Fluidity
Matt Arnold
Show overview
Fluidity has been publishing since 2021, and across the 4 years since has built a catalogue of 155 episodes, alongside 5 trailers or bonus episodes. That works out to roughly 70 hours of audio in total. Releases follow a fortnightly cadence, with the show now in its 3rd season.
Episodes typically run twenty to thirty-five minutes — most land between 16 min and 35 min — though episode length varies meaningfully from one episode to the next. None of the episodes are flagged explicit by the publisher. It is catalogued as a EN-language Society & Culture show.
The catalogue appears to be on hiatus or wound down — the most recent episode landed 1.3 years ago, with no new episodes in over a year. The busiest year was 2021, with 53 episodes published. Published by Matt Arnold.
From the publisher
After the collapse of the 20th-century systematic mode of social organization, how can we move from our internet-enabled atomized mode, toward a fluid mode? We take problems of meaning-making, typically considered spiritual, and turn them into practical problems, which are more tractable. "Meaningness" begins with this episode: https://fluidity.libsyn.com/an-appetizer-purpose "Meaningness And Time" begins with this episode: https://fluidity.libsyn.com/meaningness-and-time-how-meaning-fell-apart 'In The Cells Of The Eggplant" begins with this episode: https://fluidity.libsyn.com/intro-to-metarationality 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 This is a nonfiction audiobook narrated by Matt Arnold with the permission of the author, David Chapman. Full text at: https://meaningness.com Please email me at [email protected].
Latest Episodes
View all 155 episodesS3 Ep 148A Better Future, Without Backprop
This concludes "Gradient Dissent", the companion document to "Better Without AI". Thank you so much for listening! 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.
S3 Ep 147Better Text Generation With Science And Engineering
Current text generators, such as ChatGPT, are highly unreliable, difficult to use effectively, unable to do many things we might want them to, and extremely expensive to develop and run. These defects are inherent in their underlying technology. Quite different methods could plausibly remedy all these defects. Would that be good, or bad? https://betterwithout.ai/better-text-generators John McCarthy's paper "Programs with common sense": http://www-formal.stanford.edu/jmc/mcc59/mcc59.html Harry Frankfurt, "On Bullshit": https://www.amazon.com/dp/B001EQ4OJW/?tag=meaningness-20 Petroni et al., "Language Models as Knowledge Bases?": https://aclanthology.org/D19-1250/ Gwern Branwen, "The Scaling Hypothesis": gwern.net/scaling-hypothesis Rich Sutton's "Bitter Lesson": www.incompleteideas.net/IncIdeas/BitterLesson.html Guu et al.'s "Retrieval augmented language model pre-training" (REALM): http://proceedings.mlr.press/v119/guu20a/guu20a.pdf Borgeaud et al.'s "Improving language models by retrieving from trillions of tokens" (RETRO): https://arxiv.org/pdf/2112.04426.pdf Izacard et al., "Few-shot Learning with Retrieval Augmented Language Models": https://arxiv.org/pdf/2208.03299.pdf Chirag Shah and Emily M. Bender, "Situating Search": https://dl.acm.org/doi/10.1145/3498366.3505816 David Chapman's original version of the proposal he puts forth in this episode: twitter.com/Meaningness/status/1576195630891819008 Lan et al. "Copy Is All You Need": https://arxiv.org/abs/2307.06962 Mitchell A. Gordon's "RETRO Is Blazingly Fast": https://mitchgordon.me/ml/2022/07/01/retro-is-blazing.html Min et al.'s "Silo Language Models": https://arxiv.org/pdf/2308.04430.pdf W. Daniel Hillis, The Connection Machine, 1986: https://www.amazon.com/dp/0262081571/?tag=meaningness-20 Ouyang et al., "Training language models to follow instructions with human feedback": https://arxiv.org/abs/2203.02155 Ronen Eldan and Yuanzhi Li, "TinyStories: How Small Can Language Models Be and Still Speak Coherent English?": https://arxiv.org/pdf/2305.07759.pdf Li et al., "Textbooks Are All You Need II: phi-1.5 technical report": https://arxiv.org/abs/2309.05463 Henderson et al., "Foundation Models and Fair Use": https://arxiv.org/abs/2303.15715 Authors Guild v. Google: https://en.wikipedia.org/wiki/Authors_Guild%2C_Inc._v._Google%2C_Inc. Abhishek Nagaraj and Imke Reimers, "Digitization and the Market for Physical Works: Evidence from the Google Books Project": https://www.aeaweb.org/articles?id=10.1257/pol.20210702 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.
S3 Ep 146Classifying Images: Massive Parallelism And Surface Features
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 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.
S3 Ep 145Do AI As Engineering Instead
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.
S3 Ep 144Do AI As Science Instead
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.
S3 Ep 143Do AI As Science And Engineering Instead
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.
S3 Ep 142Backpropaganda: Anti-Rational Neuro-Mythology
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.
S3 Ep 141Artificial Neurons Considered Harmful, Part 2
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.
S3 Ep 140Gradient Dissent- Artificial Neurons Considered Harmful, Part 1
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.
S3 Ep 139Futurism, Politics, and Responsibility
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.
S3 Ep 144A Future We Would Like
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.
S3 Ep 143Scientific Progress Without AI
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/
S3 Ep 142Limits To Experimental Induction
Progress requires experimentation. Suggested ways AI could speed progress by automating experiments appear mistaken. https://betterwithout.ai/limits-to-induction 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.
S3 Ep 141Bonus Episode 8: Going Down On The Phenomenon
Forgive the sound quality on this episode; I recorded it live in front of an audience on a platform floating in a lake during the 2024 solar eclipse. This is a standalone essay by David Chapman on metarationaity.com. How scientific research is like cunnilingus: a phenomenology of epistemology. https://metarationality.com/going-down-on-the-phenomenon 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.
S3 Ep 140The Role Of Intelligence In Science
What Is The Role Of Intelligence In Science? Actually, what are "science" and "intelligence"? Precise, explicit definitions aren't necessary, but discussions of Transformative AI seem to depend implicitly on particular models of both. It matters if those models are wrong. https://betterwithout.ai/intelligence-in-science Katja Grace, "Counterarguments to the basic AI x-risk case". https://aiimpacts.org/counterarguments-to-the-basic-ai-x-risk-case/ What Do Unusually Intelligent People Do? If we want to know what a superintelligent AI might do, and how, it could help to investigate what the most intelligent humans do, and how. If we want to know how to dramatically accelerate science and technology development, it could help to investigate what the best scientists and technologists do, and how. https://betterwithout.ai/what-intelligent-people-do Patrick Collison and Tyler Cowen, "We Need a New Science of Progress," The Atlantic, July 30, 2019. https://www.theatlantic.com/science/archive/2019/07/we-need-new-science-progress/594946/ Gwern Branwen, "Catnip immunity and alternatives". https://www.gwern.net/Catnip#optimal-catnip-alternative-selection-solving-the-mdp 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.
S3 Ep 139Radical Progress Without Scary AI
Radical Progress Without Scary AI: Technological progress, in medicine for example, provides an altruistic motivation for developing more powerful AIs. I suggest that AI may be unnecessary, or even irrelevant, for that. We may be able to get the benefits without the risks. https://betterwithout.ai/radical-progress-without-AI What kind of AI might accelerate technological progress?: "Narrow" AI systems, specialized for particular technical tasks, are probably feasible, useful, and safe. Let's build those instead of Scary superintelligence. https://betterwithout.ai/what-AI-for-progress
S3 Ep 138AI Is Net Harmful, and, A Negative Public Image For AI
Recognize that AI is probably net harmful: Actually-existing and near-future AIs are net harmful—never mind their longer-term risks. We should shut them down, not pussyfoot around hoping they can somehow be made safe. https://betterwithout.ai/AI-is-harmful Create a negative public image for AI: Most funding for AI research comes from the advertising industry. Their primary motivation may be to create a positive corporate image, to offset their obvious harms. Creating bad publicity for AI would eliminate their incentive to fund it. https://betterwithout.ai/AI-is-public-relations Seth Lazar's "Legitimacy, Authority, and the Political Value of Explanations": https://arxiv.org/ftp/arxiv/papers/2208/2208.08628.pdf Kate Crawford's "Atlas Of AI": https://www.amazon.com/dp/B08WKQ1MTM/?tag=meaningness-20 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.
S3 Ep 137Spurn Artificial Ideology
"Apocalypse now" identified the corrosive influence of new viral ideologies, created unintentionally by recommender systems, as a major AI risk. These may cause social collapse if not tackled head-on. You can resist. https://betterwithout.ai/spurn-artificial-ideology Announcement tweet for the Opening Awareness, Opening Rationality discussion group starting on February 1: https://twitter.com/openingBklyn/status/1751314312415567956 Document with more details: https://docs.google.com/document/d/1YPaos3zTgdraF9VouWkHUouVHVsrbYBluUO3Kh--Ezs/edit Vaster Than Ideology (text): https://meaningness.com/vaster-than-ideology Vaster Than Ideology (Fluidity Audiobooks episode): https://fluidity.libsyn.com/vaster-than-ideology Coinbase Is A Mission Focused Company: https://www.coinbase.com/blog/coinbase-is-a-mission-focused-company 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.
S3 Ep 136Fight DOOM AI with SCIENCE! and ENGINEERING!!
Current AI practices produce technologies that are expensive, difficult to apply in real-world situations, and inherently unsafe. Neglected scientific and engineering investigations can bring better understanding of specific risks of current AI technology, and can lead to safer technologies. https://betterwithout.ai/fight-unsafe-AI 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 Music is by Kevin MacLeod. This podcast is under a Creative Commons Attribution Non-Commercial International 4.0 License.
S3 Ep 135Mistrust Machine Learning
The technologies underlying current AI systems are inherently, unfixably unreliable. They should be deprecated, avoided, regulated, and replaced. https://betterwithout.ai/mistrust-machine-learning 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 Music is by Kevin MacLeod. This podcast is under a Creative Commons Attribution Non-Commercial International 4.0 License.