require new solutions to grow and diversify. Episode Links:  Cyrus Radfar’s LinkedIn: https://www.linkedin.com/in/cyrusradfar/ Cyrus Radfar’s Twitter:  https://twitter.com/cyrusradfar?s=20 Cyrus Radfar’s Website: https://www.v1.co/ 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– 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(02:47) – The future of artificial intelligence is going to drive a huge number of trends. We're going to be building something to either replace us or replace all the things that in the positive sense we don't want to be doing with machine intelligence, artificial intelligence, robotics, etcetera(06:32) – Machines are most likely going to solve business problems. AI in general is going to support and augment us so we can focus more on doing what we love. Augmenting humans with robotics is going to replace a lot of jobs. People are going to do a lot more of what they want to do on the knowledge work side and have removed a lot of work they don't want to do. (08:49) – it's more of a political and socio-economic question of how do you structure a society where you don't necessarily need as many people working or doing the jobs people don't want to do today.(11:08) – Social media didn't exist 10 years ago or went well 15 years ago. So the whole term is new, the whole industry and everyone who claims to be in that industry, those are new jobs, and it was created by a platform. Technologists and business in general, eventually, even if the intentions of the founders may not be good, will end up changing things a lot and constantly creating good new things like social media.(14:50) – Are we going to be more or less human? Are we giving more or less empathy? Are we going to care more or less for each other or we're going to be more or less competitive because of that? I don't know the answer, but the reality is we're going to limp through seeing a generation very soon, like gen Z that has completely been immersed in this thing that we created in garages. (16:58) – We've raised a whole generation to respond to apps more comfortably in a closed setting than they do to other humans who manage them. And it's almost evolutionary that we're almost setting ourselves up for this world where we're more comfortable with our machines.(19:27) – The “always on generation”. We're always being connected, whether it's through Slack or WhatsApp or Line or WeChat or Telegram the apps just go on and on. We are being connected. We're being driven by algorithms to make decisions that maybe we wouldn't choose by ourselves, but maybe it's more efficient and better.(20:23) – We're not moving as fast as we thought we would, but we are accelerating. It is possible that the generations that are born today, our children, could be on Mars.(25:19) – With faster travel and transport, more people will move away from cities. The future is remote for a lot of companies. So it's really important that we consider that it is significantly cheaper for companies, it's better for people to be at home.(28:45) – All my experience with remote workers is that they're way more focused. They're not distracted. There's not as much disruption on day-to-day goals. They can focus and do what they need and then go on with their lives.(35:48) – There are so many people who don't actually have broadband in the U.S. alone. There's people all over the country and in rural areas who do not have broadband, which is unfathomable.(37:11) – It's an unwired world. Some have lived through that transition. The phone, then television, radio, the rise of the internet and whatever wired telecommunications and then unwired communications. It's crazy the perspective that folks have, who are still living. (40:08) – Look out for 5G. We're going to be more seamless with immigrations for real time data. Perhaps, maybe that's through the 5G, or more seamless computer vision, getting to self-driving cars or getting to consumer applications that can see things for you or read text for you, or do it more real time. 5G will get us in that direction.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

The Most Promising Tech Job of 2020 Is Cybersecurity Analyst with David Yakobovitch.Available for reading on Medium: https://medium.com/swlh/the-fastest-growing-hidden-job-in-2020-is-network-engineering-6034bdf288b .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.About HumAIn Podcast:The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy