Hugo Penedones

Some inductive biases encode beliefs about the data: bets that certain hand-crafted features matter, that certain regularities hold.

I just got Llama 3.2 1B running at 60 tokens/sec on my nucbox mini PC’s NPU (Neural Processing Unit) (AMD Ryzen AI 9 HX 370) under Ubuntu Linux — something that, as far as I can tell, hasn’t been publicly demonstrated before.

Everyone’s talking about LLMs, but it is already pretty clear what the next big wave will be: AI for the physical world.

Arriving at the Portuguese encounter “Ciência 2025”, for a roundtable discussion on AI for Science and how Inductiva.AI is contributing with a platform that makes numerical simulation and physics datasets generation really easy and affordable.

Machine Learning Summer School coming up!

🇵🇹 DeepMinders Joao Carreira and Zita Marinho, with ex-DeepMinders Wang Ling and Hugo Penedones at AIHub / Unicorn Factory Lisboa in Lisbon.

I was invited as the keynote speaker at the Dia da Investigação (Research Day) organised by the Faculdade de Medicina da Universidade de Lisboa , held at the Auditório Professor David Ferreira (A52), Edifício Egas Moniz, at 13h.

Hoje as 17:30 na Faculdade de Ciências da Universidade do Porto (FCUP), uma palestra sobre as contribuições da área de Machine Learning nos prémios Nobel de 2024.

“If there were many databases as good as the PDB, I would say, yes, this [prize] probably is just the first of many, but it is kind of a unique database in biology” - David Baker (Nobel prize in Chemistry).

Congrats Demis Hassabis and John Jumper!

Steve Crossan’s article Engineering for Science asks: “Why did AlphaFold happen at DeepMind rather than (for example) the Broad Institute ?

That feeling when Schmidhuber cites your (not well known) RL paper!

Forecasted the future 2 days ahead, not bad!

Prediction: training data from molecular dynamics simulations will be key.

I tend to agree with Blaise Aguera y Arcas and Peter Norvig, who argue in their Noema Magazine article “Artificial General Intelligence Is Already Here” that today’s most advanced AI models should already be recognised as the first true examples of AGI.

Congrats to my ex-colleagues from the AlphaFold project.

Talvez não seja óbvio, mas ser capaz de prever a próxima palavra, pode ir muito além de identificar correlações estatísticas superficiais: imaginem um romance policial em que após centenas de páginas com detalhes intrincados sobre todas as personagens, o…

Computation is at the heart of what we do.

Our Nature paper describing AlphaFold is finally out. :) You can read it at: https://rdcu.be/b0mtx #nature #proteins #structureprediction #deepmind #alphafold #deeplearning AlphaFold: Using AI for scientific discovery

Our paper was accepted at NeurIPS 2019.

I recently made public more of my programming projects from the past.

It turns out that there are several very high profile Portuguese researchers in the field of Machine Learning, even though they all seem to live and work abroad.

I have recently started working as a Research Engineer at Google DeepMind , in London.

Yesterday I attended a talk organized by the London Machine Learning meetup group, where Yoshua Bengio was the invited speaker .

I recently started playing with the torch library again.

Ten years after my Summer internship at Microsoft as an undergraduate student, I had the opportunity to be back to the Redmond campus.

Nassim Nicholas Taleb (author, scholar, statistician, trader, philosopher, risk manager) Books: The Black Swan ), Antifragile: Things That Gain from Disorder Bret Victor (engineer, designer, inventor, dreamer): Talks: Inventing on Principle , The Future of

Soon I will be in Dublin, Ireland, attending one of the best conferences in Information Retrieval.

A couple of years ago, I was doing research involving Convolutional Neural Networks for object classification tasks (see here ).

Yesterday I attended to the Machine Learning Workshop at Idiap EPFL.

Lately, I have been working with Deformable Models and I am surprised by how well they can work.

I recently read an article from Y.

I recently added to my webpage a tutorial on how to use torch5 library to train a convolutional neural network for the task of handwritten digit recognition.

The event took place yesterday at the New York Academy of Sciences, a building right next to the World Trade Center.

In the last few months (in which I haven’t posted in this blog) there were a few interesting web platforms related to Machine Learning poping-up, most notably: MLcomp.org - you can upload your datasets and/or your algorithms, and experiments will run auto…

Just a short link to an article of the New York Times about AI .

Today I assisted to the first edition of TEDx Geneva.

I’m doing research in the fields of Machine Learning and Computer Vision, so each time we have an idea for a new algorithm, I have to write code, run experiments and compare results.

My supervisor is leading a new European project called MASH, which stands for “Massive Sets of Heuristics”.

I just quickly tried the new Gmail Labs feature “Got the wrong Bob”?

My good friend Miguel called my attention to a TED talk that you might also find interesting: Ken Robinson: Do schools kill creativity?

Thoughts on Life This essay will be in beta version, longer than any Google product.

I spend lots of time wondering about the best ways to be both more productive and happy.

In the past it happened that I didn’t publish some potentially interesting thoughts in this blog, just because they didn’t exactly fit the “about intelligence” topic.

John Langford wrote a very interesting post on the failures of Artificial Intelligence research and why Machine Learning has been a safer bet.

Google already has a tradition of April fool’s jokes : this year they are introducing an Artificial Intelligence brain!

Today I tried out a great site to generate tag clouds, it is called wordle.net.

The 2008 ACM Paris Kanellakis Theory and Practice Award was awarded to Corinna Cortes and Vladimir Vapnik “for the development of Support Vector Machines, a highly effective algorithm for classification and related machine learning problems”.

This is an extremely geek picture!

If I had to explain what computer vision is all about, in just one snapshot, I would show you this: Computer Graphics algorithms go from the parameter space to the image space (rendering), computer vision algorithms do the opposite (inverse-rendering).