HumAIn Podcast

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSSagi Eliyahu is the co-founder and CEO of Tonkean, a next generation business dashboard that connects the dots between the tools organizations use every day and the insight only teams can provide. With Tonkean, Sagi seeks to help companies of all sizes and types simplify and automate the process of staying updated on the most important details they need to more successfully manage their businesses.Episode Links:  Sagi Eliyahu ’s LinkedIn: https://www.linkedin.com/in/eliyahusagi/ Sagi Eliyahu ’s Twitter: @esbsagiSagi Eliyahu ’s Website: https://tonkean.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(01:39) – Tonkean enables more people to use software. And what impact would that have on enterprises and business in everyone's life? That's what we're all about. (02:43) –  We joined from acquisition and grew the team there from a handful of people to over 150 people. Even though we had all those great tools in place and the top CRMs and the top project management tools like most companies, it didn't feel like it helped to force people into those softwares. You look at the CRM, you don't have the information that you need. You look into the project manager system, it's not there. So I tried to hack those systems together, trying to connect them together with the likes of integration platforms.(04:24) –  The biggest moment for me was to realize that business processes are actually not about data, they're about people. But software in enterprise is almost a hundred percent about data. How do you actually go about using technology in a process that is very dynamic, very asynchronous and very human-centric? And the answer is that you didn't actually have anything to do that for you. That sounds like a big enough problem to pursue. So that's when we decided to start Tonkean. (06:01) – We call it the Operating System for business operations. It's really abstract into the complexity of business processes, which are human-centric, highly dynamic, highly complex, simplifying it to non-coders business professionals, operation teams like sales operations, marketing operations, legal operations, and general operations, and so on, to be able to build their own solutions that are across a process, not necessarily creating a new app where you can view and manage data, but actually streamline a process end to end across different systems and across different teams.(09:28) – When the pandemic hit and everyone moved in almost overnight, it was really quick to be fully remote. I always had a remote team or more accurately a team that is, like you said, distributed on both sides of the globe. Everything is more measured, and not because we want to, because we're forced into it. All that coordination and all that work that was not in the spotlight becomes more in the spotlight because we're remote, and because everyone is remote. That definitely pushed a lot of the automation world in a lot of our sort of human-centric processes world to the top of mind, because now you can see how much of the work you actually do every day. And all of us are not necessarily in systems. It's between us people and how much of it is manual.(12:29) – One of the big things we're pushing forward is a concept we came up with, which is people-first process design. It's not even about what technology you have. We also believe that most companies and most people misuse technology in the way that they even structured the processes. (15:11) – If we're not designing the process into their strength, then we're actually replacing one inefficiency with another. And that's kind of where we strive to help operation teams. They know the process, they understand it, provide them with a tool set, with a platform where they can actually create efficiency on top of existing systems and on top of existing behaviors. (16:26) – There's the personalization for the role. What is important for that role? What is important for that team? What are the things that work well and what are the things that are not working well as part of this end-to-end operation? (17:47) – Work is more global. And to get the best case scenario, outcome, you need to actually leverage everyone. And that is something that I feel our platform allows to do, but more of that, the movement of no-code and low- code release, all about enabling more people to create more solutions that are more customized for their own processes, their own team, their own company, versus buying packaged solutions off the shelf.(18:55) –  No Code and Low Code are both playing on the same, call it a wave of future improvement and future next steps off software. So for many reasons they are in the same global area, but at the same time, they're night and day, they're actually very different. Low-code, by definition, is the ability for developers to do more things with less code, but it's a low code because you still need to code. And even if you're not writing scripts, like Python or any other coding language, you still expect that to be a developer mindset and skillset. The low code movement allows you to move faster. So it's basically saying the same people that can code today can code faster.(20:39) –  No-code is about expanding the pie, making the pie bigger of people that can actually build things. So it's, instead of saying, you can do more things faster, it's saying more people can do more things. And why that is important is because if you think about the impact of technology and the growth of technology over decades and over many generations, any duration in software specific to the big leaps do not come from making things faster. Those are linear growths. The big things come from opening the door for different new people that can now code.(22:15) –  With Tonkean, we believe that operation ops people, again, sales ops, legal ops, finance ops, professionals that understand processes really well, they understand what needs to happen and why, and what's important, but they don't know how to code. So they don't even understand how API works or well enough to create mission critical solutions.(22:54) – If you give them low code, it's not very useful for them. They can do toys, they can do small things that create small impact, but they will never be able to build huge complex systems with low code because the gap is not in the speed. The gap is in the knowledge that they come with. With Tonkean, being fully no-code, we focus on those business processes segments and they've created them to be fully no code in the sense that you don't need to be a developer in your mindset.(24:50) – There's always going to be the need for implementers and the need for architects. To be an architect, you would need to still be the technical person in that case, that understands how networks work and how data flows. Tonkean is a bridge between tech and IT, it incorporates engineering with the operation teams. And empower the operation teams and business analysts to implement their own solutions.(27:15) – 95% of all IT and operation teams have already adopted or planning to adopt in the next 12 months a no-code or low-code solution. The need for efficiency in those departments was always there.  What we're seeing now is the movement from personal productivity to operational efficiency.(33:47) – We're focusing mostly on large enterprises these days. So  there's a lot that we're going to add on from that perspective as well. And being able to, like I said, allow people to build true missions, critical processes and things that run for a long time.(35:02) – Get educated on what's out there. There's a lot of great technology that is very complimentary.  There's a lot of noise marketing wise. A lot of things seem or sound the same, but that's because the opportunity is so big. And there's so many things that we took for granted over the years.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSKen Grohe is SVP & Chief Revenue Officer, Taos. Additionally, Ken Grohe has had 3 past jobs including SVP & GM at Barracuda Networks. He got a BS in Business Management from Boston College.Episode Links:  Ken Grohe’s LinkedIn: https://www.linkedin.com/in/leveragegtm/ Ken Grohe’s Twitter: @LeverageSignNow (suspended)Ken Grohe’s Website:https://www.taos.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(01:27) – WEKA as you probably know, and some of the folks that might be data scientists listening in, they had to strip a wekabite. So it's 10 to the 30th power. That's a good way to future-proof it. It's all you can fit in a file system. A new way to do storage. It's all software, it's all service subscription through the people you're buying from every day. So if you run it through AWS in the cloud or on premises with Hewlett Packard, it's a great way to get things done and solve big problems. What WEKA is, is a modern and limitless parallel file system, that's easy to deploy any scale in the cloud or on premises for the people in the data center, solve big problems.(05:15) –  71% of corporate data goes unused, despite how much money was spent to create this information data. And it's going on use. So that's amazing. So the average sale for us is a petabyte and that's two thirds of the time. It's on premises. One third of the time, it's in the cloud every time to go between the two.(08:12) –  I can certainly think if you're in a university and at the end of the day, you want an AI project and I'll cut to the chase, not just for the greater good, but to recruit great talent. So when you're doing that and you're recruiting that type of talent, you're putting it into action. And that's probably going to be on premises. We allow you to put the right data at the right place at the right time to get, manage your information across the entire life cycle. So you make the money when you need it, and then you don't lose it when you really want to protect it for data protection.(13:09) – Where you live in your hat in a COVID world, doesn't matter. They kept going. When you think about it, traditional Hollywood shut down during the beginning of COVID. Because you couldn't break the unions, you couldn't get the talent, the labor, they, Brad Pitt's Reese Witherspoon's to go on site, you couldn't see, you couldn't create any of the content we watched. The tiger came and things like that. But what I've told students able to do is enable them to create content. The need to have a parallel modern file system with no limits, no compromise. It was so important because you're going to bring all these engineers and all these scientists, you want to make breakthrough discoveries.(16:22) –Some early in the career, 20 something, it says what am I going to do with the rest of my career? I heard AI is great. I'm telling you now the chief data officers are to learn. And as part of it, you may not earn that job right away, but think about, and put this individual's going to be, and typically they've come from the HPC high performance compute environment or the academic environment. So what's happened is a title has risen. It's called chief data opposite. Some of it is compliance and there's certainly a chief compliance officer in there, but more important, more exciting is building out new applications that grab market share and new revenue streams using that.(20:28) – Storage is going to have a Renaissance or is we're living in right now, part of AI. (24:53) – I see three different paradigms. GPU's being prevalent. NBME being everywhere in the network, but especially in the GPU and the server itself.(27:27) – All the intended AI practices and initiatives, it was going to be a fallout that over 50% of them were not going to have ROI. And that's unfortunate. Now that number has shrunk to less than 12% per the analysts we spoke with yesterday. You never want to have strengthened aptitude and intelligence, but you don't have the ability to use it at that time. So the pro file system lets everyone use it all the time. We take care of the locking and the overriding, all the other management is part of it. (29:52) –  You can start as small as you want and go as large as you want, but bring the ability and the imagination to solve big problems. Because storage and more importantly, AI centric accelerated storage from WEKA is certainly huge. And I love I'm going to use an ops shoot of your bottomless. I'm going to call it limitless. So it's kind of the solutions of limitless.(31:01) – You want the right data at the right place at the right time. No in all the cases. So you can capitalize, you can make, go faster and go actually press your advantage. And wherever it might be, whether it be retail or manufacturing. The reason I say extensibility is for naming conventions, whatever file you create, you want that same name and convention whether you're on premises, we on a cloud, we were an object store or whatever. And what's great about WEKA.(33:03) – The fast, eat the slow. If that's the case, the ability to move the correct data, the right naming conventions based on the right policies, the right security allows you to happen. So we're kind of known as the information life cycle management type company. (34:14) – We were involved with vaccine development, obviously with those, most of those vendors did all suppliers do that through the cloud and we have a solution for them in the cloud but ultimately a hybrid solution as well.(39:51) – We're not a very sales dominant culture. We're all about solving big problems and very technical by nature to get into your use cases. In fact, most of our people spend most of the time trying to move data scientists or people represent data scientists. So if you're in that category, we'd love to help you out.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSDr. Vasco Pedro is the co-founder and CEO of Unbabel. He owns the vision, overall business strategy and sets the direction for Unbabel’s product development. Responsible for the company’s culture, Vasco is heavily involved in recruiting and spearheads Unbabel’s fundraising efforts, which total USD$91 million in venture capital to date. He is a leading presence in the burgeoning Lisbon startup scene, with Unbabel known for being the first Portuguese company to be accepted into the Y Combinator accelerator program.Vasco received his Ph.D. in Computer Science in May 2009 from Carnegie Mellon University (CMU), working with Jaime Carbonell and Eric Nyberg. His thesis, titled “Federated Ontology Search,” focused on developing new methods using ontologies (a set of concepts which compartmentalizes variables for computations and establishes the relationships between them) in large scale data-processing scenarios. From 2001-2009 he was a Research Assistant at the Language Technologies Institute, contributing in the field of Question Answering (a computer system capable of answering questions posed in natural language), alongside the team that eventually went on to create IBM’s Watson. Vasco was a Fulbright Scholar, 2001-2005, and was awarded a scholarship from Fundação para a Ciência e a Tecnologia, Portuguese Foundation for Science and Technology (FCT), Ph.D. Scholarship, 2006-2010.Episode Links:  Vasco Pedro’s LinkedIn: https://www.linkedin.com/in/vascopedro/ Vasco Pedro’s Twitter: @justvascoVasco Pedro’s Website: https://unbabel.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(01:30) – We need to create a new version of the translation service that blends artificial intelligence and humans in a number of different varieties to provide just this very simple, straightforward API for translation. That was the original idea. (04:21) –  Companies are pressured earlier to be able to serve multiple markets. And as you expand to multiple markets, you face the fact that people in that market will speak a different language and I need to be able to serve them.(06:49) –  Our goal is to build the language operations platform that enables every enterprise to seamlessly scale across languages. And a big part of that is the full stack that we've built on translation and different components of AI, quality estimation or anonymization, or the actual interfaces for humans to translate and all the different components.(08:43) – AI will have the biggest impact in areas that are highly commoditized and require a lot of human effort. A lot of humans can acquire the knowledge and the skillset to do translation and to do transcription. Overall, AI is not replacing humans, it is augmenting humans. And it's enabling humans to be more productive as a tool, so far.(10:43) –You will need a smaller amount of human effort per unit, but that human effort overall would be more valuable, because it translates into a higher value. I don't see, unless you're talking about very basic repetitive tasks, I see the real value is in this interaction of being able to give the boring task to AI and to let the human do the higher cognitive load function type of tasks. (15:10) – We started by focusing on customer service and the drive behind that was a number of things. One, conversational interaction is particularly suited for enabling AI to have a large impact. There's this sense of almost the inequality of customer service, depending on language.(16:59) – We're still focused on text, chat and email, but in a way that I, as a customer service agent, don't have to really care about the language you're talking. You, as an agent, focus on being an amazing customer service agent and really understanding your product and providing that level of customer service. And we act, we sit in between to make sure that that communication happens at a high quality human level on both ways, both from the customer to the customer service agent and vice versa.(20:07) – Unbabel is a platform and solution for language operations that relies on multiple things. So the portal is really the product that the LangOps use to implement, manage and scale the translation layer. This is powered by the underlying platform, which is the actual bit that does a translation and would set up pipelines. And that's where a lot of the AI and human work combined to provide fast, scalable, robust and high quality translations.(24:13) –  The digital-first world that we're accelerating into, and despite all the very, really bad things that the pandemic brought, that's probably the silver lining in terms of accelerating into the future, highlighting the need for that, for the ability to overcome language challenges. It's very clear that even in Unbabel, which is a company that's focused on eliminating language barriers, everyone that we hire needs to speak English, because otherwise we can't really communicate yet at the level that we do, we need to do. You're now really being able to overcome physical barriers, but still have some sort of pseudo physical presence. And so the glaring barrier becomes language. If your appearance and location are not an issue for communication, then, really, the language that you use becomes the number one barrier for it.(29:34) – Conversational is still going to be, you mentioned the interface, but it's going to expand more beyond text into voice, which was pioneered in the media is going to migrate into a lot of business use cases because we were forced to do it.(31:49) – If you're a consumer, don't settle for bad customer service, just because they don't speak English.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSJacqueline Nolis is a Data Science consultant, who helps companies like T-Mobile, Expedia, with their data science problems.She’s got an undergrad in math. Masters in math. She got a doctorate in industrial engineering and then started working as a consultant. For the last ten years she’s been doing data science consulting for all sorts of companies and leading data science teams.Emily Robinson studied very related fields of statistics. And that's where she started programming in R, went on from there to get a Master's in organizational behavior and then did Metis, which is another data science bootcamp.Went on to Etsy DataCamp. And now she is a senior data scientist at Warby Parker. She got interested in data science because quantitative social sciences are a very good background to lead into data science.Episode Links:  Jacqueline Nolis' LinkedIn: https://www.linkedin.com/in/jnolis/ Emily Robinson’s LinkedIn: https://www.linkedin.com/in/robinsones/ Emily Robinson’s Twitter: @robinson_esJacqueline Nolis' Twitter: @skyetetraEmily Robinson’s Website: https://hookedondata.org/ Jacqueline Nolis' Website: https://jnolis.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(04:08) – There's just, clearly, some desire in the world that people are data scientists, or if you're a junior data scientist, a desire in the world to be one of these senior data scientists, giving talks at conferences and joining the community. And so we just noticed organically that this is happening more than us making some grand observation about the state of the world.  You bring up  the current moment also recognizing, how May I become even more valuable to employers? I may end up having to do a job search. What can I do to prepare so that I can be an attractive candidate to different companies? (06:23) – The book was put up into four parts, and the first part is, basically, what is data science? What does it look like at different companies? How do you find jobs? What does the interview process look like all the way up to negotiating an offer? So that's the first half. The second half of the book, and the third part is around settling into your job. Putting a machine learning model into production. And dealing with stakeholders. And then, finally, the last half is about when you start settling in it's about continuing to grow by joining the community, handling failure, which is pretty much inevitable when you're a data scientist going on to a new job. And then the final chapter is what are the things you can do even after you become a senior data scientist. So Management, independent consulting or being a principal data scientist. Finally, actually we have an interview appendix with over 30 interview questions, example answers.(08:51) – No one really knows what's happening. No one, or for the last two months, no one really knows what happened. No one knows what's going to happen for a while. That we're just in a really uncertain time. We don't know if your company is going to be around in six months, everything's more uncertain.(09:57) –A lot of companies are putting on hiring freezes in general, except for very critical roles. (12:18) – Each one of those stakeholders has a different goal, whether it's to make their engineering stronger, to make better decisions, to make their company go to a better place in the long term. And how you work with each one of these groups of people really will differ based on who they are and what their goals are. So we break down that a lot. (15:40) –  Some of the key communication strategies include messing up a lot until you remember how you messed up the last time, and then get a little bit better. And you do that for 10 or 20 years. And eventually you're okay. Being consistent. Creating a consistent framework for how you share things. You have to adapt your strategies.(18:01) – The idea of how you prioritize this work thinking through a lot of the prioritization and deciding what work to do when that's really important to good stakeholder management.(19:43) – Failure can come in all shapes and sizes. For me, I find one of the most difficult types of failure is that when you're a data scientist, you generally have to get people excited about a project before it starts. You have funding from people, and then you start working with the data. And it turns out that data doesn't have a signal in it. If you can't find it with a simple model, you're never going to find it. And that's a really big source of failure in the data science field. (20:54) – So it's also worth thinking about, as a team, maybe not taking on only pie in the sky, very high risks, new cutting edge projects and balancing that with things that you're more confident you can deliver because that can help show people the value of the team. And then, hopefully occasionally, one of those riskier projects does pay off and it will probably pay off in a bigger way.(22:38) – A lot of the work you need to do to handle a failure really starts long before the failure actually occurred. Companies do have different cultures around failure, and at some places it's not seen as valuable, you might be punished for it.Try to understand if that company has a culture of learning and ongoing feedback, because you do want to be at a place where it can be safe and understood that sometimes things do fail. Startups are more comfortable with failing fast and frequently because startups are lean and exciting.(27:40) – These softwares to monitor their employee's computers, which will take screenshots every 10 minutes, it hugely invades privacy. You should know what outcomes you're striving for. What success looks like there, trust your team to do the work well, to give them the flexibility. We're not just working remotely, we're working remotely in a pandemic. And having that human understanding that people are going through different stuff.(35:57) – I am a big component, a proponent of doing public work. In my free time, I've picked up art. So I've been doing a lot of watercolor and oil pastel, and it's been nice to just have something that is totally not tech to put a little bit of my heart into.(43:05) – At the current moment, it's certainly riskier to leave without another job lined up. You could just ditch the system entirely and become a consultant and work as a freelancer, which is what I've been doing, which can have a huge payout and huge opportunity, but also is incredibly stressful, very risky, and just almost impossible to do right now, given the virus. I really do not care for giant tech companies to come out with giant technology and we're supposed to be excited about it. I find that inaccessible. I really love seeing new projects, new things people are doing. But what I get very excited about, too, is when folks start sharing their side projects or blogs, or sharing some of their work, it's cool. There's more to be done with other groups including people of color, but I've also seen some meetup groups and other efforts for that. So that's what's exciting to me. (53:59) – My call to action is to try to find a way to help people. That's why we wrote the book. It was certainly not so we could get fabulously wealthy and retire early. Don't take conventional wisdom and assume because someone told you it has to be true, including us. Challenge conventional wisdom a little bit.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSBabak Hodjat is Vice President of Evolutionary AI at Cognizant, and former co-founder and CEO of Sentient. He is responsible for the core technology behind the world’s largest distributed artificial intelligence system. Babak was also the founder of the world's first AI-driven hedge fund, Sentient Investment Management. He is a serial entrepreneur, having started a number of Silicon Valley companies as main inventor and technologist.Prior to co-founding Sentient, Babak was senior director of engineering at Sybase iAnywhere, where he led mobile solutions engineering. He was also co-founder, CTO and board member of Dejima Inc. Babak is the primary inventor of Dejima’s patented, agent-oriented technology applied to intelligent interfaces for mobile and enterprise computing – the technology behind Apple’s Siri.He is an expert in numerous fields of AI, including natural language processing, machine learning, genetic algorithms and distributed AI and has founded multiple companies in these areas. Babak holds a Ph.D. in machine intelligence from Kyushu University, in Fukuoka, Japan.Episode Links:  Babak Hodjat's LinkedIn: https://www.linkedin.com/in/babakhodjat/ Babak Hodjat's Twitter: @babakatwork Babak Hodjat’s Website: https://digitally.cognizant.com/author/babak-hodjat 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(01:34) – Machine learning and AI based algorithms are being used to get a sense of what is happening in an organization, abstracting out patterns, and then to be able to actually forecast and make predictions into the future. We need to have our AI systems help us with the decision-making itself.(03:59) – Humans are really good at general intelligence. We know a lot of things about a lot of things. So often that state-of-the-art in AI can not capture things like common sense. The frequency of making decisions is slow enough that we can have a human in the loop.Today, it does still make sense to have a human in the loop. There are cases where we have to rely on our AI systems to make autonomous decisions for us. (06:54) – We can build models that are specialized in assessing certainty in our AI systems and the way they do that is based on familiarity on the input side, the context side and familiarity on the output side.(09:57) – Our systems have to be able to tell us how much we can rely on them. You need confidence in what the AI system is telling you to do, but then there is risk sort of projecting that confidence out. Past performance is no indication of future returns.(11:36) – Companies and enterprises have definitely reprioritized things. They have maintained, or even in some cases increased their investments in AI enablement, which says a lot about the value that people ascribe to AI based systems. It is a natural next step to digitizing your business.(14:45) –Evolutionary AI is a set of tools that we use to build AI systems and AI enabled companies. The reason why it's called evolutionary AI beyond the fact that it's an evolution in the way people should think about AI, is that a very strong core component of it is evolutionary computation. We do pull from other AI disciplines as well, such as deep learning and neural networks and so forth, but the essence, the main differentiation here is the fact that we have an element of what I call creativity that is missing in a lot of AI systems. 15:29) – We're able to search for solutions much more efficiently than we are with your typical machine learning based systems. And that speed and efficiency allows us to be much more creative and find solutions that are either very difficult to arrive at using other methods or impossible to arrive at. So it also gives us a number of very interesting capabilities. (20:09) –  What evolutionary AI allows us to do is to actually use machine learning to create what we call a surrogate for the real world. That surrogate is learned off of data that we've seen up until now.(20:47) – This is the principle of what we call evolutionary surrogate assisted prescriptions, where you have a predictor, which is the surrogate for the real world. You have a prescriptive that you evolve, which gives you a decision strategy. And often you pair that with a certainty model. So when the three of these come together, you have all the elements of a good decision augmentation system, where a human decision maker, let's say a policy maker would ask the AI, how can I achieve this balance of cost and containment.(24:46) –  Optimization is where we need to be. And that is what decision-making is about. We are constantly optimizing and trying to improve on goals and outcomes. (29:25) –  There's a lot of work around new architecture, search and evolving, basically the design and hyper parameters of any kind of deep learning based system.(36:57) – More and more companies are going to adopt this technology for decision-making and it will start with areas where the decision-making has been captured. So the data around the decision-making is already there, but it will not stay there. It will get to areas where we think decision-making is the soul.(38:20) – If you are in an organization or enterprise where there's critical decision-making happening, work back from there. You have to have a vision of AI enablement in order to even get the data and digital part of what you do. Build your Data infrastructure, modernize it, report on top of that, build your machine learning and forecasting and predictions on top of that.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSAlberto Todeschini is a Faculty director, consultant and lecturer in artificial intelligence. He has supervised over 150 projects covering a wide variety of industries and techniques, with a special focus on sustainability in energy and water. He also works with the University of California, Berkeley, GetSmarter, and aivancity. Episode Links: Alberto Todeschini's LinkedIn: https://www.linkedin.com/in/atodeschini/ Alberto Todeschini's Twitter:Alberto Todeschini's Website: https://www.ischool.berkeley.edu/people/alberto-todeschini 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(01:49) – It has been interesting because in the last few years, a lot of this is about the environment, about energy and about agriculture having been penetrated by data science. I'm pretty optimistic actually, coming out of this big dark cloud. First half of 2022 will be some good news. (03:56) – Newer energy technologies have been around for a while, but they really have become mainstream recently, such as wind and solar. They are intrinsically data-driven. So you need to squeeze every last percent of energy out of this massively capital intensive works.(06:22) – With COVID, we've been forced essentially to experiment. We will see more experimentation around the livable cities for instance. There's a lot of appetite for resilience, for community resilience, maybe at the city level, but also at the regional level and national level.(09:00) – We've seen the investment moving elsewhere to renewable, which is certainly more future proof. if you talk to the epidemiologists, they'll say, well, there will be another pandemic. As a matter of fact, it could be a lot deadlier. So it will be nice to have this distributed way of storing large amounts of essential items.(12:40) – 5G enables this distributed system and the ability to communicate incredibly quickly and also to do, technically speaking, inference on the edge.(17:08) - The market in Europe is pretty fragmented. Partially that has to do with language. So, pretty much most European countries would speak reasonable English, but that's not absolutely not true for the entire population. One of the things that maybe has changed with COVID is the sense of locality.(20:25) – There's a huge amount of work that needs to be done postmortem, in the real meaning of the term, to understand what went wrong with the data collection. So that next time, collect it better. What went wrong with communication between health authorities and political authorities and the general population.(24:49) –  Cultivated areas are very interesting because agriculture consumes the majority of fresh workers and about half of agriculture. Currently it is not sustainable. Purely from the point of view of water. And we're not talking about deforestation, we're not talking about runoff of chemicals into the ocean, purely just the water.7:05) – Some of the main carbon capture technology is very water-intensive. As we increase both the data collection, as well as the predictions, which are two of the main things that we can do with machine learning, we can just use water better.(28:45) –  These companies that are, from day one, data-driven companies, are all thriving and they're becoming ever more unmatchable.(38:45) – Let’s use technology to figure out how to improve life in the city or make places where we enjoy walking. We like walking, and we enjoy local restaurants. We enjoy going out. We like biking around the same city, livable cities. So maybe that is something we can think about and work towards.(41:31) –  It's been awful. It still is awful, but I'm optimistic. Look around your neighborhood and think of things that you want to stay with us. We've been given a great opportunity to reset a lot of our habits.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSStan Vlasimsky helps companies envision and navigate complex transformations leveraging technology to achieve business outcomes. He is currently a Senior Vice President at Pariveda Solutions focused on digital transformation and helping clients navigate change with a particular focus on innovation, operating with a product mindset, organizational health and leveraging emerging technologies.Formerly, Stan was a senior executive at Accenture, where he spent 25 years working across the Americas, Europe, and Asia focused on large scale global change initiatives, operational excellence, and technology modernization. He has had the privilege to serve some of the leading companies in the world, including Toyota, Walmart, ExxonMobil, ChevronTexaco, and AmerisourceBergen amongst others.Episode Links:  Stan Vlasimsky’s LinkedIn: https://www.linkedin.com/in/stanvlasimsky/ Stan Vlasimsky’s Twitter: @Pariveda_IncStan Vlasimsky’s Website: https://www.parivedasolutions.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(01:30) – We were moving to a more virtual world and we have been for a while, but then all of a sudden over a span of a few weeks  everything was accelerated. How do you make teams effective and motivated where we're used to walking around having team lunches, mentoring, and recognition and all those things? How do we make human relationships?(03:13) – We've been experimenting with how you scan things, all the collaboration tools we use with our clients we're now having to use with our employees from a career growth perspective.(06:19) – The most complex algorithm that exists is the human brain and how humans interact with each other, and that's the most challenging thing.(09:22) – Leveraging both what we do internally and expanding that out into the broader ecosystem condition to other third parties as well. We're doing five or 10 years in five or 10 months. (10:57) – We have been changed forever to some extent and we'll have to deal with that new normal and much of that is positive and some it's going to require some more work.(18:19) – Productivity measured in output of the consulting work we do or to clients has actually gone up.  As we've reduced some of the friction cost of commuting and all those things that happen and then there's an element of, even though we're a very employee friendly company, everybody has seen people in their ecosystem be impacted, furloughed, laid off, whatever. So,  there's an element of the Hawthorne effect, which is ultimately when people believe they're being measured, their productivity changes or generally improves.(22:22) –  There's going to be  a lot about people and a rethinking of what the models are for things such as restaurants or retail and malls and all the things are going to be similarly impacted as people try to figure out they need a certain density of customers. (25:58) – This is going to test every organization, every leader, agility and product and all those are digital, all of the words that we like to use right now, but it's real at this point in time either you figure it out or you don't survive.(28:32) – Contactless payments helps perhaps with restaurants. The core of this is being able to simplify payment transactions.(32:14) – It can all be underpinned by ultimately automation, those processes that have traditionally been more manual, but pushed in more traditional ways through different organizations and such again, going back to as things get more digital, that's going to happen. It'd be accelerated because there's so much more data to deal with and every day there's so much more data.  Again, it's never going to add to now the worry is what do you do with the data and what do you do with Intelligent data, because there's no longer a lack of data. It's because I got too much data. (35:37) – We're going to be in some sort of hybrid world and  your comments about European flare brands, recognizing what the consumer wants is going to be even more important than it ever was and you're going to have to morph to a hybrid so rather than saying the strong sales experience, people value product expertise, so rather than saying, then having somebody, this is the sales person, this is a person that can help you pick the product.(38:48) – How do you continue to build those communication skills in a world that is remote? For others of us, it's going to be about empathy.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Listen in to this episode as David Yakobovitch shares his thoughts on why open source vulnerabilities are increasing.Available for reading on Medium.🚀 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 | RSSDr. Kari L. Jordan is the Executive Director of The Carpentries. Kari has been on the Core Team of The Carpentries since 2016. Before becoming Executive Director, Kari served as the Acting Executive Director. She has expertise in engineering education, diversity & inclusion, and leadership. In addition to her work as the Acting Executive Director, Kari held the role of Senior Director of Equity and Assessment where she guided The Carpentries through development of an Equity, Inclusion, and Accessibility Roadmap and was the liaison to the Code of Conduct Committee. Before this, Kari was the Director of Assessment and Community Equity where she streamlined The Carpentries assessment strategy and expanded their mentoring program.Episode Links:  Kari Jordan’s LinkedIn: https://www.linkedin.com/in/kariljordan/ Kari Jordan’s Twitter: @DrKariLJordanKari Jordan’s Website: https://www.carpentries.org/ 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(01:38) –  I hadn't heard of open source until I started working with The Carpentries and more specifically data carpentry.(04:11) –  We're all over the place and we work remotely full-time, so the shift that we've seen over the past couple of months from a teamwork perspective has not changed, but in the way we deliver our workshops has totally changed. We’ve moving our workshops online, making sure that the quality in our brand stays the same.(08:39) – We received quite a substantial amount of support from both the Moore Foundation and the Chan-Zuckerberg Initiative and this funding will help us scale our instructor training program.(12:02) – I had no idea what open source was, but now I can advocate for it and we can offer opportunities for workshops you may not get in a university, but what does that mean for a degree program? or how can I justify paying or having someone pay for a four-year degree to learn open source or learn to reproduce or all of these things when they can come to a The Carpentries shop? It's a very interesting conversation about the curriculum and who owns it and how it’s shared. (14:50) – The growth in open source has to do with problem solving and it comes from the desire to want to solve problems in your own community or want to solve problems that you see things that have been problems for such a very long time that they have not been solved. This is why I talk so much about not only diversity, but inclusion. Bringing people together of all backgrounds and giving them the space to contribute what they have, because every contribution truly does matter.(19:48) –  There is no wrong way to get involved. There are many ways we can get involved with open source.(21:39) – There are hundreds of organizations dedicated to allocating resources, to providing opportunities for people to get involved with data and coding and it's not the responsibility of one organization to do all the work, The Carpentries I feel like our zone of genius really is that training teaching data skills training that type of pedagogy. It's really important for this opportunity for access and just sharing what we do is so important.(24:32) – What do you want your participants to walk away with? That's extremely important to the carbon truth. We don't want anyone leaving our workshop feeling worse than when they came in or feeling they’re never going to learn this. It's more so about that self confidence piece that belonging to a community that's what it's all about, and eventually you're going to learn some code, you're going to learn how to code. (28:19) – There's no wrong way, and I very much appreciate the industry acknowledging a four year degree may not be the answer for everything. There are things that I've definitely learned in college, but the industry is noticing that you can pick up skills along the way, you can take a two day course, you can take a month long seminar and be just as effective in your role and learn just as much. So it's all about pathways. (30:22) – You don't have to be proficient in any of the programs to be a maintainer, you have to be patient and know how to be organized and how to facilitate conversation around the lesson.(34:46) – If you ever thought that you could never code, you thought wrong. I have been in your shoes, I shied away from programming for a very long time and now I'm the executive director of a nonprofit that teaches foundational coding and data science skills. There is nothing to be afraid of because there is a community in The Carpentries that values you, that appreciates your contribution and that appreciates your perspective. I want you to visit Carpentries.org, check out the opportunities that we have for mentoring see if there's a workshop, all of our workshops are online right now, actually so this is actually a great opportunity and great time for you to get involvedAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSBen Byford has been a freelance web designer since 2009, and he is now mostly a freelance AI / ML teacher, speaker and ethicist and tinkerer – in his spare time he makes computer games. Ben has worked on large scale projects as a web designer with companies such as Virgin.com, medium scale projects with clients including BFI, CEH, Virgin and Virgin unite, as well as having created a myriad of sites for smaller businesses, startups and creatives' portfolios.He’s mostly been a design and front-end guy, with extensive knowledge of other tech and development languages and has previously worked as a mediator between dev teams and clients. His public speaking and lecturing blends his insights within AI and ethics, web technologies, and entrepreneurship; focusing on the usage of technology as a tool for innovation and creativity. He hosts the Machine Ethics Podcast, which consists of interviews with academics, writers, technologists and business people on the theme of AI and autonomy.He also talks about Machine Ethics.Episode Links:  Ben Byford’s LinkedIn: https://www.linkedin.com/in/ben-byford/ Ben Byford’s Twitter: @benbyfordBen Byford’s Website: https://www.benbyford.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(01:24) – The big question and a moral quandary which we're battling with is how much information do we give to organizations, governments, about our movements - and that's always been the case - but we're now having to think differently in the face of a pandemic, about  how much we can give away, and what kinds of things can be done with that data.(03:31) –  You're really concerned with whom you're giving that data to. And can they be transparent about how they're using that data, and have that data secure, and be able to delete that data when appropriate. And it's very hard to actually believe or have trust in organizations when they say these things. it's a good thing to be doing, but the trust issue is a big one. (06:51) –  Whether Americans have a similar legislation put in place is, in my opinion, irrelevant. Because the internet is cross boundary, cross continental. So, if you deal with anyone outside of your own jurisdiction, your own country, then you will fall into someone else's legislation. And it just so happens that GDPR is one of the most robust that we have at the moment, to do with data.(09:19) – We should be teaching people to reflect on the situation within our educational institutions, so that we are priming people who are going to be making this stuff in the future, to be making design decisions and technical decisions that they can implement it in full respect of other people, and for the respect of the environment. (12:20) – We should all be worried about security, as citizens and our data privacy as citizens, because we don't necessarily want to tell everyone what we're talking about, and that comes into our discrimination issue. So, you can be discriminated against in different countries, for all sorts of different things. And you might not want to tell your neighbor or your government certain things about your person, because those things aren't deemed in that country normal or acceptable or legal. (13:22) – There are many reasons why you would want to keep your privacy and your security intact.You're using a utility, and the utility doesn't respect the user. We're saying water and electricity is a general need, a civil need, I think the internet is certainly up there as a civil need.(17:40) – As you're building technology, you have to require consent under GDPR. You have to stipulate usage under GDPR, and you have to give terms of access under GDPR. So, if you are to be amended or deleted for your delayed data or have your data shared to the user, what specific data they have on them. All that has to be implemented. And if you don't implement that, then you could be taken to court and sued for a lot of money. Now it's illegal to be doing some of that stuff, but within the ethics of AI and the ethics of technology and kind of the ethics of mass automation, we have to really go beyond what is under GDPR, beyond what is legal, illegal and think about again, what is it the world we're we're making?  What is equitable to most people? What is useful to people and what isn't just useful to shareholders. (22:34) – Face tracking stuff is great. It's a microcosm of what is essentially a really big ethical quandary, which has positive and negative effects. So it is really interesting and really frightening in the same way. You have to create trust. And if it is known that these machines are very good and work very well, and the information maybe doesn't really leave the robot in any meaningful way, or is anonymized in all aspects and isn't actually restricting the citizens mobility we've built something that actually really does work and works enough, and knowing when it works enough is an ethical question. And then also, allowing humans to be in the loop somewhere.(26:53) – The obvious contradiction here is that the Chinese system seems to be very heavy handed in its use of technology to implement those social norms. We don't really have a similar approach, I don't think, in the West.(34:02) – You have all these really good applications, all these really interesting applications. And then you have applications which then restrict people's rights or human rights. And again, it might be that we have to look at what human rights actually mean in the digital world.(38:04) – We want to live in a world where George Floyd or anyone who is discriminated against traditionally in a society can walk up to a police officer, can walk up to a person of power in that society and know that they are going to be trustful, trustworthy, wherever your trust in any situation, you don't want to be in a situation where you are in grave danger and you can't trust your own environment.(39:11) –  There has been a wealth of interest in ethics and technology and, in Data Science and Machine Learning, and AI has just been an explosion. I'm seeing that with the emergence of quite a few workshops and talks and conversations around AI, responsibility, transparency, and diversity and equity and all those sorts of terms. Into the future, I am most interested in how the interaction of moral agencies appears in technologies that we actually use and within society's reaction to it. (44:19) – Be mindful. We all have our autonomy and we all should be thinking about the things that we are doing, and you should be empowered to think about what you are doing. It's easy for me to say it on this podcast, but please be mindful of how you affect the world. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy