Monthly Archives: February 2017

How to Keep Your AI From Turning Into a Racist Monster – WIRED

Posted: February 13, 2017 at 9:20 am

Slide: 1 / of 1. Caption: Getty Images

Working on a new product launch? Debuting a new mobile site? Announcing a new feature? If youre not sure whether algorithmic bias could derail your plan, you should be.

About

Megan Garcia (@meganegarcia) is a senior fellow and director of New America California, where she studies cybersecurity, AI, and diversity in technology.

Algorithmic biaswhen seemingly innocuous programming takes on the prejudices either of its creators or the data it is fedcauses everything from warped Google searches to barring qualified women from medical school. It doesnt take active prejudice to produce skewed results (more on that later) in web searches, data-driven home loan decisions, or photo-recognition software. It just takes distorted data that no one notices and corrects for.

It took one little Twitter bot to make the point to Microsoft last year. Tay was designed to engage with people ages 18 to 24, and it burst onto social media with an upbeat hellllooooo world!! (the o in world was a planet earth emoji). But within 12 hours, Tay morphed into a foul-mouthed racist Holocaust denier that said feminists should all die and burn in hell. Tay, which was quickly removed from Twitter, was programmed to learn from the behaviors of other Twitter users, and in that regard, the bot was a success. Tays embrace of humanitys worst attributes is an example of algorithmic biaswhen seemingly innocuous programming takes on the prejudices either of its creators or the data it is fed.

Tay represents just one example of algorithmic bias tarnishing tech companies and some of their marquis products. In 2015, Google Photos tagged several African-American users as gorillas, and the images lit up social media. Yonatan Zunger, Googles chief social architect and head of infrastructure for Google Assistant, quickly took to Twitter to announce that Google was scrambling a team to address the issue. And then there was the embarrassing revelation that Siri didnt know how to respond to a host of health questions that affect women, including, I was raped. What do I do? Apple took action to handle that as well after a nationwide petition from the American Civil Liberties Union and a host of cringe-worthy media attention.

One of the trickiest parts about algorithmic bias is that engineers dont have to be actively racist or sexist to create it. In an era when we increasingly trust technology to be more neutral than we are, this is a dangerous situation. As Laura Weidman Powers, founder of Code2040, which brings more African Americans and Latinos into tech, told me, We are running the risk of seeding self-teaching AI with the discriminatory undertones of our society in ways that will be hard to rein in, because of the often self-reinforcing nature of machine learning.

As the tech industry begins to create artificial intelligence, it risks inserting racism and other prejudices into code that will make decisions for years to come. And as deep learning means that code, not humans, will write code, theres an even greater need to root out algorithmic bias. There are four things that tech companies can do to keep their developers from unintentionally writing biased code or using biased data.

The first is lifted from gaming. League of Legends used to be besieged by claims of harassment until a few small changes caused complaints to drop sharply. The games creator empowered players to vote on reported cases of harassment and decide whether a player should be suspended. Players who are banned for bad behavior are also now told why they were banned. Not only have incidents of bullying dramatically decreased, but players report that they previously had no idea how their online actions affected others. Now, instead of coming back and saying the same horrible things again and again, their behavior improves. The lesson is that tech companies can use these community policing models to attack discrimination: Build creative ways to have users find it and root it out.

Second, hire the people who can spot the problem before launching a new product, site, or feature. Put women, people of color, and others who tend to be affected by bias and are generally underrepresented in tech companies development teams. Theyll be more likely to feed algorithms a wider variety of data and spot code that is unintentionally biased. Plus there is a trove of research that shows that diverse teams create better products and generate more profit.

Third, allow algorithmic auditing. Recently, a Carnegie Mellon research team unearthed algorithmic bias in online ads. When they simulated people searching for jobs online, Google ads showed listings for high-income jobs to men nearly six times as often as to equivalent women. The Carnegie Mellon team has said it believes internal auditing to beef up companies ability to reduce bias would help.

Fourth, support the development of tools and standards that could get all companies on the same page. In the next few years, there may be a certification for companies actively and thoughtfully working to reduce algorithmic discrimination. Now we know that water is safe to drink because the EPA monitors how well utilities keep it contaminant-free. One day we may know which tech companies are working to keep bias at bay. Tech companies should support the development of such a certification and work to get it when it exists. Having one standard will both ensure sectors sustain their attention to the issue and give credit to the companies using commonsense practices to reduce unintended algorithmic bias.

Companies shouldnt wait for algorithmic bias to derail their projects. Rather than clinging to the belief that technology is impartial, engineers and developers should take steps to ensure they dont accidentally create something that is just as racist, sexist, and xenophobic as humanity has shown itself to be.

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Dyson opens new Singapore tech center with focus on R&D in AI and software – TechCrunch

Posted: at 9:20 am

Dyson is expanding its footprint in Singapore, with a new Technology Centre opened today by the maker of vacuums and other smart home electronics. The UK company will be investing $561 million as part of its commitment to the new facility, which hosts working labs where research and development teams can pool their cumulative hardware and software know-how to help advance the companys growing ambitions.

If youre only passingly familiar with Dysons work, you might be wondering what a company that makes vacuums needs with a half-million dollar tech facility with a focus the company says is on artificial intelligence, machine learning and software development. But Dyson has always emphasized its tech edge in the domestic cleaning hardware market, and its only doing more to push that advantage lately, including more work in robotics, computer visions systems and machine learning with products like its Dyson 360 Eye robot vacuum.

As you can see from the photos of the facility, the company also put a lot of engineering work into one of its most recent products, the Supersonic hair dryer. There has also been some speculation that Dyson could extend some of its expertise around electric motors and battery tech into the automotive space, though the company isnt saying much one way or another about those reports just yet.

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Dyson R&D by Gareth Phillips

Dyson R&D by Gareth Phillips

Dyson R&D by Gareth Phillips

Dyson R&D by Gareth Phillips

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Dysons new facility also includes what they call The Control Tower, which shows real-time supply chain and logistics data, and which they use to help ensure things run smoothly in terms of global production and shipping, and the new tech centre is very close to Dysons West Park production facility, where the company says one of its digital motors leaves the line every 2.6 seconds, thanks to highly automated production lines.

Dyson has already said that it will do much more in robotics, machine learning and robotics according to the engineer leading its robotics program, Mike Aldred, and it seems like this new tech center will help with those pursuits. The company has already admitted its working on next-generation robot vacuums, even as it launched the first, and it also says that computer vision and other tech it created for the 360 Eye will apply more broadly across its offerings.

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Ford pledges $1bn for AI start-up – BBC News

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The Register
Ford pledges $1bn for AI start-up
BBC News
Car giant Ford has announced that it is investing $1bn (800m) over the next five years in artificial intelligence (AI) company Argo. The firms will collaborate on developing a virtual driver system for driverless cars. Ford intends to have an ...
Ford fills up ex-Google, Uber engineers' tank: $1bn pours into Argo AIThe Register
Ford invests massively in AI startupElectronics EETimes (registration)
Argo AI: Ford to invest $1 billion in Artificial IntelligenceComputer Business Review
PYMNTS.com -NDTV -Silicon UK
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An artificially intelligent pathologist bags India’s biggest funding in healthcare AI – Tech in Asia

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Tech in Asia
An artificially intelligent pathologist bags India's biggest funding in healthcare AI
Tech in Asia
The World Health Organization requires a pathologist to spend 20 minutes examining a blood smear on a slide under a microscope before ruling out malaria if no parasites are seen. You can imagine how fatigue, an urge to get home, or some other ...
SigTuple, an AI healthcare started by techies, raises $5.8M to help better diagnosisYourStory.com

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Google’s New AI Has Learned to Become "Highly Aggressive" in Stressful Situations – ScienceAlert

Posted: at 9:20 am

Late last year, famed physicist Stephen Hawking issued a warning that the continued advancement of artificial intelligence will either be "the best, or the worst thing, ever to happen to humanity".

We've all seen the Terminator movies, and the apocalyptic nightmare that the self-aware AI system, Skynet, wrought upon humanity, and now results from recent behaviour tests of Google's new DeepMind AI system are making it clear just how careful we need to be when building the robots of the future.

In tests late last year, Google's DeepMind AI system demonstrated an ability to learn independently from its own memory, and beat the world's best Go players at their own game.

It's since been figuring out how to seamlessly mimic a human voice.

Now, researchers have been testing its willingness to cooperate with others, and have revealed that when DeepMind feels like it's about to lose, it opts for "highly aggressive" strategies to ensure that it comes out on top.

The Google team ran 40 million turns of a simple 'fruit gathering' computer game that asks two DeepMind 'agents' to compete against each other to gather as many virtual apples as they could.

They found that things went smoothly so long as there were enough apples to go around, but as soon as the apples began to dwindle, the two agents turned aggressive, using laser beams to knock each other out of the game to steal all the apples.

You can watch the Gathering game in the video below, with the DeepMind agents in blue and red, the virtual apples in green, and the laser beams in yellow:

Now those are some trigger-happy fruit-gatherers.

Interestingly, if an agent successfully 'tags' its opponent with a laser beam, no extra reward is given. It simply knocks the opponent out of the game for a set period, which allows the successful agent to collect more apples.

If the agents left the laser beams unused, they could theoretically end up with equal shares of apples, which is what the 'less intelligent' iterations of DeepMind opted to do.

It was only when the Google team tested more and more complex forms of DeepMind that sabotage, greed, and aggression set in.

As Rhett Jones reports for Gizmodo, when the researchers used smaller DeepMind networks as the agents, there was a greater likelihood for peaceful co-existence.

But when they used larger, more complex networks as the agents, the AI was far more willing to sabotage its opponent early to get the lion's share of virtual apples.

The researchers suggest that the more intelligent the agent, the more able it was to learn from its environment, allowing it to use some highly aggressive tactics to come out on top.

"This model ... shows that some aspects of human-like behaviour emerge as a product of the environment and learning," one of the team, Joel Z Leibo, told Matt Burgess at Wired.

"Less aggressive policies emerge from learning in relatively abundant environments with less possibility for costly action. The greed motivation reflects the temptation to take out a rival and collect all the apples oneself."

DeepMind was then tasked with playing a second video game, called Wolfpack. This time, there were three AI agents - two of them played as wolves, and one as the prey.

Unlike Gathering, this game actively encouraged co-operation, because if both wolves were near the prey when it was captured, they both received a reward - regardless of which one actually took it down:

"The idea is that the prey is dangerous - a lone wolf can overcome it, but is at risk of losing the carcass to scavengers," the team explains in their paper.

"However, when the two wolves capture the prey together, they can better protect the carcass from scavengers, and hence receive a higher reward."

So just as the DeepMind agents learned from Gathering that aggression and selfishness netted them the most favourable result in that particular environment, they learned fromWolfpackthat co-operation can also be the key to greater individual success in certain situations.

And while these are just simple little computer games, the message is clear - put different AI systems in charge of competing interests in real-life situations, and it could be an all-out war if their objectives are not balanced against the overall goal of benefitting us humans above all else.

Think traffic lights trying to slow things down, and driverless cars trying to find the fastest route - both need to take each other's objectives into account to achieve the safest and most efficient result for society.

It's still early days for DeepMind, and the team at Google has yet to publish their study in a peer-reviewed paper, but the initial results show that, just because we build them, it doesn't mean robots and AI systems will automatically have our interests at heart.

Instead, we need to build that helpful nature into our machines, and anticipate any 'loopholes' that could see them reach for the laser beams.

As the founders of OpenAI, Elon Musk's new research initiative dedicated to the ethics of artificial intelligence, said back in 2015:

"AI systems today have impressive but narrow capabilities.It seems that we'll keep whittling away at their constraints, and in the extreme case, they will reach human performance on virtually every intellectual task.

It's hard to fathom how much human-level AI could benefit society, and it's equally hard to imagine how much it could damage society if built or used incorrectly."

Tread carefully, humans...

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Salesforce adds some artificial intelligence to customer service products – TechCrunch

Posted: at 9:19 am

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

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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 Announces Investment in Artificial Intelligence Company Argo AI – Motor Trend

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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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The artificial intelligence revolutionising healthcare – Irish Times

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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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Inside Intel Corporation’s Artificial Intelligence Strategy – Motley Fool

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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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