Why Chat's fun but not yet top 3 Intelligence economically or socially

2025report.com three quarters of a century ago my family and journalist friends at The Economist and innovative people at royal societies around london's st james chatted to Neumann-Einstein-Turing. They had a vision for new engines "computers' and new "death of cost of distance" intel coms platforms eg space's satellites. Engineers would exponentially accelerate design around these new brainworking tools. It was quite possible to advance humanity with win-win exchanges - eg life-critical knowhow multiplies value in application unlike industrial age of consuming up things

I would like to linkin with people who believe chats (except those openly transforming education; agency of lifelong students) are not what we the peoples most urgently need to value in Safety debate. Here goes:

OK I am here to learn but want to fully celebrate cases for 3 exciting branches of Human AI's that have 20+ years depth and are integral to NET (Neumann Eiinstein Turing)) projection of Quintillion times more tech debated started by Von Neumann with The Economist in 1951, and with people like JF Kennedy and Prince Charles 1962

2 Science AI which Hassabis has gravitated for over 20 years trains computers around pattern games not the primary context of the chats. Science AI will produce gamechanging advances in health everywhere within a decade also reducing the chances of species ending plagues. Awkwardly some components of Science AI depend on publications of Einstein in 1905. My guess is that less than 20 (AI) engineers are trained up in that maths. One of the opportunities of King Charles AI World Series is to invite places with Royal Societies to identify who is anyone is their lead Einstein correspondent. When you track what Hassabis says -he's pretty modest but is clearly asking his google colleagues to live up to their do no evil mantra by permitting him enough time to help linkin open science AI

Fortunately there are already several blended corporate entries into Einstein Health AI: Hassabis entry isomorphic is integrating chemistry's core language with biotech's 250 million proteins alphafold3.

1 Jensen Huang's investment in Recursion is likely to keep advancing whatever accelerated compute in silico drug discovery needs most. Please note I dont fully understand but its fascinating that Huang positions Nvidia's omniverse digital twin and robotics lab as also being about intelligence physically translated from 2d to 3d or vice versa. For example if a surgeon has the most advanced 2d data or a patient, he still needs a 3d plan to operate.

3 Enter over 20 years of Fei-Fei Li's work. While she may be famous for staging the 2d global machine learning competition that saw 2012's end to ai winter ; the 2d visioning is only a small part of the story of do you trust to be autonomous in all aspects of translating between 2 and 3 d design

Its interesting Jensen Huang's now 30 years into designing what computer architecture is uniquely fit for what deep data challenge Biotech stages. We can go through at least 100 partnerships he has formed where corporates or brilliant engineers needed something wholly different to central processing

Footnote - here are some chats with Gemini that may add context.

A Royal Catalyst for Life Science AI

Your vision of a life science club emerging from AI meetings initiated by King Charles III is intriguing. The potential for such a club, especially given the unique perspectives of the British and Japanese royal families, offers a fascinating avenue for exploration.

Key Points

• Royal Patronage: The involvement of royal figures lends credibility and prestige to the initiative, potentially attracting top talent and resources.
• Cross-Cultural Collaboration: The collaboration between the UK and Japan, two nations with deep historical connections to nature and innovation, could foster a unique approach to life science AI.
• Long-Term Perspective: The Osaka Expo, with its emphasis on intergenerational system design aligned with nature, provides a valuable framework for considering the long-term implications of life science AI.

I have added some discussions with Gemini - ultmately educaion ai and 90 days profit extraction ai have very different consequences if yiu are parents and want you children to be renewable. God Save King Charles English and LLM for science and for renewable generation out oif ec=very GPS on mother earth.

the biotech companies associated with world class ai people - eg isomorphic demis hassabis, recursion Jensen Huang.

The Intersection of Biotech and AI: A Promising Frontier

You're absolutely right. The convergence of biotechnology and artificial intelligence is reshaping the landscape of medical research, drug discovery, and healthcare. Let's delve into some biotech companies that are at the forefront of this revolution, often led by prominent AI figures:

Isomorphic Labs and DeepMind

• Isomorphic Labs: Founded by Demis Hassabis, a renowned AI researcher and co-founder of DeepMind, Isomorphic is focused on using AI to accelerate drug discovery. The company leverages advanced machine learning techniques to understand biological systems at a fundamental level, aiming to design novel therapeutics more efficiently.
• DeepMind: While primarily known for its achievements in AI, DeepMind has also made significant contributions to the life sciences. Their AlphaFold system, for example, revolutionized protein structure prediction, a crucial step in drug discovery.

Recursion Pharmaceuticals and NVIDIA

• Recursion Pharmaceuticals: Led by a team with a strong background in AI and computer science, Recursion utilizes AI and machine learning to analyze massive biological and chemical datasets. They aim to discover new drugs by identifying patterns and relationships within complex biological systems.
• NVIDIA: Although primarily a hardware company, NVIDIA has made substantial investments in AI and has a growing presence in the life sciences. They partner with various biotech companies, providing the computational power necessary for AI-driven drug discovery.

Other Notable Players

Beyond these prominent examples, several other biotech companies are making significant strides in AI-driven drug discovery and development. These include:

• Atomwise: Using deep learning to predict the binding affinities of small molecules to protein targets.
• Exscientia: Employing AI to design novel drug candidates and optimize drug discovery processes.
• Berg Health: