Yannic Kilcher Videos (Audio Only)

#adversarialexamples #dimpledmanifold #security Adversarial Examples have long been a fascinating topic for many Machine Learning researchers. How can a tiny perturbation cause the neural network to change its output by so much? While many explanations have been proposed over the years, they all appear to fall short. This paper attempts to comprehensively explain the existence of adversarial examples by proposing a view of the classification landscape, which they call the Dimpled Manifold Model, which says that any classifier will adjust its decision boundary to align with the low-dimensional data manifold, and only slightly bend around the data. This potentially explains many phenomena around adversarial examples. Warning: In this video, I disagree. Remember that I'm not an authority, but simply give my own opinions. OUTLINE: 0:00 - Intro & Overview 7:30 - The old mental image of Adversarial Examples 11:25 - The new Dimpled Manifold Hypothesis 22:55 - The Stretchy Feature Model 29:05 - Why do DNNs create Dimpled Manifolds? 38:30 - What can be explained with the new model? 1:00:40 - Experimental evidence for the Dimpled Manifold Model 1:10:25 - Is Goodfellow's claim debunked? 1:13:00 - Conclusion & Comments Paper: https://arxiv.org/abs/2106.10151 My replication code: https://gist.github.com/yk/de8d987c4e... Goodfellow's Talk: https://youtu.be/CIfsB_EYsVI?t=4280 Abstract: The extreme fragility of deep neural networks when presented with tiny perturbations in their inputs was independently discovered by several research groups in 2013, but in spite of enormous effort these adversarial examples remained a baffling phenomenon with no clear explanation. In this paper we introduce a new conceptual framework (which we call the Dimpled Manifold Model) which provides a simple explanation for why adversarial examples exist, why their perturbations have such tiny norms, why these perturbations look like random noise, and why a network which was adversarially trained with incorrectly labeled images can still correctly classify test images. In the last part of the paper we describe the results of numerous experiments which strongly support this new model, and in particular our assertion that adversarial perturbations are roughly perpendicular to the low dimensional manifold which contains all the training examples. Abstract: Adi Shamir, Odelia Melamed, Oriel BenShmuel Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#mlnews #gta #weather In this week's ML News, we look at the latest developments in the Machine Learning and AI world with updates from research, industry, and society at large. OUTLINE: 0:00 - Intro 0:20 - Hugging Face launches free course 1:30 - Sentdex releases GAN Theft Auto 2:25 - Facebook uses AI to help moderators 4:10 - Weather with Antonio 5:10 - Autonomous ship aborts mission 7:25 - PyTorch Release 1.9 8:30 - McDonald's new AI drive thru 10:20 - UBS CEO says AI won't replace humans 12:20 - Gödel paper has 90th birthday 12:55 - AugLy data augmentation library 13:20 - Programming Puzzles for autonomous coding 14:30 - Boston Dynamics' Spot turns 1 References: PyTorch 1.9 Released https://pytorch.org/blog/pytorch-1.9-... Hugging Face launches course https://huggingface.co/course/chapter1 90 years of Gödel's theory https://people.idsia.ch/~juergen/goed... AugLy: A data augmentation library https://ai.facebook.com/blog/augly-a-... Sentdex builds GAN Theft Auto https://github.com/sentdex/GANTheftAuto/ Spot turns 1 https://blog.bostondynamics.com/spots... Autonomous ship aborts mission https://www.washingtonpost.com/techno... https://mas400.com/dashboard#currentL... McDonald's tests AI drive thru https://www.zdnet.com/article/i-just-... Facebook uses AI to moderate conversations https://edition.cnn.com/2021/06/16/te... UBS CEO says AI won't replace financial advisors https://www.cnbc.com/2021/06/17/ai-wo... Programming Puzzles https://arxiv.org/abs/2106.05784 https://github.com/microsoft/PythonPr... Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/yannic-ki... BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#xcit #transformer #attentionmechanism After dominating Natural Language Processing, Transformers have taken over Computer Vision recently with the advent of Vision Transformers. However, the attention mechanism's quadratic complexity in the number of tokens means that Transformers do not scale well to high-resolution images. XCiT is a new Transformer architecture, containing XCA, a transposed version of attention, reducing the complexity from quadratic to linear, and at least on image data, it appears to perform on par with other models. What does this mean for the field? Is this even a transformer? What really matters in deep learning? OUTLINE: 0:00 - Intro & Overview 3:45 - Self-Attention vs Cross-Covariance Attention (XCA) 19:55 - Cross-Covariance Image Transformer (XCiT) Architecture 26:00 - Theoretical & Engineering considerations 30:40 - Experimental Results 33:20 - Comments & Conclusion Paper: https://arxiv.org/abs/2106.09681 Code: https://github.com/facebookresearch/xcit Abstract: Following their success in natural language processing, transformers have recently shown much promise for computer vision. The self-attention operation underlying transformers yields global interactions between all tokens ,i.e. words or image patches, and enables flexible modelling of image data beyond the local interactions of convolutions. This flexibility, however, comes with a quadratic complexity in time and memory, hindering application to long sequences and high-resolution images. We propose a "transposed" version of self-attention that operates across feature channels rather than tokens, where the interactions are based on the cross-covariance matrix between keys and queries. The resulting cross-covariance attention (XCA) has linear complexity in the number of tokens, and allows efficient processing of high-resolution images. Our cross-covariance image transformer (XCiT) is built upon XCA. It combines the accuracy of conventional transformers with the scalability of convolutional architectures. We validate the effectiveness and generality of XCiT by reporting excellent results on multiple vision benchmarks, including image classification and self-supervised feature learning on ImageNet-1k, object detection and instance segmentation on COCO, and semantic segmentation on ADE20k. Authors: Alaaeldin El-Nouby, Hugo Touvron, Mathilde Caron, Piotr Bojanowski, Matthijs Douze, Armand Joulin, Ivan Laptev, Natalia Neverova, Gabriel Synnaeve, Jakob Verbeek, Hervé Jegou Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/yannic-ki... BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#reiforcementlearning #gan #imitationlearning Learning from demonstrations is a fascinating topic, but what if the demonstrations are not exactly the behaviors we want to learn? Can we adhere to a dataset of demonstrations and still achieve a specified goal? This paper uses GANs to combine goal-achieving reinforcement learning with imitation learning and learns to perform well at a given task while doing so in the style of a given presented dataset. The resulting behaviors include many realistic-looking transitions between the demonstrated movements. OUTLINE: 0:00 - Intro & Overview 1:25 - Problem Statement 6:10 - Reward Signals 8:15 - Motion Prior from GAN 14:10 - Algorithm Overview 20:15 - Reward Engineering & Experimental Results 30:40 - Conclusion & Comments Paper: https://arxiv.org/abs/2104.02180 Main Video: https://www.youtube.com/watch?v=wySUx... Supplementary Video: https://www.youtube.com/watch?v=O6fBS... Abstract: Synthesizing graceful and life-like behaviors for physically simulated characters has been a fundamental challenge in computer animation. Data-driven methods that leverage motion tracking are a prominent class of techniques for producing high fidelity motions for a wide range of behaviors. However, the effectiveness of these tracking-based methods often hinges on carefully designed objective functions, and when applied to large and diverse motion datasets, these methods require significant additional machinery to select the appropriate motion for the character to track in a given scenario. In this work, we propose to obviate the need to manually design imitation objectives and mechanisms for motion selection by utilizing a fully automated approach based on adversarial imitation learning. High-level task objectives that the character should perform can be specified by relatively simple reward functions, while the low-level style of the character's behaviors can be specified by a dataset of unstructured motion clips, without any explicit clip selection or sequencing. These motion clips are used to train an adversarial motion prior, which specifies style-rewards for training the character through reinforcement learning (RL). The adversarial RL procedure automatically selects which motion to perform, dynamically interpolating and generalizing from the dataset. Our system produces high-quality motions that are comparable to those achieved by state-of-the-art tracking-based techniques, while also being able to easily accommodate large datasets of unstructured motion clips. Composition of disparate skills emerges automatically from the motion prior, without requiring a high-level motion planner or other task-specific annotations of the motion clips. We demonstrate the effectiveness of our framework on a diverse cast of complex simulated characters and a challenging suite of motor control tasks. Authors: Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, Angjoo Kanazawa Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher

OUTLINE: 0:00 - Intro 0:30 - Google RL creates next-gen TPUs 2:15 - Facebook launches NetHack challenge 3:50 - OpenAI mitigates bias by fine-tuning 9:05 - Google AI releases browseable reconstruction of human cortex 9:50 - GPT-J 6B Transformer in JAX 12:00 - Tensorflow launches Forum 13:50 - Text style transfer from a single word 15:45 - ALiEn artificial life simulator My Video on Chip Placement: https://youtu.be/PDRtyrVskMU References: RL creates next-gen TPUs https://www.nature.com/articles/s4158... https://www.youtube.com/watch?v=PDRty... Facebook launches NetHack challenge https://ai.facebook.com/blog/launchin... Mitigating bias by fine-tuning https://openai.com/blog/improving-lan... Human Cortex 3D Reconstruction https://ai.googleblog.com/2021/06/a-b... GPT-J: An open-source 6B transformer https://arankomatsuzaki.wordpress.com... https://6b.eleuther.ai/ https://github.com/kingoflolz/mesh-tr... Tensorflow launches "Forum" https://discuss.tensorflow.org/ Text style transfer from single word https://ai.facebook.com/blog/ai-can-n... ALiEn Life Simulator https://github.com/chrxh/alien Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#implicitfunction #jax #autodiff Many problems in Machine Learning involve loops of inner and outer optimization. Finding update steps for the outer loop is usually difficult, because of the.need to differentiate through the inner loop's procedure over multiple steps. Such loop unrolling is very limited and constrained to very few steps. Other papers have found solutions around unrolling in very specific, individual problems. This paper proposes a unified framework for implicit differentiation of inner optimization procedures without unrolling and provides implementations that integrate seamlessly into JAX. OUTLINE: 0:00 - Intro & Overview 2:05 - Automatic Differentiation of Inner Optimizations 4:30 - Example: Meta-Learning 7:45 - Unrolling Optimization 13:00 - Unified Framework Overview & Pseudocode 21:10 - Implicit Function Theorem 25:45 - More Technicalities 28:45 - Experiments ERRATA: - Dataset Distillation is done with respect to the training set, not the validation or test set. Paper: https://arxiv.org/abs/2105.15183 Code coming soon Abstract: Automatic differentiation (autodiff) has revolutionized machine learning. It allows expressing complex computations by composing elementary ones in creative ways and removes the burden of computing their derivatives by hand. More recently, differentiation of optimization problem solutions has attracted widespread attention with applications such as optimization as a layer, and in bi-level problems such as hyper-parameter optimization and meta-learning. However, the formulas for these derivatives often involve case-by-case tedious mathematical derivations. In this paper, we propose a unified, efficient and modular approach for implicit differentiation of optimization problems. In our approach, the user defines (in Python in the case of our implementation) a function F capturing the optimality conditions of the problem to be differentiated. Once this is done, we leverage autodiff of F and implicit differentiation to automatically differentiate the optimization problem. Our approach thus combines the benefits of implicit differentiation and autodiff. It is efficient as it can be added on top of any state-of-the-art solver and modular as the optimality condition specification is decoupled from the implicit differentiation mechanism. We show that seemingly simple principles allow to recover many recently proposed implicit differentiation methods and create new ones easily. We demonstrate the ease of formulating and solving bi-level optimization problems using our framework. We also showcase an application to the sensitivity analysis of molecular dynamics. Authors: Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/yannic-ki... BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq

#mlnews #wudao #academicfraud OUTLINE: 0:00 - Intro 0:25 - EU seeks to regulate AI 2:45 - AI COVID detection systems are all flawed 5:05 - Chinese lab trains model 10x GPT-3 size 6:55 - Google error identifies "ugliest" language 9:45 - McDonald's learns about AI buzzwords 11:25 - AI predicts cryptocurrency prices 12:00 - Unreal Engine hack for CLIP 12:35 - Please commit more academic fraud References: https://www.lawfareblog.com/artificia... https://blogs.sciencemag.org/pipeline... https://www.nature.com/articles/s4225... https://en.pingwest.com/a/8693 https://arxiv.org/pdf/2104.12369.pdf https://www.bbc.com/news/world-asia-i... https://www.zdnet.com/article/mcdonal... https://www.analyticsinsight.net/ai-i... https://twitter.com/arankomatsuzaki/s... https://jacobbuckman.com/2021-05-29-p... Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/yannic-ki... BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#decisiontransformer #reinforcementlearning #transformer Proper credit assignment over long timespans is a fundamental problem in reinforcement learning. Even methods designed to combat this problem, such as TD-learning, quickly reach their limits when rewards are sparse or noisy. This paper reframes offline reinforcement learning as a pure sequence modeling problem, with the actions being sampled conditioned on the given history and desired future rewards. This allows the authors to use recent advances in sequence modeling using Transformers and achieve competitive results in Offline RL benchmarks. OUTLINE: 0:00 - Intro & Overview 4:15 - Offline Reinforcement Learning 10:10 - Transformers in RL 14:25 - Value Functions and Temporal Difference Learning 20:25 - Sequence Modeling and Reward-to-go 27:20 - Why this is ideal for offline RL 31:30 - The context length problem 34:35 - Toy example: Shortest path from random walks 41:00 - Discount factors 45:50 - Experimental Results 49:25 - Do you need to know the best possible reward? 52:15 - Key-to-door toy experiment 56:00 - Comments & Conclusion Paper: https://arxiv.org/abs/2106.01345 Website: https://sites.google.com/berkeley.edu... Code: https://github.com/kzl/decision-trans... Abstract: We present a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. This allows us to draw upon the simplicity and scalability of the Transformer architecture, and associated advances in language modeling such as GPT-x and BERT. In particular, we present Decision Transformer, an architecture that casts the problem of RL as conditional sequence modeling. Unlike prior approaches to RL that fit value functions or compute policy gradients, Decision Transformer simply outputs the optimal actions by leveraging a causally masked Transformer. By conditioning an autoregressive model on the desired return (reward), past states, and actions, our Decision Transformer model can generate future actions that achieve the desired return. Despite its simplicity, Decision Transformer matches or exceeds the performance of state-of-the-art model-free offline RL baselines on Atari, OpenAI Gym, and Key-to-Door tasks. Authors: Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/yannic-ki... BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#mlnews #anthropic #eliza Anthropic raises $124M for steerable AI, peer review is threatened by collusion rings, and the original ELIZA source code was discovered. OUTLINE: 0:00 - Intro 0:40 - Anthropic raises $124M 3:25 - 65% of execs can't explain AI predictions 4:25 - DeepMind releases AndroidEnv 6:10 - Collusion rings in ML Conferences 7:30 - ELIZA's original source code discovered 10:45 - OpenAI raises $100M fund 11:25 - Outro References: https://techcrunch.com/2021/05/28/ant... https://www.anthropic.com/news/announ... https://www.anthropic.com/ https://openai.com/blog/introducing-o... https://deepmind.com/research/publica... https://cacm.acm.org/magazines/2021/6... https://venturebeat.com/2021/05/25/65... https://techcrunch.com/2021/05/26/ope... https://sites.google.com/view/elizage... http://psych.fullerton.edu/mbirnbaum/... https://en.wikipedia.org/wiki/Carl_Ro... https://openai.com/fund/ Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/yannic-ki... BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#reinforcementlearning #deepmind #agi What's the most promising path to creating Artificial General Intelligence (AGI)? This paper makes the bold claim that a learning agent maximizing its reward in a sufficiently complex environment will necessarily develop intelligence as a by-product, and that Reward Maximization is the best way to move the creation of AGI forward. The paper is a mix of philosophy, engineering, and futurism, and raises many points of discussion. OUTLINE: 0:00 - Intro & Outline 4:10 - Reward Maximization 10:10 - The Reward-is-Enough Hypothesis 13:15 - Abilities associated with intelligence 16:40 - My Criticism 26:15 - Reward Maximization through Reinforcement Learning 31:30 - Discussion, Conclusion & My Comments Paper: https://www.sciencedirect.com/science... Abstract: In this article we hypothesise that intelligence, and its associated abilities, can be understood as subserving the maximisation of reward. Accordingly, reward is enough to drive behaviour that exhibits abilities studied in natural and artificial intelligence, including knowledge, learning, perception, social intelligence, language, generalisation and imitation. This is in contrast to the view that specialised problem formulations are needed for each ability, based on other signals or objectives. Furthermore, we suggest that agents that learn through trial and error experience to maximise reward could learn behaviour that exhibits most if not all of these abilities, and therefore that powerful reinforcement learning agents could constitute a solution to artificial general intelligence. Authors: David Silver, Satinder Singh, Doina Precup, Richard S. Sutton Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/yannic-ki... BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n