Monthly Archives: December 2017

Protecting urban infrastructure against cyberterrorism

While working for the global management consulting company Accenture, Gregory Falco discovered just how vulnerable the technologies underlying smart cities and the “internet of things” — everyday devices that are connected to the internet or a network — are to cyberterrorism attacks.

“What happened was, I was telling sheiks and government officials all around the world about how amazing the internet of things is and how it’s going to solve all their problems and solve sustainability issues and social problems,” Falco says. “And then they asked me, ‘Is it secure?’ I looked at the security guys and they said, ‘There’s no problem.’ And then I looked under the hood myself, and there was nothing going on there.”

Falco is currently transitioning into the third and final year of his PhD within the Department of Urban Studies and Planning (DUSP). Currently, his is carrying out his research at the Computer Science and Artificial Intelligence Laboratory (CSAIL). His focus is on cybersecurity for urban critical infrastructure, and the internet of things, or IoT, is at the center of his work. A washing machine, for example, that is connected to an app on its owner’s smartphone is considered part of the IoT. There are billions of IoT devices that don’t have traditional security software because they’re built with small amounts of memory and low-power processors. This makes these devices susceptible to cyberattacks and may provide a gate for hackers to breach other devices on the same network.

Falco’s concentration is on industrial controls and embedded systems such as automatic switches found in subway systems.

“If someone decides to figure out how to access a switch by hacking another access point that is communicating with that switch, then that subway is not going to stop, and people are going to die,” Falco says. “We rely on these systems for our life functions — critical infrastructure like electric grids, water grids, or transportation systems, but also our health care systems. Insulin pumps, for example, are now connected to your smartphone.”

Citing real-world examples, Falco notes that Russian hackers were able to take down the Ukrainian capital city’s electric grid, and that Iranian hackers interfered with the computer-guided controls of a small dam in Rye Brook, New York.

Falco aims to help combat potential cyberattacks through his research. One arm of his dissertation, which he is working on with renown negotiation Professor Lawrence Susskind, is aimed at conflict negotiation, and looks at how best to negotiate with cyberterrorists. Also, with CSAIL Principal Research Scientist Howard Shrobe, Falco seeks to determine the possibility of predicting which control-systems vulnerabilities could be exploited in critical urban infrastructure. The final branch of Falco’s dissertation is in collaboration with NASA’s Jet Propulsion Laboratory. He has secured a contract to develop an artificial intelligence-powered automated attack generator that can identify all the possible ways someone could hack and destroy NASA’s systems.

“What I really intend to do for my PhD is something that is actionable to the communities I’m working with,” Falco says. “I don’t want to publish something in a book that will sit on a shelf where nobody would read it.”

Million investment in new lab with MIT to advance AI hardware, software, and algorithms

IBM and MIT today announced that IBM plans to make a 10-year, $240 million investment to create the MIT–IBM Watson AI Lab in partnership with MIT. The lab will carry out fundamental artificial intelligence (AI) research and seek to propel scientific breakthroughs that unlock the potential of AI. The collaboration aims to advance AI hardware, software, and algorithms related to deep learning and other areas; increase AI’s impact on industries, such as health care and cybersecurity; and explore the economic and ethical implications of AI on society. IBM’s $240 million investment in the lab will support research by IBM and MIT scientists.

The new lab will be one of the largest long-term university-industry AI collaborations to date, mobilizing the talent of more than 100 AI scientists, professors, and students to pursue joint research at IBM’s Research Lab in Cambridge, Massachusetts — co-located with the IBM Watson Health and IBM Security headquarters in Kendall Square — and on the neighboring MIT campus.

The lab will be co-chaired by Dario Gil, IBM Research VP of AI and IBM Q, and Anantha P. Chandrakasan, dean of MIT’s School of Engineering. (Read a related Q&A with Chandrakasan.) IBM and MIT plan to issue a call for proposals to MIT researchers and IBM scientists to submit their ideas for joint research to push the boundaries in AI science and technology in several areas, including:

AI algorithms: Developing advanced algorithms to expand capabilities in machine learning and reasoning. Researchers will create AI systems that move beyond specialized tasks to tackle more complex problems and benefit from robust, continuous learning. Researchers will invent new algorithms that can not only leverage big data when available, but also learn from limited data to augment human intelligence.
Physics of AI: Investigating new AI hardware materials, devices, and architectures that will support future analog computational approaches to AI model training and deployment, as well as the intersection of quantum computing and machine learning. The latter involves using AI to help characterize and improve quantum devices, and researching the use of quantum computing to optimize and speed up machine-learning algorithms and other AI applications.
Application of AI to industries: Given its location in IBM Watson Health and IBM Security headquarters in Kendall Square, a global hub of biomedical innovation, the lab will develop new applications of AI for professional use, including fields such as health care and cybersecurity. The collaboration will explore the use of AI in areas such as the security and privacy of medical data, personalization of health care, image analysis, and the optimum treatment paths for specific patients.
Advancing shared prosperity through AI: The MIT–IBM Watson AI Lab will explore how AI can deliver economic and societal benefits to a broader range of people, nations, and enterprises. The lab will study the economic implications of AI and investigate how AI can improve prosperity and help individuals achieve more in their lives.
In addition to IBM’s plan to produce innovations that advance the frontiers of AI, a distinct objective of the new lab is to encourage MIT faculty and students to launch companies that will focus on commercializing AI inventions and technologies that are developed at the lab. The lab’s scientists also will publish their work, contribute to the release of open source material, and foster an adherence to the ethical application of AI.

“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,” says John Kelly III, IBM senior vice president, Cognitive Solutions and Research. “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.”

Reduce power consumption of data center “caches” by 90 percent

Most modern websites store data in databases, and since database queries are relatively slow, most sites also maintain so-called cache servers, which list the results of common queries for faster access. A data center for a major web service such as Google or Facebook might have as many as 1,000 servers dedicated just to caching.

Cache servers generally use random-access memory (RAM), which is fast but expensive and power-hungry. This week, at the International Conference on Very Large Databases, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are presenting a new system for data center caching that instead uses flash memory, the kind of memory used in most smartphones.

Per gigabyte of memory, flash consumes about 5 percent as much energy as RAM and costs about one-tenth as much. It also has about 100 times the storage density, meaning that more data can be crammed into a smaller space. In addition to costing less and consuming less power, a flash caching system could dramatically reduce the number of cache servers required by a data center.

The drawback to flash is that it’s much slower than RAM. “That’s where the disbelief comes in,” says Arvind, the Charles and Jennifer Johnson Professor in Computer Science Engineering and senior author on the conference paper. “People say, ‘Really? You can do this with flash memory?’ Access time in flash is 10,000 times longer than in DRAM [dynamic RAM].”

But slow as it is relative to DRAM, flash access is still much faster than human reactions to new sensory stimuli. Users won’t notice the difference between a request that takes .0002 seconds to process — a typical round-trip travel time over the internet — and one that takes .0004 seconds because it involves a flash query.

Keeping pace

The more important concern is keeping up with the requests flooding the data center. The CSAIL researchers’ system, dubbed BlueCache, does that by using the common computer science technique of “pipelining.” Before a flash-based cache server returns the result of the first query to reach it, it can begin executing the next 10,000 queries. The first query might take 200 microseconds to process, but the responses to the succeeding ones will emerge at .02-microsecond intervals.

Even using pipelining, however, the CSAIL researchers had to deploy some clever engineering tricks to make flash caching competitive with DRAM caching. In tests, they compared BlueCache to what might be called the default implementation of a flash-based cache server, which is simply a data-center database server configured for caching. (Although slow compared to DRAM, flash is much faster than magnetic hard drives, which it has all but replaced in data centers.) BlueCache was 4.2 times as fast as the default implementation.

Joining Arvind on the paper are first author Shuotao Xu and his fellow MIT graduate student in electrical engineering and computer science Sang-Woo Jun; Ming Liu, who was an MIT graduate student when the work was done and is now at Microsoft Research; Sungjin Lee, an assistant professor of computer science and engineering at the Daegu Gyeongbuk Institute of Science and Technology in Korea, who worked on the project as a postdoc in Arvind’s lab; and Jamey Hicks, a freelance software architect and MIT affiliate who runs the software consultancy Accelerated Tech.

The boundaries of research on artificial intelligence

MIT and IBM jointly announced today a 10-year agreement to create the MIT–IBM Watson AI Lab, a new collaboration for research on the frontiers of artificial intelligence. Anantha Chandrakasan, the dean of MIT’s School of Engineering, who led MIT’s work in forging the agreement, sat down with MIT News to discuss the new lab.

Q: What does the new collaboration make possible?

A: AI is everywhere. It’s used in just about every domain you can think of and is central to diverse fields, from image and speech recognition, to machine learning for disease detection, to drug discovery, to financial modeling for global trade.

This new collaboration will bring together researchers working on the core algorithms and devices that make such applications possible, enabling the pursuit of jointly defined projects. We will focus on basic research and applications, but with new resources and colleagues and tremendous access to real-world data and computational power.

The project will support many different pursuits, from scholarship, to the licensing of technology, to the release of open-source material, to the creation of startups. We hope to use this new lab as a template for many other interactions with industry.

We’ll issue a call for proposals to all researchers at MIT soon; this new lab will hope to attract interest from all five schools. I’ll co-chair the lab alongside Dario Gil, IBM Research VP of AI and IBM Q, and Dario and I will name co-directors from MIT and IBM soon.

Q: What are the key areas of research that this lab will focus on?

A: The main areas of focus are AI algorithms, the application of AI to industries (such as biomedicine and cybersecurity), the physics of AI, and ways to use AI to advance shared prosperity.

The core AI theme will focus on not only advancing deep-learning algorithms and other approaches, but also the use of AI to understand and enhance human intelligence. One of the goals is to build machine learning and AI systems that excel at both narrow tasks and the human skills of discovery and explanation. In terms of applications, there are some particular targets we have in mind, including being able to detect cancer (e.g., by using AI with imaging in radiology to automatically detect breast cancer) well before we do now.

This new collaboration will also provide a framework for aggregating knowledge from different domains. For example, a method that we use for cancer detection might also be useful in detecting other diseases, or the tools we develop to enable this might end up being useful in a non-biomedical context.

The work on the physics of AI will include quantum computing and new kinds of materials, devices, and architectures that will support machine-learning hardware. This will require innovations not only in the way that we think about algorithms and systems, but also at the physical level of devices and materials at the nanoscale.

To that end, IBM will become a founding member of MIT.nano, our new nanotechnology research, fabrication, and imaging facility that is set to open in the summer of 2018.

Lastly, researchers will explore how AI can increase prosperity broadly. They will also develop approaches to mitigate data bias and to ensure that AI systems behave ethically when deployed.