by Nathan Benaich, Medium
Distilling a generally-accepted definition of what qualifies as artificial intelligence (AI) has become a revived topic of debate in recent times. Some have rebranded AI as “cognitive computing” or “machine intelligence”, while others incorrectly interchange AI with “machine learning”. This is in part because AI is not one technology. It is in fact a broad field constituted of many disciplines, ranging from robotics to machine learning. The ultimate goal of AI, most of us affirm, is to build machines capable of performing tasks and cognitive functions that are otherwise only within the scope of human intelligence. In order to get there, machines must be able to learn these capabilities automatically instead of having each of them be explicitly programmed end-to-end. Continue reading 6 areas of AI and machine learning to watch closely
by Chris Baraniuk, New Scientist
Imagine a building that tells you – before it happens – that the heating is about to fail. Some companies are using machine learning to do just that. It’s called predictive maintenance.
Software firm CGnal, based in Milan, Italy, recently analysed a year’s worth of data from the heating and ventilation units in an Italian hospital. Sensors are now commonly built into heating, ventilation and air conditioning units, and the team had records such as temperature, humidity and electricity use, relating to appliances in operating theatres and first aid rooms as well as corridors. Continue reading Smart buildings predict when critical systems are about to fail
by Jon Kleinberg, Jens Ludwig, and Sendhil Mullainathan, Harvard Business Review
It’s Sunday night. You’re the deputy mayor of a big city. You sit down to watch a movie and ask Netflix for help. (“Will I like Birdemic? Ishtar? Zoolander 2?”) The Netflix recommendation algorithm predicts what movie you’d like by mining data on millions of previous movie-watchers using sophisticated machine learning tools. And then the next day you go to work and every one of your agencies will make hiring decisions with little idea of which candidates would be good workers; community college students will be largely left to their own devices to decide which courses are too hard or too easy for them; and your social service system will implement a reactive rather than preventive approach to homelessness because they don’t believe it’s possible to forecast which families will wind up on the streets. Continue reading A Guide to Solving Social Problems with Machine Learning
by Chris Brandt, University Herald
Artificial Intelligence or AI and machine learning have been making waves these days with the rise of autonomous cars and smarter gadgets. With the latest technological advances, it won’t be long before machines will speak and respond like humans. For now, here are five uses of AI and machine learning you might not still be aware of.
Read the full article here…
by Natasha Lomas, TechCrunch
How will people sift and navigate information intelligently in the future, when there’s even more data being pushed at them? Information overload is a problem we struggle with now, so the need for better ways to filter and triage digital content is only going to step up as the MBs keep piling up.
Researchers in Finland have their eye on this problem and have completed an interesting study that used EEG (electroencephalogram) sensors to monitor the brain signals of people reading the text of Wikipedia articles, combining that with machine learning models trained to interpret the EEG data and identify which concepts readers found interesting. Continue reading Researchers use machine learning to pull interest signals from readers’ brain waves
by Chris Nicholson, TechCrunch
Quitting Twitter is easy — I’ve done it a hundred times. Someone called it “a clown car that drove into a gold mine,” and like all clown cars, Twitter makes the passengers get out once in awhile.
If I go back, it’s because I’m addicted. The tight news cycle, tweetstorms, gossip mongers, insight, argument, factoids, snark and one-liners. For an information junkie, that little bubble is hard to resist. Continue reading Machine learning can fix Twitter, Facebook, and maybe even America
by Steven Melendez, Fast Company
Five ways the image-sharing site is harnessing AI to keep people engaged
Thanks to recent gains in machine learning, computers are getting skilled at picking out patterns and features in text and images. That’s how e-commerce giants like Amazon and eBay build sophisticated recommendation systems and how social networks like Facebook and Twitter are tweaking feeds to keep users hooked. Pinterest is no exception, with 30% of engagement tied to personalized real-time suggestions. Here’s how Pinterest engineers are leveraging artificial intelligence to keep the website’s 150 million–plus users pinning and sharing. Continue reading How Pinterest Uses Machine Learning To Keep Its Users Pinned
by Tom Simonite, MIT Technology Review
Ingesting a heap of drug data allows a machine-learning system to suggest alternatives humans hadn’t tried yet.
What do you get if you cross aspirin with ibuprofen? Harvard chemistry professor Alán Aspuru-Guzik isn’t sure, but he’s trained software that could give him an answer by suggesting a molecular structure that combines properties of both drugs. Continue reading Software Dreams Up New Molecules in Quest for Wonder Drugs
by Jon Fingas, Engadget
Project Soli just needs to get close to an item to determine what it is.
Google’s Project Soli radar technology is useful for much more than controlling your smartwatch with gestures. University of St. Andrews scientists have used the Soli developer kit to create RadarCat, a device that identifies many kinds of objects just by getting close enough. Thanks to machine learning, it can not only identify different materials, such as air or steel, but specific items. It’ll know if it’s touching an apple or an orange, an empty glass versus one full of water, or individual body parts.
Continue reading Google’s mini radar can identify virtually any object