HumAIn Podcast

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSJerry Ting is the CEO and Co-Founder at Evisort Inc. He is a former Board Member at Harvard Law Entrepreneurship Project and Harvard Association for Law & Business and was an Account Executive at Yelp.Episode Links:  Jerry Ting’s LinkedIn: https://www.linkedin.com/in/jerryting/ Jerry Ting’s Twitter:  @JerryHTingJerry Ting’s Website: https://www.evisort.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(01:36) – Law is a super fascinating industry in the sense that it's one of the last ones to typically adopt technology. Nothing with automation, artificial intelligence, business intelligence. But then you go into the law firm environment or the legal environment, and then we step back 10 to 15 years in technology. Legal tech is one of those morphous terms that emerged recently, but it's a new wave of technology that addresses the question of how to make lawyers more efficient.(04:23) – There's a really big market opportunity to both modernize and also look forward, bringing in automation and artificial intelligence to help an industry that provides a lot of value, but hasn't adopted technology in the way that financial counterparts have.(06:14) – Law firms bill on an hourly basis. If you bring in tools that save 80% of time, that might not necessarily be all good for a law firm for an in-house counsel, for a lawyer at Microsoft, for a lawyer at, and name any big firm, they're driven by traditional business KPIs. Being more efficient, being able to help close deals quicker, removing roadblocks for sales and procurement. These are good things for in-house counsel. So we focus on in-house corporate counsel. (09:21) – It's actually easier to change technology than it is to change people's minds. We think we can provide legal services, whether it's tech-enabled or with alternative billing models. There is a large opportunity for disruption in the law firm space. (10:54) – Microsoft is an investor. And the Evisort part of why that's exciting is that almost 80% of our customers use SharePoint or Microsoft teams to store contracts in one way or another. One of the main use cases is taking data that already exists in the cloud and activating it using machine learning and AI. (11:45) – One is for helping accelerate deals, helping accelerate how quickly a sales team can close contracts. We can provide a layer of automation to review contracts for proof. The other one is vendor management. Being able to see across a billion dollar supply chain, software license agreements to be paid, to be cancelled, to automatically renew, all in a calendar format and visualizing it. And the third one is one that encompasses both of the previous, which is bringing data to lights. (12:21) – A centralized enterprise repository where, regardless of where your contracts are stored, sales contracts could be in Salesforce. Employment contracts could be in Workday. Vendor contracts could be in SAP Ariba, but one centralized place where management can go and find and run a report and gather insights about their contracts across the entire enterprise.(13:18) – Our AI technology does a couple of things. We can take a scan of the contract that we've never seen before, convert it to a Word file and pull out over 50 different data points, including who the contract is with, when does it expire and what are the key legal terms. We can do that all today. From a content analysis perspective based on benchmark data, how to optimize this contract is the next level of intelligence. (15:44) – We understand what the customers need and then, we go to our research team and we already have models that we built that we'll test with. And most of them are deep learning models, a lot of research being done on natural language processing on computer vision. We test it on the existing models that we have. And then, if the accuracy is not where we need it to be, we start to tune that model and then add additional features.(18:57) – We've invested a significant portion of our R&D budget in building out a proprietary dataset that now spans hundreds of thousands of labeled data points. And the modeling then follows that. But without a large enough data, you might be building a model for the wrong subset of data. It might be under a fitted model. We're creating training data that customers may not have ordered yet, but we know that as a phase two and a transformation project they may need.(21:40) – Historically, contract management and AI vendors have focused on the things to do after you sign a contract. We recently announced a full collaboration platform from generating a contract, to negotiating it, to getting it approved, all assisted by AI. That's now available to all of our clients. We are the first company to go end-to-end from the creation of a contract all the way through renewal, all AI assistants all in one platform. (25:55) – There's a big difference between SAS companies and AI companies. Our idea is to combine the two. Combine deep AI analytics that were traditionally meant for large enterprises working with consultants. Democratize the AI that's easily digestible and verticalized for business function and then wrap it in a SAS platform so that anybody can use it. AI companies mature, they're going to build more end-to-end SAS platforms. And, it is going to be hard for the SAS platforms to build the AI capabilities. And that over time to merge into end-to-end SAS and AI platforms. (25:12) – The Bay area is world-class for scaling companies. The leaders and go-to-market and marketing and sales and customer success, product management, the go-to-market team in the environment that we have in the Bay area is hard to compete with, including New York. But New York is actually one of the main bases for customers. I try to get the best of all three regions, deep research out of universities in Boston, meeting with clients in New York, and then also running my office here in California.(28:02) – To be a Forbes’ 30 under 30 has given us some credibility and some recognition for the work that we're doing. We were never doing this as a hobby, we always believed in the vision and our ability to execute and then being named to the Forbes list was a validation for the efforts that we had so far. And then shortly after Microsoft and Vertex and other VCs invested $15 million. The 30 under 30 was a way for us to go out to our colleagues, peers and say, take a chance at Evisort and join us. We're here working on something cool, something meaningful and something impactful.(31:09) – What's happening a lot with verticalized AI applications right now is it's removing some of the tedious parts of a person's job, but it's actually making that person more effective in doing what they were supposed to do in the first place. I don't think AI is going to replace people's jobs. It's actually going to replace the points that people didn't want to do in the first place, so they can spend more of their time doing the strategic work.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSDaniel Pianko is the Co-Founder and Managing Director of Achieve Partners. Pianko also serves as Managing Director of University Ventures. With nearly two decades of experience in the education industry, Pianko has built a reputation as a trusted education adviser and innovator in student finance, medical education, and postsecondary education. A frequent commentator on higher education, Pianko’s insights have been featured in national media outlets including The Wall Street Journal, CNBC, TechCrunch, Inside Higher Ed, and The Chronicle of Higher Education. He began his career in investment banking at Goldman Sachs, and quickly became intrigued by the potential of leveraging private capital to establish the next generation of socially beneficial education companies. After leaving Goldman Sachs, he invested in, founded, advised and managed a number of education-related businesses. He also established a student loan fund, served as chief of staff for the public/private investments in the Philadelphia School District, and worked as a hedge fund analyst. Daniel Pianko graduated magna cum laude from Columbia University, and holds an M.B.A. and M.A. in Education from Stanford University.Episode Links:  Daniel Pianko’s LinkedIn: https://www.linkedin.com/in/daniel-pianko-947223/ Daniel Pianko’s Twitter:  @danielpiankoDaniel Pianko’s Website: https://www.achievepartners.com/  https://www.universityventures.com/  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(01:46) – COVID is going to do a massive experiment in taking millions of learners online in the space of a week. Online education in the US will get to maybe 50% of people getting their content online. It’ll be a second massive evolution revolution in learning at all levels, as those online environments will become even more robust, even more like a replacement for the in-person. In-person education is going to go away.(05:17) – Almost no Ed Tech platform has their own video interface, but Zoom is never going to build out the ecosystem that's required to actually run an online school. Packback uses an AI system to basically put it up. It uses AI to allow professors to grade online discussion, because you're not actually looking to grade very detailed work. (08:25) – You're seeing technology bring massive consolidation. And that is happening in education because an online learning environment has to scale, and scale is a different beast in the online world. We're going to have to move these things online and it's gonna reward scale in a way people are not ready for in the traditional education consumer market.(12:15) – People don’t quite realize how important schools, K-12 schools, physical schools are. They don't have digital connectivity. I would strongly encourage schools to look at organizations like K-12 and other online environments to figure out how to solve these equity issues. Especially, if it means getting technology in the hands of these kids.  It's a failure of leadership that we can't get these devices and the internet connectivity in the hands of our students, and I know it's hard, but that's no excuse.(16:32) – It's important that we differentiate between the aversion of online education that people are experiencing this week versus a real online education, because online education shouldn't have to be synchronous. (18:29) – Predictive analytics is not quite AI. We're able to dramatically open the funnel rethinking the entire classroom experience, technology experience, that led to a predictive analytics revolution in education, in medical school education. And now we admit students or we're starting to admit students based on their success in the MSMS. You can transform equity issues through technology and through predictive analytics and through AI.(23:30) – Adaptive learning has been the buzzword in education broadly, for the better part of 25 years. And even before then, some really great work was done down by very famous education professors who basically said there are different ways people learn. I'm not a technologist, but what is important for tech hardcore, techies, to understand is learning is still one of those fundamentally human endeavors. We have failed. And the reason why is because the technologists and the educators aren't connected enough.(25:45) – We're not where online education or AI driven education is totally worthless and meaningless. We're at this kind of in-between stage where the most successful interventions are going to be those where the technologist and the education folks can come together and say, here are the areas where we can deliver a high quality program that radically improves the product and it's going to be high-performance.(28:09) – Adaptive tests are a perfect example where technology works really well. Psychometricians can basically prove it. That's a better model for testing because it levels out where you're going to end up and allows you to drive a better outcome. While the actual instructional component will stay fairly human centric for the foreseeable future, a lot of these back office, I don't call the admissions office back office, but the non-straight academic functionality will become much more consumer-friendly and tech-driven and where AI can have a massive impact.(33:02) – People learn differently in different components. Sometimes I actually really prefer online learning. I'm actually not a believer that COVID is going to radically change human existence. I don't think technology fundamentally changes that. I do believe that the vast majority of humans want human to human interaction.(38:26) – The skills gap is massive and it is not going away. There are vast areas where the connectivity between education and employment has broken down. And we see a future where a series of intermediaries develop,  intermediaries that solve the education friction and the employment friction.(45:39) – Software is eating the world and it's changing how everybody operates. But at the end of the day, things around education and workforce are very human-driven. And there's a push to automate the job search and education processes.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSDaniel Whitenack is a Ph.D. trained data scientist working with Pachyderm. Daniel develops innovative, distributed data pipelines which include predictive models, data visualizations, statistical analyses, and more. He has spoken at conferences around the world (ODSC, Spark Summit, PyCon, GopherCon, JuliaCon, and more), teaches data science/engineering with Purdue University and Ardan Labs , maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects.Episode Links:  Daniel Whitenack’s LinkedIn: https://www.linkedin.com/in/danielwhitenack/ Daniel Whitenack’s Twitter: @dwhitenaDaniel Whitenack’s Website: https://datadan.io/ 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(02:13) – Being online is pretty normal for myself and my team. I am fairly often on calls with people all across the U.S. but also in Singapore, and India, and Africa and all over mostly via zoom.  (02:55) – Our India teammates went fully remote from their office cause they're all programmers and software engineers and that sort of thing so they're all working from home. (03:56) – What's really boosted NLP in the last couple of years are these large scale language models, so oftentimes what you'll have in an AI model and that's processing text is you'll have a series either one or a series of encoders for text classification. What's really been interesting is these sort of large scale language models that have been trained like GPT-2 and BERT and ELMo, and there's a bunch of other ones. They're trained on a massive set of data, even sometimes for multiple languages, such that you really can apply that model to a wide range of tasks by just fine tuning to one of these tasks like translation or sentiment analysis, or text classification with a much smaller amount of data than was required before. That led to this explosion and application of AI and NLP(06:12) – The size of the models has increased a lot and they're processing a lot of data. These word embeddings or these representations of texts that are learned in the model encode a lot about language in general so it's been shown in a couple of studies that you can backtrack out of these embeddings, the actual traditional syntax structure of texts that linguists are familiar with like grammars and such and so in these embeddings is encoded a lot of information. (08:07) – Transfer learning depends a lot on that sort of parent model that you transfer from and there are sort of very multilingual models out there some including up to a hundred and 104 hundred nine languages maybe. There's actually 7,117 languages currently being spoken in the world. if we think about a multilingual model that has like 104 languages in it and it's Embeddings that it's language model supports, that's a drop in the bucket and some tasks like speech to text, or text to speech especially in NLP platforms only support maybe 10 to 20 languages and so there's a long way to go in terms of NLP for the world's languages. (11:29) – I'm really hoping that what we start to see in 2020 is a an acceleration of this technology through the long tail of languages because with 7,000 languages if we tackle like one language every six months or 12 months or something like that it's going to take us a long time to support things like translation or speech to text in 7,000 languages, so I'm hoping that we see some sort of rapid adaptation technology come about in 2020 that will let us tackle, 40, 50, a hundred languages more at a time.(13:46) – Teams that are starting to leverage that those existing resources, which really haven't been tapped into I don't think because they're archived in weird ways they're not in the sort of formats that like AI people typically are used to working in, so we're just at the tipping point where we can really jump in and utilize a lot of that data in creative ways. (15:17) –  There are certain languages that maybe aren't being used in the same way that they were before. There's other languages that would be used digitally, they're just not supported yet and there's economic concerns and literacy concerns and all of these things all wrapped up and so we have a lot of data around all of those things.(18:09) – For chatbots in general, I would say that there's less support for those than there is for a general technology like Google Translate or machine translation. So it's fewer languages than that, but you can do, again, some creative things to bridge the gap, like doing some of this transfer, learning and other things to build custom components under the hood to support new languages. whoever does crack the nut of rapidly.(22:38) – Imagine going into a new language community with a virtual assistant, imagine if that virtual assistant had the ability to query a natural language, that could enable there's still other pieces of that puzzle, like document search and that sort of thing but this is a big step in the right direction. (26:40) –  There's a lot of disruption and that's definitely true and there's a lot of people experiencing real suffering out there but at the same time there also some new opportunities that are arising. (36:15) – Our show is really focused on as you might have guessed the practicalities of being an AI developer these days and not only for those that are currently AI developers, but those that would like to be AI developers so we dig into a bunch of the different technology(38:03) – Reinforcement learning and generative adversarial networks scans both of those technologies get a lot of hype because of some of the things that they power like deep fakes and other things we haven't really entered into a season where reinforcement learning and GANs are really powering a lot of enterprise applications the way that deep learning models have actually penetrated.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSGideon Mendels is co-founder and the CEO of CometML. Gideon is an experienced data scientist and entrepreneur. He worked on Deep Learning research at Google and Columbia University and previously co-founded Groupwize.Episode Links:  Gideon Mendels’ LinkedIn: https://www.linkedin.com/in/gideon-mendels/ Gideon Mendels’ Twitter: @comet_ai Gideon Mendels’ Website: https://www.comet.ml/site/ 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(02:06) – Some people call Israel the start-up nation. New York's the new Mecca and it's the Mecca of technology.(02:46) –  To build a better model, especially if you're inheriting an existing one you try to figure out what people did already. You don't want to reinvent the wheel. You want to see what works, what doesn't. Where's the exact data set.(04:27) – We eventually collected most of the information, but we started from scratch because we wanted to make sure we're not basing our assumptions in something that might have been inaccurate. (04:54) – We found another approach that was much simpler than what we had in production. When you don't have the right processes and tools, it's really hard to bring ROI on these efforts.(05:29) – We have this amazing stack of tools, anything from testing, monitoring, orchestration, CI/CD, Versioning, you name it. And there's a lot of, sometimes, maybe too many, but then you go to machine learning teams and both of them are still using a combination of scripts, notebooks, and emails. And that's a fallback. There's definitely a better way to do this. It is exciting that developer tools and machine learning are helping these bigger companies to build reliable missionary models.(06:16) – Comet is a meta machine learning platform designed to help these machine learning or AI practitioners and their teams to build machinery models for real world application. The platform allows these teams to automatically track and manage their dataset, their code, experiments, models, as we solve problems around reproducibility, visibility, efficiency, and loss of institutional knowledge.(07:31) – Some engineers think Machine Learning is basically software engineering. But in machine learning, code is just one small piece of the puzzle. You have data, you have experimentation, you have results, you have models and models in production. But at the end of the day, these are different processes. And for that, we need different tools and different methodology. (08:57) –  Our approach has always been to be agnostic to what tool to use. We work with any type of machine in the library, whether it's the common ones, Perch, TensorFlow, scikit-learn, but even if you have something that's completely custom that you built in your garage or in your organization, you can still use Comet.(09:32) – Pick the best tool for the job but still have one platform where you can see everything, you can compare your results, you can share them, you can collaborate. So very similar from that perspective to what GitHub did for code, we're doing for machine learning.(11:36) – Python has definitely been the most dominant language on the machine learning side of things. We still see quite a lot of R users. Mostly those with a more traditional statistics background, but we also see people training models in things like Java.(12:31) – You can see the emergence of low-code or no-code solutions. Those will become more and more popular as we go, as well. (13:46) – Deepfakes, like with every new technology, are an amazing technology that is used and can be used for really great things. There's no question that people can abuse it. There are some similarities to hate speech in the sense that we will need to use machine learning to detect them. But we would need to make sure we set some kind of policy.(16:16) – We have major enterprise customers, multiple Fortune 100's across industry. We have some big tech companies, finance, automotives, media companies, biotech, retail, even manufacturing. We do have dedicated models and the platform to look at, computer vision problems, looking at your model predictions and debugging them same from natural language processing, tabulary data and audio. But we're not limited to a certain use case.(18:37) – We recently announced a partnership with Uber AI Labs which developed a really unique product or library called Ludwig. Ludwig is a no-code machine learning library. You kind of define the specification of the models without coding anything. And then you can train your model based on that. And Comet is the built-in experimentation management tool for that. (21:31) – For ancestry, one of the key things is they have Comet as the central place for their team to track their machinery and experiments and debug them. One of the biggest challenges in machine learning is debugging these models.  It's about figuring out where your model predicts the wrong results.(22:39) – One of the biggest value propositions in Comet is that they can look into the results of the model and track predictions over time and better understand what's going on and how to drive the research process forward. You look at the results. You decide that this model is not doing any good. Just you click a small stop button. It's very simple, but it's very valuable if you're trying to move quickly. (24:14) – Transfer learning falls under the subfield of meta machine learning; using machine learning to improve machine learning.The idea with transfer learning is by using a model or a training and a much bigger data set, you can get much better results than with your smaller one. This has two advantages: the ability to get a better result in your data set, but also saving a lot of costs.(29:11) – The predictor is an early stopping mechanism. We try to predict where your model is going, and then once things look like they're not going anywhere, or the model has converged or that the line essentially flattens in a way, you stop the model. You reiterate and try to figure out the next step. And that's essentially the research process. We can actually automate this process, and you get to move 30% faster.(32:48) – Product side, instead of trying to solve all the problems in this space, to build one end to end solution that does everything. If you have one platform that replaces AWS, New Relic, GitHub, Jenkins, all the tools in the world, one product with one login, that's something that's very hard to do.(35:07) – Machine learning is essentially becoming another tool for engineering. Things will definitely converge. And if you look into undergraduate programs, for example, machine learning and AI have become part of the core curriculum.(38:09) – If you're trying to classify some examples you can go back and do the data labeling process and get more data from this class and drive the research process for it. In production time, you won't be surprised anymore because you already looked into all these edge cases and solved them in training. These are the two main approaches people in the industry are taking.(43:16) – It's very important not to get married with a single library, use the best research that's out there. (44:11) – Overlap and collaboration between academia and industry. That convergence of being able to support both ways is very exciting. More companies are being able to get real business value from machine learning and AI.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSMike Robins is an author, speaker, coach, and podcaster who delivers keynotes and seminars (in-person and virtually) to groups of all kinds throughout the world. He’s written five books, which have been translated into 15 different languages. His latest, We're All in This Together, has just been released. Before starting his business in 2001, Mike played baseball at Stanford University and then with the Kansas City Royals organization. After baseball and prior to starting his consulting business, he worked for two tech companies in online ad sales during the "dot-com boom" of the late 1990s.Episode Links:  Mike Robins’ LinkedIn: https://www.linkedin.com/in/mrobbins/ Mike Robins’ Twitter:   mikedrobbinsMike Robins’ Website: https://mike-robbins.com/ 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(01:50) – It's definitely called upon a lot of us to dig deep and think, what can I contribute to the world? There's a desire for us to connect, even though we're all separated by time and space more than ever these days.(03:05) – In some ways the rules have changed very quickly through this pandemic, we're all getting a chance to connect with and see each other's humanity as people are trying to do work from the dining room table and their kids are off to the side.(06:52) – There's a lot of companies, especially tech companies who've been needing to embrace video and other platforms in order to communicate. There are a number of companies that we work with for which that is the primary way they communicate anyway, prior to COVID. That's going to allow us to work in a more effective and productive way, but also humanize the technology if you will, because that's been one of the challenges over the years and continues to be. (09:04) –  Everybody is panicked in the sports industry because they don't know what's going to happen. They're just hoping and waiting for things to get back to normal, but it's impacting a lot of people's lives and a lot of people's jobs in the short term.(11:47) – The technological capabilities of schools are often dependent upon the socioeconomics of that school or that community. Not everybody has access to the same technology and so just as that exists in school in person, it also exists online. The education experience, just like the meeting experience or the conversation experience, is different when it's done virtually than when it's done in person.(13:57) – We want an education just like in business, that can be innovative and creative, adaptive and adjusted to the moment.(16:31) – if you had the best players you would have the best team. If you have the most talented players, you should have the best team. But that was not the case at all and I learned it many times in sports. (18:00) – It was the intangible qualities that allowed us somehow, some way to put our little egos aside and be interested in each other's success, wanting to win as a team more than simply just succeed as an individual to cultivate an environment where people can work together in a way that actually brings out the best in everybody.(21:30) – To be good at anything and ultimately sustain that success and grow, you got to master yourself. We're talking a lot more about mindfulness, both in our schools and in businesses, because we're all dealing with similar macro experiences, even though we have our own little world. Much of our success or failure has to do with mindset and approach. We need some talent, but the same is true in business.(28:44) – We actually have a lot more common ground with each other than we think we do. We're separated in a way we've never been forced to be physically separated before, and we're simultaneously connected to each other in this global experience all at the same time.(32:30) – Maslow's Hierarchy, the third place on the pyramid once we get past the physiological and safety needs for human beings is a need to belong. The ultimate  goal is to get to a place where you create an environment where everyone irrespective of their background, their race, their seniority, their age, their gender, their all of that everyone feels like they belong and that's not easy. (35:40) – Authenticity and vulnerability is the iceberg. And it's about lowering the waterline on the iceberg so that we share a little more authentically and vulnerably how we're feeling and we really ask other people how are you and not just the corny hey, how are you? How's it going? What's up? Reach out a little bit to offer some support.(39:26) – Have some compassion for ourselves in the midst of all this, and also have some grace and compassion for other people. There are a bunch of companies and apps and pieces of technology that maybe we didn't realize were super important that are now becoming very important in a different way because of what's going on. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSRishal Hurbans is the Business Solutions Manager at Entelect where he is responsible for business development, strategic planning, ideating, and designing and developing solutions for local and international clients; whilst actively nurturing knowledge, skills, and culture within the company, community, and industry. He has a passion for business mechanics and strategy, growing people and teams, design thinking, artificial intelligence, and philosophy.Rishal is the author of Grokking Artificial Intelligence Algorithms with Manning Publications, aimed at demystifying AI algorithms for technologists by teaching the approaches through practical problem solving and visual explanations: Episode Links:  Rishal Hurbans’ LinkedIn: https://www.linkedin.com/in/rishalhurbans/ Rishal Hurbans’ Twitter:  @RishalHurbansRishal Hurbans’ Website: https://rhurbans.com/  http://bit.ly/gaia-book 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) – “Grokking Artificial Intelligence Algorithms'' consists of 10 chapters that explore different AI approaches. It's part of Manning Publications, MEAP, which means Manning Early Access Program. And the benefit of that is we get feedback from readers as we release chapters, which allows us to refine and create a better book at the end of the day. Once all the chapters have been released and we get some feedback, the book would be then printed and finalized. (03:22) – The term Grok or Grokking is to gain a deep understanding about something, but through intuition and through some sort of feeling about it, demystifying these algorithms that are sometimes underappreciated. Including the modern hyped concepts like machine learning and neural networks, to actually help the reader understand why it works and how it's useful to the day to day.(05:16) – A lot of Funding has gone into creating this kind of skills and capabilities in different organizations. There's a lot of solutions and proof of concepts that have been bolts that work in theory or work in a ring-fence environment, but perform poorly in production or don't provide the value that was originally envisioned. A lot of effort is going into understanding now, what are the critical aspects to what we're doing with this technology. How do we understand it better? And how do we target it or direct it in a better way as opposed to running a bunch of experiments and see what works.(07:03) –  It's not a lack of engineering or a lack of know-how in actual execution. In any technology that we've built, especially software, at the end of the day, it comes down to solving a real world problem, whether that's a business problem or, whatever the case might be. Usually it comes down to a business problem that you're solving. (07:49) – It's not actually addressing the problems in a meaningful way because we just tried everything. Also, partly it's because people have been trying the hype buzzwords, because they're a good idea. And you feel like if you're not doing it, you're doing something wrong. From a global decision-making perspective, the stakeholders involved there, the different people involved, they need to have a better understanding of what problems the technology is solving, as opposed to just simply using it, to implementing it for the sake of it.(09:40) – The focus on the different algorithms is driven by a theme or concept I mentioned just a bit earlier. So instead of trying any new technique that you come across, I wanted to highlight the advantages of some of the underappreciated algorithms. The goal was basically to expand a technologist or a developer's mind in terms of what the possibilities are when being faced with a problem. There's no silver bullet and here are the advantages and disadvantages of the different approaches. (12:35) – Specifically with search, it's mainly exhaustive, you had to try every possibility to find a good solution, whereas, more modern approaches try to estimate a good solution. A person would have to know what questions to ask. What modern approaches and machine learning and deep learning try to do is learn from examples and learn from previously made decisions to figure out the questions.(14:07) – Modern algorithms are geared towards different problems that we're trying to solve now, but computing has definitely made it possible for things like artificial neural networks to become more prominent.(16:11) – Large amount of data that's been collected through connecting the world, the actual value that's hidden within that data and the kind of advancements in computing have allowed us to leverage these algorithms. And as I said, old algorithms that can now do some really powerful and useful things. (17:26) – The implant search is also sometimes referred to as adversarial search. It's essentially used for two player games like chess, and the whole concept is centered around an agent predicting the future. So if I'm an agent. And I see a certain state of a chess board. I would make a move and then simulate every move that my opponent could make and score that. Games like Dota and StarCraft, they're using something completely different. So they're leveraging reinforcement learning and deep learning. (19:03) – You're not working on a two dimensional space where you're moving pieces a few blocks at a time you're working in a very fluid environment. It's almost simulating reality. Detailing every single piece of information and representing that as a state and then trying to predict every possible future for that state becomes very difficult to do with traditional adversarial search approaches.(19:52) – They try to let an agent learn from experiencing the game. What a deep mind, open AI and similar organizations have done is basically allowed an agent to play itself many times and figure out what short-term actions and mid-term actions may result in long-term rewards. I'd like people to be more pragmatic because the more pragmatic you are, the more effective you are at solving what's important.(22:49) – Technology, including data science, including software engineering or mobile development or whatever facet of technology we're working in, I see it as a tool or a vehicle to deliver value or solve a problem. There's a difference between a successful project and a successful solution. There's this deep focus on what tools and libraries and technologies and programming languages, and what are you using as opposed to, why are you using it? What are you trying to achieve with it? And that's not just a problem in data science. It's a general theme, but we're getting better as we go.(25:57) – A big misunderstanding is the glamour in bolding, something with machine learning or AI algorithms about 60, 70% fried, depending on the surveys, you look at 60 to 70% of  data scientists work is usually understanding, cleaning, preparing, enriching, augmenting that data before it becomes useful. And even after you do all that work, you don't actually know if that data is going to solve your problem or not.(26:58) – Every solution should contain some sort of data science or AI element to it. And that's not really the case. So unless there is a clear use case, not that fits the use of some sort of either classification or reinforcement learning or optimization algorithms. Unless there's a real use case for that, it shouldn't just be taken into consideration. You should think critically about how you can build a minimum solution that solves the problem in the best way. (29:19) – I would have spent a technical perspective and a growth perspective specifically in the area of AI and machine learning, I would have made a bigger effort to figure out why math is useful in these concepts. Do not give up on that and perhaps try and seek material or people or mentors or someone that can explain to you in a more human way, how these mathematical principles work, but more importantly, why they're important.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSEric Adams is a former State Senator, and current Brooklyn Borough President running to be the next Mayor of NYC. He was born in the Brownsville neighborhood of Brooklyn, went on to earn an Associate in Arts degree in data processing from the New York City College of Technology, a Bachelor of Arts degree in criminal justice from John Jay College of Criminal Justice, and a Master of Public Administration degree from Marist College. Eric graduated from the New York City Police Academy in 1984 as one of the highest-ranked students in his class. After initially serving with the New York City Transit Police Department, he was transferred to the New York City Police Department (NYPD) with the merging of the city’s police forces.Episode Links:  Eric Adams’ LinkedIn: https://www.linkedin.com/company/eric-adams-for-mayor/ Eric Adams’ Twitter:   @ericadamsfornycEric Adams’ Website: https://ericadams2021.com/ 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(01:29) – We were able historically to get away with the dysfunctionalities of cities. In the next 20 years, as we evolve into computer learning and artificial intelligence, we have to change how we run cities so we can keep pace with that.(02:16) – The real fact that we're not addressing COVID-19 in real time with real data and real on the ground response is really exposing. Our cities across America and in general, specifically, here in New York, are not prepared to see how you run cities in the 21st Century.(03:40) – We have a disproportionate negative impact on certain communities. When you look at the term of essential employees, over 70% of these central employees are black and brown people. When we see the decrease or the increase we are talking about specific populations, over 60% of the people who died from Coronavirus are black and brown.(06:37) –  Free food for all New Yorkers is open to people who are in need of a meal who can't travel far to their community.(07:27) – We have a large number of people in this city who are seniors. It is our responsibility to teach our seniors how to be introduced into the technology.(09:11) – Our influence really impacts the entire globe. And here in the city, we're in a fishbowl in that we all live together. Our technology, the technologies that we use must be part of preparing our future employee pool and how we run this city in an effective way.(11:22) – The population that was less likely to use technology, our senior population, are compelled to embrace the technology that's available. (13:30) – Government officials need to make sure students have the devices and the technology that they can remain engaged.(16:21) – The more we build out using the free wifi, and it should be a right in all communities, the more we learn where our gaps are. And it's important to do a GIS mapping of the entire cities.(18:25) – It's not a one day strike. It is imperative that as we go through this crisis, we're thinking about rebuilding in the meantime. How do we look at this new norm that we are going to embrace?(21:11) – The New York City Employee Retention Grant program is a great program because many jobs are being impacted, they want to lose employees. And if you hold onto your employees through this program for a particular period of time, you are able to take the benefits of this program. (22:39) –  We should do a 90 day moratorium on rent as well, as long as it's matched together with the moratorium on mortgage payments. (26:53) – What we must do is continue to get the information out into the crevices of all of our communities. (29:26) – We need to try to provide personal protection equipment to all essential employees. We need to make sure that any employee that's considered an essential employee, that they have some form of healthcare package(31:55) – You don't have to break your traditional bonds of coming together as a family, we just have to be more creative in doing so. We are a resilient community, city and country, we've had hard times before and all we have to do is come together. Show a level of compassion, commitment, and dedication, not only to each other, but to ourselves. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSGabriele Columbro is the Founder and current Executive Director, FINOS at Linux Foundation and Member at Forbes Finance Council. Columbro is an open source leader and technologist at heart, having spent more than 10 years building thriving communities and delivering business value through open source. He thrives in working with open source communities to drive disruptive innovation, whether it’s for an early stage tech startup, a Fortune 500 firm or a non profit organization. Gabriele brings expertise in executive and technical leadership, ranging from FinTech to enterprise collaboration, from developer platforms to SaaS ARR business models. Previously Director of Product Management at Alfresco, as Executive Director, Gabriele built the Symphony Software Foundation from the ground up, with the vision of creating a trusted arena for Wall Street to accelerate the digital transformation, engaging in a new model of open source FinTech innovation, backed by the largest global investments banks like Goldman Sachs, JPMorgan Chase, Morgan Stanley, Citibank, Deutsche Banks, Nomura, Wells Fargo, UBS, Credit Suisse. Gabriele is also a PMC Member for the Apache Software Foundation and an advisor for Bankex.com.Episode Links:  Gabriele Columbro’s LinkedIn: https://www.linkedin.com/in/columbro/ Gabriele Columbro’s Twitter: @mindthegabzGabriele Columbro’s Website: http://mindthegab.com/ http://mindthegab.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(02:01) – There are some major shifts happening in the industry and all the arrows pointing to open source as a brand new way forward for this industry. There are systemic reasons why we're seeing the rise of open source, same financial services margins. Revenues of nowhere nearly where they were 10 years ago in this industry, the cost of regulation keeps rising. (04:20) – So there is not an infinite amount of money to be thrown at every single technology problem in the industry. And open source certainly has had a history of reducing technology costs when using TCO. That's one of the main driving reasons for financial institutions looking at open source collaboration. Open source provides a much larger, much broader talent pool, and allows every individual to continue fostering its own portfolio. Open source doesn't equal free, there's a lot to be saved, but also a lot of money to be made on open source.(09:07) – This generation has grown up with social tools and a really different way of even interacting with each other. The new generation of developers that we see coming up comes with being born and bred in GitHub.(11:19) – Open source is not charity. There's an element of conscience, of openness. Everyone, and most corporations participate in open source right now. And even our foundation, they do it with a business goal. So it's not per se charity. it's not just talent acquisition, it’s certainly a lot of talent retention as well. (13:04) –The rise of open source out there and the rise of non-profit open source foundations is because open source is not easy. Especially if you're a large corporate who's seeking to collaborate either with its competitors or with its customers and ecosystem at large through open source. (13:51) – Code is certainly important. And the quality of the open source code is higher. Everyone feels a bit more accountable for what they put out there than necessarily what you do behind the firewall. But that's just the tip of the iceberg. (10:05) –There's an element of internal and external policies. Regulated industries are very understandably risk averse, and very much careful about what degree of collaboration they have with their competitors. That's why foundations like ours provide a very structured governance framework, conflict of interest policies, antitrust policies, making sure that it's clear that through transparency, you can achieve a very productive level of collaboration without any compliance concerns.(145:39) – Policies are one element. You mentioned standards. The world of open standards and the world of open source had historically been very different, but they are more and more colliding because they reinforce each other. When you add the open source reference implementation to an existing standard, that drastically speeds up the rate of adoption, and certainly the rate of compatibility, cross compatibility through the standard.(16:25) – The generational cultural aspects include a lot to learn before you can be effective and productive in an open source community, the same way you do it in an internal project. You need to relinquish control in favor of influence. And that's a big step for hierarchical organizations, large corporate hierarchy organizations. But there's also an element of code of conduct and behavior. Open source communities that are driven by meritocracy, or even by the more contribution, the more sweat equity you put into, the more influence you have.(19:03) – Government is one of the models that we're using for modeling the collaboration in our community. Governance and code governance and corporate governance. All of our governance is public and transparent, which leads us to traceability. Every decision is traceable and is auditable(24:05) – There is an intention and a goal for the industry to better model the data in a collaborative way to be clear, not only to collaborate on the code itself, on the visual modeler and on the language, but on the models themselves. Create this common modeling tool and common set of data models in the hope that with common data models, we can start building on top of it common tools and common ideally AI and ML and intelligence around it. (30:56) – It is understandable how institutions who have not only such a regulatory nature, but very sensitive information about their customers would think twice before sharing information with some of their competitors, whether that's because it's a unique differentiator or even just because of fear of breaking some regulation. There’s lack of data standardization, and we’re identifying more technical solutions to enable shedding in a safe way. Big tech through open sourcing is enabling some of these better collaborations happening also in financial services. (34:46) – Open source can be a means for financial institutions to really have an alternative to the usual maker by decision. Its transparent nature, which is a talent pool expanding nature, goes back to traceability. That's really a good driver for financial institutions and fintechs to collaborate in the open. Rather than having to train and specialize people in every single system that you're going to have to go and regulate, you can build a broader talent pool if the implementation and the process is dealt with in the open.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSLorna Davis has served as President of multinational consumer goods companies for 20+ years, in Danone, Kraft and Mondelez. Lorna has been a key leader in Danone’s purpose journey and is a Global Ambassador for the B Corp movement. In 2017, she served as CEO and Chairwoman of Danone Wave (now Danone North America), where she established that $6 Billion entity as a Public Benefit Corporation and achieved B Corp status in 2018, making it the largest B Corp in the world. Lorna is a member of the Social Mission Board of Seventh Generation, the Integrity Board of Sir Kensington (both Seventh Generation and Sir Kensington are owned by Unilever)the Advisory Board of Radical Impact and the Board of the Stone Barns Center for Food and Agriculture.She has lived and led businesses in 7 countries including the UK, France and the USA and served on the Global board of Electrolux for 6 years.Lorna was also based in Shanghai, China for 6 years where she was the CEO of the merged Danone and Kraft business.Episode Links:  Lorna Davies' LinkedIn: https://www.linkedin.com/in/lorna-davis-3366ab14/ Lorna Davies' Twitter: @lorna_davis10Lorna Davies' Website: https://www.lornadavis.net/ 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) – When people ask me how I'm doing i noticed that the answer is only relevant for this moment. I'm variable like everybody else and I'm trying to just take it one moment at a time really. (04:17) – What we will also come out of this with is a really good understanding of ourselves, which will be very important for the next phase of the world. We'll be more self aware and hopefully more compassionate and more loving leaders in the future.(06:31) – Work out how to calibrate, how to be supportive, but now how not to be helpful. Because being helpful is a pain in the neck, nobody wants to be helped. But how can we really provide support for each other at a time when people are still trying to work out what support they want. (09:24) – Very interesting to see how businesses are pivoting. I'm loving the innovation that's coming out of this and I'm also loving the new relationships, new collaboration, new interdependence that's coming from this.(13:26) –We're going to see things that we have never seen before. And we're also going to see a complete reshaping of traditional blocks of time. This sort of neat disruption of the day is challenging for some people. These fixed boundaries between these periods of our lives have dissolved perhaps forever. We'll be easily able to segue away from laying on the couch, reading a book to getting up, to do a yoga class, to doing an hour of work, to going to learn the tuba. There'll be fun. (17:27) – The inclusion of people who are shyer than others. With that hand raising function, people who would otherwise struggle to fight their way into a conversation can put their hand up. These are all things that will enhance intimacy and connection that I hope we hold on to when we go back to more in person meetings.(21:50) –  It was unthinkable before that you and I might build this kind of relationship and never meet. And people have old fashioned ideas about how people need to be face-to-face to really build a relationship. I don't think that that's true.(30:25) – The big question is if you really want to solve the climate challenge, countries need to work together and they need to have a line of legislation on carbon reduction. They obviously need to sign up to agreements and they need to have a shared view that the world has a problem that the world has to solve together. (33:22) – There will be stories that as human activity slows down, natural activity will rectify itself or come back to life. And hopefully we will fall back in love with the world, fall back in love with nature, fall back in love with the universe really. And that'll give us a new sensibility. It is a better, more grounded place to act from when you are in love with other humans and in love with nature than when you're frightened, angry, defensive, and think that your money is going to save you, which is kind of what, has been predominant in parts of the world recently.(35:58) – The water will deliver us into common ground, or intercommon waters, and then we'll be able to find our ground. Everybody knows that it's chaotic. Nobody can pretend that it isn't. So it is, so let that be.(41:50) – Women's ability to deal with ambiguity and complexity and interconnectedness is better than men's. This time of ambiguity, complexity, multitasking is the time for them to really step up. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

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