HumAIn Podcast

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSBrad Schneider is the Founder and CEO of NoMad Data. He was previously the CEO of Adaptive Management. Throughout his career, Brad has focused on using alternative data to improve decision making and prediction. Brad has been a Portfolio Manager at Tiger Management, and Managing Director at Jericho Capital, a $2bn AUM TMT-focused hedge fund. Prior to Jericho, Brad also worked at Palo Alto Investors as an equity analyst and was a co-founder and head of product development for InfoLenz, a predictive analytics company. Brad holds a Bachelor of Science degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and is a CFA charterholder.  Please support this podcast by checking out our sponsors:-Work Patterns: https://www.workpatterns.com-Imagine Golf: https://www.imaginegolf.com-Art of Manliness: https://www.artofmanliness.com/podcast/-Keep Optimising: https://keepoptimising.comEpisode Links:  Brad Schneider’s LinkedIn: https://www.linkedin.com/in/bradschneider/ Brad Schneider’s Twitter: https://twitter.com/bschneider222?s=20 Brad Schneider’s Website: https://www.nomad-data.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(01:16) –A tech guy who started in the analytics space and moved to the world of investment, which led him back to the field of data(02:33) – Building software over the years helped him, as the user of data, to more easily interact with that data and find ways to connect the use case to the dataset. (03:57) – NoMad Data's goal is at a high level to be the search engine for these datasets, making it a lot easier for people in the AI space, for researchers, for computer science, for marketers, for strategy professionals, consultants, investors, help them connect those everyday business problems that they have to real datasets.(05:33) – Data that is more frequently purchased include credit transaction data and customs data, which allows to see trade flows (06:48) – Data sets are so powerful, but they're also so broad.Customs data set help to understand a single company on the aspect of one company or region and economic competitive wins and losses for factories. And because they're so broad it's very hard to describe on a webpage what this dataset can be used for.(08:07) – The build vs. buy dilemma: it really depends on your timeline and the availability of the data you need. Even if the data we collected was a hundred percent accurate, it would become very challenging, because we wouldn’t have enough data points to even make a simple linear regression model. So, in a lot of cases, it's better to buy. (10:25) – Getting that data from where it started, whoever is creating it or whoever you're purchasing it from, and getting it somewhere that you can write that first query has historically been a bottleneck. Some services like Snowflake are creating these marketplaces where people are putting the data in a common database format.(12:05) – It's hard to fully automate the data search process today, and the main reason being the data you need, the metadata about the data, doesn't really exist, and the term metadata is used very broadly. Cutting edge NLP and machine learning is used to find similar concepts.(13:47) – The biggest change that the pandemic caused was really the need for data. Buyers are looking at more and more datasets to fill in the holes in their understanding. And because of the increasing number of those holes in their knowledge, there's been an increasing need for data.(15:49) – Searching the area that we're focused on is one of the biggest problems holding back the market. People know they want to see something, they want to be able to calculate some statistics, but they don't really know the data that would provide the requirement to do that.(16:33) – Companies need to be really pinpointed on what they focus on, and because people have a really difficult time finding the right data, finding the best data to address their use case, services like Nomad help unlock this industry, which ultimately means you bring more and more buyers into the market. (19:08) – Many of the companies today haven't given much thought to data as they have for software. The data revolution has already started. And the first step in that was companies looking at their internal data. The next frontier is external data or alternative data. It's these data sets that are coming from outside your four walls, and in a lot of different businesses, it gives you a perspective that you don't have. It gives you a perspective that isn't biased by your own internal processes(21:00) – If you're a company where your brand is extremely important, you’d be more reticent to sell data because there's potential brand risk associated with doing that. We support anonymity on both sides of the market. In Nomad, they can post their data. It's completely anonymous.(22:40) – Nomad has raised $1.6 million and that was led by Bloomberg beta and some other higher profile VCs as well. Some great angels in the data space.(23:51) – As we get out three to five years, awareness of this space and interest in this space is going to explode in orders of magnitude growth on both the number of people selling data and the number of people buying data.(24:40) – If you're a startup, NYC is a wonderful environment to be in. It's also helping a lot, that housing is coming down.It’s attracting more and more people. People that don't want to commute here don't have to anymore. It's going to be a Renaissance for the city.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Ashutosh Garg: How To Leverage AI To Recognize And Improve Diversity In Hiring [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSAshutosh Garg works with startups across the enterprise stack. He is particularly excited about how machine learning and deep learning are reinventing existing software categories and creating new consumer experiences. Ashutosh has invested in AI-enabled business applications (such as marketing technology and HR technology), data platforms, data center infrastructure, security & privacy, as well as online video. Before joining Foundation Capital in 2008, Ashutosh was the general manager for Microsoft’s online-advertising business and led field marketing for the software businesses. Previously, Ashutosh worked at McKinsey & Company, helping technology companies scale their go-to-market efforts. Earlier in his career, Ashutosh founded TringTring.com, one of the first search engines in Asia, set up Unilever’s Nepal operations, and led the marketing and pre-sales teams at Cadence Design Systems.Ashutosh has a bachelor’s degree from the Indian Institute of Technology (IIT) in New Delhi and an MBA from the Indian Institute of Management at Bangalore, where he received the President’s Gold Medal.Episode Links:  Ashutosh Garg’s LinkedIn: https://www.linkedin.com/in/ashugargvc/ Ashutosh Garg’s Twitter: https://twitter.com/ashugarg?s=20 Ashutosh Garg’s Website: https://foundationcapital.com/member/ashu-garg/ 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(01:31) –Eightfold.ai was created in 2016 as a talent intelligence platform that is being used by the leading enterprises across the globe to hire, engage, and retain a diverse workforce.(04:21) – Large enterprises’ number one challenge is people. They are not able to hire fast enough. Enterprises should think about diversity, about their own biases, to understand what talent exists. We added exits to bring the right people on board and that is where data and AI comes into play.(05:43) – We can't keep looking for people who have done the work. We have to look at the people who can do the work, and that is a fundamental shift in the mindset.(09:00) – We need to reach out to the people who may not have had all the privileges that we have and support them. We have to look at people beyond what we perceive for their face color, age.(10:14) – Machines have the ability to forget and ignore. We have our biases because of the lack of knowledge. Knowledge and moving out of biases can really help us solve this problem when hiring candidates.(11:59) – There has to be an audit process to ensure that your algorithms are not going crazy and that they are doing the right thing. Let's use them to help humans do a better job. (13:53) – It's all about humans. These systems are designed to come in and replace humans. In that case, not only are you taking the snitch system correctly, you're teasing that: I really don't need to worry about humans, and that has to be front and center.(16:00) – One of the things Eightfold believes is that it's not that people are good or bad, or one is better or worse, but who is the best fit for which flow in that company.(18:24) – You have to really assess the people at their full potential.(22:32) – What Eightfold.ai is trying to do through machines is help hiring managers understand that candidates past, be able to dig deeper with you, look at the peer group of the community to see what their peer group is doing today.(25:27) – Some of the success stories of the companies that we know today in the world come from combining experience with young talent. (27:26) – The talent market rate landscape is completely going to go through a massive shift in next 18 months. This is also a good time to hire great talent, because many people are looking up.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

#148- Robert Stevenson: Why The Future Hospitality Guest Experience is Mobile [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSRobert Stevenson is the Chief Executive Officer at INTELITY. He is a business and technology executive with 20 years’ of rich experience across a wide array of disciplines. Robert specializes in the productization, strategy and market delivery of new technologies. In addition to undergraduate studies in Design and Computer Science, Robert holds an MBA from the Schulich School of Business at York University and the Kellogg School of Management at Northwestern University, including work at the Hong Kong University of Science & Technology.Episode Links: Robert Stevenson’s LinkedIn: linkedin.com/in/robertstevensonRobert Stevenson’s Twitter: https://twitter.com/intelity?lang=en Robert Stevenson’s Website: https://intelity.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.(01:45) – Hospitality Tech has been reluctant to embrace the latest and greatest technologies.(03:28) – INTELITY is a mobile platform being built to modernize the guest experience.(05:36) – INTELITY customer segment and customer ecosystem and market is that 80% who are not major hotel brands.(08:09) – INTELITY has been conceived as a B2B2C.(12:41) – How the pandemic stroke Hospitality industry but leveraged a long-expected change.(13:53) – Mobile experience and automation to improve the market.(14:53) – Using AI and data to drive revenue.(18:31) – Using AI and data to predict customers behavior and offer a better service.(19:59) –Automate the experience to elevate the guest and improve the travel P&L for the hospitality space.(21:17) – The voice space in hospitality has been slow to customize and adapt these tools.(23:54) – Mobile technology has led the way, but major changes will emerge in mobile computing devices.(27:56) – The power of the devices will continue to get stronger, better and more demanded.(28:38) – The trend will be to see new hotel apps rolling out to promote contactless experiences because of COVID.(29:55) – The hospitality industry needs AI and Machine Learning to adapt to customer needs.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

How Platforms Leverage The Extended AI Community To Address Misinformation with Claire LeibowiczClaire Leibowicz currently leads the AI and Media Integrity program at the Partnership on AI. She holds a BA in Psychology and Computer Science from Harvard College, and a master’s degree in the Social Science of the Internet from Balliol College, University of Oxford, where she studied as a Clarendon Scholar.Not only tech companies should be involved in creating good, responsible, ethical AI, but also civil society organizations, academic venues, other parts of industry and especially media.AI and media integrity proposes a very simple way to have good, healthy, beneficial information online by using AI systems to do that. Not everyone agrees what type of content should be allowed online. Even humans don't agree about what misinformation is or what content should be shown to people through technology. Some tech companies feel empowered to take comments off platforms. So, not only just to declare a label or more context around people, but really to take a public figure off a platform, which is really an emboldening of platform agency in contributing to who is allowed to speak and who's not.In terms of tactics for misinformation, how people create misinformation, how they spread content, is generally applicable to social media. There's misinformation flowing in WhatsApp groups, in texts, in all these different venues. There is a real movement towards this kind of misinformation that's not just total misrepresentation of an event or a fact, but a slant or a leaning, or a caption that may make a post have a different connotation than it would if it was written by someone else.AI and Media integrity seeks to reach a public that can distinguish credible information from misleading information. Labeling is an interesting, almost in-between option, because it's not limiting speech or saying you can't share this post or saying someone's information shouldn't be seen. It's giving you more context. The idea is to find a middle ground for platforms to seem like they're giving the user control and autonomy, and being able to judge for themselves what's credible. Some people are really skeptical about platforms. Labels might encourage major division in user attitudes between those who think they're important for people to be healthy consumers of content and those who find them biased and partisan and error prone. Automating that label deployment is really complicated. And we don't really know what the best intervention is right now to help bolster credible content consumption. With the de-platforming of Donald Trump, we're living in a new society where we are giving the rights of freedoms to platforms to say, we can get content so that we're providing the best interest for our users without acknowledging whether the users really want that.The platforms have been emboldened, and that has a connotation that we're going to become the arbiters of truth. Those who value free speech and principles might frown upon, since the internet was founded as a venue for democratizing speech and allowing people to speak. There are other solutions that the platforms can take to change how content gets shown beyond just labeling. Platform labels alone are insufficient to address the question of what people trust and why there is this general distrust, in the principle of platforms to self-regulate and for fact-checkers and media companies to offer non-politicized ratings. We need to better design interventions that don't repress people, but really respect the intelligence and autonomy that has raised awareness of looking into a source and media literacy. So holistic, digital literacy, educational interventions to focus community-centric moderation,. And that people in the community rather than the platform itself, are the ones doing the moderation, which might increase trust in how the speech is being labeled and ultimately decided upon.A lot of the policies that platforms have about speech on the platforms have to do with the way in which they cause real world harm. You may have a policy that says we don't label speech, we don't do anything until there's a perception that post might prompt real-world harm. Manipulated media is basically any visual artifact that has been altered in some way by any means, and whereas there's no harm to the public square, there might be harm to other types of political speech or those that are misleading. So when we talk about manipulated media, it's really important to underscore what makes that misleading or problematic. So a lot of people have advocated for AI-based solutions to deal with manipulated media. It's not just how an artifact has been manipulated that matters. It's partially the intent, why it's been manipulated and what it conveys that really matters. Just because something has been manipulated doesn't mean it's inherently misleading or automatically misinformation.But rather, what's the effect of that manipulation. And that's a really hard task for machines to gauge, let alone people. Shownotes Linkshttps://www.linkedin.com/in/claire-leibowicz-17156a65/  https://twitter.com/CLeibowicz   https://www.partnershiponai.org/manipulated-media-detection-requires-more-than-tools-community-insights-on-whats-needed/    https://medium.com/partnership-on-ai/a-field-guide-to-making-ai-art-responsibly-f7f4a5066ee  https://arxiv.org/abs/2011.12758  https://medium.com/swlh/it-matters-how-platforms-label-manipulated-media-here-are-12-principles-designers-should-follow-438b76546078  About HumAIn PodcastThe 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

Episode Show Notes: - Eric Daimler is the CEO & Co-Founder of Conexus.com. Daimler is an authority in Artificial Intelligence with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. Daimler has co-founded six technology companies that have done pioneering work in fields ranging from software systems to statistical arbitrage.- Daimler believes the Obama administration made big efforts to bring in more technologists into government for innovation and digital modernization, and is optimistic that sensibility around a digitally native environment will be expressed inside of the Federal Government, and continue to trickle down into states' governments for the benefit of all. - Human failure has come before machines got trained on human failures. Therefore, technologists can't use massive amounts of data on every human problem and expect to come out with mind blowing results. So there's limitations on technology. What can be done is to transform these whole domains of knowledge and map them onto others through a new type of math.-There's a discovery in this domain called category theory. Categorical mathematics, category theory, is really at a level above all those other mathematics that transforms a problem from geometry, into another problem called safe set theory, applying it to databases. The math of category theory changes how we relate to data. This is “the math of the future”.-It's at a higher level of math, a level of abstraction to model the world in which companies operate their business, and make bigger decisions better and faster, reasoning large amounts of data at a higher level to power a whole new change in our environment, as business people, as academics, as citizens. -Daimler suggests three ways to solve data issues: matching data in a unified database, create a silo and then they sell a subscription to data silos and data interoperability math analysis through category theory.-AI definition has been misinterpreted over the years as algorithms that collect data and have machines do stuff, when in reality, AI should be understood as a system that senses plans, acts and learns from the experience. And it senses plans and acts from inputs that are given to it. -Not everyone needs to be a programmer in a basement. People need to be playing a multitude of roles. There's not just a choice between computer science or an English degree. What the current world of tech needs is policy considerations, places to get involved, and a way to focus educational efforts. Automation doesn't mean no human intervention. Societies benefit by that exchange of ideas and communication of values.Shownotes Links: https://www.linkedin.com/in/ericdaimler  https://youtu.be/YP9kodLGvT8  https://youtu.be/jqn4wnSBKuE  https://youtu.be/c92rK_UZaXU  About HumAIn PodcastThe 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

How We Can Design Autonomous Systems for Values with Steven UmbrelloShow notes:Steven Umbrello is the Managing Director at the Institute for Ethics and Emerging Technologies with a research focus on responsible innovation and the ethical design methods for emerging technologies. His work focuses on ethics and design thinking around building AI systems, and how policy can shape the future of these autonomous systems. Ethics clarification of what would normally be abstract, philosophical concepts like human values to engineers can be implemented into design requirements. Design has to be approached so that engineering can incorporate human values, which are often abstract, into technological design.The difficulty with AI and with many technologies in a globalized world is that technology can be developed in X, but unfortunately, that technology has cross-cultural, cross-domain, cross-border impacts. So, it's about trying to incorporate different understandings of values from across the globe into a single technology. These are some of the difficulties that designers are facing right now.Technology is not purely deterministic. Nor is society purely constructive and nor is technology purely instrumental. It's just a neutral tool. It doesn't embody any type of values whatsoever. And that really is important, because that means that the decisions that engineers make today, as designers, philosophers, do have a real substantive impact into the future.We can begin to really break down the debate on whether we should ban or not ban autonomous weapon systems. Technological innovations have always played a key role in military operations. And autonomous weapon systems, at least within the last few years are receiving asymmetric attention, both in public and, as well, academic discussions.  Scientists should not apologize for, but show the nuance in debate that level five autonomy in and of itself is not the problematic point of interest, but rather what type of system has this level five autonomy. There's all these assessments. This is the nuance in the debate. For those who are interested in the philosophical foundations of meaningful human control, or even value sensitive design more generally you can find my work on my website and my social media. If people are interested in following the debate on the prohibition of AWS they can watch many of the online multilateral meetings, both hosted by the UN and outside their auspices as they take place. People can check out Human Rights Watch and the Campaign to Stop Killer Robots for news on these events.Show notes Links: https://www.frontiersin.org/articles/10.3389/frobt.2018.00015/full https://www.hrw.org/https://www.stopkillerrobots.org/About HumAIn PodcastThe 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

How to Power Enterprises with Intelligent Applications with Jordan Tigani of SingleStoreJordan Tigani is the Chief Product Officer at SingleStore. He was the co-founding engineer on Google BigQuery. He also led engineering teams then product teams at BQ. SingleStore powers Comcast with their streaming analytics to drive proactive care and real-time recommendations for their 300K events per second. Since switching to SingleStore, Nucleus Security converted its first beta account to a paying customer, increased the number of scans Nucleus can process in one hour by 60X, and saw speed improvement of 20X for the slowest queries.  To be more competitive in our new normal, organizations must make real-time data-driven decisions. And to create a better customer experience and better business outcomes, data needs to tell customers and users what is happening right now. With the pandemic accelerating digitization, and new database companies going public (Snowflake) and filing IPOs (Couchbase), the database industry will continue to grow exponentially, with new advanced computing technologies emerging over the next decade. Companies will begin looking for infrastructure that can give real-time analytics -- they can no longer afford to use technology that cannot handle the onslaught of data brought by the pandemic. True Digital in Thailand utilizes SingleStore’s in-the-moment analytics to develop heat maps around geographies with large COVID-19 infection rates to see where people are congregating, pointing out areas to be avoided, and ultimately, flattening the curve of COVID-19. In two weeks’ time, SingleStore built a solution that could perform event stream processing on 500K anonymized location events every second for 30M+ mobile phones. Businesses need to prioritize in-app analytics: This will allow you to influence customer's behaviors within your application or outside of it based on data. Additionally, businesses must utilize a unified database that supports transactions and analytics to deliver greater value to customers and business. Enterprises must access technology that can handle different types of workloads, datasets and modernize infrastructure, and use real-time analytics.Shownotes Links: - https://www.linkedin.com/in/jordantigani - https://twitter.com/jrdntgn  - www.SingleStore.com  - https://www.linkedin.com/company/singlestore/-https://www.singlestore.com/media-hub/releases/research-highlights-spike-in-data-demands-amid-pandemic/  -https://www.singlestore.com/media-hub/releases/businesses-reconsidering-existing-data-platforms/   About HumAIn PodcastThe 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

How AI will impact the Future of Jobs and Work with Jeff WaldJeff Wald is an Entrepreneur, Speaker and author of the book “The End Of Jobs: The Rise Of On-demand Workers And Agile Corporations”. Wald has started three technology companies, the most recent, WorkMarket , sold to ADP, is enterprise software that enables companies to organize, manage and pay their freelance workforce. He is also a Board member to other companies with an expertise in audit, governance and cyber security. Robotics, AI and technology as a whole are the key factors in what’s being called the fourth Industrial Revolution. Wald mentions three phases: fear-mongering, where society believes all jobs will be automated, dislocation, when job losses occur, and finally, changes in the way of work and society’s standard of living.New technology doesn't replace existing jobs. Companies, workers and society adjust differently to changes in labor, but eventually, that transition is slow and social and economic dislocations do happen, but not immediately. Plus, from a technology standpoint, there is a need for customer service and a human factor which cannot be disregarded.The pandemic has definitely impacted the labor market, but is a complete guess what the outcome will be in a post pandemic world. Economic growth can be predicted, but only as the economy recovers, real estimations could be made related to unemployment rates.The hard tech jobs are growing even through the pandemic, and they will grow post pandemic. They were growing pre pandemic. The pandemic is not impacting that. But hard human jobs, those that involve human connection are also predicted to grow because computers and AI systems can’t do those jobs. Automation is easily applicable to those jobs that are repetitive, high-volume, task-driven jobs.Remote and flexible work have also been growing due to the pandemic. Companies had been reluctant to change their mindsets, infrastructures, policies and procedures for remote work. But now that they've been forced to do it, there is a great number of people who prefer working under the current work arrangements. Not meaning that workers will never again be at the office, just less often than prior to the pandemic, and more frequently than now, pursuing human interaction. But no prediction is accurate until vaccination can really incide in variants.Everyone needs to become a lifelong learner, constantly upskilling in industries that will continue to grow or rescaling because an industry is at very high risk of automation and displacement,https://www.amazon.com/End-Jobs-Demand-Workers-Corporations/dp/1642934356/https://www.jeffwald.com/Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

The Future of Augmented Reality and Apple Glasses with Robert ScobleEpisode Show Notes: Robert Scoble works with companies that are implementing Spatial Computing technologiesRobert is a futurist and technology strategist and the author of four books about technology trends, being the first to report on technologies from autonomous vehicles to Siri. Previous positions held by Robert include being a strategist at Microsoft, a futurist at Rackspace, Chief Strategy Officer at Infinite Retina, and the producer and host of a video show about technology at Fast Company.Spatial computing is the next step in the ongoing convergence of the physical and digital worlds. It does everything virtual-reality and augmented-reality apps do: digitize objects that connect via the cloud; allow sensors and motors to react to one another; and digitally represent the real world.Spatial computing will soon bring human-machine and machine-machine interactions to new levels of efficiency in many walks of life, among them industry, health care, transportation and the home. Major companies, including Microsoft and Amazon, are heavily invested in the technology.    Is computer vision about to change everything (and already is) and what should business people do to prepare for the changes that will come in 2022? How will the war between Facebook and Apple go and why we will soon give a LOT more data to these big companies?Call to Action: Learn about spatial computing and how it will roll up all the other AI advantages into a new way of computing.Links: - https://twitter.com/Scobleizer  - https://www.linkedin.com/in/scobleizer/  - https://varjo.com/  About HumAIn PodcastThe 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

How Data Scientists Transform the Financial Industry with Geoffrey Horrell from London Stock Exchange GroupEpisode Show Notes: Geoffrey Horrell was the Head of Refinitiv Labs, London and currently the Global Head of Innovation and Labs at the London Stock Exchange Group. This episode unpacks key trends from Refinitiv’s new global research report ‘The Rise of the Data Scientist’. Data scientists are moving across teams and realizing (and scaling) new opportunities for AI across their firms - e.g. NLP. In addition, 75% of firms are now using Deep Learning while 17% of firms rely solely on unstructured data for AI/ML use-cases. Data strategy is set to overtake tech strategy in importance. There is a further evolution of teams and talent across finance - more citizen data scientists in trading, investment teams etc.Data scientists are going from supportive to strategic roles as investments in tech and talent put data strategy in the spotlight. COVID-19 is upsetting ML models where you need to recalibrate and calculate disruptive events. NLP use-cases are going into production and impacting accuracy with more advancements increasing the need for ‘explainability’  Models, data and people are prepared for tomorrow’s unexpected shocks - COVID proved that models need re-calibrating / new data to calculate disruptive events (e.g. data enrichment). Learn More about Refinitiv Labs:https://www.refinitiv.com/mlreport2020 https://www.refinitiv.com/en/labs https://www.refinitiv.com/en/artificial-intelligence-machine-learningAbout HumAIn PodcastThe 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