DALL-E

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Four Battlegrounds by Paul Scharre

2021 United States Capitol attack, 3D printing, active measures, activist lawyer, AI winter, AlphaGo, amateurs talk tactics, professionals talk logistics, artificial general intelligence, ASML, augmented reality, Automated Insights, autonomous vehicles, barriers to entry, Berlin Wall, Big Tech, bitcoin, Black Lives Matter, Boeing 737 MAX, Boris Johnson, Brexit referendum, business continuity plan, business process, carbon footprint, chief data officer, Citizen Lab, clean water, cloud computing, commoditize, computer vision, coronavirus, COVID-19, crisis actor, crowdsourcing, DALL-E, data is not the new oil, data is the new oil, data science, deep learning, deepfake, DeepMind, Demis Hassabis, Deng Xiaoping, digital map, digital rights, disinformation, Donald Trump, drone strike, dual-use technology, Elon Musk, en.wikipedia.org, endowment effect, fake news, Francis Fukuyama: the end of history, future of journalism, future of work, game design, general purpose technology, Geoffrey Hinton, geopolitical risk, George Floyd, global supply chain, GPT-3, Great Leap Forward, hive mind, hustle culture, ImageNet competition, immigration reform, income per capita, interchangeable parts, Internet Archive, Internet of things, iterative process, Jeff Bezos, job automation, Kevin Kelly, Kevin Roose, large language model, lockdown, Mark Zuckerberg, military-industrial complex, move fast and break things, Nate Silver, natural language processing, new economy, Nick Bostrom, one-China policy, Open Library, OpenAI, PalmPilot, Parler "social media", pattern recognition, phenotype, post-truth, purchasing power parity, QAnon, QR code, race to the bottom, RAND corporation, recommendation engine, reshoring, ride hailing / ride sharing, robotic process automation, Rodney Brooks, Rubik’s Cube, self-driving car, Shoshana Zuboff, side project, Silicon Valley, slashdot, smart cities, smart meter, Snapchat, social software, sorting algorithm, South China Sea, sparse data, speech recognition, Steve Bannon, Steven Levy, Stuxnet, supply-chain attack, surveillance capitalism, systems thinking, tech worker, techlash, telemarketer, The Brussels Effect, The Signal and the Noise by Nate Silver, TikTok, trade route, TSMC

It can allow for models that can connect concepts across different types of data, such as explaining the content of images in words or creating new images based on text descriptions. Text-to-image models, such as OpenAI’s DALL·E and DALL·E 2, Google Brain’s Imagen, and Stability AI’s Stable Diffusion, can create new AI-generated images based on a text description. Multimodal training data can lead to models that have a richer understanding of the world. DALL·E’s concept of a “cat,” for example, encompasses pictures of actual cats, cartoon sketches of cats, and the word “cat.” Researchers have discovered that the multimodal models actually have artificial neurons tied to underlying concepts.

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., Learning Transferable Visual Models From Natural Language Supervision (arXiv.org, February 26, 2021), https://arxiv.org/pdf/2103.00020.pdf; Gabriel Goh et al., “Multimodal Neurons in Artificial Neural Networks,” OpenAI Blog, March 4, 2021, https://openai.com/blog/multimodal-neurons/; Romero, “GPT-3 Scared You?” 295Text-to-image models: Ramesh et al., “DALL·E”; Ramesh et al., Zero-Shot Text-to-Image Generation; Aditya Ramesh et al., “DALL·E 2,” OpenAI Blog, n.d., https://openai.com/dall-e-2/; Aditya Ramesh et al., Hierarchical Text-Conditional Image Generation with CLIP Latents (arXiv.org, April 13, 2022), https://arxiv.org/pdf/2204.06125.pdf; Chitwan Saharia et al., “Imagen,” Google Research, n.d., https://imagen.research.google/; Chitwan Saharia et al., Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (arXiv.org, May 23, 2022), https://arxiv.org/pdf/2205.11487.pdf; Emad Mostaque, “Stable Diffusion Public Release,” Stability AI blog, August 22, 2022, https://stability.ai/blog/stable-diffusion-public-release; Emad Mostaque, “Stable Diffusion Launch Announcement,” Stability AI blog, August 10, 2022, https://stability.ai/blog/stable-diffusion-announcement. 295artificial neurons tied to underlying concepts: Goh et al., “Multimodal Neurons in Artificial Neural Networks” (OpenAI Blog); Gabriel Goh et al., “Multimodal Neurons in Artificial Neural Networks” (full paper), distill.pub, March 4, 2021, https://distill.pub/2021/multimodal-neurons/; “Unit 550,” OpenAI Microscope, n.d., https://microscope.openai.com/models/contrastive_4x/image_block_4_5_Add_6_0/550 295larger, more diverse datasets: Radford et al., Learning Transferable Visual Models From Natural Language Supervision. 295Gato: Scott Reed et al., “A Generalist Agent,” DeepMind blog, May 12, 2022, https://www.deepmind.com/publications/a-generalist-agent; Scott Reed et al., A Generalist Agent (arXiv.org, May 19, 2022), https://arxiv.org/pdf/2205.06175.pdf. 295interrogate the inner workings of multimodal models: Goh et al., “Multimodal Neurons in Artificial Neural Networks” (full paper). 295new ways of attacking models: Goh et al., “Multimodal Neurons in Artificial Neural Networks” (full paper). 296trend toward ever-larger AI models: Jared Kaplan et al., Scaling Laws for Neural Language Models (arXiv.org, January 23, 2020), https://arxiv.org/pdf/2001.08361.pdf.

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., Machine Learning Model Sizes and the Parameter Gap (arXiv.org, July 5, 2022), https://arxiv.org/pdf/2207.02852.pdf. 295multimodal models: Ilya Sutskever, “Multimodal,” OpenAI Blog, January 2021, https://openai.com/blog/tags/multimodal/; Aditya Ramesh et al., “DALL·E: Creating Images from Text,” OpenAI Blog, January 5, 2021, https://openai.com/blog/dall-e/; Aditya Ramesh et al., Zero-Shot Text-to-Image Generation (arXiv.org, February 26, 2021), https://arxiv.org/pdf/2102.12092.pdf; Alec Radford et al., “CLIP: Connecting Text and Images,” OpenAI Blog, January 5, 2021, https://openai.com/blog/clip/; Alec Radford et al., Learning Transferable Visual Models From Natural Language Supervision (arXiv.org, February 26, 2021), https://arxiv.org/pdf/2103.00020.pdf; Gabriel Goh et al., “Multimodal Neurons in Artificial Neural Networks,” OpenAI Blog, March 4, 2021, https://openai.com/blog/multimodal-neurons/; Romero, “GPT-3 Scared You?”

Blood in the Machine: The Origins of the Rebellion Against Big Tech by Brian Merchant

"World Economic Forum" Davos, Ada Lovelace, algorithmic management, Amazon Mechanical Turk, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, basic income, Bernie Sanders, Big Tech, big-box store, Black Lives Matter, Cambridge Analytica, Charles Babbage, ChatGPT, collective bargaining, colonial rule, commoditize, company town, computer age, computer vision, coronavirus, cotton gin, COVID-19, cryptocurrency, DALL-E, decarbonisation, deskilling, digital rights, Donald Trump, Edward Jenner, Elon Musk, Erik Brynjolfsson, factory automation, flying shuttle, Frederick Winslow Taylor, fulfillment center, full employment, future of work, George Floyd, gig economy, gigafactory, hiring and firing, hockey-stick growth, independent contractor, industrial robot, information asymmetry, Internet Archive, invisible hand, Isaac Newton, James Hargreaves, James Watt: steam engine, Jeff Bezos, Jessica Bruder, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Roose, Kickstarter, Lyft, Mark Zuckerberg, Marshall McLuhan, means of production, military-industrial complex, move fast and break things, Naomi Klein, New Journalism, On the Economy of Machinery and Manufactures, OpenAI, precariat, profit motive, ride hailing / ride sharing, Sam Bankman-Fried, scientific management, Second Machine Age, self-driving car, sharing economy, Silicon Valley, sovereign wealth fund, spinning jenny, Steve Jobs, Steve Wozniak, super pumped, TaskRabbit, tech billionaire, tech bro, tech worker, techlash, technological determinism, Ted Kaczynski, The Future of Employment, The Wealth of Nations by Adam Smith, Thomas Malthus, Travis Kalanick, Uber and Lyft, uber lyft, union organizing, universal basic income, W. E. B. Du Bois, warehouse automation, warehouse robotics, working poor, workplace surveillance

Ambitious start-ups like Midjourney, and well-positioned Silicon Valley companies like OpenAI, are already offering on-demand AI image and prose generation. DALL-E spurred a backlash when it was unveiled in 2022, especially among artists and illustrators, who worry that such generators will take away work and degrade wages. If history is any guide, they’re almost certainly right. DALL-E certainly isn’t as high in quality as a skilled human artist, and likely won’t be for some time, if ever—but as with the skilled cloth workers of the 1800s, that ultimately doesn’t matter. DALL-E is cheaper and can pump out knockoff images in a heartbeat; companies will deem them good enough, and will turn to the program to save costs.

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With app-based work, jobs are precarious, are subject to sudden changes in workload and pay rates, come with few to no benefits and protections, place the worker under intense, nonstop surveillance, and are highly volatile and unpredictable. And the boss is the algorithm; HR consists of a text box that workers can log complaints into, and which may or may not generate a response. The modern worker can sense the implications of this trend. It’s not just ride-hailing either—AI image-generators like DALL-E and neural net–based writing tools like ChatGPT threaten the livelihoods of illustrators, graphic designers, copywriters, and editorial assistants. Streaming platforms like Spotify have already radically degraded wages for musicians, who lost album sales as an income stream years ago. Much about Andrew Yang may be suspect, but he did predict correctly that anger would again spread like wildfire as skilled workers watched algorithms, AI, and tech platforms erode their earnings and status.

Nobody's Fool: Why We Get Taken in and What We Can Do About It by Daniel Simons, Christopher Chabris

Abraham Wald, Airbnb, artificial general intelligence, Bernie Madoff, bitcoin, Bitcoin "FTX", blockchain, Boston Dynamics, butterfly effect, call centre, Carmen Reinhart, Cass Sunstein, ChatGPT, Checklist Manifesto, choice architecture, computer vision, contact tracing, coronavirus, COVID-19, cryptocurrency, DALL-E, data science, disinformation, Donald Trump, Elon Musk, en.wikipedia.org, fake news, false flag, financial thriller, forensic accounting, framing effect, George Akerlof, global pandemic, index fund, information asymmetry, information security, Internet Archive, Jeffrey Epstein, Jim Simons, John von Neumann, Keith Raniere, Kenneth Rogoff, London Whale, lone genius, longitudinal study, loss aversion, Mark Zuckerberg, meta-analysis, moral panic, multilevel marketing, Nelson Mandela, pattern recognition, Pershing Square Capital Management, pets.com, placebo effect, Ponzi scheme, power law, publication bias, randomized controlled trial, replication crisis, risk tolerance, Robert Shiller, Ronald Reagan, Rubik’s Cube, Sam Bankman-Fried, Satoshi Nakamoto, Saturday Night Live, Sharpe ratio, short selling, side hustle, Silicon Valley, Silicon Valley startup, Skype, smart transportation, sovereign wealth fund, statistical model, stem cell, Steve Jobs, sunk-cost fallacy, survivorship bias, systematic bias, TED Talk, transcontinental railway, WikiLeaks, Y2K

Their expertise in developing sophisticated computational models is genuine, but it is not the expertise necessary to evaluate whether a model’s output constitutes generally intelligent behavior. People who make these predictions appear to be swayed by the most impressive examples of how well new machine learning models like ChatGPT and DALL-E do in producing realistic language and generating beautiful pictures. But these systems tend to work best only when given just the right prompts, and their boosters downplay or ignore the cases where similar prompts make them fail miserably. What seems like intelligent conversation often turns out to be a bull session with a bot whose cleverness comes from ingesting huge volumes of text and responding by accessing the statistically most relevant stuff in its dataset.

AI 2041 by Kai-Fu Lee, Chen Qiufan

3D printing, Abraham Maslow, active measures, airport security, Albert Einstein, AlphaGo, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, blue-collar work, Cambridge Analytica, carbon footprint, Charles Babbage, computer vision, contact tracing, coronavirus, corporate governance, corporate social responsibility, COVID-19, CRISPR, cryptocurrency, DALL-E, data science, deep learning, deepfake, DeepMind, delayed gratification, dematerialisation, digital map, digital rights, digital twin, Elon Musk, fake news, fault tolerance, future of work, Future Shock, game design, general purpose technology, global pandemic, Google Glasses, Google X / Alphabet X, GPT-3, happiness index / gross national happiness, hedonic treadmill, hiring and firing, Hyperloop, information security, Internet of things, iterative process, job automation, language acquisition, low earth orbit, Lyft, Maslow's hierarchy, mass immigration, mirror neurons, money: store of value / unit of account / medium of exchange, mutually assured destruction, natural language processing, Neil Armstrong, Nelson Mandela, OpenAI, optical character recognition, pattern recognition, plutocrats, post scarcity, profit motive, QR code, quantitative easing, Richard Feynman, ride hailing / ride sharing, robotic process automation, Satoshi Nakamoto, self-driving car, seminal paper, Silicon Valley, smart cities, smart contracts, smart transportation, Snapchat, social distancing, speech recognition, Stephen Hawking, synthetic biology, telemarketer, Tesla Model S, The future is already here, trolley problem, Turing test, uber lyft, universal basic income, warehouse automation, warehouse robotics, zero-sum game

Consider that just months after its release, people had built applications on top of GPT-3 that included a chatbot that lets you talk to historical figures, a music composition tool that finishes guitar tabs that you start, an app capable of taking half an image and completing the full image, and an app called DALL.E that can draw a figure based on a natural language description (such as “a baby daikon radish in a tutu walking a dog”). While these apps are mere curiosities at present, if the flaws above are fixed, such a platform could evolve into a virtuous cycle in which tens of thousands of smart developers create amazing apps that improve the platform while drawing more users, just like what happened with Windows and Android.

American Ground: Unbuilding the World Trade Center by William Langewiesche

DALL-E, William Langewiesche

Tra le prime affermazioni imbarazzanti ci sono state quelle di Van Romero, esperto in esplosivi e vicepresidente del New Mexico Institute of Mining and Technology, il quale ha pubblicamente dichiarato che secondo lui la responsabilità dei crolli andava attribuita a cariche esplosive piazzate in precedenza, aggiungendo che gli aerei non erano altro che un’esca per attirare le squadre di soccorso. È stato subito sommerso dalle e-mail dei teorici della cospirazione. Dopo appena una settimana ha tentato di rimangiarsi tutto, sconfessando pubblicamente se stesso e sottoscrivendo l’opinione sempre più diffusa secondo cui le torri erano crollate per l’effetto combinato degli incendi e dei danni causati dall’impatto. «Sono molto turbato...