Category Archives: Artificial Intelligence

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This Startup Has Developed A New Artificial Intelligence That Can (Sometimes) Beat Google Forbes The entire tech industry has fallen hard for a branch of artificial intelligence called deep learning. Also known as deep neural networks, the AI involves throwing massive amounts of data at a neural network to train the system to understand things ... AI's Factions Get Feisty. But Really, They're All on the Same TeamWIRED Artificial intelligence is expected to get smarter much faster thanks to GamalonDigital Trends all 11 news articles »

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This Startup Has Developed A New Artificial Intelligence That Can (Sometimes) Beat Google - Forbes

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Whenever a hot new field starts to take off, youll inevitably hear sighs of regret by the many who wish theyd gotten into it when they had the chance. The billion-dollar question is, why didnt they? The answer is, they chose not to. Thats what separates successful people from the pack: the choices they make.

Theres currently a talent war going on in the deep learning space. Web service leaders Amazon, Google and Microsoft are scooping up talent and buying startups left and right in a race for facial and speech recognition technology used in cloud-based searches and other red-hot machine learning applications.

Last week, Ford invested $1 billion to become majority shareholder of Argo AI, a self-driving car startup. Microsoft bought natural- language research lab Maluuba in January. Last summer, Intel paid more than $400 million to acquire 48-person AI startup Nervana and Apple bought Turi. Salesforce acquired MegaMind in April. And so on.

Related:The Growth ofArtificial Intelligencein Ecommerce (Infographic)

The thing is, the entrepreneurs involved in those ventures didnt just wake up one day and opportunistically decide to do something thatll make them a fortune. Some were pioneers in the field. Others took big risks and even bigger leaps of faith over long periods of time to get to where they are today.

Argo CEO Bryan Selesky led hardware development for Googles self-driving car project and hails from Carnegie Mellons famed National Robotics Engineering Center. Naveen Rao was an engineer for a decade before the AI lightbulb went off in his head. He went back to school, earneda PhD in neuroscience, themcofounded Nervana.

The point is, none of these people knew the field would take off. Maybe they thought it might, but they certainly did not know in advance. Thats not why they got into it. They got into it because that was the path that felt right to them and only them.

Im always telling young up-and-comers to take their time finding the right career that captures their imagination --the one thing that gets them excited, sparks their creativity, and makes them happy to do, day in, day out --and focus on being the best at it. And Im always hearing dumb excuses about why they cant, wont or shouldnt do that.

They read a book or an article somewhere by someone who said they should build their personal brand, fake it til they make it, join the growing hoard of self-employed gig workers, or start lots of little online businesses and hope that it all adds up to something someday.

Thats all nonsense that will get you nowhere, except maybe living hand-to-mouth and further in debt.

Related:How This Entrepreneur Kept His Day Job While Starting a Business

The spoils go to those who follow their passion, take risksand focus only on doing what they do best. That may not apply to everyone, but if you dont want to end up looking longingly at success stories and wondering why youre not among them, then it applies to you.

Nobody ever knows in advance if what they do for a living will pay off in the long run. Nobody has a crystal ball, and everybody gets just one life to live. There are no do-overs. So career decisions always come down to basic fundamentals thathavent changed in ages:

First, understand that everything you do is a choice made of your own free will. Make it wisely. Make it because it makes sense and feels right to you, not because someone you dont know from Adam said you should do it. After all, youre the one who is going to have to live with the consequences. Own it.

Second, the only way to make good choices is to have good choices. That means getting out in the world, working hard, and learning from those with experience doing what you aspire to do. The more exposure you have to smart people and new opportunities, the more experience you gain, the better your available choices will become.

Related:HasArtificial IntelligenceArrived At The Sales Function Yet?

Lastly, remember that you are human. The rules do apply to you. There are laws of physics, biologyand economics that no amount of wishful thinking or positive psychology can overcome. By all means, shoot for the stars, but try to keep at least one foot planted on the ground at all times.

Those who got into AI arent any smarter, luckier, or more privileged than you or me. All they did was stay true to the path that felt right to them. Simple as that.

Steve Tobakis a management consultant, columnist, former senior executive, and author ofReal Leaders Dont Follow: Being Extraordinary in the Age of the Entrepreneur(Entrepreneur Press, October 2015).Tobak runs...

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How Artificial Intelligence Startups Struck Gold - Entrepreneur

Elon Musk is saying outlandish things again. Several months ago, the Tesla and SpaceX CEO said that chances are we are all living in a simulation. Thankfully, other writers have contested this in a kinder manner than I would have (the words I have for Musks theory aresomething along the lines of utter nonsense and logically self-defeating, but I digress).

Well, now Musk thinks that humans must merge with machines, or else become defunct from the threat of advanced artificial intelligence. I guess he no longer thinks we live in a computer simulation. Why worry about humans becoming defunct if we are all brains in a vat?

Having millions of dollars does not mean that one can construct logically coherent chains of thought.

All that aside, I have several major issues with Musks assessment.

On an argumentative level Musks claims seem to paint artificial intelligence as some sort monster we have no control over. He talks about the threat of A.I. while ignoring that humans are the ones who create and control it, thus ignoring that we could easily stop working on it as it currently stands (as this Skynet-esque threat) if we are really so concerned about it displacing people.

Further, claims like Musks ignore the reality that no matter how advanced A.I. becomes, it is still artificial and reliant on programming put into it by human minds that are ontologically distinct from mere neurological matter and functions.

But really, the underlying presupposition of Musks confused plea for the merger of humans and machines is the biggest problem here. It implicitly assumes that humans are mere technology to be exploited for profit and material success. In this view humans are not persons, with an ultimate goal of flourishing, but mere biological machinery that need to be upgraded to a biomechanical level. When ones ultimate meaning has no transcendent anchor or reference point (e.g. God as the transcendent Source and Ground of reality), humans will inevitably be reduced down to mere technology. The bloodbath that is secularized 20th century bears stark witness to this.

Of course, Musk and those like him fundamentally misunderstand that mind is quite distinct from brain. True, the mental and the neurological are inextricably related. But to think that consciousness is derived or secreted from neurological matter is a fundamental confusion of categories, the product of an age that has forgotten to think deeply about the nature of reality and what persons not just human beings, but human persons really and truly are.

Artificial intelligence, no matter how complex, is not the same as human consciousness:

Computational models of the mind would make sense if what a computer actually does could be characterized as an elementary version of what the mind does, or at least as something remotely like thinking. In fact, though, there is not even a useful analogy to be drawn here. A computer does not even really compute. We compute, using it as a tool. We can set a program in motion to calculate the square root of pi, but the stream of digits that will appear on the screen will have mathematical content only because of our intentions, and because wenot the computerare running algorithms. The computer, in itself, as an object or a series of physical events, does not contain or produce any symbols at all; its operations are not determined by any semantic content but only by binary sequences that mean nothing in themselves. The visible figures that appear on the computers screen are only the electronic traces of sets of binary correlates, and they serve as symbols only when we represent them as such, and assign them intelligible significances. The computer could just as well be programmed so that it would respond to the request for the square root of pi with the result Rupert Bear; nor would it be wrong to do so, because an ensemble of merely material components and purely physical events can be neither wrong nor right about anythingin fact, it cannot be about anything at all. Software no more thinks than a minute hand knows the time or the printed word pelican knows what a pelican is.

David Bentley Hart The Experience of God: Being, Consciousness, Bliss p. 219

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Artificial Intelligence and The Confusion of Our Age - Patheos (blog)

Image from Wikimedia Commons

Artificial intelligence, machine learning, cognitive computing, deep learning, and related terms have become interchangeable jargon referring to AI. Although it's hard to believe, the level of marketing hype around AI has even surpassed digital transformation.

To break through the hype and nonsense, I asked the Chief Data Scientist of Dun and Bradstreet to explain AI in straightforward business terms. It's a complicated assignment, so I went to Anthony Scriffignano, one of the smartest, most accomplished data scientists I know. Anthony is a brilliant communicator, making him an ideal candidate to explain AI.

In the short video embedded above, Anthony gives a succinct introduction to AI for business people. Watch the video and enjoy un-hyped truth about an important topic.

How to Implement AI and Machine Learning

The next wave of IT innovation will be powered by artificial intelligence and machine learning. We look at the ways companies can take advantage of it and how to get started.

The conversation is part of the CXOTALK series, where you can watch the full-length, unedited discussion with Anthony Scriffignano and read a complete transcript.

If there's nothing else that our industry is good for, it's creating terms that people can use that have ambiguous meaning, and can be taken to mean almost anything in any situation. And this is certainly one of them. So, it's one of those things that you understand, but then when you try to define it, scholars will disagree on the exact definition. But, artificial intelligence collectively is a bunch of technologies that we run into. So, you'll hear "AI." You'll hear "machine learning." You'll hear "deep learning," [or] sometimes "deep belief." "Neuromorphic computing" is something that you might run into, or "neural networks;" "natural language processing;" "inference algorithms;" "recommendation engines." All of these fall into that category.

And some of the things that you might touch upon are autonomous systems bots. Sometimes, we will hear talk of... Well, Siri is probably the most obvious example that anybody runs into (or any of the other I won't try to name them all because I'll forget one), but things of that nature where you have these assistants that try to sort of mimic the behavior of a person. When you're on a website, and it says, "Click here to talk to Shelly!" or "Click here to talk to Doug!" You're not talking to a person; you're talking to a bot. So, those are examples of this.

Generally speaking, that's the broad brush. And then if you think about it as a computer scientist, you would say that these are systems processes that are designed to do any one of several things. One of them is to mimic human behavior. Another one is to mimic human thought process. Another is to "behave intelligently" you know, put that in quotes. Another is to "behave rationally," and that's a subject of a huge debate. Another one is to "behave ethically," and that's an even bigger debate. Those are some of the categories that these systems and processes fall into.

And then there are ways to categorize the actual algorithms. So, there are deterministic approaches; there are non-deterministic approaches; there are rules-based approaches. So, there are different ways you can look at this: you can look at it from the bottom up; the way it just ended; or regarding what you see and touch and experience.

They're not synonymous. So, cognitive computing is very different than machine learning, and I will call both of them a type of AI. Just to try and describe those three. So, I would say artificial intelligence is all of that stuff I just described. It's a collection of things designed to either mimic behavior, mimic thinking, behave intelligently, behave rationally, behave empathetically. Those are the systems and processes that are in the collection of soup that we call artificial intelligence.

Cognitive computing is primarily an IBM term. It's a phenomenal approach to curating massive amounts of information that can be ingested into what's called the cognitive stack. And then to be able to create connections among all of the ingested material, so that the user can discover a particular problem, or a particular question can be explored that hasn't been anticipated.

Machine learning is almost the opposite of that. Where you have a goal function, you have something very specific that you try and define in the data. And, the machine learning will look at lots of disparate data, and try to create proximity to this goal function basically try to find what you told it to look for. Typically, you do that by either training the system, or by watching it behave, and turning knobs and buttons, so there's unsupervised, supervised learning. And that's very, very different than cognitive computing.

So, a model is a method of looking at a set of data in the past, or a set of data that's already been collected, and describing it in a mathematical way. And we have techniques based on regression, where we continue to refine that model until it behaves within a certain performance. It predicts the outcome that we intend it to predict, in retrospect. And then, assuming that we can extrapolate from the frame we're into the future, which is a big assumption, we can use that model to try to predict what happens going forward mathematically.

The most obvious example of this that we have right now is the elections, right? So we look at the polling data. We look at the phase of the moon. We look at the shoe sizes. Whatever we decide to look at, we say, "This is what's going to happen." And then, something happens that maybe the model didn't predict.

So, now we get into AI. The way some systems work, not all, is they say: "Show me something that looks like what you're looking for, and then I'll go find lots of other things that look just like it. So train me. Give me a webpage, and tell me on that web page which things you find to be interesting. I'll find a whole bunch of other web pages that looks like that. Give me a set of signals that you consider to be a danger, and then when I see those signals, I'll tell you that something dangerous is happening." That's what we call "training."

Sure. So imagine that I gave a whole bunch of people, and the gold standard here is that they have to be similarly incentivized and similarly instructed, so I can't get, you know, five computer scientists and four interns ... You try to get people that more or less have either they're completely randomly dispersed, or they're all trying to do the same thing. There are two different ways to do it, right? And you show them lots and lots of pictures, right? You show them pictures of mountains, mixed in with pictures of camels, and pictures of things that are maybe almost mountains, like ice cream cones; and you let them tell you which ones are mountains. And then, the machine is watching and learning from people's behavior when they pick out mountains, to pick out mountains like people do. That's called a heuristic approach.

AI, Automation, and Tech Jobs

There are some things that machines are simply better at doing than humans, but humans still have plenty going for them. Here's a look at how the two are going to work in concert to deliver a more powerful future for IT, and the human race.

When we look at people, and we model their behavior by watching it, and then doing the same thing they did. That's a type of learning. That heuristic modeling is one of the ways that machine learning can work, not the only way.

There's a lot of easy ways to trick this. So, people's faces are a great example. When you look at people's faces, and we probably all know that there are techniques for modeling with certain points on a face, you know, the corners of the eyes. I don't want to get into any IP here, but there are certain places where you build angles between these certain places, and then those angles don't typically change much. And then you see mugshots with people with their eyes wide open, or with crazy expressions in their mouth. And those are people trying to confound those algorithms by distorting their face. It's why you're not supposed to smile in your passport picture. But, machine learning has gotten much better than that now. We have things like the Eigenface, and other techniques for modeling the rotation and distortion of the face and determining that it's the same thing.

So, these things get better and better and better over time. And sometimes, as people try to confound the training, we learn from that behavior as well. So, this thing all feeds into itself, and these things get better, and better, and better. And eventually, they approach the goal, if you will, of yes, it only finds mountains. It never misses a mountain, and it never gets confused by an ice cream cone.

The original way that this was done was through gamification or just image tagging. So, they either had people play a game, or they had people trying to help, saying, "This is a mountain," "This is not a mountain," "This is Mount Fuji," "This is Mount Kilimanjaro." So, they got a bunch of words. They got a bunch of people that use words to describe pictures (like Amazon Mechanical Turk).

Using those techniques, they just basically curated a bunch of words and said, "Alright, the word 'mountain' is often associated with there's a high correlation statistically between the use of the word 'mountain' and this image. Therefore, when people are looking for a mountain, give them this image. When they're looking for Mount Fuji, give them this image and not this image." And that was a trick of using human brains and using words. That's not the only way it works today. There are many more sophisticated ways today.

Please see the list of upcoming CXOTALK episodes. Thank you to my colleague, Lisbeth Shaw, for assistance with this post.

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No hype, just fact: Artificial intelligence in simple business terms - ZDNet

Last Fall when Salesforce introduced Einstein, its artificial intelligence initiative, it debuted with some intelligence built into the core CRM tool, but with a promise that it would expand into other products over time. Today it announced it was adding Einstein AI to its ServiceCloud customer service platform.

The goal is to make life easier for customer service reps and their managers. For the reps, it gives information that is supposed to help them better understand the needs of the customer theyre dealing with. For the managers, its been designed to help give deeperinsight into their customer service center operation. The ultimate goal is improving the key customer satisfaction metric known as CSAT.

For the customer service rep, it starts with how the call gets routed to them. It uses underlying intelligence to route the call to the best available rep based on known information, and it provides the rep with some background before they even interact with the customer.

All that should help the CSR do their jobs better and be more efficient with the customer. They also get fed somedata on the right side of the customer service window, which the system thinks will help improve the CSAT score.

Einstein case management window. Photo: Salesforce

This could be a case of too much information when youre dealing with a customerbecause it forces you to look atthe classification that Einstein believes is the correct one for this interaction. You also have to absorb several data points, which Einstein has determined could be havingan impact on the projected score. Thats all well and good, butviewing this data requires taking your attention away from the customer.

Regardless, thatindividual CSAT data gets compiled into a view for the customer service manager, who can see how the customer service team isdoing in terms of agent availability, the size of queues and wait times at any given moment. All of this is useful in compiling and improving that all important CSAT score.

Salesforce has been developing its artificial intelligence technology for some time. As I wrote at the time of the announcement in September:

The company pulled together 175 data scientists to help create Salesforce Einstein, while leveraging acquisitions such as MetaMind, PredictionIO and RelateIQ. In fact, MetaMind founder Richard Socher, holds the title of Chief Data Scientist at Salesforce now. Salesforce Einstein will touch every one of its products in some way eventually.

Indeed todays announcement is a continuation of that original vision, and we can expect that over the coming months and years, additional Salesforce products will get the Einstein treatment.

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Salesforce adds some artificial intelligence to customer service products - TechCrunch

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Ford has announced that it will invest $1 billion in Argo AI, an artificial intelligence startup, to help develop the automakers autonomous vehicles, which are scheduled to arrive in 2021. Argo AIs main responsibility will be the development of a virtual driver system for Fords self-driving cars.

The next decade will be defined by the automation of the automobile, and autonomous vehicles will have as significant an impact on society as Fords moving assembly line did 100 years ago, said Mark Fields, Fords president and CEO. As Ford expands to be an auto and a mobility company, we believe that investing in Argo AI will create significant value for our shareholders by strengthening Fords leadership in bringing self-driving vehicles to market in the near term and by creating technology that could be licensed to others in the future.

As part of Fords continued development of autonomous vehicles, the automakers team responsible for developing a virtual driver system will be combined with Argo AI. The combined development team will then be charged of creating SAE level 4 self-driving cars. Ford, however, will continue to be in charge of developing vehicle platforms, systems integration, exterior and interior designs, manufacturing, and managing regulatory policies related to autonomous cars.

The investment also includes Ford becoming a majority stakeholder in the Argo AI but will remain independent from the automaker. Fords autonomous vehicle project will be the Argo AIs key initial focus but in the future, the automaker says that the startup could also license its self-driving technologies to other companies.

Source: Ford

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Ford Announces Investment in Artificial Intelligence Company Argo AI - Motor Trend

Ford is investing $1-billion into a new artificial intelligence company.

The investment will go towards developing a virtual driver system for Fords upcoming self-driving cars, with the potential to license the technology to other companies. The $1-billion investment is in Argo AI, founded by former Google and Uber leaders and features a team of experts in robotics and artificial intelligence led bycompany founders Bryan Salesky and Peter Rander.Salesky serves as CEO of Argo AIand was previously a leader on the self-driving car team of Google, while Rander is company COO and formerly had a similar role as Salesky at Uber.

The current team working on Fords virtual driver system will be combined with the roboticstalent and expertise of Argo AI. The virtual driver system is a machine-learning software that acts as the brain of autonomous vehicles. Both companies hope to bring SAE level 4 self-driving vehicles to Fords lineup.

SEE ALSO:Ford Turns its Attention Back to US Manufacturing, Dumps Plans for Mexico Plant

The automaker hopes to have fully autonomous vehiclesto marketin 2021 and by becoming majority stakeholder in Argo AI, it moves one step closer to that goal. The investment will bemade over five years.

The next decade will be defined by the automation of the automobile, and autonomous vehicles will have as significant an impact on society as Fords moving assembly line did 100 years ago, said Ford President and CEO Mark Fields. As Ford expands to be an auto and a mobility company, we believe that investing in Argo AI will create significant value for our shareholders by strengthening Fords leadership in bringing self-driving vehicles to market in the near term and by creating technology that could be licensed to others in the future.

Discuss this story on our Ford Forum

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Ford Invests $1-Billion in Artificial Intelligence - AutoGuide.com

More and more, health technologies originally viewed as futuristic have become reality. Photograph: Carmen Murillo/Getty Images/iStockphoto

Last year, it was reported that supercomputer IBM Watson diagnosed a rare form of leukaemia in a patient at a University of Tokyo-affiliated hospital whose case had baffled her medical team.

The cloud-based, artificial intelligence-powered supercomputer is capable of cross-referencing and analysing data from tens of millions of oncology papers from research institutes all over the world. From vast volumes of data, it can instantly pull out the information it needs, much faster than humans can.

The University of Tokyo reported that the 60-year-old Japanese woman was correctly diagnosed in just 10 minutes by Watson, after her genetic data was cross-referenced with the computers own database.

More and more, health technologies originally viewed as futuristic like virtual avatars and chatbots have become reality. These technologies use artificial intelligence (AI) to mimic conversation with people, interact on the internet and perform other tasks that would normally require human intelligence.

One example of this is Sensely, a mobile triage smartphone app currently being trialled by the National Health Service (NHS) in the United Kingdom.

Olivia, Senselys artificially intelligent virtual nurse, guides patients naturally through their personal healthcare needs on demand 24/7, 365 days a year. The blue-eyed, dewy skinned young woman in blue NHS scrubs, gathers information by listening to the patient and asking questions, similar to a person-to-person interaction with a clinician.

Sensely was developed by a Californian start-up, but as Richard Corbridge, chief executive of eHealth Ireland points out, theres no need to go to California to see examples of how AI is revolutionising healthcare. Five out of the top 10 start-ups in Dublin last year were in the digital health arena, he says.

Corbridge will be speaking at this weeks Dublin Technology Summit 2017 (February 15th to 16th) on the topic of Health Reality, Not Science Fi.

Things are moving so fast that technologies we would have regarded as sci-fi last year, will become a reality this year. Over the last couple of years, Ireland has made some really big strides in digital healthcare, he says.

We are still the last first world country not to have a national electronic health record (EHR) in place, yet we are way ahead in other areas, like DNA genome sequencing.

The eHealth Epilepsy Lighthouse Project has developed the infrastructure to sequence the genome (figure out the order of DNA nucleotides in a complete set of genes) in patients and to record this information for clinicians to use in the delivery of care. The significance of sequencing the genome is that it can be used by healthcare systems across the world to predict what will happen to an individual patients health.

Corbridge remarks: Take a patient with epilepsy who has had an epileptic seizure every day for 20 years at least. By taking a sample of that patients DNA, we can sequence the genome and enter the information into his/her EHR.

The multidisciplinary team can then use this data to change or adapt the patients care plan. Within a week of one patient on the project changing his diet, he went a full day without having a fit for the first time in 20 years.

Over the past few weeks, every maternity hospital in Ireland has been visited by teams from eHealth Ireland to identify where the gaps are in their digital health capabilities and to close them.

Going forward, every newborn baby in hospital will have three devices in their cot, monitoring respiration, temperature and heart rate. All of this information is automatically transferred to the babys EHR.

Instead of constantly checking these levels in individual patients, each nurse has a tablet PC where they can see the vital information on all the babies in their care at their fingertips, including requests for tests and scans and results. Within the next two years, every hospital in Ireland will have this technology. Its an amazing leap for Ireland in a short space of time, says Corbridge.

With an increased emphasis on getting patients to self-manage their health where possible, rapid advances are being made in smartphone and wearable devices. Another eHealth project is an app for patients with bipolar disorder which uses a chatbot to engage with the user, monitor their mood and try to keep them on the right track. With the patients consent, the app can contact their carer or GP if it feels they need support.

Dublin-based start-up TickerFit enables health professionals to prescribe, educate and monitor a heart patients recovery from a distance through a wearable device. Founder Avril Coleman is another of the speakers at this weeks summit which brings global leaders in innovation, technology and business together to shape the future of global trends and technologies. The two-day summit will host 10,000 members of the tech community at the Convention Centre Dublin this Thursday and Friday.

Fabian Bolin, cofounder of War on Cancer, will be talking about waroncancer.com, an online storytelling community to help people deal with the mental challenges that come with a cancer diagnosis.

Musics new role in healthcare and the evolving world of HealthTunes will be explored in a session entitled When Medicine Rocks, with the panel discussing the possibility of a time when music, given its undeniable influence on our emotions, could be prescribed along with conventional medicines.

To learn more visit dublintechsummit.com.

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The artificial intelligence revolutionising healthcare - Irish Times

A much discussed area in technology these days is artificial intelligence, a type of machine learning. Artificial intelligence is a workload that requires an immense amount of processing power, which is why companies like microprocessor giant Intel (NASDAQ:INTC) -- a company that brings in tens of billions of dollars from sales of processors -- see this market as an interesting long-term growth opportunity.

Interestingly, although Intel is a major supplier of processors for artificial intelligence workloads, the company doesn't get nearly as much attention for its efforts in this market as does graphics specialist NVIDIA (NASDAQ:NVDA) -- a company that has seen significant revenue and profit growth from artificial intelligence applications as its long-term investments in this space are paying off.

Intel CEO Brian Krzanich at the company's AI day back in November 2016. Image source: Intel.

Intel went over its artificial intelligence strategy at its Feb. 9 investor meeting. Let's look at what the company had to say about the market and how it plans to win in it.

According to Intel, only 7% of server sales in 2016 were used for artificial intelligence workloads, but it is the "fastest-growing data center workload."

Within that 7%, the company says that 60% of those servers were used for "classical machine learning" while the remaining 40% were used for "deep learning."

The company then went on to show that of the servers used for classical machine learning, 97% used Intel Xeon processors to handle the computations, 2% used alternative architectures, and 1% used Intel processors paired with graphics processing units (likely from NVIDIA).

Among servers used for deep learning applications, the chipmaker says that 91% use just Intel Xeon processors to handle the computations, 7% use Xeon processors paired with graphics processing units, while 2% use alternative architectures altogether.

The point that Intel is trying to make is that its chips overwhelmingly dominate the market for servers that run artificial intelligence workloads today.

Intel clearly views graphics processors from the likes of NVIDIA as a threat to its position in the artificial intelligence market -- a reasonable viewpoint considering that NVIDIA's data center graphics processor business continues to grow at a phenomenal rate (revenue was up 145% in the company's fiscal year 2017).

The risk is that that those graphics processors, though usually paired with Intel Xeon processors, will reduce the demand for said Xeon processor (i.e., if some number of Xeon processors can be replaced by one Xeon processor and some smaller number of graphics processors, then Intel loses).

Intel's strategy, then, appears to be to cast a very wide net with a wide range of different architectures and hope that it can offer better solutions for specific types of artificial intelligence workloads than the graphics chipmakers like NVIDIA can.

Intel's broad AI product portfolio. Image source: Intel.

Look at the slide above and you'll notice Intel has different solutions for different types of workloads. It's promoting its next-generation Xeon processor (known as Skylake-EP) as the standard, general-purpose artificial intelligence processor.

From there, the offerings get more targeted. For some workloads, it will offer a specialized version of its Xeon Phi processor called Knights Mill. For others, it's going to offer combined Xeon processor with Field Programmable Gate Array (FPGA) chips. And, for still others, the company plans to offer a chip that combines a Xeon processor with a specialized deep learning chip called Lake Crest (based on technology that Intel acquired when it picked up start-up Nervana Systems).

Intel's strategy looks as solid as it can possibly be as it seems to be throwing its entire technical arsenal at the problem -- I'd say the company is well positioned to profit from the continued proliferation of artificial intelligence workloads.

What will only become evidence in time, though, will be how much market share Intel will ultimately be able to capture in this market. The underlying market growth should mean that Intel's revenue and profits here will grow, but obviously, the magnitude of that growth will depend on its ability to defend its market share while at the same time defending its average selling prices.

Ashraf Eassa owns shares of Intel. The Motley Fool owns shares of and recommends Nvidia. The Motley Fool recommends Intel. The Motley Fool has a disclosure policy.

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Inside Intel Corporation's Artificial Intelligence Strategy - Motley Fool

In a 2015 interview with Elon Musk and Bill Gates, Musk argued that humanitys greatest concern should be the future of artificial intelligence. Gates adamantly voiced his alignment with Musks concerns, making clear that people need to acknowledge how serious of an issue this is.

So I try not to get to exercised about this problem, but when people say its not a problem then I really start to get to a point of disagreement, Gates said.

The fears surrounding unchecked advances in AI are rooted in the potential threat posed by machine superintelligence an intelligence that at first matches human-level capabilities, but then quickly and radically surpasses it. Nick Bostrom, in his book Superintelligence, warns that once machines possess a level of intelligence that surpasses that of our own, control of our future may no longer be in our hands.

Once unfriendly superintelligence exists, it would prevent us from replacing it or changing its preferences. Our fate would be sealed, Bostrom said.

For Musk, Gates and Bostrom, the arrival of superintelligent machines is not a matter of if, but when. Their arguments seem grounded and cogent, but their scope is too far-sighted. They offer little in the way of what we can expect to see from AI in the next 10 to 20 years, or of how best to prepare for the changes to come.

Dr. Michael Mauk, chairman of the UT neuroscience department, has made a career out of building computer simulations of the brain. His wide exposure to AI has kept him close to the latest developments in the field. And while Mauk agrees in principle with plausibility of superintelligent AI, he doesnt see its danger, or the timeline of its arrival, in the same way as those mentioned before.

I think theres a lot of fearmongering in this that is potentially, in some watered-down way, touching a reality that could happen in the near future, but they just exaggerate the crap out of it, Mauk said. Is (the creation of a machine mind) possible? I believe yes. Whats cool is that it will one day be an empirically answerable question.

For Mauk, hype of the sort propagated by Musk, Gates and Bostrom is out of balance, and doesnt reflect what we can realistically expect to see from AI. In fact, Mauk claims that current developments in neuroscience and computer science are not moving toward the development of superintelligence, but rather toward what Mauk calls IA, or Intelligent Automation.

Most computer scientists are not trying to build a sentient machine, Mauk said. They are trying to build increasingly clever and useful machines that do things we think of as intelligent.

And we see evidence of this all around us. IA has grown rapidly in recent years. From self-driving cars to Watson-like machines with disease diagnosing capabilities superior to that of even the best doctors, IA is set to massively disrupt the current social and economic landscape.

Students and professionals alike should sober any fears about a future occupied by superintelligent AI, and instead focus on the very real, and near future reality where IA will be profoundly impacting their career. And theres a beautiful irony to this. As humanity works to adapt to a world with greater levels of Intelligent Automation, along with its many challenges increased social strife, economic restructuring, the need for improved global cooperation it will inadvertently be preparing itself to face a potential future occupied by superintelligent AI.

Hadley is a faculty member in biology and a BS 15 in neuroscience from Southlake.

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Artificial intelligence predictions surpass reality - UT The Daily Texan