NEW YORK - The Pentagon is adopting new ethical principles as it prepares to accelerate its use of artificial intelligence technology on the battlefield. The new principles call for people to "exercise appropriate levels of judgment and care" when deploying and using AI systems, such as those that scan aerial imagery to look for targets. Defense Department officials outlined the new approach Monday. "The United States, together with our allies and partners, must accelerate the adoption of AI and lead in its national security applications to maintain our strategic position, prevail on future battlefields, and safeguard the rules-based international order," said Defense Secretary Mark Esper. It follows recommendations made last year by the Defense Innovation Board, a group led by former Google CEO Eric Schmidt.

All Arm trademarks featured in this course are registered or unregistered trademarks of Arm Limited (or its subsidiaries) in the US or elsewhere. Welcome to the Deep Learning From Ground Up on ARM Processors course. We are going to embark on a very exciting journey together. We are going to learn how to build deep neural networks from scratch on our microcontrollers. We shall begin by learning the basics of deep learning with practical code showing each of the basic building blocks that end up making a giant deep neural network.

Signalling the resistance of the robotic process automation market despite a poorly performing economy, RPA solutions provider Blue Prism announced that it has raised over $124 million in equity financing at a valuation of around $1.24 billion. According to CEO Jason Kingdon, a bulk of the capital will go into bolstering the company's automation suite. Just last month, Blue Prism had launched a COVID-19 response task team to perform a variety of functions. This team has been working with the UK's National Health Service, University of California, and the Leeds Building Society to automate personnel, vaccine development, finance, and related health care support functions. "Our RPA solution is more important than ever in this environment," Kingdon said in a report. "The impact of this pandemic is unknown, and as a result, we are taking action to invest and reinforce our product differentiation in preparation for the opportunities that will occur," he added.

The SETI Institute, a Silicon Valley-based nonprofit that seeks to explore and explain the nature and origins of life in the universe, is gearing up to host the fifth iteration of its competitive, NASA-funded summer program, the Frontier Development Lab. FDL brings together a diverse cadre of researchers each year since its inception to rapidly leverage artificial intelligence, machine learning and advanced computing capabilities--all to ultimately help America's space agency accelerate its own research and discoveries. While SETI envelops the "search for extraterrestrial intelligence," its inside efforts touch a range of areas across space, science and beyond. But the 2020 program might run a little differently than those that came before. "Now, what's interesting is, we may--for the first time actually--undertake the program virtually because of the COVID-19 pandemic," Bill Diamond, president and CEO of the SETI Institute, told Nextgov recently. "All indications are that this is going to be with us through at least the early part of the summer, and it may preclude the in-person working system that normally is characterized by the FDL program."

Because you belong at Twilio. Twilio seeks a Machine Learning Engineer to be a key leader in defining a new product offering at Twilio in the customer engagement space. The person in this role will be critical in shaping Twilio's data and intelligence strategy, which will empower our customers to create highly personalized communications and experiences for their contacts. Come be part of a team that's building a set of ML-driven APIs that deliver intelligent audience and personalization recommendations. Increasingly, we're hearing from our B2C customers that they're struggling to harness the massive amounts of valuable data they generate, much of which stems from the communications we help them send.

Machine learning experts working at Google Health have published a new study in tandem with the University of California San Francisco's (UCSF) computational health sciences department that describes a machine learning model the researchers built that can anticipate normal physician drug prescribing patterns, using a patient's electronic health records (EHR) as input. That's useful because around 2% of patients who end up hospitalized are affected by preventable mistakes in medication prescriptions, some instances of which can even lead to death. The researchers describe the system as working in a similar manner to automated, machine learning-based fraud detection tools that are commonly used by credit card companies to alert customers of possible fraudulent transactions: They essentially build a baseline of what's normal consumer behavior based on past transactions, and then alert your bank's fraud department or freeze access when they detect a behavior that is not in line with an individual's baseline behavior. Similarly, the model trained by Google and UCSF worked by identifying any prescriptions that "looked abnormal for the patient and their current situation." That's a much more challenging proposition in the case of prescription drugs versus consumer activity -- because courses of medication, their interactions with one another and the specific needs, sensitivities and conditions of any given patient all present an incredibly complex web to untangle.

Amazon A2I helps developers add human review for model predictions to new or existing applications using reviewers from Mechanical Turk, third party vendors, or their own employees. Amazon A2I makes it easier for developers to build the human review system, structure the review process, and manage the human review workforce. For example, developers could use Amazon A2I to quickly spin up and manage a workforce of humans to review and validate the accuracy of machine learning predictions for an application that extracts financial information from scanned mortgage documents or an application that uses image recognition to identify counterfeit items online, so that the quality of results improve over time. There are no upfront commitments to use Amazon A2I, and users pay only for each review needed. Today, machine learning provides highly accurate predictions (known as "inferences") for a variety of use cases, including identifying objects in images, extracting text from scanned documents, or transcribing and understanding spoken language.

Normally, it takes pricey equipment and expertise to create an accurate 3-D reconstruction of someone's face that's realistic and doesn't look creepy. Now, Carnegie Mellon University researchers have pulled off the feat using video recorded on an ordinary smartphone. Using a smartphone to shoot a continuous video of the front and sides of the face generates a dense cloud of data. A two-step process developed by CMU's Robotics Institute uses that data, with some help from deep learning algorithms, to build a digital reconstruction of the face. The team's experiments show that their method can achieve sub-millimeter accuracy, outperforming other camera-based processes.

The algorithmic revolution is here, and nations are losing control of not only their understanding of the potential impact of artificial intelligence but also the governance model that enforced accountability on the advances in science and technology over the years at all levels. While each new technology innovation claims its territory for the economic advances in the human ecosystem with significant ramifications across cyberspace, geospace and/or space (CGS), the rise of artificial intelligence (AI) has not only undermined governance, management, and growth models, but it has also broken all barriers to boundaries defined by human decision makers. In addition, it is both blurring the boundaries between human intelligence and machine intelligence, and the boundaries between man and machine and real and fake. As a result, the power dynamics are shifting away from the select few across nations (and is moving away from humans entirely to algorithms)--re-defining the criteria upon which geopolitics was framed--and thereby threatening the foundations of global peace and security. Since the beginning of the technological age, each new idea, innovation, and invention has helped humans across nations usher in a new era of economic growth, changing the fundamentals of respective nations and their security.

The study of causal effects of an intervention or treatment on a specific outcome based on observational data is a fundamental problem in many applications. Examples include understanding the effects of massive wildfires on a person's mental health, of teaching methods on a student's employability, or of disease outbreaks on the global stock market. A critical problem of causal inference from observational data is confounding. A variable that affects both the treatment and the outcome is known as a confounder of the treatment effects on the outcome. Standard ways to measure observable confounders include propensity score matching and their variants (Rubin, 2005). However, if a confounder is hidden, the treatment effect on the outcome cannot be directly estimated without further assumptions (Pearl, 2009; Louizos et al., 2017). For example, household income, which cannot be easily measured, can affect both the therapy options available to a patient and the health condition after therapy of that patient.