Lifeboat Foundation

WASHINGTON — The U.S. military got is first big taste of artificial intelligence with Project Maven. An Air Force initiative, it began more than a year ago as an experiment using machine learning algorithms developed by Google to analyze full-motion video surveillance.

The project has received high praise within military circles for giving operators in the field instant access to the type of intelligence that typically would have taken a long time for geospatial data analysts to produce.

Project Maven has whetted the military’s appetite for artificial intelligence tools. And this is creating pressure on the National Geospatial-Intelligence Agency to jump on the AI bandwagon and start delivering Maven-like products and services.

Continue reading “Artificial intelligence is changing everything, ‘We need a different mentality’” »

When you take a picture of a cat and Google’s algorithms place it in a folder called “pets,” with no direction from you, you’re seeing the benefit of image recognition AI. The exact same technology is used by doctors to diagnose diseases on a scale never before possible by humans.

Diabetic retinopathy, caused by type two diabetes, is the fastest-growing cause of preventable blindness. Each of the more than 415 million people living with the disease risks losing their eyesight unless they have regular access to doctors.

In countries like India there are simply too many patients for doctors to treat. There are 4,000 diabetic patients for every ophthalmologist in India, where the US has one for every 1,500 patients.

Continue reading “Seeing is believing: How AI could prevent blindness for 415 million people (right now)” »

So much talk about AI and robots taking our jobs. Well, guess what, it’s already happening and the rate of change will only increase. I estimate that about 5% of jobs have been automated — both blue collar manufacturing jobs, as well as, this time, low-level white collar jobs — think back office, paralegals, etc. There’s a thing called RPA, or Robot Process Automation, which is hollowing out back office jobs at an alarming rate, using rules based algorithms and expert systems. This will rapidly change with the introduction of deep learning algorithms into these “robot automation” systems, making them intelligent, capable of making intuitive decisions and therefore replacing more highly skilled and creative jobs. So if we’re on an exponential curve, and we’ve managed to automate around 5% of jobs in the past six years, say, and the doubling is every two years, that means by 2030, almost all jobs will be automated. Remember, the exponential math means 1, 2, 4, 8, 16, 32, 64, 100%, with the doubling every two years.

We are definitely going to need a basic income to prevent people (doctors, lawyers, drivers, teachers, scientists, manufacturers, craftsmen) from going homeless once their jobs are automated away. This will need to be worked out at the government level — the sooner the better, because exponentials have a habit of creeping up on people and then surprising society with the intensity and rapidity of the disruptive change they bring. I’m confident that humanity can and will rise to the challenges ahead, and it is well to remember that economics is driven by technology, not the other way around. Education, as usual, is definitely the key to meeting these challenges head on and in a fully informed way. My only concern is when governments will actually start taking this situation seriously enough to start taking bold action. There certainly is no time like the present.

Just about everyone agrees that America’s health care system is broken. Is better data—and the ability to harness it—the medicine we’ve been looking for?

A German mathematician, physicist and astronomer, Johann Carl Friedrich Gauss rose from humble origins to become one of the world’s greatest minds.

Born in 1777 in Brunswick, then part of the Holy Roman Empire, Gauss was the only child of poor parents who had received little or no formal education. His mother was illiterate. But when Gauss started school at age seven, he was quickly recognized as a child prodigy who could solve complex math problems in his head.

While still a teenager, Gauss became the first person to prove the Law of Quadratic Reciprocity, a math theory to determine whether quadratic equations can be solved.

Another problem is that we still don’t have enough data about every compound, according to Wolverton, and a lack of data means algorithms aren’t very smart. That said, he and Mehta are now interested in using their method on other types of materials beside metallic glass. And they hope that one day, you won’t need a human to do experiments at all, it’ll just be AI and robots. “We can create really a completely autonomous system,” Wolverton says, “without any human being involved.

For hundreds of years, new materials were discovered through trial and error, or luck and serendipity. Now, scientists are using artificial intelligence to speed up the process.

Think Java code-completion on steriods

Code boffins at Rice University in Texas have developed a system called Bayou to partially automate the writing of Java code with the help of deep-learning algorithms and training data sampled from GitHub.

Breakthrough in predicting chaotic systems using machine learning may significantly improve forecasting the weather, earthquakes, cardiac arrest, and many other chaotic phenomena.

In new computer experiments, artificial-intelligence algorithms can tell the future of chaotic systems.

There is an enduring fear in the music industry that artificial intelligence will replace the artists we love, and end creativity as we know it.

As ridiculous as this claim may be, it’s grounded in concrete evidence. Last December, an AI-composed song populated several New Music Friday playlists on Spotify, with full support from Spotify execs. An entire startup ecosystem is emerging around services that give artists automated songwriting recommendations, or enable the average internet user to generate customized instrumental tracks at the click of a button.

But AI’s long-term impact on music creation isn’t so cut and dried. In fact, if we as an industry are already thinking so reductively and pessimistically about AI from the beginning, we’re sealing our own fates as slaves to the algorithm. Instead, if we take the long view on how technological innovation has made it progressively easier for artists to realize their creative visions, we can see AI’s genuine potential as a powerful tool and partner, rather than as a threat.

Continue reading “How Music Generated” »