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Right before the final scenes of the film.

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Will Artificial Intelligence (AI) Effect Your Business?
Artificial Intelligence (AI) is rapidly evolving and affecting every area of our lives. It #39;s affecting your communications, transportation, manufacturing and delivery of products and services....

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#39;SUPERINTELLIGENT #39; ROBOTS - TREAT HUMANS AS PETS, KILL UNHAPPY, VIOLENT ONES, BREED HUMANS
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The most exciting future - if we aren #39;t killed on the way | Gabrielle Grobler | TEDxYouth@HITECCity
We #39;ve all heard of Artificial Intelligence, but how many of us really know what it is? Have any of us stopped to consider the consequences of what may be the most influential invention of the...

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So what's a CEO to do?

1. Expect that Software as a Service (SaaS) will become more and more like "Everything as a Service."

That goes for everything from sourcing talent to getting probabilistic predictions of the sales of your products. Your company needs to keep up with the times, embrace new services and A.I.-based technologies or fall behind. Similarly to what happened a decade ago, when enterprise mobility entered the strategic road map of major corporations, A.I.-enabled technologies will become an integral part of the strategy planning process in the near future.

2. If you think A.I. is not hereat least not in a conspicuous way when it comes to your businessthink twice.

Do your marketing people hire research or customer insights reports? Most likely your contractors are using big data analytics to deliver their conclusions to you. Is your competitor doing so in-house? That may indicate they can react and move faster in decision-making and eventually interact rapidly and more decisively with (your) customers.

3. Conduct an A.I. inventory.

Map out existing internal and external resources of your company and match them to available big data, analytics and A.I.-related technologies and tools.

4. Delegate it.

To make it simple and to monitor A.I. readiness of your company, you should earmark a tech champion in your management team (if there isn't one already). The chief information officer promoted to chief digital officer or chief marketing officer will do for the time being. Task the tech champion to screen for any of these referred tools or technologies currently in use either internally or externally. If the answer is a hard no internally, run a supplier and partner appraisal to understand how far A.I. is from the core of your business. Give yourself a score on a scale of 1 to 10 and start pushing the company to embark on the A.I. journey. You probably already did this when you told your management team to start using mobiles, tablets or smartphones.

5. Think ahead and don't wait; there's no reason you can't, or shouldn't, be the one to A.I.-innovate.

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How CEOs can outwit artificial intelligence

No level of security screening short of mind-reading could have prevented the crash of Germanwings flight 9525. But what can be done? The New York Times editorial todaycalls for the American standard that requires two crew members be in the cockpit at all times to be adopted by all airlines. Thissuggestion isreasonable,but would not prevent a team of two pilotsfrom accomplishing a similarly evil deed.

The Times correctly assertsAir travel over all remains incredibly safe. The plane in question, the Airbus A320, has among the worlds best safety records and was the first commercial airliner to have an all-digital fly-by-wire control system. Much of the criticism over the years of these fly-by-wire systems has focused on the problem of pilots becoming too dependent on technology, but these systems could also be a means of preventing future tragedies. In fly-by-wire planes, a story on a previous Airbus crash in Popular Mechanicsreports, The vast majority of the time, the computer operates within whats known as normal law, which means that the computer will not enact any control movements that would cause the plane to leave its flight envelope. The flight control computer under normal law will not allow an aircraft to stall, aviation experts say. If autopilot is disconnected or reset, as the New York Times reports it was on the Germanwings plane, it can be switched to alternate law, a regime with far fewer restrictions on what a pilot can do.

Germanwings Airbus A320

I just happened tohave scheduled an interview with AI pioneer Jeff Hawkins today to talk about the recent upswell of fears about AI and superintelligence that he addressed in a post on Re/code. The Terminator Is Not Coming, his title announces. The Future Will Thank Us. I thought of this story as the news unfolded from the Alps. We are so concerned, it seems, about giving machines too much power that we appear to miss the fact that the largest existential threat to humans isother humans. Such seems to be the case with Germanwings 9525.

Hawkinsis the inventor of the Palm Pilot (the first personal digital assistant or PDA) and the Palm Treo (one of the first smartphones). Heis also the co-founder, with Donna Dubinsky, of the machine intelligence company Numenta. Grok, the companys first commercial product, sifts through massive amounts of server activity data on Amazon Web Services (AWS) to identify anomalouspatterns of events. This same approach could easily be used to monitor flight data from airplanes and alert ground control in real time of the precise nature of unexpected activity. Numenta open sources its software (see Numenta.org) and is known to DARPA and other government research agencies, so multiple parties could already be at work on such a system.

Hawkins approach to machine intelligence, Hierarchical Temporal Memory (HTM), has some distinct advantages overthe highly-publicized technique of deep learning(DL). Both use hierarchies of matrices to learn patterns from large data sets. HTMtakes its inspiration from biology and uses the layering of neurons in the brain as a model for its architecture. DL is primarily mathematical and projects the abstraction of the brains hierarchy to deeper and deeper levels. HTM uses larger matrices and flatter hierarchies to store patterns than DL and the data in these matrices is characterized by sparse distributions. Most important, HTM processestime-based data whereas DL trains mostly on static data sets.

For the emerging Internet of Things (IoT), time-based and real-time data is incredibly important. Systems that can learn continuously from these data streams, like Numentas, will be particularly valuable for keeping track of all of those thingsincluding errant airplanes. Could machine intelligence have prevented this tragedy? Hawkins thinks sobut notes, All the intelligence in the world in the cockpit wont solve any problem if the pilot decides to turn it off. There will need to be aviation systems designed for potential override from ground. What are we the most scared of, individual agency or systematic control? Based on the Germanwings evidence so far, lack of override control from the ground is the greater threat.

I contacted my colleague Dan Reed, who covers aviation and logistics for Forbes.com. He wrote recently on how inexpensive it would be for the airlines to increase their tracking of flights using existing signals. He raised the additional issue of the bandwidth that would be required to control a plane reliably from the ground without significant time delay. Thishardware, he says, would require a substantial investment. Securing those transmissions is also important to make sure that the failsafe does not become a backdoor for bad actors. The most important impediment to controlling planes remotely (even temporarily), is philosophical, he says. Even if machines become statistically safer than humans, as Google contends with cars, how do you prove it would be safer, Reed asks?

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Artificial Intelligence Could Have Prevented The Germanwings Crash

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Apple Co-Founder Steve Wozniak Warns Artificial Intelligence May Enslave Humans
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The Super Rich Technologists Making Dire Predictions About Artificial Intelligence club gained another fear-mongering member this week: Apple co-founder Steve Wozniak.

In an interview with the Australian Financial Review, Wozniak joined original club members Bill Gates, Stephen Hawking and Elon Musk by making his own casually apocalyptic warning about machines superseding the human race.

"Like people including Stephen Hawking and Elon Musk have predicted, I agree that the future is scary and very bad for people," Wozniak said. "If we build these devices to take care of everything for us, eventually they'll think faster than us and they'll get rid of the slow humans to run companies more efficiently."

[Bill Gates on dangers of artificial intelligence: I dont understand why some people are not concerned]

Doling out paralyzing chunks of fear like gumdrops to sweet-toothed children on Halloween, Woz continued: "Will we be the gods? Will we be the family pets? Or will we be ants that get stepped on? I don't know about that But when I got that thinking in my head about if I'm going to be treated in the future as a pet to these smart machines well I'm going to treat my own pet dog really nice."

Seriously? Should we even get up tomorrow morning, or just order pizza, log onto Netflix and wait until we find ourselves looking through the bars of a dog crate? Help me out here, man!

Wozniak's warning seemed to follow the exact same story arc as Season 1 Episode 2 of Adult Swim's "Rick and Morty Show."Not accusing him of apocalyptic plagiarism or anything; just noting.

For what it's worth, Wozniak did outline a scenario by which super-machines will be stopped in their human-enslaving tracks. Citing Moore's Law -- "the pattern whereby computer processing speeds double every two years" -- Wozniak pointed out that at some point, the size of silicon transistors, which allow processing speeds to increase as they reduce size, will eventually reach the size of an atom, according to the Financial Review.

"Any smaller than that, and scientists will need to figure out how to manipulate subatomic particles a field commonly referred to asquantum computing which has not yet been cracked," Quartz notes.

Wozniak's predictions represent a bit of a turnaround, the Financial Review pointed out. While he previously rejected the predictions of futurists such asthe pill-popping Ray Kurzweil, who argued that super machines will outpace human intelligence within several decades, Wozniak told the Financial Review that he came around after he realized the prognostication was coming true.

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Apple co-founder on artificial intelligence: The future ...

Google Inc. announced this month that it had developed the most accurate facial-recognition technology to date called FaceNet, which the company said trumped Facebook Inc.s rival software called DeepFace by almost three percentage points in a test of accuracy. That was a tough truth for Facebook to swallow, because both companies have invested heavily in artificial-intelligence and computer-logic research to fuel the accuracy and speed of their respective systems, and because a billion monthly users alreadyrely on a form of Facebooks version to tag photographs when they log into the site. It appeared Facebook was getting beat at its own game.

Yann LeCun, head of Facebooks Artificial Intelligence Research lab, spoke Tuesday about how Facebook originally built the tools that currently handle the sites many photos and how his team plans to expand on that proficiency to build the next generation of artificial-intelligence software at an event co-sponsored by Facebook, Medidata and New York Universitys Center for Data Science that was held at the formers offices in New York. Its complicated, but its simpler than you might think, LeCun said.He leads a 40-member group of artificial-intelligence experts thatis only a year old, and split between Facebooks offices in New York, the companys headquarters in Menlo Park, California, and the firms new branch in Paris.

That team and Facebooks developers are in a race against other major technological companies, including Google, to create the fastest and most sophisticated systems not only for facial recognition but also for a whole suite of products built on the tenets of artificial intelligence. Along with Facebook and Google, Alibaba Group Holding Ltd. and Amazon.com Inc. also have stated interests in this area,as Bloomberg Business reported. Last year, 16 artificial-intelligence startups were funded, while in 2010 the comparable figure was only two.

Facebook and its competitors believe people will increasingly rely on artificial intelligence to communicate with each other and to interact with the digital world. To stay ahead in this stiff competition, LeCun said his team needs to make breakthroughs in the field of deep learning, or the process by which machines can help humans at tasks that people have always proven best at, including making decisions or reasoning.

A computer capable of the advanced machine logic known as deep learning would require more inputs, outputs, levels and layers than Facebooks facial recognition and photo-tagging software, but LeCun said both projects would rely on many of the same fundamental methods that computers and programmers currently use to organize and prioritize information.

At any given moment, Facebook software is busy tagging and categorizing the 500 million photos that users upload to the site each day, all within two seconds of when the images first appear. At nearly the same time, the systems logic decides which photos to display to which users based not only on permissions but also on their preferences. Although the volume of data that this program processes would be mind-boggling for any human, the methods by which it sorts through those images are crafted by LeCuns team.

Most Facebook users have seen friends names pop up in suggested tags when they upload photos to the site, but the company also uses tags to categorize the objects within images and help its software to decide which photos to display on the site. Although the system could display as many as 1,500 photos a day in a users stream, the average Facebook user will spend only enough time on the site to see between 100 and 150 images a day. A form of artificial intelligence helps Facebook ensure users are seeing the most important ones.

To create a similar system that would fuel the company's foray into deep learning, developers and experts began with a large database of images and tags such as ImageNet, and they built programs that learned to associate characteristics of each tag with specific types of images. For example, differentiating between colors and shapes helps the software pick out a black road versus a gray sidewalk in an image of a city street. The network is able to take advantage of the fact that the world is compositional, LeCun said.

Once the program recognizes features such as streets or sidewalks in a photo, it can draw a box around each object and identify them as separate from each other, or highlight examples of only one or the other. LeCun demonstrated this last concept in a shaky video taken on a walk through Washington Square Park in New York. The software picked out pedestrians as they moved past, drawing a rectangular box around them on the screen.

A sophisticated tagging program should also be able to first distinguish between a black road and a black car, and then assign names and categories to these objects. To do this, experts teach the system to grab contextual clues from the pixels surrounding an unidentified object to determine its most likely identity. So in that photo of a city street, the software may identify and tag a road based on its shape, its color and the presence of a nearby sidewalk. Then, it could surmise that the bulky shape in the center of that road is probably a black car.

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Facebook And Artificial Intelligence: Company's AI Chief Explains How He Tags Your Photos

Comments made in an interview with scientist Neil deGrasse Tyson Musk said fears are based on something known as 'superintelligence' Apple co-founder Steve Wozniak made similar comments this year He says AI predictions are coming true and it is a dangerous reality Musk also recently said robots could soon replace humans as drivers

By Ellie Zolfagharifard and Victoria Woollaston for MailOnline

Published: 12:00 EST, 25 March 2015 | Updated: 14:12 EST, 25 March 2015

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Robots will use humans as pets once they achieve a subset of artificial intelligence known as 'superintelligence'.

This is according to SpaceX-founder Elon Musk who claims that when computers become smarter than people, they will treat them like 'pet Labradors'.

His comments were made in a recent interview with scientist Neil deGrasse Tyson, who added that computers could choose to breed docile humans and eradicate the violent ones.

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Elon Musk claims artificial intelligence will treat humans like 'labradors'

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The Super Rich Technologists Making Dire Predictions About Artificial Intelligence club gained another fear-mongering member this week: Apple co-founder Steve Wozniak.

In an interview with the Australian Financial Review, Wozniak joined original club members Bill Gates, Stephen Hawking and Elon Musk by making his own casually apocalyptic warning about machines superseding the human race.

"Like people including Stephen Hawking and Elon Musk have predicted, I agree that the future is scary and very bad for people," Wozniak said. "If we build these devices to take care of everything for us, eventually they'll think faster than us and they'll get rid of the slow humans to run companies more efficiently."

[Bill Gates on dangers of artificial intelligence: I dont understand why some people are not concerned]

Doling out paralyzing chunks of fear like gumdrops to sweet-toothed children on Halloween, Woz continued: "Will we be the gods? Will we be the family pets? Or will we be ants that get stepped on? I don't know about that But when I got that thinking in my head about if I'm going to be treated in the future as a pet to these smart machines well I'm going to treat my own pet dog really nice."

Seriously? Should we even get up tomorrow morning, or just order pizza, log onto Netflix and wait until we find ourselves looking through the bars of a dog crate? Help me out here, man!

Wozniak's warning seemed to follow the exact same story arc as Season 1 Episode 2 of Adult Swim's "Rick and Morty Show."Not accusing him of apocalyptic plagiarism or anything; just noting.

For what it's worth, Wozniak did outline a scenario by which super-machines will be stopped in their human-enslaving tracks. Citing Moore's Law -- "the pattern whereby computer processing speeds double every two years" -- Wozniak pointed out that at some point, the size of silicon transistors, which allow processing speeds to increase as they reduce size, will eventually reach the size of an atom, according to the Financial Review.

"Any smaller than that, and scientists will need to figure out how to manipulate subatomic particles a field commonly referred to asquantum computing which has not yet been cracked," Quartz notes.

Wozniak's predictions represent a bit of a turnaround, the Financial Review pointed out. While he previously rejected the predictions of futurists such asthe pill-popping Ray Kurzweil, who argued that super machines will outpace human intelligence within several decades, Wozniak told the Financial Review that he came around after he realized the prognostication was coming true.

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Apple co-founder on artificial intelligence: The future is scary and very bad for people

17 hours ago by David Stauth

The most realistic risks about the dangers of artificial intelligence are basic mistakes, breakdowns and cyber attacks, an expert in the field says more so than machines that become super powerful, run amok and try to destroy the human race.

Thomas Dietterich, president of the Association for the Advancement of Artificial Intelligence and a distinguished professor of computer science at Oregon State University, said that the recent contribution of $10 million by Elon Musk to the Future of Life Institute will help support some important and needed efforts to ensure AI safety.

But the real risks may not be as dramatic as some people visualize, he said.

"For a long time the risks of artificial intelligence have mostly been discussed in a few small, academic circles, and now they are getting some long-overdue attention," Dietterich said. "That attention, and funding to support it, is a very important step."

Dietterich's perspective of problems with AI, however, is a little more pedestrian than most not so much that it will overwhelm humanity, but that like most complex engineered systems, it may not always work.

"We're now talking about doing some pretty difficult and exciting things with AI, such as automobiles that drive themselves, or robots that can effect rescues or operate weapons," Dietterich said. "These are high-stakes tasks that will depend on enormously complex algorithms.

"The biggest risk is that those algorithms may not always work," he added. "We need to be conscious of this risk and create systems that can still function safely even when the AI components commit errors."

Dietterich said he considers machines becoming self-aware and trying to exterminate humans to be more science fiction than scientific fact. But to the extent that computer systems are given increasingly dangerous tasks, and asked to learn from and interpret their experiences, he says they may simply make mistakes.

"Computer systems can already beat humans at chess, but that doesn't mean they can't make a wrong move," he said. "They can reason, but that doesn't mean they always get the right answer. And they may be powerful, but that's not the same thing as saying they will develop superpowers."

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Artificial intelligence systems more apt to fail than to destroy

Elon Musk has already ignited a debate over the dangers of artificial intelligence. The chief executive of Tesla and SpaceX has called it humanitysgreatest threat, and something even more dangerous than nuclear weapons.

Musk publicly hasnt offered a lot of detail about why hes concerned, and what could go wrong. That changed in an interview with scientist Neil deGrasse Tyson, posted Sunday.

Musks fears lie with a subset of artificial intelligence, called superintelligence. Its defined by Nick Bostrom, author of the highly-cited book Superintelligence, as any intellect that greatly exceed the cognitive performance of humans in virtually all domains of interest.

Musk isnt worried about simpler forms of artificial intelligence, such as a driverless car or smart conditioning unit. The danger is when a machine can rapidly educate itself, as Musk explained:

If there was a very deep digital superintelligence that was created that could go into rapid recursive self-improvement in a non-algorithmic way it could reprogram itself to be smarter and iterate very quickly and do that 24 hours a day on millions of computers, well

Then thats all she wrote, interjected Tysonwith a chuckle.

Thats all she wrote, Musk answered. I mean, we wont be like a pet Labrador if were lucky.

A pet Lab, laughed Tyson.

I have a pet Labrador by the way, Musk said.

Well be their pets, Tysonsaid.

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Innovations: Elon Musk, Neil deGrasse Tyson laugh about artificial intelligence turning the human race into its pet ...

Artificial Intelligence (AI) has been enjoying a major resurgence in recent months and for some seasoned professionals, who have been in the AI industry since the 1980s, it feels like dj vu all over again.

AI, being a loosely defined collection of techniques inspired by natural intelligence, does have a mystic aspect to it. After all, we do culturally assign positive value to all things smart, and so we naturally expect any system imbued with AI to be good, or it is not AI. When AI works, it is only doing what it is supposed to do, no matter how complex an algorithm being used to enable it, but when it fails to workeven if what was asked of it is impractical or out of scopeit is often not considered intelligent anymore. Just think of your personal assistant.

For these reasons, AI has typically gone through cycles of promise, leading to investment, and then under-delivery, due to the expectation problem noted above, which has inevitably led to a tapering off of the funding.

This time, however, the scale and scope of this surge in attention to AI is much larger than before. During the latter half of 2014, there was an injection of nearly half a billion dollars into the AI industry.

What are the drivers behind this?

For starters, the infrastructure speed, availability, and sheer scale has enabled bolder algorithms to tackle more ambitious problems. Not only is the hardware faster, sometimes augmented by specialized arrays of processors (e.g., GPUs), it is also available in the shape of cloud services. What used to be run in specialized labs with access to super computers can now be deployed to the cloud at a fraction of the cost and much more easily. This has democratized access to the necessary hardware platforms to run AI, enabling a proliferation of start-ups.

Furthermore, new emerging open source technologies, such as Hadoop, allow speedier development of scaled AI technologies applied to large and distributed data sets.

A combination of other events has helped AI gain the critical-mass necessary for it to become the center of attention for technology investment. Larger players are investing heavily in various AI technologies. These investments go beyond simple R&D extensions of existing products, and are often quite strategic in nature. Take for example, IBMs scale of investment in Watson, or Googles investment in driverless cars, Deep Learning (i.e., DeepMind), and even Quantum Computing, which promises to significantly improve on efficiency of machine learning algorithms.

On top of this, theres a more wide scale awareness of AI in the general population, thanks in no small part to the advent and relative success of natural language mobile personal assistants. Incidentally, the fact that Siri can be funny sometimes, which ironically is technically relatively simple to implement, does add to the impression that it is truly intelligent.

But theres more substance to this resurgence than the impression of intelligence that Siris jocularity gives its users. The recent advances in Machine Learning are truly groundbreaking. Artificial Neural Networks (deep learning computer systems that mimic the human brain) are now scaled to several tens of hidden layer nodes, increasing their abstraction power. They can be trained on tens of thousands of cores, speeding up the process of developing generalizing learning models. Other mainstream classification approaches, such as Random Forest classification, have been scaled to run on very large numbers of compute nodes, enabling the tackling of ever more ambitious problems on larger and larger data-sets (e.g., Wise.io).

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The AI Resurgence: Why Now?