This week we share an excerpt of our conversation with Raquel Urtasun, CEO and Founder of the AI-first self-driving company, Waabi.
Investor Sanjana Basu shares her perspective on the areas where we expect to see AI generate outsized value including innovations in health data sets, genomics and precision medicine, generative AI applied to biology, and AI-enabled sleep solutions.
Canada’s AI pedigree is no accident. CIFAR and NSERC were funding the work of AI pioneers Geoffrey Hinton, Yoshua Bengio, and Richard Sutton long before the work of these individuals would define a new era of innovation.
Aidan Gomez offers advice to researchers looking to become the next generation of AI founders. He says, “Operate in a way that assumes success…”
In a candid conversation with Radical Ventures Partner and Co-Founder Tomi Poutanen, Richard Socher offers advice to the researchers looking to become the next generation of AI founders.
In a candid conversation with Radical Ventures Partner Rob Toews, Fei-Fei Li offered advice to the researchers looking to become the next generation of AI founders.
What does it take to found a successful AI company? Radical Ventures Managing Partner and Co-Founder Jordan Jacobs explored this question with AI roboticist and multi-time founder Pieter Abbeel in the kick-off to this year’s AI Founders Master Class.
The AI Founders Master Class will be held virtually from October 12th to November 2rd, 2022 with speakers such as Fei-Fei Li, Co-Director of Stanford’s Human-Centered AI Institute, Pieter Abbeel, Co-Founder of Covariant and Director of the Berkeley Robot Learning Lab, Richard Socher, Co-Founder and CEO of You.com, Raquel Urtasun, Founder and CEO of Waabi, and Aidan Gomez, Co-Founder and CEO of Cohere.
Right now, we must learn the computer’s language. I went to school for five years to learn how to talk to a computer, to tell it what to do, and to code it. I learned to speak its language. We’re not yet in a place where our products speak our language. That will change…
![Objects generated by 3D-GAN from vectors, without a reference image/object. We show, for the last two objects in each row, the nearest neighbor retrieved from the training set. We see that the generated objects are similar, but not identical, to examples in the training set. For comparison, we show objects generated by the previous state-of-the-art [Wu et al., 2015] (results supplied by the authors). We also show objects generated by autoencoders trained on a single object category, with latent vectors sampled from empirical distribution. See paper Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling.](https://radicalventure.wpenginepowered.com/wp-content/uploads/2022/09/Screen-Shot-2022-09-26-at-9.51.58-AM-1024x479.png)
An idea, of course, is the domain of the human mind. Breakthrough advances in generative AI technology, however, are changing the notion of what it means to substantiate an idea, offering new mechanisms for creation and iteration that were – until just recently – entirely bound by our biological capacity…
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