The new microscope features something called "photonic time stretch". Essentially, the microscope takes pictures of blood cells with the help of flashing lasers. Alongside optics that boost clarity within images, the new microscope can track information not possible in the past. Then, the AI uses deep learning to distinguish the difference between healthy and cancer riddled white blood cells.

The great irony is that AI technology being honed and implemented right now could actually help increase diversity within the field itself, as tech companies leverage machine learning programs to pinpoint unconscious gender bias in the workplace. A slate of machine learning programs on the market utilize data and algorithms to spot diversity blind spots and help companies fill in the gaps. But eradicating bias isn't just politically correct; increasing gender diversity could change the face of AI research as well. There's a new theory floating around the engineering and computer science industries that women are far more likely to enroll and stay invested in the field if the work being produced is more societally meaningful. Programs that focus on humanistic applications for the greater good perform remarkably better where diversity is concerned: A new UC Berkeley Ph.D. program in development engineering boasted a 50 percent female enrollment rate in its inaugural 2014 class, and MIT's D-Lab, which aims to build technology to improve the lives of the impoverished, is 74 percent female.

A recent article by a Foreign-Exchange Journalist suggests the'Skynet' of Finance is not too far away. In particular, Transfer/Payments business expect to lose 28% of their business to FinTech in the next 5 years, and Banks expect to lose 24% of their business. The silver lining to this takeover could however be, the article points out, the greater emphasis on the'human touch' in key customer interfacing areas. For example, a human hand at the wheel to prevent another'flash crash' or a human interpreter of the decisions of an Artificial Intelligence made lending / investment decision. Whatever happens, we are likely to see more automation, lower costs for the customer, and smarter decision making – albeit in the near term.

PITTSBURGH (KDKA) – Scientists say they have developed a new technique for identifying cancer cells in a blood sample more accurately and faster thanks to artificial intelligence. Researchers at the California NanoSystems Institute at UCLA developed the technique that combines a special microscope with an artificial intelligence algorithm to identify cancerous cells. Using the new method could reduce the time and energy to diagnose cancer, allowing doctors to treat it more quickly. The new microscope is called a photonic time stretch microscope, which uses nanosecond-long pulses of light to capture images of hundreds of thousands of cells per second. Researchers say it works by taking pictures of flowing blood cells, the way a camera uses a flash.

Welcome to 2016, where you can buy flowers, book a flight and order an Uber through messaging apps like Facebook Messenger and Slack. Last week, Taco Bell and its agency, Deutsch, unveiled the TacoBot, a Siri-like version of the cashiers that take your order at its restaurants. Taco Bell built the bot for workplaces that use Slack's messaging platform to communicate internally. Now, instead of someone jotting everyone's orders on a Post-it and hoping the drive-thru attendant gets everything right, they can ask TacoBot to put in the order for them. "I've described TacoBot as your own personal Taco Bell butler," said Andy McCraw, Taco Bell's digital innovation and on-demand product manager.

Artificial intelligence is one of the greatest goals of the 21st century. Major developments in AI do astound, machines learning how to turn words into images and how to beat world class players in Go. A new microscope, developed by researchers from UCLA, uses AI in helping detect and spot blood samples with cancer cells. Faster and more accurate than its contemporary techniques, it can analyze 36 million images every second without damaging the blood samples. The new microscope features something called "photonic time stretch".

Recent research has identified independent predictors of dementia to include age at diagnosis, transient ischemic attack and stroke status, and years of education, with vascular factors playing a greater role in disease pathogenesis than previously thought. The findings were presented at the 2016 annual meeting of the American Academy of Neurology (AAN). In the abstract, the researchers wrote that dementia encompasses a broad set of neurologic diseases, producing progressive declines in memory and/or thinking faculties, sometimes alongside personality and emotional disturbances. "Worldwide, approximately 35.6 million people have dementia, and this number is only expected to grow due to an aging population," they wrote. "Unfortunately, it is exceedingly difficult to predict who will develop dementia, let alone what type. This makes it difficult to mobilize various preventive strategies supported by mounting evidence."

IBM announced a deal with sports and fitness retailer Under Armour Inc. to use machine learning technology from Watson and showed off an application for diabetic care developed with the supercomputer's data, highlighting the company's effort to expand Watson's capabilities for the health-care industry. IBM and Under Armour released an updated fitness application for Apple Inc.'s iPhones that uses data powered by Watson, IBM Chief Executive Officer Ginni Rometty said Wednesday in a speech at the International Consumer Electronics Show in Las Vegas. Separately, Medtronic Plc CEO Omar Ishrak joined Rometty on stage to unveil a prototype for a diabetes-management app that tests have shown may be capable of predicting hypoglycemic events as early as three hours in advance. The application still needs to go through regulatory review -- it will roll out this summer, Rometty said. The ability to predict the hypoglycemic events is a "breakthrough," she said.

The "photonic time stretch" was invented by Professor Barham Jalali, who holds a patent for this technology, and its use in microscopes is just one of many possible applications. It works by taking pictures of flowing blood cells using laser bursts in the way that a camera uses a flash. This process happens so quickly – in nanoseconds, or billionths of a second – that the images would be too weak to be detected and too fast to be digitised by normal instrumentation. The new microscope overcomes those challenges using specially designed optics that boost the clarity of the images and simultaneously slow them enough to be detected and digitised at a rate of 36 million images per second. It then uses deep learning to distinguish the cancer cells from healthy white blood cells. Deep learning is a form of artificial intelligence that uses complex algorithms to extract meaning from data, with the goal of achieving accurate decision making.

A microscope, invented by a professor at the University of California, uses artificial intelligence in order to locate cancer cells more efficiently than ever before. The device uses photonic time stretch and deep learning to analyze 36 million images every second without damaging the blood samples. This new technique for identifying problematic cells is faster and more accurate than standard methods currently in practice. Commonly, doctors will add biochemicals to blood samples in order to check for cells containing cancer. The biochemicals attach what scientists call "biological labels" to damaged cells, which enables instruments to both locate and identify differences.