Monthly Archives: March 2017

Googles director of engineering Ray Kurzweil has said the AI singularity will happen in the year 2029, and just a few years later humans will merge with machines.

The AI singularity is the point where machines match human-level intelligence.

Speaking at the SXSW Conference in Austin, Texas, Mr Kurzweil said: "By 2029, computers will have human-level intelligence.

He added that the process has already begun.

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The Google employee said: "That leads to computers having human intelligence, our putting them inside our brains, connecting them to the cloud, expanding who we are.

Today, that's not just a future scenario.

"It's here, in part, and it's going to accelerate.

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Mr Kurzweil continued by stating that predictions that AI will enslave humans is not realistic, adding that it is already ubiquitous.

He said: "We don't have one or two AIs in the world. Today we have billions.

What he envisions is actually a world where AIs purpose is to benefit humanity, rather than exceed it, before predicting that we will one day finally merge with machines which, he believes, will massively improve us as beings.

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The 69-year old computer scientist said: "What's actually happening is [machines] are powering all of us.

"They're making us smarter.

They may not yet be inside our bodies, but, by the 2030s, we will connect our neocortex, the part of our brain where we do our thinking, to the cloud.

"We're going to get more neocortex, we're going to be funnier, we're going to be better at music. We're going to be sexier.

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"We're really going to exemplify all the things that we value in humans to a greater degree.

"Ultimately, it will affect everything.

"We're going to be able to meet the physical needs of all humans.

We're going to expand our minds and exemplify these artistic qualities that we value."

Excerpt from:

AI will be smarter than HUMANS by 2029 before we MERGE with ... - Express.co.uk

The road to human-level artificial intelligence is long and wildly uncertain. Most AI programs today are one-trick ponies. They can recognize faces, the sound of your voice, translate foreign languages, trade stocks and play chess. They may well have got the trick down pat, but one-trick ponies they remain. Google's DeepMind program, AlphaGo, can beat the best human players at Go, but it hasn't a clue how to play tiddlywinks, shove ha'penny, or tell one end of a horse from the other.

Humans, on the other hand, are not specialists. Our forte is versatility. What other animal comes close as the jack of all trades? Put humans in a situation where a problem must be solved and, if they can leave their smartphones alone for a moment, they will draw on experience to work out a solution.

The skill, already evident in preschool children, is the ultimate goal of artificial intelligence. If it can be distilled and encoded in software, then thinking machines will finally deserve the name.

DeepMind's latest AI has cleared one of the important hurdles on the way to human-level AGI -- artificial general intelligence. Most AIs can perform only one trick because to learn a second, they must forget the first. The problem, known as "catastrophic forgetting," occurs because the neural network at the heart of the AI overwrites old lessons with new ones.

DeepMind solved the problem by mirroring how the human brain works. When we learn to ride a bike, we consolidate the skill. We can go off and learn the violin, the capitals of the world and the finer rules of gaga ball, and still cycle home for tea. This program's AI mimics the process by making the important lessons of the past hard to overwrite in the future. Instead of forgetting old tricks, it draws on them to learn new ones.

Because it retains past skills, the new AI can learn one task after another. When it was set to work on the Atari classics -- Space Invaders, Breakout, Defender and the rest -- it learned to play seven out of 10 as well as a human can. But it did not score as well as an AI devoted to each game would have done. Like us, the new AI is more the jack of all trades, the master of none.

There is no doubt that thinking machines, if they ever truly emerge, would be powerful and valuable. Researchers talk of pointing them at the world's greatest problems: poverty, inequality, climate change and disease.

They could also be a danger. Serious AI researchers, and plenty of prominent figures who know less of the art, have raised worries about the moment when computers surpass human intelligence. Looming on the horizon is the Singularity, a time when super-AIs improve at exponential speed, causing such technological disruption that poor, unenhanced humans are left in the dust. These superintelligent computers needn't hate us to destroy us. As the Oxford philosopher Nick Bostrom has pointed out, a superintelligence might dispose of us simply because it is too devoted to making paper clips to look out for human welfare.

In January the Future of Life Institute held a conference on Beneficial AI in Asilomar, California. When it came to discussing threats to humanity, researchers pondered what might be the AI equivalents of nuclear control rods, the sort that are plunged into nuclear reactors to rein in runaway reactions. At the end of the meeting, the organizers released a set of guiding principles for the safe development of AI.

While the latest work on DeepMind edges scientists towards AGI, it does not bring it, or the Singularity, meaningfully closer. There is far more to the general intelligence that humans possess than the ability to learn continually. The DeepMind AI can draw on skills it learned on one game to play another. But it cannot generalize from one learned skill to another. It cannot ponder a new task, reflect on its capabilities, and work out how best to apply them.

The futurist Ray Kurzweil sees the Singularity rolling in 30 years from now. But for other scientists, human-level AI is not inevitable. It is still a matter of if, not when. Emulating human intelligence is a mammoth task. What scientists need are good ideas, and no one can predict when inspiration will strike.

2017 Guardian Web syndicated under contract with NewsEdge/Acquire Media. All rights reserved.

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AI Is Getting Brainier: Will Machines Leave Us in the Dust? - Top Tech News

Im going to let you in on a little secret: Ive broken the terms of use for SaaS software and shared a license before.

Surprised? My guess would be no because youve probably done it too.

In general, per-seat licensing has been a great way for SaaS companies to charge a subscription and collect reliable revenue. Its helped companies like Salesforce, Zoom, and Box grow into large, successful organizations. But theres also no question that this success and revenue reliability comes at a cost, where pricing is not tied directly to how much a customer uses a service.

In short, seat-based subscription models have lots of problems but have been good enough for a long time. However, as more SaaS services leverage AI to augment human work, it will make less and less sense to charge per human seat and more sense to charge for what is actually being used to get work done: the compute power needed to run increasingly intelligent and useful AI-enhanced services.

This shift from human to AI-based productivity is going to fundamentally alter how SaaS companies sell their services. If SaaS companies dont start thinking about this inevitability, and pricing it into their models, AI may cannibalize their revenue over time.

For service models in which AI can provide value, such as in customer service or CRM, the AI itself is going to actively reduce human work over time. What does this mean in practice? In the customer service sphere, for example, bots will work alongside humans, so humans will operate with greater productivity. But SaaS companies that integrate AI while continuing to charge on a per-seat basis will actually be dis-incentivized from making users more efficient. Think about it: companies will lose revenue as they increase AI, because each person (each seat they sell) will be able to do more, and fewer people will be needed to do the same job. So this pushes vendors to drag their heels on innovation.

On top of all of that, it gets pretty darn expensive to do the research for developing good AI and to run the system 24/7. Compute power can easily take a solid chunk of revenue. So, SaaS companies with AI integration will start to sell fewer seats while their system becomes more expensive to develop and run.

Given these trends, the calculus for the vast majority of SaaS companies needs to change both for the customer and for their own long-term viability. Otherwise, in five or 10 years, many of these companies will be in for a rude surprise as AI cannibalizes their revenue.

Expect to see SaaS companies start charging based on usage. That might mean charging for AI work because it costs compute cycles. The more efficiency a customer wants, and the more they rely on the AI, the more they will end up paying for service, but the less they will pay for staff.

Usage-based pricing isnt a novel idea. Amazon has been the obvious pioneer behind pay-as-you-go SaaS pricing. It was no surprise for AWS to introduce a pay-as-you-go model, because the service provided with AWS is not based on human users or account management time. Instead, customers are charged for the type of computing unit being consumed. For example, EC2 charges in cloud compute units. Getting even more granular, Lambda charges by the execution second, while S3 charges by the gigabyte of used disk space.

Usage-based pricing opens the door to a more granular experience in which the customer only pays for what they use. Its the equivalent to buying a ticket to a single football game, versus being forced to buy a season pass, even if you can only make it to that one game. But usage-based models also have other positive byproducts. They take away the ability for customers to cheat by sharing accounts, and they remove the incentive for the SaaS provider to push customers to overbuy licenses in order to plan for growth.

Just like Amazons services, AI-enhanced SaaS companies that charge based on usage will introduce greater elasticity, better user experience, and more efficiency into their systems, leading to less churn and more long-term revenue stability.

Fred Hsu is CEO of Agent.ai.

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AI is going to kill seat-based SaaS models - VentureBeat

In a book thats become the darling of many a Silicon Valley billionaire Sapiens: A Brief History of Humankind the historian Yuval Harari paints a picture of humanitys inexorable march towards ever greater forms of collectivization. From the tribal clans of pre-history, people gathered to create city-states, then nations, and finally empires. While certain recent political trends, namely Brexit and the nativism of Donald Trump would seem to belie this trend, now another luminary of academia has added his voice to the chorus calling for stronger forms of world government. Far from citing some ancient historical trends though, Stephen Hawking points to artificial intelligence as a defining reason for needing stronger forms of globally enforced cooperation.

Its facile to dismiss Stephen Hawking as another scientist poking his nose into problems more germane to politics than physics. Or even to suggest he is being alarmist, as many AI experts have already done. Its worth taking his point seriously, though, and weighing the evidence to see if theres any merit to the cautionary note he rings.

Lets first take the case made by the naysayers who claim we are a long time away from AI posing any real threat to humanity. These are often the same people who suggest Isaac Asimovs three laws of robotics are sufficient to ensure ethical behavior from machines never mind that the whole thrust of Asimovs stories is to demonstrate how things can go terribly wrong despite of the three laws of robots. Leaving that aside, itsexceedingly difficult to keep pace with the breakneck pace of research in AI and robotics. One may be an expert in a small domain of AI or robotics, say pneumatic actuators, and have no clue what is going on in reinforcement learning. This tends to be the rule rather than the exception among experts, since their very expertise tends to confine them to a narrow field of endeavor.

As a tech journalist covering AI and robotics on a more or less full-time basis, I can cite many recent developments that justify Mr. Hawkings concern namely the advent of autonomous weapons, DARPA sponsored hacking algorithms, and a poker playing AI that resembles a strategic superpower, to highlight just a few. Adding to this, its increasingly clear theres already something of an AI arms race underway, with China and the United States pouring increasingly large sums into supercomputers that can support the ever-hungry algorithms underpinningtodays cutting-edge AI.

And this is just the tip of the iceberg, thanks to the larger and more nebulous threat poised by superintelligence that is an algorithm or collection of them that achieved a singleton, in any of the three domains of intelligence outlined by Nick Bostrom in Superintelligence: paths, dangers and strategies those being Speed, Quality/Strategic planning, and Collective intelligence.

The dangers poised to humanity by AI, being somewhat more difficult to conceptualize than atomic weapons since they dont involve dramatic mushroom clouds or panicked basement drills, are all the more pernicious. Even the so called Utopian scenario, in which AI merely replaces large segments of the workforce, would bring with it a concomitant set of challenges that could best be met by stronger and more global government entities. In this light, it seems if anything, Dr. Hawking has understated the case for taking action at a global level, to ensure the transition into an AI-first world is a smooth rather than apocalyptic one.

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Stephen Hawking calls for creation of world government to meet AI challenges - ExtremeTech

Forbes

Why You Don't Need To Worry About AI Forbes Any great sci-fi movie has artificial intelligence (AI), but to be entertaining, a movie needs drama. So in the real world, advances in AI are less about robot overlords and more about Siri, take me home. Below, a few members of Forbes Technology ...

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Why You Don't Need To Worry About AI - Forbes

Automation on the Up

Andrew Ng (founding lead of the Google Brain team, former director of the Stanford Artificial Intelligence Laboratory, and now overall lead of Baidus AI team) in an article at the Harvard Business Reviewpoints out that if executives had a better understanding of what machine learning is already capable of, millions of people would be out of a job today. Many executives ask me what artificial intelligence can do. They want to know how it will disrupt their industry and how they can use it to reinvent their own companiesthe biggest harm that AI is likely to do to individuals in the short term is job displacement, as the amount of work we can automate with AI is vastly bigger than before.

How much bigger? A report from Mckinsey says that 51% of economic activitycould be automated by existingtechnology.

This is not a new phenomenon, new technologies have alwaysdisplaced the need for human labor. In the past the change was gradual and people had time to learn new skills that the economy needed. But the pace at which it is happening this time may betoo rapid for people to adapt.

This is not some issue that you will have to figure out how to deal with in the distant future, it is already happening,relatively quietly,all around us. Somewhere, in agiant tech company ortiny startup, there is someone trying to figure out how to get a computer to do your job better than you ever could.

Below is a handy graph from McKinsey of a variety of skills that canbe replaced by AI and the industries that will be most affected. For a more detailed visualization click hereand here.

Its impact is already being felt from manufacturing jobs in China to insurance claim workersin Japan to tophedge fund managersin America.And this is just the beginning, as AI develops and its array of skills grow, more and more people whose jobs revolve around those skills will be replaced.

Of course, just because the technical ability is there doesnt mean it can be implemented right away. Still, this is an issue thatshould be getting a lot more attention than it does because it will impact you.

This excellent talk was delivered by Robert Reich, Secretary of Labor under Bill Clinton, delivered at Google back in February. He highlights what will be the pressing need of our times, for people to be able to find fulfillment outside of their job.

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AI Can Likely Already Do Your Job Better Than You Can - Futurism - Futurism