The MIT-IBM Watson AI Lab is focused on fundamental artificial intelligence (AI) research with the goal of propelling scientific breakthroughs that unlock the potential of AI.
Artificial intelligence and machine learning are playing larger roles in software, from data consumption and analysis to test automation and user experience. These cognitive services will drive the next wave of technology innovation. And industry heavyweights Facebook, IBM and Microsoft are leading the charge with new investments for innovation.
IBM yesterday announced plans to create an AI research partnership with the Massachusetts Institute of Technology to unlock AI’s potential by advancing hardware, software and algorithms around deep learning, the company said in the announcement.
IBM will make a 10-year, $240 million commitment to the MIT-IBM Watson AI Lab, which will be located in Cambridge, Mass., where IBM has a research lab and where MT’s campus is located. Dario Gil, IBM Research VP of AI, and Dean Anantha P. Chandrakasan of MIT’s School of Engineering, will co-chair the new lab. The project will draw from the expertise of more than 100 AI scientists and MIT professors and students.
“The field of artificial intelligence has experienced incredible growth and progress over the past decade. Yet today’s AI systems, as remarkable as they are, will require new innovations to tackle increasingly difficult real-world problems to improve our work and lives,” said Dr. John Kelly III, IBM senior vice president, Cognitive Solutions and Research, in a statement. “The extremely broad and deep technical capabilities and talent at MIT and IBM are unmatched, and will lead the field of AI for at least the next decade.”
Among the efforts the lab team will pursue are creating AI algorithms that can tackle more complex problems, understanding the physics of AI, how AI applies to vertical industries, and delivering societal and economic benefits through AI.
Meanwhile, Microsoft yesterday announced the Open Neural Network Exchange in conjunction with Facebook. Microsoft’s Cognitive Toolkit, along with Caffe2 and PyTorch, will all support the open-source ONNX.
According to Microsoft’s announcement, the ONNX representation of neural networks will provide framework interoperability, allowing developers to use their preferred tools while moving between frameworks. ONNX also offers shared optimization, so organizations looking to improve the performance of their neural networks can do so to multiple frameworks at once by simply targeting the ONNX representation.
ONNX, the announcement explained, “provides a definition of an extensible computation graph model, as well as definitions of built-in operators and standard data types.” Initially, the project is focused on inferencing capabilities.
ONNX code and documentation are available on GitHub.
Digital operations management company PagerDuty is using machine learning and advanced response automation to help businesses orchestrate the correct response to any situation. Among the new capabilities in PagerDuty’s platform are the ability to group related alerts to provide context, the ability to recognize similar incidents with the context of who dealt with the similar issue in the past and what steps were taken to resolve it, the ability to design automated response patterns, and more.
“Today’s dynamic digital business climate has exponentially increased both opportunity for growth and downside risks to mitigate. The latest Digital Operations Management capabilities announced [yesterday] – machine learning and automation – tackle the real-time, all-the-time demands of consumers and business, translating complex events and signals into actionable insights, and orchestrating teams across businesses in service or revenue and productivity,” said Jennifer Tejada, CEO of PagerDuty.
Lastly, Cloudera yesterday announced the acquisition of Fast Forward Labs, an applied research and advisory services company specializing in machine learning and applied AI.
Now known as Cloudera Fast Forward Labs, the company is focused on practical research into data science, and applying that research to broad business problems.