Accelerating Discovery to Solve Grand Challenges
Bibliographic record
Abstract
<!--HTML--> For the last 60 years, the world of computing has been dominated by binary bits representing the intersection of information and mathematics. We have constantly pushed the boundaries of computation in this paradigm, with innovations in semiconductors reducing energy or increasing performance to enable more sophisticated calculations. Now, working at the intersection of information and biology, artificial intelligence advances and permeates through ever more applications affecting business and science. We have witnessed its power to learn and reason in human language. Powerful models are now emerging that evolve AI from discrimination to creation, enabling AI to create in new domains. We will discuss the underlying techniques enabling this future and their transformative impact. Finally, we are witnessing the growth of a new computing paradigm combining physics and information—quantum computing, with the potential to solve problems out of reach for even the most powerful supercomputers. We will discuss the opportunities and challenges defining the future of quantum computing. We marvel at the power of each of these computing technologies, but we haven’t fully grasped their most profound implication, one we will see this decade when we witness their convergence. The result will be the creation of unseen computational power accelerating the rate of scientific discovery. We will conclude with a reflection on this future of computing and the implications of this convergence of technologies. About the speaker Dr. Gil leads the technology roadmap and the technical community of IBM, directing innovation strategies in areas including hybrid cloud, AI, semiconductors, quantum computing, and exploratory science. Dr. Gil is responsible for IBM Research, one of the world’s largest and most influential corporate research labs, with over 3,000 researchers. He is the 12th Director in its 76-year history. He is also responsible for IBM's intellectual property strategy and business. Dr. Gil is a globally recognized leader of the quantum computing industry. Under his leadership, IBM was the first company in the world to build programmable quantum computers and make them universally available through the cloud. An advocate of collaborative research models, Dr. Gil co-chairs the MIT-IBM Watson AI Lab, which advances fundamental AI research to the broad benefit of industry and society. He also co-chairs the COVID-19 High-Performance Computing Consortium, which provides access to the world’s most powerful high-performance computing resources in support of COVID-19 research. Dr. Gil is a member of the National Science Board (NSB), the governing body of the National Science Foundation (NSF), serves on the President’s Research Council of the Canadian Institute for Advanced Research (CIFAR), and the MIT School of Engineering Dean's Advisory Council. Dr. Gil is on the boards of the Semiconductor Industry Association (SIA), New York Academy of Sciences, New York Hall of Science, and Research!America. Dr. Gil received his Ph.D. in Electrical Engineering and Computer Science from MIT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.013 | 0.035 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.035 | 0.021 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".