Episode 133: Nigel Toon, Graphcore - How AI Thinks

Gaule's Question Time

In this interview hosted by Andrew Gaule (linkedin.com/in/andrew-gaule-aimava), Graphcore CEO Nigel Toon (https://www.linkedin.com/in/nigeltoon/) shares his perspectives on artificial intelligence and the hardware powering the latest capabilities. Nigel and Andrew are mentors on the AI Stream at Creative Destruction Labs based in Oxford University. See more of the topics below.
The discussion includes key insights from Nigel's book "How AI Thinks: How we built it, how it can help us, and how we can control it" https://amzn.eu/d/bkDXk7Y
The context of tech and business change featured in Andrew's book "Purpose to Performance: Innovative New Value Chains" https://amzn.eu/d/6cK5C6Q and AI was discussed in this context.
To book a meeting to discuss AI and other business change with Andrew - https://calendly.com/andrew-gaule/30min?
You can listen to this interview as a podcast on Gaule's Question Time on Apple, Spotify, Google and many other podcast channels. https://www.podomatic.com/podcasts/gaulesqt
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See this and other video content at Aimava Purpose to Performance Channel - https://www.youtube.com/channel/UCV9o-htFNIk9Yt7jp2XcdWw
Nigel outlines how far AI has advanced, from beating the world champion at Go to the recent explosion in popularity of chatbots like ChatGPT. Underpinning these leaps in software are rapid gains in semiconductor chips, improving at an astonishing 25 billion fold over 60 years. Graphcore builds specialty AI chips to provide an alternative to dominant player Nvidia, allowing more researchers to accelerate discoveries.
Beyond keeping up with technical progress, Nigel stresses that education must transform to prioritize creativity over rote learning. He welcomes AI-assisted teaching tailored to individuals. Regarding ethical concerns, Nigel argues biases come from flawed data rather than being inherent to AI systems. Still, developers must pledge transparency while testing for unfair impacts on diverse groups. With sensible safeguards, AI can augment human intelligence to solve previously intractable problems. The technology itself is neither good nor bad; it merely amplifies our own goals and values.
Here are six key topics from the interview
Graphcore's AI chips
Enable more parallel processing like the human brain
Optimized to accelerate neural networks for complex AI workloads
Provide an alternative to Nvidia for AI compute in data centers
Comparing AI and human cognition
Many subconscious brain skills remain beyond AI systems currently
Things easy for people often prove difficult computationally
Future computing may better approximate biological infrastructure
AI adoption in China
Leading aggressive deployment of AI across many sectors
Rapid integration into education at early ages
Authoritarian system enables swift data collection and trials
AI's economic impact
Potential to augment productivity on par with industrial revolution
Requires rethinking of education models and job training
Risk of automating certain jobs must be mitigated
Ethics of AI systems
Biases originate from flawed training data rather than inherently
Guidelines needed to ensure transparency and test for harm
Clearly unethical applications should be banned
Hardware progress enabling AI
Exponential improvements in semiconductors underlying gains
Future quantum and molecular computing shifts possible
Enormous data digitization also crucial for progress
To book a meeting to discuss AI and other business change with Andrew - https://calendly.com/andrew-gaule/30min?