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The madness of king altman: how AI became the god of silicon valley
The madness of king altman: how AI became the god of silicon valley

"At this point, it doesn’t matter if tech executives and VCs believe their own hype and wild claims about what’s next for AI — though I firmly believe that they genuinely buy into the things they’re preaching and chugging their own kool aid — because they’ve made the grand pronouncements, they started their cults, and they promised a future of AGI-powered wonder. They sold equity to investors who give them billions every year, and those investors now expect steady double-digit percentage returns."

A fun (and hopefully concerning) take on the AIification of everything.

#AI#business#hype#money
·wowt.news·
The madness of king altman: how AI became the god of silicon valley
Why “Everyone Dies” Gets AGI All Wrong
Why “Everyone Dies” Gets AGI All Wrong
"Everyone Dies" is on my to-read list. From what I've seen it sounds pretty alarmist, but also that feels like that's the point. This article seems pretty negative on it (not that I've read the article at the time of saving this) so I'm keeping a link to this to read at some point in the future, once I've read the book.
#AI#book#machine-learning#philosophy#review#scepticism
·bengoertzel.substack.com·
Why “Everyone Dies” Gets AGI All Wrong
Why Large Language Models Won’t Replace Engineers Anytime Soon
Why Large Language Models Won’t Replace Engineers Anytime Soon

In short: an attempt at a mathematical proof as to why humans won't be replaced by LLMs any time soon.

Not sure I have the background to really follow all of this, and I'm not sure this is as reassuring as some might want it to be; but it's an interesting read. Finally I sense the "replacement" won't happen or fail to happen because of facts of the world, but because business will decide it's for the best and will cause things to collapse.

#AI#blog#engineering#LLM#machine-learning#research#science
·fastcode.io·
Why Large Language Models Won’t Replace Engineers Anytime Soon
Why I'm declining your AI generated MR - Stuart Spence Blog
Why I'm declining your AI generated MR - Stuart Spence Blog

I've been thinking recently about AI/LLM-created PRs on GitHub. I've only received a couple, and they've not been great. This has got me wondering if I should entertain them at all from both a software development point of view, and also an ethical point of view.

This blog post is an interesting read for someone else's take on things.

#AI#engineering#github#LLM#machine-learning#programming#rant
·blog.stuartspence.ca·
Why I'm declining your AI generated MR - Stuart Spence Blog
Software Engineering for Machine Learning: A Case Study
Software Engineering for Machine Learning: A Case Study
Abstract—Recent advances in machine learning have stimulated widespread interest within the Information Technology sector on integrating AI capabilities into software and services. This goal has forced organizations to evolve their development processes. We report on a study that we conducted on observing software teams at Microsoft as they develop AI-based applications. We consider a nine-stage workflow process informed by prior experiences developing AI applications (e.g., search and NLP) and data science tools (e.g. application diagnostics and bug reporting). We found that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace. We collected some best practices from Microsoft teams to address these challenges. In addition, we have identified three aspects of the AI domain that make it fundamentally different from prior software application domains: 1) discovering, managing, and versioning the data needed for machine learning applications is much more complex and difficult than other types of software engineering, 2) model customization and model reuse require very different skills than are typically found in software teams, and 3) AI components are more difficult to handle as distinct modules than traditional software components — models may be “entangled” in complex ways and experience non-monotonic error behavior. We believe that the lessons learned by Microsoft teams will be valuable to other organizations.
#AI#engineering#machine-learning#paper#programming
·microsoft.com·
Software Engineering for Machine Learning: A Case Study
Repomix
Repomix
While the tool is aimed at repos, more generally a tool for taking a ton of different files and turning them into a single source that can be thrown at some sort of learning system so you can ask questions of it.
#AI#LLM#machine-learning#programming
·repomix.com·
Repomix
I Will Fucking Piledrive You If You Mention AI Again — Ludicity
I Will Fucking Piledrive You If You Mention AI Again — Ludicity

"The recent innovations in the AI space, most notably those such as GPT-4, obviously have far-reaching implications for society, ranging from the utopian eliminating of drudgery, to the dystopian damage to the livelihood of artists in a capitalist society, to existential threats to humanity itself."

A rant that's pretty much about how money-grabbing idiots are slapping AI on any old shit. We all know who they are.

#AI#business#humour#llm#machine-learning#rant
·ludic.mataroa.blog·
I Will Fucking Piledrive You If You Mention AI Again — Ludicity