HumAIn Podcast

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Jed Dougherty is the VP of Field Engineering at Dataiku. He specializes in helping companies construct enterprise-grade data platforms and has helped teams around the world build successful production infrastructures across the various clouds and on-prem. He holds a master’s degree from the QMSS Program at Columbia University and Degrees in Mathematics and Political Science from Arizona State.

Episode Links:  

Jed Doughtery’s LinkedIn: https://www.linkedin.com/in/jediv/ 

Jed Doughtery’s Twitter:  https://twitter.com/dataiku 

Jed Doughtery’s Website: https://www.dataiku.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.

(02:03) – Making AI and Data Science relatively easy to use instead of limiting it to a few smart dudes encourages a more evenly distribution of the power that comes from it.

(03:27) – Google and Amazon want to keep control of the actual algorithms. 

(04:37) – No big company in America, except Google, Amazon, Facebook and Netflix is able to hit the median income for their data scientists these giants have, which means they have a different pool of talent to pull from.

(07:22) – Universal Basic Income as a solution for a feasible future of jobs being replaced as a result of automation.

(12:27) – Empathy mapping to design AI systems to be diverse, inclusive and trained for multiple scenarios. AI has been about prediction and not an explanation of these predictions. Models should be more explainable than accurate.

(13:02) – Some of the new product lines for the explainability of an AI built by Google and Amazon .

(17:08) – Pushing the power back to the user immediately to empower them to have decisions driven by AI. 

(11:53) – AI governance and ethical decision-making. If you don't have people connected to the things you're trying to predict, it's easy to miss a trend to assume that you have complete data when you do not.

(20:29) – Fair AI systems: labels are generated by humans, which means they have all the failures and foibles of our current society. Pushing those into a model makes that model exactly as good as our current society or worse. 

(23:06) – We overestimated human enthusiasm for autonomous driving.

(28:51) – Computer centralized systems are weak. If Google, Amazon and Facebok were one single company, they would have this complete idea of your life and be able to predict every moment of it and say, how much of a worthy individual you were to society.

(32:35) – In NYC, people from all walks of life run into each other, touch each other accidentally. Nobody owns New York. You have the people who are gonna be affected by AI, the business knowledge and a growing tech base of folks who can implement technology. NYC could be the center of machine learning.

(36:22) – Linux command line is running 99% of the servers in the world right now

(38:32) – Knowledge to become tech-relevant: this is such a new industry that 10 years at school, you may have learned 5% of the industry.

(43:03) – Humans and the machines. Things from an ethical perspective or a human perspective combined with technical knowledge.

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John Spence is an author, international executive coach, professional development educator, virtual trainer, strategic planning facilitator, keynote speaker and developer of online learning programs. John is recognized as one of the top business thought leaders and leadership development experts in the world and was named by the American Management Association as one of America’s Top 50 Leaders to Watch along with Sergey Brin and Larry Page of Google and Jeff Bezos of Amazon. As a consultant and coach to organizations worldwide, from startups to the Fortune 10, John is dedicated to helping people and businesses be more successful.

Episode Links:  

John Spence’s LinkedIn: linkedin.com/in/johnbspence

John Spence’s Twitter: AwesomelySimple

John Spence’s Website: https://johnspence.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.

(03:45) –  Technology is impacting not only the financial industry, but every industry there is.

(04:41) – Major trends that are impacting business: computer speed, which leads to big data, Artificial intelligence, robotics, virtual reality, augmented reality, synthetic medicine, genetic decoding and recoding.

(06:57) –Technology is evolving. A lot of industries are taking off virtual reality, augmented reality, AI and FinTech.

(07:45) – Monsanto: from agriculture and farming to Big Data.

(09:09) – Ubers for harvesters in Australia. Get on the app, order the harvesters, the harvesters are brought to the field and they're all autonomous.

(11:00) – All strategy is just valued differentiation, multiplied by disciplined execution.

(11:30) – Bring something unique and compelling to the marketplace that your target customer wants to buy, impossible for your competition to copy and that you can execute on flawlessly and consistently.

(11:53) – Required criteria for success in business: the quality of the people in your company, the relationships you have with your customers, the strength of your brand, the data you have collected on your customers in the industry with the caveat of how well you deploy that data.

(13:08) –  Data that will allow to significantly increase competitive advantages in the marketplace.

(14:33) – Businesses are going to change through automation, through AI, through big data.

(15:25) – Intelligence Quotient, Emotional Intelligence and Agility Quotient.

(19:15) – All the technology, all the leadership, all the business models, all the strategies are focused on one thing only, to satisfy your customer. 

(28:58) – The three wheels of the Hedgehog Concept: high technical expertise, passion  and a strong economic driver in the marketplace.

(24:10) – “Adjacent news” to anticipate the market.

(29:42) – Technology is going to outrun our ability to keep up with it.

(33:35) – Younger people coming into the marketplace look for stability, dignity and purpose.

(39:13) – Committing yourself to

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Kristen has been ranked #8 Global LinkedIn Top Voice in Data Science & Analytics. She has 10 years’ experience in data science delivering innovative and actionable machine learning solutions across the eCommerce, healthcare, & utility industries. Kristen is currently an instructor at UC Berkeley Ext. teaching Practical Data Science in collaboration with Emeritus Institute of Management, holds an MS in Applied Statistics from Worcester Polytechnic Institute and a BS in Mathematics.

Episode Links:  

Kristen Kehrer’s LinkedIn: https://www.linkedin.com/in/kristen-kehrer-datamovesme/

Kristen Kehrer’s Twitter: DataMovesHer 

Kristen Kehrer’s Website: https://datamovesme.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

(02:39) – Remote learning environments are the world of Work 2.0

(03:33) – Many people believe Data Science means working remotely, but you need to build reputation and experience first

(05:41) –Education and work are moving towards remote environments but we’re not completely there yet

(08:18) – Working remotely simplifies busy schedules. It's a model that works for people and the lives that they've built

(11:49) – Without diminishing the experience of being able to learn on campus and build real relationships, online learning is very useful for professional development 

(13:46) – Saving for future education requires financial advice, considering the college admission scandal and changing world conditions

(17:21) – Models must be interpretable. AutoML does not allow to set parameters

(18:38) – Some tasks can be automated by interpreting these AutoML models and some others should be done manually, depending on the factors to be determined by them

(19:29) – Dehumanization by automation: products that we're using each and every day implementing AutoML or auto AI solutions.

(22:29) – There will always be a need for analysts who have business context and understand how variables interact

(23:28) – Data science, the beginning of machine learning, is the foundation that helps get into more advanced algorithms

(24:45) – Stop learning and start applying to positions. Get feedback from the market in order to know what skills or gaps you have on your resume that keep you from getting a job in the industry. Work your way up.

(28:14) – We're living in a data-swamped life among devices, information and messages that are distracting 

(29:40) – Defining the role of a Data Scientist is quite complicated. People trying to get into the industry, data getting bigger, tools getting easier. Things are becoming more automated. Data is the ‘new oil’

(33:41) – Data Science is going to focus the highest priority on the objectives that are going to result in the highest ROI

(35:15) – A data-obsessed culture that wants every single decision to be made on data. And it does not need to be used for everything, if it's not actually going to inform what it is that we're going to do

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Lex Sokolin is Global Fintech Co-Head and Head Economist at ConsenSys. He is a New York & London entrepreneur with senior operating and board-level Fintech experience in blockchain, digital investing, and wealth management. He founded the Fintech practice at Autonomous, a financial services equity research firm, where he focused on artificial intelligence, blockchain, and mixed reality. 

Episode Links:  

Lex Sokolin’s LinkedIn: linkedin.com/in/alexeysokolin

Lex Sokolin’s Twitter: LexSokolin 

Lex Sokolin’s Website: https://www.lexsokolin.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

(02:12) – Banking, investing, lending and insurance, which used to be intermediated by human beings, are now targeted by artificial intelligence companies. 

(04:09) – The digitization of all the industries from media to retail to now finance and healthcare.

(05:14) –Machine Learning on top of large data sets. Services that used to be physical now have digital chassis that create data exhaust.

(06:28) – Removing humans and running on autopilot makes it way cheaper and more accessible to manufacture a financial product. 

(09:12) – Mobile apps usage rises because phones are more distributed than banking services.

(11:07) – 800 million or so users in China power the engine for the algorithmic decision-making for credit there.

(12:30) – Free internet costs your data, your money or tracking of your behavior.

(15:35) – Chinese model is successful because fixed costs of research and development are covered by government spending, and the U.S is fairly disadvantaged in that regard.

(16:13) – Asymmetrical markets are controlled by very few players. Capitalism allows for that sort of selection mechanism to occur. The other option is decentralized direction, which is less profit-driven and more humanistic.

(22:16) – Low marginal costs is the direction that we're moving towards from a macro level, driven by lower cost manufacturing and lower cost distribution.

(24:05) – Finance jobs are threatened by automation.

(28:40) – Encourage venture capitalism for innovation and risk-taking by the population.

(34:36) – Distribution of wealth and income are getting more unequal because of unassailable structures of society.

(38:21) – The internet age accentuates inequality. The large tech firms trend towards monopoly.

(42:19) – Mental health tactics to keep at baseline

(42:55) – A system that manufactures anxiety. Scientists develop newsfeeds to break down your behavioral defenses

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Brett Greenstein is a Senior Vice President and Global Head of Artificial Intelligence at Cognizant. His experience in the Internet of Things, technology consulting, solutions in banking, healthcare, customer service, and retail with organizations include IBM and many Fortune 500 products.   

Episode Links:  

Brett Greenstein’s LinkedIn: https://www.linkedin.com/in/bretgreenstein/

Brett Greenstein’s Twitter:  https://twitter.com/bretgreenstein?s=20

Brett Greenstein’s Website: https://www.cognizant.com 

Podcast Details: 

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

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

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

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

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

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

Support and Social Media:  

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

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

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

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

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

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

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

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction.

(04:12) –How technology immerses our lives and the internet reaches every corner.

(05:51) – The chinese pace to adapt to changes in technology versus the slowness of the US.

(07:57) – Technology integration might become a liability. 

(10:28) – IOT and technology as a game of forces between the US and China.

(13:34) – 5G opens up massive potential for communications.

(19:45) – HTML geeks are now strategists who use data and to make companies be more digitally centric, AI-powered.

(24:00) – Cultural and transformational change of a business is inhibited by the power structures. 

(29:01) – The human touch to an AI system is critically important for its acceptance and adoption.

(31:23) – AI is transforming industries.

(40:55) – Breakthrough devices are going to be health-related. 

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T. Scott Clendaniel is an Artificial Intelligence Pioneer with 35 years' proven track record of ROI improvements. He’s also a Guest Lecturer at Johns Hopkins University and University of Maryland, Harvard Innovation Labs’ Experfy, Artificial Intelligence course author and the Chief Data Officer of the Board of Directors at Gartner/ Evanta (DC region) 

Episode Links

T. Scott’s LinkedIn: https://www.linkedin.com/in/tscottclendaniel/

T. Scott’s Twitter: https://twitter.com/Strat_AI?s=20 

T. Scott’s Website: https://www.boozallen.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

(01:43) – The pace of advancement has changed but problem solving leans more towards software development than problem solving itself.

(03:18) – Deep learning can’t provide solutions unless data is applied beyond the models.

(05:38) – Model building must be fully interpretable to be able to be fixed if needed

(07:15) – Protecting the rights of consumers and increasing the requirements on transparency of the models.

(12:55) – Ethics groups, reviewing policies and the “adverse impact test” for algorithms.

(15:46) –Overestimating AI's impact in the future of work.

(16:49) – Automation and augmented intelligence: humans using computers to solve existing problems, as opposed to being replaced by them.

(21:22) – AI applications in specific industries for specific problems, focusing education on the good and the bad in AI.

(25:10) – Sharing the "wealth of knowledge" about predictive analytics.. 

(27:09) – Open sourcing education so that anyone can learn how to build and use models that are going to impact them.

(31:06) – New research on algorithms to find advanced sophisticated solutions to problems.

(34:07) – Data in general and Artificial Intelligence, specifically, can be used in good ways or detrimental ways.

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John Lynn is the Co-Founder of Cela, an Accelerator in NYC. He has worked closely with acceleration at places like Studio Project and Techstars, which represent a new kind of educational model. John has been involved with many innovation, acceleration and incubator programs. 

Episode Links:  

John Lynn’s LinkedIn: https://www.linkedin.com/in/jayeffelle/

John Lynn’s Twitter:   https://twitter.com/jmlynn7?s=20 

John Lynn’s Website: https://about.me/jlynn 

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

(01:41) – The trends and patterns in innovation are set by New York City as a tech capital.

(02:37) –  New York City’s tech ecosystem invites more participation.

(06:47) – New York City is the capital for crowdfunding.

(11:01) – Better access to education make NYC a place for innovation.

(12:59) – The rapid change and growth of NYC due to the rise of technology.

(16:43) – The virtualization of education as a shift in Accelerators’ models.

(22:08) –  NYC has unique elements that make it a tech capital demographically and for business. 

(24:30) – All sorts of capital investments make NYC the best place at the best timing. 

(26:54) – The Global Accelerator Summit will be scheduled soon.

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Dr. JT Kostman is a data scientist, mathematician, and psychologist. He is widely regarded as one of the world's leading experts in applied artificial intelligence and cognitive computing. JT has hunted terrorists for the U.S. intelligence agencies, tracked criminal networks for the FBI, advised the Department of Defense on analytic strategies and led social media analysis for the 2012 Obama campaign. In the corporate sector, he served as Chief Data Officer and member of the Executive Committee for Time Inc. and as Chief Data Scientist for Samsung. Prior to attending graduate school, JT served as a paramedic, police officer, deep-sea rescue driver and team leader of a U.S. Army Special Forces scout/sniper reconnaissance team. JT received a PhD in psychology from City University of New York an MS in psychology from Baruch College and did post-doctoral research on nonlinear Dynamical Systems Theory at Moscow State University of Economics, Statistics and Informatics (MESI)

Episode Links:  

Dr. JT Kostman’s LinkedIn: https://www.linkedin.com/company/protectedbyai/

Dr. JT Kostman’s Twitter: jt_kostman 

Dr. JT Kostman’s Website: https://protectedby.ai/  

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

(01:47) – The trends in the consumer space keep being 5G, virtual reality and quantum computing.

(02:23) – Diversity is not just needed, but essential to our success. Most industries do little to truly accommodate,welcome, incentivize and attract a more diverse and more heterogeneous population into tech.

(04:35) – Minorities portrayed in the media are editorial selections. The seats of power, keep being in hands of white males.

(05:36) – We need to think beyond traditional concerns of gender, of ethnicity. We need to start talking about neurodiversity. We need to start talking about a difference of perspective. 

(06:26) – Fast growing demographics are not wholly represented within technology.

(08:27) – Cognitive diversity needs to be transferred over into AI startups and AI applications. 

(09:57) – Diversity of thinking has really contributed to each of the organizations. And that's what we need need to be able to better communicate to the rest of the field.

(211:57) – Brain power and diversity of cognitive thinking is it's going to start with humans, training humans in essence, to think diversity-first.

(12:35) – Artificial intelligence and machine learning has, in its current incarnation, been coded by white men “teaching the machines to do what white men do”.

(15:10) – The things that make us quintessentially human; Empathy, caring, wisdom, perspective, equanimity, patience. Let machines do what machines do, and let us be left to be human.

(16:27) – The lost generation 2.0.

(19:09) – Multiculturalism and neurodiversity.

(20:12) – HR teams do not assess technical fit, but cultural fit. People who have a diverse perspective are not going to fit into that common mold.

(24:52) – The Dunning Kruger effect and the skills assessed by the Industry. 

(26:41) – Symbiotech means people and the machines partnering most effectively, how in an almost transhumanist perspective of how do we work symbiotically with the machines so that both end up being able to do better.

(27:28) – The Great Tech Debate, a dialogue with all the great thinkers, citizenry, consumers, whose voices will be included in these conversations. 

(28:43) – We should talk about technology. The implications, applications, ramifications, data privacy, the petroleum problem, the economic implications of artificial intelligence and the impact that will have on jobs.

(30:05) – We need to reclaim the ability to actually hear other perspectives, other attitudes, other beliefs.

(32:01) – AI is not going to run a mocking and kill us all. We need to stop worrying about silly things and take that same energy and worry about climate change, how joblessness is already impacting such a large segment of the population, the economic implications of artificial intelligence.

(32:22) – We need to democratize talent more. That leaves us to worry about things like immigration and writing.

(35:58) – We have to stop abrogating our responsibility to the leaders of the tech companies or to the politicians.

(37:01) –Every citizen needs to know how to code. Technology is going to change the world and every aspect of it. “Edutainment”, something that teaches you and entertains you.

[Audio] 

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Sameer Maskey is the Founder & CEO at Fusemachines, a services and solutions provider on a mission to democratize AI. He is also an Adjunct Associate Professor at Columbia University. Sameer holds a PhD, a MPhil & an MS in Computer Science from Columbia University and a BS in Math & Physics from Bates College.

Episode Links:  

Sameer Maskey’s LinkedIn: https://www.linkedin.com/in/sameer-maskey/

Sameer Maskey’s Twitter:   https://twitter.com/sameermaskey 

Sameer Maskey’s Website: http://www.sameermaskey.com/

                                                 https://fusemachines.com/  https://www.fuse.ai/  

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.

(01:42) – Fuse.ai Scholarship program intended for underserved communities to democratize AI.

(04:55) – AI should be a global opportunity. Lots of companies are trying to build AI systems and there's not enough AI talent.

(06:30) – Neural networks have come back in full force with deep learning to build solutions in computer vision, NLP, and others.

(07:46) – AI, ML and Deep Learning, made simple.

(10:06) – Neural networks and the human brain functions. 

(11:24) – We’re still far from making computers use data to process it like a human being.

(14:09) – Fusemachines is working on medical dental medicine delivery in Nepal, and  various language recognition systems, speech recognition systems and dialogue systems. 

(16:25) –In spite of research and advancements in translation systems, language learning is not redundant yet.

(17:22) – New breakthroughs will be in language translation systems with data collected from the tech Giants.

(19:04) –  Job automation will happen, so people should learn about AI, Machine Learning and computer science.

(21:27) –  Self-driving cars will be transformational for the way we travel.

(24:03) –  Chatbots are useful for reducing the customer service load, but cannot completely replace customer service reps. 

(26:29) – Global recession might slow down investments into AI-related businesses, but if otherwise, AI will keep pace with it, or actually outpace economic growth.

(28:47) – Fusemachines and Fuse.ai  will have wide-world impact by training AI practitioners to solve some of the biggest challenges humans face.

[Audio] 

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Oliver Christie is the Co-Founder of Voltare Consulting. He has a degree in Mathematics, Economics and Fine Art from Peter Symonds College, Winchester, UK and a BFA in Fine and Studio Arts from University of SoutHampton. His experience includes working in the Financial, Media and Transportation sectors, and using technologies such as IBM Watson, Microsoft Azure, Google Tensorflow, H2O.ai and Intel.

Episode Links:  

Oliver Christie’s LinkedIn: https://www.linkedin.com/in/oliverchristie/

Oliver Christie’s Twitter: https://twitter.com/OliverChristie?s=20

Oliver Christie’s Website: https://oliverchristie.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

(02:08) – Automation means men and machines working together.

(05:55) – People’s skills and the need for a more human technology.

(07:05) – Technical education for a more inclusive workforce.

(10:57) – The need for Government policies to regulate optimization through AI.

(15:56) – Building more human connections through Technology.

(23:08) – People are a company’s best asset to leverage true changes through technology.

(23:34) – Real success is not only technological advancements. Humans need connection, family and personal growth.

(30:15) – Changing the model for automation to make it more human-centric. 

(31:55) – AI Transparency for consumer-focused products.

(34:18) – Human-centric AI tools, Data and technology for human development.