astronomy

Timothy Lillicrap

Timothy Lillicrap is a astronomy topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Timothy Lillicrap rather than just read about it. In short: Timothy P. Lillicrap is a Canadian neuroscientist and artificial intelligence (AI) researcher, adjunct professor at University College London, and staff research scientist at Google DeepMind, where he has been involved in the AlphaGo and AlphaZero projects mastering the games of Go, Chess and Shogi.

Key takeaways

Timothy Lillicrap belongs to astronomy; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Timothy Lillicrap to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Timothy Lillicrap from memory before moving on to harder problems.

Reference excerpt

Timothy P. Lillicrap is a Canadian neuroscientist and artificial intelligence (AI) researcher, adjunct professor at University College London, and staff research scientist at Google DeepMind, where he has been involved in the AlphaGo and AlphaZero projects mastering the games of Go, Chess and Shogi. His research focuses on machine learning and statistics for optimal control and decision making, as well as using these mathematical frameworks to understand how the brain learns. He has developed algorithms and approaches for exploiting deep neural networks in the context of reinforcement learning, and new recurrent memory architectures for one-shot learning. His numerous contributions to the field have earned him a number of honours, including the Governor General's Academic Medal, an NSERC Fellowship, the Centre for Neuroscience Studies Award for Excellence, and numerous European Research Council grants. He has also won a number of Social Learning tournaments.

Biography Lillicrap attained a B.Sc. in cognitive science and artificial intelligence from University of Toronto in 2005, and a Ph.D. in systems neuroscience from Queen's University in 2012 under Stephen H. Scott. He then went on to become a postdoctoral research fellow at Oxford University, and joined Google DeepMind as a research scientist in 2014. Following a series of promotions, he eventually became a DeepMind staff research scientist in 2016, a position he still held in 2021. In 2016, Lillicrap accepted an adjunct professorship at University College London.

Select publications Timothy Lillicrap has an extensive publication record. A selection of works is listed below:

Timothy Lillicrap (2014). Modelling Motor Cortex using Neural Network Controls Laws. Ph.D. Systems Neuroscience Thesis, Centre for Neuroscience Studies, Queen's University, advisor: Stephen H. Scott Timothy Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra (2015). Continuous Control with Deep Reinforcement Learning. arXiv:1509.02971 Nicolas Heess, Jonathan J. Hunt, Timothy Lillicrap, David Silver (2015). Memory-based control with recurrent neural networks. arXiv:1512.04455 David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis (2016). Mastering the game of Go with deep neural networks and tree search. Nature, Vol. 529 » AlphaGo Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu (2016). Asynchronous Methods for Deep Reinforcement Learning. arXiv:1602.01783v2 Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine (2016). Continuous Deep Q-Learning with Model-based Acceleration. arXiv:1603.00748 Shixiang Gu, Ethan Holly, Timothy Lillicrap, Sergey Levine (2016). Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates. arXiv:1610.00633 Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine (2016). Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. arXiv:1611.02247 Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy Lillicrap, Matthew Botvinick, Nando de Freitas (2017). Learning to Learn without Gradient Descent by Gradient Descent. arXiv:1611.03824v6, ICML 2017 David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, Demis Hassabis (2017). Mastering the game of Go without human knowledge. Nature, Vol. 550 David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis (2017). Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. arXiv:1712.01815 » AlphaZero Jack W. Rae, Chris Dyer, Peter Dayan, Timothy Lillicrap (2018). Fast Parametric Learning with Activation Memorization. arXiv:1803.10049 David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, Vol. 362, No. 6419 Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, David Silver (2019). Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. arXiv:1911.08265

Notable awards NSERC Fellowship Queen's University Graduate Award Governor General's Academic Medal Social Learning Strategies Tournament Winner 2nd Social Learning Strategies Tournament Winner European Research Council Proof of Concept Grant Centre for Neuroscience Studies Award for Excellence University College Howard Ferguson Entrance Scholarship HPCVL / Sun Microsystems of Canada, Inc. Scholarship in Computational Sciences and Engineering

References Content in this article was copied from Timothy Lillicrap at the Chess Programming wiki, which is licensed under the Creative Commons Attribution-Share Alike 3.0 (Unported) (CC-BY-SA 3.0) license.

External links

Homepage of Timothy P. Lillicrap Timothy P. Lillicrap - Google Scholar Citations

Worked examples

Example 1 — a first encounter with Timothy Lillicrap

Start with the simplest possible case. Write down what Timothy Lillicrap claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In astronomy, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Timothy Lillicrap before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Timothy Lillicrap ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Timothy Lillicrap

In research: Timothy Lillicrap appears in astronomy research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Timothy Lillicrap in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Timothy Lillicrap is common in secondary-school and first-year university syllabi. It links to neighbouring topics Academics of University College London, Canadian artificial intelligence researchers, Canadian neuroscientists, so understanding it makes those chapters shorter.
In everyday life: Look for Timothy Lillicrap outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

Ask Teacher Smith questions about this articleOpens your AI tutor with a question about “Timothy Lillicrap” →

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Timothy Lillicrap in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Timothy Lillicrap means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Timothy Lillicrap out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Timothy Lillicrap in simple terms?

Why does Timothy Lillicrap matter?

Because it connects several astronomy ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Timothy Lillicrap?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Timothy Lillicrap.