What can the medieval 100 Years War between #France and #England tell us about OpenAI's boardroom drama + dramatic (un-)firing of #SamAltman? I peer into the distant past in hopes of giving you a useful brainstorm about the impending future.
The puny shark attacks of Jaws and AI doomsaying
What can the impact of the classic shark attack film Jaws tell us about #ArtificialIntelligence doomsaying?
I envy you the sturdy rock you slumbered under if you did not hear about OpenAI's recent surprise firing of CEO #SamAltman. It seems that at this point no one on the outside has quite the confidence they'd like about the rationale, yet all accounts make it clear #AISafety concerns we're an important part of the story. It attracts my attention broadly how people seem eager to participate in something they believe is so likely to produce an extinction level event, and how little these dire warnings seem to affect people's behavior. Everyone, it seems, would like a job ending humanity.
This video contains what you might call a cultural analysis of doomsaying broadly and I hope to provide some gentle suggestions about why we think about the end of the world much more than we seem to take it seriously in practice.
A prosaic idea for AI application with poetic gravity
#ArtificialIntelligence is a term that covers a lot of ground and not everyone agrees what it means. To find success in a corporate, private-sector setting you will need more specific ideas, yet you may still see a representative of your company on Bloomberg suggesting #AI is the reason your stock price should go up. Being greatly blessed with vision, I have an idea to share with you that might help you solve this problem. Better yet, I will articulate a familiar problem, a realistic yet sexy AI solution, and even a narrative as to why it could all spiral into lucrative megacorporate domination down the road.
We are often in positions where we have no choice but to produce lots of documentation about how to run the corporation, yet these efforts often undermining themselves when the mountain of documents is so large no one has time to sift through it. #RetrievalAugmentedGeneration (#RAG) is an approach to making these mountains more accessible to everyday people that involves sexy cutting edge techniques like #LargeLanguageModels and #GenerativeAI more broadly yet is also achievable and helps contains various risks and rough edges. Successful use of a technique like this leads to something like AI-as-management-enhancement that could provide a huge competitive advantage in successfully administering a larger and more diverse conglomerate - you might call it a #rhizocorp.
I should note I got the term rhizocorp from Matthew Gladden and I have not been able to discern another source from which he might have taken it. Credit is his pending further information.
What a weekend at OpenAI...
If you are behind on current events let me catch you up OpenAI's surprise firing of #SamAltman and the wild boardroom intrigue over the weekend. I will do my best to summarize events, analyze some of the players and their angles, and finally break down how these might matter to you. Short story: there was a clumsy midnight coup fueled by a known bad corporate governance situation, everything spiraled out of control, and lots of people aren't going to enjoy their holiday.
- OpenAI is hemorrhaging talent, has a board with no credibility with anyone, and will be lucky to avoid heavy-duty shareholder lawsuits. They could spiral into nothing.
- OpenAI's investors, outside of Microsoft, got screwed over to a semi-illegal degree and don't have great options. One not-great option they are talking about at this very second is shareholder lawsuits.
- Sam Altman is going to Microsoft. He had lots of options and he liked this one. Other people will go with him.
- Microsoft looked like the big loser last night yet they emerge today as the big winner. Altman puts them in a great position to build entirely within Microsoft and become the unquestioned leader in commercializing #LargeLanguageModels
- If you are at a large corporation that uses #Azure, and you were comfortable using #LLM inside Azure, this is a positive turn of events for you in the short to medium term. You are buying it from Microsoft will likely have better luck making it for you in house.
Don't LLM so hard you forget about vector embeddings
If it is news to you, I would like 10 minutes of your time to tell you a bit about #VectorEmbeddings and #VectorDatabases as part of my weekly theme exploring #RetrievalAugmentedGeneration or #RAG (please forgive some past errors where "aided" was my "A" word). The #LargeLanguageModel component will handle the generation, but #LLM is not a great tool for the retrieval. For some problems, vector embeddings are the answer.
I think it is a little dangerous to set out in pursuit of #ArtificialIntelligence. It is much better to have specific goals, and RAG is a technique that fits very well onto some very common megacorporate problems. Specific goals are good because they help spotlight specific tasks that add up to success. Shopping for a vector database is one such task that might turn out be very relevant to you but you will probably not see in the headlines like LLM.
Intro to retrieval aided generation (RAG)
The appearance of a new name is an interesting signal and I am here to introduce you to the term #RetrievalAidedGeneration (#RAG).
This is notably something I have discussed before without a name - you might remember either a widely circulated story on a Morgan Stanley internal #LargeLanguageModel application or my video on it - and I might say the appearance of a new name is a harbinger of the appearance of a consensus best practice. RAG gives you a functionality as if you were using #LLM for expert knowledge but it also gives you control over the the hallucination problem and the provenance of information in general. It is a natural fit onto the basically universal big business problem that documentation of how to run the corporation quickly accumulates in volume beyond what readers can usefully sift through.
Teaser for later this week: one can almost imagine these technologies opening new frontiers in how a large and decentralized conglomerate could be administered as a sort of #ArtificialIntelligence powered #RhizoCorp.
Analysis: AI and the quest to duct-tape our toys together
#OpenAI recently added some interesting new features and this video is about deeply reading these tea leaves for useful information about your own internal AI initiatives as well as the evolution of the broader marketplace for these tools. Specifically, they opened beta testing on a new #ChatGPT feature for uploading and ingesting .pdf files and announced a roadmap for broadening the input and output options in general. In this video, I pursue three lines of analysis...
1) #ArtificialIntelligence is going to need a lot of help with duct-taping itself into other things before it can take your job. The success or failure of these (otherwise very boring) duct-taping operations is what you need to watch.
2) #GenerativeAI is about it in terms of areas in which startups can raise money recently, and many were working similar add-on features. They may be in trouble, and some may have difficulty finding niches that can stay away from this kind of trouble.
3) We already have an aging tradition of stock dialog about how to handle AI inside your corporate organization and I think recent progress has rendered some of it awkwardly half out-of-date. There is a promise that AI can now help resolve data quality issues that previously held you back from doing AI.
Let's turn sorrow to joy. Send me your complaints!
Are things not as they should be? Feel like no one listens? I want to listen!
It's at best a lost opportunity how much we trumpet our real or fabricated success while concealing our stumbles and failures. A story about a way things can go wrong is actually a pretty useful thing, and we're probably destroying each other's mental health endlessly shouting at one another about our flawless success. That being said, I know you all have lots of good reasons you can't complain too hard about your work on social media.
So... complain to me! I won't tell a soul unless I hear similar complaints repeatedly, and if I do I will make a video about the pattern I see and not anyone's individual story. We can share useful anti-knowledge, give some hints at our real selves (at least in large, consistent groups), and you won't have to look like a malcontent in front of your boss.
Do you like this idea? Please send me a message or an email. I want to hear from you!
Vague BS is the biggest danger your AI program faces
I've got the envelope here and the academy says the biggest danger to your AI program is [drumroll]... !vague bullshit!
What is AI? That might actually be a profound question, and perhaps it is like the House speaker race in that the difficulty is certainly not lack of candidates but lack of consensus and clarity. If you are blessed with success, it is going to be fueled by specificity on business value, infrastructure, algorithms, talent [coach’s clipboard folds out and the list drops to the floor]...
Emerging technology in the private sector tends to suffer from a code-switching problem - the language you need to excite and persuade is deeply unlike the language you need to build. If you don't see that both codes are there, and don't know how to speak in each, you may be in trouble.
In this video, I elaborate on these ideas and give a couple examples. One is about the old tension regarding what is fancy enough to call AI. The other is new: some of our older commentary on AI is very appropriate to #GenerativeAI in some applications and very inappropriate in others.
?? LLM = Large Libel Model ??
Does #LLM stand for #LargeLanguageModel or Large Libel Model? In any event, you should take note that law bloggers are telling these jokes.
I discuss a specific lawsuit in #Georgia, Walters vs. #OpenAI, to provide an example of a #ChatGPT hallucination triggering a credible lawsuit. The specific dynamics of this particular lawsuit also provide us an opportunity to drill down on why it matters that #OpenAI is quite unusually incorporated as a for-profit subsidiary of a not for-profit corporation.
Sanity on AI in the Hollywood labor negotiations
Yesterday there were some pleasing signs of sanity on artificial intelligence in the Hollywood labor negotiations. Specifically, in the end it is probably unavoidable for respectable large businesses that...
1. Products made using AI must be "labeled" as such.
2. Some specific human is on the record as responsible for the AI's output.
AWS invests in Anthropic vs. Microsoft invests in OpenAI
I cover the basic facts of Amazon / AWS 's recently announced $1.25 - $4 billion investment in generative AI firm Anthropic, creator of the well-known large language model Claude. More fun: I point out that this is kind of a weird deal that not only mirrors the weird deal between Microsoft and Open AI but has involved many of the same people over time.
Morgan Stanley seems to have the right idea on generative AI
There is a lot of media circulating about a new internal artificial intelligence tool at Morgan Stanley and I've really liked many of the things I have read. Specifically, what I could glean about the architecture fits well into some of my ongoing narratives about how you maybe don't want to directly use your large language model as an expert, but rather you should have it read information from trusted sources and write a synthesis for you to read in turn. In this video, I go into some detail about why this is the case.
Evaluating the hype on new large language models
Here I discuss new or upcoming large language models including Technology Innovation Institute's latest Falcon model, Meta's (f.k.a. Facebook's) upcoming iteration on it's influential LLaMA models, and Alphabet Inc.'s (f.k.a. Google's) highly but vaguely anticipated new model Gemini. My real aim is to discuss how you might evaluate these different models, how the outcomes track the goals of their makers, the potential value in embracing open source models, and what sort of marketing language prevails in this area when test drives are not available.
In the heat of the moment, I also accidentally provide some arguments about naming and renaming things...
Music, technology, and AI looking backwards and forwards
It is not terribly hard to see that generative AI is likely to put a lot of pressure on the music business. Music, as a business and cultural institution, is actually a great case study looking backwards as it has been disrupted again and again by new technology with consequences that are an accepted part of your everyday life.
Is AI smarter than people? It certainly is when you can't hold people's attention
Are recent, state-of-art artificial intelligence algorithms smarter than people? I would say AI is still pretty shabby relative to motivated humans that are paying attention, yet motivating humans to pay attention is a hard problem in itself that we all spends a lot of time discussing in frustration.
Intellectual property is a freakish modern invention
It's not hard to see that artificial intelligence is going to put a lot of pressure on our existing system of intellectual property law but easy to forget that that system is hardly eternal. I weave together diverse strands like Homer, James Joyce, cryptocurrency, Immanuel Kant, and the human author as a large language model to make a point about how the status quo we are afraid to leave is actually a pretty fleeting and weird moment in history.
The AI unemployment apocalypse that isn't
...a silly case study about shoes explaining why the effect of artificial intelligence on labor and unemployment won't be too bad in the long run...
Breaking down ChatGPT Enterprise
Let me give you the high-level story on OpenAI’s new enterprise tier for ChatGPT, including...
...the data privacy and cybersecurity they are trying to assuage...
...the legal concerns that are mostly still at large...
...some very nice although not radically different new features...
...and the landscape of competing goods like the recent new Microsoft Azure offering and the zoo of open source models like Llama 2.
Don't let ChatGPT trade cryptocurrency for you!
I'd like explain why letting ChatGPT trade cryptocurrency for you is a very terrible idea especially because it provides a great prototype for recognizing other terrible ideas...
I was just making a Capnion TikTok account, so you can watch there if it is your preferred platform, and while the advice I observed was terrible it provided great ideas for videos. In this particular case, the story is that the cryptocurrency universe is full of propaganda for pump-and-dump scams and all ChatGPT can realistically be doing is uncritically passing through bad information it scraped off the internet. You are much better off closer to the source because you can take a guess at what manner of game the source may be playing.