Leticia L. Rodríguez

Computer Science Professor - ML & Data Scientist - ex @Google, ex @mercadolibre y otros exes.
I 💖 algorithms beauty. Mom*2. 💕💙

[The Institute for Ethics in AI @EthicsInAI]

What is AI ethics and why does it matter?

As artificial intelligence (AI) becomes increasingly embedded in society, questions about responsibility, fairness, accountability and human values become ever more important.

At the Institute for Ethics in AI, we explore fundamental questions at the heart of AI ethics, from transparency and governance to the ways AI may shape human life and what we value.

📖 Learn more about ethics in AI and why it matters: https://t.co/NNEUPCCtbh

[Leticia L. Rodríguez @letyrodri]

Armando la agenda del segundo semestre de TEGAI Consulting!
Nueva oferta de servicios a empresas y c-level. Asesoramiento estratégico en Inteligencia Artificial. Cursos MLOps IA Generativa Modelos de Lenguajes y Agentes.

[Leticia L. Rodríguez @letyrodri]

@rbenzaquen @googlegemma @AMD Podes hacer inferencia local con CUDA sin nube con una NVIDIA RTX 3060 y un Gemma quantizado modelos chicos de Gemma. No entendí . Haya unas cuentas muy piolas para contar la GPU que precisas según la cantidad de parámetros de la LLM :)

[Leticia L. Rodríguez @letyrodri]

@hthieblot AI

[Rohan Paul @rohanpaul_ai]

Another great paper from Google.

Shows general LLMs can solve formal math by planning proofs and checking each step. Raised general LLM performance from under 10% to 70%.

A general LLM failed badly when asked to write full formal proofs in 1 try, but became much stronger when it planned, split the work into smaller claims, reused past claims, and learned from Lean’s feedback.

The paper shows the weakness was not just the model’s math ability, but the way it was being used - the absence of structured interaction with a verifier.

The key idea is that the model does not try to write one giant perfect proof at once, because that usually fails on long and tricky problems.

Instead, LEAP stores the proof as a graph of goals and subgoals, so useful lemmas can be reused instead of rediscovered every time.

The authors tested LEAP on Putnam 2025 and a new Lean benchmark built from 60 IMO-style problems, where ordinary one-shot proof writing did very poorly.

LEAP solved all 12 Putnam 2025 problems and raised general LLM performance on the Lean IMO benchmark from under 10% to 70%.

----

Link – arxiv. org/abs/2606.03303

Title: "LEAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks"

[Jia-Bin Huang @jbhuang0604]

POV: attending CVPR

[Leticia L. Rodríguez @letyrodri]

@jbhuang0604 🤣

[Leticia L. Rodríguez @letyrodri]

@readswithravi The little prince

[Leticia L. Rodríguez @letyrodri]

@emmaiarussi Nunca hubo que dejar de tomarlo! Es una excelente oportunidad para que los estudiantes verifiquen lo que saben e intercambiar con el docente. 100% a favor del coloquio como evaluación andamiaje y oportunidad de seguir aprendiendo

[LatinX in AI (LXAI) @_LXAI]

LXAI is happy to welcome you today to the LatinX in Computer Vision (LXCV) workshop!

🏢 Location: Colorado Convention Center, Room 106
🕗 Time: Doors open at 8:00 AM for registration, with presentations kicking off at 8:30 AM.

[LatinX in AI (LXAI) @_LXAI]

We are officially opening nominations for our keynote slots. We highly encourage you to nominate yourself, a colleague, or a friend!

Drop your nominations here: https://t.co/VQCy2HNajo

[Emmanuel Iarussi @emmaiarussi]

Sorpresón! Automatizar con IA un proceso sesgado, como la contratación, no elimina el sesgo. De yapa, lo hace más difícil de detectar, de discutir, y es mucho más fácil que afecte simultáneamente a miles de personas.

[Leticia L. Rodríguez @letyrodri]

Hey! I'm happy to share that I will be teaching about Agents for a week at Universidad de Buenos Aires at the end of July. More informaction here:
https://t.co/jbYU50fZYQ @ECIDCUBA @Exactas_UBA

[Leticia L. Rodríguez @letyrodri]

Hey! I'm happy to share that I will be teaching about Agents for a week at Universidad de Buenos Aires at the end of July. More informaction here:
https://t.co/jbYU50fZYQ @ECIDCUBA @Exactas_UBA

[Andrew Ng @AndrewYNg]

Harvard University just voted to limit the number of A grades given in undergraduate classes to about 20% of the class. I’m not in favor of this. It deeply runs counter to how I believe education should be. We should hold a high bar, but also work mightily to support the success of 100% of learners, rather than a fraction.

Harvard’s administration took this step — over the objections of a large fraction of the student body — to counter grade inflation. Grade inflation is real: Many universities have been awarding A and B grades to ever larger fractions of students, and this has caused grade point averages (GPAs) to become less useful as signals of student skill. At the same time, we want students to succeed. The heart of the question is the role of educational institutions. Should our goal be:
- To help students succeed?
- To judge students?

Both of these have value. But my focus when working in education is almost entirely helping students succeed.

To me, it is clear that many people want to learn, to be empowered, to build skills that let them do new things! This is what we focus on at DeepLearningAI. This philosophy is also why my online courses (going back to my early online Stanford courses on Coursera) permitted an unlimited number of retries for graded assignments.

I believe in letting — and even encouraging — someone to redo something until they succeed. This is as opposed to standing in judgement of the fact they didn’t get it right the first time. Further, I want homework assignments to be designed primarily to help people practice and learn, rather than to judge their skill level. This is why I prefer to create “Practice Problems” and “Practice Labs” — questions that, when you think through them, help you to gain practice and reinforce what you know. As opposed to “Assessment Problems” designed primarily to judge skill.

But won’t Harvard’s move make GPAs more meaningful and help prospective employers identify strong candidates? Having hired a large number of people from Harvard and other institutions, I can say confidently that GPA is not an important signal. We have screening and interviewing processes that give far more accurate ways to figure out if someone is truly skilled. I do not need a wider spread in applicant GPA scores to figure out who's really good!

To be clear, there is also value in assessment. Even though standardized testing is much hated, high-quality tests like the SAT, ACT, GRE, TOEFL, etc. provide objective measures of ability in a domain. I find that most people want to learn and succeed. There are also people who want rigorous assessment (for example, to apply for school admissions), but this is a lesser need, and is not my focus when building educational products.

Harvard is often described as an “elite” educational institution. There are two ways to be elite: One option involves limiting enrollments, and then even among admitted students, cap the number of people that do well at 20%. I would rather pursue a different path: Set a high bar and teach elite, cutting-edge skills, but strive relentlessly to help everyone succeed. This way, eliteness is defined not by excluding people but by helping as many people as possible to be excellent.

[Original text: The Batch newsletter]

[Elon Musk @elonmusk]

SpaceX is actively hiring world-class engineers/physicists for SpaceXAI, even if you have zero prior experience in AI. Smart humans figure it out fast.

Please send an email with ~3 bullet points demonstrating evidence of exceptional ability to [email protected].

[Leticia L. Rodríguez @letyrodri]

Interesting

[hinata @HinataMotivates]

Terence Tao says AI makes his math papers richer not easier.

[Leticia L. Rodríguez @letyrodri]

Sábado arrancó explicando la arquitectura de Von Neumann a mi hijo de 9 años 🤓

[Haider. @haider1]

Yann LeCun says you cannot build a reliable agentic system without a world model

LLMs don't have world models. They can't predict the consequences of their actions before taking them

"they just act, and whatever happens next is someone else's problem"

Without that, it's not intelligence

[Sundar Pichai @sundarpichai]

TPU 8t, optimized for training and TPU 8i, optimized for inference.

Looking good!