You predict we’ll reach a point with artificial intelligence that you call the singularity. How will that affect us?

By the time we get to the 2040s, we’ll be able to multiply human intelligence a billionfold. That will be a profound change that’s singular in nature. Computers are going to keep getting smaller and smaller. Ultimately, they will go inside our bodies and brains and make us healthier, make us smarter. We’ll be online all the time. Search engines won’t wait to be asked.

Probably nothing new here for those well-versed in Kurzweil’s ideas, but it’s still neat to see transhumanist ideas being published in a magazine with as wide a circulation as TIME.

Why the Singularity isn’t going to happen (10/16/10) – “That is how Singularity-level technologies work in real life. They solve dire problems, sure. They save lives. But they also create problems we’d never imagined – problems that might have been inconceivable before that Singularity tech was invented.” (io9)

Artificial intelligence has a feel for laboratory science (10/14/10) - “‘The artificial experimenter will provide a tool for scientists, which will not only allow them to reduce experimentation costs, but will also allow them to redirect their time from monotonous characterisation experiments, to analysing the results, building theories and determining uses for those results,’ say the researchers in their paper.” (The Engineer)

In New Study, Babies Think A Silvery Robot Is Human, As Long As It Acts Friendly (10/14/10) – “At 18 months old, babies have begun to make conscious delineations between sentient beings and inanimate objects. But as robots get more and more advanced, those decisions may become harder to make. What causes a baby to decide a robot is more than bits of metal? As it turns out, it takes more than humanoid looks–babies rely on social interaction to make that call.” (PopSci)

Scary Smart: The Next Trillion-Dollar Industry (10/12/10) – “I’m convinced the next trillion-dollar industry will be built around all the stuff that makes us smarter. This would include things that go into our bodies–performance-enhancing supplements and drugs, chip implants and the like–and such traditional tools as smartphones, Web search and databases.” (Forbes.com)

Two Days Among The Immortals (10/11/10) - “Exercise, eat healthy and not too much, sleep well, don’t stress out… and you may well live long enough to live forever.” (h+ Magazine)

Over at Big Think, physicist  Michio Kaku appears in a video where he talks about the progression of Moore’s Law, the evolution of AI, and how to prevent robots from doing us harm:

Right now our machines are as smart as insects.  Eventually they’ll be smart as mice.  After that they’ll be smart as dogs and cats.  Probably by the end of the century, who knows, they’ll be as smart as monkeys.  At that point they could become potentially dangerous because monkeys can formulate their own plans.  They don’t have to listen to you.  They can formulate their own strategies, their own goals and I would say therefore at that point let’s put a chip in their brain to shut them off if they get murderous thoughts.  Isaac Asimov advocated something like that with his “Three Laws.”  I say hey, put a chip in their brain to shut them off if they start to get murderous.

Of course, we can avoid this scenario altogether by developing AI with benevolent motivations.

“In twenty years time, it is likely that most astronomers will never go near a cutting-edge telescope,” says Ray Norris at the Commonwealth Scientific and Industrial Research Organisation in Epping, Australia. So begins a fascinating discussion about the future of humanity’s oldest science.

Norris paints an optimistic picture. For him, the future is filled with automation that will make astronomers’ jobs easier. He says, for example, that in twenty years time: “I expect to be able to click on an object in a paper, and see its image at all wavelengths.” This data will be provided more or less automatically by a new generation of smart telescopes that calibrate and edit data on the fly and then send it to a Virtual Observatory that anybody can access.

The job for astronomers will be to theorise about this data, to look for patterns within it and to see how it explains some problems and creates others. They might then suggest what other data to collect.

That should free up much of their time. Norris says the time not spent fiddling with equitorial mounts and lens cloths will allow them up to better engage with the public who pay their wages.

The article notes that as technology progresses, the ability of machines to find patterns and evaluate data may eclipse that of humans, as well. Machines in other scientific disciplines have made great strides in automated research. However, given the nature of astronomy, it’s likely that humans in this field may be among the first scientists to find themselves replaced by computers.

(Via Instapundit)

Many people around the world (and in the United States) do not have reliable access to medical care. This may be due to geographical factors, financial factors, cultural factors, or a combination thereof. However, mobile phone adoption continues to grow rapidly in both developed and underdeveloped countries.

With fast rates of adoption and increasing levels of computing power available through mobile phones to people around the world, the X PRIZE Foundation is looking to “inspire creation” of “The AI Physician X PRIZE,” which would use artificial intelligence to “diagnose patients better than board certified doctors.”

While this technology would certainly be a boon for many, a diagnosis is only the first part in healing a patient. Those without access to care would still be left without a means of actual treatment due to the same three factors that would prevent them from getting diagnosed. Gotta crawl before you walk, though, I suppose.

(Via Scope)

In order to assist with deciphering writing systems, researchers and students at MIT have developed software that can decipher unknown scripts by comparing them to those we understand:

A computer successfully deciphered an ancient language Ugaritic in just a couple of hours.

Regina Barzilay, an associate professor in MIT’s Computer Science and Artificial Intelligence Lab, Ben Snyder, a grad student in her lab, and the University of Southern California’s Kevin Knight are the creators. [...]

The system makes certain assumption of the language’s similarity to another, Hebrew in this case. It also depends on a systematic way to map the alphabet of one language on to the alphabet of the other.

And it assumes a similar mapping for parts of words. A word like “overloading,” for instance, has both a prefix – “over” – and a suffix – “ing.”

“We iterate through the data hundreds of times, thousands of times and each time, our guesses have higher probability, because we’re actually coming closer to a solution where we get more consistency,” said Snyder.

While the software isn’t threatening to put human deciphers out of work, the developers say it does have the potential to make their jobs a bit easier.

Quiz show Jeopardy! has been stumping human contestants and viewers for more than 45 years. Part of the show’s challenge lies in the way contestants are required to phrase answers, which must be in the form of a question. It can be flummoxing for humans, and would understandably be difficult for a machine.

Apparently IBM’s “Watson” supercomputer is not one of those machines.

Watson is the result of IBM’s effort to create a machine capable of understanding questions asked in “natural language” and responding in kind. In mock Jeopardy! matches, Watson has done remarkably well, handily defeating its human opponents. It’s been so successful, in fact, that IBM has agreed to feature the machine on an upcoming episode of Jeopardy! that could air as early as the fall.

Aside from racking up winnings on trivia game shows, Watson’s technology could revolutionize search engines, enabling computers to correctly interpret questions and provide more accurate answers, rather than require humans to phrase questions in ways machines can understand.

The New York Times has a fascinating feature on Watson, which is part of their ongoing series on artificial intelligence.

(Via Engadget)

He also provides his opinion on who is helping bring about this potential future:

Ok, so who is working towards this eventuality? Well, if it happens this way, pretty much everybody in the NBIC fields – everybody working on nanotech and biotech and AI and brain science, whether as citizen scientists in a collaborationist project or working for a corporation, or those wacky surrealists at DARPA – they’re all pushing this potentiality forward. Of course, we may have to “hijack the singularity” from them eventually – or even now (think gene patent v. open source bio). But mainly, I think all the people who are engaging in open source collaborationist tinkering and culture, the citizen scientists – particularly the more sophisticated and educated young people that are choosing to invest themselves in “garage” projects – I think they all may be taking us there.

I also think the best, smartest critics and skeptics and SF writers and creators are helping – by problematizing these scenarios in advance, by giving us arguments and narratives that remind us about human behaviors and emotions and political and economic and scientific realities. Brilliant fiction adds to our foresight… our pattern recognition… by playing out dramatic, difficult, dark, challenging, ambiguous or dystopian scenarios based on similar technological possibilities.

Like all of R.U. Sirius’ writings, it’s well worth reading.

Currently, Furber and his team are testing a version that includes a mere 50 “neurons” that can navigate a simple virtual environment described as “Pac-Man-like.”

Furber has big plans for the computer once it is completed:

That’s good enough for Furber, who wants to start teaching his brain-like computer about the world as soon as possible. His first goal is to teach it how to control a robotic arm, before working towards a design to control a humanoid. A robot controller with even a dash of brain-like properties should be much better at tasks like image recognition, navigation and decision-making, says Furber.

“Robots offer a natural, sensory environment for testing brain-like computers,” says Furber. “You can instantly tell if it is being useful.”

Processors for the project are currently being manufactured in Taiwan, and Furber intends to have a 10,000 chip version of the machine operational before the end of the year.

(Via PopSci)

Now, I’m in the process of developing completely automated sports content, which will take the form of blogs.  I’m not talking just a “stat of the day” or game recaps, but a lot more. I’ve identified 21 different types of sports stories that can be automated.  You could say I’m trying to make the process of writing a sports blog so easy you don’t have to do anything at all.  My goal for these blogs in version 1.0 is that at least 90% of the readers think the content was created by a human. One of the nice attributes of algorithmic content is that it can be improved over time.  A blogger/writer’s internal script is pretty much set.  They generally don’t change or improve the quality or comprehensiveness of their content over time in a significant way, but algorithms can be upgraded continuously. The “voice” of the content can be improved.

The author points out that, while not all sports news can be easily automated (thus requiring human intervention), this algorithm should go a big way toward helping develop content, and may be especially helpful for smaller markets, where local news outlets may not have the resources to devote to covering sports.