Researchers from the Institute for Medical Engineering and Science (IMES) at MIT; the National University of Ireland Galway (NUI Galway); and AMBER, the SFI Research Centre for Advanced Materials and BioEngineering Research, recently announced a significant breakthrough in soft robotics that could help patients requiring in-situ (implanted) medical devices such as breast implants, pacemakers, neural probes, glucose biosensors, and drug and cell delivery devices. The implantable medical devices market is currently estimated at approximately $100 billion, with significant growth potential into the future as new technologies for drug delivery and health monitoring are developed. These devices are not without problems, caused in part by the body's own protection responses. These complex and unpredictable foreign-body responses impair device function and drastically limit the long-term performance and therapeutic efficacy of these devices. One such foreign body response is fibrosis, a process whereby a dense fibrous capsule surrounds the implanted device, which can cause device failure or impede its function.

As the leader in AI Assisted Customer Analytics, Syntasa is helping enterprises generate real-time, actionable customer insight to enhance the customer experience and drive conversions. Syntasa's software can be installed on-premise, allowing the enterprise to integrate individual-level clickstream data with sensitive enterprise data. By leveraging open-source technologies, Syntasa seamlessly fits in the new enterprise big data analytics ecosystem and provides easy-to-use behavioral analytics applications which significantly reduce time-to-value. The company is headquartered in the Washington, DC metropolitan area with an office in London.

Stears, after the launch of Nigeria's first real-time election database, is looking for an experienced data scientist to join Stears Intelligence: its data science and analytics team. Your role and project may change quicker than you expect. The only constant is that you will lead processes from data collection, cleaning, and preprocessing to training models and deploying them to production. You will also help in delivering products that empowers governments, investors, researchers and professionals in their quest for accurate and accessible data and intelligence. Our ideal candidate will be passionate about artificial intelligence and stay up-to-date with the latest developments in the field.

Palo Alto has inked a definitive agreement to acquire Internet of Things (IoT) start-up Zingbox for $75m (£60.8m). San Francisco-based Zingbox was established in 2014 and had raised $23.5m in funding rounds prior to its purchase by the security vendor. It is the second acquisition of 2019 for the vendor after it splashed out $560m for Israeli start-up Demisto . Palo Alto stated that it will incorporate Zingbox's artificial intelligence (AI) and machine-learning capabilities into its own Firewall and Cortex platforms.

Anonymous hacker programmer uses a laptop to hack the system in the dark. Phone scams are nothing new, but the mark usually isn't an accomplished CEO. According to a new report in The Wall Street Journal, the CEO of an unnamed UK-based energy firm believed he was on the phone with his boss, the chief executive of firm's the German parent company, when he followed the orders to immediately transfer €220,000 (approx. In fact, the voice belonged to a fraudster using AI voice technology to spoof the German chief executive. Rüdiger Kirsch of Euler Hermes Group SA, the firm's insurance company, shared the information with WSJ. He explained that the CEO recognized the subtle German accent in his boss's voice--and moreover that it carried the man's "melody."

You likely know Paris-based L'Oréal as a global cosmetics and beauty care company (the largest in the world), but you might not be aware of the company's commitment to research, innovation, and technology. In fact, since 2012, L'Oréal operates its own technology incubator, a group that operates like a start-up but focused on where beauty and technology meet. Here's an overview of the company's incubator and some other ways they are using artificial intelligence such as with its AI-powered digital skin diagnostic. L'Oréal's first incubator lab was located in New Jersey, but it now also operates additional labs in San Francisco, Paris, and Tokyo that are focused on a small number of products--a mix of apps and wearables and objects to help cosmetics be connected and customized to meet the specific needs of each customer. The incubator partners with entrepreneurs and academia to develop the latest and greatest products by using technology.

Empowering the human is a piece of the puzzle often missing from the fast-paced tech world but remains one of the most important drivers of success and true disruption. Think about the people behind the companies creating or using the most innovative technologies--even the biggest businesses rely on human creativity and emotional intelligence as much as they rely on technological development to survive, let alone thrive in the digital age. These are all digital advancements that are discussed in the context of technology and the sheer computational power of the machine. But what many business leaders fail to understand is that machines can't solve problems alone. Machines are the enabler, but without situational context and logic, these technologies can never serve as a replacement for humans. So, what exactly does this mean for the future of humans and intelligent technology?

Now, there is a computer model that can help forecasters recognize potential severe storms more quickly and accurately, thanks to a team of researchers at Penn State, AccuWeather, Inc., and the University of Almería in Spain. They have developed a framework based on machine learning linear classifiers -- a kind of artificial intelligence -- that detects rotational movements in clouds from satellite images that might have otherwise gone unnoticed. This AI solution ran on the Bridges supercomputer at the Pittsburgh Supercomputing Center. Steve Wistar, senior forensic meteorologist at AccuWeather, said that having this tool to point his eye toward potentially threatening formations could help him to make a better forecast. "The very best forecasting incorporates as much data as possible," he said.

For this research, computer scientists at the University of Southern California (USC) and the University of California, Los Angeles, probed two state-of-the-art natural language systems: OpenAI's small GPT-2 model, which sports 124 million parameters, and Google's recurrent neural network [PDF] – referred to as LM_1B in the Cali academics' paper [PDF] – that was trained using the 1 Billion Word Language Benchmark. Machine-learning code, it seems, picks up all of its prejudices from its human creators: the software ends up with sexist, racist, and homophobic tendencies by learning from books, articles, and webpages subtly, or not so subtly, laced with our social and cultural biases. Multiple experiments have demonstrated that trained language models assume doctors are male, and are more likely to associate positive terms with Western names popular in Europe and America than African-American names, for instance. "Despite the fact that biases in language models are well-known, there is a lack of systematic evaluation metrics for quantifying and analyzing such biases in language generation," Emily Sheng, first author of the study and a PhD student at the USC, told The Register. And so, to evaluate the output of GPT-2 and LM_1B in a systematic way, the researchers trained two separate text classifiers, one to measure bias, and the other to measure sentiment.

An artificial intelligence system developed by the Allen Institute for Artificial Intelligence successfully passed an eighth-grade multiple-choice science test, correctly answering over 90% of the questions. The Allen Institute for Artificial Intelligence introduced an artificial intelligence (AI) system that successfully passed an eighth-grade multiple-choice science test, correctly answering over 90% of the questions, as well as scoring more than 80% on a 12th-grade test. The Aristo system's milestone suggests understanding the language and logic that high school students are expected to possess is no longer outside AI's capabilities. Aristo took standard exams written for students in New York schools, with questions including pictures and diagrams removed; some questions required simple information retrieval, while others required logical thinking. Aristo was built atop Google's Bert, a language-model system that learned via guessing missing words in sentences.