In this post, we have separated the wheat from the chaff in the AI ecosystem, surfacing and highlighting some of the most prescient AI/ML breakthroughs and promising public/private partnerships. Companies and organizations featured here include Google DeepMind, The Allen Institute for Artificial Intelligence (AI2), The National Science Foundation (NSF), The International Union for Conservation of Nature (IUCN), Strider Technologies, and Scale AI.
AI Breakthroughs and Promising Partnerships
The vision statement from the team at DeepMind: “We live in an exciting time when AI research and technology are delivering extraordinary advances.In the coming years, AI — and ultimately artificial general intelligence (AGI) — has the potential to drive one of the greatest transformations in history. We’re a team of scientists, engineers, ethicists and more, working to build the next generation of AI systems safely and responsibly. By solving some of the hardest scientific and engineering challenges of our time, we’re working to create breakthrough technologies that could advance science, transform work, serve diverse communities — and improve billions of people’s lives.”
“Newly discovered materials can be used to make better solar cells, batteries, computer chips, and more.”
From EV batteries to solar cells to microchips, new materials can supercharge technological breakthroughs. But discovering them usually takes months or even years of trial-and-error research. Google DeepMind hopes to change that with a new tool that uses deep learning to dramatically speed up the process of discovering new materials. Called graphical networks for material exploration (GNoME), the technology has already been used to predict structures for 2.2 million new materials, of which more than 700 have gone on to be created in the lab and are now being tested. It is described in a paper published in Nature [in November 2023].
Alongside GNoME, Lawrence Berkeley National Laboratory also announced a new autonomous lab. The lab takes data from the materials database that includes some of GNoME’s discoveries and uses machine learning and robotic arms to engineer new materials without the help of humans. Google DeepMind says that together, these advancements show the potential of using AI to scale up the discovery and development of new materials.
GNoME can be described as AlphaFold for materials discovery, according to Ju Li, a materials science and engineering professor at the Massachusetts Institute of Technology. AlphaFold, a DeepMind AI system announced in 2020, predicts the structures of proteins with high accuracy and has since advanced biological research and drug discovery. Thanks to GNoME, the number of known stable materials has grown almost tenfold, to 421,000.
“…a language model that was able to train itself by synthesising millions of theorems and their proofs…”
Google Deepmind says that a new artificial intelligence system has made a major breakthrough in one of the most difficult tests for AI. The company says that it has created a new AI system that can solve geometry problems at the level of the very top high-school students. Geometry is one of the oldest branches of mathematics, but has proven particularly difficult for AI systems to work with. It has been difficult to train them because of a lack of data, and succeeding requires building a system that can take on difficult logical challenges. Typically, engineers train such systems using machine learning, which involves providing them with data on how to successfully complete a task, and have them learn how to do so. But there are few such human demonstrations available for proving theorems, especially in geometry.
Instead, researchers say they used a different approach to build the new system known as AlphaGeometry. They instead used a language model that was able to train itself by synthesising millions of theorems and their proofs, and then combined that with a system that can search through branching points in challenging problems. Taken together, that system is able to learn and then solve complex geometrical problems without human input, the creators claim. It was put to the test with 30 problems from the International Mathematical Olympiad, which is a competition in which the top-performing high school students are asked to prove mathematical theorems. AlphaGeometry was able to solve 25 of them. That is far better than the previous best method, which was only able to solve 10 problems. It gets it close to the average gold medallist, who solved 25.9 theorems.
The Allen Institute for Artificial Intelligence (AI2)
AI2 is a nonprofit research institute with the mission of conducting high-impact AI research and engineering designed to solve the largest problems facing humanity today. AI2 was founded in 2014 by the late Paul Allen, philanthropist, and Microsoft co-founder. AI2 is committed to developing transparent, open and equitable AI. Learn more at allenai.org and stay up to date with new research and news from AI2 on X @allen_ai.
“OLMo will be comparable in scale to other state-of-the-art large language models at 70 billion parameters, and is expected in early 2024”
AI2 is embarking on the creation of an open, state-of-the-art generative language model: AI2 OLMo (Open Language Model).
OLMo will be a uniquely open language model intended to benefit the research community by providing access and education around all aspects of model creation. OLMo will be a new avenue for many people in the AI research community to work directly on language models for the first time. We will be making all elements of the OLMo project accessible — not only will our data be available, but so will the code used to create the data. We will release the model, the training code, the training curves, and evaluation benchmarks. We will also openly share and discuss the ethical and educational considerations around the creation of this model to help guide the understanding and responsible development of language modeling technology.
This broad availability of all aspects of OLMo will allow the research community to directly take what we create and work to improve it. We believe that millions of people want to better understand and engage with language models, and we aim to create an environment where they actually can, leading to faster and safer progress for everyone. Our goal is to collaboratively build the best open language model in the world.
Learn more here.
“One of the NAIRR Pilot’s key goals of democratizing access to AI research resources for the broad research and education community.”
In response to the Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence released last week, The Allen Institute for Artificial Intelligence (AI2) announces a commitment to the National Artificial Intelligence Research Resource (NAIRR) pilot program as part of AI2’s Open Ecosystem for AI.
“AI2 is deeply committed to working with the NAIRR Pilot community to support the design and development of truly open large-scale model training and evaluation software,” said Ali Farhadi, AI2 CEO.
As part of this commitment, AI2 will make datasets, large-scale models, training, and evaluation software available to the NAIRR Pilot via AI2’s Open Ecosystem for AI. To start, AI2 will provide access to Dolma, a 3 trillion token open corpus for language model pre-training. Dolma is derived from a diverse mix of web content, academic publications, code, books, and encyclopedic materials. Dolma is the pre-training dataset for AI2’s Open Language Model, OLMo, which is comparable in performance to state-of-the-art large language models, but, notably, the entire model, including training data, will be open and accessible via AI2’s Open Ecosystem for AI. Dolma is available to download today from Hugging Face and OLMo will be available in the new year.
“We are thrilled that AI2 has made an initial contribution to the NAIRR Pilot. AI2’s commitment to open AI research, data, and evaluation is well aligned with one of the NAIRR Pilot’s key goals of democratizing access to AI research resources for the broad research and education community. Critically, AI2 will provide their considerable technical expertise to aid in the design of the NAIRR Pilot and NAIRR software stack,” said Katie Antypas, Director of the Office of Advanced Cyberinfrastructure at NSF.
“…developing countries interested in carrying out the Treaty access to AI2’s monitoring and analysis software, Skylight, at no cost…”
The International Union for Conservation of Nature (IUCN) and the Allen Institute for AI (AI2) are partnering to equip governmental and non-governmental organisations with advanced artificial intelligence (AI) technology to protect our oceans. Following the signing of the Biodiversity Beyond National Jurisdiction Agreement (BBNJ) at the New York UN SDG Summit, the partnership will help fast-track the effective and equitable implementation of the agreement.
The remoteness and vastness of the high seas present a unique challenge for countries planning to establish and monitor marine protected areas (MPAs) under the BBNJ Agreement – also known as the UN High Seas Treaty. Together, IUCN, a global authority on conservation with over 1,400 member organisations, and AI2, a non-profit research institute building AI for the common good, will give developing countries interested in carrying out the Treaty access to AI2’s monitoring and analysis software, Skylight, at no cost, ensuring equitable implementation of the High Seas Treaty in the Global South and beyond. Countries will also receive technical assistance, capacity building, and policy advice from IUCN.
Skylight, which over 300 organisations use in nearly 70 countries, combines satellite technology and AI to deliver automated monitoring and detection capabilities to assist in tackling illegal, unreported, and unregulated (IUU) fishing. With the ability to process and analyse millions of data points daily, the platform also provides policymakers and MPA managers with near real-time and historical intelligence to inform conservation actions..
“Partnership will enable federal agencies to leverage Strider’s unique data assets and Scale AI’s Donovan platform to enrich government data holdings to drive critical economic and national security missions.”
Strider Technologies, Inc. and Scale AI have announced…a strategic partnership to deliver federal government agencies the data and AI technologies to identify, contextualize, and respond to important U.S. economic and national security issues.
The partnership will combine Strider’s public data, a collection of over 10 billion documents from primary sources in multiple languages, with Donovan, Scale’s AI-powered decision-making platform for helping public sector operators, analysts, and decision-makers understand, plan, and act in minutes instead of weeks. The goal will be to support mission objectives with additional data to better understand state-sponsored risk around the globe from strategic adversaries.
Specifically, Strider’s publicly collected data will inform Donovan’s large language model platform that helps users make sense of vast amounts of structured and unstructured data to make smarter decisions. As a result, government agencies will have access to billions of documents via a production-ready platform that delivers the intelligence needed to identify, evaluate, and respond to critical national security threats.
Additional OODA Loop Resources
Ten Noteworthy Global Developments in the Double Exponential Growth of Artificial Intelligence: 2023 has been marked by what some are characterizing as the double exponential pace of artificial intelligence innovation and commercial deployment. Following are some of the most vital developments from last year.
Will the “Double Exponential” Growth of Artificial Intelligence Render Global AI Governance and Safety Efforts Futile?: Major global, multinational announcements and events related to AI governance and safety took place recently. We provide a brief overview here. In an effort to get off the beaten path, however, and move away from these recent nation-state based AI governance efforts, two recent reports are framing some really interesting isues: How Might AI Affect the Rise and Fall of Nations? and “Governing AI at the Local Level for Global Benefit: A Response to the On-Going Calls for the Establishment of a Global AI Agency.”
Technology Convergence and Market Disruption: Rapid advancements in technology are changing market dynamics and user expectations. See: Disruptive and Exponential Technologies.
The New Tech Trinity: Artificial Intelligence, BioTech, Quantum Tech: Will make monumental shifts in the world. This new Tech Trinity will redefine our economy, both threaten and fortify our national security, and revolutionize our intelligence community. None of us are ready for this. This convergence requires a deepened commitment to foresight and preparation and planning on a level that is not occurring anywhere. The New Tech Trinity.
AI Discipline Interdependence: There are concerns about uncontrolled AI growth, with many experts calling for robust AI governance. Both positive and negative impacts of AI need assessment. See: Using AI for Competitive Advantage in Business.
Benefits of Automation and New Technology: Automation, AI, robotics, and Robotic Process Automation are improving business efficiency. New sensors, especially quantum ones, are revolutionizing sectors like healthcare and national security. Advanced WiFi, cellular, and space-based communication technologies are enhancing distributed work capabilities. See: Advanced Automation and New Technologies
Emerging NLP Approaches: While Big Data remains vital, there’s a growing need for efficient small data analysis, especially with potential chip shortages. Cost reductions in training AI models offer promising prospects for business disruptions. Breakthroughs in unsupervised learning could be especially transformative. See: What Leaders Should Know About NLP