Germán CM

Lead data scientist, ML product owner. In numbers I trust, with music I play.

[Germán CM @GermanicusCM]

@sama Transparency about reliable sources of truth when the model answers about verifiable facts (science, technology…); more intelligent models should give more credit to good data sources @sama

[Google AI @GoogleAI]

AI is rapidly accelerating scientific discovery and contributing to the effort to better understand cancer. We're proud to announce two major open-source breakthroughs from @GoogleResearch and @GoogleDeepMind that demonstrate the immense potential of AI in this field:

First, DeepSomatic can significantly improve cancer diagnostics by accurately identifying key genetic variants within cancer cells, outperforming current state-of-the-art methods. This provides researchers and clinicians with a more precise and reliable tool for mapping cancer's blueprint, which is essential for targeted therapy development.

Second, our new Gemma C2S-Scale 27B model (built using our open Gemma family), achieved an exciting milestone: it generated a novel hypothesis to convert "cold" tumors—which are notoriously resistant to current immunotherapies—into "hot" tumors that can be successfully treated. This discovery may offer a new avenue for cancer drug development.

[Germán CM @GermanicusCM]

@sama Example of a great MCP by Alpha Vantage, to get accurate stock prices

[Germán CM @GermanicusCM]

@sama Hi @sama after using MCP connections in developer mode, I can see great power to get accurate answers, but it´s still unstable (you have to grant several times access, it is also not possible to activate it in conversations inside a project…). Any plan to fix these issues?

[Germán CM @GermanicusCM]

@googleaidevs What an amazing release, thinking about proposing to use it for fine tuning this to our corporative terminology in a semantic search engine (we are using openAI ada 3 large without customization)

[Germán CM @GermanicusCM]

@googleaidevs This sounds great. Can you explain the difference with what we already have in VS code using Gemini 2.5 pro?

[Colaboratory @GoogleColab]

Big news for data science in higher ed! 🚀Colab now offers 1-year Pro subscriptions free of charge for verified US students/faculty, interactive Slideshow Mode for lectures, & an AI toggle per notebook. Enhance teaching & learning in the upcoming academic year! Read all about it here: https://t.co/OCgVqxrHmU. #GoogleColab #EdTech.

[Germán CM @GermanicusCM]

@dreamtheaternet @swedenrockfest @WorkByWolfe Amazing band, it will be my fifth time I see them live next Tuesday in Fuengirola Spain; God bless us if they play Metropolis part 1 🙌🏻

[Germán CM @GermanicusCM]

@10X_AI_ This is the kind of message the developers community need to listen; looking at the generative AI not as a threat, but as an opportunity

[Germán CM @GermanicusCM]

@dreamtheaternet The best album together with Scenes and Six Degrees 🫶🏻

[Germán CM @GermanicusCM]

@dreamtheaternet please @dreamtheaternet @MikePortnoy @JPetrucci
play in Fuengirola Metropolis part 1, Under a glass moon and Octavarium for my friend @Jandroa, it was amazingly GREAT to see that in Madrid, YOU THE BEST

[Germán CM @GermanicusCM]

@dreamtheaternet @WorkByWolfe please @dreamtheaternet @MikePortnoy @JPetrucci play in Fuengirola Metropolis part 1, Under a glass moon and Octavarium for my friend @Jandroa , it was amazingly GREAT to see that in Madrid, YOU THE BEST!

[María @tucaranena]

@perezreverte “La guerra es un lugar donde jóvenes que no se conocen y no se odian se matan entre sí, por decisión de viejos que sí se conocen y se odian, pero no se matan". Erich Hartmann

[Germán CM @GermanicusCM]

@sama This, together with a nicer experience when using the models selector, could be the best update for plus users

[Germán CM @GermanicusCM]

@jameslabrie What a huge work on vocals, you are great @jameslabrie

[Andrew Ng @AndrewYNg]

The buzz over DeepSeek this week crystallized, for many people, a few important trends that have been happening in plain sight: (i) China is catching up to the U.S. in generative AI, with implications for the AI supply chain. (ii) Open weight models are commoditizing the foundation-model layer, which creates opportunities for application builders. (iii) Scaling up isn’t the only path to AI progress. Despite the massive focus on and hype around processing power, algorithmic innovations are rapidly pushing down training costs.

About a week ago, DeepSeek, a company based in China, released DeepSeek-R1, a remarkable model whose performance on benchmarks is comparable to OpenAI’s o1. Further, it was released as an open weight model with a permissive MIT license. At Davos last week, I got a lot of questions about it from non-technical business leaders. And on Monday, the stock market saw a “DeepSeek selloff”: The share prices of Nvidia and a number of other U.S. tech companies plunged. (As of the time of writing, some have recovered somewhat.)

Here’s what I think DeepSeek has caused many people to realize:

China is catching up to the U.S. in generative AI. When ChatGPT was launched in November 2022, the U.S. was significantly ahead of China in generative AI. Impressions change slowly, and so even recently I heard friends in both the U.S. and China say they thought China was behind. But in reality, this gap has rapidly eroded over the past two years. With models from China such as Qwen (which my teams have used for months), Kimi, InternVL, and DeepSeek, China had clearly been closing the gap, and in areas such as video generation there were already moments where China seemed to be in the lead.

I’m thrilled that DeepSeek-R1 was released as an open weight model, with a technical report that shares many details. In contrast, a number of U.S. companies have pushed for regulation to stifle open source by hyping up hypothetical AI dangers such as human extinction. It is now clear that open source/open weight models are a key part of the AI supply chain: Many companies will use them. If the U.S. continues to stymie open source, China will come to dominate this part of the supply chain and many businesses will end up using models that reflect China’s values much more than America’s.

Open weight models are commoditizing the foundation-model layer. As I wrote previously, LLM token prices have been falling rapidly, and open weights have contributed to this trend and given developers more choice. OpenAI’s o1 costs $60 per million output tokens; DeepSeek R1 costs $2.19. This nearly 30x difference brought the trend of falling prices to the attention of many people.

The business of training foundation models and selling API access is tough. Many companies in this area are still looking for a path to recouping the massive cost of model training. Sequoia’s article “AI’s $600B Question” lays out the challenge well (but, to be clear, I think the foundation model companies are doing great work, and I hope they succeed). In contrast, building applications on top of foundation models presents many great business opportunities. Now that others have spent billions training such models, you can access these models for mere dollars to build customer service chatbots, email summarizers, AI doctors, legal document assistants, and much more.

Scaling up isn’t the only path to AI progress. There’s been a lot of hype around scaling up models as a way to drive progress. To be fair, I was an early proponent of scaling up models. A number of companies raised billions of dollars by generating buzz around the narrative that, with more capital, they could (i) scale up and (ii) predictably drive improvements. Consequently, there has been a huge focus on scaling up, as opposed to a more nuanced view that gives due attention to the many different ways we can make progress. Driven in part by the U.S. AI chip embargo, the DeepSeek team had to innovate on many optimizations to run on less-capable H800 GPUs rather than H100s, leading ultimately to a model trained (omitting research costs) for under $6M of compute.

It remains to be seen if this will actually reduce demand for compute. Sometimes making each unit of a good cheaper can result in more dollars in total going to buy that good. I think the demand for intelligence and compute has practically no ceiling over the long term, so I remain bullish that humanity will use more intelligence even as it gets cheaper.

I saw many different interpretations of DeepSeek’s progress here in X, as if it was a Rorschach test that allowed many people to project their own meaning onto it. I think DeepSeek-R1 has geopolitical implications that are yet to be worked out. And it’s also great for AI application builders. My team has already been brainstorming ideas that are newly possible only because we have easy access to an open advanced reasoning model. This continues to be a great time to build!

[Original text: https://t.co/yiOHeGJgLZ ]

[Germán CM @GermanicusCM]

First interaction with Grok
https://t.co/61rbBlv1Gv

[Germán CM @GermanicusCM]

@perezreverte @coge1libro Ver esta respuesta de alguien como usted, habiéndose “molestado” en contestar con esmero este tipo de pregunta, me genera una mayor admiración, grande @perezreverte

[Arturo Pérez-Reverte @perezreverte]

Ilíada. Odisea. Eneida. Biblia. Quijote. Vidas paralelas. Anales. Los doce césares. Cartas a Lucilio. Meditaciones de M. Aurelio. Ensayos de Montaigne. La montaña mágica. El conde de Montecristo. Los tres mosqueteros. Lord Jim. Episodios nacionales. El rojo y el negro. El halcón maltés. La máscara de Dimitrios. El asesinato de Rogelio Ackroyd. Hamlet. Macbeth. La Regenta. Suave es la noche. Coplas a la muerte de mi padre. El sí de las niñas. Madame Bovary. El gatopardo...

[Germán CM @GermanicusCM]

@poldos26 @perezreverte @IreneMontero @guardiacivil Llámame borracho, pero yo veo antifaces negros, que son más eficaces en la oscuridad para que no te vean