Category Archives: Artificial Intelligence

Worried that the rise of Artificial Intelligence technologies will make the role of the salesperson obsolete? Maybe you should be... but not if you focus on what really matters where sales, and customers relationships, are concerned. Embrace AI and you might find yourself becoming an even better salesperson.

How?

The following is from Justin Shriber, Vice President of Marketing, LinkedIn Sales Solutions.

Here's Justin:

Over the past year, AI has taken the world by storm. In 2016, AI startups saw record highs in deals and funding, while tech companies like Facebook, Amazon and Google banded together to conduct AI research and promote best practices. A recent Bloomberg Terminal analysis even revealed the number of companies mentioning "artificial intelligence" in their quarterly earnings shot up from under 20 in 2014 to nearly 200 today.

While AI will improve the workplace (think virtual assistants), people worry it will also kill jobs. Manufacturing jobs have already been lost to automation, while self-driving cars and trucks are well on their way to replacing professional drivers. McKinsey reports that machines or robots can take over 49 percent of worker activities, such as stocking supermarket shelves, serving food at restaurants and crunching numbers.

Even sales professionals, whose skill sets are in high demand, are fearful. Forrester predicts that 1 million B2B sales people will lose their jobs to self-service eCommerce by 2020. If that prediction pans out, that's 20 percent of the B2B sales force, gone, three years from now. No wonder everyone is scared.

But let's be clear -- not all sales are the same. While some purely transactional sales positions will move to this self-serve model, jobs that involve selling "high consideration" products through a complex sales process will be enhanced, not replaced, by AI.

Like other professions, sales involves some repetitive tasks that could be easily automated, and while AI will certainly change how we work, it will never replace all salespeople. In fact, it may actually make them better at their jobs. Here's why:

By automating mundane work, AI will save salespeople time.

Automation is already starting to replace rote tasks, which benefits busy sales professionals. Calendly, for example, automatically schedules meetings and sends invites. This frees up salespeople's time for more important tasks that require critical thinking, such as crafting customized emails or teeing up a conversation with a prospective buyers.

But let's take things a step further. One of the biggest challenges for sales professionals is prioritizing their time. Instead of guessing whether now is the best time to reach out, or keeping track of all correspondence with dozens of prospects, sales reps could rely on AI to determine when and how to take their "next best action" to move a deal forward.

Not all data is stored in computers.

One of the most exciting possibilities of AI is its potential to analyze vast amounts of data. In the future, AI will seamlessly digest data and provide smart suggestions, such as prompting you to follow up with a prospective buyer after a phone call. We're already starting to see this kind of technology in its early stages.

For example, Salesforce's Einstein, a smart cloud analytics platform, learns your CRM data, email, calendar, social, ERP and IoT, and delivers predictions and recommendations based on your goals. It can even suggest next steps if it detects a change in customer sentiment.

If selling enterprise software was as simple as processing data from a single source and spitting out the optimal decision, we'd all be toast. Luckily it's not. Data that informs these decisions comes from all kinds of sources, including the human brain.

Great sales professionals can read the room, connect the dots, and make sense of the intangibles that make each deal unique. Statistics do inform purchasing decisions, but reason has its limitations. Other types of data that humans excel at-- like observing others' emotions and body language and reacting immediately -- still factor in.

Relationships still drive business.

Enterprise sales is high stakes by nature. Deals often exceed six figures, and on average, 6.8 people are involved in the buying process. This means several people's careers and reputations are on the line if something goes wrong. I've seen people fired over poor technology decisions or deployments. Unsurprisingly, risk makes people uneasy. That's why a salesperson's job is so crucial.

When it comes down to making a huge purchasing decision, buyers need to trust their sellers. They want to meet that person and ask questions; they want to make sure their fears, hesitations and needs are understood. In an ideal buyer-seller relationship, sales professionals make their buyers feel informed, secure and comfortable with the product. Emotional intelligence is therefore crucial to the process.

Because sales touches on deep emotions like trust and empathy, it's one of the biggest reasons salespeople will never lose their jobs to machines. Robots still haven't grasped natural language understanding, let alone the subtle nuances of emotions. Amazon's Alexa and Apple's Siri, for instance, rely on pre-defined scripts and are easily baffled by simple questions.

While AI's ability to interpret language and emotion will definitely improve, it's unlikely it will ever fully replace the human ability to connect and build trust.

Over the past few years, machines have made great strides in becoming more human-like, from walking on two legs to understanding language. But humans are complex creatures that have evolved over six million years. By comparison, machines are in their infancy. While AI will take over some transactional sales positions, it won't replace sellers who manage intricate, multi-million dollar deals involving executive stakeholders.

Many job functions thatrequire human connection, like sales, just aren't that easy to replace.

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3 Reasons Why Artificial Intelligence Will Never Replace Sales Jobs - Inc.com

Do not go gentle into that good night, Old age should burn and rave at close of day; Rage, rage against the dying of the light.

Welsh poet Dylan Thomas famous lines are a passionate plea to fight against the inevitability of death. While the sentiment is poetic, the reality is far more prosaic. We are all going to die someday at a time and place that will likely remain a mystery to us until the very end.

Or maybe not.

Researchers are now applying artificial intelligence, particularly machine learning and computer vision, to predict when someone may die. The ultimate goal is not to play the role of Grim Reaper, like in the macabre sci-fi Machine of Death universe, but to treat or even prevent chronic diseases and other illnesses.

The latest research into this application of AI to precision medicine used an off-the-shelf machine-learning platform to analyze 48 chest CT scans. The computer was able to predict which patients would die within five years with 69 percent accuracy. Thats about as good as any human doctor.

The results were published in the Nature journal Scientific Reports by a team led by the University of Adelaide.

In an email interview with Singularity Hub, lead author Dr. Luke Oakden-Rayner, a radiologist and PhD student, says that one of the obvious benefits of using AI in precision medicine is to identify health risks earlier and potentially intervene.

Less obvious, he adds, is the promise of speeding up longevity research.

Currently, most research into chronic disease and longevity requires long periods of follow-up to detect any difference between patients with and without treatment, because the diseases progress so slowly, he explains. If we can quantify the changes earlier, not only can we identify disease while we can intervene more effectively, but we might also be able to detect treatment response much sooner.

That could lead to faster and cheaper treatments, he adds. If we could cut a year or two off the time it takes to take a treatment from lab to patient, that could speed up progress in this area substantially.

In January, researchers at Imperial College London published results that suggested AI could predict heart failure and death better than a human doctor. The research, published in the journal Radiology, involved creating virtual 3D hearts of about 250 patients that could simulate cardiac function. AI algorithms then went to work to learn what features would serve as the best predictors. The system relied on MRIs, blood tests, and other data for its analyses.

In the end, the machine was faster and better at assessing risk of pulmonary hypertensionabout 73 percent versus 60 percent.

The researchers say the technology could be applied to predict outcomes of other heart conditions in the future. We would like to develop the technology so it can be used in many heart conditions to complement how doctors interpret the results of medical tests, says study co-author Dr. Tim Dawes in a press release. The goal is to see if better predictions can guide treatment to help people to live longer.

These sorts of applications with AI to precision medicine are only going to get better as the machines continue to learn, just like any medical school student.

Oakden-Rayner says his team is still building its ideal dataset as it moves forward with its research, but have already improved predictive accuracy by 75 to 80 percent by including information such as age and sex.

I think there is an upper limit on how accurate we can be, because there is always going to be an element of randomness, he says, replying to how well AI will be able to pinpoint individual human mortality. But we can be much more precise than we are now, taking more of each individuals risks and strengths into account. A model combining all of those factors will hopefully account for more than 80 percent of the risk of near-term mortality.

Others are even more optimistic about how quickly AI will transform this aspect of the medical field.

Predicting remaining life span for people is actually one of the easiest applications of machine learning, Dr. Ziad Obermeyer tells STAT News. It requires a unique set of data where we have electronic records linked to information about when people died. But once we have that for enough people, you can come up with a very accurate predictor of someones likelihood of being alive one month out, for instance, or one year out.

Obermeyer co-authored a paper last year with Dr. Ezekiel Emanuel in the New England Journal of Medicine called Predicting the FutureBig Data, Machine Learning, and Clinical Medicine.

Experts like Obermeyer and Oakden-Rayner agree that advances will come swiftly, but there is still much work to be done.

For one thing, theres plenty of data out there to mine, but its still a bit of a mess. For example, the images needed to train machines still need to be processed to make them useful. Many groups around the world are now spending millions of dollars on this task, because this appears to be the major bottleneck for successful medical AI, Oakden-Rayner says.

In the interview with STAT News, Obermeyer says data is fragmented across the health system, so linking information and creating comprehensive datasets will take time and money. He also notes that while there is much excitement about the use of AI in precision medicine, theres been little activity in testing the algorithms in a clinical setting.

Its all very well and good to say youve got an algorithm thats good at predicting. Now lets actually port them over to the real world in a safe and responsible and ethical way and see what happens, he says in STAT News.

Preventing a fatal disease is one thing. But preventing fatal accidents with AI?

Thats what US and Indian researchers set out to do when they looked over the disturbing number of deaths occurring from people taking selfies. The team identified 127 people who died while posing for a self-taken photo over a two-year period.

Based on a combination of text, images and location, the machine learned to identify a selfie as potentially dangerous or not. Running more than 3,000 annotated selfies collected on Twitter through the software resulted in 73 percent accuracy.

The combination of image-based and location-based features resulted in the best accuracy, they reported.

Whats next? A sort of selfie early warning system. One of the directions that we are working on is to have the camera give the user information about [whether or not a particular location is] dangerous, with some score attached to it, says Ponnurangam Kumaraguru, a professor at Indraprastha Institute of Information Technology in Delhi, in a story by Digital Trends.

This discussion begs the question: Do we really want to know when were going to die?

According to at least one paper published in Psychology Review earlier this year, the answer is a resounding no. Nearly nine out of 10 people in Germany and Spain who were quizzed about whether they would want to know about their future, including death, said they would prefer to remain ignorant.

Obermeyer sees it differently, at least when it comes to people living with life-threatening illness.

[O]ne thing that those patients really, really want and arent getting from doctors is objective predictions about how long they have to live, he tells Marketplace public radio. Doctors are very reluctant to answer those kinds of questions, partly because, you know, you dont want to be wrong about something so important. But also partly because theres a sense that patients dont want to know. And in fact, that turns out not to be true when you actually ask the patients.

Stock Media provided by photocosma / Pond5

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Artificial Intelligence Predicts Death to Help Us Live Longer - Singularity Hub

Google may be an "AI first" company, but few people who work there actually use the term artificial intelligence.

That's because it doesn't actually describe the seismic shift currently happening across all of the Alphabet Inc. (GOOGL) unit's products.The better word for that process is machine learning, which is the technology that's making our computers think and act more like humans, said Peter Norvig, an AI scientist and a director of research at Google.

"Sundar has come out and said we're an AI first company, and that's a pretty bold statement," Norvig told The Street. "Internally we use machine learning more...it's what we're going to use to become an AI-first company."

Google CEO Sundar Pichai has been charting a transformation at the company ever since he took over as chief executive in 2015. Google's next big step is to navigate a future where mobile devices fade away and are replaced by omnipresent intelligence assistants -- an "AI first world," as Pichai has said.

But before that future can become a reality, Silicon Valley giants will have to overcome the obstacle of helping average people understand just what exactly AI is, as well as how it can be used in their everyday lives. The invention of products such as Google Assistant, Amazon.com Inc.'s (AMZN) Alexa and Apple Inc.'s (AAPL) Siri has demystified a lot of the confusion surrounding AI, Norvig said. It's helped people realize that AI isn't going to materialize as Skynet from "The Terminator" oras the so-called singularity-- the theory that one day machines will become smarter than humans.

Alphabet is a holding in Jim Cramer's Action Alerts PLUS Charitable Trust Portfolio. Want to be alerted before Cramer buys or sells GOOGL? Learn more now.

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One of Google's Top Scientists Explains Artificial Intelligence's Biggest Challenge Right Now - TheStreet.com

Instagram started to automatically block offensive comments Thursday, using artificial intelligence to go beyond simple keyword filters. The use of this technology is also a test case for Facebook as it is looking to improve its own moderation and filtering.

The Facebook-owned photo sharing service officially announced the launch of a new comment filter Thursday morning. Many of you have told us that toxic comments discourage you from enjoying Instagram and expressing yourself freely, wrote Instagram CEO and co-founder Kevin Systrom in a blog post. To help, weve developed a filter that will block certain offensive comments on posts and in live video.

Instagram also announced a new spam filter, which it had quietly been testing over the last couple of months. Filtering abusive comments will for now only be available in English, but spam is being detected if its written inSpanish, Portuguese, Arabic, French, German, Russian, Japanese, or Chinese as well. Comment filters are enabled by default, but can be turned off by each user.

Both filters are powered by machine learning, which means that the technology used to filter comments has been trained with a test set of data, and is looking at not just keywords but also contexts and relationships. An f-word between friends may have a completely different meaning than a slur hurled at an outsider, and song lyrics can include a lot of offensive language without actually offending anyone.

Instagrams comment and spam filters are based on DeepText, an artificial intelligence effort developed in-house at Facebook, as Wired reported Thursday.

Thats notable in part because Facebook itself has yet to officially commit to AI as a means to moderate content and comments. Executives previously said that it may take some time before AI can play a role in moderation, and Facebook has responded to recent controversies related to inappropriate content with the hire of thousands of additional human moderators.

However, more recently, the company seems to have changed its tune on AI a bit. Theres an area of real debate about how much we want AI filtering posts on Facebook, wrote CEO Mark Zuckerberg in a blog post earlier this month. Its a debate we have all the time and wont be decided for years to come. But in the case of terrorism, I think theres a strong argument that AI can help keep our community safe and so we have a responsibility to pursue it.

Does this mean that Facebook may also eventually use AI to moderate comments the way Instagram is now? A Facebook spokesperson stressed that both platforms are unique in a statement emailed toVariety:

Facebook and Instagram are different platforms with different user experiences from the follow model to how comments are used. Although we share the same goal of creating safe communities, we are going to have different approaches. Instagrams new tools are a great first step that both companies will be able to learn from.

In other words: Facebook may not copy Instagrams new AI-powered comment filters 1:1, but the company surely is looking to this as a test case as it evaluates if and how it may one day use artificial intelligence for moderation on Facebook proper as well.

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Instagram Starts Using Artificial Intelligence to Moderate Comments. Is Facebook Up Next? - Variety

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Microsoft is working to bring artificial intelligence to extremely small devices, but the work requires compression techniques and other technologies so it can run on the tiniest of CPUs.

The Raspberry Pi 3 is one of the most popular devices you can buy. For $35, this inexpensive all-in-one computer board can be used to power numerous projectsranging from miniature Linux-based PCsto gaming consolesand more. Now Microsoft reports that it built a device based on the Raspberry Pi 3 that uses artificial intelligence to hunt down pesky rodents.

The device was created by the head of Microsofts Machine Learning and Optimization group, Ofer Dekel. He discovered that squirrels were stealing flower bulbs along with seeds in the bird feeder in his backyard garden. Naturally, he couldnt literally keep watch in the shadows and chase down the furry-tail rodents with his bare hands, so he came up with a plan.

Using his team located at the Redmond, Washington, research lab (he has one in India, too), they trained a computer-vision model to detect squirrels. The artificial intelligence was then deployed onto a Raspberry Pi 3 board inside a special device he mounted in his backyard. Thus, when a squirrel rears its head, the device will turn on the sprinkler system, thwarting the rodents thieving habits.

This backyard project is just part of Microsofts overall picture of an artificial intelligence-first world. Were moving from what is todays mobile-first, cloud-first world to a new world that is going to be made up of an intelligent cloud and intelligent edge, Microsoft CEO Satya Nadella saidduring the recent Build developer conference.

The big accomplishment in the squirrel-hunting project, according to Microsoft, was cramming a deep neural network onto an extremely small chip. Dekel and his team used a variety of techniques to compress the neural network, which is essentially a class of predictors inspired by our brains.

One technique is called weight quantization, capable of cramming more parameters into a smaller physical space. This compression enables the artificial intelligence to work faster, too. Additionally, Dekels group is examining a technique called pruning, which removes redundancies in neural networks. This has a double benefit: the ability to run a neural network on extremely small processors, and faster evaluation times.

However, the team wants to get artificial intelligence running on the smallest ARM-based processor to date: the Cortex M0. According to ARM, this processor has a floorplan area of 0.007mm squared. Thats very, very tiny, and will require the team to make its machine learning models up to 10,000 times smaller than what theyre compressing for the Raspberry Pi 3.

There is just no way to take a deep neural network, have it stay as accurate as it is today, and consume 10,000 less resources. You cant do it, said Dekel. So, for that, we have a longer-term approach, which is to start from scratch. To start from math on the white board and invent a new set of machine-learning technologies and tools that are tailored for these resource-constrained platforms.

To see what the team is currently working on, early previews can be downloaded from Microsofts GitHub depot here. It also provides previews of the compression techniques and training algorithms.

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Microsoft manages to cram artificial intelligence on the Raspberry Pi 3 PC board - Digital Trends

In the early days, identifying malicious programs such as viruses involved matching their code against a database of known malware. But this technique was only as good as the database; new malware variants could easily slip through.

So security companies started characterizing malware by its behavior. In the case of ransomware, software could look for repeated attempts to lock files by encrypting them. But that can flag ordinary computer behavior such as file compression.

Newer techniques involve looking for combinations of behaviors. For instance, a program that starts encrypting files without showing a progress bar on the screen could be flagged for surreptitious activity, said Fabian Wosar, chief technology officer at the New Zealand security company Emsisoft. But that also risks identifying harmful software too late, after some files have already been locked up.

An even better approach identifies malware using observable characteristics usually associated with malicious intent for instance, by quarantining a program disguised with a PDF icon to hide its true nature.

This sort of malware profiling wouldn't rely on exact code matches, so it couldn't be easily evaded. And such checks could be made well before potentially dangerous programs start running.

The rest is here:

How artificial intelligence is taking on ransomware - CNBC.com - CNBC

Virtual Reality's New Look panel at the 2016 Thrival Innovation + Music Festival. Image courtesy of Ascender.

When the East Liberty-based incubator Ascender started thinking about how to approach their latest Thrival Innovation + Music Festival, they saw an opportunity to hone in on one important subject: artificial intelligence.

Thrival Innovation in the past has always been almost this intentional shotgun approach, covering as many different topics as we possibly could, and bringing together that intersection of people, says Ascender Program Director Kenny Chen. He adds that they chose artificial intelligence (AI) because it has become a primary area of interest, risk, opportunity, concern, or curiosity for a lot of people.

Titled Intelligence: Humans X Tech, the Innovation portion of Thrival, which will take place from September 27-28, offers programming at cultural spaces throughout Pittsburghs East End, including the Ace Hotel and Kelly-Strayhorn Theater, as well as Ascenders home base in Bakery Square. The two-day event will examine the fast-growing relationship between humans and technology.

The theme was inspired by Pittsburghs fast-growing reputation as a global leader in AI, machine learning and robotics, a standing bolstered by the presence of autonomous vehicle technology, companies like Google, Facebook and Amazon, and research at Carnegie Mellon University and the University of Pittsburgh.

Thrival Director Dan Law believes narrowing Innovations focus provides balance to a festival where the music portion has received far more attention.

The scale and scope of Thrival have come a long way, but weve noticed that Thrival Music became extremely high-profile extremely fast, he says. For the entire festival holistically to be successful, the two elements need to hold equal weight.

Thrival crowds gather for an event at Alloy 26. Image courtesy of Ascender.

He and Chen also see it as an opportunity to showcase Pittsburgh as a hub for AI, not just in terms of driving the technology, but in determining its impact. The approach ties in with Ascenders collaboration with the XPRIZE organization, which has explored the moral applications of AI. The relationship recently took Chen to theAI for Good Global Summit in Geneva, where he met with AI experts from around the world.

Wed really like to make Pittsburgh not just an AI robotics development hub, but also a center where AI ethics and the responsible development of these technologies are also happening, says Chen. We saw [Thrival] as an opportunity to bring together as many of the local, national and international players to speak on it so that the public can be more factually aware of whats actually going on in this space and make decisions about how best to both protect themselves from the risk, but also perhaps take part in some of the really exciting opportunities.

The speaking roster boastsWIRED founding editor and NewCo CEO John Battelle, Twitter brand strategistJoel Lunenfeld andGrammy Award-nominated DJ and producerSteve James. The growing list also includes representatives from Fortune 500 companies, global research universities and fast-rising startups from Silicon Valley and beyond.

Thrival Innovation concludes in Oakland with an event at the Carnegie Museum of Art, marking the first time Ascender has collaborated with the cultural institution.

We thought the museum of art would be a really fascinating place to take that deep dive into how technology is poised to affect our lives and civilization as a whole, says Chen.

The evening will feature more than a dozen technology and art exhibitsand coincides with a new installation by world-renowned digital simulation artist Ian Cheng, whose work will be on display at the museum from September 21-January 28. NEXtpittsburgh is a media sponsor of the event which will feature talks by experts on the complexities of AI.

It is an important juxtaposition when youre exploring AI right next to great sculptures of antiquity and dinosaur bones, says Law. It creates this really interesting contrast that I think will speak to people.

Passes for Thrival Innovation are on sale now. Options include $15 passes for evening programs, $45 single-day full-access passes, and $75 two-day all-access passes. A limited amount of VIP passes are also available for $150.

ascenderDan LawKenny ChenThrival Innovation + Music Festival

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Thrival Innovation Festival ups its game with national speakers and a focus on AI - NEXTpittsburgh

Artificial intelligence is being used in a variety of ways in the healthcare industry, and one area where it is proving to be an effective asset is cybersecurity. Healthcare CIOs and CISOs should recognize that AI has the ability to enhance technologys ability to identify malicious activity and attackers and to protect systems and data, healthcare cybersecurity experts said. And AI does so in different ways.

Machine learning and artificial intelligence can be used to augment and/or replace traditional signature-based protections, said Robert LaMagna-Reiter, senior director of information security at First National Technology Solutions, a managed IT services company that, among other things, advises on cybersecurity issues. One area is security information and event management alerting, or anti-virus solutions.

[Also:Barracuda unveils AI-driven tech to combat spear-phishing]

With the immense amount of data, security personnel cannot efficiently sift through every event or alert, whether legitimate or a false-positive machine learning and AI solve this problem by looking at behavior versus signatures, as well as taking into account multiple data points from a network, LaMagna-Reiter explained.

By acting on behavior and expected actions versus outdated or unknown signatures, the systems can take immediate actions on threats instead of alerting after the fact, he added.

Artificial intelligence also can assist with self-healing or self-correcting actions, LaMagna-Reiter said.

[Also:Healthcare AI poised for explosive growth, big cost savings]

For example, if an antivirus or next-generation firewall system incorporates AI or behavioral monitoring information, assets with abnormal behavior signs of infection, abnormal traffic, anomalies can automatically be placed in a quarantined group, removed from network access, he said. Additionally, AI can be used to take vulnerability scan results and exploit information to move assets to a safe-zone to prevent infection, or apply different security policies in an attempt to virtually patch devices before an official patch is released.

Further, if abnormal activity is observed, prior to any execution AI can wipe the activity and all preceding actions from a machine, LaMagna-Reiter explained. Essentially, every action is recorded and monitored for playback, if necessary, he said.

Cybersecurity is one of the most prominent use-cases for machine learning and artificial intelligence, said Viktor Kovrizhkin, a security expert at DataArt, which builds custom software for businesses.

The main niche for applying machine learning and complex AI systems in healthcare cybersecurity is reactive analysis and notification or escalation of potential problems, Kovrizhkin said. In combination with other infrastructure components, a machine learning-based approach may respond with actions to anticipate potential data leaks.

Making use of artificial intelligence is a progressive action, where a system constantly trains and identifies patterns of behavior and can discriminate between those considered normal and those that require attention or action, said Rafael Zubairov, a security expert at DataArt.

For this, the machine can use a variety of available data sources, such as network activity, errors or denial of access to data, log files, and many more, Zubairov said. Continuous interaction with a person and information gathering after deep analysis allow systems to self-improve and avoid future problems.

But successful use of artificial intelligence in healthcare requires a top-down approach that includes an executive in the know, LaMagna-Reiter said.

An organization must implement a defense-in-depth, multi-layer security program and have an executive-sponsored information security function in order to fully realize the benefits of implementing machine learning and AI, LaMagna-Reiter explained. Without those, machine learning and AI would be under-utilized tools that dont have the opportunity to take the security program to the next step. Machine learning and AI are not a silver bullet, or even a one-size-fits-all solution.

See more here:

Artificial intelligence is giving healthcare cybersecurity programs a boost - Healthcare IT News

Artificial intelligence could be another business driver for chipmaker Broadcom (AVGO), RBC Capital Markets said Thursday.

Broadcom's custom application-specific integrated circuits could provide upside for the company's stock over the next few years, RBC analyst Amit Daryanani said in a note to clients.

"While investors tend to focus on Broadcom's wireless assets, we think their custom ASIC solutions could unlock further upside especially as we think about revenues over the next 2-4 years," Daryanani said.

Broadcom has been involved in co-designing ASICs for hyperscale enterprise systems to handle artificial intelligence and machine learning workloads. These include projects with Alphabet's (GOOGL) Google, Alibaba (BABA) and Cisco Systems (CSCO), he said.

Daryanani estimates that ASICs for the artificial intelligence market could be a revenue opportunity for Broadcom that's worth $2 billion to $3 billion over the next few years.

IBD'S TAKE:For the latest news on chip stocks, visit IBD's news page Chip Stocks To Watch And Semiconductor Industry News.

Broadcom has limited competition in the custom ASIC business, Daryanani said. Rivals include STMicroelectronics (STM) and GlobalFoundries.

Daryanani maintainedhis "top pick" buy rating and 270 price target on Broadcom.

Broadcom lost 2.9% to close at 234.04on the stock market today.

Elsewhere in the semiconductor sector Thursday, Oppenheimer reiterated its outperform rating on Monolithic Power Systems (MPWR) after meeting with management this week.

"Tone remains bullish as management outlined the long-term vision for continued outsized (organically driven) growth," Oppenheimer analyst Rich Schafer said in a note to clients.

"Monolithic Power Systems has delivered high-teens annual (percentage) growth the past several years," Schaefer said. "We believe this growth is poised to accelerate as new opportunities in four key vectors take root. These four pillars of growth are high-end notebook, server, game console, and auto. We believe each of these verticals represents about $100 million incremental revenue potential over the next 3 years."

Oppenheimer has a price target of 100 on Monolithic Power.

Monolithic stock fell 1.9% to close at97.12 Thursday.

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Artificial Intelligence Could Be Another Catalyst For Broadcom - Investor's Business Daily

The future of the smartphone is rooted in advancements of artificial intelligence and machine learning. Through the wonders of AI, your phone will be able to track, interpret, and respond to patterns and trends that it recognizes as desirable or necessary. It will organize, match, and learn every single day about who you are and how you operate. It might sound alarming, but its reality for the 77 percent of Americans who own a smartphone.

Sometimes its hard to imagine how our phones could get any smarter, but companies like Apple, Samsung, and Google keep upping the ante. What enables them to do that is artificial intelligence, and more specifically, deep learning. Deep learning is a branch of AI that recognizes sensory patterns as they happen and its the reason image recognition, speech transcription, and translation have become more accurate.

Picture the human brain its a network made up of signals, sensors, and processing algorithms. AI chips, similarly to the brain, can digest massive data sets based on your usual habits, daily patterns, and past behaviors. They can retrieve supporting information from mobile apps, fitness trackers, digital watches, and even browsing history all to make predictions about what youll do next. Whats more, this analysis will be able to take place without internet connection. That is a revolutionary thought.

Another application for AI is augmented reality. Thats where digital effects provide an additional visual layer on top of your camera or captured image. You see this on Instagram, Snapchat, and Pinterest, where users apply creative effects to images and static or live videos. Pinning is another AI-backed tool that will allow users to attach digital objects to specific locations in the real world.

While many fear that machines are taking over the world, some pretty amazing things are happening in the meantime. For instance, AI is improving the emotional intelligence of customer support representatives, enhancing predictive algorithms for concierge services, and forcing car manufacturers to rethink who/what will be behind the wheel.

From your phone, youll start to see personal assistance like never before a device that understands your interests and tastes, emotions and moods, even prioritizing notifications. Your health app will scan your body, pull readings from phone sensors, and determine if anything is unbalanced. If it is, youll be notified immediately. Soon your phone will be able to detect precursors for illnesses such as dementia, Parkinsons or cardiovascular diseases. AI-based software makes that possible.

For businesspeople who are constantly multitasking, an improved AI phone can declutter your calendar, schedule your conference calls, even record and transcribe notes from a presentation. Itll boost battery life, increase storage space, and charge faster. Unless consumer spending on mobile phones and apps slows down which it wont expect to see these features rolled out in the near term.

The more data your phone collects, the more data it can make use of. When you download an application, youre agreeing to allow that company to use your data, within reason. AI becomes helpful here because it can learn how you use the service and when you share information. Instead of shooting off your data to a company server for harvesting, AI can analyze your data on-device, which keeps it personal and under your control.

Other ways AI is keeping things private is by crowdsourcing anonymized information of multiple consumers without knowing the individual user in a process known as differential privacy. This process still garners links, vocabulary, and emojis used it just doesnt associate a person with them.

Its important to remember that AI and machine learning is in its nascent stage. In the future, localized learning will privatize data while opening doors to anonymous mining, which in turn will expand its benefits. The most noticeable changes AI will bring are processing speed and efficiency letting us do things we already do, but faster and without subjecting our phone to multiple charges. In the end, the whole point of AI is to create a more personalized, user-friendly relationship with our smartphones. Based on the advancements in technology and the increased demand for smart applications, itll be a perfect match.

Tom Coughlin is an IEEE Senior Member and the president of Coughlin Associates, where he covers the storage industry.

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How AI will make smartphones much smarter - VentureBeat