Smash Notes
Raia Hadsell
Episodes
Notes
Pint of Science · Dr Raia Hadsell - Artificial Intelligence researcher
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Is There a Connection Between Neural Networks and the Human Brain
The human brain and other nervous systems in the biological world are the only examples we have of artificial intelligence. Dr. Hadsell and her team takes inspiration from neuroscience, but they want to make it efficient to run on modern computers, which means that it looks different.
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What Is a Problem of Learning Across a Lifetime of Experience
Continually learning, having the knowledge continue over time. It’s far away from what scientists can do with the neural networks. Learning from data set is different from how humans learn. It’s critical to learn in stages and to grow the knowledge and capability of the system to educate the AI to start with the small problems first.
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What’s a Challenge That AI Is Least Suited to Address
The most challenging things are when humans are making decisions that are abstract, vague, and contain inconsistent information. It’s when they are trying to come up with a not so black and white decision. When it involves a lot of human interaction and bringing together uncertain information, it remains to be a complex challenge for AI.
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Are the Scientists Trying to Make Tools to Solve What Humans Can’t Solve or Replicate Human Intelligence
Dr. Hadsell believes the former not the latter, that they are trying to develop AI as a general problem-solving approach. On the way, they are inspired a lot by human intelligence. Right now, mouse level AI is what they can successfully achieve.
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How Much of the Codes Are Shared With Others, and How Much Do You Keep to Yourself
When writing code in Google, it's hard to not use a lot of Google tools that are proprietary. Dr. Hadsell’s team open sources things that provide a lot of value for others. You want to share the code so you can build a community of people working toward the same goal. You also want other labs to try to reproduce. When having disagreements between results, you learn.
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How Do Raia Hadsell's Colleagues Catch a False Win?
On Pint of Science, Dr. Raia Hadsell says a central question is how individual AI agents solve tasks, which tricks they find, and which behaviors emerge. One example she gives is an agent using a neural network as a memory bank to pass a navigation test. Sometimes the scientists think an agent is clever and has solved the problem when it has not. They catch that through careful analysis, further experiments, and ablations, rather than stopping at the result they hoped to see. She says that check is what keeps the work from turning into confirmation bias.
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Can Neural Networks Help Teach Students?
Having a curated curriculum for each student could be really valuable since each student learns different. Dr. Hadsell thinks that it could be possible, and that the roles of human teaches are very important. Humans are much better and more efficient at learning a curriculum of knowledge, applying it, and putting it all together. There are not machines that can learn that way-- as of now.
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What Areas Are We Seeing AI Applied in Our Normal Lives
It depends on how you define AI. Realistically, anything that involves media or understanding things that scientists couldn’t do before having large neural networks that could solve these core tasks, like in videos, to summarize or caption what's happening. We are also seeing changes in the medical world to automatically analyze and read medical scans.
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Is There Any Potential Research That Focuses on Creating an AI Neural Network That Can Be Applied to the Brain
There has been a rise in research of seeing things that look and act like grid cells in the neural network and training, when trained in a particular way, which have opened up new avenues for exploration and research.
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What Is Deep Reinforcement Learning
Deep reinforcement learning is when artificial neural networks are brought together. When it comes to deep reinforcement learning, it uses a neural network that can have millions of neurons that are adapted and changed based on the input and output of the networks, which creates an action in the world.
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How Do You Program an AI to Produce Something That Is Pure Creative
It’s difficult to do. There are algorithms that try to mimic curiosity. When an AI agent is playing the creative mode in Minecraft and comes across something it didn't predict, it will be surprised, and that gives it a happy little bonus. So naturally, you'll try to seek new areas. Those type of algorithms aren't as well developed compared to maximizing an objective or win rate.
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How Would an AI React in Minecraft's Creative Mode
Dr. Hadsell states that this would be a problem in the AI study. She says that the algorithms would have a tough time in the creative mode because it's an open world with no objectives.
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What Does AI Look Like When Watching It on a Screen
Dr. Hadsell states, unfortunately, that it’s nowhere near exciting as in the movies. It looks like a set of graphs and plots over time that reflects the AI’s expectation of winning and what the probabilities of winning and losing are at any point. Also, it’s visible to see the prediction of what the opponent will play next.
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What Is Go, and How Does It Help With AI Research
Go is an ancient, simple game that involves one or two players that put down black or white stones and capture territory. It’s popularly played in Korea. There are many different ways and options that the game can go. DeepMind learned the game from scratch with an AGI approach of having a neural network that learns from the visible data and the experience of the game.
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What Is DeepMind, and What's the Company's Goal
AI was intended for any computer program meant to emulate the decision processes of a human. DeepMind was originally a startup company pre-2014. It was bought by Google, but it is still separate. They act like an independent research group, and their goal is to solve the problem of intelligence and figure out new problems to solve once the company has figured out intelligence.
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Who Is Dr. Raia Hadsell
Dr. Raia Hadsell is a senior research scientist that's part of the world renowned artificial intelligence company, DeepMind. Originally from California, she transitioned from her undergrad degree in religion and philosophy to computer science at a Ph.D. level.