Category Archives: Ai

Two and a half years ago, President Obama called on the FCC to classify broadband internet as a utility. It joined a small club that you know well:electricity, gas, and running water. And now this club may be welcoming yet another new member. Is artificial intelligence the newest utility? Are we witnessing the dawn of AI being as ubiquitous asrunning water?

Six years ago, Netscape cofounder Marc Andreessen wrote thatsoftware is eating the world. Well, now AI is eating software.

You know the times are changing when AI is going from the subject of science fiction to the subject of an organization like the Financial Stability Board. Thats a global organization of central bankers who are responsible for the security of the worlds banking system. This is a critical task when we are facing an epidemic of data breaches.

Consider the attack that happened last February, when hackers managed to withdraw over $100 million from a Bangladesh bank account at the Federal Reserve Bank of New York. The fraudsters used malware to compromise a computer network, observed how transfers were done, and gained access to the banks credentials. This is a new kind of stealing in the digital age, where a heist of eight figures can happen in an instant, all with the unseen movement of 1s and 0s through cables and between satellites.

The infrastructure behind our global financial system is vulnerable. So if you ask me, attainable AI is here just in time. Thats what I told the FSB delegation when I stood up and presented a vision of AI like running water.

Its hard to imagine an industry that wont be transformed. Search. Health care. Law. Self-driving cars, of course. Even journalism.

The funny thing is, AI isnt even new. It was invented by a man named Arthur Samuel, who taught a computer to play checkers in 1962. But like Da Vincis flying machine, the idea of AI was born before the technology was in place tosupport it. AI was ahead of its time, and governments across the globe, from the U.S. to Japan, pulled its research funding. Thatled to an AI winter that lasted until recently.

But now that technology has caught up to our AI aspirations, we are seeing a rebirth of AI thanks to something Im calling the AI convergence. Its a perfect storm where multiple distinct threads of technology are coming together in a moment thats about to change everything. What are the threads of the AI convergence?

About a decade ago, Google innovated a method for computers to work in parallel, MapReduce, that introduced us to a new order of magnitude for processing power.

Before we just had one kind of processing unit: the CPU. Now we have a second: the GPU. Forrester analyst Mike Gualtieri called this a hardware renaissance, and its opened up a new dimension of computing for machine learning.

You are aware of Moores law, which describes the exponential growth for our capacity to store memory. Experts keep predicting that Moores law will have to slow down at some point. Its growth rate seems impossible. In fact, Moores law continues to this day, because we keep creating new ways of looking at data.

Big is an understatement. In the thousands of years between the dawn of humanity and the year 2003, humans created five exabytes of data. Now we create that much data every day. And our data footprint only continues to double every year.

Maybe you remember voice recognition software 15 years ago. It took three months of training before it could recognize your voice. Now this software can recognize any voice, instantly. The reason? We invented a better algorithm. Every time we do that, we have the power to model the universe with even more accuracy.

Add this all up and you have a cognitive revolution. The Industrial Revolution was the last time something like this happened, and it required huge physical resources: railroads, steel, and factories. But this time, with a small amount of money and access to attainable AI, small companies can upend capital-intensive industries in a way that was unthinkable before.

Take NuTonomy. Theyre a small startup of 100 employees out of MIT, and they beat Uber and Google to the coveted self-driving taxi market. NuTonomy taxis are driving around Singapore, and soon theyll be in business in Boston. Why? Access to AI.

For another example, I used to be an aerospace engineer, so I love to look at what SpaceX is up to. Could you imagine a private citizen joining the space race inthe 60s? In 2014, SpaceX released the total combined development costs for both its Falcon 9 launch vehicle and the Dragon capsule. The total was $846 million. As a comparison, NASA alone spent $38 billion on its comparable rocket, Orion. Thats 320 times the cost.

More than that, about half of SpaceXs funding came fromyou guessed it NASA! So not only did Elon Musk figure out how to do this cheaper, but NASA recognized this fact. If you cant beat them, fund them. This is the future: code over capital.

The Industrial Revolution sounded like the clanking of steel. This new cognitive revolution, powered by AI like running water, sounds more like a whoosh. Like water flowing through the pipes.

Nuno Sebastiao is the CEO of Feedzai,a data science company that detects fraud in omnichannel commerce.

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Why AI is the new electricity - VentureBeat

Timelines show 50 percent probability intervals for achieving various AI milestones.

Google has hung up its AlphaGo gloves after trouncing the world's best human Go players, but when will AI beat humans at other tasks, such as writing a best-selling novel or doing surgery?

To answer that question, a team of researchers led by Katja Grace of Oxford University's Future of Humanity Institute surveyed several hundred machine-learning experts to get their educated guess. The researchers used the responses to calculate the median number of years it would take for AI to reach key milestones in human capabilities.

Teachers may need to be on the alert for machine-written essays by 2026 and truck drivers could be made redundant by 2027, according to the results.

Meanwhile, AI will surpass human capabilities in retail by 2031. The experts also predict that AI will be capable of writing a best-seller by 2049, and doing a surgeon's work by 2053.

Overall, the respondents believe there is a 50 percent chance that AI beats humans at all tasks in 45 years and will automate all human jobs within 120 years.

The researchers invited the views of all 1,634 authors of papers published in 2015 at two of the leading machine-learning conferences, Neural Information Processing Systems and the International Conference on Machine Learning. A total of 352 researchers responded.

Interestingly, the researchers predict that AI won't beat the best human Go players until about 2028. As we know, Google beat Korean Go champ, Lee Sedol, in 2016, and just beat Chinese grandmaster Ke Ji. Google is now putting its AlphaGo developers from its DeepMind lab to work on solving bigger challenges to society.

But as Grace et al point out in the paper, the machine-learning experts were asked when AI could beat a human at Go on the condition that opponents had played or been trained on the same number of games.

"For reference, DeepMind's AlphaGo has probably played a hundred million games of self-play, while Lee Sedol has probably played 50,000," they note.

In fact, if the researchers' predictions are right, we're likely to see a two-legged robot beat humans in a 5km road race before AI beats a human Go player on equal terms.

The survey also asked the researchers about the likelihood of an AI "intelligence explosion", or the point at which AI becomes better than humans at AI design. As physicist Stephen Hawking explained, if that situation occurs, it could result in "machines whose intelligence exceeds ours by more than ours exceeds that of snails".

Specifically, researchers were asked about the chances of an intelligence explosion happening within two years of machines having learned to do every task better and more cheaply than humans. That is, within about 45 years.

Respondents overall see it as "possible but improbable", with a median probability of 10 percent. They also see it as likely to have positive outcomes but there is a five percent chance of an "extremely bad" outcome, like human extinction.

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AI experts predict the future: Truck drivers out of jobs by 2027, surgeons by 2053 - ZDNet

Weve seen many stories and theories about artificial intelligence, enough to know that its likely computers will gain some sort of sentience one day. But how will that happen? How will humans respond to it? And, perhaps most importantly, what will an artificial intelligence (AI) think of the human race?

These are the difficult questions that C.A. Higgins tackles in her provocative Lightless trilogy. The first book in the series, Lightless (2015), is set in a future in which humans have spread across space, and Earth and its solar system are controlled by an interstellar organization called the System. The Ananke is an experimental ship thats taken over by terrorists, and its up to Althea, an engineer with a special bond with the ship, to resist them. Thats an interesting plot on the surface, for sure, but its the story of Ananke, the ship, as it gains consciousness and awakens to its potential that is the crux of this trilogy. Higgins continued Althea and the Anankes journey through the series second novel, Supernova (2016), and concludes the trilogy in Radiate, released last month.

Again and again, Ananke finds itself in mortal peril, with only the being it considers its mother, Althea, to guide it. It would be easy to make a motherchild comparison in their relationship, yet in any traditional relationship between parents and children the power resides overwhelmingly in the hands of the parents. If children dont learn and obey, they are punished. How then do you handle a situation where the roles are flipped so completely? Althea has no control over what Ananke does, and as an artificial intelligence, the ships power is almost immeasurable. The damage it can do is, quite simply, catastrophic.

Althea does her best to guide the ship through its tumultuous awakening and adolescence, but the journey is rough. Anankes desires are understandable: to know the people who created it; to find companionship, another being like it. These childlike, and yet very adult, requests are heartbreaking. Higgins is able to make an all-powerful ship surprisingly sympathetic; Ananke may hurt others in its frustration, but it also does not understand the consequences of its actions. Its wholly sad and utterly frightening, given the ships capabilities.

Anankes actions and journey of self-discovery are all the more fraught given the complicated political situation. The man that Ananke considers its father, Matthew, is a member of a terrorist organization that has launched a devastating attack on the organization that controls Earth. The solar system is in chaos, and Ananke is adding to it through a determined search for Matthew, with a reluctant Althea along for the ride.

Its clear from the beginning of Radiate that the book is leading to an explosive ending. But what does explosive mean in this context? Sometimes the quietest story lines can make for the most earth-shattering revelations, as each character in this book goes on their own journey of self-discovery. They must each decide what matters to them and what theyll risk in order to do what they believe is right.

Although Radiate can be confusing as it jumps back and forth in time, fleshing out the past while pushing the thrust of the narrative forward, it is rewarding for readers who stick with it. After a stellar first outing in Lightless and an uneven and somewhat bleak sequel in Supernova, Higgins is in fine form closing out her space opera.

For more than science-fiction fans, the Lightless trilogy is great for those who have little experience with the genre because of its narrative style. Its a perfect sci-fi entry point that matches a rich, character-driven story with fundamental questions about who we are and why were here.

Krishna writes for Paste Magazine and Syfy Wire and is one-half of the podcast Desi Geek Girls. Shes on Twitter @skrishna

Radiate

C.A. Higgins

Del Rey: 336 pp., $27

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The AI in CA Higgins 'Radiate' poses fundamental questions - Los Angeles Times

Forbes

US Falls Behind China & Canada In Advancing Healthcare With AI Forbes The United States leads the world in artificial intelligence, but lags behind other countries in applying technical innovations to the field of healthcare. Globally, machine learning is used to increase efficiency, lower error rates, and decrease ...

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US Falls Behind China & Canada In Advancing Healthcare With AI - Forbes

Big news for artificial intelligence watchers: Google's AlphaGo AI has beaten the world's top-ranked Go player 3-0 at a match in Wuzhen, China. The AI is a vastly improved version of the one that beat a legendary 18-time world champion just one year ago.

It's a big accomplishment forAlphabet (NASDAQ:GOOGL)(NASDAQ:GOOG). But the headline isn't the highlight -- it's actually the manner of AlphaGo's victory that sheds new light on what the future might hold for the budding AI industry.

Ke Jie vs. AlphaGo. Image source: DeepMind.

Go, a 3,000-year-old board game, has simple rules. Players take turns placing a stone on the board. If one player surrounds an opponent's entire group of stones, the surrounded stones are captured and removed from the board. The object of the game is to surround the most empty territory.

Despite Go's apparent simplicity, the subtlety and complexity of its strategy and tactics have long confounded AI researchers. Until last year, it was the only game of perfect information AI had been unable to master. Never had a machine come close to beating any professional player.

A full-sized 19 by 19 board has 361 positions. Because almost every empty position is a legal move, there are a vast number of possible game sequences -- too vast for a human or a computer to calculate the best move using the same brute force techniques that IBM's Deep Blue used to master chess. (The number of possible board states in Go exceeds the number in chess by a factor of more than the number of atoms in the known universe.)

Playing Go, therefore, requires a kind of intuitive thinking that computers have difficulty mastering.

Alphabet subsidiary DeepMind made a leap in this direction incorporating machine learning alongside the more traditional statistical-algorithmic approach known as the Monte Carlo Tree Search.

To match the intuitive skills of human players, programmers taught AlphaGo pattern recognition. They fed AlphaGo data from millions of internet forum games to teach it to recognize what good moves "look" like. AlphaGo then played against itself millions of times over several months to further refine its skills.

The latest version of AlphaGo that beat number-one-ranked Ke Jieis even more impressive than the one that defeated legendary player Lee Sedollast year. It's now 1,000% more efficient with computing power and takes mere weeks instead of months to train.

It's also a stronger player. Instead of learning on a data set of strong human players, DeepMind wiped AlphaGo's memory and freakishly retrained it entirely on data from millions of games it had played against itself in the past. Its personality has also evolved. AlphaGo 2.0 is more tactical, more territorial, and somewhat more aggressive. It's also added a few more unusual maneuvers to its arsenal -- for example, a type of invasion that it plays in situations that any good human player would perceive as too early.

The latest match tells us a lot about the pace of AI improvement.

Ke Jie attempted to unsettle AlphaGo by playing some of its own unusual strategies and tactics against it. Known for his extremely accurate and quick ability to read possible game sequences, Ke Jie also tried to confuse AlphaGo by creating games so complicated that the computer would have difficulty keeping up. The second gamespiraled off into eight simultaneous, interconnected battles spanning the entire board. But in the end, AlphaGo was able to handle itself.

Complex fight in game two. Image source: DeepMind.

The 3-0 match result seems to be further vindication for companies in the AI space, most notably NVIDIA (NASDAQ:NVDA). But it may be a mixed blessing for processing manufacturers. Last year's match featured 1,920 CPUs and 280 of the souped-up graphics processing units that NVIDIA sells for AI uses. Despite losing the series, Lee Sedol managed to push all that processing power to the breaking point.

AlphaGo's tenfold efficiency gain in just one year could mean wider adoption of AI technology and processors, but it would also suggest a less processing-intensive AI future. Customers can't just buy the same amount of hardware to get higher performance -- there are diminishing returns to processing power.

What's more, opportunities attract competition -- Google has been developing its own AI processors.

Machine-learning AIs are turning to fields with large databases that combine pattern recognition and strategic reasoning, like medical diagnostics and treatment, that will also involve some level of teamwork between trained humans and trained computers.The summit in Wuzhen contained an unusual game that foreshadowed the future of AI applications.

For the first time, AlphaGo played on a team alongside a human in a game of "pair Go." Two teams of two play against one another, each player alternating moves for his/her teammate (a little bit like bridge). It can be tricky because players aren't allowed to communicate with their teammate. They must understand what their teammate's moves accomplish and what possible scenarios their teammate may be considering. It's an interesting way to simulate the tag-team future of man-machine problem-solving.

The teams were Lian Xiao-AlphaGo versus Gu Li-a second AlphaGo. In the game, the machine both won and lost.

Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. Ilan Moscovitz owns shares of Alphabet (A shares) and Alphabet (C shares). The Motley Fool owns shares of and recommends Alphabet (A shares), Alphabet (C shares), and Nvidia. The Motley Fool has a disclosure policy.

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AI's Latest Milestone Could Be a Mixed Blessing - Motley Fool

A loss for humanity! Man succumbs to machine!

If you heard about AlphaGos latest exploits last week crushing the worlds best Go player and confirming that artificial intelligence had mastered the ancient Chinese board game you may have heard the news delivered in doomsday terms.

There was a certain melancholy to Ke Jies capitulation, to be sure. The 19-year-old Chinese prodigy declared he would never lose to an AI following AlphaGos earthshaking victory over Lee Se-dol last year. To see him onstage last week, nearly bent double over the Go board and fidgeting with his hair, was to see a man comprehensively put in his place.

But focusing on that would miss the point. DeepMind, the Google-owned company that developed AlphaGo, isnt attempting to crush humanity after all, the company is made up of humans itself. AlphaGo represents a major human achievement and the takeaway shouldnt be that AI is surpassing our abilities, but instead that AI will enhance our abilities.

When speaking to DeepMind and Google developers at the Future of Go Summit in Wuzhen, China last week, I didnt hear much about the four games AlphaGo won over Lee Se-dol last year. Instead, I heard a lot about the one that it lost.

We were interested to see if we could fix the problems, the knowledge gaps as we call them, that Lee Se-dol brilliantly exposed in game four with his incredible win, showing that there was a weakness in AlphaGos knowledge, DeepMind co-founder and CEO Demis Hassabis said on the first day of the event. We worked hard to see if we could fix that knowledge gap and actually teach, or have AlphaGo learn itself, how to deal with those kinds of positions. Were confident now that AlphaGo is better in those situations, but again we dont know for sure until we play against an amazing master like Ke Jie.

AlphaGo Master has become its own teacher.

As it happened, AlphaGo steamrolled Ke into a 3-0 defeat, suggesting that those knowledge gaps have been closed. Its worth noting, however, that DeepMind had to learn from AlphaGos past mistakes to reach this level. If the AI had stood still for the past year, its entirely possible that Ke would have won; hes a far stronger player than Lee. But AlphaGo did not stand still.

The version of AlphaGo that played Ke has been completely rearchitected DeepMind calls it AlphaGo Master. The main innovation in AlphaGo Master is that its become its own teacher, says Dave Silver, DeepMinds lead researcher on AlphaGo. So [now] AlphaGo actually learns from its own searches to improve its neural networks, both the policy network and the value network, and this makes it learn in a much more general way. One of the things were most excited about is not just that it can play Go better but we hope that thisll actually lead to technologies that are more generally applicable to other challenging domains.

AlphaGo is comprised of two networks: a policy network that selects the next move to play, and a value network that analyzes the probability of winning. The policy network was initially based on millions of historical moves from actual games played by Go professionals. But AlphaGo Master goes much further by searching through the possible moves that could occur if a particular move is played, increasing its understanding of the potential fallout.

The original system played against itself millions of times, but it didnt have this component of using the search, Hassabis tells The Verge. [AlphaGo Master is] using its own strength to improve its own predictions. So whereas in the previous version it was mostly about generating data, in this version its actually using the power of its own search function and its own abilities to improve one part of itself, the policy net. Essentially, AlphaGo is now better at assessing why a particular move would be the strongest possible option.

The whole idea is to reduce your reliance on that human bootstrapping step.

I asked Hassabis whether he thought this system could work without the initial dataset taken from historical games of Go. Were running those tests at the moment and were pretty confident, actually, he said. The initial results have been that its looking pretty good. Thatll be part of this future paper that were going to publish, so were not talking about that at the moment, but its looking promising. The whole idea is to reduce your reliance on that human bootstrapping step.

But in order to defeat Ke, DeepMind needed to fix the weaknesses in the original AlphaGo that Lee exposed. Although the AI gets ever stronger by playing against itself, DeepMind couldnt rely on that baseline training to cover the knowledge gaps nor could it hand-code a solution. Its not like a traditional program where you just fix a bug, says Hassabis, who believes that similar knowledge gaps are likely to be a problem faced by all kinds of learning systems in the future. You have to kind of coax it to learn new knowledge or explore that new area of the domain, and there are various strategies to do that. You can use adversarial opponents that push you into exploring those spaces, and you can keep different varieties of the AlphaGo versions to play each other so theres more variety in the player pool.

Another thing we did is when we assessed what kinds of positions we thought AlphaGo had a problem with, we looked at the self-play games and we identified games algorithmically we wrote another algorithm to look at all those games and identify places where AlphaGo seemed to have this kind of problem. So we have a library of those sorts of positions, and we can test our new systems not only against each other in the self-play but against this database of known problematic positions, so then we could quantify the improvement against that.

None of this increase in performance has required an increase in power. In fact, AlphaGo Master uses much less power than the version of AlphaGo that beat Lee Se-dol; it runs on a single second-gen Tensor Processing Unit machine in the Google Cloud, whereas the previous version used 50 TPUs at once. You shouldnt think of this as running on compute power thats beyond the access of normal people, says Silver. The special thing about it is the algorithm thats being used as opposed to the amount of compute.

AlphaGo learned from humans, and humans are learning from AlphaGo

AlphaGo is learning from humans, then, even if it may not need to in the future. And in turn, humans have learned from AlphaGo. The simplest demonstration of this came in Ke Jies first match against the AI, where he used a 3-3 point as part of his opening strategy. Thats a move that fell out of favor over the past several decades, but its seen a resurgence in popularity after AlphaGo employed it to some success. And Ke pushed AlphaGo to its limits in the second game; the AI determined that his first 50 moves were perfect, and his first 100 were better than anyone had ever played against the Master version.

Although the Go community might not necessarily understand why a given AlphaGo move works in the moment, the AI provides a whole new way to approach the game. Go has been around for thousands of years, and AlphaGo has sparked one of the most profound shifts yet in how the game is played and studied.

But if youre reading this in the West, you probably dont play Go. What can AlphaGo do for you?

Say youre a data center architect working at Google. Its your job to make sure everything runs efficiently and coolly. To date, youve achieved that by designing the system so that youre running as few pieces of cooling equipment at once as possible you turn on the second piece only after the first is maxed out, and so on. This makes sense, right? Well, a variant of AlphaGo named Dr. Data disagreed.

What Dr. Data decided to do was actually turn on as many units as possible and run them at a very low level, Hassabis says. Because of the switching and the pumps and the other things, that turned out to be better and I think theyre now taking that into new data center designs, potentially. Theyre taking some of those ideas and reincorporating them into the new designs, which obviously the AI system cant do. So the human designers are looking at what the AlphaGo variant was doing, and then thats informing their next decisions. Dr. Data is at work right now in Googles data centers, saving the company 40 percent in electricity required for cooling and resulting in 15 percent overall less energy usage.

DeepMind believes that the same principle will apply to science and health care, with deep-learning techniques helping to improve the accuracy and efficiency of everything from protein-folding to radiography. Perhaps less ambitiously but no less importantly, it may also lead to more sensible workflows. You can imagine across a hospital or many hospitals you might be able to figure out that theres this process one hospitals using, or one nurse is using, thats super effective over time, says Hassabis. Maybe theyre doing something slightly different to this other hospital, and perhaps the other hospital can learn from that. I think at the moment youd never know that was happening, but you can imagine that an AI system might be able to pick up on that and share that knowledge effectively between different doctors and hospitals so they all end up with the best practice.

These are areas particularly fraught with roadblocks and worries for many, of course. And its natural for people to be suspicious of AI I experienced it myself somewhat last week. My hotel was part of the same compound as the Future of Go Summit, and access to certain areas was gated by Baidus machine learning-powered facial recognition tech. It worked instantly, every time, often without me even knowing where the camera was; Id just go through the gate and see my Verge profile photo flash up on a screen. I never saw it fail for the thousands of other people at the event, either. And all of this worked based on nothing more than a picture of me taken on an iPad at check-in.

I know that Facebook and Google and probably tons of other companies also know what I look like. But the weird feeling I got from seeing my face flawlessly recognized multiple times a day for a week shows that companies ought to be sensitive about the way they roll out AI technologies. It also, to some extent, probably explains why so many people seem unsettled by AlphaGos success.

But again, that success is a success built by humans. AlphaGo is already demonstrating the power of what can happen not only when AI learns from us, but when we learn from AI. At this stage, its technology worth being optimistic about.

Photography by Sam Byford / The Verge

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AI, the humanity! - The Verge

Sometimes the lips dont say what the heart feels, but instead of humanity working together on our own collectivesense of caring and empathy, we have made the brave decision to build computers that can interpret emotions for us.

In this installment of Judah vs. the Machines, actor Judah Friedlander touches down in Miami to discover the wits of Kairos, a computer visiontechnologythatclaims to understand people with face recognition technology.

The startup claims that their technology can detect emotions like anger, fear, disgust, sadness and joy (as well as a lack of emotion). Friedlander seemedmost concerned about whether the machine would be able to detect the emotion of victory that he soon planned to be feeling.

Friedlander sat down with Kairos CEO Brian Brackeen to figure out how the machine worked and see if he could get an upper hand in beating it head-to-head.

There are 85 points on your face and the distance between those points is like a fingerprint or a faceprint Brackeen told Friedlander. We feed the algorithm millions and millions of faces, and we say to the algorithm, This is a male, This is a female, or This is Brian and it learns over time who these people are or what they are.

Things are a bit more scrappy at Kairos machine learning team than when Friedlander visited the sprawling offices of Facebook. Kairos AR was founded in 2012 and has received just over $4.2 million in funding. Their primary customers appear to be companies looking to gauge brand perception or organize data through facial recognition, though they also tout their AIs ability to serve as an authentication tool.

In his head-to-head challenge with Kairos, Friedlander was forced to guess the emotions of strangers watching videos designed to elicit reactions ranging from surprise to dislike to delight. Check out the video above to see how Friedlander fares.

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Judah vs. the Machines: Kairos face recognition AI can tell how you ... - TechCrunch

People.aiis a startup usingAI to give sales managers a predictive playbook forthe best way to close a deal. The company is announcing it has raised $7 million in Series A funding led by Lightspeed Venture Partners. Index Ventures and Shasta Ventures also participated in the round, alongside existing investors Y Combinator and SV Angel. Nakul Mandan, partner at Lightspeed,is also joining People.ais board of directors.

The problem the sales management platformis trying to solve is that managers coach teams based on intuition rather than data. People.ai wants to change this by providing a holistic view of every outreach and action reps take to close deals. The softwarelets yousee where in the pipeline sales repsare spending the most timeand identify the metrics tied to success. Work smarter, not harder, right?

The goal is to have full visibility intosalespeoples processeswith a visualization that showsmuch time top performers are spending at each phase of a deal, and where struggling reps may bedeviating from typically successful methodology. Are salespeople too zoned in on one phase of a deal? Not spending enough time talking to product managers, executives or other decision makers? Are they even focusingon the right leads? Those are the questions People.ais algorithms seek to answer.

Thesolution tracks activity across different communication touch points between salespeople and clients. The tech scans email, phone calls, calendar meetings and produces a dashboard showing how much time is spentin each phase of a deal, who was contacted and what the outcome was.

When People.ai launched last year, CEO and machine learning veteran Oleg Rogynskyy wanted to build an AI that would help automate sales ops as a function. Since then, the company realized they wanted to refocus on solving this same problem but with an eye on the enterprise.

There are many companies building out features related to conversational AI like Chorus.ai and VoiceOps. People.ai sees these companies as data sources, but that their own solution isthe backbone that readsall types of sales activity.

Rogynskyy tells me that recently, the company isseeing strong interest coming from the enterprise and Fortune 500 companies. People.ai will use the funding to scale out its product and sales teams, and engage in more enterprise-focused R&D.

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People.ai raises $7M to automate sales ops for the enterprise ... - TechCrunch

Nvidia founder and CEO Jensen Huang shows off the company's vision for the future -- self-training AI.

According to Nvidia, the age of Moore's Law is coming to an end.

The solution? We don't just need to get smaller, we need to get smarter.

Nvidia took to the stage at Computex in Taipei today, talking up the future of artificial intelligence and machine learning, all powered by its GPU computing technology and what it's calling the Isaac Initiative.

The name might look back to Asimov, but the Isaac Initiative is all about building an AI future on four key pillars: smart processors (Nvidia certainly has a legacy on this front), smart software, reference designs for robots (created by partners like Ford) and something called Isaac's Lab.

That last part is where we get futuristic. Nvidia wants create a virtual world -- what it's calling a Holodeck -- where machine learning can be developed and artificial intelligence can self train. Think of it like the Matrix, but for AI.

Nvidia demo'd a version of Isaac's Lab on screen at its keynote, where row upon row of 3D-rendered robots were practising hitting a 3D hockey puck into a goal.

In the words of Nvidia CEO Jensen Huang, "We train for a while ... we replicate the smartest brain, and then we continue... Imagine if we could teach children like that!"

If you want a vision of the future, imagine a robot playing hockey, forever.

Nvidia's Jensen Huang explains the company's vision for the future of AI -- the Isaac Initiative.

Nvidia is no stranger to machine learning. At CES this year, the company showed off a supercomputer designed for self-driving cars.

The company's driverless concept car, BB8, also got a showing today, but Nvidia isn't stopping at driverless cars.

"If we can solve this technology [machine-learning] for self-driving cars, it's the beginning of the road for solving it for all kinds of machines," said Huang.

That means you won't just see driverless cars, you'll see smart drones that can intelligently map their surroundings, robots that can learn to mimic famous artists to paint their own works and technology that can identify diseases.

And plenty of ice hockey, too.

Be sure to check out CNET's full coverage from the Computex 2017 show floor right here.

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Nvidia wants to drive the future of AI (with ice hockey) - CNET

Scared of a future where you can no longer discern if you're dealing with a human or a computer? A team of Australian researchers have come up with what they call theAnti-AI AI.

The wearable prototype device is designed to identify synthetic speech and alert the user that the voice they're listening doesn't belong to a flesh-and-blood individual.Developed as a proof of concept in just five days, the prototype makes use of a neural network powered by Google'sTensorflowmachine learning software.

As artificial intelligence (AI) and robotic technology rapidly evolve, we're facing an uncertain future where machines can seemingly do all sorts of things better than people can from mastering gamesto working our jobs, and even making new, more powerful forms of AI.

While the gravest concerns envision a future dystopia where unregulated, super-powerful AIs threaten humanity's very existence, the truth is we're already entering a new, unsettling era in which machines can deceive humans by impersonating the ways we speak and look.

DT

As this technology gets even more sophisticated, it's becoming easier to imagine a world where soon it may be difficult or even impossible to tell when a 'person' you're talking to on the phone or watching on TV is or isn't a real human being.

But while AI is what empowers this nightmare scenario, it could also be what helps us reveal these synthetic impostors for what they are.

A team at Australian creative technology agency DTtrained its AI up on a database of synthetic voices, teaching the offline network to recognise artificial speech patterns.

When the wearable prototype operates, it captures audio spoken in the device's presence and sends it to this neural network in the cloud. If the AI detects an actual human voice (code green), all is fine:

But if the system picks up on synthetic speech, it has a unique way of subtly letting the human know that they're talking to a digital clone.

Rather than using light, sound, or vibration to alert the user, the prototype includes a miniature thermoelectric cooling element to reinforce that the voice they're hearing is coming from a "a cold, lifeless machine".

"We wanted the device to give the wearer a unique sensation that matched what they were experiencing when a synthetic voice is detected," the team explains on DT's R&D blog.

"By using a 4x4 mm thermoelectric Peltier plate, we were able to create a noticeable chill on the skin near the back of the neck without drawing too much current."

DT

That's right, guys, this device literally sends a chill down your spine when you're talking to a digital doppelgnger made up of 0s and 1s, and we can't think of a more fitting example of UI feedback.

Of course, because the Anti-AI AI is just a work-in-progress concept piece for now, it's unlikely the device will actually be released any time soon.

But the researchers behind it say that they're still refining their prototype and intend to improve the neural net with more synthetic content in the future.

Is this something you and I might need in the future? It's possible.

After all, in a post-truth world dominated by fake news misinformation where world leaders can so easily be manipulated to say things they never actually said nothing's for certain.

More here:

This dystopian device warns you when AI is trying to impersonate ... - ScienceAlert