Accelerating Discovery to Solve Grand Challenges
Notice bibliographique
Résumé
<!--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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,010 |
| Communication savante | 0,013 | 0,035 |
| Science ouverte | 0,003 | 0,013 |
| Intégrité de la recherche | 0,009 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,035 | 0,021 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».