In a paper published in Nature on 28th January 2016, we describe a new approach to computer Go. This is the first time ever that a computer program “AlphaGo” has defeated a human professional player.
The game of Go is widely viewed as an unsolved “grand challenge” for artificial intelligence. Games are a great testing ground for inventing smarter, more flexible algorithms that have the ability to tackle problems in ways similar to humans. The first classic game mastered by a computer was noughts and crosses (also known as tic-tac-toe) in 1952. But until now, one game has thwarted A.I. researchers: the ancient game of Go.
Despite decades of work, the strongest computer Go programs only played at the level of human amateurs. AlphaGo has won over 99% of games against the strongest other computer Go programs. It also defeated the human European champion by 5–0 in tournament games, a feat previously believed to be at least a decade away. In March 2016, AlphaGo will face its ultimate challenge: a 5-game challenge match in Seoul against the legendary Lee Sedol—the top Go player in the world over the past decade.
An international team of researchers has developed a new algorithm that could one day help scientists reprogram cells to plug any kind of gap in the human body. The computer code model, called Mogrify, is designed to make the process of creating pluripotent stem cells much quicker and more straightforward than ever before.
A pluripotent stem cell is one that has the potential to become any type of specialised cell in the body: eye tissue, or a neural cell, or cells to build a heart. In theory, that would open up the potential for doctors to regrow limbs, make organs to order, and patch up the human body in all kinds of ways that aren’t currently possible.
It was Japanese researcher Shinya Yamanaka who first reprogrammed cells in this way back in 2007 — it later earned him a Nobel Prize — but Yamanaka’s work involved a lot of labourious trial and error, and the process he followed is not an easy one to reproduce. Mogrify aims to compute the required set of factors to change cells instead, and it’s passed its early tests with flying colours.
Understanding time is one of the big open questions of physics, and it has puzzled philosophers throughout history. What is time? Why does it appear to have a direction? The concept is defined as the “arrow of time,” which is used to indicate that time is asymmetric – even though most laws of the universe are perfectly symmetric.
A potential explanation for this has now been put forward. Physicist Sean Carroll from CalTech and cosmologist Alan Guth from MIT created a simulation that shows that arrows of time can arise naturally from a perfectly symmetric system of equations.
The arrow of time comes from observing that time does indeed seem to pass for us and that the direction of time is consistent with the increase in entropy in the universe. Entropy is the measure of the disorder of the world; an intact egg has less entropy than a broken one, and if we see a broken egg, we know that it used to be unbroken. Our experience tells us that broken eggs don’t jump back together, that ice cubes melt, and that tidying up a room requires a lot more energy than making it messy.
A new algorithm has been developed that will drastically reduce the time and effort needed to create induced pluripotent stem cells (iPSCs). As a result of this breakthrough, we can expect a dramatic revolution in regenerative medicine in the near future.
What if you could directly reprogram cells to develop into whatever you wished? What if you could take an undifferentiated, incipient cell, full of the unrealized potential to become any one of the many specialized cells in the human body, and nudge it into becoming ocular tissue, or neural cells, even a new heart to replace an old or damaged one?
This is the promise afforded by Mogrify, the result of the application of computational and mathematical science to the problems of medicine and biology. It was developed by an international collaboration of researchers from the Duke-NUS Medical School in Singapore, the University of Bristol in the United Kingdom, Monash University in Australia, and RIKEN in Japan. The new research was published online in the journalNature Genetics.
I have seen this model so many times over the decades. And, I even was engaged in some of these experiments in the past. The continued problem we saw is “subjective reasoning” by humans which makes the experiments flawed.
And, as Yampolskiy suggested this is not true AI; it is using human insights and identifying patterns based on human input whch also includes subjective reasoning.
While AI focuses on creating intelligent machines that perform human tasks, a human-based algorithm, harnessing the power of the crowd to make predictions, shows remarkable accuracy.
Chrome is about to load web pages a lot faster than you’ve experienced up until now. Thanks to a new compression algorithm called Brotli, which Google introduced last September, Chrome will be able to compress data up to 26 percent more than its existing compression engine, Zopfli, which is an impressive jump.
According to Google’s web performance engineer Ilya Grigorik, Brotli is ready to roll out, so Chrome users should expect to see a bump in load times once the next version of Chrome is released. Google also says Brotli will help mobile Chrome users experience “lower data transfer fees and reduced battery use.” The company is hailing Brotli as “a new data format” that Google hopes will be adopted by other web browsers in the near future, with Firefox seemingly next in line to adopt it. But for now, expect to notice your web pages loading a bit faster in the coming weeks.
Update:January 20th 10:30AM: Updated to note that Firefox will also adopt Brotli in a future update.
This totally makes sense to me. Whenever, you’re monitoring any type of patterns for collective reasoning or predictive analysis such measuring what voters clap to or respond positively to as well as build out your entire campaign strategy and speeches; AI is your go to solution. So, AI is a must have tool for politicians who strategically plan to win in future elections. No more need for a campaign manager like Karl Rove, etc.
Political speeches are often written for politicians by trusted aides and confidantes. Could an AI algorithm do as well?
Ask the average passerby on the street to describe artificial intelligence and you’re apt to get answers like C-3PO and Apple’s Siri. But for those who follow AI developments on a regular basis and swim just below the surface of the broad field , the idea that the foreseeable AI future might be driven more by Big Data rather than big discoveries is probably not a huge surprise. In a recent interview with Data Scientist and Entrepreneur Eyal Amir, we discussed how companies are using AI to connect the dots between data and innovation.
Image credit: Startup Leadership Program Chicago
According to Amir, the ability to make connections between big data together has quietly become a strong force in a number of industries. In advertising for example, companies can now tease apart data to discern the basics of who you are, what you’re doing, and where you’re going, and tailor ads to you based on that information.
“What we need to understand is that, most of the time, the data is not actually available out there in the way we think that it is. So, for example I don’t know if a user is a man or woman. I don’t know what amounts of money she’s making every year. I don’t know where she’s working,” said Eyal. “There are a bunch of pieces of data out there, but they are all suggestive. (But) we can connect the dots and say, ‘she’s likely working in banking based on her contacts and friends.’ It’s big machines that are crunching this.”
Amir used the example of image recognition to illustrate how AI is connecting the dots to make inferences and facilitate commerce. Many computer programs can now detect the image of a man on a horse in a photograph. Yet many of them miss the fact that, rather than an actual man on a horse, the image is actually a statue of a man on a horse. This lack of precision in analysis of broad data is part of what’s keep autonomous cars on the curb until the use of AI in commerce advances.
“You can connect the dots enough that you can create new applications, such as knowing where there is a parking spot available in the street. It doesn’t make financial sense to put sensors everywhere, so making those connections between a bunch of data sources leads to precise enough information that people are actually able to use,” Amir said. “Think about, ‘How long is the line at my coffee place down the street right now?’ or ‘Does this store have the shirt that I’m looking for?’ The information is not out there, but most companies don’t have a lot of incentive to put it out there for third parties. But there will be the ability to…infer a lot of that information.”
This greater ability to connect information and deliver more precise information through applications will come when everybody chooses to pool their information, said Eyal. While he expects a fair bit of resistance to that concept, Amir predicts that there will ultimately be enough players working together to infer and share information; this approach may provide more benefits on an aggregate level, as compared to an individual company that might not have the same incentives to share.
As more data is collected and analyzed, another trend that Eyal sees on the horizon is more autonomy being given to computers. Far from the dire predictions of runaway computers ruling the world, he sees a ‘supervised’ autonomy in which computers have the ability to perform tasks using knowledge that is out-of-reach for humans. Of course, this means developing a sense trust and allowing the computer to make more choices for us.
“The same way that we would let our TiVo record things that are of interest to us, it would still record what we want, but maybe it would record some extras. The same goes with (re-stocking) my groceries every week,” he said. “There is this trend of ‘Internet of Things,’ which brings together information about the contents of your refrigerator, for example. Then your favorite grocery store would deliver what you need without you having to spend an extra hour (shopping) every week.”
On the other hand, Amir does have some potential concerns about the future of artificial intelligence, comparable to what’s been voiced by Elon Musk and others. Yet he emphasizes that it’s not just the technology we should be concerned about.
“At the end, this will be AI controlled by market forces. I think the real risk is not the technology, but the combination of technology and market forces. That, together, poses some threats,” Amir said. “I don’t think that the computers themselves, in the foreseeable future, will terminate us because they want to. But they may terminate us because the hackers wanted to.”
“[H]alf the people in the world cram into just 1 percent of the Earth’s surface (in yellow), and the other half sprawl across the remaining 99 percent (in black).”
