Despite advancing threats from hackers and nation states, human error remains the top cybersecurity concern for both C-suite executives and policymakers, according to a Wednesday report from Oracle. To combat this issue, professionals must invest more in employees--via training and hiring--than in technologies in the coming two years, the report found. Only 38% of C-suite executives said they plan to invest in artificial intelligence (AI) and machine learning to improve security in the next two years, though these technologies can aid in minimizing human error, the report said. In terms of other security investments over that time frame, 44% of C-suite executives said they plan to purchase new software with improved security, and 37% said they plan to invest in new infrastructure solutions, according to the report. In the last five years, C-suite executives said they have upgraded existing software (60%), trained existing staff (57%), purchased new software with enhanced security features (54%), and invested in new infrastructure solutions (40%) to improve security, the report found.

There are many headline studies about artificial intelligence making strides in medicine, but the reality can be somewhat more prosaic. What gets used in hospitals and clinicians' offices may be much simpler, and a lot less like AI than you would think. In the latest issue of Nature magazine, DeepMind researchers published the results of a deep learning project that can predict kidney failure of patients in the hospital up to 48 hours before the onset of symptoms, with far greater accuracy than existing computer programs for such predictive uses. Also this week, the DeepMind team published the results of a third-party survey of the use of a computer program called "Streams," which uses no artificial intelligence but which can be useful to physicians for things such as being alerted to warning signs about a patient. The first project, the deep learning one, has some ways to go to be put into practice, while the Streams software is already in use by doctors and hospital staff.

Amazon says that U.S. government requests for customer data have seen a substantial spike so far this year. As reported by TechCrunch, the most recent figures released from the company -- which date between January and June 2019 -- show a 14 percent increase in subpoenas and a nearly 35 percent increase in the number of search warrants. Information handed over by the company comes from several sources according to TechCrunch, including Amazon's Echo voice assistant, Alexa, its e-reader, the Kindle, and even its home security devices sold by Ring. The company also experienced an uptick in interest for its cloud services, Amazon Web Services, which separately reported a 77 percent uptick in the number of subpoena requests over the last six-month period. According to data released in the company's latest report, Amazon's response varied depending on the type of requests.

Ada Lovelace was an English mathematician who lived in the first half of the 19th century. In 1842, Lovelace was tasked with translating an article from French into English for Charles Babbage, the "Grandfather of the Computer." Babbage's piece was about his Analytical Engine, a revolutionary new automatic calculating machine. Although originally retained solely to translate the article, Lovelace also scribbled extensive ideas about the machine into the margins, adding her unique insight, seeing that the Analytical Engine could be used to decode symbols and to make music, art, and graphics. Her notes, which included a method for calculating the Bernoulli numbers sequence and for what would become known as the "Lovelace objection," were the first computer programs on record, even though the machine could not actually be built at the time.1 Though never formally trained as a mathematician, Lovelace was able to see beyond the limitations of Babbage's invention and imagine the power and potential of programmable computers; also, she was a woman, and women in the first half of the 19th century were typically not seen as suited for this type of career. Lovelace had to sign her work with just her initials because women weren't thought of as proper authors at the time.2 Still, she persevered,3 and her work, which would eventually be considered the world's first computer algorithm, later earned her the title of the first computer programmer.

Somewhat unceremoniously, Facebook this week provided an update on its brain-computer interface project, preliminary plans for which it unveiled at its F8 developer conference in 2017. In a paper published in the journal Nature Communications, a team of scientists at the University of California, San Francisco backed by Facebook Reality Labs -- Facebook's Pittsburgh-based division devoted to augmented reality and virtual reality R&D -- described a prototypical system capable of reading and decoding study subjects' brain activity while they speak. It's impressive no matter how you slice it: The researchers managed to make out full, spoken words and phrases in real time. Study participants (who were prepping for epilepsy surgery) had a patch of electrodes placed on the surface of their brains, which employed a technique called electrocorticography (ECoG) -- the direct recording of electrical potentials associated with activity from the cerebral cortex -- to derive rich insights. A set of machine learning algorithms equipped with phonological speech models learned to decode specific speech sounds from the data and to distinguish between questions and responses.

Monsters and titans share the stage of mythology across cultures as the necessary realizations of the human imagination. From stone cave to urban dwelling, the theme is unremitting; kept in the imagination, such creatures perform, innocently enough, benign functions. The catch here is the human tendency to realize such creatures. They take the form of social engineering and utopia. Folly bound, such projects and ventures wind up corrupting and degrading.

In April last year, a medical device powered by artificial intelligence (AI) received approval from the US Food and Drug Administration (USFDA), marking a historic moment in healthcare globally. The IDx-DR, a software algorithm that uses AI to analyse images of the eye using a camera, achieved an 87.4% accuracy rate while detecting'more than mild' diabetic retinopathy, a condition where high blood sugar levels damage the blood vessels in the retina. For IT services firms, which are already developing AI and machine language (ML) tools for other uses and industries, extending AI and ML capabilities to healthcare is a fairly non-complex process, and comes with a large upside. Rather than doing it entirely on their own though, these companies are partnering hospital chains and niche players in the field to acquire the required domain expertise. For instance, Japanese technology firm NTT DATA Services tied up with Pune's Deenanath Mangeshkar Hospital last year to use an AI-based solution to diagnose emphysema, a chronic condition of the lungs.

The UK's Royal Navy wants to use artificial intelligence to guide a fleet of robot submarines, plotting the best course to find and destroy underwater mines. Mines are a cheap and effective form of naval warfare. Laid by aircraft, ships or submarines, they lurk in shallow water until a vessel passes nearby and triggers a magnetic sensor, setting off the explosive. Ensuring waterways are safe requires regular sweeps.

Since deep learning became a key player in natural language processing (NLP), many deep learning models have been showing remarkable performances in a variety of NLP tasks, and in some cases, they are even outperforming humans. Such high performance can be explained by efficient knowledge representation of deep learning models. While many methods have been proposed to learn more efficient representation, knowledge distillation from pretrained deep networks suggest that we can use more information from the soft target probability to train other neural networks. In this paper, we propose a new knowledge distillation method self-knowledge distillation, based on the soft target probabilities of the training model itself, where multimode information is distilled from the word embedding space right below the softmax layer. Due to the time complexity, our method approximates the soft target probabilities. In experiments, we applied the proposed method to two different and fundamental NLP tasks: language model and neural machine translation. The experiment results show that our proposed method improves performance on the tasks.

This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the contributions of individual agents to the global reward. The health-informed credit assignment is then extended to a multi-agent variant of the proximal policy optimization algorithm and demonstrated on simple particle environments that have elements of system health, risk-taking, semi-expendable agents, and partial observability. We show significant improvement in learning performance compared to policy gradient methods that do not perform multi-agent credit assignment.