The team at the IBM-MIT Watson AI Lab presented a new study at the AAAI Conference on AI, Ethics, and Society on “Learning Occupational Task-Shares Dynamics for the Future of Work” that shows how to predict changes in the economy’s demand for different tasks.
IBM Research had 21 papers accepted to SPIE, and throughout the four-day conference IBM researchers will present on topics ranging from EUV lithography, patterning materials, etch, selective deposition, and novel device integration.
One year ago, we announced the creation of the IBM Research AI Hardware Center, a global research hub headquartered in Albany, New York. Building on work of the last few years, the launch of the Center initiated the next phase in a long-term effort to combine evolving, fundamental advances in AI with new computing accelerators, […]
At the thirty-fourth AAAI conference on Artificial Intelligence (AAAI-20), we will present two papers on recent advancements in Project Debater on two core tasks, both utilizing BERT.
IBM Research AI is leading the push to develop new tools that enable AI to process and understand natural language. Our goal: empower enterprises to deploy and scale sophisticated AI systems that leverage natural language processing (NLP) with greater accuracy and efficiency, while requiring less data and human supervision.
IBM Research will present more than fifty technical papers at AAAI-20, as well a rich set of demos of our latest work, reflecting our focus on key areas of AI research including AutoAI, mastering language, planning, computational argumentation, the future of work and security.
AutoAI is a novel approach of designing, training and optimizing machine learning models automatically. With AutoAI, anyone could soon build machine learning pipelines from raw data directly, without writing complex code and performing tedious tuning and optimization, to then automate complicated, labor-intensive tasks. Several IBM papers selected for the AAAI-20 conference in New York demonstrate the value of AutoAI and different approaches to it in great detail.
IBM’s leadership in AI continued in earnest in 2019, which was notable for a growing focus on critical topics such as making trustworthy AI work in practice, creating new AI engineering paradigms to scale AI for a broader use, and continuing to advance core AI capabilities.
A new paper from the MIT-IBM Watson AI Lab and MIT CSAIL considers how the optimal transport can efficiently “summarize” this uncertainty for a class of popular decision making problems.