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Prof. Emily M. Bender(she/her) (@emilymbender@dair-community.social)
Prof. Emily M. Bender(she/her) (@emilymbender@dair-community.social)
As OpenAI and Meta introduce LLM-driven searchbots, I'd like to once again remind people that neither LLMs nor chatbots are good technology for information access. A thread, with links: Chirag Shah and I wrote about this in two academic papers: 2022: https://dl.acm.org/doi/10.1145/3498366.3505816 2024: https://dl.acm.org/doi/10.1145/3649468 We also have an op-ed from Dec 2022: https://iai.tv/articles/all-knowing-machines-are-a-fantasy-auid-2334
·dair-community.social·
Prof. Emily M. Bender(she/her) (@emilymbender@dair-community.social)
Venture capital is having a bad time — and AI isn’t going to fix it
Venture capital is having a bad time — and AI isn’t going to fix it

Venture capitalists are the worst people

We’re tempted to found a guillotine-as-a-service startup.

Venture capitalists are the worst people We’re tempted to found a guillotine-as-a-service startup.
·pivot-to-ai.com·
Venture capital is having a bad time — and AI isn’t going to fix it
Petzt die KI? Schlimm? - Das Netz ist politisch
Petzt die KI? Schlimm? - Das Netz ist politisch
Es gibt viel Unsicherheit über Datenschutz und Datensicherheit rund um KI-Textgeneratoren wie ChatGPT oder Gemini. Was darf man ihnen anvertrauen? Was soll
·dnip.ch·
Petzt die KI? Schlimm? - Das Netz ist politisch
Feilner IT (@FeilnerIT@mastodon.social)
Feilner IT (@FeilnerIT@mastodon.social)
Nein! Doch! Oooh! #KI ist teuer und bringt keinen ROI, sagt Gartner??? Gartner sounds alarm on AI cost, data challenges | CX Dive https://www.customerexperiencedive.com/news/gartner-symposium-keynote-AI/731122/
·mastodon.social·
Feilner IT (@FeilnerIT@mastodon.social)
(25) Post | LinkedIn
(25) Post | LinkedIn
🚑Crazy case yesterday in the ER: fulminant Glianorex infection with REALLY high Neurostabilin levels. Figured I'd ask ChatGPT for help and it honestly would… | 35 comments on LinkedIn
·linkedin.com·
(25) Post | LinkedIn
#PromptOber - Mustererkennung » Harald-Schirmer.de
#PromptOber - Mustererkennung » Harald-Schirmer.de
Wobei KI besonders gut helfen kann, ist Muster aus Daten, Texten oder in Bildern oder Videos zu erkennen. Was wiederholt sich, was ergänzt sich, wo ist ein „Bruch“ in einer Folge… Heute 3 Experimente dazu: Experiment 1 – KI Profilbewertung – Was ich nicht weiß Nadja Schwind hat vor ein paar Tagen folgendes Experiment geteilt: […]
·harald-schirmer.de·
#PromptOber - Mustererkennung » Harald-Schirmer.de
Rp24
Rp24
Hatdware rack power
·thomasfricke.de·
Rp24
KI-Paradoxien
KI-Paradoxien
Widersprüche im Umgang mit Künstlicher Intelligenz in der Schule
·joschafalck.de·
KI-Paradoxien
Papers-Literature-ML-DL-RL-AI/General-Machine-Learning/The Hundred-Page Machine Learning Book by Andriy Burkov/Links to read the chapters online.md at master · tirthajyoti/Papers-Literature-ML-DL-RL-AI · GitHub
Papers-Literature-ML-DL-RL-AI/General-Machine-Learning/The Hundred-Page Machine Learning Book by Andriy Burkov/Links to read the chapters online.md at master · tirthajyoti/Papers-Literature-ML-DL-RL-AI · GitHub
Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning - tirthajyoti/Papers-Literature-ML-DL-RL-AI
·github.com·
Papers-Literature-ML-DL-RL-AI/General-Machine-Learning/The Hundred-Page Machine Learning Book by Andriy Burkov/Links to read the chapters online.md at master · tirthajyoti/Papers-Literature-ML-DL-RL-AI · GitHub
Challenging The Myths of Generative AI
Challenging The Myths of Generative AI
Eryk Salvaggio says we must dispense with myths if we are to think more clearly about what AI actually is and does.
·techpolicy.press·
Challenging The Myths of Generative AI
Visualisierung der Aufmerksamkeit, das Herz eines Transformators | Kapitel 6, Deep Learning - YouTube
Visualisierung der Aufmerksamkeit, das Herz eines Transformators | Kapitel 6, Deep Learning - YouTube
Demystifying attention, the key mechanism inside transformers and LLMs. Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support Special thanks to these supporters: https://www.3blue1brown.com/lessons/attention#thanks An equally valuable form of support is to simply share the videos. Demystifying self-attention, multiple heads, and cross-attention. Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support The first pass for the translated subtitles here is machine-generated, and therefore notably imperfect. To contribute edits or fixes, visit https://translate.3blue1brown.com/ And yes, at 22:00 (and elsewhere), "breaks" is a typo. ------------------ Here are a few other relevant resources Build a GPT from scratch, by Andrej Karpathy https://youtu.be/kCc8FmEb1nY If you want a conceptual understanding of language models from the ground up, @vcubingx just started a short series of videos on the topic: https://youtu.be/1il-s4mgNdI?si=XaVxj6bsdy3VkgEX If you're interested in the herculean task of interpreting what these large networks might actually be doing, the Transformer Circuits posts by Anthropic are great. In particular, it was only after reading one of these that I started thinking of the combination of the value and output matrices as being a combined low-rank map from the embedding space to itself, which, at least in my mind, made things much clearer than other sources. https://transformer-circuits.pub/2021/framework/index.html Site with exercises related to ML programming and GPTs https://www.gptandchill.ai/codingproblems History of language models by Brit Cruise,  @ArtOfTheProblem  https://youtu.be/OFS90-FX6pg An early paper on how directions in embedding spaces have meaning: https://arxiv.org/pdf/1301.3781.pdf ------------------ Timestamps: 0:00 - Recap on embeddings 1:39 - Motivating examples 4:29 - The attention pattern 11:08 - Masking 12:42 - Context size 13:10 - Values 15:44 - Counting parameters 18:21 - Cross-attention 19:19 - Multiple heads 22:16 - The output matrix 23:19 - Going deeper 24:54 - Ending ------------------ These animations are largely made using a custom Python library, manim. See the FAQ comments here: https://3b1b.co/faq#manim https://github.com/3b1b/manim https://github.com/ManimCommunity/manim/ All code for specific videos is visible here: https://github.com/3b1b/videos/ The music is by Vincent Rubinetti. https://www.vincentrubinetti.com https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown https://open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. If you're reading the bottom of a video description, I'm guessing you're more interested than the average viewer in lessons here. It would mean a lot to me if you chose to stay up to date on new ones, either by subscribing here on YouTube or otherwise following on whichever platform below you check most regularly. Mailing list: https://3blue1brown.substack.com Twitter: https://twitter.com/3blue1brown Instagram: https://www.instagram.com/3blue1brown Reddit: https://www.reddit.com/r/3blue1brown Facebook: https://www.facebook.com/3blue1brown Patreon: https://patreon.com/3blue1brown Website: https://www.3blue1brown.com
·m.youtube.com·
Visualisierung der Aufmerksamkeit, das Herz eines Transformators | Kapitel 6, Deep Learning - YouTube
Google’s GameNGen AI Doom video game generator: dissecting a rigged demo
Google’s GameNGen AI Doom video game generator: dissecting a rigged demo
Did you know you can play Doom on a diffusion model now? It’s true, Google just announced it! Just don’t read the paper too closely. In their paper “Diffusion models are real-time game engines,” Go…
·pivot-to-ai.com·
Google’s GameNGen AI Doom video game generator: dissecting a rigged demo
Le "nuove" tre leggi della robotica nell'era della AI - Gravita Zero: comunicazione scientifica e istituzionale
Le "nuove" tre leggi della robotica nell'era della AI - Gravita Zero: comunicazione scientifica e istituzionale
Negli ultimi decenni, il mondo della robotica ha visto enormi cambiamenti, portando alla nascita di nuove tecnologie e, soprattutto, di nuove sfide etiche e legali. Isaac Asimov, celebre scrittore di fantascienza, aveva anticipato queste problematiche negli anni ’40, quando formulò le sue tre leggi della robotica. Queste leggi immaginate da Asimov, nonostante siano state sviluppate …
·gravita-zero.it·
Le "nuove" tre leggi della robotica nell'era della AI - Gravita Zero: comunicazione scientifica e istituzionale
AI generates covertly racist decisions about people based on their dialect
AI generates covertly racist decisions about people based on their dialect
Nature - Despite efforts to remove overt racial prejudice, language models using artificial intelligence still show covert racism against speakers of African American English that is triggered by...
·nature.com·
AI generates covertly racist decisions about people based on their dialect
Productivity gains in Software Development through AI
Productivity gains in Software Development through AI
Especially in IT and software development numbers keep popping up about “savings” through AI. Amazon for example claims to have “saved” 4500 person years of work. These numbers have to be taken with a grain of salt and shouldn’t be interpreted as “oh, we will save massive amounts of work by using AI, let’s fire […]
·tante.cc·
Productivity gains in Software Development through AI
To Understand The Future of AI, Study Its Past
To Understand The Future of AI, Study Its Past
A schism lies at the heart of the field of artificial intelligence. Since its inception, the field has been defined by an intellectual tug-of-war between two opposing philosophies: connectionism and symbolism. These two camps have deeply divergent visions as to how to "solve" intelligence, with differing research agendas and sometimes bitter relations.
·forbes.com·
To Understand The Future of AI, Study Its Past