Analysis of image classifiers demonstrates that it is possible to understand backprop networks at the task-relevant run-time algorithmic level. In these systems, at least, networks gain their power from deploying massive parallelism to check for the presence of a vast number of simple, shallow patterns.

https://betterwithout.ai/images-surface-features

This episode has a lot of links:

David Chapman's earliest public mention, in February 2016, of image classifiers probably using color and texture in ways that "cheat": twitter.com/Meaningness/status/698688687341572096

Jordana Cepelewicz’s “Where we see shapes, AI sees textures,” Quanta Magazine, July 1, 2019: https://www.quantamagazine.org/where-we-see-shapes-ai-sees-textures-20190701/

“Suddenly, a leopard print sofa appears”, May 2015: https://web.archive.org/web/20150622084852/http://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.html

“Understanding How Image Quality Affects Deep Neural Networks” April 2016: https://arxiv.org/abs/1604.04004   Goodfellow et al., “Explaining and Harnessing Adversarial Examples,” December 2014: https://arxiv.org/abs/1412.6572

“Universal adversarial perturbations,” October 2016: https://arxiv.org/pdf/1610.08401v1.pdf

“Exploring the Landscape of Spatial Robustness,” December 2017: https://arxiv.org/abs/1712.02779

“Overinterpretation reveals image classification model pathologies,” NeurIPS 2021: https://proceedings.neurips.cc/paper/2021/file/8217bb4e7fa0541e0f5e04fea764ab91-Paper.pdf

“Approximating CNNs with Bag-of-Local-Features Models Works Surprisingly Well on ImageNet,” ICLR 2019: https://openreview.net/forum?id=SkfMWhAqYQ

Baker et al.’s “Deep convolutional networks do not classify based on global object shape,” PLOS Computational Biology, 2018: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006613

François Chollet's Twitter threads about AI producing images of horses with extra legs: twitter.com/fchollet/status/1573836241875120128 and twitter.com/fchollet/status/1573843774803161090

“Zoom In: An Introduction to Circuits,” 2020: https://distill.pub/2020/circuits/zoom-in/

Geirhos et al., “ImageNet-Trained CNNs Are Biased Towards Texture; Increasing Shape Bias Improves Accuracy and Robustness,” ICLR 2019: https://openreview.net/forum?id=Bygh9j09KX

Dehghani et al., “Scaling Vision Transformers to 22 Billion Parameters,” 2023: https://arxiv.org/abs/2302.05442

Hasson et al., “Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks,” February 2020: https://www.gwern.net/docs/ai/scaling/2020-hasson.pdf

Current AI practice is not engineering, even when it aims for practical applications, because it is not based on scientific understanding. Enforcing engineering norms on the field could lead to considerably safer systems.   https://betterwithout.ai/AI-as-engineering   This episode has a lot of links! Here they are.   Michael Nielsen’s “The role of ‘explanation’ in AI”. https://michaelnotebook.com/ongoing/sporadica.html#role_of_explanation_in_AI   Subbarao Kambhampati’s “Changing the Nature of AI Research”. https://dl.acm.org/doi/pdf/10.1145/3546954   Chris Olah and his collaborators: “Thread: Circuits”. distill.pub/2020/circuits/ “An Overview of Early Vision in InceptionV1”. distill.pub/2020/circuits/early-vision/   Dai et al., “Knowledge Neurons in Pretrained Transformers”. https://arxiv.org/pdf/2104.08696.pdf   Meng et al.: “Locating and Editing Factual Associations in GPT.” rome.baulab.info “Mass-Editing Memory in a Transformer,” https://arxiv.org/pdf/2210.07229.pdf   François Chollet on image generators putting the wrong number of legs on horses: twitter.com/fchollet/status/1573879858203340800   Neel Nanda’s “Longlist of Theories of Impact for Interpretability”, https://www.lesswrong.com/posts/uK6sQCNMw8WKzJeCQ/a-longlist-of-theories-of-impact-for-interpretability   Zachary C. Lipton’s “The Mythos of Model Interpretability”. https://arxiv.org/abs/1606.03490   Meng et al., “Locating and Editing Factual Associations in GPT”. https://arxiv.org/pdf/2202.05262.pdf   Belrose et al., “Eliciting Latent Predictions from Transformers with the Tuned Lens”. https://arxiv.org/abs/2303.08112   “Progress measures for grokking via mechanistic interpretability”. https://arxiv.org/abs/2301.05217   Conmy et al., “Towards Automated Circuit Discovery for Mechanistic Interpretability”. https://arxiv.org/abs/2304.14997   Elhage et al., “Softmax Linear Units,” transformer-circuits.pub/2022/solu/index.html   Filan et al., “Clusterability in Neural Networks,” https://arxiv.org/pdf/2103.03386.pdf   Cammarata et al., “Curve circuits,” distill.pub/2020/circuits/curve-circuits/   You can support the podcast and get episodes a week early, by supporting the Patreon: https://www.patreon.com/m/fluidityaudiobooks   If you like the show, consider buying me a coffee: https://www.buymeacoffee.com/mattarnold   Original music by Kevin MacLeod.   This podcast is under a Creative Commons Attribution Non-Commercial International 4.0 License.

Few AI experiments constitute meaningful tests of hypotheses. As a branch of machine learning research, AI science has concentrated on black box investigation of training time phenomena. The best of this work is has been scientifically excellent. However, the hypotheses tested are mainly irrelevant to user and societal concerns. https://betterwithout.ai/AI-as-science This chapter references Chapman's essay, "How should we evaluate progress in AI?" https://metarationality.com/artificial-intelligence-progress "Troubling Trends in Machine Learning Scholarship", Zachary C. Lipton and Jacob Steinhardt: https://arxiv.org/abs/1807.03341 You can support the podcast and get episodes a week early, by supporting the Patreon: https://www.patreon.com/m/fluidityaudiobooks   If you like the show, consider buying me a coffee: https://www.buymeacoffee.com/mattarnold   Original music by Kevin MacLeod.   This podcast is under a Creative Commons Attribution Non-Commercial International 4.0 License.

Do AI As Science And Engineering Instead - We’ve seen that current AI practice leads to technologies that are expensive, difficult to apply in real-world situations, and inherently unsafe. Neglected scientific and engineering investigations can bring better understanding of the risks of current AI technology, and can lead to safer technologies.   https://betterwithout.ai/science-engineering-vs-AI   Run-Time Task-Relevant Algorithmic Understanding - The type of scientific and engineering understanding most relevant to AI safety is run-time, task-relevant, and algorithmic. That can lead to more reliable, safer systems. Unfortunately, gaining such understanding has been neglected in AI research, so currently we have little.   https://betterwithout.ai/AI-algorithmic-level   For more information, see David Chapman's 2017 essay "How should we evaluate progress in AI?" https://betterwithout.ai/artificial-intelligence-progress   You can support the podcast and get episodes a week early, by supporting the Patreon: https://www.patreon.com/m/fluidityaudiobooks   If you like the show, consider buying me a coffee: https://www.buymeacoffee.com/mattarnold   Original music by Kevin MacLeod.   This podcast is under a Creative Commons Attribution Non-Commercial International 4.0 License.

Current AI results from experimental variation of mechanisms, unguided by theoretical principles. That has produced systems that can do amazing things. On the other hand, they are extremely error-prone and therefore unsafe. Backpropaganda, a collection of misleading ways of talking about “neural networks,” justifies continuing in this misguided direction.   https://betterwithout.ai/backpropaganda   You can support the podcast and get episodes a week early, by supporting the Patreon: https://www.patreon.com/m/fluidityaudiobooks   If you like the show, consider buying me a coffee: https://www.buymeacoffee.com/mattarnold   Original music by Kevin MacLeod.   This podcast is under a Creative Commons Attribution Non-Commercial International 4.0 License.

The conclusion of this chapter.   So-called “neural networks” are extremely expensive, poorly understood, unfixably unreliable, deceptive, data hungry, and inherently limited in capabilities. In short: they are bad.  https://betterwithout.ai/artificial-neurons-considered-harmful Sayash Kapoor and Arvind Narayanan’s "The bait and switch behind AI risk prediction tools": https://aisnakeoil.substack.com/p/the-bait-and-switch-behind-ai-risk A video titled "Latent Space Walk": https://www.youtube.com/watch?v=bPgwwvjtX_g Another video showing a walk through latent space: https://www.youtube.com/watch?v=YnXiM97ZvOM You can support the podcast and get episodes a week early, by supporting the Patreon: https://www.patreon.com/m/fluidityaudiobooks If you like the show, consider buying me a coffee: https://www.buymeacoffee.com/mattarnold Original music by Kevin MacLeod. This podcast is under a Creative Commons Attribution Non-Commercial International 4.0 License.

This begins "Gradient Dissent", the companion material to "Better Without AI". The neural network and GPT technologies that power current artificial intelligence are exceptionally error prone, deceptive, poorly understood, and dangerous. They are widely used without adequate safeguards in situations where they cause increasing harms. They are not inevitable, and we should replace them with better alternatives. https://betterwithout.ai/gradient-dissent Artificial Neurons Considered Harmful, Part 1 - So-called “neural networks” are extremely expensive, poorly understood, unfixably unreliable, deceptive, data hungry, and inherently limited in capabilities. In short: they are bad. https://betterwithout.ai/artificial-neurons-considered-harmful You can support the podcast and get episodes a week early, by supporting the Patreon: https://www.patreon.com/m/fluidityaudiobooks If you like the show, consider buying me a coffee: https://www.buymeacoffee.com/mattarnold Original music by Kevin MacLeod. This podcast is under a Creative Commons Attribution Non-Commercial International 4.0 License.

The five short chapters in this episode are the conclusion of the main body of Better Without AI. Next, we'll begin the book's appendix, Gradient Dissent. Cozy Futurism - If we knew we’d never get flying cars, most people wouldn’t care. What do we care about? https://betterwithout.ai/cozy-futurism Meaningful Futurism - Likeable futures are meaningful, not just materially comfortable. Bringing one about requires imagining it. I invite you to do that! https://betterwithout.ai/meaningful-future The Inescapable: Politics - No realistic approach to future AI can avoid questions of power and social organization. https://betterwithout.ai/inescapable-AI-politics Responsibility https://betterwithout.ai/responsibility This is about you https://betterwithout.ai/about-you You can support the podcast and get episodes a week early, by supporting the Patreon: https://www.patreon.com/m/fluidityaudiobooks If you like the show, consider buying me a coffee: https://www.buymeacoffee.com/mattarnold Original music by Kevin MacLeod. This podcast is under a Creative Commons Attribution Non-Commercial International 4.0 License.

A Future We Would Like - The most important questions are not about technology but about us. What sorts of future would we like? What role could AI play in getting us there, and also in that world? What is your own role in helping that happen?

https://betterwithout.ai/a-future-we-would-like

How AI Destroyed The Future -We are doing a terrible job of thinking about the most important question because unimaginably powerful evil artificial intelligences are controlling our brains.

https://betterwithout.ai/AI-destroyed-the-future

A One-Bit Future - Superintelligence scenarios reduce the future to infinitely good or infinitely bad. Both are possible, but we cannot reason about or act toward them. Messy complicated good-and-bad futures are probably more likely, and in any case are more feasible to influence.

https://betterwithout.ai/one-bit-future

This episode mentions David Chapman's essay "Vaster Than Ideology" for getting AI out of your head.

Text link: https://meaningness.com/vaster-than-ideology

Episode link: https://fluidity.libsyn.com/vaster-than-ideology

You can support the podcast and get episodes a week early, by supporting the Patreon: https://www.patreon.com/m/fluidityaudiobooks   If you like the show, consider buying me a coffee: https://www.buymeacoffee.com/mattarnold Original music by Kevin MacLeod.   This podcast is under a Creative Commons Attribution Non-Commercial International 4.0 License.

Stop obstructing scientific progress! We already know how to dramatically accelerate science: by getting out of the way.   https://betterwithout.ai/stop-obstructing-science   How to science better. What do exceptional scientists do differently from mediocre ones? Can we train currently-mediocre ones to do better?   https://betterwithout.ai/better-science-without-AI   Scenius: upgrading science FTW. Empirically, breakthroughs that enable great progress depend on particular, uncommon social constellations and accompanying social practices. Let’s encourage these!   https://betterwithout.ai/human-scenius-vs-artificial-genius   Matt Clancy reviews the evidence for scientific progress slowing, with citations and graphs. https://twitter.com/mattsclancy/status/1612440718177603584   "Scenius, or Communal Genius", Kevin Kelly, The Technium. https://kk.org/thetechnium/scenius-or-comm/