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How AI Happens

How AI Happens

Sama

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1 Creator

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1 Creator

How AI Happens is a podcast featuring experts and practitioners explaining their work at the cutting edge of Artificial Intelligence. Tune in to hear AI Researchers, Data Scientists, ML Engineers, and the leaders of today’s most exciting AI companies explain the newest and most challenging facets of their field. Powered by Sama.
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Top 10 How AI Happens Episodes

Goodpods has curated a list of the 10 best How AI Happens episodes, ranked by the number of listens and likes each episode have garnered from our listeners. If you are listening to How AI Happens for the first time, there's no better place to start than with one of these standout episodes. If you are a fan of the show, vote for your favorite How AI Happens episode by adding your comments to the episode page.

Academic turned entrepreneur Michel Valstar joins How AI Happens to explain how his behaviomedics company, Blueskeye AI, prioritizes building trust with their users. Much of the approach features data opt-ins and on-device processing, which necessarily results in less data collection. Michel explains how his team is able to continue gleaning meaningful insight from smaller portions of data than your average AI practitioner is used to.

Michel Valstar on LinkedIn

Blueskeye AI

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Rob Carpenter is the founder and CEO of Valyant AI, which is on a journey to solve the complex problem of conversational AI in the food service industry. In today’s episode, Rob explains the three main components of AI speech processing (and the challenges that arise at each of these nodes), how conversational AI has the capacity to improve conditions for human workers in the food service industry, and what this technology is going to be like in the future. After this episode, you’ll understand the importance of being more thoughtful about how you communicate your next burger and fries order to a conversational AI system.

Key Points From This Episode:

  • Rob’s early interest in entrepreneurship.
  • The original idea that Rob wanted to center his company around, and why it didn’t work out that way.
  • What Valyant AI does.
  • Long term goals that Rob has for his company.
  • Challenges of conversational AI in the food service industry.
  • Benefits of being an industry newcomer.
  • The “three amigos” of speech processing.
  • Examples of customer statements which highlight why a natural language processor is such a vital part of AI speech processing.
  • How people need to learn to communicate with AI systems.
  • The deficit of employees in the restaurant industry.
  • Ways that conversational AI improves working conditions for food service industry employees.
  • Progress that we have made as a society as a result of innovation.
  • What we can expect from conversational AI in the next five to ten years.

Tweetables:

“I thought the hologram was the hard part and that the conversational AI was solved, but it was basically the inverse of that.” — Rob Carpenter [0:06:47]

“There’s benefits when you get into a new industry or technology not knowing the problems, because you don’t know what your limitations are. I think a lot of times that frees you up to be more creative and innovative.” — Rob Carpenter [0:08:38]

“If I was to postulate where things would end up, I’d say it’s probably a 90/10. 90% is that the technology has to be better, and keep getting better. 90% of everything needs to be handled by the AI. The other 10%, people need to be more thoughtful when they communicate with these systems.” — Rob Carpenter [0:17:22]

“There’s 1.7 million unfilled positions in the restaurant industry right now. 1 in 6 of every position available right now is in restaurants.” — Rob Carpenter [0:20:26]

“Innovation is not only built into economies, but it’s essential for their health and long-term safety.” — Rob Carpenter [0:23:28]

Links Mentioned in Today’s Episode:

Rob Carpenter on LinkedIn

Valyant AI

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How AI Happens - Kelvin Wursten: Tensor Flow Models in Healthcare
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07/30/21 • 19 min

Kelvin Wursten, leader of PointClickCare's Data Science team, explains how they are utilizing AI to help solve complicated supply vs. demand calculations in hospital emergency departments, as well as the challenge of balancing building awesome technology while still prioritizing the user's needs.

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LeanTaas CEO Mohan Giridharadas explains how his team is solving the Supply & Demand challenge within healthcare, how the algorithms are stress tested in order to handle extreme edge cases, as well as how drift is defined, detected, and resolved in a customer-centric fashion.

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Walmart's SVP of Global Technology Anshu Bhardwaj and VP of Emerging Technology Desirée Gosby join Sama CEO Wendy Gonzalez for a roundtable discussion about representation in AI, explainable & ethical AI, and how representative teams are a key way to reduce biases in AI technology.

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Dr. John Kane, Head of Signal Processing & Machine Learning at Cogito, explains the challenges brought on by the oft-repeated truism "speech is more than text", and how Cogito addresses these challenges to deliver real-time conversational insight to their users. Later, John explains the holistic approach to ensuring machine learning technology is created in a bias-free environment.

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In recent years, the focus of AI developers has been to implement technologies that replace basic human labor. Talking to us today about why this is the wrong application for AI (right now), is Katya Klinova, the Head of AI, Labor, and the Economy at The Partnership on AI. Tune in to find out why replacing human labor doesn't benefit the whole of humanity, and what our focus should be instead. We delve into the threat of "so-so technologies" and what the developer's role should be in approaching ethical vendors and looking after the workers supplying them with data. Join us to find out more about how AI can be used to better the whole of society if there’s a shift in the field’s current aims.

Key Points From This Episode:

  • An introduction to Katya Klinova, Head of Al, Labor and the Economy at The Partnership on AI.
  • How her expectations of the world after her undergraduate degree shaped her.
  • Pursuing a degree in economics to understand how AI impacts labor and economics.
  • The role of The Partnership on AI in dissipating technological gains.
  • Who is impacted when AI is introduced to a market: the consumers and the workers.
  • How different companies are deficient in the ways they benefit everyone.
  • Find out what the “threat of so-so technology” is.
  • Should people become shareholders in AI technology that they helped to train?
  • How capitalism incentivizes “so-so technologies”.
  • The role of developers in selecting vendors and responsible sourcing.
  • Why it's important to realize that data labelers are employees and not just numbers.
  • Shifting the focus of AI from automation to complementarity.
  • Why now is not the time to be replacing human labor.

Tweetables:

“Creating AI that benefits all is actually a very large commitment and a statement, and I don't think many companies have really realized or thought through what they're actually saying in the economic terms when they're subscribing to something like that.” — @klinovakatya [0:09:45]

"It’s not that you want to avoid all kinds of automation, no matter what. Automation, at the end of the day, has been the force that lifted living conditions and incomes around the world, and has been around for much longer than AI." — @klinovakatya [0:11:28]

“We compensate people for the task or for their time, but we are not necessarily compensating them for the data that they generate that we use to train models that can displace their jobs in the future.” — @klinovakatya [0:14:49]

"Might we be automating too much for the kind of labor market needs that we have right now?" — @klinovakatya [0:23:14]

”It’s not the time to eliminate all of the jobs that we possibly can. It’s not the time to create machines that can match humans in everything that they do, but that’s what we are doing.” — @klinovakatya [0:24:50]

Links Mentioned in Today’s Episode:

Katya Klinova on LinkedIn

"Automation and New Tasks: How Technology Displaces and Reinstates Labor"

The Partnership on AI: Responsible Sourcing

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How AI Happens - Trailer: Introducing How AI Happens
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05/20/21 • 1 min

How AI Happens is a podcast featuring experts and practitioners explaining their work at the cutting edge of Artificial Intelligence. Tune in to hear AI Researchers, Data Scientists, ML Engineers, and the leaders of today’s most exciting AI companies explain the newest and most challenging facets of their field. Powered by Sama.

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Sama CEO Wendy Gonzalez explains how the Sama Digital Basics program teaches AI skills to individuals in Africa's largest slum, and reflects on the findings of MIT's 6 year study measuring the program's effect.

RCT Results from MIT: Evaluating the Impact of Sama’s Training and Job Programs

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How AI Happens - Predictive & Declarative AI with Theresa Benson
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08/26/21 • 33 min

Theresa Benson, Product Storyteller for InRule Technology, explains the opportunity of combining declarative AI with predictive AI, and how InRule Technology is using predictive AI to empower non AI experts to develop algorithms from their existing domain knowledge.

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FAQ

How many episodes does How AI Happens have?

How AI Happens currently has 112 episodes available.

What topics does How AI Happens cover?

The podcast is about Computer Science, Mathematics, Podcasts, Big Data, Technology, Science, Artificial Intelligence and Machine Learning.

What is the most popular episode on How AI Happens?

The episode title 'Building Trustworthy Behaviomedics with Blueskeye CEO Michel Valstar' is the most popular.

What is the average episode length on How AI Happens?

The average episode length on How AI Happens is 29 minutes.

How often are episodes of How AI Happens released?

Episodes of How AI Happens are typically released every 7 days, 5 hours.

When was the first episode of How AI Happens?

The first episode of How AI Happens was released on May 20, 2021.

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