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Machine Learning Street Talk

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This is the audio podcast for the ML Street Talk YouTube channel at

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We think that scientists and engineers are the heroes of our generation. Each week we have a hard-hitting discussion with the leading thinkers in the AI space. Street Talk is unabashedly technical and non-commercial, so you will hear no annoying pitches. Corporate- and MBA-speak is banned on street talk, "data product", "digital transformation" are banned, we promise :)

Dr. Tim Scarfe, Dr. Yannic Kilcher and Dr. Keith Duggar.
61 Episodes
The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. Many high-dimensional learning tasks previously thought to be beyond reach -- such as computer vision, playing Go, or protein folding -- are in fact tractable given enough computational horsepower. Remarkably, the essence of deep learning is built from two simple algorithmic principles: first, the notion of representation or feature learning and second, learning by local gradient-descent type methods, typically implemented as backpropagation. While learning generic functions in high dimensions is a cursed estimation problem, most tasks of interest are not uniform and have strong repeating patterns as a result of the low-dimensionality and structure of the physical world. Geometric Deep Learning unifies a broad class of ML problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way to construct new types of problem-specific inductive biases. This week we spoke with Professor Michael Bronstein (head of graph ML at Twitter) and Dr. Petar Veličković (Senior Research Scientist at DeepMind), and Dr. Taco Cohen and Prof. Joan Bruna about their new proto-book Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. See the table of contents for this (long) show at 
The ultimate goal of neuroscience is to learn how the human brain gives rise to human intelligence and what it means to be intelligent. Understanding how the brain works is considered one of humanity’s greatest challenges.  Jeff Hawkins thinks that the reality we perceive is a kind of simulation, a hallucination, a confabulation. He thinks that our brains are a model reality based on thousands of information streams originating from the sensors in our body.  Critically - Hawkins doesn’t think there is just one model but rather; thousands.  Jeff has just released his new book, A thousand brains: a new theory of intelligence. It’s an inspiring and well-written book and I hope after watching this show; you will be inspired to read it too. Panel: Dr. Keith Duggar Connor Leahy
The field of Artificial Intelligence was founded in the mid 1950s with the aim of constructing “thinking machines” - that is to say, computer systems with human-like general intelligence. Think of humanoid robots that not only look but act and think with intelligence equal to and ultimately greater than that of human beings. But in the intervening years, the field has drifted far from its ambitious old-fashioned roots. Dr. Ben Goertzel is an artificial intelligence researcher, CEO and founder of SingularityNET. A project combining artificial intelligence and blockchain to democratize access to artificial intelligence. Ben seeks to fulfil the original ambitions of the field.  Ben graduated with a PhD in Mathematics from Temple University in 1990. Ben’s approach to AGI over many decades now has been inspired by many disciplines, but in particular from human cognitive psychology and computer science perspective. To date Ben’s work has been mostly theoretically-driven. Ben thinks that most of the deep learning approaches to AGI today try to model the brain. They may have a loose analogy to human neuroscience but they have not tried to derive the details of an AGI architecture from an overall conception of what a mind is. Ben thinks that what matters for creating human-level (or greater) intelligence is having the right information processing architecture, not the underlying mechanics via which the architecture is implemented. Ben thinks that there is a certain set of key cognitive processes and interactions that AGI systems must implement explicitly such as; working and long-term memory, deliberative and reactive processing, perc biological systems tend to be messy, complex and integrative; searching for a single “algorithm of general intelligence” is an inappropriate attempt to project the aesthetics of physics or theoretical computer science into a qualitatively different domain. TOC is on the YT show description Panel: Dr. Tim Scarfe, Dr. Yannic Kilcher, Dr. Keith Duggar Artificial General Intelligence: Concept, State of the Art, and Future Prospects The General Theory of General Intelligence: A Pragmatic Patternist Perspective
Since its beginning in the 1950s, the field of artificial intelligence has vacillated between periods of optimistic predictions and massive investment and periods of disappointment, loss of confidence, and reduced funding. Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected.  Professor Melanie Mitchell thinks one reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. YT vid- Main show kick off [00:26:51] Panel: Dr. Tim Scarfe, Dr. Keith Duggar, Letitia Parcalabescu (
It has been over three decades since the statistical revolution overtook AI by a storm and over two  decades since deep learning (DL) helped usher the latest resurgence of artificial intelligence (AI). However, the disappointing progress in conversational agents, NLU, and self-driving cars, has made it clear that progress has not lived up to the promise of these empirical and data-driven methods. DARPA has suggested that it is time for a third wave in AI, one that would be characterized by hybrid models – models that combine knowledge-based approaches with data-driven machine learning techniques.  Joining us on this panel discussion is polymath and linguist Walid Saba - Co-founder ONTOLOGIK.AI, Gadi Singer - VP & Director, Cognitive Computing Research, Intel Labs and J. Mark Bishop - Professor of Cognitive Computing (Emeritus), Goldsmiths, University of London and Scientific Adviser to FACT360. Moderated by Dr. Keith Duggar and Dr. Tim Scarfe #machinelearning #artificialintelligence
Dr. Ishan Misra is a Research Scientist at Facebook AI Research where he works on Computer Vision and Machine Learning. His main research interest is reducing the need for human supervision, and indeed, human knowledge in visual learning systems. He finished his PhD at the Robotics Institute at Carnegie Mellon. He has done stints at Microsoft Research, INRIA and Yale. His bachelors is in computer science where he achieved the highest GPA in his cohort.  Ishan is fast becoming a prolific scientist, already with more than 3000 citations under his belt and co-authoring with Yann LeCun; the godfather of deep learning.  Today though we will be focusing an exciting cluster of recent papers around unsupervised representation learning for computer vision released from FAIR. These are; DINO: Emerging Properties in Self-Supervised Vision Transformers, BARLOW TWINS: Self-Supervised Learning via Redundancy Reduction and PAWS: Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples. All of these papers are hot off the press, just being officially released in the last month or so. Many of you will remember PIRL: Self-Supervised Learning of Pretext-Invariant Representations which Ishan was the primary author of in 2019. References; Shuffle and Learn - DepthContrast - DINO - Barlow Twins - SwAV - PIRL - AVID - (best paper candidate at CVPR'21 (just announced over the weekend) -   Alexei (Alyosha) Efros   Exemplar networks   The bitter lesson - Rich Sutton   Machine Teaching: A New Paradigm for Building Machine Learning Systems   POET
Professor Gary Marcus is a scientist, best-selling author, and entrepreneur. He is Founder and CEO of Robust.AI, and was Founder and CEO of Geometric Intelligence, a machine learning company acquired by Uber in 2016. Gary said in his recent next decade paper that — without us, or other creatures like us, the world would continue to exist, but it would not be described, distilled, or understood.  Human lives are filled with abstraction and causal description. This is so powerful. Francois Chollet the other week said that intelligence is literally sensitivity to abstract analogies, and that is all there is to it. It's almost as if one of the most important features of intelligence is to be able to abstract knowledge, this drives the generalisation which will allow you to mine previous experience to make sense of many future novel situations.   Also joining us today is Professor Luis Lamb — Secretary of Innovation for Science and Technology of the State of Rio Grande do Sul, Brazil. His Research Interests are Machine Learning and Reasoning, Neuro-Symbolic Computing, Logic in Computation and Artificial Intelligence, Cognitive and Neural Computation and also AI Ethics and Social Computing. Luis released his new paper Neurosymbolic AI: the third wave at the end of last year. It beautifully articulated the key ingredients needed in the next generation of AI systems, integrating type 1 and type 2 approaches to AI and it summarises all the of the achievements of the last 20 years of research.   We cover a lot of ground in today's show. Explaining the limitations of deep learning, Rich Sutton's the bitter lesson and "reward is enough", and the semantic foundation which is required for us to build robust AI.
Bob Coercke is a celebrated physicist, he's been a Physics and Quantum professor at Oxford University for the last 20 years. He is particularly interested in Structure which is to say, Logic, Order, and Category Theory. He is well known for work involving compositional distributional models of natural language meaning and he is also fascinated with understanding how our brains work. Bob was recently appointed as the Chief Scientist at Cambridge Quantum Computing. Bob thinks that interactions between systems in Quantum Mechanics carries naturally over to how word meanings interact in natural language. Bob argues that this interaction embodies the phenomenon of quantum teleportation. Bob invented ZX-calculus, a graphical calculus for revealing the compositional structure inside quantum circuits - to show entanglement states and protocols in a visually succinct but logically complete way. Von Neumann himself didn't even like his own original symbolic formalism of quantum theory, despite it being widely used! We hope you enjoy this fascinating conversation which might give you a lot of insight into natural language processing.  Tim Intro [00:00:00] The topological brain (Post-record button skit) [00:13:22] Show kick off [00:19:31] Bob introduction [00:22:37] Changing culture in universities [00:24:51] Machine Learning is like electricity [00:31:50] NLP -- what is Bob's Quantum conception? [00:34:50] The missing text problem [00:52:59] Can statistical induction be trusted? [00:59:49] On pragmatism and hybrid systems [01:04:42] Parlour tricks, parsing and information flows [01:07:43] How much human input is required with Bob's method? [01:11:29] Reality, meaning, structure and language [01:14:42] Replacing complexity with quantum entanglement, emergent complexity [01:17:45] Loading quantum data requires machine learning [01:19:49]  QC is happy math coincidence for NLP [01:22:30] The Theory of English (ToE) [01:28:23]  ... or can we learn the ToE? [01:29:56]  How did diagrammatic quantum calculus come about? [01:31:04 The state of quantum computing today [01:37:49]  NLP on QC might be doable even in the NISQ era [01:40:48]  Hype and private investment are driving progress [01:48:34]  Crypto discussion (moved to post-show) [01:50:38]  Kilcher is in a startup (moved to post show) [01:53:40 Debrief [01:55:26] 
Performing reliably on unseen or shifting data distributions is a difficult challenge for modern vision systems, even slight corruptions or transformations of images are enough to slash the accuracy of state-of-the-art classifiers. When an adversary is allowed to modify an input image directly, models can be manipulated into predicting anything even when there is no perceptible change, this is known an adversarial example. The ideal definition of an adversarial example is when humans consistently say two pictures are the same but a machine disagrees. Hadi Salman, a Ph.D student at MIT (ex-Uber and Microsoft Research) started thinking about how adversarial robustness  could be leveraged beyond security. He realised that the phenomenon of adversarial examples could actually be turned upside down to lead to more robust models instead of breaking them. Hadi actually utilized the brittleness of neural networks to design unadversarial examples or robust objects which_ are objects designed specifically to be robustly recognized by neural networks.  Introduction [00:00:00] DR KILCHER'S PHD HAT [00:11:18] Main Introduction [00:11:38] Hadi's Introduction [00:14:43] More robust models == transfer better [00:46:41] Features not bugs paper [00:49:13] Manifolds [00:55:51] Robustness and Transferability [00:58:00] Do non-robust features generalize worse than robust? [00:59:52] The unreasonable predicament of entangled features [01:01:57] We can only find adversarial examples in the vicinity [01:09:30] Certifiability of models for robustness [01:13:55] Carlini is coming for you! And we are screwed [01:23:21] Distribution shift and corruptions are a bigger problem than adversarial examples [01:25:34] All roads lead to generalization [01:26:47] Unadversarial examples [01:27:26]
In today's show we are joined by Francois Chollet, I have been inspired by Francois ever since I read his Deep Learning with Python book and started using the Keras library which he invented many, many years ago. Francois has a clarity of thought that I've never seen in any other human being! He has extremely interesting views on intelligence as generalisation, abstraction and an information conversation ratio. He wrote on the measure of intelligence at the end of 2019 and it had a huge impact on my thinking. He thinks that NNs can only model continuous problems, which have a smooth learnable manifold and that many "type 2" problems which involve reasoning and/or planning are not suitable for NNs. He thinks that many problems have type 1 and type 2 enmeshed together. He thinks that the future of AI must include program synthesis to allow us to generalise broadly from a few examples, but the search could be guided by neural networks because the search space is interpolative to some extent. Tim's Whimsical notes;
Dr. Christian Szegedy from Google Research is a deep learning heavyweight. He invented adversarial examples, one of the first object detection algorithms, the inceptionnet architecture, and co-invented batchnorm. He thinks that if you bet on computers and software in 1990 you would have been as right as if you bet on AI now. But he thinks that we have been programming computers the same way since the 1950s and there has been a huge stagnation ever since. Mathematics is the process of taking a fuzzy thought and formalising it. But could we automate that? Could we create a system which will act like a super human mathematician but you can talk to it in natural language? This is what Christian calls autoformalisation. Christian thinks that automating many of the things we do in mathematics is the first step towards software synthesis and building human-level AGI. Mathematics ability is the litmus test for general reasoning ability. Christian has a fascinating take on transformers too.  With Yannic Lightspeed Kilcher and Dr. Mathew Salvaris Whimsical Canvas with Tim's Notes: YouTube version (with detailed table of contents)
The race is on, we are on a collective mission to understand and create artificial general intelligence. Dr. Tom Zahavy, a Research Scientist at DeepMind thinks that reinforcement learning is the most general learning framework that we have today, and in his opinion it could lead to artificial general intelligence. He thinks there are no tasks which could not be solved by simply maximising a reward.  Back in 2012 when Tom was an undergraduate, before the deep learning revolution he attended an online lecture on how CNNs automatically discover representations. This was an epiphany for Tom. He decided in that very moment that he was going to become an ML researcher.  Tom's view is that the ability to recognise patterns and discover structure is the most important aspect of intelligence. This has been his quest ever since. He is particularly focused on using diversity preservation and metagradients to discover this structure.  In this discussion we dive deep into meta gradients in reinforcement learning.  Video version and TOC @
First episode in a series we are doing on ML DevOps. Starting with the thing which nobody seems to be talking about enough, security! We chat with cyber security expert Andy Smith about threat modelling and trust boundaries for an ML DevOps system.  Intro [00:00:00] ML DevOps - a security perspective [00:00:50] Threat Modelling [00:03:03] Adversarial examples? [00:11:27] Nobody understands the whole stack [00:13:53] On the size of the state space, the element of unpredictability [00:18:32] Threat modelling in more detail [00:21:17] Trust boundaries for an ML DevOps system [00:25:45] Andy has a YouTube channel on cyber security! Check it out @ Video version:
Christoph Molnar is one of the main people to know in the space of interpretable ML. In 2018 he released the first version of his incredible online book, interpretable machine learning. Interpretability is often a deciding factor when a machine learning (ML) model is used in a product, a decision process, or in research. Interpretability methods can be used to discover knowledge, to debug or justify the model and its predictions, and to control and improve the model, reason about potential bias in models as well as increase the social acceptance of models. But Interpretability methods can also be quite esoteric, add an additional layer of complexity and potential pitfalls and requires expert knowledge to understand. Is it even possible to understand complex models or even humans for that matter in any  meaningful way?  Introduction to IML [00:00:00] Show Kickoff [00:13:28] What makes a good explanation? [00:15:51] Quantification of how good an explanation is [00:19:59] Knowledge of the pitfalls of IML [00:22:14] Are linear models even interpretable? [00:24:26] Complex Math models to explain Complex Math models? [00:27:04] Saliency maps are glorified edge detectors [00:28:35] Challenge on IML -- feature dependence [00:36:46] Don't leap to using a complex model! Surrogate models can be too dumb [00:40:52] On airplane pilots. Seeking to understand vs testing [00:44:09] IML Could help us make better models or lead a better life [00:51:53] Lack of statistical rigor and quantification of uncertainty [00:55:35] On Causality [01:01:09] Broadening out the discussion to the process or institutional level [01:08:53] No focus on fairness / ethics? [01:11:44] Is it possible to condition ML model training on IML metrics ? [01:15:27] Where is IML going? Some of the esoterica of the IML methods [01:18:35] You can't compress information without common knowledge, the latter becomes the bottleneck [01:23:25] IML methods used non-interactively? Making IML an engineering discipline [01:31:10] Tim Postscript -- on the lack of effective corporate operating models for IML, security, engineering and ethics [01:36:34] Explanation in Artificial Intelligence: Insights from the Social Sciences (Tim Miller 2018) Seven Myths in Machine Learning Research (Chang 19)  Myth 7: Saliency maps are robust ways to interpret neural networks Sanity Checks for Saliency Maps (Adebayo 2020) Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Christoph Molnar: Please show your appreciation and buy Christoph's book here; Panel:  Connor Tann Dr. Tim Scarfe  Dr. Keith Duggar Video version:
Academics think of themselves as trailblazers, explorers — seekers of the truth. Any fundamental discovery involves a significant degree of risk. If an idea is guaranteed to work then it moves from the realm of research to engineering. Unfortunately, this also means that most research careers will invariably be failures at least if failures are measured via “objective” metrics like citations. Today we discuss the recent article from Mark Saroufim called Machine Learning: the great stagnation. We discuss the rise of gentleman scientists, fake rigor, incentives in ML, SOTA-chasing, "graduate student descent", distribution of talent in ML and how to learn effectively.   With special guest interviewer Mat Salvaris.  Machine learning: the great stagnation [00:00:00] Main show kick off [00:16:30] Great stagnation article / Bad incentive systems in academia [00:18:24] OpenAI is a media business [00:19:48] Incentive structures in academia [00:22:13] SOTA chasing [00:24:47] F You Money [00:28:53] Research grants and gentlemen scientists [00:29:13] Following your own gradient of interest and making a contribution [00:33:27] Marketing yourself to be successful [00:37:07] Tech companies create the bad incentives [00:42:20] GPT3 was sota chasing but it seemed really... "good"? Scaling laws? [00:51:09] Dota / game AI [00:58:39] Hard to go it alone? [01:02:08] Reaching out to people [01:09:21] Willingness to be wrong [01:13:14] Distribution of talent / tech interviews [01:18:30] What should you read online and how to learn? Sharing your stuff online and finding your niece [01:25:52] Mark Saroufim: Dr. Mathew Salvaris:
Microsoft has an interesting strategy with their new “autonomous systems” technology also known as Project Bonsai. They want to create an interface to abstract away the complexity and esoterica of deep reinforcement learning. They want to fuse together expert knowledge and artificial intelligence all on one platform, so that complex problems can be decomposed into simpler ones. They want to take machine learning Ph.Ds out of the equation and make autonomous systems engineering look more like a traditional software engineering process. It is an ambitious undertaking, but interesting. Reinforcement learning is extremely difficult (as I cover in the video), and if you don’t have a team of RL Ph.Ds with tech industry experience, you shouldn’t even consider doing it yourself. This is our take on it! There are 3 chapters in this video; Chapter 1: Tim's intro and take on RL being hard, intro to Bonsai and machine teaching  Chapter 2: Interview with Scott Stanfield [recorded Jan 2020] 00:56:41 Chapter 3: Traditional street talk episode [recorded Dec 2020] 01:38:13 This is *not* an official communication from Microsoft, all personal opinions. There is no MS-confidential information in this video.  With: Scott Stanfield Megan Bloemsma Gurdeep Pall (he has not validated anything we have said in this video or been involved in the creation of it) Panel:  Dr. Keith Duggar Dr. Tim Scarfe Yannic Kilcher
Today we are going to talk about the *Data-efficient image Transformers paper or (DeiT) which Hugo is the primary author of. One of the recipes of success for vision models since the DL revolution began has been the availability of large training sets. CNNs have been optimized for almost a decade now, including through extensive architecture search which is prone to overfitting. Motivated by the success of transformers-based models in Natural Language Processing there has been increasing attention in applying these approaches to vision models. Hugo and his collaborators used a different training strategy and a new distillation token to get a massive increase in sample efficiency with image transformers.  00:00:00 Introduction 00:06:33 Data augmentation is all you need 00:09:53 Now the image patches are the convolutions though? 00:12:16 Where are those inductive biases hiding? 00:15:46 Distillation token 00:21:01 Why different resolutions on training 00:24:14 How data efficient can we get? 00:26:47 Out of domain generalisation 00:28:22 Why are transformers data efficient at all? Learning invariances 00:32:04 Is data augmentation cheating? 00:33:25 Distillation strategies - matching the intermediatae teacher representation as well as output 00:35:49 Do ML models learn the same thing for a problem? 00:39:01 How is it like at Facebook AI? 00:41:17 How long is the PhD programme? 00:42:03 Other interests outside of transformers? 00:43:18 Transformers for Vision and Language 00:47:40 Could we improve transformers models? (Hybrid models) 00:49:03 Biggest challenges in AI? 00:50:52 How far can we go with data driven approach?
Professor Mark Bishop does not think that computers can be conscious or have phenomenological states of consciousness unless we are willing to accept panpsychism which is idea that mentality is fundamental and ubiquitous in the natural world, or put simply, that your goldfish and everything else for that matter has a mind. Panpsychism postulates that distinctions between intelligences are largely arbitrary. Mark’s work in the ‘philosophy of AI’ led to an influential critique of computational approaches to Artificial Intelligence through a thorough examination of John Searle's 'Chinese Room Argument' Mark just published a paper called artificial intelligence is stupid and causal reasoning wont fix it. He makes it clear in this paper that in his opinion computers will never be able to compute everything, understand anything, or feel anything.  00:00:00​ Tim Intro 00:15:04​ Intro  00:18:49​ Introduction to Marks ideas  00:25:49​ Some problems are not computable  00:29:57​ the dancing was Pixies fallacy  00:32:36​ The observer relative problem, and its all in the mapping  00:43:03​ Conscious Experience  00:53:30​ Intelligence without representation, consciousness is something that we do  01:02:36​ Consciousness helps us to act autonomously  01:05:13​ The Chinese room argument  01:14:58​ Simulation argument and computation doesn't have phenomenal consciousness  01:17:44​ Language informs our colour perception  01:23:11​ We have our own distinct ontologies  01:27:12​ Kurt Gödel, Turing and Penrose and the implications of their work 
Today we have professor Pedro Domingos and we are going to talk about activism in machine learning, cancel culture, AI ethics and kernels. In Pedro's book the master algorithm, he segmented the AI community into 5 distinct tribes with 5 unique identities (and before you ask, no the irony of an anti-identitarian doing do was not lost on us!). Pedro recently published an article in Quillette called Beating Back Cancel Culture: A Case Study from the Field of Artificial Intelligence. Domingos has railed against political activism in the machine learning community and cancel culture. Recently Pedro was involved in a controversy where he asserted the NeurIPS broader impact statements are an ideological filter mechanism. Important Disclaimer: All views expressed are personal opinions. 00:00:00 Caveating 00:04:08 Main intro 00:07:44 Cancelling culture is a culture and intellectual weakness  00:12:26 Is cancel culture a post-modern religion?  00:24:46 Should we have gateways and gatekeepers?  00:29:30 Does everything require broader impact statements?  00:33:55 We are stifling diversity (of thought) not promoting it.  00:39:09 What is fair and how to do fair? 00:45:11 Models can introduce biases by compressing away minority data  00:48:36 Accurate but unequal soap dispensers  00:53:55 Agendas are not even self-consistent  00:56:42 Is vs Ought: all variables should be used for Is  01:00:38 Fighting back cancellation with cancellation? 01:10:01 Intent and degree matter in right vs wrong.  01:11:08 Limiting principles matter  01:15:10 Gradient descent and kernels  01:20:16 Training Journey matter more than Destination  01:24:36 Can training paths teach us about symmetry? 01:28:37 What is the most promising path to AGI?  01:31:29 Intelligence will lose its mystery
Dr. Simon Stringer. Obtained his Ph.D in mathematical state space control theory and has been a Senior Research Fellow at Oxford University for over 27 years. Simon is the director of the the Oxford Centre for Theoretical Neuroscience and Artificial Intelligence, which is based within the Oxford University Department of Experimental Psychology. His department covers vision, spatial processing, motor function, language and consciousness -- in particular -- how the primate visual system learns to make sense of complex natural scenes. Dr. Stringers laboratory houses a team of theoreticians, who are developing computer models of a range of different aspects of brain function. Simon's lab is investigating the neural and synaptic dynamics that underpin brain function. An important matter here is the The feature-binding problem which concerns how the visual system represents the hierarchical relationships between features. the visual system must represent hierarchical binding relations across the entire visual field at every spatial scale and level in the hierarchy of visual primitives. We discuss the emergence of self-organised behaviour, complex information processing, invariant sensory representations and hierarchical feature binding which emerges when you build biologically plausible neural networks with temporal spiking dynamics.  00:00:09 Tim Intro  00:09:31 Show kickoff  00:14:37 Hierarchical Feature binding and timing of action potentials  00:30:16 Hebb to Spike-timing-dependent plasticity (STDP)  00:35:27 Encoding of shape primitives  00:38:50 Is imagination working in the same place in the brain  00:41:12 Compare to supervised CNNs  00:45:59 Speech recognition, motor system, learning mazes  00:49:28 How practical are these spiking NNs  00:50:19 Why simulate the human brain  00:52:46 How much computational power do you gain from differential timings  00:55:08 Adversarial inputs  00:59:41 Generative / causal component needed?  01:01:46 Modalities of processing i.e. language  01:03:42 Understanding  01:04:37 Human hardware  01:06:19 Roadmap of NNs?  01:10:36 Intepretability methods for these new models  01:13:03 Won't GPT just scale and do this anyway?  01:15:51 What about trace learning and transformation learning  01:18:50 Categories of invariance  01:19:47 Biological plausibility
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