You know what happens in cold water? Everything shrinks.
Bing's market share hasn't grown at all. Bing's share of search It's still stuck at a lousy 3%.
So did traffic to ChatGPT and other LLMs. What could be the possible reasons?
Again, a realization the AI revolution won't take weeks, but months/years, if at all. Despite that, people are losing jobs and engaging in so-called 'sweatshops' - often it's the only possible income source. Yet still they are faced with platform bans, below-minimum-wage payouts and exploitation. It's a social issue I feel deserves outcrying.
Personally, I see it as a combination of trying to hastily pump out deterministic outputs from a non-deterministic system + a one-armed bandit we're relentlessly pushing the lever of, because each time we're getting an output which is approximately needed and plausible - yet all the time it's not the jackpot
AI AI AI AI… AI!
AI raises and faces legal, regulatory, and public challenges - everyone's suddenly afraid of big corp using his/her personal data, scamming, yada yada. However, AI seems to excel (thanks to open source!) at deception, citing:
AI is now used in a series of elaborate ransom scams.
New AI bots create malware on demand.
Cheap AI music is used to replace human songs—not because it's better, but because it's cheaper, and puts more power in the hands of technocrat platforms.
Students are cheating with the aid of AI.
AI threatens to disrupt the 2024 election with fake videos.
Publications are misleading readers, who get served up AI articles with little disclosure.
So, consumer demand is shrinking; with companies adopting the useful parts - the ones allowing them to run cheaper, extract more revenue - the usual. Remind something?
Yeah, too for me. Are there any ways forward/around/inside? I believe yeah, one possibility lying in reasoning.
Graphs + ML = ❤️
Some of the papers' topics:
New GNN architectures: novel ideas of dynamically rewired message passing with delay and slow nodes for long-range tasks
Generative Models: e.g. diffusion models for graph generation (think Stable Diffusion that makes graphs)
Molecules & proteins: generating molecules, protein description (by its structure), predicting high resolution mass spectra (in 19 min instead of 126 hours - x398 speedup)
Knowledge: knowledge graph embedding ('packing'), discovering both unseen entities and relations
By Knowable Magazine
Day 1. Crash Course in Neuro-Symbolic AI (Aug 29, 2023)
Day 2: Diverse Approaches at the Research Frontier of Neurosymbolic AI (Aug 30, 2023)
A huge repository of IBM-developed neurosymbolic AI OSS, divided into 8 categories:
Logical Neural Network (LNN)
Natural language processing via reasoning (NLP)
Knowledge foundation (KF)
Learning with less (LwL)
Knowledge augmented sequential decision making (SDM)
Human in the loop (HIL)
Datasets and environments (DS)
Related advances (RA)
Plus a nice taxonomy of neurosymbolic systems, which I won't include for brevity.
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