Something I’ve been thinking about a lot in the current battle over the future of (pseudo) AI is the cotton gin.
I live in a country where industrial progress is always considered a positive. It’s such a fundamental concept to the American exceptionalism claim that we are taught never to question it, let alone realize that it’s propaganda.
One such myth, taught early in grade school, is the story of Eli Whitney and the cotton gin. Here was a classic example of a labor-saving device that made millions of lives better. No more overworked people hand cleaning the cotton (slaves, though that was only mentioned much later, if at all). Better clothes and bedding for the world. Capitalism at its best.
But that’s only half the story of this great industrial time saver. Where did those cotton cleaners go? And what was the impact of speeding up the process?
Now that the cleaning bottleneck was gone, the focus was on picking cotton as fast as possible. Those cotton cleaners likely, and millions of other slaves definitely, were sent to the fields to pick cotton. There was an unprecedented explosion in the slave trade. Industrial time management and optimization methods were applied to human beings using elaborate rule-based systems written up in books. How hard to punish to get optimal productivity. How long their lifespans needed to be to get the lost production per dollar. Those techniques, practiced on the backs and lives of slaves, became the basis of how to run the industrial mills in the North. They are the ancestors of the techniques that your manager uses now to improve productivity.
Millions of people were sold into slavery and worked to death because of the cotton gin. The advance it provided did not, in fact save labor overall. Nor did it make life better overall. It made a very small set of people much much richer; especially the investors around the world who funded the banks who funded the slave purchases. It made a larger set of consumers more comfortable at the cost of the lives of those poorer. Over a hundred years later this model is still the basis for our society.
Modern “AI” is a cotton gin. It makes a lot of painstaking things much easier and available to everyone. Writing, reading, drawing, summarizing, reviewing medical cases, hiring, firing, tracking productivity, driving, identifying people in a lineup…they all can now be done automatically. Put aside whether it’s actually capable of doing any of those things well; the investors don’t care if their products are good, they only care if they can make more money off of them. So long as they work enough to sell, the errors, and the human cost of those errors, are irrelevant. And like the cotton gin, AI has other side effects. When those jobs are gone, are the new jobs better? Or are we all working that much harder, with even more negative consequences to our life if we fall off the treadmill? One more fear to keep us “productive”.
The Luddites learned this lesson the hard way, and history demonizes them for it; because history isn’t written by the losers.
They’ve wrapped “AI” with a shiny ribbon to make it fun and appealing to the masses. How could something so fun to play with be dangerous? But like the story we are told about the cotton gin, the true costs are hidden.
This picture is hilarious because the words “Meat ball” were transliterated into Arabic. The resulting Arabic words (ميت بول) were then interpreted (incorrectly) as Arabic, THEN translated into English. The first word “Meat” can be misread as the Arabic word for “is dead”. The second word “ball” is how an Arabic speaker would pronounce the name “Paul.” Therefore, the English description of this dish is “Paul is dead.”
Anybody out there looking for an ML or software engineer with >30 years total experience and ~20 years in the industry?
I have extensive experience with #Python and #ML frameworks, particularly #TensorFlow, and I've worked on #NLP and #ImageProcessing both in the workplace and in personal open source projects. My resume is available here:
We really need a different term than ‘AI’ to describe private, on-device machine learning that benefits individuals but (a) doesn’t violate anyone’s privacy, (b) doesn’t destroy the environment and, (c) doesn’t enrich smug Silicon Valley tech douchebros like Sam Altman.
Whenever I see OpenAI's Sam Altman with his pseudo-innocent glance, he always reminds me of Carter Burke from Aliens (1986), who deceived the entire spaceship crew in favor of his corporation, with the aim of getting rich by weaponizing a newly discovered intelligent lifeform.
Please boost for reach if this kind of stuff interests you. Will post more on this later.
Once upon a time, there was a cool emulator frontend called Retroarch. This emulator wasn't accessible until I and a few other gamers went to them and asked about adding accessibility. An amazing person known as BarryR made it happen. Now, if you turn on accessibility mode in settings, or pass the "--accessibility" (or something like that) flag on the command line, you get spoken menus, including the emulator's pause menu, good for saving states and such. Then, using PIL and other image processing Python utilities, running a server and hooking into Retroarch, the script allowed players to move around the map, battle, talk to NPC's, ETC. The only problem was, no one wanted to test it. The blind gaming community pretty much spoke, saying that we want new games. We want cool new, easy accessibility. So that's what we have no, follow the beacon or get sighted help in the case of diablo and such. It's sad, but meh. It's what we wanted I guess. No Zelda for us. So, this is about as far as he got:
To expand on what devinprater was saying: I am working on an accessibility pack/service for Final Fantasy 1 for the NES (this was what was shown in the latest RetroArch update). The idea is similar to how Pokemon Crystal access works, but it's using the RetroArch AI Service interface to do so.
Right now, the FF1 access service is mostly done, but I need more testers to try it out and give me feedback on how it's working. Right now, you can get up to the point where you get the ship, but there's no code to deal with how the ship moves, so that still needs to be done. Likewise with the airship later on.
The service works the latest version of RetroArch, on linux and mac, but not windows. This is due to how nvda reads out the text and until the next major update to nvda (which will have a feature to fix this), it'll have to wait. If you have those, I (or maybe devinprater) can help you set it up on mac/linux to test out. The package itself is available at: https://ztranslate.net/download/ff1_pac … zip?owner=
"[...] as companies like Coca-Cola start making huge investments to use generative AI to sell more products, it’s becoming all too clear that this new tech will be used in the same ways as the last generation of digital tools: that what begins with lofty promises about spreading freedom and democracy ends up micro targeting ads at us so that we buy more useless, carbon-spewing stuff."
I'm happy to announce the start of a new free and open online course on neuroscience for people with a machine learning or similar background, co-developed by @marcusghosh. YouTube videos and Jupyter-based exercises will be released weekly. There is a Discord for discussions.
For more details about the structure of the course, and to watch the first video "Why neuroscience?" go straight to the course website:
Currently available are videos for "week 0" and exercises for "week 1", but more coming soon.
Why did I create this course? Well, I think both neuroscience and ML can be enriched by knowing about each other and my feeling is that a general purpose intro to neuro or comp-neuro isn't the right way to inspire people in ML to be interested in neuro.
I hear a lot about neuroscience inspiring AI, but I think there's understandable scepticism about that from ML people. I don't want people to take neuro ideas and apply directly to ML, I just think we get a richer picture of what both fields are doing if we think more widely.
In other words, we should be thinking that we are somehow studying the same problem in different ways. You see that in the early history of the field, and it's very inspiring. (Yes, this is pretty much just saying that cognitive science is cool, but my scope is a bit narrower.)
The focus then is not on how neuroscientists think the brain works, but on the mechanisms the brain uses. These are strange, inspiring, and often their contribution to intelligent behaviour is still deeply mysterious.
The first video of the main part, on the structure of neurons, finishes with recent research (from @ilennaj and @kordinglab among others) on what the function of dendritic structure might be. No answers, just ideas.
And that's going to be another key part of this course. Research level problems are not hard to find in neuroscience, and the aim of this course is to empower students with the tools to start finding and working on them straight away.
Most of the exercises in the course won't have correct answers. They're starting points for further investigation. We'll be downloading and exploring open neuroscience datasets using methods from computational neuroscience and ML.
The course is not supposed to be comprehensive. It's a short course and the aim is more to get inspired and start on a longer road. I'd expect everyone to get something different out of it, and I'm happy if for some people their take home is "neuroscience is not for me"!
In some ways, it's the course I would have liked to get me into neuroscience and for my incoming PhD students from non-neuro backgrounds to be able to take. It's personal, and full of the sort of stuff that inspires me to be interested in neuroscience.
Well, I hope that some of you might be interested to follow along in the next few weeks, and since it's the first time I'm giving this course please do give feedback by email, Discord or however you like. Also, please feel free to re-use materials however you like.
#ML systems can leak confidential data in their training set even with a very silly attack. This is a direct and clear #MLsec issue that applies well beyond the #LLM case
A conversation that keeps popping up in my mind since FOSDEM centers around open source projects and “AI,” and I still don’t know what I think. So let me share some thoughts here on the famously nuance-friendly Internet. 😜
During a chat w/folks from several open source organizations, someone suggested GNOME could attract funding by “sprinkling some AI on it.” Several folks laughed at the topical joke, but then realized it was in earnest. 🧵
Maybe we have an open source competitor for ElevenLabs? Check out their demo which they switch between original and synthesized. I can't tell. lol Apparently they're going to fully open source codebase and model weights. #TTS#AI#MLhttps://jasonppy.github.io/VoiceCraft_web/
Cool tip for running LLMs on Apple Silicon! By default, MacOS allows GPU to use up to 2/3 of RAM on machines with <=36GB and 3/4 on machines with >36GB. I used the command sudo sysctl iogpu.wired_limit_mb=57344 to override and allocate 56GB/64GB for GPU. This allowed me to load all layers of larger models for a faster speed! #MacOS#LLM#AI#ML