Ideas: Economics and computation with Nicole Immorlica

NICOLE IMMORLICA: Thank you.  HUIZINGA: So before we get into specifics on the big ideas behind your work, let’s find out a little bit about how and why you started doing it. Tell us your research origin story and, if there was one, what big idea or animating “what if” inspired young Nicole and launched a career in theoretical economics and computation research?  IMMORLICA: So I took a rather circuitous route to my current research path. In high school, I […]

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Ideas: The journey to DNA data storage

[MUSIC FADES] GUEST HOST KARIN STRAUSS: I’m your guest host Karin Strauss, a senior principal research manager at Microsoft. For nearly a decade, my colleagues and I—along with a fantastic and talented group of collaborators from academia and industry—have been working together to help close the data creation–data storage gap. We’re producing far more digital information than we can possibly store. One solution we’ve explored uses synthetic DNA as a medium, and over the years, we’ve contributed to steady and […]

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Abstracts: November 14, 2024

TONG WANG: Thank you, Bonnie. KRUFT: Microsoft Research is one of the earliest institutions to apply AI in biomolecular simulation research. Why did the AI for Science team choose this direction, and—with this work specifically, AI2BMD—what problem are you and your coauthors addressing, and why should people know about it? WANG: So as Richard Feynman famously said, “Everything that living things do can be understood in terms of the jigglings and the wigglings of atoms.” To study the mechanisms behind […]

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Collaborators: AI and the economy with Brendan Lucier and Mert Demirer

[TEASER ENDS]  GRETCHEN HUIZINGA: You’re listening to Collaborators, a Microsoft Research Podcast showcasing the range of expertise that goes into transforming mind-blowing ideas into world-changing technologies. I’m Dr. Gretchen Huizinga. [MUSIC FADES]  On today’s episode, I’m talking to Dr. Brendan Lucier, a senior principal researcher in the economics and computation group at Microsoft Research, and Dr. Mert Demirer, an assistant professor of applied economics at the MIT Sloan School of Management. Brendan and Mert are exploring the economic impact of […]

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Tracing the path to self-adapting AI agents

The games industry has long been a frontier of innovation for AI. In the early 2000s, programmers hand-coded neural networks to breathe life into virtual worlds (opens in new tab), creating engaging AI characters (opens in new tab) that interact with players. Fast forward two decades, neural networks have grown from their humble beginnings to colossal architectures with billions of parameters, powering real-world applications like

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Research Focus: Week of April 15, 2024

Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. NEW RESEARCH Appropriate reliance on Generative AI: Research synthesis Appropriate reliance on AI happens when people accept correct AI outputs and reject incorrect ones. It requires users of AI systems to know when to trust the AI and when to trust themselves. But fostering appropriate reliance comes with new complexities when  

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An online application powered by machine learning algorithms

About GIA Greenstocks Investment Advisor (GIA) is an online application powered by machine learning algorithms, that facilitates reliable stock market investments in companies that implement a high Environmental, Social and Governance (ESG) policy. What is ESG Investing Investors interested in ESG are increasingly applying these non-financial factors as part of their analysis process to identify material risks and growth opportunities. ESG metrics are not commonly part of mandatory financial reporting, though companies are increasingly making disclosures in their annual report […]

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An algorithm for exploiting Reinforcement Learning (RL) on Multi-agent Path Finding tasks

PICO is an algorithm for exploiting Reinforcement Learning (RL) on Multi-agent Path Finding tasks.It is developed by the Multi-Agent Artificial Intelligence Lab (MAIL) in East China Normal University and the AI Research Institute in Geekplus Technology Co., Ltd.PICO is constructed based on the framework of PRIMAL:Pathfinding via Reinforcement and Imitation Multi-Agent Learning and focuses more on the collision avoidance rather than manual post-processing when collision occurs.Exploiting the design of decentralized communication and implicit priority in these secenarios benifits better path […]

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Bpe algorithm can finetune tokenizer

“# bpe_algorithm_can_finetune_tokenizer” this is an implyment for https://github.com/huggingface/transformers/issues/15153 I just add tens of lines of code into the py_bpe algorithm.function finetune_tokenizer is main function added. Details can be see in example.py , actuctally it is very simple.the official python library tokenizer is written is rust. I am learning hoping to give a rust version of this code. ps:the_factor_of_new_added_token_divided_unk_number is the only param you should set.hoping can find a auto algorithm to set it. GitHub View Github    

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