Where Today Meets Tomorrow


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

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:


Connect with Ron Bodkin:


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