There's a science behind getting people to open your emails. One tech startup is helping companies master that. London-based marketing technology software company Phrasee uses artificial intelligence to deliver human-sounding copy for companies like eBay and Domino's. The software injects AI-optimized language into a company's email subject lines, push notifications, and ads to boost clicks and conversations from copy. And it can do it all in matter of minutes.

In a study published today in the journal JAMA Network Open, researchers demonstrated that machine-learning algorithms could help improve the accuracy of breast cancer screenings when used in combination with assessments from radiologists. The study was based on results from the Digital Mammography (DM) DREAM Challenge, a crowd-sourced competition that kicked off in 2016 to engage a broad, international scientific community to assess whether artificial intelligence (AI) algorithms could meet or beat radiologist interpretive accuracy. New study in @JAMANetworkOpen shows #AI may help improve the accuracy of breast cancer screenings when used in combination with assessments from radiologists. "This DREAM Challenge allowed for a rigorous, apples-to-apples assessment of dozens of state-of-the-art deep learning algorithms in two independent datasets," said Dr. Justin Guinney, VP of Computational Oncology at Sage Bionetworks and Chair of DREAM Challenges. "This is a much-needed comparison effort given the importance and activity of AI research in this field."

BROOKLYN, New York, Wednesday, March 4, 2020 - Robots hold promise for a large number of people with neurological movement disorders severely affecting the quality of their lives. Now researchers have tapped artificial intelligence techniques to build an algorithmic model that will make the robots more accurate, faster, and safer when battling hand tremors. Their model, which is ready for others to deploy, appears this month in Scientific Reports, an online journal of Nature. The international team reports the most robust techniques to date to characterize pathological hand tremors symptomatic of the common and debilitating motor problems affecting a large number of aging adults. One million people throughout the world have been diagnosed with Parkinson's disease, just one of the neurodegenerative diseases that can cause hand tremors. While technology such as sophisticated wearable exoskeleton suits and neurorehabilitative robots could help people offset some involuntary movements, these robotic assistants need to precisely predict involuntary movements in real-time - a lag of merely 10 or 20 milliseconds can thwart effective compensation by the machine and in some cases may even jeopardize safety.

Deep AI or General AI has been one of the biggest fascinations of much of the tech world. Many kinds of research are being carried out to achieve it. This is simply because it is the highest level that the Artificial Intelligence (AI) technology can achieve. In fact, it is the closest that technology can get towards human intelligence. This is to say it will be able to almost do all the mental tasks that humans are able to perform. There are both people who are welcoming of the idea of a deep AI, and those who are apprehensive about it.

Artificial intelligence (AI) is increasingly useful for central banks. While it may be used only in low-level roles today, technological advances and cost savings will likely embed AI deeper and deeper into core central bank functions. Maybe each central bank will have their own AI engine, maybe a future'BoB' (the Bank of England Bot). What will be the impact of BoB and its counterparts? BoB could today, or soon, help with many central bank tasks, such as information gathering, data analysis, forecasting, risk management, financial supervision, and monetary policy analysis. The technology is mostly here; what prevents adoption are cultural, political, and legal factors.

The state of Utah has given an artificial intelligence company real-time access to state traffic cameras, CCTV and "public safety" cameras, 911 emergency systems, location data for state-owned vehicles, and other sensitive data. The company, called Banjo, says that it's combining this data with information collected from social media, satellites, and other apps, and claims its algorithms "detect anomalies" in the real world. The lofty goal of Banjo's system is to alert law enforcement of crimes as they happen. It claims it does this while somehow stripping all personal data from the system, allowing it to help cops without putting anyone's privacy at risk. As with other algorithmic crime systems, there is little public oversight or information about how, exactly, the system determines what is worth alerting cops to. In its pitches to prospective clients, Banjo promises its technology, called "Live Time Intelligence," can identify, and potentially help police solve, an incredible variety of crimes in real-time. Banjo says its AI can help police solve child kidnapping cases "in seconds," identify active shooter situations as they happen, or potentially send an alert when there's a traffic accident, airbag deployment, fire, or a car is driving the wrong way down the road. Banjo says it has "a solution for homelessness" and can help with the opioid epidemic by detecting "opioid events." It offers "artificial intelligence processing" of state-owned audio sensors that "include but may not be limited to speech recognition and natural language processing" as well as automatic scene detection, object recognition, and vehicle detection on real-time video footage pulled in from Utah's cameras.

The coronavirus disease COVID-19 has so far caused about 3,380 deaths, infected about 98,300 people, and is significantly impacting the economy in many countries. We used predictive analytics, a branch of artificial intelligence (AI), to forecast how many confirmed COVID-19 cases and deaths can be expected in the near future. Our method predicts that by March 13, the virus death toll will have climbed to 3,913, and confirmed cases will reach 116,250 worldwide, based on data available up to March 5. To develop contingency plans and hopefully head off the worst effects of the coronavirus, governments need to be able to anticipate the future course of the outbreak. This is where predictive analytics could prove invaluable.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Since the early days of artificial intelligence, computer scientists have been dreaming of creating machines that can see and understand the world as we do. The efforts have led to the emergence of computer vision, a vast subfield of AI and computer science that deals with processing the content of visual data. In recent years, computer vision has taken great leaps thanks to advances in deep learning and artificial neural networks. Deep learning is a branch of AI that is especially good at processing unstructured data such as images and videos.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Since the early days of artificial intelligence, computer scientists have been dreaming of creating machines that can see and understand the world as we do. The efforts have led to the emergence of computer vision, a vast subfield of AI and computer science that deals with processing the content of visual data. In recent years, computer vision has taken great leaps thanks to advances in deep learning and artificial neural networks. Deep learning is a branch of AI that is especially good at processing unstructured data such as images and videos.

Scrabble is among classic casual games getting new life on smartphones and tablets as the mobile video game audience continues to grow.