OpenAI has introduced a groundbreaking technique to better understand the internal workings of its GPT-4 language model. By employing sparse autoencoders, the company has successfully identified 16 million interpretable features within GPT-4. This advancement allows researchers to disentangle the model's internal representations, offering a more transparent view of how the AI processes information. The new method scales better than previous approaches and has been praised for its potential to enhance AI interpretability. Additionally, GPT-4 has surpassed human performance in theory of mind tasks. This development comes amid criticisms of OpenAI's superalignment team, which has recently been disbanded.
Discover the latest research on how LLMs are surpassing humans as informed ethicists in this insightful Psychology Today article. Explore the data and implications here: https://t.co/31bKv4Uu7Z
🚨Can LLMs Become Our New Moral Compass? 👉New data suggest that LLMs outperform humans as informed ethicists. It's clear that large language models (LLMs) are smart—but moral, too? A recent paper suggests that these models can provide moral guidance that surpasses even expert… https://t.co/oBwZ3C0nH9
#OpenAI is democratizing access to advanced ChatGPT features. https://t.co/zcMdV71K1P
OpenAI Offers a Peek Inside the Guts of ChatGPT | WIRED https://t.co/ShwF704NBD
🚨 Psychology Today's Essential Read... Sentient Minds in the Cloud, Savant Servants in Your Pocket Behind the curious bifurcation of LLM development. https://t.co/dbCIulrel0 #AI #GI #LLMs #sentience #consciousness @PsychToday @lexfridman @jordanbpeterson @BrianRoemmele
"Chatbot Teamwork Makes the AI Dream Work" — Wired See the highlights of the story below! 1/11 🧵 https://t.co/9OHUeWLiFi
Alignment, of a sort: this paper conducts what they call a “moral Turing Test,” asking people to compare GPT-4o to humans on ethical questions. “Here we find that LLMs appear to have a strong aptitude for moral reasoning on par with expert ethicists.” https://t.co/0kNVv8hqYA https://t.co/Cqpk42A2H1
"OpenAI Offers a Peek Inside the Guts of ChatGPT" — Wired Take a peek at the essence of the story! 1/12 🧵 https://t.co/tUuoC3xMw7
There is unsurprisingly a lot of excitement around Sparse Auto Encoders for interpretability work. With our lord and savior Golden Gate Claude leading the way. OpenAI released what seems like the super alignment team's last paper detailing how they found 16m+ features in GPT4…
Understanding the secrets of large language models is a challenging task, but a new OpenAI paper offers a promising approach. Sparse autoencoders, like detectives, sift through the vast web of connections in these LLM models to find interpretable features, the hidden concepts… https://t.co/1o6KQRogOr
This is super cool work! Sparse autoencoders are the currently most promising approach to actually understanding how models "think" internally. This new paper demonstrates how to scale them to GPT-4 and beyond – completely unsupervised. A big step forward! https://t.co/jZ36peImDr
OpenAI's GPT-4 Surpasses Human Performance in Theory of Mind, Identifies 16 Million Features https://t.co/IIkWTEqNvc
1/n The Rise of Artificial Empathy: Can AI Decipher Our Thoughts? Can LLMs truly understand us, grasping not just the meaning of our words but also the intricate dance of thoughts, beliefs, and desires that underpin human interaction? This question lies at the heart of a recent… https://t.co/cziMGCenAT
OpenAI has come under fire for letting its superalignment team disappear but the company just published work from @ilyasut, @janleike, and others on a new way to peer inside GPT-4 to better understand how it works. https://t.co/YFRJ7xTLXY
OpenAI Offers a Peek Inside the Guts of ChatGPT https://t.co/nOcNVbGndH
https://t.co/Mhzh95J1la “Today, we are sharing improved methods for finding a large number of "features"—patterns of activity that we hope are human interpretable. Our methods scale better than existing work, and we use them to find 16 million features in GPT-4”
.@OpenAI just dropped a new technique to break GPT-4 down into 16,000,000 #interpretable features 🧵 https://t.co/vhKbUU5GzZ
We're sharing progress toward understanding the neural activity of language models. We improved methods for training sparse autoencoders at scale, disentangling GPT-4’s internal representations into 16 million features—which often appear to correspond to understandable concepts.… https://t.co/UFP0EfEKSL
When AI chatbots team up, magic happens! 🤖💬 Experiments reveal collaboration can overcome individual shortcomings. #AIDreamWork #TechInnovation https://t.co/PGdULBc8YN