Monthly Archives: August 2017

One hundred and sixteen roboticists and AI researchers, including SpaceX founder Elon Musk and Google Deepmind co-founder Mustafa Suleyman, have signed a letter to the United Nations calling for strict oversight of autonomous weapons, a.k.a. "killer robots." Though the letter itself is more circumspect, an accompanying press release says the group wants "a ban on their use internationally."

Other signatories of the letter include executives and founders from Denmarks Universal Robotics, Canadas Element AI, and Frances Aldebaran Robotics.

The letter describes the risks of robotic weaponry in dire terms, and says that the need for strong action is urgent. It is aimed at a group of UN officials considering adding robotic weapons to the UNs Convention on Certain Conventional Weapons . Dating back to 1981, the Convention and parallel treaties currently restrict chemical weapons, blinding laser weapons, mines, and other weapons deemed to cause unnecessary or unjustifiable suffering to combatants or to affect civilians indiscriminately.

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Robotic warriors could arguably reduce casualties among human soldiers at least, those of the wealthiest and most advanced nations. But the risk to civilians is the headline concern of Musk and Suleymans group, who write that these can be weapons of terror, weapons that despots and terrorists use against innocent populations, and weapons hacked to behave in undesirable ways."

The letter also warns that failure to act swiftly will lead to an arms race towards killer robots but thats arguably already underway. Autonomous weapons systems or precursor technologies are available or under development from firms including Raytheon , Dassault , MiG , and BAE Systems.

Element AI founder Yoshua Bengio had another intriguing warning that weaponizing AI could actually hurt the further development of AIs good applications. Thats precisely the scenario foreseen in Frank Herberts sci-fi novel Dune, set in a universe where all thinking machines are banned because of their role in past wars.

The UN weapons group was due to meet on Monday, August 21, but that meeting has reportedly been delayed until November.

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Elon Musk and AI Experts Call for Total Ban on Robotic Weapons - Fortune

Prisma, the Russian company best known for its AI-powered photo filters, is shifting to B2B. The company wont retire its popular app, but says in the future, it will focus on selling machine vision tools to other tech firms.

We see big opportunities in deep learning and communication, Prisma CEO and co-founder Alexey Moiseenkov told The Verge. We feel that a lot of companies need expertise in this area. Even Google is buying companies for computer vision. We can help companies put machine vision in their app because we understand how to implement the technology. The firm has launched a new website prismalabs.ai in order to promote these services.

Prisma will offer a number of off-the-shelf vision tools, including segmentation (separating the foreground of a photo from the background), face mapping, and both scene and object recognition. The companys expertise is getting these sorts of systems powered by neural networks to run locally on-device. This can be a tricky task, but avoiding using the cloud to power these services can result in apps that are faster, more secure, and less of a drain on phone and tablet battery life.

Getting copied by Facebook might help account for its pivot to B2B

Although Prismas painting-inspired filters were all the rage last year (the app itself was released in June 2015), they were soon copied by the likes of Facebook, which might account for the Russian companys change in direction.

Moiseenkov denies this is the case, and says it wasnt his intention to compete with bigger social networks. We never thought we were a competitor of Facebook were a small startup, with a small budget, he said. But, he says, the popularity of these deep learning filters shows there are plenty of consumer applications for the latest machine vision tech.

Moiseenkov says his company will continue to support the Prisma app, and that it will act as a showcase for the firms latest experiments. He says the app still has between 5 million and 10 million monthly active users, most of which are based in the US. The company also started experimenting with selling sponsored filters on its main app last year, and says it will continue to do so. It also launched an app for turning selfies into chat stickers.

There have been rumors that Prisma would get bought out by a bigger company. Moiseenkov visited Facebooks headquarters last year, and the US tech giant has made similar acquisitions in the past buying Belarus facial filter startup MSQRD in March 2016. When asked if the company would consider a similar deal, co-founder Aram Airapetyan replied over email: We want to go on doing what we do and what we can do best. The whole team is super motivated and passionately committed to what we do! So the rest doesn't matter (where, when, with whom). Make of that what you will.

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AI-powered filter app Prisma wants to sell its tech to other companies - The Verge

AI is one of hottest trends in tech at the moment, but what happens when its merged with another fashionable and extremely promising tech?

Researchers are looking for ways to take big data to the next level by combining it with AI. Weve just recently realized how powerful big data can be, and by uniting it with AI, big data is swiftly marching towards a level of maturity that promises a bigger, industry-wide disruption.

The application of artificial intelligence on big data is arguably the most important modern breakthrough of our time. It redefines how businesses create value with the help of data. The availability of big data has fostered unprecedented breakthroughs in machine learning, that could not have been possible before.

With access to large volumes of datasets, businesses are now able to derive meaningful learning and come up with amazing results. It is no wonder then why businesses are quickly moving from a hypothesis-based research approach to a more focused data first strategy.

Businesses can now process massive volumes of data which was not possible before due to technical limitations. Previously, they had to buy powerful and expensive hardware and software. The widespread availability of data is the most important paradigm shift that has fostered a culture of innovation in the industry.

The availability of massive datasets has corresponded with remarkable breakthroughs in machine learning, mainly due to the emergence of better, more sophisticated AI algorithms.

The best example of these breakthroughs is virtual agents. Virtual agents (more commonly known as chat bots), have gained impressive traction over the course of time. Previously, chatbots had trouble identifying certain phrases or regional accents, dialects or nuances.

In fact, most chatbots get stumped by the simplest of words and expressions, such as mistaking Queue for Q and so on. With the union of big data and AI however, we can see new breakthroughs in the way virtual agents can self-learn.

A good example of self-learning virtual agents is Amelia, a cognitive agent recently developed by IPSoft. Amelia can understand everyday language, learn really fast and even gets smarter with time!

She is deployed at the help desk of Nordic bank SEB along with a number of public sector agencies. The reaction of executive teams to Amelia has been overwhelmingly positive.

Google is also delving deeper into big data-powered AI learning. DeepMind, Googles very own artificial intelligence company, has developed an AI that can teach itself to walk, run, jump and climb without any prior guidance. The AI was never taught what walking or running is but managed to learn it itself through trial and error.

The implications of these breakthroughs in the realm of artificial intelligence are astounding and could provide the foundation for further innovations in the times to come. However, there are dire repercussions of self-learning algorithms too and, if werent too busy to notice, you may have observed quite a few in the past.

Not long ago, Microsoft introduced its own artificial intelligence chatbot named Tay. The bot was made available to the public for chatting and could learn through human interactions. However, Microsoft pulled the plug on the project only a day after the bot was introduced to Twitter.

Learning at an exponential level mainly through human interactions, Tay transformed from an innocent AI teen girl to an evil, Hitler-loving, incestuous, sex-promoting, Bush did 9/11-proclaiming robot in less than 24 hours.

Some fans of sci-fi movies like Terminator also voice concerns that with the access it has to big data, artificial intelligence may become self-aware and that it may initiate massive cyberattacks or even take over the world. More realistically speaking, it may replace human jobs.

Looking at the rate of AI-learning, we can understand why a lot of people around the world are concerned with self-learning AI and the access it enjoys to big data. Whatever the case, the prospects are both intriguing and terrifying.

There is no telling how the world will react to the amalgamation of big data and artificial intelligence. However, like everything else, it has its virtue and vices. For example, it is true that self-learning AI will herald a new age where chatbots become more efficient and sophisticated in answering user queries.

Perhaps we would eventually see AI bots on help desks in banks, waiting to greet us. And, through self-learning, the bot will have all the knowledge it could ever need to answer all our queries in a manner unlike any human assistant.

Whatever the applications, we can surely say that combining big data with artificial intelligence will herald an age of new possibilities and astounding new breakthroughs and innovations in technology. Lets just hope that the virtues of this union will outweigh the vices.

Read next: Military-funded prosthetic technologies benefit veterans, but also kids

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Merging big data and AI is the next step - TNW

In this April 30, 2015, file photo, Tesla Motors CEO Elon Musk unveils the company's newest products, in Hawthorne, Calif. Ringo H.W. Chiu/AP hide caption

In this April 30, 2015, file photo, Tesla Motors CEO Elon Musk unveils the company's newest products, in Hawthorne, Calif.

One of Tesla CEO Elon Musk's companies, the nonprofit start-up OpenAI, manufactures a device that last week was victorious in defeating some of the world's top gamers in an international video game (e-sport) tournament with a multi-million-dollar pot of prize money.

We're getting very good, it seems, at making machines that can outplay us at our favorite pastimes. Machines dominate Go, Jeopardy, Chess and as of now at least some video games.

Instead of crowing over the win, though, Musk is sounding the alarm. Artificial Intelligence, or AI, he argued last week, poses a far greater risk to us now than even North Korean warheads.

No doubt Musk's latest pronouncements make for good advertising copy. What better way to drum up interest in a product than to announce that, well, it has the power to destroy the world.

But is it true? Is AI a greater threat to mankind than the threat posed to us today by an openly hostile, well-armed and manifestly unstable enemy?

AI means, at least, three things.

First, it means machines that are faster, stronger and smarter than us, machines that may one day soon, HAL-like, come to make their own decisions and make up their own values and, so, even to rule over us, just as we rule over the cows. This is a very scary thought, not the least when you consider how we have ruled over the cows.

Second, AI means really good machines for doing stuff. I used to have a coffee machine that I'd set with a timer before going to bed; in the morning I'd wake up to the smell of fresh coffee. My coffee maker was a smart, or at least smart-ish, device. Most of the smart technologies, the AIs, in our phones, and airplanes, and cars, and software programs including the ones winning tournaments are pretty much like this. Only more so. They are vastly more complicated and reliable but they are, finally, only smart-ish. The fact that some of these new systems "learn," and that they come to be able to do things that their makers cannot do like win at Go or Dota is really beside the point. A steam hammer can do what John Henry can't but, in the end, the steam hammer doesn't really do anything.

Third, AI is a research program. I don't mean a program in high-tech engineering. I mean, rather, a program investigating the nature of the mind itself. In 1950, the great mathematician Alan Turing published a paper in a philosophy journal in which he argued that by the year 2000 we would find it entirely natural to speak of machines as intelligent. But more significantly, working as a mathematician, he had devised a formal system for investigating the nature of computation that showed, as philosopher Daniel Dennett puts it in his recent book, that you can get competence (the ability to solve problems) without comprehension (by merely following blind rules mechanically). It was not long before philosopher Hilary Putnam would hypothesize the mind is a Turing Machine (and a Turing Machine just is, for all intents and purposes, what we call a computer today). And, thus, the circle closes. To study computational minds is to study our minds, and to build an AI is, finally, to try to reverse engineer ourselves.

Now, Type 3 AI, this research program, is alive and well and a continuing chapter in our intellectual history that is of genuine excitement and importance. This, even though the original hypothesis of Putnam is wildly implausible (and was given up by Putnam decades ago). To give just one example: the problem of the inputs and the outputs. A Turing Machine works by performing operations on inputs. For example, it might erase a 1 on a cell of its tape and replace it with a 0. The whole method depends on being able to give a formal specification of a finite number of inputs and outputs. We can see how that goes for 1s and 0s. But what are the inputs, and what are the outputs, for a living animal, let alone a human being? Can we give a finite list, and specify its items in formal terms, of everything we can perceive, let alone, do?

And there are other problems, too. To mention only one: We don't understand how the brain works. And this means that we don't know that the brain functions, in any sense other than metaphorical, like a computer.

Type 1 AI, the nightmare of machine dominance, is just that, a nightmare, or maybe (for the capitalists making the gizmos) a fantasy. Depending on what we learn pursuing the philosophy of AI, and as luminaries like John Searle and the late Hubert Dreyfus have long argued, it may be an impossible fiction.

Whatever our view on this, there can be no doubt that the advent of smart, rather than smart-ish, machines, the sort of machines that might actually do something intelligent on their own initiative, is a long way off. Centuries off. The threat of nuclear war with North Korea is both more likely and more immediate than this.

Which does not mean, though, that there is not in fact real cause for alarm posed by AI. But if so, we need to turn our attention to Type 2 AI: the smart-ish technologies that are everywhere in our world today. The danger here is not posed by the technologies themselves. They aren't out to get us. They are not going to be out to get us any time soon. The danger, rather, is our increasing dependence on them. We have created a technosphere in which we are beholden to technologies and processes that we do not understand. I don't mean you and me, that we don't understand: No one person can understand. It's all gotten too complicated. It takes a whole team or maybe a university to understand adequately all the mechanisms, for example, that enable air traffic control, or drug manufacture, or the successful production and maintenance of satellites, or the electricity grid, not to mention your car.

Now this is not a bad thing in itself. We are not isolated individuals all alone and we never have been. We are a social animal and it is fine and good that we should depend on each other and on our collective.

But are we rising to the occasion? Are we tending our collective? Are we educating our children and organizing our means of production to keep ourselves safe and self-reliant and moving forward? Are we taking on the challenges that, to some degree, are of our own making? How to feed 7 billion people in a rapidly warming world?

Or have we settled? Too many of us, I fear, have taken up a "user" attitude to the gear of our world. We are passive consumers. Like the child who thinks chickens come from supermarkets, we are hopelessly alienated from how things work.

And if we are, then what are we going to do if some clever young person some where maybe a young lady in North Korea writes a program to turn things off? This is a serious and immediate pressing danger.

Alva No is a philosopher at the University of California, Berkeley, where he writes and teaches about perception, consciousness and art. He is the author of several books, including his latest, Strange Tools: Art and Human Nature (Farrar, Straus and Giroux, 2015). You can keep up with more of what Alva is thinking on Facebook and on Twitter: @alvanoe

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Is AI More Threatening Than North Korean Missiles? - NPR