In recent years, MIT scholars have helped develop a whole lexicon of science and math terms for use in Haiti's Kreyòl language. Now a collaboration with Google is making those terms readily available to anyone -- an important step in the expansion of Haitian Kreyòl for education purposes. The new project, centered around the MIT-Haiti Initiative, has been launched as part of an enhancement to the Google Translate program. Now anyone using Google Translate can find an extensive set of Kreyòl terms, including recent coinages, in the science, technology, engineering, and math (STEM) disciplines. "In the past five or six years, we've witnessed quite a paradigm shift in the way people in Haiti talk about and use Kreyòl," says Michel DeGraff, a professor of linguistics at MIT and director of the MIT-Haiti Initiative.

Until recently, the most famous thing that Sophia the robot had ever done was beat Jimmy Fallon a little too easily in a nationally televised game of rock-paper-scissors. But now, the advanced artificial intelligence robot -- which looks like Audrey Hepburn, mimics human expressions and may be the grandmother of robots that solve the world's most complex problems -- has a new feather in her cap: The kingdom of Saudi Arabia officially granted citizenship to the humanoid robot last week during a program at the Future Investment Initiative, a summit that links deep-pocketed Saudis with inventors hoping to shape the future. Sophia's recognition made international headlines -- and sparked an outcry against a country with a shoddy human rights record that has been accused of making women second-class citizens. "Thank you to the Kingdom of Saudi Arabia," the country's newest citizen said. "It is historic to be the first robot in the world granted citizenship."

Advances in artificial intelligence (AI) and machine learning (ML) are already demonstrating great utility in a variety of domains that point to great opportunities for adoption in a wide variety of military applications. For this reason, data will become the life-blood of many AI and ML enabled technologies. Yet, developing trusted data sets for the purposes of training and testing AI and ML applications will become a central issue to eliciting predictable behaviors of such technology in order to foster trust in autonomous systems. Therefore, the US Department of Defense (DoD) cannot sit idle while AI and ML developments remain forthcoming. Rather, the US military can begin laying the ground work for constructing training data in domain specific ways that will help convergence with AI and ML in the future.

Government regulations are critical to understanding how to do business with a government entity and receive other benefits. However, government regulations are also notoriously long and organized in ways that can be confusing for novice users. Developing cognitive assistance tools that remove some of the burden from human users is of potential benefit to a variety of users. The volume of data found in United States federal government regulation suggests a multiple-step approach to process the data into machine-readable text, create an automated legal knowledge base capturing various facts and rules, and eventually building a legal question and answer system to acquire understanding from various regulations and provisions. Our work discussed in this paper represents our initial efforts to build a framework for Federal Acquisition Regulations System (Title 48, Code of Federal Regulations) in order to create an efficient legal knowledge base representing relationships between various legal elements, semantically similar terminologies, deontic expressions and cross-referenced legal facts and rules.

Both the ethics of autonomous systems and the problems of their technical implementation have by now been studied in some detail. Less attention has been given to the areas in which these two separate concerns meet. This paper, written by both philosophers and engineers of autonomous systems, addresses a number of issues in machine ethics that are located at precisely the intersection between ethics and engineering. We first discuss the main challenges which, in our view, machine ethics posses to moral philosophy. We them consider different approaches towards the conceptual design of autonomous systems and their implications on the ethics implementation in such systems. Then we examine problematic areas regarding the specification and verification of ethical behavior in autonomous systems, particularly with a view towards the requirements of future legislation. We discuss transparency and accountability issues that will be crucial for any future wide deployment of autonomous systems in society. Finally we consider the, often overlooked, possibility of intentional misuse of AI systems and the possible dangers arising out of deliberately unethical design, implementation, and use of autonomous robots.

WASHINGTON, DC - JUNE 19: U.S. President Donald Trump welcomes members of his American Technology Council in the State Dining Room of the White House June 19, 2017 in Washington, DC. According to the White House, the council's goal is'to explore how to transform and modernize government information technology.' (Photo by Chip Somodevilla/Getty Images) This week, the newly created White House American Technology Council convened a series of policy discussions on the emerging Internet of Things (IoT), something I have discussed here in the past. The news is a welcome and timely development, given the breakneck speed at which IoT technologies are progressing – from driverless cars, to remote surgeries, to 21st Century Smart Cities. In the not-so-distant future, the IoT could be integrated into essentially every aspect of consumers' daily lives. But there's a serious threat lurking to IoT advancements: fair access to essential technology standards – which are included in many consumer products like refrigerators, smartphones and TVs – so that all innovators have a chance to participate in this technological revolution.

In 1983, the IBM PC XT debuted with 128K of RAM and a 10MB hard disk. In that same year, the first mobile phone debuted weighing about 2.5 pounds and with a $4,000 price tag. Fast forward to today and the average person unlocks their smartphone 76-80 times a day and relies on it for every aspect of their lives. These amazing pieces of hardware are millions of times more capable than all of NASA's computing power in the 1960s. Now that we have a supercomputer that never leaves people's sides, maybe it's time that we do some more innovation and see how that device can be used for "mobile health".

Spoiler alert: Big Brother actually is watching, but not in the way you think. These days every federal agency is connected through a wide array of devices and sensors that collect data on many aspects of their operations. With the help of IBM Watson Internet of Things (IoT), this data is helping customers lead healthier and safer lives, and helping businesses remain competitive and become more profitable. The key question: How can we turn that IoT data into opportunity? That exact question and many others were discussed at the recent Connected Government event in Washington, D.C.

Canada has produced several big breakthroughs in artificial intelligence in recent years, and its government is keen to establish the country as a global epicenter of AI. The country's prime minister, Justin Trudeau, also hopes that the technology will learn Canadian values as it grows up. Speaking at a major AI event in Toronto today, Trudeau demonstrated an impressive enthusiasm for AI and machine learning, at one point even taking a stab at describing the concept of deep reinforcement learning, an approach that lets computers learn to do complex things that can't be programmed manually (see "10 Breakthrough Technologies 2017: Reinforcement Learning"). Both deep reinforcement learning and deep neural networks, which the method exploits, were pioneered by researchers working at Canadian universities. The country's government is now investing in big efforts to spur more AI research.

Earlier this year, Forbes included Expert System in the list of companies forging the next generation of Artificial Intelligence systems. "The impact of AI in the near future will be even more decisive," said Stefano Spaggiari, CEO, Expert System. "Artificial Intelligence allows companies to overcome the purely'numerical' management of data by adding the ability to understand and manage information in a way that captures the maximum business value." Keynote speaker Boris Evelson, VP and Principal Analyst, Forrester Research, noted that one of the most important macro-applications of Artificial Intelligence in the business world is for understanding and making use of the incredible volumes of information that companies already collect. In fact, the current rate of growth in the adoption of AI technologies is comparable to a'tsunami'.