Utterance and Objective: Issues in Natural Language Communication

Natural Language Understanding

Research in Progress at the Massachusetts Institute of Technology Artificial Intelligence Laboratory

Every serious technology company now has an Artificial Intelligence team in place. These companies are investing millions into intelligent systems for situation assessment, prediction analysis, learning-based recognition systems, conversational interfaces, and recommendation engines. Companies such as Google, Facebook, and Amazon aren't just employing AI, but have made it a central part of their core intellectual property. As the market has matured, AI is beginning to move into enterprises that will use it but not develop it on their own. They see intelligent systems as solutions for sales, logistics, manufacturing, and business intelligence challenges. They hope AI can improve productivity, automate existing process, provide predictive analysis, and extract meaning from massive data sets.

When Jason Nichols joined GE Global Research in 2011, soon after completing postdoctoral work in organic chemistry at the University of California, Berkeley, he anticipated a long career in chemical research. But after four years creating materials and systems to treat industrial wastewater, Nichols moved to the company's machine-learning lab. This year he began working with augmented reality. Part chemist, part data scientist, Nichols is now exactly the type of hybrid employee crucial to the future of a company working to inject artificial intelligence into its machines and industrial processes. Fifteen years ago, GE's machine operators and technicians monitored its aircraft engines, locomotives, and gas turbines by listening to their clanks and whirs and checking their gauges.

Every day, financial markets and global economies produce a flood of data. As a result, stock traders now have more information about more industries and sectors than ever before. That deluge, combined with the rise of cloud technology, has inspired hedge funds to develop new quantitative strategies that they hope can generate greater returns than the experience and judgement of their own staff. At the Future of Fintech conference hosted by research company CB Insights in New York City, three hedge fund insiders discussed the latest developments in quantitative trading. A session on Tuesday featured Christina Qi, the co-founder of a high-frequency trading firm called Domeyard LP; Jonathan Larkin, an executive from Quantopian, a hedge fund taking a data-driven systematic approach; and Andy Weissman of Union Square Ventures, a venture capital firm that has invested in an autonomous hedge fund. Many of the world's largest hedge funds already rely on powerful computing infrastructure and quantitative methods--whether that's high-frequency trading, incorporating machine learning, or applying data science--to make trades.

Apple's Siri virtual assistant can be used for a variety of tasks on the iPhone and iPad. And soon, the program will be getting some help from its own editor. The company is hiring a Siri "event maven" to join the virtual assistant's team. Apple's job listing says the event maven will help to "provide strategic awareness of cultural happenings in the collective zeitgeist." As an example, Apple mentions themed pop culture holidays like Star Wars Day or Pi Day as events it wants the event maven to bring to Siri's attention. The maven will also help to ensure Siri has coverage and knowledge of general pop culture and trending events.

Apple has acquired German eye tracking company SensoMotoric Instruments (SMI), MacRumors reported Monday. SMI has been working on eye-tracking and vision related technology since its inception in 1991. It became active in the virtual reality segment in 2016 when it announced an eye-tracking development kit for the HTC Vive VR headset. In the report, MacRumors cited the power of attorney signed by German law firm Hiking Kühn Lüer Wojtek giving power to Delaware-based shell company Vineyard Capital Corporation to represent it in all business related to the acquisition. Curiously, the agreement has been signed by Gene Levoff, Apple's vice president of corporate law, which indicates Vineyard Capital Corporation might be one of the shell companies owned by Apple that it uses to hide its acquisitions.

Elon Musk has hired a new director of AI research at Tesla, and it may signal a plan to rethink the way its automated driving works. This week, Musk poached Andrej Karpathy, an expert on vision, deep learning, and reinforcement learning, from OpenAI, a nonprofit that Musk and others are funding that's dedicated to "discovering and enacting the path to safe artificial general intelligence." After Stanford, Karpathy interned with DeepMind, where reinforcement learning is a major focus. Appointing Karpathy a Tesla's director of AI research indicates something else about the challenge of autonomous driving: there's some distance left to go before it's solved (see "What to Know Before You Get in a Self-Driving Car").

An ordinary smart meter gives your local utility useful information about how much energy you are using--every hour, or even as often as every minute. This helps utility planners efficiently adjust electricity generation to meet demand or encourage reductions in demand when necessary. But machine learning systems, looking at that data, can tell something else about your home besides its energy use--they can tell if you are home, or if you are not. That's what University of California at Berkeley researchers Ming Jin, Ruoxi Jia, and Costas Spanos found out. That information, Jin says, is also useful for utilities--they can call or show up to perform necessary maintenance when you are home, and not waste personnel time trying to reach you.