India's Infosys Ltd. said it plans to hire 10,000 American employees in the next two years, following criticism from the Trump administration that the company and other outsourcing firms are unfairly taking jobs away from U.S. workers. Infosys, which employs about 200,000 people around the world, will expand its local hiring in the U.S. while adding research capabilities. It plans to open four hubs in the country focused on cutting-edge technologies such as artificial intelligence and machine learning, with the first location to open in Indiana in 2017. That center is expected to create 2,000 jobs for American workers by 2021, the company said. The moves come after India's outsourcing firms have come under fierce attack for using foreign workers in place of American employees.

Atlanta Braves coaches and players wearing the No. 42 in honor of Jackie Robinson stand during the national anthem before a game against the San Diego Padres at SunTrust Park. If you ask the media, who took the informal marker of the presidency as an opportunity to dive into Donald Trump's early record in office, it was a lot of talk and international outreach, but not much movement on the domestic issues -- like healthcare and tax reform -- that made him popular as a candidate. If you ask budget chief Mick Mulvaney, as NewsHour's Judy Woodruff did on air last week, the first hundred days was spent undoing damage from the previous administration. As for the chief: The presidency is harder than he thought, he told Reuters. No matter how you feel about the administration's first three-and-a-half months in the Oval, here are five important stories overlooked in the 100-day fanfare that are still worth your attention.

An RQ-4 Global Hawk surveillance drone arrived at Yokota Air Base Monday night, starting a five-month operation in Tokyo, the U.S. base announced. A Defense Ministry source said that the aircraft could be used to conduct surveillance of North Korea, over which political tensions have been ratcheted up over the past month. The aircraft is part of the 69th Reconnaissance Group Detachment 1 and provides near real-time aerial imagery reconnaissance support to U.S. and partner nations, according to the base's website. The aircraft was flown from its home at Andersen Air Force Base, Guam, to avoid typhoons there, the Stars and Stripes quoted Detachment 1 commander Col. Jeremy Fields as saying. The drones and members of the detachment normally travel from their Guam home base to operate out of Misawa Air Base in Aomori Prefecture during the summer to avoid typhoons, but that wasn't possible last year or this year because of renovations to the runway at Misawa, the paper quoted Fields as saying.

In the present paper, we studied a Dynamic Stochastic Block Model (DSBM) under the assumptions that the connection probabilities, as functions of time, are smooth and that at most $s$ nodes can switch their class memberships between two consecutive time points. We estimate the edge probability tensor by a kernel-type procedure and extract the group memberships of the nodes by spectral clustering. The procedure is computationally viable, adaptive to the unknown smoothness of the functional connection probabilities, to the rate $s$ of membership switching and to the unknown number of clusters. In addition, it is accompanied by non-asymptotic guarantees for the precision of estimation and clustering.

A Canadian machine learning outfit has received ringing endorsements with the voices of Donald Trump, Hillary Clinton, and Barack Obama discussing its technology. Montreal-based LyreBird uses machine learning to produce realistic-sounding voice audio from preexisting voices, but you wouldn't know it [f. By analyzing the voices of all three U.S. political figures, it was able to (openly) produce all three fake quotes, and while America may have need such an unlikely alliance of political ideologies, the public is anything but enthused. LyreBird takes a sample of any voice, at least one full minute of speech, and looks at the aspects of the voice that define it, that differentiate it most reliably from any other voice in the world. It then takes these reliable deviations from the platonic ideal of an English voice and tells its voice-synthesis component to make those same adjustments to its audio "wave-forms" as those sound curves are generated.

One of the more interesting panels at last week's Health Datapalooza featured four speakers involved in the application of artificial intelligence to healthcare, including the creation of predictive models. In areas involving massive amounts of information in the diagnostic and genomic space, machine learning is already in use today, and the FDA is starting to approve applications of deep learning. For instance, a company called Arterys recently won FDA approval for its Cardio DL application, which uses deep learning to automate time-consuming analyses and tasks that are performed manually by clinicians today. Although they each come at it from a different angle based on their company's focus, there were several overarching themes the Datapalooza panelists tackled about the application of algorithms in healthcare, including the importance of transparency to getting clinician engagement. Getting buy-in from clinicians is a huge challenge, said Eric Just, a senior vice president for product development at Health Catalyst, which builds analytics and decision support tools for its health system customers.

Could advances in technology, genetics and artificial intelligence lead to a world in which economic inequality turns into biological inequality? asks the historian and writer Yuval Noah Harari. Hunter-gatherers were more equal than subsequent societies. They had very little property, and property is a pre-requisite for long-term inequality. In the 19th and 20th Centuries, however, something changed. Equality became a dominant value in human culture, almost all over the world.

If a quiet theme can be found in Washington's debates over taxes, trade, budgets, and regulations, it is the difficulty of settling on actions that will bring back the high productivity that the United States enjoyed just 20 years ago. Productivity growth, or a rising output per worker, has slowed, as it has in much of the world, reducing living standards. What can bring it back? The first step is for elected leaders to focus on ways to foster innovation, such as investments in education, infrastructure, and research. One model for such a singular political focus is New Zealand.

Last week I came across this all-too-true tweet poking fun at the ubiquity of the Iris dataset. I'm sure many people who've taken a stats course can relate! While Iris may be one of the most popular datasets on Kaggle, our community is bringing much more variety to the ways the world can learn data science. There's so much data to analyze! Me: Cool let me at it.

Swedish startup Gavagai AB, a language technology company that originated at the Swedish Institute of Computer Science, has mastered 40 human languages with its language analysis software. Now, researchers from the KTH Royal Institute of Technology are teaming up with Gavagai AB to take on the language of dolphins, in a project undoubtedly focused as much on testing and expanding the system's capabilities as deciphering the thoughts of dolphins. The team will monitor bottlenose dolphins in a wildlife park and use Gavagai's artificial intelligence (AI) language analysis technology to decode the sounds and, if all goes according to plan, compile a dictionary of dolphin language. The team is confident that they'll be able to do this, thanks not only to the AI capabilities of the Gavagai AB system, but also to the availability of more dolphin data, larger computational resources, and newer recording methods. "We hope to be able to understand dolphins with the help of artificial intelligence technology," KTH adjunct professor and Gavagai co-founder Jussi Karlgren said in an interview with Bloomberg.