A link roundup for curious non-techies
I feel like every few weeks or so – just as the news media adopts a more critical and balanced perspective on the use of artificial intelligence technology – some clueless Boomer or GenX media personality or CEO gets AI-pillled and kicks off a whole new round of breathless hype.
It now takes one of two forms: ‘OMG, AI is amazing and there totally isn’t a bubble at all,’ or ‘AI is big and scary and dangerous, and there is totally not a bubble at all. But we need to slow it down so it doesn’t wipe out humanity.’
As I wrote last week, I believe we are at risk of the general public becoming so disillusioned with the specter of the fake, overhyped fantasy ‘AI’ that we won’t have broad-based support for pursuing really useful technological advances.
A central problem is that we can’t even agree on what AI is.
The leaders of the big U.S.-based AI hyperscalers , namely Anthropic and OpenAI, have been deliberately vague and often vastly overstated the capabilities of what their models can do. And the general public (and many well-known AI critics) now conflate these huge commercial, cloud-based models with all AI.
Instead of attempting to put together a long article myself, I put together this round up of links to articles and videos from sources that know way more about it than I do, and that explain things in a way that I understand.
Where possible, I will link to articles that are freely accessible. But some are behind a paywall. I will also update the list as needed.
Definitions – What do we mean when we say ‘AI?’
IBM.com – AI vs. machine learning vs. deep learning vs. neural networks: What’s the difference?
Stanford Human-Centered Artificial Intelligence – What is a Large Language Model?
Forbes – The Important Difference between Generative AI and AGI
Forbes – What is a Hyperscaler?
IBM.com – What are Open-Source Large Language Models? Benefits, Risks and Types
OpenAI, Anthropic and the ‘AI bubble’
Note: When most people talk about an”AI bubble,” they are almost exclusively referring to the vast amounts of venture capital that have been poured into the top Silicon Valley hyperscaler companies.
The authors of the following articles are using the term AI as shorthand for this private, for-profit industry rather than the technology as a whole. And while I think this is a misleading habit, I am including these links because I think they provide important perspective about the speciousness of the claims made by the leaders of these companies, as well as the harm that their actions have had on society and the world economy.
Ed Zitron – Where’s Your Ed At? – AI is Slowing Down, AI Doesn’t Have ROI, AI is Too Expensive
Will Lockett: It’s Bubbles within Bubbles, NVIDIA’s 500 Billion Plan, The AI Industry is Panicking, AI Hysteria: No, AI Isn’t Going to Kill Us All
Cory Doctorow – How an AI Moratorium Can Save AI Bosses
AI mythbusting from people who work with AI
Teaching Computers How to Talk: Why Hallucinations Will Never Be Eliminated
Machine Learning Mastery: 5 Practical Techniques to Detect and Mitigate LLM Hallucinations Beyond Prompt Engineering
Health Tech World: Large Language Models Cannot Learn Independently or Acquire New Skills
Marcus on AI: LLMs are Not Like You or Me–and Never Will Be
Beyond the hype – Where the industry is moving
Neil Sahota – Local LLM: When Running AI In-House Becomes the Smarter Choice
Ignesa.com – The Truth About Local LLMs: When You Actually Need Them
Open Source Initiative – Open Weights are Good. Open Source is Better
LifeHacker – You Can (Maybe) Run Meta’s Latest AI Model Locally on Your Computer1
That’s it for now. Is there an angle to this you want to know more about but I haven’t covered? Leave a comment or send me a message and let me know. I will update this list going forward.
- This is not a recommendation. I would no sooner trust a Meta product with sensitve data than I would email a Nigerian prince my bank account information. For informational purposes only. ↩︎
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