Radical Reads: Advice for Aspiring AI Founders – Radical Exclusive with Professor Fei-Fei Li

Editorial Team

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This week Radical Ventures launched our inaugural AI Founders Master Class, a program aiming to help AI founders create AI companies. Over 200 students and faculty from the Vector Institute for Artificial Intelligence, the Alberta Machine Intelligence Institute, Mila (the Quebec Institute for Machine Intelligence), Stanford University, and Oxford University registered for the four week session.

Below, we are sharing an exclusive excerpt from week one of the AI Founders Master Class. It features a conversation between computer vision pioneer Fei-Fei Li and Radical Ventures Co-Founder and Managing Partner, Jordan Jacobs. The focus of the discussion was the founder mindset.

The following has been edited for length and clarity.

Jordan Jacobs: How much does your own experience play into your personal sense of resolve?


Fei-Fei Li: I know Jordan that you are a parent, and I’m a parent. This is the million dollar question that I ask myself: Do we educate resolve and grit into a child, let life teach them, or is it imprinted into a piece of the genome? The truth is we all have our own unique journey. My personal journey, as an immigrant teenager going through a New Jersey public high school in a low income situation, allowed me to gradually build that resolve.

One thing I do want to say to all of the aspiring founders here, especially having been through multiple universities, Illinois, Stanford, Princeton, and having spent time in Silicon Valley advising young people: there is a difference between, resolve, grit, and stubbornness.

At least for myself, I think that ‘North Star’ resolve, knowing the mission and pursuing it, is really important. But the path toward that North Star, building the toolsets and learning the execution pathway, is also really important. To use a fashionable term, you need to have a “growth mindset” and be a learner and a listener. You need to always reflect and understand where your weaknesses are and either strengthen yourself or invite partners and people who can compliment your path to that North Star. I think that is the subtle art of life. As someone who has seen young people through their journey for the past few decades, those who can blend that magical combination of resolve and growth are more likely to succeed.

JJ: Is there any other advice you would like to share with AI researchers contemplating building a startup?


FL: We are living the AI revolution. This AI revolution is, whether we like it or not, one of the biggest technological revolutions that will change the fate of humanity. We have an incredible opportunity. 

We have trillion dollar companies yet to be born using this general technology. But, I still hope those of you who are aspiring entrepreneurs keep a heart in all this.

Aspire to do the right thing and do it for the right reason. There are no independent machine values. Machine values are human values. What you are creating with this technology will fundamentally impact human lives. It may bring you fame and wealth if that matters to you. But, at the end of the day, I would encourage all of you to think of the human responsibility of what you’re creating. Can we do everything within an ethical framework that can truly make the world better, rather than focusing solely on the business goals? I would love to see more entrepreneurs keep their heart in the business journey.

Radical Ventures was created to work with visionaries like Fei-Fei Li and to support them in harnessing the transformative power of AI technology. The AI Founders Master Class continues this week, featuring a conversation with Pieter Abbeel, Director of the Berkeley Robot Learning Lab and co-founder of Radical portfolio company, Covariant. Stay tuned for more exclusives from the program.

For more information on the AI Founders Master Class or other Radical Ventures AI ecosystem projects reach out to Leah Morris at leah@radical.vc.

5 Noteworthy AI and Deep Tech Articles: week of October 18, 2021

1) The State of AI Report 2021 (Benaich and Hogarth)
There are massive implications across policy, industry, and the workforce due to technology breakthroughs in AI. This annual report compiles research contributions from major AI innovators including Stanford, DeepMind, IBM Research, Mila, and Berkeley, amongst others. Based on this research, the State of AI Report has a number of predictions for 2022, including Transformer architecture replacing recurrent networks and surpassing human performance in large and rich game environments. Aidan Gomez is a co-author of the breakthrough paper “Attention is All You Need,” which invented the Transformer architecture. Aidan is the Co-Founder and CEO of NLP platform company Cohere – a Radical Ventures portfolio company. 

2) Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report (Stanford University)
The AI100 Report looks at the state of the AI field every five years. This year’s instalment addresses new found limits of deep learning and its ability to drive the field in the future. The report notes, “the field of AI has made remarkable progress in the past five years and is having real-world impact on people, institutions and culture. The ability of computer programs to perform sophisticated language and image-processing tasks, core problems that have driven the field since its birth in the 1950s, has advanced significantly. Although the current state of AI technology is still far short of the field’s founding aspiration of recreating full human-like intelligence in machines, research and development teams are leveraging these advances and incorporating them into society-facing applications.”

3) Deep learning helps predict traffic crashes before they happen (MIT CSAIL)
What if we could predict when a roadway was likely to have a car accident? Scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Qatar Center for Artificial Intelligence (QCAI) developed a deep learning model that predicts very high-resolution crash risk maps. The risk maps identify high-risk locations through a combination of historical crash data, road maps, satellite imagery, and GPS traces. These maps describe the expected number of crashes over a period of time in the future, identifying high-risk areas and predicting future crashes. Translating this knowledge to active drivers on high-risk roadways has yet to be designed. 

4) Learning Indoor Inverse Rendering with 3D Spatially-Varying Lighting (arXiv)
Vector Institute co-founder Sanja Fidler, and Vector-affiliated researchers Zian Wang, and Jonah Philion, are improving augmented reality (AR) with AI. Their paper offers a new approach to estimating reflectance, shape, and 3D spatially varying lighting by formulating the complete rendering process in an end-to-end trainable way with a 3D lighting representation. This method allows virtual objects to be more realistic by making better predictions about appearances in complex environments such as reflections and shadows from indoor lighting. 

5) AI and maths to play bigger role in global diplomacy, says expert (The Guardian)
AI could be helpful to negotiations by identifying patterns in economic data underpinning free trade deals and standardizing negotiations. Math in negotiations is far from new. Game theory was developed in the 1920s to describe problems with asymmetrical information mathematically. AI, like game theory, could prove to be a useful tool to support decision-making and clarify the level of certainty and risk associated with a decision. 

–R–

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