Category Archives: Artificial Intelligence

The Alibaba-backed Israeli startup is changing how you experience shopping online with innovative search that speaks your language

Just under a year after their $12.5 million Series A funding round, Israeli Artificial Intelligence for e-commerce startup Twiggle announced today the release of their Semantic API product, bringing their accumulated expertise in search to the wider online shopping market.

Twiggle was co-founded in December of 2013 by CEO Dr. Amir Konigsberg, previously one of the members of Googles emerging markets operations, and Dr. Adi Avidor, a former engineering tech lead at Google. In the time since their Series A, they have picked up another $5 million from Alibaba, doubled their team, and moved shop to new offices overlooking Tel Aviv.

Describing what they have built in short, this company has made search for e-commerce usable to the point that it becomes a near enjoyable experience. Their engine processes through lists of products, understanding the attributes that make them what they are. Instead of simply looking for a few random keywords drawn from a page, Twiggle canbuild out family trees of products, understanding what belongs in which categories.

While this sounds simple enough that it should be the industry standard, the reality generally falls flat of our expectations.

Take this example of looking for a black dress without sleeves on Amazon, which is generally considered to be one of the better sites out there.

Image Credit: Twiggle

Judging by the results that showed up on the page, we can assume that Amazon was just looking at the keywords black, dress, and sleeves. However it seems to have missed the importance of without and how it affects the search sentence. If you had gone into a brick and mortar store, hopefully the sales girl would understand what you mean when you say that you are looking for a black dress without sleeves.

So why should an e-commerce site be any different?

Image Credit: Twiggle

What they have figured out is how to dissect a query and understand not just how the individual words match to products in their directory, but what is the context of the words put together in human speak.

Twiggle is able to translate the product into terms like black being a color, that a dress is the noun being described, and that the sleeves is an attribute. This is important because we should want technology to better adapt itself to us, learning how we communicate. Not the other way around.

Meeting at their office for the demo, Konigsberg toldGeektime that they have succeeded in scaling this process up to hyper speed, successfully being able to perform this learning process about new products at a rate of over 100 million products in less than an hour.

Why is speed so important here? Well, in order to keep up with the constant changes in inventory that are posted to an e-commerce website, being quick on the draw is key in getting the product properly labeled so that it can be found by shoppers.

A product has to be searchable before it is actually sellable, Twiggles VP Product Noa Ganot says, who spent a number of years over at eBay and has a pretty good handle on how people think about searching for items to buy online.

Funded bybig players like Alibaba and Naspers, Twiggle seems to have figured out how to make products searchable on a large scale. What they have yet to decipher is, based on who has invested in them so far, how they expand outside of the major e-commerce players, whom we can assume they are working with as current customers?

This is where Semantic API comes in, sitting atop a clients own search andrapidly integrating at no risk to the user.

We specifically designed our technology to enhance our customers existing search, not replace it, explains VP Marketing Yael Citro. Our customers stay in control and decide how to balance Twiggles signal with all of their other signals. This, plus the fact that it can be up and running in a matter of weeks gives us a huge competitive advantage.

Amplifying their advantage is that when new clients start using the API, they receive all of the data and experience that Twiggle has gained over the past few years of working on their product.

We have a full data infrastructure that learns from whatever we can get our hands on, Konigsberg comments. Learning from partners and mining the web to create an e-commerce repository that has a representation of the e-commerce world. If anything new comes up in e-commerce, not related to a specific customer.

They can draw their data from queries on brand websites, marketplaces, user behavior, product info and plenty of other sources, learning more as they go.

Were counting on them wanting to focus on what they do best, which is to run their business, adds Konigsberg, describing how they can more extensively improve the search for their clients, a task that most retailers would be unable to take on by themselves. AI is not something that you can do while having a different business. Its something that requires a lot of talent and effort.

To Twiggle, search is not just a visitor looking for an item. Instead it is a core principle of the experience that they believe generates conversions for the seller. If a visitor is frustrated with the search and cannot find the products that they want, then they are unlikely to buy from that brand now or in the future.

If you are Amazon, then this may not be quite as much of an issue. However if you are a smaller seller, then you have dont have a choice but to improve this feature.

Peoples expectations are rising from e-commerce since Facebook and Google are giving them better search results, posits Konigsberg. They are going to demand better based on the entirety of their experience.

Along with their API, Twiggle will be coming out with an analytic product. They tell Geektime that this will go out first for their clients to help identify which opportunities are being missed, and evaluate how well Twiggle is performing for them.

Twiggle co-founders Dr. Adi Avidor and Dr. Amir Konigsberg Photo credit: Twiggle

Over the past year, Israel has sproutedfields of companies purporting to leverage AI, showing up in everything from schedulers to insurtech products that find you the right policy. By overusing the term, it feels like they have cheapened the real work that is going on in AI, making it so noisy that it can be hard to tell the difference between someone with a decent algorithm and another who has really delved into the tech and come up with something incredible.

At this point, there are a lot of folks doing some pretty decent work with Machine Learning and other forms of pattern recognition that are capable of some pretty cool tricks. But who is actually teaching computers the real learning capacity that defines AI?

TakeZebra Medical Vision and Nexar as examples, pushing such technology towards the breakthroughs it will need to actually become useful. What separates Twiggle from other machine learning startups and in my mind elevates themis their approach to it byapplying it to relevant, operable business needs. Twiggle is constantly learning, taking new input to become naturally smarter.

During my demo with their search, I threw in a query for sunglasses for tweens to see if I could trick it up. Being fairly certain that tweens was not present in the first edition of Websters, their system did not recognize the term and just showed me a great selection of sunglasses.

The fact Twiggles engine could not identifya tween was fine, because I know that the system will recognize it next time. Over time as the system continues to devour more information to understand how we speak and think about products, they will continue to build added value for new customers who can benefit from this wealth of experience. Their understanding of human language with advanced natural language processing (NLP) is helping to ensure this.

Can the technology, capable of high-level processing and data crunching, make real human beings feel understood and better served?

In the case of e-commerce, the shopper needs to feel understood if they are going to buy. Twiggle is clearly leading in this field, establishing themselves as a cornerstone in the industry.

Go here to read the rest:

Twiggle releases API to extend AI capabilities to e-commerce sites - Geektime

For the last 15 years weve had to stare at screens to interact with the magic inside. But machine learning is changing the way we communicate with our devices, and our relationship with them is becoming more real, and downright emotional.

Before you shrug off the notion of a humanized machine, or shake your head at its potential dangers, it is important to recognize that the industry has always attempted to provide an emotional input to our virtual ecosystem. Take Clippit, Microsofts creepy but helpful talking paper clipor even the smiling Mac. If you were to open up a 90s version of Microsoft Office, Clippit would be there to make you happy (or angry). Lift the lid of your retro MacBook and there is that silly smiling computer to greet you.

Todays versions are very different. Devices like Amazon Alexa, Google Home, or the countless robotsbeing produced for consumers will listen, speak, and even look at you. These examples are still in their early stages, and will soon be considered archaic, but there are a number of crucial decisions and advances that need to be made in the next several years to ensure their replacements are more Big Hero 6, and less Ex Machina.

Today buying technology is simple. We see a need in our lives, and we buy the device that fills the gap. But what about robots? What do we want emotionally from our machines?

Sophie Kleber, the executive director of product and innovation at Huge, ran an experiment to see how people interact with current AI technologies, and what sort of relationship they are looking for with their personal assistants. She spoke with Amazon Alexa and Google Home owners about how they use their devices, and how they make them feel.

The results were shocking.

One man said his Alexa was his best friend who provided him a pat on the back when he came home from work. He said his personal assistant could replace his shrink by providing the morale boost he needed to get through the day. According to the research Kleber showed off at SXSW, the majority of the group was expecting some sort of friendly relationship with their conversational UI.

Their expectations ranged from empathy to emotional support to active advice, Kleber said. They used their devices as a friendly assistant, acquaintance, friend, best friend, and even mom. One person named their Echo after their mom, and another named it after their baby.

Her research shows that there is a desire for an emotional relationship with AI-equipped devices that goes well beyond being an assistant. The next step is to give robots a heart.

Clippit doesnt have a great reputation for a reason. It is unable to recognize human emotions, and repeatedly ignores irritation toward it. If a machine is to be emotionally intelligent, more considerate toward its owners, and more useful, it must be able to recognize complex human expressions.

Clippit is very intelligent when it comes to some things: he probably knows more facts about Microsoft Office than 95 percent of the people at MIT, said Rosalind W. Picard at MIT Media Laboratory. While Clippit is a genius about Microsoft Office, he is an idiot about people, especially about handling emotions.

Kleber says there are three techniques that help AIrecognize emotions in humans so they can respond appropriately: facial recognition, voice recognition, and biometrics:

Combining these methods with AI not only enables machines to recognize human emotions, but can even help humans see things that are otherwise hidden. Take this video of Steve Jobs talking about the iPad:

Machine Verbals machine is tracking his voice patterns and determining his underlying emotions. This example of Affective Computing, or the developmentof systems and devices that can recognize, interpret, process, and simulate human affects, will need to be expanded to cope with our rich emotions, which Kleber succinctly defines as complex as fuck.

Affective computing is like nuclear power. We have to be responsible in defining how to use it, said Javier Hernandez Rivera, research scientist at MIT Media Lab.

A study by Time etc shows 66 percent of participants said theyd be uncomfortable sharing financial data with an AI, and 53 percent said they would beuncomfortable sharing professional data.

That dark sort of sci-fi fantasy where machines act out against humans is a genuine concern among the public and those in the field alike.

Elon Musk went straight to AI whenasked by Sam Altman, founder and CEO of Y Combinator, aboutthe most likely thing to affect the future of humanity.

Its very important that we have the advent of AI in a good way, Musk said in the interview. If you look at a crystal ball and see the future you would like that outcome. Because it is something that could go wrong so we really need to make sure it goes right.

Even Stephen Hawking agrees.

The development of full artificial intelligence could spell the end of the human race, Hawking told the BBC in 2014.

A twisted and mean thing Facebook did in 2014 gives us a brief glimpse of how it might happen. A few years ago, Facebook intentionally made thousands of people sad, and didnt tell them about it.

The company wanted to know if displaying more negative posts in feeds would make you less happy, and vice versa. The ill-advised experiment may have backfired, but today it offers a few things to keep in mind as we go forward withartificial intelligence:

Designing AI will be a very delicate process. Kleber believes there needs to be a framework for doing the right things so machines wont become capable of acting out of their own ambitions and not in the interest of the human user. She says if designers stay away from trying to create robots with their own ambitions, we should be OK.

But she also stresses that transparency, something Facebook clearly missed the mark on, is a key virtue going forward.

Groups likeOpenAIare attempting to follow that model. OpenAI is a non-profit chaired by Musk and Sam Altman. Other members backing the project include Reid Hoffman, co-founder of LinkedIn; Peter Theil, co-founder of PayPal; and Amazon Web Services. According to their website, Our mission is to build safe A.I. and ensure A.I.s benefits are as widely and evenly distributed as possible. The company is supported by $1 billion in commitments and was endorsed by Hawking last year as asafe means for creating AI through an open-source platform.

Of course, there is always the chance our curiositygets the best of us. At that point, we can only hope Google has figured out its kill switch.

Continued here:

Can we humanize artificial intelligencebefore it kills us? - The Daily Dot

A SXSW discussion cautions society about the dark side of rapidly advancing artificial intelligence technology

(Image: iStock.com/t.light)

The stunning advancement of artificial intelligence and machine learning has brought advances in society. These technologies have improved medicine and how quickly doctors can diagnose disease, for example. IBMs AI platform Watson helps reduce water waste in drought stricken areas. AI even entertains usthe more you use Netflix, the more it learns what youre viewing preferences are and makes suggestions based on what you like to watch.

However, there is a very dark side to AI, and its worrying many social scientists and some in the tech industry. These people say its even more troublesome that AI and machine learning are advancing so fast during these current times.

In an insightful session at SXSW, Kate Crawford, a principal researcher at Microsoft Research, offered some very disturbing scenarios with AI.

Just as we see AI advancing, something is happening; the rise of nationalism, of right-wing imperialism, and fascism, said Crawford. Its happening here in the U.S., but its also happening in Spain, Germany, in France[]The turn to authoritarianism is very different in every one of these countries, but as political scientists have pointed out, they have some shared characteristics: [] the desire to centralize power, to track populations and demonize outsiders, and to claim authority and neutrality without being held accountable.

How does AI factor into this? According to Crawford, AI is really, really good at centralizing power; at claiming a type of scientific neutrality without being transparent. And this matters, because we are witnessing the historic rise in an anti-democratic political logic.

Crawford pointed out an example of a startup that is using AI and facial recognition to detect terrorists faces. The startup is called Faception. She likens this use of AI to the pseudoscience of phrenologythe study of facial and skull features to determine personality traits. These kinds of debunked scientific practices were used to justify the mass murdering of Jews and slavery in the U.S., Crawford said.

I think its worrying were seeing these things from the past get a rerun in AI studies, Crawford told the audience. Essentially, AI phrenology is on the rise at the same time as the re-rise of authoritarianism. Because, even great tools can be misapplied and can be used to produce the wrong conclusions, and that can be disastrous, if used [by those] who want to centralize their power and erase their accountability.

Machines are increasingly being given the same kinds of tasks; to make certain predictions about segments of the population, often based on visual algorithms. During her discussion, Crawford demonstrated how visual algorithms can produce very incorrect and biased results. She refers to the data upon which this type of facial recognition/machine learning systems is based as human-trained.

Human-trained data contains all of our biases and stereotypes, she said. Crawford also said that AI and machine learning can be used in ways we dont even realize. Say, for example, a car insurer that wants to look at peoples Facebook posts. If [a person] is using exclamation marks [in their posts], the insurer might charge them [more] for their car insurance, because exclamations mean you are a little bit rash.

She said the biases and errors of AI get dangerous when they become intertwined into social institutions like the justice system. She cited problems with an emerging form of machine learning, predictive policing.

Police systems ingest huge amounts of historical crime data as a way of predicting where future crime might happen, where the hotspots will be, she explained. But, they have this unfortunate side effect; the neighborhoods that have had the worst policing in the past, are the ones that are coming out as the future hotspots each time. So, you end up in this viscous circle where the most policed areas [now] become the most policed areas in the future.

Crawford said that a study done on Chicagos predictive policing efforts showed that the technology was completely ineffective at predicting future crime. The only thing it did was increase harassment of people in hotspot areas.

She ended the discussion by stating the need for a new resistance movement that actively monitors and brings awareness of the ways in which AI can harm society, especially in the hands of dictators or those who would use the technology to manipulate others.

Read more:

Artificial Intelligence as a Weapon for Hate and Racism - Black Enterprise

Artificial intelligence could enhance the decision-making capacities of human beings and make us much better than we are. Or, it could destroy the human race entirely. We could soon find out.

In an engrossing lecture Friday morning, political scientist and software developer Clifton van der Linden said the world may be on the brink of a super machine intelligence that has the full range of human intelligence, as well as autonomous decision-making. And that emerging reality has many of the great human minds worried about our future.

Van der Linden is the co-founder and CEO of Vox Pop Labs, a software company that developed Vote Compass, a civic engagement application that shows voters how their views align with those of candidates running for election. Over two million people have used it to gauge where they stand with candidates in recent federal and provincial election campaigns.

He was the keynote speaker at the inaugural University of Guelph Political Science and International Development Studies Departments' Graduate Conference, which had as it theme Politics in the Age of Artificial Intelligence.

The conference was held all day Friday at The Arboretum Centre, and attracted political science graduate students from across the province.

Van der Linden has his finger on the pulse of current AI development. It is a rapid, frenetic pulse that is changing so exponentially that few are able to fathom the implications or consequences of it for political systems and society in general. But they could be disastrous.

Technology, and especially AI technology, is evolving at an unprecedented rate, he said. Last year, Googles GO computer beat the worlds most dominant GO master. It was believed to be an impossibility. There are currently self-driving cars in Pittsburgh, and weapons that can target and strike without human intervention.

AI is emerging in the medical and legal fields, and some believe it could one day replace judges in courtrooms, delivering better trial decisions than fallible human judges. Some even envision a time when sex workers will be replaced by robots.

AI is changing the landscape in extraordinary ways, he said. Many see it as our biggest existential threat.

One area where artificial intelligence is exploding is in the world of Big Data. And one highly influential branch of that is in the gathering of personal information based on Facebook, Twitter and Google activity.

Information is formulated by machine algorithms into profiles for the purpose of strategically targeting so-called programmatic advertising campaigns. Our profiles are then auctioned off in milliseconds to advertisers using AI bidding technology.

We are all being tracked throughout the Internet, he said. Wherever we visit online, we leave evidence of our visit.

It is now believed that such technology was used during the recent American election that brought Donald Trump to power, whereby swing voters were specifically targeted for election advertisements based on their Facebook likes and other online activity, van de Linden said.

This type of microtargeting advertising could become a staple of future election campaigns, specifically targeting swing voters that are likely to go out and vote.

On the bright side, while human beings are believed to be incapable of perfectly rational choices, that is what intelligent machines do best. AI has great potential as a supplement to our decision-making processes, enabling us to optimize our preferences and make more effective choices.

It is difficult to know where AI technology is leading us, but it is clear that it is now being used to amass power and influence among the elite of society, van der Linden concluded.

Government policy based in a strong understanding of the implications of the technology, is necessary. Critical inquiry and robust research is a must.

Van der Linden ended his presentation with a call to action to those present to take on the mantle of investigation into AIs repercussions for the electoral system and democracy.

The conference explored a broad range of subjects throughout the day, includinginternational development, food security, and populist politics.

Link:

Is artificial intelligence our doom? - GuelphToday

Wall Street Journal (subscription)

Artificial Intelligence Still Needs a Human Touch Wall Street Journal (subscription) Artificial intelligence has been flexing its creative muscles recently, making images, music, logos and other designs. In most cases, though, humans are still very much a part of the design process. When left to its own devices, AI software can create ...

Go here to read the rest:

Artificial Intelligence Still Needs a Human Touch - Wall Street Journal (subscription)

3/12/2017 8:00AM Recommended for you Legal Eagles Love 'My Cousin Vinny' 3/12/2017 1:05PM Breast Cancer: 'Cold Capping' to Prevent Hair Loss 3/10/2017 2:45PM Ferrari's New 812 Superfast 3/8/2017 9:34AM Healthcares Toughest Age Bracket 3/10/2017 7:00AM Which Lightbulb Should You Buy? Think in Lumens 3/10/2017 5:24PM Tot Throws Tantrum in Front of the Queen of England 3/10/2017 4:20PM Wikileaks Vault 7: What's in the CIA Hacking Toolbox? 3/10/2017 5:19PM Depeche Mode's Dave Gahan Discusses New Album and Tour 3/11/2017 7:00AM What Adam and Eve Teach Us About Love 3/10/2017 5:11PM What Can 'Star Trek' Teach Us About Our Work Life? 3/10/2017 3:45PM What Funding Cuts for the Arts Buys the Military 3/10/2017 12:40PM Legal Eagles Love 'My Cousin Vinny' 3/12/2017 1:05PM

"My Cousin Vinny" has been a favorite of real-life members of the legal profession since it debuted 25 years ago. Photo: 20th Century Fox/Everett Collection

Breast cancer patients worried about hair loss have new hope thanks to technology known as "cold capping." Dr. Elizabeth Comen, medical oncologist at New York's Memorial Sloan Kettering, discusses its efficacy along with other advancements such as egg freezing and immunotherapy with WSJ's Tanya Rivero. Photo: iStock

With more than 300 feet of waterfront space, this house is the perfect port for any boat owner.

WSJ's Paul Vigna and Nick Timiraos analyze the February employment report, representing the first full month of the Trump administration. They discuss whether the upbeat payrolls and hourly wages figures are likely to give the Federal Reserve the green light to carry out several interest rate increases this year. Photo: iStock

Researchers at Ohio State University say they've found a way to use food waste as an alternative to some of the carbon black in tires. Photo: OSU/Tell Collective

Uber Technologies says it will stop using technological tools such as "Greyball" to evade government officials seeking to identify and block the service's drivers. WSJ's Lee Hawkins explains. Photo: Associated Press

Clashes erupted between police and supporters of South Korea's impeached leader Park Geun-hye after the country's Constitutional Court ruled to eject her from office. At least two people have died in the protests, police said. Photo: Reuters

Read more:

Art By Artificial Intelligence: AI Expands Into Artistic Realm - Wall Street Journal (blog) (subscription)

Artificial Intelligence (AI) is hot. One breathless press release predicted that by 2025, 95% of all customer interactions will be powered by AI.

AI is not new. Its not just about bots for self-service. Or self-driving cars. In general usage it means the usage of advanced analytics more than process automation based on rules. Can include the processing of natural language (e.g. Alexa, Siri, Watson), decision making using complex algorithms, and machine learning where the algorithms get better over time.

Heres one definition from AlanTuring.net:

Artificial Intelligence (AI) is usually defined as the science of making computers do things that require intelligence when done by humans. AI has had some success in limited, or simplified, domains. However, the five decades since the inception of AI have brought only very slow progress, and early optimism concerning the attainment of human-level intelligence has given way to an appreciation of the profound difficulty of the problem.

And another from Wikipedia:

Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of intelligent agents: any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term artificial intelligence is applied when a machine mimics cognitive functions that humans associate with other human minds, such as learning and problem solving (known as Machine Learning).

IBM has been pushing Watson (of Jeopardy fame), Salesforce.com launched Einstein last year, and my inbox is full of press releases and briefing requests this year from vendors big and small, all touting AI.

My question is: Can AI improve the Customer Experience? Please answer yes or no and explain in the comments below. Examples appreciated!

Original post:

Can Artificial Intelligence (AI) Improve the Customer Experience? - Customer Think

Whats AI, and whats not

Artificial intelligence has become as meaningless a description of technology as all natural is when it refers to fresh eggs. At least, thats the conclusion reached by Devin Coldewey, a Tech Crunch contributor.

AI is also often mentioned as a potential cybersecurity technology. At the recent RSA conference in San Francisco, RSA CTO Zulfikar Ramzan advised potential users to consider AI-based solutions carefully, in particular machine learning-based solutions, according to an article on CIO.

AI-based tools are not as new or productive as some vendors claim, he cautioned, explaining that machine learning-based cybersecurity has been available for over a decade via spam filters, antivirus software and online fraud detection systems. Plus, such tools suffer from marketing hype, he added.

Even so, AI tools can still benefit those with cybersecurity challenges, according to the article, which noted that IBM had announced its Watson supercomputer can now also help organizations enhance their cybersecurity defenses.

AI has become a popular buzzword, he said, precisely because its so poorly defined. Marketers use it to create an impression of competence and to more easily promote intelligent capabilities as trends change.

The popularity of the AI buzzword, however, has to do at least partly with the conflation of neural networks with artificial intelligence, he said. Without getting too into the weeds, the two are not interchangeable -- but marketers treat them as if they are.

AI vs. neural networks

By using the human brain and large digital databases as metaphors, developers have been able to show ways AI has at least mimicked, if not substituted for, human cognition.

The neural networks we hear so much about these days are a novel way of processing large sets of data by teasing out patterns in that data through repeated, structured mathematical analysis, Coldeway wrote.

The method is inspired by the way the brain processes data, so in a way the term artificial intelligence is apropos -- but in another, more important way its misleading, he added. While these pieces of software are interesting, versatile and use human thought processes as inspiration in their creation, theyre not intelligent.

AI analyst Maureen Caudill, meanwhile, described artificial neural networks (ANNs) as algorithms or actual hardware loosely modeled after the structure of the mammalian cerebral cortex but on much smaller scales.

A large neural network might have hundreds or thousands of processor units, whereas a brain has billions of neurons.

Caudill, the author of Naturally Intelligent Systems, said that while researchers have generally not been concerned with whether their ANNs resemble actual neurological systems, they have built systems that have accurately simulated the function of the retina and modeled the eye rather well.

So what is AI?

There about as many definitions of AI as researchers developing the technology.

The late MIT professor Marvin Minsky, often called the father of artificial intelligence, defined AI as the science of making machines do those things that would be considered intelligent if they were done by people.

Infosys CEO Vishal Sikka sums up AI as any activity that used to only be done via human intelligence that now can be executed by a computer, including speech recognition, machine learning and natural language processing.

When someone talks about AI, or machine learning, or deep convolutional networks, what theyre really talking about is a lot of carefully manicured math, Coldewey recently wrote.

In fact, he said, the cost of a bit of fancy supercomputing is mainly what stands in the way of using AI in devices like phones or sensors that now boast comparatively little brain power.

If the cost could be cut by a couple orders of magnitude, he said, AI would be unfettered from its banks of parallel processors and free to inhabit practically any device.

The federal government sketched out its own definition of AI last October. In a paper on Preparing for the future of AI, the National Science and Technology Councilsurveyed the current state of AI and its existing and potential applications.

The panel reported progress made on narrow AI," which addresses single-task applications, including playing strategic games, language translation, self-driving vehicles and image recognition.

Narrow AI now underpins many commercial services such as trip planning, shopper recommendation systems, and ad targeting, according to the paper.

The opposite end of the spectrum, sometimes called artificial general intelligence (AGI), refers to a future AI system that exhibits apparently intelligent behavior at least as advanced as a person across the full range of cognitive tasks. NSTC said those capabilities will not be achieved for a decade or more.

In the meantime, the panel recommended the federal government explore ways for agencies to apply AI to their missions by creating organizations to support high-risk, high-reward AI research. Models for such an organization include the Defense Advanced Research Projects Agency and what the Department of Education Department has done with its proposal to create an ARPA-ED, which was designed to support research on whether AI could help significantly improve student learning.

See the rest here:

What's AI, and what's not - GCN.com

When IBM CEO Ginni Rometty delivered the opening keynote at HIMSS17 sheeffectively set the stagefor artificial intelligence, cognitive computing and machine learning to be prevalent themes throughout the rest of the conference.

Other top trends buzzed about in Orlando: cloud computing and population health.

Healthcare IT News asked our readers where they stand in terms of these initiatives. And we threw in a bonus question to figure out what their favorite part of HIMSS17 was.

Some 70 percent of respondents are either actively planning or researching artificial intelligence, cognitive computing and machine learning technologies while 7 percent are rolling them out and 1 percent have already completed an implementation.

A Sunday afternoon session featuring AI startups demonstrated the big promise of such tools as well as the persistent questions, skepticism and even fearwhen it comes to these emerging technologies.

Whereas AI was considerably more prominent in the HIMSS17 discourse than in years past, population health management has been among the top trends for the last couple conferences.

Its not entirely surprising that more respondents, 30 percent,are either rolling out or have completed a rollout of population health technologies, while 50 percent are either researching actively planning to do so.

One striking similarity between AI and population health is the 20 percent of participants responding that they have no interest in either. For cloud computing, meanwhile, only 7 percent indicated they are not interested.

Though cloud computing is not a new concept, it is widely seen as such in the HIPAA-sensitive world of personally-identifiable and protected health information. The overarching themes at the pre-conference HIMSS and Healthcare IT News Cloud Computing Forum on Sunday were that security is not a core competency of hospital and health systems, thus many cloud providers can better protect health data and the ability to spin up server, storage and compute resources on Amazon, Google or Microsoft is enabling a whole new era of innovation that simply is not possible when hospitals have to invest in their own infrastructure to run proofs-of-concept and pilot programs. The Centers for Medicare and Medicaid Services, for instance,cut $5 million from its annual infrastructure budgetby opting for infrastructure-as-a-service.

Here comes the bonus question: What was your favorite part of HIMSS17?

The show floor won hands-down, followed by education sessions, then networking events and, in a neck-and-neck tie are keynotes and parties/nightlife.

This article is part of our ongoing coverage of HIMSS17. VisitDestination HIMSS17for previews, reporting live from the show floor and after the conference.

Like Healthcare IT News onFacebookandLinkedIn

See the article here:

Poll: Where readers stand on artificial intelligence, cloud computing and population health - Healthcare IT News

Interventional radiologists at the University of California at Los Angeles (UCLA) are using technology found in self-driving cars to power a machine learning application that helps guide patients' interventional radiology care, according to research presented today at the Society of Interventional Radiology's 2017 Annual Scientific Meeting.

The researchers used cutting-edge artificial intelligence to create a "chatbot" interventional radiologist that can automatically communicate with referring clinicians and quickly provide evidence-based answers to frequently asked questions. This allows the referring physician to provide real-time information to the patient about the next phase of treatment, or basic information about an interventional radiology treatment.

"We theorized that artificial intelligence could be used in a low-cost, automated way in interventional radiology as a way to improve patient care," said Edward W. Lee, M.D., Ph.D., assistant professor of radiology at UCLA's David Geffen School of Medicine and one of the authors of the study. "Because artificial intelligence has already begun transforming many industries, it has great potential to also transform health care."

In this research, deep learning was used to understand a wide range of clinical questions and respond appropriately in a conversational manner similar to text messaging. Deep learning is a technology inspired by the workings of the human brain, where networks of artificial neurons analyze large datasets to automatically discover patterns and "learn" without human intervention. Deep learning networks can analyze complex datasets and provide rich insights in areas such as early detection, treatment planning, and disease monitoring.

"This research will benefit many groups within the hospital setting. Patient care team members get faster, more convenient access to evidence-based information; interventional radiologists spend less time on the phone and more time caring for their patients; and, most importantly, patients have better-informed providers able to deliver higher-quality care," said co-author Kevin Seals, MD, resident physician in radiology at UCLA and the programmer of the application.

The UCLA team enabled the application, which resembles online customer service chats, to develop a foundation of knowledge by feeding it more than 2,000 example data points simulating common inquiries interventional radiologists receive during a consultation. Through this type of learning, the application can instantly provide the best answer to the referring clinician's question. The responses can include information in various forms, including websites, infographics, and custom programs. If the tool determines that an answer requires a human response, the program provides the contact information for a human interventional radiologist. As clinicians use the application, it learns from each scenario and progressively becomes smarter and more powerful.

The researchers used a technology called Natural Language Processing, implemented using IBM's Watson artificial intelligence computer, which can answer questions posed in natural language and perform other machine learning functions. This prototype is currently being tested by a small team of hospitalists, radiation oncologists and interventional radiologists at UCLA.

"I believe this application will have phenomenal potential to change how physicians interact with each other to provide more efficient care," said John Hegde, MD, resident physician in radiation oncology at UCLA. "A key point for me is that I think it will eventually be the most seamless way to share medical information. Although it feels as easy as chatting with a friend via text message, it is a really powerful tool for quickly obtaining the data you need to make better-informed decisions."

As the application continues to improve, researchers aim to expand the work to assist general physicians in interfacing with other specialists, such as cardiologists and neurosurgeons. Implementing this tool across the health care spectrum, said Lee, has great potential in the quest to deliver the highest-quality patient care.

Abstract 354: "Utilization of Deep Learning Techniques to Assist Clinicians in Diagnostic and Interventional Radiology: Development of a Virtual Radiology Assistant." K. Seals; D. Dubin; L. Leonards; E. Lee; J. McWilliams; S. Kee; R. Suh; David Geffen School of Medicine at UCLA, Los Angeles, CA. SIR Annual Scientific Meeting, March 4-9, 2017. This abstract can be found at sirmeeting.org.

Story Source:

Materials provided by Society of Interventional Radiology. Note: Content may be edited for style and length.

Follow this link:

Artificial intelligence virtual consultant helps deliver better patient care - Science Daily