London Futurists

Inventing the future of computing, with Alessandro Curioni

January 18, 2023 London Futurists Season 1 Episode 22
Inventing the future of computing, with Alessandro Curioni
London Futurists
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London Futurists
Inventing the future of computing, with Alessandro Curioni
Jan 18, 2023 Season 1 Episode 22
London Futurists

OpenAI's ChatGPT and picture generating AI systems like MidJourney and Stable Diffusion have got a lot more people interested in advanced AI and talking about it. Which is a good thing. It will not be pretty if the transformative changes that will happen in the next two or three decades take most of us by surprise.

A company that has been pioneering advanced AI for longer than most is IBM, and we are very fortunate to have with us in this episode one of IBM’s most senior executives.

Alessandro Curioni has been with the company for 25 years. He is an IBM Fellow, Director of IBM Research, and Vice President for Europe and Africa.

Topics discussed in this conversation include:

*) Some background: 70 years of inventing the future of computing
*) The role of grand challenges to test and advance the world of AI
*) Two major changes in AI: from rules-based to trained, and from training using annotated data to self-supervised training using non-annotated data
*) Factors which have allowed self-supervised training to build large useful models, as opposed to an unstable cascade of mistaken assumptions
*) Foundation models that extend beyond text to other types of structured data, including software code, the reactions of organic chemistry, and data streams generated from industrial processes
*) Moving from relatively shallow general foundation models to models that can hold deep knowledge about particular subjects
*) Identification and removal of bias in foundation models
*) Two methods to create models tailored to the needs of particular enterprises
*) The modification by RLHF (Reinforcement Learning from Human Feedback) of models created by self-supervised learning
*) Examples of new business opportunities enabled by foundation models
*) Three "neuromorphic" methods to significantly improve the energy efficiency of AI systems:  chips with varying precision, memory and computation co-located, and spiking neural networks
*) The vulnerability of existing confidential data to being decrypted in the relatively near future
*) The development and adoption of quantum-safe encryption algorithms
*) What a recent "quantum apocalypse" paper highlights as potential future developments
*) Changing forecasts of the capabilities of quantum computing
*) IBM's attitude toward Artificial General Intelligence and the Turing Test
*) IBM's overall goals with AI, and the selection of future "IBM Grand Challenges" in support of these goals
*) Augmenting the capabilities of scientists to accelerate breakthrough scientific discoveries.

Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration

Selected follow-up reading:
https://researcher.ibm.com/researcher/view.php?person=zurich-cur
https://www.zurich.ibm.com/st/neuromorphic/
https://www.nist.gov/news-events/news/2022/07/nist-announces-first-four-quantum-resistant-cryptographic-algorithms

Show Notes

OpenAI's ChatGPT and picture generating AI systems like MidJourney and Stable Diffusion have got a lot more people interested in advanced AI and talking about it. Which is a good thing. It will not be pretty if the transformative changes that will happen in the next two or three decades take most of us by surprise.

A company that has been pioneering advanced AI for longer than most is IBM, and we are very fortunate to have with us in this episode one of IBM’s most senior executives.

Alessandro Curioni has been with the company for 25 years. He is an IBM Fellow, Director of IBM Research, and Vice President for Europe and Africa.

Topics discussed in this conversation include:

*) Some background: 70 years of inventing the future of computing
*) The role of grand challenges to test and advance the world of AI
*) Two major changes in AI: from rules-based to trained, and from training using annotated data to self-supervised training using non-annotated data
*) Factors which have allowed self-supervised training to build large useful models, as opposed to an unstable cascade of mistaken assumptions
*) Foundation models that extend beyond text to other types of structured data, including software code, the reactions of organic chemistry, and data streams generated from industrial processes
*) Moving from relatively shallow general foundation models to models that can hold deep knowledge about particular subjects
*) Identification and removal of bias in foundation models
*) Two methods to create models tailored to the needs of particular enterprises
*) The modification by RLHF (Reinforcement Learning from Human Feedback) of models created by self-supervised learning
*) Examples of new business opportunities enabled by foundation models
*) Three "neuromorphic" methods to significantly improve the energy efficiency of AI systems:  chips with varying precision, memory and computation co-located, and spiking neural networks
*) The vulnerability of existing confidential data to being decrypted in the relatively near future
*) The development and adoption of quantum-safe encryption algorithms
*) What a recent "quantum apocalypse" paper highlights as potential future developments
*) Changing forecasts of the capabilities of quantum computing
*) IBM's attitude toward Artificial General Intelligence and the Turing Test
*) IBM's overall goals with AI, and the selection of future "IBM Grand Challenges" in support of these goals
*) Augmenting the capabilities of scientists to accelerate breakthrough scientific discoveries.

Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration

Selected follow-up reading:
https://researcher.ibm.com/researcher/view.php?person=zurich-cur
https://www.zurich.ibm.com/st/neuromorphic/
https://www.nist.gov/news-events/news/2022/07/nist-announces-first-four-quantum-resistant-cryptographic-algorithms