Your mother was right: you are special. While each of us is a perfect little snowflake in our own right, that doesn’t necessarily mean we possess world-shaking skills. But back in the lab, data scientists are cranking out algorithms that exceed human capability on a regular basis.
About a year ago, FacebookCEO Mark Zuckerberg predicted that artificial intelligence (AI) would generally surpass humans in core sensory capabilities (like seeing and hearing) in about five to 10 years. AI still can’t “actually look at the photo and deeply understand what’s in it or look at the videos and understand what’s in it,” he said at the time.
Zuck is right: deep learning approaches still can’t match humans step for step across the board. But in a few targeted areas, AI has already left humans in the dust. Here are five of them:
According to a May story in IEEE Spectrum, AI can be better at predicting heart attacks than standard doctor’s methods.
Researchers at the University of Nottingham in the UK found that their machine learning models were more accurate at predicting which patients would have a heart attack within the next 10 years.
A neural network was trained on actual patient healthcare records, which contained data on individual’s medical conditions, prescription drugs, lab results, hospital visits, and demographics. Given a data set composed of records for nearly 400,000 individuals, the researchers used 75% for training the model while reserving 25% for testing the accuracy.
About 7,400 patients in the test dataset had heart attacks. Out of those patients, the model accurate predicted the heart attack among about 5,000 patients. The standard method relied upon by doctors predicted 355 fewer heart attacks, which means the AI was about 5% more accurate than the human.
poker may seem like a decidedly human game, one that hinges on the believability of one’s “poker face” and the ability to see through a bluff. Surely, no faceless algorithm could win when some of the cards are face down on the table?
Actually, machine learning excels in this vein, too. According to an April Bloomberg story, an AI system powered by a supercomputer beat some of the best human poker players in a five-day tournament of no-limit Texas Hold ‘Em in a Chinese casino, claiming $290,000 in prize money.
The AI, dubbed Lengpudashi (or “cold poker master” – whoever said computer scientists lack a sense of humor?), completely annihilated its challenger over 36,000 hands of poker. It wasn’t even close.
“People think that bluffing is very human — it turns out that’s not true,” said Carnegie Mellon computer science PhD student Noam Brown, who developed Lengpudashi with computer science professor Tuomas Sandholm. “A computer can learn from experience that if it has a weak hand and it bluffs, it can make more money.”
Lengpudashi was an upgraded version of Libratus, another Poker-winning AI developed at Carnegie Mellon that won tournaments. The prize money will go to Strategic Machine, a firm founded by Sandholm and Brown.
Machine learning algorithms are wonderful at detecting very subtle patterns buried in the data that may correlate with real-world phenomenon that impact humans. When enough of these correlations pile up, we start believing in their predictive power. This cold, scientific approach works very all kinds of data-rich environments, but surely it doesn’t translate into the world of art, right?
But it turns out the algorithms may have a finer eye or ear than we sometimes give them credit for.
