How the Speed of a Trade Got Down to Nearly the Speed of Light
The average person can enter a stock trade on their computer, hit refresh, and the trade is done. As fast as that seems, there are professional traders moving even faster, executing thousands of trades per second. Over the years, the need for speed got so intense that competing firms would aim to get their own systems closer and closer to the exchange's computers, so as to minimize the length of the wires and get their trades in even faster. How did this happen? And how does this change the nature of trading itself? On this episode, we speak with Donald Mackenzie, a professor of sociology at the University of Edinburgh in Scotland. Professor Mackenzie has been studying the intersection of finance and tech for a long time, and in 2021 wrote the book, Trading at the Speed of Light. We discuss the history of finance technology and look at where the technological arms race is going next.
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Abstraction & Idealization: AI's Plato Problem [Mazviita Chirimuuta]
Professor Mazviita Chirimuuta joins us for a fascinating deep dive into the philosophy of neuroscience and what it really means to understand the mind.*What can neuroscience actually tell us about how the mind works?* In this thought-provoking conversation, we explore the hidden assumptions behind computational theories of the brain, the limits of scientific abstraction, and why the question of machine consciousness might be more complicated than AI researchers assume.Mazviita, author of *The Brain Abstracted,* brings a unique perspective shaped by her background in both neuroscience research and philosophy. She challenges us to think critically about the metaphors we use to understand cognition — from the reflex theory of the late 19th century to today's dominant view of the brain as a computer.*Key topics explored:**The problem of oversimplification* — Why scientific models necessarily leave things out, and how this can sometimes lead entire fields astray. The cautionary tale of reflex theory shows how elegant explanations can blind us to biological complexity.*Is the brain really a computer?* — Mazviita unpacks the philosophical assumptions behind computational neuroscience and asks: if we can model anything computationally, what makes brains special? The answer might challenge everything you thought you knew about AI.*Haptic realism* — A fresh way of thinking about scientific knowledge that emphasizes interaction over passive observation. Knowledge isn't about reading the "source code of the universe" — it's something we actively construct through engagement with the world.*Why embodiment matters for understanding* — Can a disembodied language model truly understand? Mazviita makes a compelling case that human cognition is deeply entangled with our sensory-motor engagement and biological existence in ways that can't simply be abstracted away.*Technology and human finitude* — Drawing on Heidegger, we discuss how the dream of transcending our physical limitations through technology might reflect a fundamental misunderstanding of what it means to be a knower.This conversation is essential viewing for anyone interested in AI, consciousness, philosophy of mind, or the future of cognitive science. Whether you're skeptical of strong AI claims or a true believer in machine consciousness, Mazviita's careful philosophical analysis will give you new tools for thinking through these profound questions.---TIMESTAMPS:00:00:00 The Problem of Generalizing Neuroscience00:02:51 Abstraction vs. Idealization: The "Kaleidoscope"00:05:39 Platonism in AI: Discovering or Inventing Patterns?00:09:42 When Simplification Fails: The Reflex Theory00:12:23 Behaviorism and the "Black Box" Trap00:14:20 Haptic Realism: Knowledge Through Interaction00:20:23 Is Nature Protean? The Myth of Converging Truth00:23:23 The Computational Theory of Mind: A Useful Fiction?00:27:25 Biological Constraints: Why Brains Aren't Just Neural Nets00:31:01 Agency, Distal Causes, and Dennett's Stances00:37:13 Searle's Challenge: Causal Powers and Understanding00:41:58 Heidegger's Warning & The Experiment on Children---REFERENCES:Book:[00:01:28] The Brain Abstractedhttps://mitpress.mit.edu/9780262548045/the-brain-abstracted/[00:11:05] The Integrated Action of the Nervous Systemhttps://www.amazon.sg/integrative-action-nervous-system/dp/9354179029[00:18:15] The Quest for Certainty (Dewey)https://www.amazon.com/Quest-Certainty-Relation-Knowledge-Lectures/dp/0399501916[00:19:45] Realism for Realistic People (Chang)https://www.cambridge.org/core/books/realism-for-realistic-people/ACC93A7F03B15AA4D6F3A466E3FC5AB7<truncated, see ReScript>---RESCRIPT:https://app.rescript.info/public/share/A6cZ1TY35p8ORMmYCWNBI0no9ChU3-Kx7dPXGJURvZ0PDF Transcript:https://app.rescript.info/api/public/sessions/0fb7767e066cf712/pdf
#039 - Lena Voita - NLP
ena Voita is a Ph.D. student at the University of Edinburgh and University of Amsterdam. Previously, She was a research scientist at Yandex Research and worked closely with the Yandex Translate team. She still teaches NLP at the Yandex School of Data Analysis. She has created an exciting new NLP course on her website lena-voita.github.io which you folks need to check out! She has one of the most well presented blogs we have ever seen, where she discusses her research in an easily digestable manner. Lena has been investigating many fascinating topics in machine learning and NLP. Today we are going to talk about three of her papers and corresponding blog articles;
Source and Target Contributions to NMT Predictions -- Where she talks about the influential dichotomy between the source and the prefix of neural translation models.
https://arxiv.org/pdf/2010.10907.pdf
https://lena-voita.github.io/posts/source_target_contributions_to_nmt.html
Information-Theoretic Probing with MDL -- Where Lena proposes a technique of evaluating a model using the minimum description length or Kolmogorov complexity of labels given representations rather than something basic like accuracy
https://arxiv.org/pdf/2003.12298.pdf
https://lena-voita.github.io/posts/mdl_probes.html
Evolution of Representations in the Transformer - Lena investigates the evolution of representations of individual tokens in Transformers -- trained with different training objectives (MT, LM, MLM)
https://arxiv.org/abs/1909.01380
https://lena-voita.github.io/posts/emnlp19_evolution.html
Panel Dr. Tim Scarfe, Yannic Kilcher, Sayak Paul
00:00:00 Kenneth Stanley / Greatness can not be planned house keeping
00:21:09 Kilcher intro
00:28:54 Hello Lena
00:29:21 Tim - Lenas NMT paper
00:35:26 Tim - Minimum Description Length / Probe paper
00:40:12 Tim - Evolution of representations
00:46:40 Lenas NLP course
00:49:18 The peppermint tea situation
00:49:28 Main Show Kick Off
00:50:22 Hallucination vs exposure bias
00:53:04 Lenas focus on explaining the models not SOTA chasing
00:56:34 Probes paper and NLP intepretability
01:02:18 Why standard probing doesnt work
01:12:12 Evolutions of representations paper
01:23:53 BERTScore and BERT Rediscovers the Classical NLP Pipeline paper
01:25:10 Is the shifting encoding context because of BERT bidirectionality
01:26:43 Objective defines which information we lose on input
01:27:59 How influential is the dataset?
01:29:42 Where is the community going wrong?
01:31:55 Thoughts on GOFAI/Understanding in NLP?
01:36:38 Lena's NLP course
01:47:40 How to foster better learning / understanding
01:52:17 Lena's toolset and languages
01:54:12 Mathematics is all you need
01:56:03 Programming languages
https://lena-voita.github.io/
https://www.linkedin.com/in/elena-voita/
https://scholar.google.com/citations?user=EcN9o7kAAAAJ&hl=ja
https://twitter.com/lena_voita
Masked Autoregressive Flow for Density Estimation with George Papamakarios - TWiML Talk #145
In this episode, University of Edinburgh Phd student George Papamakarios and I discuss his paper “Masked Autoregressive Flow for Density Estimation.” George walks us through the idea of Masked Autoregressive Flow, which uses neural networks to produce estimates of probability densities from a set of input examples. We discuss some of the related work that’s laid the groundwork for his research, including Inverse Autoregressive Flow, Real NVP and Masked Auto-encoders. We also look at the properties of probability density networks and discuss some of the challenges associated with this effort. The notes for this show can be found at twimlai.com/talk/145.