THURSDAY, Sept. 26, 2019 (HealthDay News) -- Early evidence suggests that diagnostic performance of deep learning models is equivalent to that of health care professionals for interpreting medical imaging, according to a study published online Sept. 25 in The Lancet Digital Health. Xiaoxuan Liu, M.B.Ch.B., from the University Hospitals Birmingham NHS Foundation Trust in the United Kingdom, and colleagues conducted a systematic review and meta-analysis to assess the diagnostic accuracy of deep learning algorithms versus health care professionals in classifying disease using medical imaging. Binary diagnostic accuracy data were extracted and contingency tables were constructed to derive the outcomes of interest: sensitivity and specificity. Data from 82 studies, describing 147 patient cohorts were included. The researchers found that based on 69 studies, sensitivity ranged from 9.7 to 100 percent and specificity ranged from 38.9 to 100 percent.

Rapidly maturing technologies such as 5G, artificial intelligence (AI), and quantum computing will "shift fundamentally the global balance of power", according to Dr Tobias Feakin, Australia's Ambassador for Cyber Affairs. "Those [nations] that really are at the forefront of AI and the way that it works will genuinely be at the forefront of the emerging 21st century economy," he told the Australian Cybersecurity Conference, or CyberCon, in Melbourne on Wednesday. Some nations are already positioning themselves to take advantage of these technologies. "Geopolitics now is being shaped and harnessed in a way that we probably didn't think conceivable a decade ago," Feakin said. "We need to be thinking about grand strategy in technology. How do we ensure that we're plugging into this conversation and the kinds of areas that are shaping technology, not only the technology development itself, but the kinds of legislation that shape the absorption of that technology into the global environment," he said.

For the first time ever, a human drug has been created entirely by artificial intelligence (AI). This news comes from a team at Flinders University in Australia, who claims to have created an enhanced influenza vaccine using an AI program known Search Algorithm for Ligands (SAM). Though computers have been used to make drugs before, this was the first time it was done independently by an AI system. The researchers described this drug as a flu vaccine with an added compound that better stimulates the human immune system. This addition causes more antibodies to be formed against the flu virus than with the traditional vaccination, increasing the vaccine's efficacy.

Welcome to TechTalks' AI book reviews, a series of posts that explore the latest literature on AI. The media is rife with stories that warn of AI algorithms bringing people back from the dead, AI algorithms developing secret languages, mass technological unemployment, and a looming robot apocalypse. Movies and TV series like Her, The Circleand Westworld,which present a mystic portrayal of conscious machines and human-level AI being just around the corner. Rebooting AI is a refreshing read and a much-needed reality check on the current confusing state of artificial intelligence. Consider the following text, mentioned in Rebooting AI: "Elsie tried to reach her aunt on the phone, but she didn't answer." You don't need to be a genius to quickly make the following assumptions after reading this sentence: But even the most sophisticated AI algorithm would struggle to draw the same conclusions.

Social media platforms like Facebook use a combination of artificial intelligence and human moderators to scout out and eliminate hate speech. But now researchers have developed a new AI tool that wouldn't just scrub hate speech, but would actually craft responses to it, like: 'The language used is highly offensive. All ethnicities and social groups deserve tolerance.' "And this type of intervention response can hopefully short circuit the hate cycles that we often get in these types of forums." The idea, she says, is to fight hate speech with more speech.

Thanks to all this, Watanabe explained, no longer would gymnasts -- many of whom, he noted, had started gymnastics as young as age 3 and had trained competitively for more than a decade -- risk having their efforts unceremoniously wasted by human error or interference. "This is a step toward the challenge of justice through technology," Watanabe said. The debut of such technology at the biggest gymnastics meet outside the Olympics represented a meaningful milestone in a sport periodically marred by judging controversies and often wracked with questions about political influence in scoring decisions. For all the grand language, and for all the big-picture prophesying it has inspired about the future of sports -- baseball is already experimenting with robot umpires, and tennis is starting to expand electronic line-calling -- the steps unveiled in Stuttgart were preliminary, and fairly subtle. In gymnastics, at least, humans very much remain in control.

FEW IDEAS have enthused technologists as much as the self-driving car. Advances in machine learning, a subfield of artificial intelligence (AI), would enable cars to teach themselves to drive by drawing on reams of data from the real world. The more they drove, the more data they would collect, and the better they would become. Robotaxis summoned with the flick of an app would make car ownership obsolete. Best of all, reflexes operating at the speed of electronics would drastically improve safety. Car- and tech-industry bosses talked of a world of "zero crashes".

Researchers have found a way to marry human creativity and artificial intelligence (AI) creativity to dramatically boost the performance of deep learning. A team led by Alexander Wong, a Canada Research Chair in the area of AI and a professor of systems design engineering at the University of Waterloo, developed a new type of compact family of neural networks that could run on smartphones, tablets, and other embedded and mobile devices. The networks, called AttoNets, are being used for image classification and object segmentation, but can also act as the building blocks for video action recognition, video pose estimation, image generation, and other visual perception tasks. "The problem with current neural networks is they are being built by hand and incredibly large and complex and difficult to run in any real-world situation," said Wong, who also co-founded a startup named DarwinAI to commercialize the technology. "These on-the-edge networks are small and agile and could have huge implications for the automotive, aerospace, agriculture, finance, and consumer electronics sectors."

AI heralds a step-change in the performance and capability of wireless networks and other critical infrastructures. However, it may also cause irreversible environmental damage due to their high energy consumption. Here, we address this challenge in the context of 5G and beyond, where there is a complexity explosion in radio resource management (RRM). On the one hand, deep reinforcement learning (DRL) provides a powerful tool for scalable optimization for high dimensional RRM problems in a dynamic environment. On the other hand, DRL algorithms consume a high amount of energy over time and risk compromising progress made in green radio research. This paper reviews and analyzes how to achieve green DRL for RRM via both architecture and algorithm innovations. Architecturally, a cloud based training and distributed decision-making DRL scheme is proposed, where RRM entities can make lightweight deep local decisions whilst assisted by on-cloud training and updating. On the algorithm level, compression approaches are introduced for both deep neural networks and the underlying Markov Decision Processes, enabling accurate low-dimensional representations of challenges. To scale learning across geographic areas, a spatial transfer learning scheme is proposed to further promote the learning efficiency of distributed DRL entities by exploiting the traffic demand correlations. Together, our proposed architecture and algorithms provide a vision for green and on-demand DRL capability.

Learned representations of source code enable various software developer tools, e.g., to detect bugs or to predict program properties. At the core of code representations often are word embeddings of identifier names in source code, because identifiers account for the majority of source code vocabulary and convey important semantic information. Unfortunately, there currently is no generally accepted way of evaluating the quality of word embeddings of identifiers, and current evaluations are biased toward specific downstream tasks. This paper presents IdBench, the first benchmark for evaluating to what extent word embeddings of identifiers represent semantic relatedness and similarity. The benchmark is based on thousands of ratings gathered by surveying 500 software developers. We use IdBench to evaluate state-of-the-art embedding techniques proposed for natural language, an embedding technique specifically designed for source code, and lexical string distance functions, as these are often used in current developer tools. Our results show that the effectiveness of embeddings varies significantly across different embedding techniques and that the best available embeddings successfully represent semantic relatedness. On the downside, no existing embedding provides a satisfactory representation of semantic similarities, e.g., because embeddings consider identifiers with opposing meanings as similar, which may lead to fatal mistakes in downstream developer tools. IdBench provides a gold standard to guide the development of novel embeddings that address the current limitations.