June 14, 2022

Artifice of Intelligence

CyberGunk 01

This video is all about neural networks, how they work and how they don’t really work. Really, just doing all the boring explanation up top. For the longest time it was just a bunch of bits that I kept adding and removing from the Automation (Episode 2) script, and it was a pretty late decision to make a standalone video out of it. Weirder, denser, less thought out. A good one to start with.


Membership Content


Key Research

The Smart, the Stupid, and the Catastrophically Scary: An Interview with an Anonymous Data Scientist

Pretty sure this is where I got the idea to frame the whole thing around the perceptron and be really reductive about it.

Andrej Karpathy – AI for Full-Self Driving at Tesla

A pretty frank and direct talk about where Tesla is at with self-driving. And how it sucks to work for Elon Musk.

Obviously he tries to hype it up but underneath that it shows just how shallow this tech is.

my brain

This video is basically made up of a bunch of little pieces that didn’t really fit into episode two. So it’s a weird outlier in that I didn’t really do any specific research for it, and it’s largely just stuff that I’ve learned through osmosis and messing around with neural networks myself.

Research List

Filtered to Algorithms View all
Title URL Notes Video Relevance
📰Article Ad Tech Could Be the Next Internet Bubble https://www.wired.com/story/ad-tech-could-be-the-next-internet-bubble/ Targeted advertising doesn’t work Advertising ★★★
📰Article Buying Consumer Data? Tread Carefully. https://hbr.org/2020/05/buying-consumer-data-tread-carefully Consumer data is innaccurate Advertising ★★★
📰Article Coors Light Can’t Beam Ads Into Your Brain https://gizmodo.com/no-brands-cant-target-ads-at-your-dreams-1847113888?rev=1623886445493 This story that keeps cropping up about ads in your dreams is so fucking dumb Advertising
📰Article Deconstructing the Disinformation War https://mediawell.ssrc.org/expert-reflections/deconstructing-the-disinformation-war/ Exaggerated narratives Advertising
📰Article Digital Marketing’s Last Decade Is Digital Marketing’s Lost Decade https://www.forbes.com/sites/augustinefou/2021/05/16/digital-marketings-lost-decadetime-to-think-different-do-different/ Programmatic advertising is a waste Advertising ★★
📰Article How AWS and Other Cloud Providers Became the Internet’s Most Powerful Moderators https://onezero.medium.com/how-aws-and-other-cloud-providers-became-the-internets-most-powerful-moderators-9b23a954d68a Amazon is behind the internet Advertising
📰Article How to Start Fixing Social Media. https://onezero.medium.com/how-to-start-fixing-social-media-196297eb6b71 What social media could be Advertising ★★
📰Article ICO’s final report into Cambridge Analytica invites regulatory questions https://www.ft.com/content/43962679-b1f9-4818-b569-b028a58c8cd2 CA didn’t do anything Advertising ★★★★
📕Book In the Plex: How Google Thinks, Works & Shapes Our Lives https://www.worldcat.org/title/in-the-plex-how-google-thinks-works-shapes-our-lives/oclc/1244174326&referer=brief_results Background on Google/AdWords Advertising ★★★★
📰Article Is Using a Data Broker the Most Secure Marketing Strategy? https://www.cmswire.com/digital-marketing/is-your-data-broker-the-most-secure-marketing-strategy/ Data brokers worse than random chance Advertising ★★★
📰Article Labour HQ used Facebook ads to deceive Jeremy Corbyn during election campaign https://www.thetimes.co.uk/article/labour-hq-used-facebook-ads-to-deceive-jeremy-corbyn-during-election-campaign-grlx75c27 A use case for targeted advertising Advertising ★★
📰Article Large Publishers Buy Incentivized Traffic & Popups Too https://deepsee.io/blog/large-publishers-buy-incentivized-traffic-too On incentivised traffic Advertising
📰Article New Zealand’s Stuff newspaper group joins Facebook boycott as ‘experiment’ https://www.theguardian.com/world/2020/jul/06/new-zealands-stuff-newspaper-group-joins-facebook-boycott-as-experiment Facebook boycott Advertising ★★★
📰Article News site Stuff left Facebook. Seven months later, traffic is just fine and trust is higher https://reutersinstitute.politics.ox.ac.uk/news/news-site-stuff-left-facebook-seven-months-later-traffic-just-fine-and-trust-higher Programmatic advertising doesn’t do anything Advertising ★★★
📕Book Subprime Attention Crisis: Advertising and the Time Bomb at the Heart of the Internet https://www.worldcat.org/title/subprime-attention-crisis-advertising-and-the-time-bomb-at-the-heart-of-the-internet/oclc/1225234235&referer=brief_results Programatic advertising doesn’t work/some fun GFC analogies Advertising ★★★★★
📰Article The Big Lie Behind the “Pivot to Video” https://slate.com/technology/2018/10/facebook-online-video-pivot-metrics-false.html On the Pivot to video Advertising ★★★★
📰Article The downfall of adtech means the trust economy is here https://techcrunch.com/2020/11/23/the-downfall-of-ad-tech-means-the-trust-economy-is-here/ No-one likes targeted advertising Advertising ★★★★
📰Article The Internet That Was (and Still Could Be) https://www.theatlantic.com/technology/archive/2015/06/medium-is-the-message-paradise-paved-internet-architecture/396227/ Internet without perverse incentives Advertising ★★★
📰Article The Madness of the Crowd https://logicmag.io/intelligence/the-madness-of-the-crowd/ Systems don’t work for people Advertising ★★
📰Article The Web We Lost http://anildash.com/2012/12/13/the_web_we_lost/ Social networks are partly a step backwards Advertising ★★
📰Article Unredacted Antitrust Complaint Unsealed: Google Internal Documents Show AMP Pages Brought 40% Less Revenue to Publishers https://wptavern.com/unredacted-antitrust-complaint-unsealed-google-internal-documents-show-amp-pages-brought-40-less-revenue-to-publishers Google pushed amp. It only makes things worse Advertising
📕Book What Tech Calls Thinking: An Inquiry Into the Intellectual Bedrock of Silicon Valley https://www.worldcat.org/title/what-tech-calls-thinking-an-inquiry-into-the-intellectual-bedrock-of-silicon-valley/oclc/1136959585&referer=brief_results Misc ramblings Advertising
📰Article When Big Brands Stopped Spending On Digital Ads, Nothing Happened. Why? https://www.forbes.com/sites/augustinefou/2021/01/02/when-big-brands-stopped-spending-on-digital-ads-nothing-happened-why/ Huge companys didn’t suffer for turning off programatic ads Advertising ★★★★★
📃Paper Why Do People Avoid Advertising On The Internet? https://www.tandfonline.com/doi/abs/10.1080/00913367.2004.10639175 Banner blindness and inneffectual online advertising Advertising ★★★
📰Article AI is wrestling with a replication crisis https://www.technologyreview.com/2020/11/12/1011944/artificial-intelligence-replication-crisis-science-big-tech-google-deepmind-facebook-openai/ Replication crisis in AI Algorithms ★★★
📰Article AI: Still Just Curve Fitting, Not Finding a Theory of Everything https://mindmatters.ai/2020/12/ai-still-just-curve-fitting-not-finding-a-theory-of-everything/ AI hasn’t moved beyond curve fitting Algorithms ★★★★
📺Video Andrej Karpathy – AI for Full-Self Driving at Tesla https://www.youtube.com/watch?v=hx7BXih7zx8 So much manual work to do Algorithms ★★★★★
📺Video Andrej Karpathy: Tesla Autopilot and Multi-Task Learning for Perception and Prediction https://www.youtube.com/watch?v=IHH47nZ7FZU Talking through self-driving Algorithms ★★★
📰Article Artificial Intelligence Isn’t an Arms Race With China, and the United States Shouldn’t Treat It Like One https://foreignpolicy.com/2019/12/11/artificial-intelligence-ai-not-arms-race-china-united-states/ AI hype and narrative Algorithms ★★
📺Video Building the Software 2.0 Stack (Andrej Karpathy) https://www.youtube.com/watch?v=y57wwucbXR8 Vast majority still 1.0 code Algorithms ★★★
📰Article Cars in driver-assist mode hit a third of cyclists, all oncoming cars in tests https://www.theregister.com/2022/05/16/automous_car_ada_system_tests/?s=09 Self-driving tests Algorithms ★★
📰Article Deep Learning’s Carbon Emissions Problem https://www.forbes.com/sites/robtoews/2020/06/17/deep-learnings-climate-change-problem/ Ecological cost of Machine Learning Algorithms
📰Article Elon Musk Describes the Future of Self-Driving Cars https://gizmodo.com/elon-musk-describes-the-future-of-self-driving-cars-1692076449 Stupid bullshit 2015 Algorithms
📃Paper Energy And Policy Considerations For Modern Deep Learning Research https://ojs.aaai.org/index.php/AAAI/article/view/7123 The material costs of deep learning Algorithms
📰Article Fear itself is the real threat to democracy, not tall tales of Chinese AI https://www.theguardian.com/commentisfree/2021/mar/06/fear-itself-is-the-real-threat-to-democracy-not-tall-tales-of-chinese-ai AI doomerism is nonsense Algorithms
📰Article Here’s Proof Tesla’s FSD Beta 9.0 Street Testing Is a Calamity Waiting to Happen https://www.autoevolution.com/news/here-s-proof-tesla-s-fsd-beta-90-street-testing-is-a-calamity-waiting-to-happen-165597.html General Tesla hate Algorithms
📃Paper Hierarchical Text-Conditional Image Generation With Clip Latents https://arxiv.org/abs/2204.06125 Dall-e 2 release paper Algorithms ★★★★
📰Article How Tesla Is Bad For The Earth. An electric car is still a car, and… https://indi.ca/how-tesla-is-bad-for-the-earth-31e644540a26 Green growth is the opposite of what’s needed Algorithms
📰Article Is California’s electric car revolution bad for the planet? https://www.latimes.com/politics/0000017a-c69a-da5f-affa-dfbbb1050001-123 Electric vehicles aren’t the answer Algorithms
📰Article It’s disturbingly easy to trick AI into doing something deadly https://www.vox.com/future-perfect/2019/4/8/18297410/ai-tesla-self-driving-cars-adversarial-machine-learning Adversarial attacks Algorithms ★★★
📃Paper Jukebox: A Generative Model For Music https://arxiv.org/abs/2005.00341 JukeBox release paper Algorithms ★★
📃Paper Language Models Are Few-Shot Learners https://arxiv.org/abs/2005.14165 GPT-3 release paper Algorithms ★★★★
📰Article Lines of Sight https://logicmag.io/commons/lines-of-sight/ Run-down of self-driving tech Algorithms ★★★★
📃Paper MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction with Automation https://ieeexplore.ieee.org/abstract/document/8751968/ Full self driving is a ways off Algorithms ★★
📰Article Notes on the Turing Test https://yeetgenstein.substack.com/p/notes-on-the-turing-test Deep learning sucess vs full intelligence Algorithms ★★★
📺Video PyTorch at Tesla – Andrej Karpathy, Tesla https://www.youtube.com/watch?v=oBklltKXtDE Talking through self-driving Algorithms ★★
📃Paper Release Strategies And The Social Impacts Of Language Models https://arxiv.org/abs/1908.09203 GPT-2 release paper Algorithms ★★★★
📰Article The Double Exploitation of Deepfake Porn https://thewalrus.ca/the-double-exploitation-of-deepfake-porn/ Everyones freaking out about politics when its really only good for porn Algorithms
📺Video The Lighthill Debate (1973) https://www.youtube.com/watch?v=yReDbeY7ZMU Beginning of the first major AI winter Algorithms ★★★
📰Article Waymo is dropping the term ‘self-driving,’ but not everyone in the industry is on board https://techcrunch.com/2021/01/07/waymo-is-dropping-the-term-self-driving-but-not-everyone-in-the-industry-is-on-board/ It’s not “self-driving” Algorithms ★★
📰Article What Full Autonomy Means for the Waymo Driver https://spectrum.ieee.org/full-autonomy-waymo-driver Current Waymo tech Algorithms
📰Article Why deep learning won’t give us level 5 self-driving cars https://bdtechtalks.com/2020/07/29/self-driving-tesla-car-deep-learning/ Title Algorithms ★★
📃Paper Zero-Shot Text-To-Image Generation https://arxiv.org/abs/2102.12092 Dall-e release paper Algorithms ★★★
📰Article Amazon Recruited Twitter Army to Defend Company and CEO Jeff Bezos, Leaked Document Reveals https://www.ign.com/articles/amazon-twitter-army-defend-company-ceo-jeff-bezos-leaked-document-reveals Amazon’s in-house astroturfing campaign Astroturfing ★★
📃Paper An Examination of the Impact of Astroturfing on Nationalism: A Persuasion Knowledge Perspective https://www.mdpi.com/402650 Actual research into astroturfing Astroturfing ★★★
📰Article Bird Box challenge: How Netflix made a few tweets into a global trend https://inews.co.uk/news/bird-box-challenge-netflix-twitter-warning-explained-241879 Faked “Bird Box Challenge” Astroturfing ★★★
📰Article Bird Box: How Netflix Harnesses It’s Memeing-ful Relationship with Fans | Research World https://www.researchworld.com/bird-box-how-netflix-harnesses-its-memeing-ful-relationship-with-fans/ Bird Box campaign overview Astroturfing ★★★
📃Paper Characterizing Retweet Bots: The Case of Black Market Accounts https://arxiv.org/abs/2112.02366 “Bot checkers” don’t catch retweet bots Astroturfing ★★★★★
📰Article Data Against Democracy https://logicmag.io/failure/data-against-democracy/ Platforms warping politics Astroturfing ★★
📰Article Elon Musk’s not-so-secret weapon: An army of Twitter bots touting Tesla https://www.msn.com/en-us/news/technology/elon-musk-s-not-so-secret-weapon-an-army-of-twitter-bots-touting-tesla/ Tesla astroturfing Astroturfing ★★
📃Paper Ephemeral Astroturfing Attacks: The Case of Fake Twitter Trends https://arxiv.org/abs/1910.07783 How to fake a twitter trend Astroturfing ★★★★★
📰Article Facebook Campaign Nets Big Returns For ‘Bird Box’ https://www.mediapost.com/publications/article/330557/facebook-campaign-nets-big-returns-for-bird-box.html No trace of official Twitter Bird Box ad spend beyond hashflag Astroturfing ★★★
📃Paper Group topic-author model for efficient discovery of latent social astroturfing groups in tourism domain https://link.springer.com/article/10.1186/s42400-019-0029-8 Actual research into astroturfing Astroturfing ★★★★
📰Article How Custom Emojis Crowned Twitter the Hashtag Registrar https://medium.com/@justinogarrity/how-custom-emojis-crowned-twitter-the-hashtag-registrar-43e5fa60c557 History of hashflags Astroturfing ★★
📰Article Lil Nas X Was A Popular Twitter User Before ‘Old Town Road’ https://nymag.com/intelligencer/2019/04/lil-nas-x-was-a-popular-twitter-user-before-old-town-road.html Lil Nas X, the tweetdecker Astroturfing ★★★
📃Paper Misleading Repurposing on Twitter https://arxiv.org/abs/2010.10600 Sockpuppets start as real accounts Astroturfing ★★★★
📰Article Netflix apologizes for using actors to meet press at Canadian launch https://www.theglobeandmail.com/technology/netflix-apologizes-for-using-actors-to-meet-press-at-canadian-launch/article4326706/ Netflix doing good old irl astroturfing Astroturfing ★★
📰Article Netflix denies spreading ‘Bird Box’ memes with Twitter bots https://www.dailydot.com/unclick/bird-box-memes-fake-netflix/ Bird Box wasn’t astroturfed, but why are all these links dead Astroturfing ★★
📃Paper Political Astroturfing on Twitter: How to Coordinate a Disinformation Campaign https://www.tandfonline.com/doi/abs/10.1080/10584609.2019.1661888 Actual research into astroturfing Astroturfing ★★★
📰Article Power of Suggestion https://www.chronicle.com/article/power-of-suggestion/ Many priming studies can’t be replicated Astroturfing ★★★
📕Book Public Relations and Participatory Culture: Fandom, social media and community engagement https://www.worldcat.org/title/public-relations-and-participatory-culture-fandom-social-media-and-community-engagement/oclc/1127300821&referer=brief_results PR and fandoms Astroturfing ★★★
📕Book Social Media and Public Relations: Fake friends and powerful publics https://www.worldcat.org/title/social-media-and-public-relations-fake-friends-and-powerful-publics/oclc/1089492876&referer=brief_results General new media PR textbook Astroturfing ★★★
📕Book Strategic Communication, Social Media and Democracy: The Challenge of the Digital Naturals https://www.worldcat.org/title/strategic-communication-social-media-and-democracy-the-challenge-of-the-digital-naturals/oclc/1064560421&referer=brief_results General new media PR textbook Astroturfing ★★★
📰Article Survive Reddit And Make An Impact https://wearesocial.com/uk/blog/2019/10/survive-reddit-and-make-an-impact/ How to infiltrate Reddit Astroturfing ★★★★
📰Article The Bird Box Effect: How Memes Drive Users to Netflix https://www.theringer.com/movies/2019/1/3/18167278/bird-box-memes-netflix-bots-marketing Bird Box wasn’t astroturfed, but whats all this bot activity Astroturfing ★★
📃Paper The disconcerting potential of online disinformation: Persuasive effects of astroturfing comments and three strategies for inoculation against them https://journals.sagepub.com/doi/abs/10.1177/1461444820908530 Actual research into astroturfing Astroturfing ★★★
📰Article The lesson of the dust-up over Trump’s fake Twitter followers. https://slate.com/technology/2017/06/the-lesson-of-the-dust-up-over-trumps-fake-twitter-followers.html We only have conjecture but there is manipulation happening Astroturfing ★★
📰Article The Stans Who Post So Much, Twitter Thinks They’re Bots — And Shuts Them Down https://melmagazine.com/en-us/story/the-stans-who-post-so-much-twitter-thinks-theyre-bots-and-shuts-them-down Stans and “harmless bots, including ones that promoted Netflix shows” Astroturfing
📃Paper There’s Nothing Social About Social Priming: Derailing The “Train Wreck” https://www.tandfonline.com/doi/abs/10.1080/1047840X.2021.1889312 On “priming” post replication crisis Astroturfing ★★★
📃Paper Thinking Taxonomically about Fake Accounts: Classification, False Dichotomies, and the Need for Nuance https://arxiv.org/abs/2006.04959 “Bots” encompasses so much Astroturfing ★★★
📰Article TikTok “Deeply Concerned” by Facebook’s Paid Campaign to Promote Negative Coverage of Social App https://www.hollywoodreporter.com/business/digital/tiktok-facebook-meta-targeted-victory-1235122641/ Facebook shady anti-competitive campaign Astroturfing
📰Article Weekly Nielsen Streaming Charts – Archive https://www.spoilertv.com/2020/11/weekly-nielsen-streaming-charts-updated.html Viewership numbers Astroturfing ★★★★
📰Article Who Really Writes Twitter’s ‘Trending’ Summaries https://onezero.medium.com/who-really-writes-twitters-trending-summaries-89e819255599 On the new team behind the twitter trends Astroturfing ★★★
📰Article Automaton https://en.wikipedia.org/wiki/Automaton Automatons! Automation ★★★★
📰Article Doctors turn to imperfect AI to spend more quality time with patients https://www.protocol.com/enterprise/doctor-burnout-ai-patient-risk AI causing medical harm Automation ★★★
📰Article From Manchester to Barcelona https://logicmag.io/nature/from-manchester-to-barcelona/ Techlash ignores capital Automation ★★★
📕Book Greek and Roman Technology: A Sourcebook: Annotated Translations of Greek and Latin Texts and Documents https://www.worldcat.org/title/greek-and-roman-technology-a-sourcebook-annotated-translations-of-greek-and-latin-texts-and-documents/oclc/1243344417&referer=brief_results Ancient technophobia Automation ★★★★★
📰Article Hundreds of AI tools have been built to catch covid. None of them helped. https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/ AI didn’t help with COVID Automation ★★★
📰Article Maelzel’s Chess Player https://www.eapoe.org/works/essays/maelzel.htm Edgar Allen Poe grappling with the Mechanical Turk Automation ★★★★
📰Article Of Flying Cars and the Declining Rate of Profit https://thebaffler.com/salvos/of-flying-cars-and-the-declining-rate-of-profit Technological change has stalled Automation ★★★
📰Article Once billed as a revolution in medicine, IBM’s Watson Health is sold off in parts https://www.statnews.com/2022/01/21/ibm-watson-health-sale-equity/ AI failure Automation ★★
📰Article Robots: what do they mean for jobs and incomes? https://thenextrecession.wordpress.com/2018/02/26/robots-what-do-they-mean-for-jobs-and-incomes/ Economic argument against automation discourse Automation ★★
📕Book Smart Machines and Service Work: Automation in an Age of Stagnation https://www.worldcat.org/title/smart-machines-and-service-work-automation-in-an-age-of-stagnation/oclc/1266189478&referer=brief_results I didn’t really enjoy this but I think I’ve entirely ripped it off Automation ★★★★★
📰Article Tellers, toil and technology https://www.atmmarketplace.com/articles/tellers-technology-and-atms/ ATMs doubled the amount of bank tellers Automation ★★★
📺Video Tesla AI Day https://www.youtube.com/watch?v=j0z4FweCy4M Stupid Elon bullshit Automation ★★
📰Article The Automation Charade https://logicmag.io/failure/the-automation-charade/ The illusion of automation Automation ★★★★
📰Article The Mechanical Chess Player That Unsettled the World https://slate.com/human-interest/2015/08/the-turk-a-chess-playing-robot-was-a-hoax-that-started-an-early-conversation-about-ai.html On the Mechanical Turk Automation ★★
📰Article The Modern World Has Finally Become Too Complex for Any of Us to Understand https://onezero.medium.com/the-modern-world-has-finally-become-too-complex-for-any-of-us-to-understand-1a0b46fbc292 Nodes in networks Automation
📰Article The Smart, the Stupid, and the Catastrophically Scary: An Interview with an Anonymous Data Scientist https://logicmag.io/intelligence/interview-with-an-anonymous-data-scientist/ AI hype vs reality Automation ★★★★★
📰Article Uber’s algorithm and the mirage of the marketplace. https://slate.com/technology/2015/07/ubers-algorithm-and-the-mirage-of-the-marketplace.html Uber designed to maximise worker exploitation Automation ★★
📰Article Why algorithms beat experts (even in shipping) https://splash247.com/why-algorithms-beat-experts-even-in-shipping/ Math has use cases Automation
📰Article Despair Fatigue https://thebaffler.com/salvos/despair-fatigue-david-graeber Art, welfare, future Creators ★★★★★
📰Article Don’t be a creator if you can’t do it for free https://marker.medium.com/if-you-cant-afford-to-do-it-for-free-don-t-be-a-creator-569488f3ef72 There’s no money in creating Creators ★★★★
📰Article Ed Sheeran’s Vinyls https://dadadrummer.substack.com/p/ed-sheerans-vinyls Big artists stifling others Creators ★★
📰Article HBO Max Might Have Outsmarted the Streaming Wars https://www.wired.com/story/hbo-max-outsmart-streaming-wars/ HBO burning millions to make a dent Creators ★★
📰Article Hollywood is quietly using AI to help decide which movies to make https://www.theverge.com/2019/5/28/18637135/hollywood-ai-film-decision-script-analysis-data-machine-learning AI for script reading and casting and stuff Creators ★★★
📰Article How Can We Pay for Creativity in the Digital Age? https://www.newyorker.com/magazine/2020/09/14/how-can-we-pay-for-creativity-in-the-digital-age Money in art Creators ★★★★★
📰Article Is Spotify’s Model Wiping Out Music’s Middle Class? https://www.theringer.com/tech/2019/1/16/18184314/spotify-music-streaming-service-royalty-payout-model No money in streaming Creators ★★★★
📰Article Jobless, Selling Nudes Online and Still Struggling https://www.nytimes.com/2021/01/13/business/onlyfans-pandemic-users.html Coronavirus and OnlyFans Creators ★★
📰Article Netflix loses subscribers for first time in 10 years. https://www.nytimes.com/2022/04/19/business/netflix-earnings-q1.html How could this happen Creators ★★★
📰Article Netflix will consider buybacks as it returns to positive cash flow after 2021 https://www.cnbc.com/2021/01/19/netflix-says-cash-flow-positive-after-2021-no-more-external-financing.html Whoops Creators ★★★
📰Article Netflix’s subscriber growth has slowed dramatically. Blame competition and the pandemic https://www.latimes.com/entertainment-arts/business/story/2021-07-20/netflixs-subscriber-growth-has-slowed-dramatically Oh dear Creators ★★★
📰Article No One Makes A Living On Patreon https://theoutline.com/post/2571/no-one-makes-a-living-on-patreon No One Makes A Living On Patreon Creators ★★★★
📰Article Tears in Rain https://dadadrummer.substack.com/p/tears-in-rain Spotify fraud Creators ★★★★
📰Article The creator economy is failing to spread the wealth https://www.axios.com/2021/10/11/creator-economy-revenue-content?utm_campaign=organic&utm_medium=socialshare&utm_source=twitter Title Creators ★★★
📰Article The Data Journalism That Wasn’t http://futureofmusic.org/blog/2015/08/21/data-journalism-wasnt Some weird internet beef and also a good overview of the money in streaming Creators
📰Article The Death Of Subculture part 3: The role of poverty and social class https://gravityglobal.com/news/the-death-of-subculture-part-3/ The working class has been priced out of art Creators ★★★
📕Book The Death of the Artist https://www.worldcat.org/title/death-of-the-artist-how-creators-are-struggling-to-survive-in-the-age-of-billionaires-and-big-tech/oclc/1246727096&referer=brief_results Easier to create, harder to make money, a social issue Creators ★★★★★
📰Article The Economics of OnlyFans https://xsrus.com/the-economics-of-onlyfans Losing money from creating Creators ★★★
📰Article The Gig Economy is failing, enter the hustle economy https://onezero.medium.com/the-gig-economy-is-failing-say-hello-to-the-hustle-economy-13ae3aa91954 Work is increasingly atomised Creators ★★
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Shooting Script

We live in a time of incredible technological innovation, there’s the iPhone from 2007…

Incredible technological innovation…

AI doesn’t work

Now despite the fact that we don’t have flying cars, VR is a stupid waste of time, and there is no computer magic to reverse global warming, we do seem to be scared of all the technological developments. Boston Dynamics robots are like the terminator, GPT-3 can write code, and Tesla has been promising full self-driving by the end of the year, every year, for the past decade. 

But, while we dream of Artificial Intelligence, it’s pretty far from our current reality of machine-learning-driven statistics.

In the 50’s some guy made a mechanical model of a biological neuron. A big old fucking computer literally motors would move to simulate the neuron firing.

And it could do linear binary classification: Just by giving it examples, it can learn to separate stuff into 2 categories. So you can say limes are green and small and lemons are yellow and larger.

–RESHUFFLE–

Actually the usual example is apples and oranges. Which is very clever, comparing apples to oranges. But I’ve changed it to other fruit because. It’s a fucking stretch. It doesn’t really work as an example, and it’s not really a joke either.

So anyway after you’ve shown it enough examples of limes being green and small and lemons being yellow and larger, it will try to create a function that will attempt to separate that data into those two categories. So eventually, you could say I’ve got something green and small here, and it will tell you you’ve got a lime.

And this was super exciting at the time, right? Full AI was just around the corner. And then obviously, it didn’t deliver. Inaccurately separating things into two categories when you already know the categories isn’t particularly useful. 

But um, they knew how it could be improved. You’d make a network of multiple perceptrons feeding into each other, and run backwards through the network as well as forwards, and use unfathomably large data sets. But obviously they were limited by the technology of the time.

Now, we’re no longer limited by the technology of that time. So we’re doing that stuff that they suggested. The end. Yeah, what we’re currently calling artificial intelligence is just that. Very basic pattern recognition.


OPTIONAL

But if you wanted to make a calculator without having to develop the underlying rules, you could just run a lot of examples through a network. Going:

“What’s 2+2?” And it’ll go “36” or whatever, it’s just random. And then you go “No it’s 4.”

And then it makes some adjustments so next time it sees the pattern “2+2” It categorises it’s answer to be less like 36 and more like 4.

Just over and over, with millions of examples, and you might get something kind of accurate. 

The exciting thing is, that’s kinda how we learn, right? They’re not teaching field theory in primary schools. But the thing is with any rules-based system it’s possible to infer the logic behind it. After you’ve show enough multiplication tables to a human, they’ll start to figure out the underlying rules.

Now the problem is, our machine learning calculator will never learn the underlying rules, the only rule it can learn is “nice patterns.”

With enough examples and time you can get it to memorise those examples, and improve the chance that it will guess other answers correctly. But it still has no idea what it’s saying and all we can do is chip diminishing percentages off of its failure rate.

That calculator will never replace an actual calculator, and accuracy in machine learning is still subhuman across the board.


That’s not to say it’s entirely useless. Of course, “nice patterns” can do a lot of things, like identify patterns, or create patterns. That’s why it’s really pretty good at image manipulation and knock off dril tweets. But for something with actual internal logic, like math, or thought, or being regular, it fucking sucks.

Pattern recognition really is the solution to everything, and nothing. It’s the solution to everything because it will always give you an answer, and nothing, because it’s never right. One of the worst human impulses. Prioritising pattern recognition over fact is why we stereotype, why we gamble, and why the girlies love idrlabs.com. Hows that for a fucking punchline, no way in hell you were expecting me to say that.

Now when the perceptron failed to be “full artificial intelligence” it lead to the first AI winter. Research in machine learning is punctuated by AI winters: hype builds, far outstrips the technological reality, then all of a sudden people realise that, so hype, massively, drops away. Only to start all over again. As the fundamental technologies remain unchanged and we find out over and over that no, the computer cannot learn to love.

So progress in AI has somewhat stalled, it’s a very popular research field with hundreds of papers coming out every day. But no one is looking at fundamentally new approaches. It’s such a popular field because you can put a new wad of data into an existing algorithm, and that’s it, you’ve just done a research paper. And that’s most innovation in AI right now, just more data, more data, more data.

OpenAI thinks that more data is the solution because as they add exponentially more data to GPT, it gets better at replicating English sentences. It seems to improve at a linear rate… Because you’re adding exponentially more sentences for it to copy.

Another way of thinking about that, is as there’s a linear increase in the amount of sentences, the improvement exponentially decreases. We don’t have an infinite supply of sentences, at this rate, they’ll soon require more examples of sentences than have ever existed, and more processing power than has ever existed.

But right now, that’s all we’ve got, just add more data, and hope improvement happens.

The best of the best right now, DALL-E 2. It’s reached a near-photorealistic aesthetic. And this is probably the perfect use-case for a neural network, in theory this is the thing that it’s best at. Producing copies of patterns it’s seen with absolutely no logic behind it. And while it is cool, it’s also kind of dogshit. Everything it produces is just a copy of its input data. Based on pattern recognition and noise. 

It has that aesthetic sense but if you ask it for a builder it’ll show you a white guy with a hard hat. A wedding is a white man and white woman holding hands. CEO is a white guy, and “a CEO” is an asian man. Whatever patterns it’s seen the most of, it will replicate, and that’s all it can do.

Forgetting about the ethics for a second, since they seem to have. It shows us that these algorithms cannot create anything new.

Like “This person does not exist.” It’s trained on a massive dataset of faces, and it tries to generate a new image based on the patterns it’s learned. Now, a lot of the time, that person does exist, it’s reproducing an image of a person, to the point that you can recognise them from the training data.

So our current artificial intelligence is just copies of the past with no intelligence. And it cannot present anything new.

No one’s got the rights to these images by the way, this isn’t a replacement for art it’s a copyright risk.

Now Tesla has the biggest, loudest dreams of Artificial Intelligence, which is interesting because they’ve never invested in AI research. Instead they rely on combining existing technologies and publicly available research. Elon Musk says that Tesla will have full AI Self-driving this year, as he has done every year for the past 8 years.

Full-self driving is what’s called level 5 automation. For reference, the best self-driving out there right now is Google’s. They have a truck that only drives one route, with cameras and sensors all around it, and has to be remotely assisted, but it doesn’t technically need to have a driver inside. And that’s at level 3.5.

Tesla is currently at level 2 and their developers don’t think that’s gonna be changing anytime soon.

But it’s in use and kind of working. The go to example for AI.

Now, there’s two halves to the self-driving system. There’s observation, which is the eyes: detecting what’s around you, then there’s driving, which is deciding when to accelerate and turn.

The observation is done by machine learning algorithms (visually demonstrate), then the driving is written in traditional explicit code. C++. “If we detect a green arrow and no oncoming traffic, accelerate to speed and turn.” That sort of thing. Actual manually written out rules for every possibility. Which is insane, the idea of doing that caused one of those previous AI winters, they we’re like “What? We have to manually write out rules for every possibility? No way, that’s insane.” So now we’re just doing that.

Now on the detection side, for every object you could encounter while driving they make a detector, and train the detector on a dataset of thousands of images of that object. But then every object has variants, you wouldn’t have trouble differentiating a stop sign from a stop sign with an unusual amount of bird shit on it, but a computer will. So they have to collect a massive data set of stop signs with an unusual amount of bird shit on them, and train that variant.

And all these variants need to have equal representation in the dataset, or else it’ll incorrectly prioritise stuff it’s seen more. Like they struggle with orange traffic lights, you don’t see orange traffic lights as often as red or green ones. Now, incorrectly thinking that the moon is an orange light is a bit of fun. But these biases in the input data mean that, due to the demographic makeup of areas where Teslas are driven, self-driving is more likely to hit a black person than a white person.

Elon Musk arbitrarily decided to remove their depth sensors, so they have no actual depth detection, everything is extrapolated from the low-res cameras on the car. And without actual depth information, their lane detection often gets thrown off by motorway bumpers, and the car will actually steer people into a crash.

So historically Tesla’s had an unusually high fatality rate when it comes to driving full speed straight into a motorway bumper. But they’ve always denied that any of their fatal crashes have involved autopilot.

Now just within the last year they’ve started to be under actual formal investigation, and right away, it turns out, a bunch of those crashes which they’d denied were caused by autopilot.

And their current plan is to work day-in-day out, to manually factor in every wild, never-before-seen possibility, and constantly re-train their classifiers from scratch. The problem with wild, never-before-seen possibilities is they’re famously hard to account for.

But they’re trying anyway. Every car is constantly providing them new data, and they have an insane amount of underpaid workers processing it. And improving the dataset is basically all they do.

So last year, comparing their training data to their testing data, they’d reached a stop sign detection rate of 99.51%. Which doesn’t sound bad, but the human equivalent is, you’re looking directly at a stop sign, can you tell you’re looking at a stop sign? You should never be answering no. We should, at the very least, be looking at a lot more nines in that percentage.

So both of these systems are sort of working but require a literally infinite process of manual coding and massaging of datasets to ever truly work. And they’re stuck with the tech that they put in those cars 8 years ago. And they’re not looking at alternative solutions, they’ve never invested in AI research.

And that’s not unique to Tesla. All the tech companies are pitching a vision of the future, while not investing in that future at all. They’re fundamentally not interested in advancing the technology. They’re interested in repackaging the old technology. Actual progress is a threat to their dominance. While pretending that it’s happening improves their image.

But look, these things will happen, the next text generation algorithm is gonna be that much better, the next image generation algorithm is gonna be that much better, they’ll keep chipping away at that self-driving kill count. But our current technologies aren’t going to magically evolve into full artificial intelligence.

What we call AI is basically spreadsheets. Weights, functions and a fuckton of data processed by a fuckton of workers. It can recognise patterns, but that’s it. And there’s interesting applications for that, you can even apply it to areas where it doesn’t have an application.

But we’re not on the brink of major technological development here. We’re in the middle of some fiddling at the edges of what came before.

Comparatively, we’re not in an age of technological innovation. Just like the algorithms, we’re stuck repeating the aesthetics of the past.