Seven Affordable Ways To Incorporate Machine Learning And AI Into Your Business


From chatbots and automation to smart office gadgets, artificial intelligence (AI) technology has become increasingly prominent in the workplace. As smaller businesses see their larger competitors taking advantage of AI and machine learning, they've realized they need to jump on the bandwagon to keep up -- but they might be wondering how they'll be able to afford it. Fortunately for businesses on a budget, you don't need to break the bank to start incorporating AI and machine learning (ML) into your operations. By starting on a smaller scale with ready-made solutions, you can harness the power of this technology and improve your business performance. Seven members of Forbes Technology Council explain how.

Robots Ride to the Rescue Where Workers Can't Be Found


Many are doing brisk business as companies around Eastern Europe accelerate an automation drive. At Rittal, a maker of switch gears and control cabinets for industrial robots, orders rose 15 percent last year and have jumped 25 percent since January. "Companies aren't able to produce more, so their competitiveness is falling," said Jaromir Zeleny, Rittal's managing director. "They don't want to be so dependent on people." Eastern Europe became a manufacturing powerhouse by luring multinationals with low wages.

This algorithm automatically spots "face swaps" in videos


The ability to take one person's face or expression and superimpose it onto a video of another person has recently become possible. In particular, pornographic videos called "deepfakes" have emerged on websites such as Reddit and 4Chan showing famous individuals' faces superimposed onto the bodies of actors. This phenomenon has significant implications. At the very least, it has the potential to undermine the reputation of people who are victims of this kind of forgery. It poses problems for biometric ID systems.

ICYMI: A huge robot and presenter in the pool

BBC News

A Japanese engineer builds an 8.5m (28 ft) robot - and other stories you might have blinked and missed this week.

Nebraska Prisons See Technology Used to Smuggle Contraband

U.S. News

The Lincoln Journal Star reports that a crashed drone attached with bags of marijuana and tobacco was found at the Lincoln Correctional Center two months ago. The Justice Department reported last year an increasing number of attempts to use drones to smuggle contraband into federal prisons over the past five years.

Your fancy new car steers and brakes for you; so why keep your hands on the wheel?


USA Today's Nathan Bomey takes Cadillac's Super Cruise for a test drive. In this Friday March 23, 2018 photo provided by KTVU, emergency personnel work a the scene where a Tesla electric SUV crashed into a barrier on U.S. Highway 101 in Mountain View, Calif. The National Transportation Safety Board has sent two investigators to look into a fatal crash and fire Friday in California that involved a Tesla electric SUV. The agency says on Twitter that it's not clear whether the Tesla Model X was operating on its semi-autonomous control system called Autopilot at the time. Investigators will study the fire that broke out after the crash.

Luminar's New Lidar Could Dominate the Self-Driving Car Market


Self-driving cars are nearly ready for primetime, and so are the laser sensors that help them see the world. Lidar, which builds a 3-D map of a car's surroundings by firing millions of laser points a second and measuring how long they take to bounce back, has been in development since 2005, when a guy named Dave Hall made one for the Darpa Grand Challenge, an autonomous vehicle contest. In the decade-plus since then, if you wanted a lidar for your self-driving car, Velodyne was your only choice. Yet Velodyne's one-time monopoly has eroded in recent years, as dozens of lidar startups came to life, and robocar makers found their own way. Google's sister company Waymo put years and millions of dollars into developing a proprietary system.

How Do Machine Learning Algorithms Handle Such Large Amounts Of Data?


How do Machine Learning algorithms handle such large amount of data in companies (or real-life cases)? Machine learning algorithms face two main constraints: Memory and processing speed. Let's talk about memory first, which is usually the most limiting constraint. A modern PC typically has something like 16 GB RAM. Consequently, it can load datasets up to a few GBs in memory, which means millions, if not billions, of data points.

Amazon files for Alexa patent to let it listen to people all the time and work out what they want

The Independent

The Amazon Alexa of the future could be listening to you all the time – and building up a detailed picture of what you want to buy. That's the suggestion of a patent filed by the company that details the idea of'voice-sniffing' technology. Such software would allow the device to eavesdrop on conversations and analyse them, feeding that into a database for ads. At the moment, Amazon's Echo products are hardwired so they will only listen to users when they say the "Alexa" wake word. Amazon has denied that it uses voice recordings for advertising at the moment, and said that the patent might never actually come to the market.

Machine learning offers new way of designing chiral crystals: Logistic regression analysis model predicts ideal chiral crystal


Chirality describes the quality of possessing a mirror image to something else, but without the ability to superimpose it. Your left foot, for example, is a mirror of your right. They look similar, but they are not the same. This is why you cannot wear a left shoe on your right foot. The idea is similar in chemistry.