6174 @wm_entropy

AI will create more jobs than any other technology in history. The doomers' fundamental error isn't just the lump of labor fallacy. It's deeper than that. They assume a finite problem space. This is the fundamental error of AI and job doomers. They look at the economy and see a fixed amount of work to be done, a pie that can only be sliced thinner as machines take bigger bites. They see humans a competitive resource for a finite amount of work and a finite amount of problems to solve that must be eliminated. This is fundamentally, totally and completely wrong. The pie isn't fixed. It never was. And the reason it isn't fixed is baked into the very nature of technology itself. Technology is nothing but abstraction stacking. And abstraction stacking is infinite. Therefore the work is infinite. The hammer didn't reduce the amount of work. It moved the work up the stack. And the new work was more complex, more varied, and more interesting than the old work. Complexity breeds more complexity and more variety. Once you have houses instead of mud huts, you have a cascade of new problems that didn't exist before. Plumbing. Wiring. Insulation. Roofing materials that don't rot. Drainage systems so the foundation doesn't flood. Fire codes so your neighbor's bad wiring doesn't burn down the whole block. Each of those problems becomes a job. A plumber. An electrician. An insulator. A roofer. A civil engineer. A building inspector. None of those jobs existed when we lived in mud huts. They exist because we solved the mud hut problem. Think of all of human technological development as a stack of abstraction layers, each one built on top of the ones below it. At the bottom: raw survival. Finding food. Building shelter. Making fire. These are the base-layer problems. Each major technology wave solved a base-layer problem and in doing so created an entirely new layer of problems above it: Agriculture solved "how do we reliably eat?" — and created problems of land ownership, irrigation, crop rotation, storage, trade, taxation, and governance. Writing solved "how do we remember things across generations?" — and created problems of literacy, education, record-keeping, law, bureaucracy, and literature. The printing press solved "how do we spread knowledge at scale?" — and created problems of intellectual property, censorship, journalism, publishing, public opinion, and democratic discourse. The steam engine solved "how do we generate mechanical power without muscles?" — and created problems of factory design, worker safety, urban planning, railroad engineering, coal mining, labor relations, and environmental pollution. Electricity solved "how do we deliver energy anywhere?" — and created problems of grid design, power generation, appliance manufacturing, electrical safety codes, utility regulation, and an entire consumer electronics industry. The Internet solved "how do we connect all human knowledge?" — and created problems of cybersecurity, digital privacy, online commerce, content moderation, network infrastructure, cloud computing, social media dynamics, and an entire digital economy that employs tens of millions. Notice the pattern? Each solution didn't just solve a problem. It created an entirely new problem space that was larger, more complex, and more varied than the one it replaced. The stack grows. It never shrinks. It's turtles all the way down and all the way up.

If you use "AI agents" (LLMs that call tools) you need to be aware of the Lethal Trifecta Any time you combine access to private data with exposure to untrusted content and the ability to externally communicate an attacker can trick the system into stealing your data!

@Artoftheproblem That's interesting! As always.

El video y la foto. Gabriel Medina 🇧🇷 Puntaje 9.90 en los #JuegosOlímpicos 📸Jerome Brouillet/ AFP via Getty Images.

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@ubernion @teknium or a lot of memory, multithread cpus and patience.

@Artoftheproblem @ylecun Without knowing Newton's laws, you can estimate where an arrow will land just by looking at the results of several of them. Knowing these laws, it is possible to have an equation that describes these results, but true intelligence is realizing that you can place objects in orbit.

Someone shared this paper, which implies that the estimate above is possibly off by as much as a factor of 1000: sciencedirect.com/science/articl…

🥁 Llama3 is out 🥁 8B and 70B models available today. 8k context length. Trained with 15 trillion tokens on a custom-built 24k GPU cluster. Great performance on various benchmarks, with Llam3-8B doing better than Llama2-70B in some cases. More versions are coming over the next few months. llama.meta.com/llama3/

@onlydomains hey guys... could you please verify if the main site is online? I'm trying to access it, but it is not working, at least in Brazil. the dns points to 119.252.177.37

@felps_bra @NandoDF @DrJimFan Each physics equation we have is an interpolation of data we've seen in the past... but with much less power than billions of matrix multiplications that these models are capable of. I wouldn't bet against algebra.

@kaylacardillo @NandoDF @apples_jimmy @DrJimFan Computers are getting better every day at understanding what they "read." Much of our knowledge of physics is described in books, articles, equations, and code. It shouldn't take long for them to start understanding more about physics than we do.

@TechEmails Eric, minutes after...

@moschella_luca @iclr_conf I truly admire your work. In a world of articles with indecipherable equations or requiring thousands of dollars worth of computers to find mere matrix weights, yours with MNIST shows how cool studying computer science can be. Thank you!

@teknium great!

@mcvalor_art @svpino I guess is because google is good enough and is "free" for users. They sell your attention.

@crislenta @svpino AWS and Akamai are huge because of Linux.

@gordic_aleksa @ggerganov @Apple @AMD Is it a good selling point? I don't know, but I bought 3x 5700U mini PCs, 2x 7730U, and one 5950X just to run GGMLs.

@gordic_aleksa @ggerganov @apple I don't know how a chip maker hasn't acquired GGML yet and assembled a superteam to become highly specialized in its own chip. I would prefer @AMD, especially for the number of threads available in their chips, but @apple is a fair choice.

@Artoftheproblem @jeremyphoward this guy is a monster

@samhogan peer-reviewed