NNUE (Efficiently Updatable Neural Networks) has revolutionized chess engine development, with nearly all top engines adopting NNUE models to maintain competitive performance. A key challenge in NNUE training is the creation of high-quality datasets, particularly in complex domains like chess, where tactical and strategic evaluations are essential. However, methods for constructing effective datasets remain poorly understood and under-documented. In this paper, we propose an algorithm for generating and filtering datasets composed of "quiet" positions--positions that are stable and free from tactical volatility. Our approach provides a clear methodology for dataset creation, which can be replicated and generalized across various evaluation functions. Testing demonstrates significant improvements in engine performance, confirming the effectiveness of our method.

Given all the relentless hype about its Artificial Intelligence and its transformative potential for healthcare, it would be understandable if some health systems might be casting about in search of AI or machine learning projects they could try. But that sort of rushed, ad hoc approach is precisely the wrong one to take, says Tushar Mehrotra, senior vice president of analytics at Optum. "The only way you are going to get value out of AI is to link the clinical or business problem to the organization's overall strategy and make sure you have a rich enough data set to train the model so it generates actionable insights," said Mehrotra. "Making sure you are building and designing your AI effort the right way means putting in the work up front to create a clear understanding of what you are trying to solve so it can be embedded in the decision-making workflow," he said. "Too often, AI projects start with a quest for academic insight." At HIMSS20, Mehrotra and his colleague, Optum SVP of Artificial Intelligence and Analytics Sanji Fernando will offer their perspectives on how AI can be applied to promote growth and speed strategies for digital transformation.

Vered Shwartz is a final year PhD student researching lexical semantic relations in the Natural Language Processing lab at Bar-Ilan University. In between her studies, she has also worked on natural language processing as part of the R&D teams at Google and IBM. Away from the lab, Vered is the creative mind behind Probably Approximately a Scientific Blog, where she discusses key NLP concepts for a general audience. We sat down with her to discuss a range of AI topics, from the lexical problems causing a buzz in the academic community to where she gets her training data. Lionbridge AI: What was it that led you to specialize in NLP? Vered: I took two NLP courses as an undergrad and when I decided to do my Masters it was an easy answer to "what's the most interesting field in computer science I've been exposed to so far?".

Given all the relentless hype about its artificial intelligence and its transformative potential for healthcare, it would be understandable if some health systems might be casting about in search of AI or machine learning projects they could try. But that sort of rushed, ad hoc approach is precisely the wrong one to take, says Tushar Mehrotra, senior vice president of analytics at Optum. "The only way you are going to get value out of AI is to link the clinical or business problem to the organization's overall strategy and make sure you have a rich enough data set to train the model so it generates actionable insights," said Mehrotra. "Making sure you are building and designing your AI effort the right way means putting in the work up front to create a clear understanding of what you are trying to solve so it can be embedded in the decision-making workflow," he said. "Too often, AI projects start with a quest for academic insight."

I think implementing papers is a good approach. Depending on the paper, this might involve just constructing a network of standard layers in the proposed architecture, or it could involve creating custom layers / operations / training loops. Most papers will claim results on standard / openly available datasets, so you can see if you can reproduce their claimed accuracy. Check out Papers with Code: Image Classification subtasks dataset ideas.