First proposed by Professor John McCarthy at Dartmouth College in the summer of 1956,1 Artificial Intelligence (AI) – human intelligence exhibited by machines – has occupied the lexicon of successive generations of computer scientists, science fiction fans, and medical researchers. The aim of countless careers has been to build intelligent machines that can interpret the world as humans do, understand language, and learn from real-world examples. In the early part of this century, two events coincided that transformed the field of AI. The advent of widely available Graphic Processing Units (GPUs) meant that parallel processing was faster, cheaper, and more powerful. At the same time, the era of'Big Data' – images, text, bioinformatics, medical records, and financial transactions, among others – was moving firmly into the mainstream, along with almost limitless data storage.

Zhou Yi was terrible at math. He risked never getting into college. Then a company called Squirrel AI came to his middle school in Hangzhou, China, promising personalized tutoring. He had tried tutoring services before, but this one was different: instead of a human teacher, an AI algorithm would curate his lessons. The 13-year-old decided to give it a try. By the end of the semester, his test scores had risen from 50% to 62.5%. Two years later, he scored an 85% on his final middle school exam. "I used to think math was terrifying," he says. "But through tutoring, I realized it really isn't that hard. It helped me take the first step down a different path."

Texas A&M University is modifying its self-driving pilot program in the city of Bryan, Texas, to have humans remotely monitor and operate the shuttles starting in September, making it one of the first commercial deployments of teleoperation technology in the country. The teleoperation technology is being provided by a Portland, Oregon-based startup called Designated Driver. It will allow humans at Texas A&M to remotely control the shuttles in situations where the self-driving system may not be up to snuff, and they'll also be able to interact with passengers on board. The new functionality could help solve a problem that similarly nascent autonomous shuttle programs have run into: crashes. The low-speed autonomous shuttles currently whispering their way around a handful of downtown areas and campuses across the country are among the first real-world tests of self-driving technology.

A new research collaboration focused on developing a solution that leverages artificial intelligence (AI) to detect and monitor brain aneurysms on scans faster and more efficiently was recently announced. As reported, Australia's Macquarie University will work with an ICT company, a medical tech company, and a medical imaging company to improve brain aneurysm diagnoses. The project has already received a Cooperative Research Centres Projects (CRC-P) grant of AU$ 2.1M from the Department of Industry, Innovation and Science. Brain aneurysms are a common disorder caused by a weakness in the wall of a brain artery. Aneurysms are present in between 2% and 8% of adults, with multiple aneurysms in more than 10% of these people.

Oracle OpenWorld 2019 will take place September 16-19 in San Francisco, with over 1,000 planned sessions on topics including transformational technologies, growth acceleration, intelligent cloud applications, and more. Here are 5 of the upcoming, fascinating marketing sessions. You can also view the full CX session catalog here. Today, companies are struggling to provide a complete customer experience due to the disconnected nature of the underlying data. Staying ahead of the curve by connecting all customer intelligence across all interactions will shape the leaders of tomorrow.

The reason behind many of these failures is that startups are adopting an approach ill-suited to healthcare – a complex and regulated industry with its own set of rules, specific workflows and complicated landscape of stakeholders – patients, doctors, regulators and insurers – all of whom have a say in whether the technology is adopted. Roeland Pater, founder of early stage health tech, Nori Health, is confident that his'need driven approach' to building and developing his AI-powered digital coach aimed at supporting millions with Inflammatory Bowel Disease and IBS will ensure his business will flourish – could this same approach pull startups out of the AI tech bubble? It's set to be a record-breaking year for health tech investments, surpassing the $8.1bn ( €7.3bn) invested in 2018. Babylon Health closed a $550M funding round this August, valuing the company at $2bn! With such sums pouring into this space, waves of digital health startups are making claims that are big, brash and bold.

Tencent today announces the launch of an AI-supported auxiliary diagnostic system for conducting digital colposcopy at the Global Digital Ecosystem Summit being held in Kunming, China. This latest technology can rapidly identify the cervical transformation zone and the location of a lesion, enabling doctors to more accurately and efficiently diagnose cervical cancer – the most common cause of malignant tumors in the female reproductive organs. "There is a pressing need for smart technology in the healthcare information system," said Tencent's vice president Ding Ke. "Tencent is exploring ways to provide the medical industry with targeted solutions and is spearheading the use of new technologies in the sector." He said, "The launch of this technology is another breakthrough in AI-assisted diagnosis for major diseases and follows close collaboration with medical experts and other partners. In line with our Tech for Good vision, it also realizes substantial social value."

Artificial intelligence will reach human levels by around 2029. Follow that out further to, say, 2045, we will have multiplied the intelligence, the human biological machine intelligence of our civilization a billion-fold.

In a recent blog post, Amazon announced the general availability (GA) of Amazon Forecast, a fully managed, time series data forecasting service. Amazon Forecast uses deep learning from multiple datasets and algorithms to make predictions in the areas of product demand, travel demand, financial planning, SAP and Oracle supply chain planning and cloud computing usage. While Amazon Forecasts leverages machine learning within its service, users of the service do not require machine learning expertise. Amazon Forecast was originally announced at re:Invent 2018 and is now available for production use via the AWS Console, AWS Command Line Interface (CLI) and AWS SDKs. The service was conceived as a result of customer demand, since Amazon has extensive experience in forecasting for their own lines of business.

IMAGE: Map colors indicate estimates of migration traffic from measurements at 143 radar stations. Locations are indicated by white circles, with size proportional to migration traffic at the station. On many evenings during spring and fall migration, tens of millions of birds take flight at sunset and pass over our heads, unseen in the night sky. Though these flights have been recorded for decades by the National Weather Services' network of constantly scanning weather radars, until recently these data have been mostly out of reach for bird researchers. That's because the sheer magnitude of information and lack of tools to analyze it made only limited studies possible, says artificial intelligence (AI) researcher Dan Sheldon at the University of Massachusetts Amherst.