As the investigation into last week's fatal crash where an autonomous Uber SUV struck and killed a pedestrian in Tempe, AZ, the state's governor has suspended Uber's permission to test its cars there. While the company had already halted testing nationwide after the test, this is a turnaround after Gov. Doug Ducey (R) had welcomed self-driving testing from many companies with open arms in a relationship that stretches back a few years. Just a few weeks ago Ducey updated his autonomous vehicle executive order to allow testing without a safety driver. In this crash, there was a test driver behind the wheel, but neither they nor the car reacted in time to avoid a woman who crossed in front of the car. In a letter to Uber, the Wall Street Journal reports Ducey said "my expectation is that public safety is also the top priority for all who operate this technology in the state of Arizona..The incident that took place on March 18 is an unquestionable failure to comply with this expectation."

Tempe police have released two angles of a fatal crash involving a self-driving Uber SUV and a pedestrian on March 18, 2018. Gov. Doug Ducey on Monday sent a letter to the CEO of Uber saying he was suspending the ride-sharing company's tests of self-driving cars on Arizona roads following a fatal accident March 18 in Tempe. That night, a self-driving Volvo operated by Uber struck and killed a pedestrian who was jaywalking on Mill Avenue near Arizona State University. Uber pulled its self-driving cars off the roads, but after video was released of the accident last week, Ducey said he needed to act to protect the safety of Arizonans. "Improving public safety has always been the emphasis of Arizona's approach to autonomous vehicle testing, and my expectation is that public safety is also the top priority for all who operate this technology in the state of Arizona," Ducey said in his letter to CEO Dara Khosrowshahi.

The ability to manipulate and understand data is increasingly critical to discovery and innovation. As a result, we see the emergence of a new field--data science--that focuses on the processes and systems that enable us to extract knowledge or insight from data in various forms and translate it into action. In practice, data science has evolved as an interdisciplinary field that integrates approaches from such data-analysis fields as statistics, data mining, and predictive analytics and incorporates advances in scalable computing and data management. But as a discipline, data science is only in its infancy. The challenge of developing data science in a way that achieves its full potential raises important questions for the research and education community: How can we evolve the field of data science so it supports the increasing role of data in all spheres? How do we train a workforce of professionals who can use data to its best advantage? What should we teach them? What can government agencies do to help maximize the potential of data science to drive discovery and address current and future needs for a workforce with data science expertise?

I was fortunate to enter computing in the era of site funding by the Defense Advanced Research Projects Administration (DARPA). "DARPA sites" from 1960s through the mid-1990s had sustained investment of $10M/year (inflation adjusted). The talent and vision combined with sustained funding at scale enabled the undertaking of bold transformative ideas, such as timesharing, entire new operating systems, novel computing system architectures, new models of networking, and a raft of exciting artificial intelligence technologies--in robotics, self-driving cars, computer vision, and more. For example, I joined Arvind's Tagged-token Dataflow Computing project as an MIT graduate student. This single project, which involved a dozen graduate and undergraduates plus staff and faculty, garnered $13.5M support with a run rate exceeding $4M/year.

The Federal Trade Commission (FTC) confirmed Monday that it is investigating Facebook's privacy practices following reports that the company allowed personal data to be extracted from users without expressed permission. The investigation was spurred by reports that Facebook allowed political data analytics firm Cambridge Analytica to gain access to the personal information of more than 50 million Facebook users and use that data to craft targeted political advertising campaigns. "The FTC takes very seriously recent press reports raising substantial concerns about the privacy practices of Facebook," Tom Pahl, Acting Director of the Federal Trade Commission's Bureau of Consumer Protection, said in a statement. "Today, the FTC is confirming that it has an open non-public investigation into these practices." The FTC previously declined to comment on an investigation into Facebook.

Artificial intelligence is currently a white-hot buzzword in healthcare. Both in everyday conversations and in large context settings like HIMSS, the term is guaranteed to generate interest. But what can it actually achieve in the world of healthcare? This question will be a topic of discussion at the upcoming MedCity INVEST conference in Chicago. IDx chairman and CEO Gary Seamans, who will be speaking at the event, commented on the popular nature of the technology.

Arizona governor Doug Ducey suspended Uber's self-driving vehicle testing on Monday following a pedestrian fatality in a Phoenix suburb last week. Ducey told Uber's chief executive Dara Khosrowshahi that video footage of the crash raised concerns about the company's ability to safely test its technology in Arizona. He said he expects public safety to be the top priority for those who operate self-driving cars. "The incident that took place on 18 March is an unquestionable failure to comply with this expectation," Ducey said. The move by the Republican governor marks a major step back from his embrace of self-driving vehicles.

FILE - In this March 1, 2017 file photo, people enter the headquarters of Uber in San Francisco. Uber suspended all of its self-driving testing Monday, March 19, 2018, after what is believed to be the first fatal pedestrian crash involving the vehicles. The testing has been going on for months in the Phoenix area, Pittsburgh, San Francisco and Toronto as automakers and technology companies compete to be the first with the technology. Uber's testing was halted after police in a Phoenix suburb said one of its self-driving vehicles struck and killed a pedestrian overnight Sunday.

Recently, AAAI coordinated and The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) cosigned a statement with CRA, and the Thirty-First Conference on Innovative Applications of Artificial expressing concern about the proposed Intelligence (IAAI-19), will be held in Honolulu, Hawaii, USA, January tax bill and its ramifications for graduate 27 - February 1, 2019. The technical conference will continue its student stipends. Other organizational 3.5-day schedule, preceded by the workshop and tutorial programs.

We present memory-efficient and scalable algorithms for kernel methods used in machine learning. Using hierarchical matrix approximations for the kernel matrix the memory requirements, the number of floating point operations, and the execution time are drastically reduced compared to standard dense linear algebra routines. We consider both the general $\mathcal{H}$ matrix hierarchical format as well as Hierarchically Semi-Separable (HSS) matrices. Furthermore, we investigate the impact of several preprocessing and clustering techniques on the hierarchical matrix compression. Effective clustering of the input leads to a ten-fold increase in efficiency of the compression. The algorithms are implemented using the STRUMPACK solver library. These results confirm that --- with correct tuning of the hyperparameters --- classification using kernel ridge regression with the compressed matrix does not lose prediction accuracy compared to the exact --- not compressed --- kernel matrix and that our approach can be extended to $\mathcal{O}(1M)$ datasets, for which computation with the full kernel matrix becomes prohibitively expensive. We present numerical experiments in a distributed memory environment up to 1,024 processors of the NERSC's Cori supercomputer using well-known datasets to the machine learning community that range from dimension 8 up to 784.