As Amazon does not believe my #orms book is worth delivery in Italy, I just published the #kindle e-book format of both the Italian and the English versions.
https://t.co/ks9KgNIRfT
https://t.co/1bfz5eKO30
🗣️We have invited Giovanni Di Liberto (@diliberg), from @tcddublin to speak us about investigating auditory #cognition with natural speech and music.
We are eager to learn more about his work!
Discover more about it here👇
https://t.co/q52OcNLD8D
I sat down with @JonHernandezIA in Madrid to discuss the growing risks and impacts of AI and the urgent need to improve our social, political, and technical safeguards.
Thanks for an excellent conversation!
I write about this in more detail in a blog post with a guest contribution from Isaac Rajagopal, a student at MIT on whose work ChatGPT built, who gives his assessment of the level of mathematical ability displayed by the model.
https://t.co/K10U8ZktcJ
Anthropic CEO: "AI will write 100% of code within a year"
developers spend 4 years in university learning to code
Claude learned it from every book ever written
if the hardest skill is already handled - the gap is no longer about what you know
it's about how well you've configured the tool that knows everything
most people haven't done that yet
the article below is where you start
Prof. Donald Knuth opened his new paper with "Shock! Shock!"
Claude Opus 4.6 had just solved an open problem he'd been working on for weeks — a graph decomposition conjecture from The Art of Computer Programming.
He named the paper "Claude's Cycles."
31 explorations. ~1 hour. Knuth read the output, wrote the formal proof, and closed with: "It seems I'll have to revise my opinions about generative AI one of these days."
The man who wrote the bible of computer science just said that. In a paper named after an AI.
Paper: https://t.co/juSOmK9vOt
Don't forget to submit your abstract for the VeRoLog 2026 conference! The deadline is on Monday, the 23rd of February and this is this the website with all the relevant information https://t.co/JaJgRL7mwS See you in Bath! #vehicleRouting
Here's my conversation all about AI in 2026, including technical breakthroughs, scaling laws, closed & open LLMs, programming & dev tooling (Claude Code, Cursor, etc), China vs US competition, training pipeline details (pre-, mid-, post-training), rapid evolution of LLMs, work culture, diffusion, robotics, tool use, compute (GPUs, TPUs, clusters), continual learning, long context, AGI timelines (including how stuff might go wrong), advice for beginners, education, a LOT of discussion about the future, and other topics.
It's a great honor and pleasure for me to be able to do this kind of episode with two of my favorite people in the AI community:
1. Sebastian Raschka (@rasbt)
2. Nathan Lambert (@natolambert)
They are both widely-respected machine learning researchers & engineers who also happen to be great communicators, educators, writers, and X posters.
This was a whirlwind conversation: everything from the super-technical to the super-fun.
It's here on X in full and is up everywhere else (see comment).
Timestamps:
0:00 - Introduction
1:57 - China vs US: Who wins the AI race?
10:38 - ChatGPT vs Claude vs Gemini vs Grok: Who is winning?
21:38 - Best AI for coding
28:29 - Open Source vs Closed Source LLMs
40:08 - Transformers: Evolution of LLMs since 2019
48:05 - AI Scaling Laws: Are they dead or still holding?
1:04:12 - How AI is trained: Pre-training, Mid-training, and Post-training
1:37:18 - Post-training explained: Exciting new research directions in LLMs
1:58:11 - Advice for beginners on how to get into AI development & research
2:21:03 - Work culture in AI (72+ hour weeks)
2:24:49 - Silicon Valley bubble
2:28:46 - Text diffusion models and other new research directions
2:34:28 - Tool use
2:38:44 - Continual learning
2:44:06 - Long context
2:50:21 - Robotics
2:59:31 - Timeline to AGI
3:06:47 - Will AI replace programmers?
3:25:18 - Is the dream of AGI dying?
3:32:07 - How AI will make money?
3:36:29 - Big acquisitions in 2026
3:41:01 - Future of OpenAI, Anthropic, Google DeepMind, xAI, Meta
3:53:35 - Manhattan Project for AI
4:00:10 - Future of NVIDIA, GPUs, and AI compute clusters
4:08:15 - Future of human civilization
The 5th Spanish Young Statisticians and Operational Researchers Meeting #SYSORM2025 has officially started!
The opening session welcomed participants to three days dedicated to exchanging knowledge and fostering collaboration in Statistics, Operational Research and Data Science.
Our last but not least plenary speaker, Maurizio Boccia, has just given an inspiring talk on the Truck-and-Drone delivery problem. A whole world with still many doors to explore.
Thank you, Maurizio, for accepting our invitation!
#EUROYoung2025#orms
We couldn’t have had a better way to end for today than with the excellent talk of our third plenary speaker, @DoloresRomeroM. Thank you so much for sharing your enthusiasm for academia for us, and for eXplaining your research with so much transparency 😉
#EUROYoung2025#orms
Have questions about optimization best practices? Gurobot delivers fast, reliable answers—helping you troubleshoot, model, and optimize more efficiently. Log in to your Gurobi User Portal to get started. Learn more: https://t.co/BuTVNMjCGs #Gurobot#Optimization#Gurobi
It's a strange time to be a programmer—easier than ever to get started, but easier to let AI steer you into frustration. We've got an antidote that we've been using ourselves with 1000 preview users for the last year: "solveit"
Now you can join us.🧵
https://t.co/GLKm0woI8b
Upcoming Public Lecture: The Traveling Salesman Problem — Package Deliveries, Pub Walks, and Astro Tours on October 22, 2025 in Seoul near Gangnam Station
https://t.co/y7z7p9MQI1
OCEAN is evolving 🌊, check it out! https://t.co/Dnh0cxNdfW
v2.0 offers a one-click Python library for optimal counterfactual explanations in tree ensembles (RFs, boosting...) based on MILP and CP models. Install with "pip install oceanpy". Unlike unstable heuristics that may treat similar users differently, OCEAN guarantees an explanation whenever one exists and delivers reliable results under a variety of plausibility or actionability conditions.
The library is actively maintained by https://t.co/RGlGelg0fA and members of the SCALE-AI Chair at @polymtl, with various additional features coming soon.
#MachineLearning #XAI #Optimization #ORMS