I like to listen to childhood nostalgia, and 1980s Italo Disco is among that.
There's a 1985 song by Den Harrow called Future Brain, and I always had to grin listening to the naive song text, but with the current AI developments, that song text is starting to get a new, retro-prophetic meaning.
Comprehensive review on Self-Supervised Learning ("the dark matter of intelligence") with focus on vision tasks and lowering the high barrier to entry.
Content moderation and #RecommenderSystems are the most widely deployed #MachineLearning based action taking systems. By action taking, I mean an algorithm that suggests a particular decision that impacts human actions. Using this view of recommender systems, we can consider how to increase the #diversity of online content by re-ranking recommended items to encourage creation of diverse content in the long term.
@dianor We learned that lesson hard with conventional machine learning, trying to detect mentions of violence against education on Twitter a few years ago.
No matter how hard we trained and retrained the model, it still struggled to distinguish actual talk of violence (usually just repeating old news reports, so no value for early detection anyway) from kids talking about school sports, video games, strict teachers, or even "une bombe" meaning a sexy person in French.
The basic rationale is to use random split-half data to identify what's "true" versus sampling error. Scores are based on similarities between eigenvectors or cluster centres, rather than, e.g., the shape of the eigenvalue plot.
Wait, if this scales up would this let us run these things on, say, our phones? If it replaces the transformer, could it replace other uses of the transformer, like Whisper? If it can do that and deliver equivalent or better results for those kinds of speed-ups, ML applications on edge computing will explode, again. And given the accessibility benefits of some existing tech, imagine being able to run that on something other than the cloud!
This new technology could blow away GPT-4 and everything like it https://www.zdnet.com/article/this-new-technology-could-blow-away-gpt-4-and-everything-like-it/#AI#MachineLearning
"Tech companies have grown secretive about what they feed the AI. So The Washington Post set out to analyze one of these data sets to fully reveal the types of proprietary, personal, and often offensive websites that go into an AI’s training data."
Such systems carry additional burdens that are foreign to more consumer/business-level #MachineLearning systems - in particular, the need to exhaustively quantify "the unseen" through objective analysis.
It is something that, most notably, #Tesla fails to recognize with respect to their #FSDBeta program, likely by design.
If you were going to hand someone a single resource to get a basic, workable knowledge of #MachineLearning, neural networks, deep learning, #AI, etc. what would it be? Not looking to prime someone for a doctoral class but enough for the person to be able to talk about the concepts and play with some pre-built libraries and tools. Could be a book, a class, a video, anything.
Autopoiesis. A beautiful word.
And an utterly superfluous one.
You cannot do anything useful with it.
The word "living" is entirely sufficient. So forget about autopoiesis and work on living systems, making them "even more alive!" #systemsthinking#systemstheory#systems#betacodex#autopoiesis
@gimulnautti
Thanks for commenting!
I think you are mixing up things.
"A.I." is just software. To be exact: A.I. does not really exist, what exists is #machinelearning And that is complicated, dead. Just like any tech.
Only complex, living systems are autopoietic. All complex, living things are. "Living", in this context, of course does not mean "biologically alive".
I recently moved from mastodon.social to hachyderm.io. I'm a Computer Scientist who moves between software engineering and research. I may post about code, papers, and conferences.
The attached image helps my colleagues differentiate my cats. 🙂 🐈⬛ 🐈