Tianxiang Wu

It’s been a pleasure to talk alongside such great researchers from Google DeepMind and top Taiwanese universities at Google AI Taiwan Academic Forum. (Details at https://lnkd.in/gnbcWZyf) My topic — “Challenges of Applied ML” — covered 4 observations: - We ought to build product, not merely expose LLMs. - Making them useful and trustworthy requires handling input and output properly. (Read more at https://lnkd.in/gcNDC54R) - Making those model useful requires high-quality data for tuning (and evaluation). (More at https://lnkd.in/gyVYQaaD) - There’s many practical deployment considerations, one of the main ones being cost.

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Engineers and scientists develop AI to judge "creativity" of "Breaking," a new event at the Paris Olympics Engineers and scientists have developed an AI-based training system to evaluate the originality of dance moves in "breaking," a new sport featured at the Paris Olympics. The system, named "THE FRESHEST AI," assesses dance performance on several criteria, including originality. Breaking originated in the 1970s South Bronx as a non-violent means of expression and has evolved into a competitive dance sport. The AI technology uses deep learning to analyze and score various dance movements and has previously created systems for judging basic moves and emotional states. While the AI is still in the research phase, its developers aim to improve dancer performance through reflection on their creativity. The breaking competitions at the Olympics will feature 16 male and female athletes each, judged on criteria such as musicality and technique. Additionally, nutritional support for athletes has been highlighted, focusing on the importance of proper diet during intense performances. #AI #Japan https://lnkd.in/gjH-uSQM

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Huge release by Daekeun Kim and Hyo (HK) Choi from our AI GBB team in Korea, sharing Korean language benchmarks for our latest Azure OpenAI model, gpt-4o-mini - like gpt-4o, the model excels on Korean language tasks, in this case mini completely eclipses gpt-35-turbo and nearly achieves gpt-4-turbo performance. The benchmarks and code are open source, extensible beyond our Azure models, so we encourage our customers and the larger community to try it out and even fork/share your own findings - and please let me in the comments what other Asia languages beyond Korean you may be looking to evaluate with your language models.

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From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future Haolin Jin, Linghan Huang, Haipeng Cai, Jun Yan, Bo Li, Huaming Chen University of Sydney 2024 https://lnkd.in/gVZ3r8G7 "As LLMs have demonstrated significant capabilities in tasks such as code generation and vulnerability detection, researchers are increasingly interested in their potential applications. However, the study highlights the limitations of LLMs, including their lack of autonomy and self-improvement, which LLM-based agents aim to address. These agents represent a promising advancement towards achieving Artificial General Intelligence (AGI) by integrating LLMs for decision-making and action-taking. Despite the growing body of research on LLMs in software engineering, there remains a lack of clarity distinguishing between LLMs and LLM-based agents, as well as a need for standardized benchmarks to evaluate these technologies. The survey systematically investigates current practices and solutions related to both LLMs and LLM-based agents, focusing on six key areas: requirement engineering, code generation, autonomous decision-making, software design, test generation, and software maintenance. By reviewing and contrasting the contributions of LLMs and LLM-based agents across these topics, the survey provides insights into their respective tasks, benchmarks, and evaluation metrics. It also discusses the models and benchmarks utilized in this domain, offering a comprehensive analysis of their effectiveness and applications in software engineering. Ultimately, the authors aim to illuminate the path forward for LLM-based agents in software engineering, paving the way for future research and development in this promising area."

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I recently completed a detailed review of the paper "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations" by Lan et al.! This fascinating study presents an innovative approach to language modelling, emphasizing efficiency and effectiveness through a lighter, more parameter-efficient architecture compared to traditional models like BERT. In my review, I delved into the strengths and weaknesses of the paper, discussing its impact and potential areas for improvement. This work has made a significant contribution to the field of natural language processing and has already influenced many advancements in the area. You can read my full review of the paper here: https://lnkd.in/ehxm5jXt #NLP #MachineLearning #ArtificialIntelligence #AI #Research #TechReview #DeepLearning

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