Students sometimes ask me if it still makes sense, in this accelerating age, to pursue a PhD in AI. Perhaps counterintuitively, I think it's a great time to do so. I wrote up some thoughts on this here: web.mit.edu/phillipi/www...
Who's Who of AI
Phillip Isola
Why they matter
Researcher with public evidence across Vision & synthetic media, AI research, Agents & robotics.
- AI signals
- 4 past 30d
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- 2 distinct domains
- Discussions
- 5 past 30d
- Latest signal
- 10d ago
Associate Professor in EECS at MIT. Neural nets, generative models, representation learning, computer vision, robotics, cog sci, AI.
https://web.mit.edu/phillipi/
What they're sharing
On the Value of Doing a PhD in the Age of AI web.mit.edu
Robot-use agents web.mit.edu
Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale arxiv.org
Revisiting the Platonic Representation Hypothesis: An Aristotelian View arxiv.org
What Converges in the Platonic Representation Hypothesis? Structure over Geometry arxiv.org
Articles & links
Recently, there have been a lot of impressive demos of AI agents, like Claude, controlling robots. I wrote a short blog post with my thoughts on the advent of these "robot-use agents." web.mit.edu/phillipi/www... I think it's an important change in the trajectory of robotics!
AI Weekly's analysis
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- Phillip Isola argues newer LLMs like Fable and Astra are eroding the case that models can't yet drive robots in general.
- In his cloud-puppeteer model, any internet-connected robot could gain AI capability via a software update, with no new hardware.
- Isola concedes LLM-controlled robots remain far less performant than dedicated solutions, with latency and reliability still limiting safety-critical use.
Read full analysis →
These results surprised me. There's more to do to fully understand which structures align! Here are some great readings on other perspectives: * local relational: arxiv.org/abs/2602.14486 * coarse-grained: arxiv.org/abs/2604.18572 * global relational: arxiv.org/abs/2609.27252 n/n
AI Weekly's analysis
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- Scaling from 1,024 text-image pairs to 15 million drops measured cross-modal alignment from 13.5% to 0.81%, the paper reports.
- Testing 55 language models, the authors found that newer and stronger LLMs do not seem more aligned with vision models.
- One-to-one image-text pairings inflate alignment scores; real many-to-many data with multiple captions per image reduces it further.
Read full analysis →
These results surprised me. There's more to do to fully understand which structures align! Here are some great readings on other perspectives: * local relational: arxiv.org/abs/2602.14486 * coarse-grained: arxiv.org/abs/2604.18572 * global relational: arxiv.org/abs/2609.27252 n/n
These results surprised me. There's more to do to fully understand which structures align! Here are some great readings on other perspectives: * local relational: arxiv.org/abs/2602.14486 * coarse-grained: arxiv.org/abs/2604.18572 * global relational: arxiv.org/abs/2609.27252 n/n
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