MILES O'BRIEN: We're going to get a better picture tomorrow of how strong job creation is when the monthly employment report comes out. But whatever that snapshot looks like, there are concerns about the rise of robotics and automation, and what that means for the future of the work force. Our economics correspondent, Paul Solman, has been exploring that subject. PAUL SOLMAN: In Silicon Valley, author Vivek Wadhwa says he already lives in the future. OK, so, your car can open the garage door and greet you in the driveway?

The California DMV has just updated the list of companies that can test self-driving technologies in the state, and there's one notable addition: Samsung Electronics. In a statement, a company spokesperson confirmed that it's participating in California's Autonomous Vehicle Tester Program. However, he clarified that the Korean conglomerate still has "no plans to enter the car-manufacturing business." Samsung will instead continue to develop sensors that use its AI and deep learning software, as well as other components for autonomous vehicles. Samsung first got a permit to test self-driving technologies in its home country earlier this year.

Property insurers are preparing to fly dozens of drones over homes and businesses to assess damage in the wake of Tropical Storm Harvey, the first widespread use of unmanned aircraft to size up catastrophe claims. Insurers have been testing drones and using them on a small scale since getting Federal Aviation Administration approval in 2015 to use the technology for U.S. inspections. Drones provide aerial images that can help insurance adjusters inspect buildings faster and more safely, executives say, part of a larger industry effort to speed up time-consuming claims. The storm presents the first opportunity for some of these insurers to test their new fleets on a large scale. Harvey, which made landfall in Texas last week and moved to Louisiana on Wednesday, is estimated to have caused up to $20 billion in insurable damage.

Richard Anderson, the tall, handsome actor best known for costarring simultaneously in the popular 1970s television shows "The Six Million Dollar Man" and "The Bionic Woman," has died at age 91. Anderson died of natural causes on Thursday, family spokesman Jonathan Taylor told The Associated Press. "The Six Million Dollar Man" brought a new wave of supernatural heroes to television. Based on the novel "Cyborg" by Martin Caidin, it starred Lee Majors as U.S. astronaut Steve Austin, who is severely injured in a crash. The government saves his life by rebuilding his body with atom-powered artificial limbs and other parts, giving him superhuman strength, speed and other powers.

The recently launched LinkedIn Salary product has been designed with the goal of providing compensation insights to the world's professionals and thereby helping them optimize their earning potential. We describe the overall design and architecture of the statistical modeling system underlying this product. We focus on the unique data mining challenges while designing and implementing the system, and describe the modeling components such as Bayesian hierarchical smoothing that help to compute and present robust compensation insights to users. We report on extensive evaluation with nearly one year of de-identified compensation data collected from over one million LinkedIn users, thereby demonstrating the efficacy of the statistical models. We also highlight the lessons learned through the deployment of our system at LinkedIn.

There are many benefits that can come with the adoption and implementation of artificial intelligence (AI), but experts believe such widespread acceptance will lead to more effective and more dangerous cyberattacks. And that they'll happen soon. At this year's Black Hat cybersecurity conference, 100 attendees were polled about various aspects of artificial intelligence, and 62 percent said they firmly believe AI will be used by hackers within the next twelve months. Despite the idea that AI may be the best defense against such attacks, its increasing availability will likely lead to more advanced hacking techniques. Hackers have already hacked into huge institutions and disrupted the lives of many.

A wide-ranging, UK government-commissioned industrial strategy review of the life sciences sector, conducted by Oxford University's Sir John Bell, has underlined the value locked up in publicly funded data held by the country's National Health Service -- and called for a new regulatory framework to be established in order to "capture for the UK the value in algorithms generated using NHS data". The NHS is a free-at-the-point of use national health service covering some 65 million users -- which gives you an idea of the unique depth and granularity of the patient data it holds. And how much potential value could therefore be created for the nation by utilizing patient data-sets to develop machine learning algorithms for medical diagnosis and tracking. "AI is likely to be used widely in healthcare and it should be the ambition for the UK to develop and test integrated AI systems that provide real-time data better than human monitoring and prediction of a wide range of patient outcomes in conditions such as mental health, cancer and inflammatory disease," writes Bell in the report. His recommendation for the government and the NHS to be pro-active about creating and capturing AI-enabled value off of valuable, taxpayer-funded health data-sets comes hard on the heels of the conclusion of a lengthy investigation by the UK's data protection watchdog, the ICO, into a controversial 2015 data-sharing arrangement between Google-DeepMind and a London-based NHS Trust, the Royal Free Hospitals Trust, to co-develop a clinical task management app.

We consider the problem of noisy matrix completion, in which the goal is to reconstruct a structured matrix whose entries are partially observed in noise. Standard approaches to this underdetermined inverse problem are based on assuming that the underlying matrix has low rank, or is well-approximated by a low rank matrix. In this paper, we propose a richer model based on what we term the "permutation-rank" of a matrix. We first describe how the classical non-negative rank model enforces restrictions that may be undesirable in practice, and how and these restrictions can be avoided by using the richer permutation-rank model. Second, we establish the minimax rates of estimation under the new permutation-based model, and prove that surprisingly, the minimax rates are equivalent up to logarithmic factors to those for estimation under the typical low rank model. Third, we analyze a computationally efficient singular-value-thresholding algorithm, known to be optimal for the low-rank setting, and show that it also simultaneously yields a consistent estimator for the low-permutation rank setting. Finally, we present various structural results characterizing the uniqueness of the permutation-rank decomposition, and characterizing convex approximations of the permutation-rank polytope.

Big data was the first step in creating value from transactions and accumulated data. Now that we have warehouses filled with data the challenge has turned to delivering actionable insights to not just customers, but also employees. Smart businesses have realized that their employees, across teams, can make better decisions and scale expertise by democratizing the access to real-time data insights. These six companies are just a small sampling of how AI is helping companies empower all their employees to be as good as their best ones. H&R Block trains its tax pros to create tax returns for its customers but understanding and interpreting the 74,000 page tax code is daunting.

Unseen data conditions can inflict serious performance degradation on systems relying on supervised machine learning algorithms. Because data can often be unseen, and because traditional machine learning algorithms are trained in a supervised manner, unsupervised adaptation techniques must be used to adapt the model to the unseen data conditions. However, unsupervised adaptation is often challenging, as one must generate some hypothesis given a model and then use that hypothesis to bootstrap the model to the unseen data conditions. Unfortunately, reliability of such hypotheses is often poor, given the mismatch between the training and testing datasets. In such cases, a model hypothesis confidence measure enables performing data selection for the model adaptation. Underlying this approach is the fact that for unseen data conditions, data variability is introduced to the model, which the model propagates to its output decision, impacting decision reliability. In a fully connected network, this data variability is propagated as distortions from one layer to the next. This work aims to estimate the propagation of such distortion in the form of network activation entropy, which is measured over a short- time running window on the activation from each neuron of a given hidden layer, and these measurements are then used to compute summary entropy. This work demonstrates that such an entropy measure can help to select data for unsupervised model adaptation, resulting in performance gains in speech recognition tasks. Results from standard benchmark speech recognition tasks show that the proposed approach can alleviate the performance degradation experienced under unseen data conditions by iteratively adapting the model to the unseen datas acoustic condition.