Ron Bodkin's guest appearances

Ron Bodkin

The AI in Business Podcast

Big Data Beard

AI and the Future of Work 

Where Today Meets Tomorrow

Neil Patel

Bob Baxley

Ben Goertzel

Jill Bolte Taylor

Scott Clark

John Collins

Joanna Bryson

Gary Marcus

Nick Bostrom

Robin Hanson

Stuart Russell

Michael Shermer

Amit Goswami

Zoltan Istvan

John Danaher

OwlTail

Ron Bodkin is a self-described “serial entrepreneur focused on beneficial uses of AI”. Ron founded ChainML in April 2022 to make it easier to integrate AI models into applications. The AI we know today is immature in so many ways and many of them relate to how crude the tooling is for traditional developers building AI-first features. The ChainML protocol is a cost-efficient, decentralized network built for compute-intensive applications running on blockchain technology. Prior to founding ChainML Ron had a distinguished entrepreneurial career having founded Think Big Analytics before it was eventually acquired by Teradata after which he spent three years in applied AI at Google. Ron is also an active investor and advisor and has degrees in Computer Science from McGill and MIT.Listen and learn...What led Ron to focus on how AI can have a positive impact on the worldWhy Hinton's right when he says "we've invented a superior form of learning"Where the current toolstack for building LLM apps is incredibly immatureHow to control the cost and performance of LLM appsWhy human brains are inefficientWhy the "effective cost of computing" is being reduced by 50% every yearHow we may get to AGI within 20 yearsWhy proprietary datasets and commercial issues will slow down AI innovationThe right way to regulate AIReferences in this episode...Meredith Broussard, professor and author, on AI and the Future of WorkAttorney relies on court cases made up by ChatGPTThe Microsoft Sparks of AGI paper

<a href="https://www.linkedin.com/in/ronbodkin/" rel="nofollow" target="_blank">Ron Bodkin</a> is a self-described “serial entrepreneur focused on beneficial uses of AI”. Ron founded ChainML in April 2022 to make it easier to integrate AI models into applications. The AI we know today is immature in so many ways and many of them relate to how crude the tooling is for traditional developers building AI-first features.  The ChainML protocol is a cost-efficient, decentralized network built for compute-intensive applications running on blockchain technology. Prior to founding ChainML Ron had a distinguished entrepreneurial career having founded Think Big Analytics before it was eventually acquired by Teradata after which he spent three years in applied AI at Google. Ron is also an active investor and advisor and has degrees in Computer Science from McGill and MIT. Listen and learn...<ol><li>What led Ron to focus on how AI can have a positive impact on the world</li><li>Why Hinton's right when he says "we've invented a superior form of learning"</li><li>Where the current toolstack for building LLM apps is incredibly immature</li><li>How to control the cost and performance of LLM apps</li><li>Why human brains are inefficient</li><li>Why the "effective cost of computing" is being reduced by 50% every year</li><li>How we may get to AGI within 20 years</li><li>Why proprietary datasets and commercial issues will slow down AI innovation</li><li>The right way to regulate AI</li></ol>References in this episode...<ul><li><a href="https://aiandthefutureofwork.buzzsprout.com/520474/12284213-meredith-broussard-nyu-professor-ai-ethics-authority-and-featured-expert-in-coded-bias-discusses-the-social-implications-of-ai" rel="nofollow" target="_blank">Meredith Broussard, professor and author, on AI and the Future of Work</a></li><li><a href="https://www.bbc.com/news/world-us-canada-65735769" rel="nofollow" target="_blank">Attorney relies on court cases made up by ChatGPT</a></li><li><a href="https://arxiv.org/abs/2303.12712" rel="nofollow" target="_blank">The Microsoft Sparks of AGI paper</a></li></ul>

Ron Bodkin, ChainML founder and CEO and ex-Google exec, shares how to ensure AI is used to benefit humanity

You’re taking advantage of the benefits of AI every day in ways you might not even be aware of. When you “talk” to an automated voice on the other end of the phone, when you call a Lyft or an Uber, and when you’re asking Siri or Alexa to play your favorite song while you wash dishes. AI is everywhere, and its uses are expanding rapidly. With the application of any new technology, there’s always a period of time during which kinks that creators didn’t plan for become visible. As new systems gain traction, those unaccounted for faults can become amplified, creating patterns, which in turn can start to erode trust. One example of this when it comes to AI is how racial and gender biases that the technology was actually built to avoid can creep into the decision-making process. Another is how the AI-based algos in social media amplify extreme views and keep us all in our filter bubbles, too often fostering division. To better broadly consider the effects of such systems, it’s perhaps useful to first understand how they work – by building upon their own intelligence, collecting information from our cues and habits. We all collectively create AI in our clicks and swipes, often without considering how the data will be used by bots and algos to make decisions. In order to make this technology work well, and work well for everyone, we need to map out the channels of its proverbial brain. Our guests today are Mohsen Rezayat and Ron Bodkin. Rezayat is our Chief Solutions Architect here at Siemens Digital Industries Software. Bodkin spent the past few years as Technical Director of Applied Artificial Intelligence at Google. Currently, he’s the Vice President of AI Engineering and CIO at Vector Institute and Engineering Lead at the Schwartz Reisman Institute for Technology and Society. In today’s episode, we’re talking about machine learning and artificial intelligence, including the complexity of establishing a system of ethics in AI so that it makes conscientious decisions and better serves our collective human community. And find more information on industrial AI at Siemens here.Some Questions I Ask:What is an example of AI in practice? (5:58)How are some AI models demonstrating bias? (7:59)What is the potential to deliberately misuse digital systems? (10:31)With the loss of public trust in AI, when do you think we’ll be able to regain our trust of this technology? 12:51)What do you think about how tech companies can safeguard us against bias and unfair treatment from algorithms? (19:48)Do you think we’ll achieve the goal of embedding ethics into future models of AI? (21:39)What You’ll Learn in This Episode:The definition of machine learning (2:20)An example of how machine learning works (2:51)How racial bias makes its way into AI algorithms (8:45)The three components of trustworthy AI (12:56)How we can build ethical AI (14:37)Why humility is a good quality (15:10)How AI could help us see the future when it comes to catastrophic events (16:50)Connect with Mohsen Rezayat:LinkedInConnect with Ron Bodkin:LinkedIn Our GDPR privacy policy was updated on August 8, 2022. Visit acast.com/privacy for more information.

You’re taking advantage of the benefits of AI every day in ways you might not even be aware of. When you “talk” to an automated voice on the other end of the phone, when you call a Lyft or an Uber, and when you’re asking Siri or Alexa to play your favorite song while you wash dishes. AI is everywhere, and its uses are expanding rapidly. With the application of any new technology, there’s always a period of time during which kinks that creators didn’t plan for become visible. As new systems gain traction, those unaccounted for faults can become amplified, creating patterns, which in turn can start to erode trust. One example of this when it comes to AI is how racial and gender biases that the technology was actually built to avoid can creep into the decision-making process. Another is how the AI-based algos in social media amplify extreme views and keep us all in our filter bubbles, too often fostering division. To better broadly consider the effects of such systems, it’s perhaps useful to first understand how they work – by building upon their own intelligence, collecting information from our cues and habits. We all collectively create AI in our clicks and swipes, often without considering how the data will be used by bots and algos to make decisions. In order to make this technology work well, and work well for everyone, we need to map out the channels of its proverbial brain. Our guests today are Mohsen Rezayat and Ron Bodkin. Rezayat is our Chief Solutions Architect here at Siemens Digital Industries Software. Bodkin spent the past few years as Technical Director of Applied Artificial Intelligence at Google. Currently, he’s the Vice President of AI Engineering and CIO at Vector Institute and Engineering Lead at the Schwartz Reisman Institute for Technology and Society. In today’s episode, we’re talking about machine learning and artificial intelligence, including the complexity of establishing a system of ethics in AI so that it makes conscientious decisions and better serves our collective human community. And find more information on industrial AI at Siemens <a href="https://new.siemens.com/global/en/company/stories/home/artificial-intelligence.html" rel="noopener noreferrer nofollow" target="_blank">here</a>.Some Questions I Ask:<ul><li>What is an example of AI in practice? (5:58)</li><li>How are some AI models demonstrating bias? (7:59)</li><li>What is the potential to deliberately misuse digital systems? (10:31)</li><li>With the loss of public trust in AI, when do you think we’ll be able to regain our trust of this technology? 12:51)</li><li>What do you think about how tech companies can safeguard us against bias and unfair treatment from algorithms? (19:48)</li><li>Do you think we’ll achieve the goal of embedding ethics into future models of AI? (21:39)</li></ul> What You’ll Learn in This Episode:<ul><li>The definition of machine learning (2:20)</li><li>An example of how machine learning works (2:51)</li><li>How racial bias makes its way into AI algorithms (8:45)</li><li>The three components of trustworthy AI (12:56)</li><li>How we can build ethical AI (14:37)</li><li>Why humility is a good quality (15:10)</li><li>How AI could help us see the future when it comes to catastrophic events (16:50)</li></ul> Connect with Mohsen Rezayat:<a href="https://www.linkedin.com/in/mohsen-rezayat-28b8498/" rel="noopener noreferrer nofollow" target="_blank">LinkedIn</a>Connect with Ron Bodkin:<a href="https://www.linkedin.com/in/ronbodkin/" rel="noopener noreferrer nofollow" target="_blank">LinkedIn</a> Our GDPR privacy policy was updated on August 8, 2022. Visit <a href="https://acast.com/privacy" rel="noopener noreferrer nofollow" target="_blank">acast.com/privacy</a> for more information.

The Future of AI and Machine Learning with Mohsen Rezayat & Ron Bodkin

Today's guest is the great and brilliant, Ron Bodkin. Ron has served as Director of Applied AI at Google, VP/GM of AI at Teradata, founded multiple technology ventures, and now leads AI &amp; Engineering at Vector Institute and Schwartz Reisman Institute. In this episode, Ron shares how to avoid scattered projects by following a reliable roadmap for leveled investment in AI capabilities. Want to learn about Emerj's best practices and frameworks? Get Emerj's report: "Generating AI ROI": emerj.com/roi1

Today's guest is the great and brilliant, Ron Bodkin. Ron has served as Director of Applied AI at Google, VP/GM of AI at Teradata, founded multiple technology ventures, and now leads AI &amp; Engineering at Vector Institute and Schwartz Reisman Institute. In this episode, Ron shares how to avoid scattered projects by following a reliable roadmap for leveled investment in AI capabilities. Want to learn about Emerj's best practices and frameworks? Get Emerj's report: "Generating AI ROI": <a href="http://emerj.com/roi1" rel="nofollow" target="_blank">emerj.com/roi1</a>

Building an Enterprise AI Adoption Roadmap - with Ron Bodkin, Technical Director of Applied AI at Google