arXiv AI
Aug 17
The Past and Future of AI Scientists
arXiv:2608. 14407v1 Announce Type: new Abstract: We present a survey of the past and future of AI Scientists: machines capable of automating science.
Can AI build a jet engine? JARVIS Challenge tests role of AI copilots in tough-tech engineering
MIT students designed, built, and tested a jet engine with AI copilots, assessing AI’s usefulness in developing high-performance aerospace systems.
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MIT News AI
Jul 7
How novice coders can develop AI programs for military applications
A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems.
MIT News AI
Sep 9
MIT Schwarzman College of Computing launches pilot to help educators teach AI across disciplines
MIT Schwarzman College of Computing launched a pilot program that hosted a weeklong summer workshop for higher education faculty. The workshop focused on exploring how AI and machine learning materials can be adapted for use in their classrooms across various disciplines.
OpenAI Blog
Mar 21, 2024
Embedding AI into developer software
JetBrains uses OpenAI’s API to build its fastest-growing product ever.
Mistral AI
May 27
Introducing physics AI at Mistral: the foundation for engineering acceleration.
A new class of AI models that predict the behavior of physical systems, powering the engineers and hardware products of tomorrow.
OpenAI Blog
Aug 13, 2024
Introducing SWE-bench Verified
We’re releasing a human-validated subset of SWE-bench that more reliably evaluates AI models’ ability to solve real-world software issues.
arXiv AI
Sep 25
Neuro-symbolic AI for Industrial Configuration
The paper "Neuro-symbolic AI for Industrial Configuration" discusses how Large Language Models (LLMs) fall short for industrial product configuration due to their probabilistic nature, which conflicts with the need for syntactically valid, semantically consistent outputs that align with extensive feature and rule knowledge bases. It proposes Neuro-symbolic (NeSy) AI as a promising solution, outlining three integration strategies—hybrid inference, hybrid fine‑tuning, and hybrid training—and presents a taxonomy of these approaches. The authors describe their efforts to implement a NeSy-based configuration copilot, derive practical design choices for trustworthy AI deployment in engineering settings, and highlight key research challenges, especially scaling NeSy methods from academic prototypes to full‑scale industrial configurators.
MIT News AI
May 21
Technology usually creates jobs for young, skilled workers. Will AI do the same?
A new study of the postwar U. S.
arXiv AI
Jun 29
Reasoning Beyond Prediction: From Data-Driven to Causal Software Engineering
arXiv:2606. 27960v1 Announce Type: cross Abstract: Software engineering is an intellectually demanding, creative discipline that juggles a web of interdependent tasks to design, build, and assure the quality of increasingly complex systems.
DeepMind Blog
Aug 21
From Atari to EVE Online: Building on 15 Years of AI Research in Games
Google DeepMind partners with game studios to prototype breakthrough AI gameplay.
arXiv AI
Aug 12
Automating and Scaling Behavioral Scientific Research on AI Agents
arXiv:2608. 10030v1 Announce Type: new Abstract: As AI agents are increasingly deployed in complex environments, understanding their behaviors becomes critical.
arXiv AI
Aug 17
Agent-Orchestration in Autonomous Chip Design
arXiv:2608. 14035v1 Announce Type: new Abstract: Recent developments in large language models (LLMs) and tool-using agents encourage people to explore the potential of using agents in chip design.