AI can learn to show its workings through trial and error
Category: 7. Maths
-
AI can learn to show its workings through trial and error
-
Trade-off between diversity and provision of specialized healthcare in US cities
Fullman, N. et al. Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: a systematic analysis from the Global Burden of Disease Study 2016. Lancet 391, 2236–2271…
Continue Reading
-
Utilizing unified progressive hybrid censored data in parametric inference under accelerated life tests
Scientific and technological advancements have resulted in the development of intricate and highly durable products, including lamp LEDs, computers, and silicone seals. Product quality is contingent upon reliability; consequently, manufacturers…
Continue Reading
-
Leveraging large language and vision models for knowledge extraction from large-scale image–text colonoscopy records
Arnold, M. et al. Global patterns and trends in colorectal cancer incidence and mortality. Gut 66, 683–691 (2017).
Google Scholar
Bray, F. et al. Global cancer statistics 2018:…
Continue Reading
-
Organic photovoltaic prediction model based on Bayesian optimization and explainable AI
BO-Bagging, the proposed optimized predictive model, accurately predicts four essential OPV parameters (Voc, Jsc, FF, and PCE) by Bayesian optimization and bootstrap aggregation trees. Ensemble learning is a sophisticated machine learning…
Continue Reading
-
On the compatibility of generative AI and generative linguistics
Chomsky, N. Three models for the description of language. IRE Trans. Inf. Theory 2, 113–124 (1956).
Google Scholar
Chomsky, N. Syntactic Structures (De Gruyter Mouton, 1957).
Chomsky, N. &…
Continue Reading
-
Constant-overhead magic state distillation | Nature Physics
Gottesman, D. An introduction to quantum error correction. Proc. Symp. Appl. Math. 58, 221–236 (2002).
Google Scholar
Aharonov, D. & Ben-Or, M. Fault-tolerant quantum…
Continue Reading
-
Neuromorphic principles in self-attention hardware for efficient transformers
Neuromorphic principles in self-attention hardware for efficient transformers
Continue Reading
-
Current applications and future directions in natural language processing for news media and mental health
In this section, we present our results from the scoping review by considering the grouping of traditional and artificial intelligence-based NLP techniques. We then highlight our findings in the context of each group: traditional content analysis…
Continue Reading
-
Target-specific de novo design of drug candidate molecules with graph-transformer-based generative adversarial networks
Rifaioglu, A. S. et al. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief. Bioinform. 20, 1878–1912 (2019).
Google Scholar
…Continue Reading