The Story: Embracing AI and All Life’s Uncertainties w/ David Spiegelhalter
Statistician David Spiegelhalter is no stranger to AI – he used it to help him research his recent book and, back in the late 70s, he helped develop foundational algorithms for the tech. So, he understands the pandora’s box that technology can represent, as well as the uncertainty embedded in its future development. Spiegelhalter sits down with Oz to unpack how we should interpret AI predictions, why better data matters and why we should consciously embrace uncertainty in our own lives.
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#305 – Martin Rees: Black Holes, Alien Life, Dark Matter, and the Big Bang
Lord Martin Rees is a cosmologist and astrophysicist at Cambridge University and co-founder of the Centre for the Study of Existential Risk. Please support this podcast by checking out our sponsors:
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OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(06:39) – Understanding the universe
(15:09) – Human limitations and AI
(24:05) – Dark matter
(31:25) – Vast universe
(39:07) – Alien life
(52:59) – Space exploration
(1:17:22) – Future technology
(1:28:44) – Newton and Einstein
(1:32:46) – Black holes
(1:46:27) – Cosmological threats
(2:13:31) – Advice for young people
(2:16:02) – Mortality
The Unreasonable Effectiveness of the Forget Gate with Jos Van Der Westhuizen - TWiML Talk #240
Today we’re joined by Jos Van Der Westhuizen, PhD student in Engineering at Cambridge University.
Jos’ research focuses on applying LSTMs, or Long Short-Term Memory neural networks, to biological data for various tasks. In our conversation, we discuss his paper "The unreasonable effectiveness of the forget gate," in which he explores the various “gates” that make up an LSTM module and the general impact of getting rid of gates on the computational intensity of training the networks.
Making Algorithms Trustworthy with David Spiegelhalter - TWiML Talk #212
Today we’re joined by David Spiegelhalter, Chair of Winton Center for Risk and Evidence Communication at Cambridge University and President of the Royal Statistical Society. David, an invited speaker at NeurIPS, presented on “Making Algorithms Trustworthy: What Can Statistical Science Contribute to Transparency, Explanation and Validation?”. In our conversation, we explore the nuanced difference between being trusted and being trustworthy, and its implications for those building AI systems.