As the world's largest beverage company, Coca-Cola serves more than 1.9 billion drinks every day, across over 500 brands, including Diet Coke, Coke Zero, Fanta, Sprite, Dasani, Powerade, Schweppes and Minute Maid. Big data and artificial intelligence (AI) power everything that the business does – the global director of digital innovation, Greg Chambers, said: "Artificial intelligence is the foundation for everything we do. Artificial intelligence is the kernel that powers that experience." Marketing soft drinks around the world is not a "one-size-fits-all affair". Coca-Cola products are marketed and sold in over 200 countries.

From the "Mona Lisa" to the "Girl with a Pearl Earring," some images linger in the mind long after others have faded. Ask an artist why, and you might hear some generally-accepted principles for making memorable art. Now there's an easier way to learn: ask an artificial intelligence model to draw an example. A new study using machine learning to generate images ranging from a memorable cheeseburger to a forgettable cup of coffee shows in close detail what makes a portrait or scene stand out. The images that human subjects in the study remembered best featured bright colors, simple backgrounds, and subjects that were centered prominently in the frame.

North Carolina's public community colleges had a problem. Although the system had a wealth of learning resources faculty members could use to develop their courses, there was no simple way to share and organize those materials across all 58 campuses. That will soon change, however, with the help of artificial intelligence (AI). Through the machine learning company Tanjo, the community college system is rolling out a custom AI "brain" in the coming months that will map and organize its digital content. The new tool will be critical to linking faculty to relevant resources, said Richard Boyd, Tanjo's CEO. Rather than wade through thousands of files in disparate places, faculty members will be able to use the AI system to source documents of interest to them from a central location.

We have been experiencing an "AI Spring" (e.g. This was due to technological breakthroughs, commercialization of Deep Learning, and cheap computation. Such uptick in interest in AI was largely driven by the work from Alex Krizhevsky (a student of Geoff Hinton and co-worker of mine) and investment from firms like Google and Nvidia. We had similar AI Springs every decade since the 60s. However, AI Winters, defined by 1) skepticism and 2) cut in funding, followed every time.

Artificial intelligence (AI) is "summoning the demon," Elon Musk warned, continuing a great tradition of fearful warnings about new technology. In the 16th century, the Vicar of Croyden warned how Gutenberg's demonic press would destroy the faith: "'We must root out printing or printing will root us out,' the Vicar told his flock."1 Preceding Musk's invocation of the devil by a couple of centuries, an Ohio school board declared the new steam railroad technology to be "a device of Satan to lead immortal souls to hell."2 Others warned of secular effects: The passing of a steam locomotive would stop cows from grazing, hens from laying, and precipitate economic havoc as horses became extinct and hay and oats farmers went bankrupt.3 Only a few years later, demonic fears appeared once again when Samuel Morse's assistant telegraphed from Baltimore suggesting suspension of the trial of the first telegraph line. Because the city's clergy were preaching that messages by sparks could only be the work of the devil. Morse's assistant feared these invocations would incite riots to destroy the equipment.4

Babylon, a UK start-up, plans to "put an accessible and affordable health service in the hands of every person on earth" by putting artificial intelligence (AI) tools to work. Currently, the company has operations in the UK and Rwanda and hopes to expand to the Middle East, the United States, and China. The company's strategy is to combine the power of AI with the medical expertise of humans to deliver unparalleled access to healthcare. The Amazing Ways Babylon Health Is Using Artificial Intelligence To Make Healthcare Universally ... [ ] Accessible Babylon's engineers, doctors, and scientists developed an AI system that can receive data about the symptoms someone is suffering from, compare the information to a database of known conditions and illnesses to find possible matches, and then identify a course of action and related risk factors. People can use the "Ask Babylon" feature to inquire about their medical concerns to get an initial understanding of what they might be dealing with, but this service is not intended to replace the expertise of a doctor or be used in a medical emergency.

Just not as first envisioned. The business model of this Google sister company is based on monitoring people's movements and connecting the dots. But this week, before signing on the dotted line, Toronto tried to turn the tables on Sidewalk Labs. Now, the watcher is being watched and its surveillance is under scrutiny. That is as it should be.

After a hugely successful first day at the Deep Learning Summit and Responsible AI Summit, we were back in Montreal for day two. Sportlogiq were up first on the Deep Learning stage with Bahar Pourbabaee, Machine Learning Team Lead, discussing some of the main challenges in developing and deploying deep learning algorithms at scale. The sheer size of this scale was reiterated with Bahar suggesting that they are processing more than 60,000 sports videos from different sources, all of which include many thousands of frames. Bahar's first example of Sportloqiq's latest work depicted that of a fast moving Premier League Soccer game, with examples showing the sheer depth of analysis suggesting that both decisions and non-decisions alongside their consequences can be looked at and scrutinised, whilst individual joints of players and their lateral movement was observed under the microscope. Bahar then continued to detail some of the problems with the visual perception of their learning representation model which included player/object detection, player/team identification, state estimation and data association.

All over the world, healthcare systems are struggling to provide the service their patients demand while also working within the legal and budgetary constraints set by governments or other institutions like insurance companies and hospitals. Digitization of medical records and new medical technologies have yet to touch large swathes of the world's medical institutions. In many countries, because of differences in the way records are kept, healthcare services and organizations are unable to "talk" to each other and cannot share data. That makes for inefficiency; if data cannot be shared between institutions, things as basic as blood tests need to be repeated each time a patient goes to a different doctor or institution. Israel's health care system, although consistently highly rated in international surveys, suffers from the same infrastructure issues: hospitals can be overcrowded, and non-critical patients sometimes wait for operations.

An AI project which successfully cut A&E attendances by a third has been greenlit for a wider rollout. Over 1,000 patients were involved in a trial of an AI system developed by Health Navigator at York Teaching Hospitals Foundation Trust over the last four years. AI was used to identify patients at risk of unplanned hospital admissions. By highlighting these patients, nurses were deployed to help coach them over six months on how to improve their health and reduce the risk of visiting A&E. The trial resulted in a 30 percent reduction in unplanned hospital admissions and a 25 percent reduction in planned admissions.