From Medicine to Mathematics: 2023's Most Exciting AI Innovations
Discover how AI's amazing breakthroughs this year have transformed the way we approach healthcare, science, and mathematics
AI Outperforms Traditional Methods in Differential Diagnosis
Differential diagnosis is crucial in medical care, involving a combination of clinical history, physical examination, and investigations. With medical knowledge constantly expanding, it's challenging for clinicians to stay updated on every condition. Doctors may be influenced by personal experience or recent cases, potentially leading to diagnostic errors. Especially in rural or underserved areas, access to specialist knowledge can be limited, impacting the accuracy of DDx.
Researchers at Google compared the accuracy of differential diagnoses made by doctors using LLM (Large Language Models) against those working in traditional ways. The difference was significant, favoring AI, with the best results achieved entirely without human involvement.
Imagine you’re in a bustling hospital. The air buzzes with urgency, doctors darting from room to room, their minds a whirlpool of diagnoses and treatment plans. In one corner, a group of doctors huddles over thick medical textbooks and case reports, their brows furrowed in concentration. In stark contrast, another group works with a sense of calm assurance, their eyes fixed on screens displaying the outputs of a Large Language Model (LLM) chat bots.
This is not a scene from the future but the summary of an intriguing study conducted by Google's DeepMind team. They decided to challenge the status quo and put to test something rather controversial: can an AI, trained with vast medical data, outperform our seasoned doctors in making differential diagnoses?
The doctors were divided into two groups. One relied on their years of experience and conventional medical resources, while the other had an ace up their sleeve – the assistance of an LLM. The task was to unravel 302 complex, real-world medical cases sourced from the New England Journal of Medicine. Picture this as a high-stakes game of medical Clue, where each diagnosis could lead to vastly different outcomes.
The group assisted by the LLM didn't just perform well – they outshone their counterparts. The AI's standalone performance in generating differential diagnoses was notably superior to that of the unassisted clinicians. AI backed group achieved 59.1% accuracy level compared to 33.6% for the doctors working solo.
But perhaps the most jaw-dropping moment of the study was the revelation that the AI, working independently, without human intervention, demonstrated even greater proficiency.
This study isn't just a testament to the advancements in AI. It opens a new chapter in medical diagnostics, highlighting the untapped potential of AI in enhancing diagnostic accuracy, especially in challenging cases. The implications are huge – from assisting in remote areas with limited access to specialist knowledge to becoming a vital tool in medical education, shaping the future of healthcare.
So, what does this mean for our medical professionals? Are we witnessing the dawn of an era where AI becomes the preferred diagnostician?
Not quite. The study, while groundbreaking, also underlines the importance of human expertise in guiding and interpreting AI's suggestions. It's a partnership where each complements the other, leading to better patient outcomes.
The study also acknowledges limitations such as the difference between real-world clinical encounters and the format of NEJM case reports, and the fact that the LLM had access only to text, not images or tables included in the case reports.
Problem Solving in Mathematics
Just a month ago, I read a several articles that skeptically remarked, “LLM will never be able to make scientific discoveries.” Today, Google’s publication in Nature turns this skepticism on its head.
DeepMind's pioneering research, spearheaded by Alhussein Fawzi and Bernardino Romera Paredes and published in Nature on 14 December 2023, introduces "FunSearch," a groundbreaking method that reshapes our approach to solving complex mathematical problems. This innovation marries the ingenuity of Large Language Models (LLMs) with sophisticated evaluative algorithms, setting a new benchmark in scientific discovery.
FunSearch marks a significant departure from traditional problem-solving methods. It pairs a pre-trained LLM with an automated evaluator, iterating between creative solution generation and meticulous refinement. This process has not only tackled but also made remarkable progress in the notoriously challenging cap set problem in extremal combinatorics, achieving the largest known cap sets to date.
This is undoubtedly a sensation because:
This is the 2nd mathematical discovery in human history made by a machine. The 1st discovery was also made by DeepMind, which created AlphaTensor in 2022 (an agent in the AlphaZero style), which discovered algorithms superior to human ones for tasks such as matrix multiplication;
This is the 1st mathematical discovery in human history made by a large language model (LLM) – a prime candidate for evolving into a Super AI;
But the prowess of FunSearch doesn't stop there. It also excelled in addressing the practical bin-packing problem, surpassing established heuristics. This dual success in theoretical and practical realms underscores FunSearch's adaptability and potential to revolutionize problem solving across various fields.
What makes FunSearch truly groundbreaking is its transparent methodology. It produces programs that explain the solution process, offering insights for further research and refinement. This characteristic is vital for scientific exploration, fostering a deeper understanding of complex problems.
The universality of FunSearch is its most promising aspect. Its ability to adapt and evolve suggests that it could be the key to solving new problems in mathematics in the near future. This AI-driven approach is not just a tool but a transformative force in mathematical sciences, bridging human intellect with computational efficiency.
Revolutionary Advancements in Materials Science
In a groundbreaking development that marks a new era in materials science, researchers have harnessed the power of artificial intelligence to make unprecedented discoveries in stable materials.
The innovative team of researches developed GNoME (Graph Networks for Materials Exploration), an approach that uses large-scale active learning and state-of-the-art GNNs to predict stability and guide materials discovery. GNoME models have discovered over 2.2 million structures, expanding the known stable materials by an order of magnitude.
All the materials found (according to DeepMind, 380,000 stable and 2.2 million in total) will be made available in the open access;
Over the last ten years, computational methods have discovered about 28,000 stable crystal structures. DeepMind's work equates to approximately 800 years of research in this field.
Among the discoveries are 52,000 potentially new graphene-like structures (superconductors) and 528 analogs of lithium conductors (batteries).
This research not only dramatically expands our knowledge of stable materials but also signifies a new era in materials science, where AI-driven discovery becomes central to technological advancements.
Integrating these discoveries with the autonomous laboratory known as A-Lab, developed at Berkley for the accelerated synthesis of novel inorganic materials, further enhances the potential for rapid material innovation. The A-Lab, as reported in a Nature article published on November 29, 2023, combines robotics, computational data from the Materials Project and Google DeepMind, machine learning, and active learning. This integration enables the A-Lab to autonomously generate synthesis recipes and execute them robotically, achieving a 71% success rate in synthesizing 41 out of 58 targeted materials over 17 days of operation (for comparison, it could takes months to synthesize only one material using traditional approach) .
Google DeepMind's findings are intricately connected to the operation of the A-Lab. The vast database of new materials identified by DeepMind can directly feed into the A-Lab's workflow, where computational data from DeepMind is used alongside historical synthesis data to generate and optimize synthesis recipes. This symbiotic relationship between DeepMind's AI-driven discovery methods and A-Lab's autonomous synthesis capabilities represents a significant leap forward in the field of materials science.
Many more to go…
As we anticipate the upcoming advancements in 2024, outlined here:
the advancements in AI during 2023 represent a significant leap forward in our journey towards integrating intelligent technology into every facet of our lives.
With groundbreaking developments in healthcare, materials science, and mathematical problem-solving, AI has not only demonstrated its vast capabilities but also pointed towards a future brimming with untapped potential. As we move forward, AI continues to stand at the forefront of innovation, promising to reshape our world in ways we are only beginning to imagine.
🔍 Explore more