Category Archives: Artificial Intelligence

Ambiguity Abounds

To borrow aclich opening from the last high schoolcommencement or Maid of Honor speech you heard, the dictionary definesartificial Iintelligence (AI) as1:a branch of computer science dealing with the simulation of intelligent behavior in computers; and2:the capability of a machine to imitate intelligent human behavior.

But, dothese definitions really explain the difference betweenan artificially intelligent system and one thats just programmed to be useful? What is intelligent behavior or, more specifically, intelligent human behavior?

in most cases, the systems making claims to artificial intelligence arent sentient, self-aware, volitional, or even surprising. Theyre just software. Ian Bogost

The definition is clearly open to some level of interpretation. Combine that ambiguity with the terms cool sci-fi connotations, and you get a world in which, asThe Atlantics Ian Bogost puts it, deflationary examples of AI are everywhere.

In an article titled Artificial Intelligence Has Become Meaningless,Bogost takes issue with thewidespread overuse of the term AI both within and outside the tech realm. [I]n most cases, the systems making claims to artificial intelligence arent sentient, self-aware, volitional, or even surprising. Theyre just software, he argues, noting the use of the term to describe everything from fairly simple pattern-matching filters to easily fooled algorithms.

So, if everything is revolutionary AI tech, then nothing is, right? Maybe not. Perhaps we just need to revisit that definition, and to that end, we must turn to the experts.

For many, the term artificial intelligence draws to mindhumanoid robots like C-3PO from Star Wars or Doloresfrom Westworld. SoftBank Robotics has its own version of that type of AI: Pepper. Omar Abdelwahed,SoftBank Robotics Americas Head of Studio, is therefore particularly well-suited to share his own definition of AI.

At base, for a system to exhibit artificial intelligence, it should be able to learn in some manner and then take actions based on that learning, says Abdelwahed. These actions are new behaviors or features of the system evolved from the learnings.

A spokesperson for IBM, home to Watson, perhaps the most famous AI this side of science fiction,went one step further, positing that an AI should not only be able to learn and reason, it should also be able to interact and react:

AI platforms should do more than answer simple questions. They should be able to learn at scale, reason with purpose, and naturally interact with humans. They should gain knowledge over time as they continue to learn from their interactions, creating new opportunities for business and positively impacting society.

By those definitions, Bogost is clearly right that a great number of AI systems dont deserve the name. Facebooks suicide-detecting system, for example, was heralded as being assisted by artificial intelligence, and true, it can learn something (that a users behaviorindicates that they may be suicidal) and then take action (by offering the number of a helpline), but it doesnt evolve. If it works as designed, it could positively impact society, but is it able to naturally interact with humans or learn from its interactions? Not so much.

In a recent survey by Pega Systems, 72 percent of the 6,000 adult consumers polled claimed to understand what was meant by artificial intelligence, and yet only34 percent said they had ever come in contact with AI. The actual percentage who had? Eighty-four.The survey indicates a clear level of misunderstanding of the term amongst the average person.

Perhaps this hearkens back to that idea of C-3PO and Dolores. Both are machines that largely look and act like humans, and they, along with other sci-fi figures, may have clouded public consciousness when it comes to not only identifying an artificial intelligence, but trusting it.

Truly, a system neednt be human-like at all tobe AI. Though named after a person, Watsonhas very little in common with how humans behave, save a knack for language processing, and Teslas Autopilot system is artificially intelligent, but it couldnt hold a conversation with a Jedi, let alone help one save the Galaxy. The Jetsons aproned maidRosie and Skynet from The Terminator franchise are both examples of AI, but they couldnt be more different, and, depending on which represents your first intellectual encounter with AI, you might have a very different internalized bias toward future iterations of the tech.

With artificial intelligence poised to disrupt everything from our roads to our schools andour workplaces, its time we get on the same page as to what AI is and, perhaps more importantly, what we want it to beso that we can regulate and control the technology.

If we dont, anyone looking for a way to draw buzz to their latest product will continue to co-opt the phrase and the definition will get even more clouded. Our society is on the verge ofbecoming the proverbial boy who cried wolf when it comes to AI technology. If the trendcontinues, the public might not even notice when a truly revolutionary AI system does arrive, and anyone whos seen a movie in the last 50 years should be able to tell you how problematic that could be.

Neither Futurism nor the author of this article received monetary compensation (or any other form of compensation) in exchange for writing this piece.

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This Is What a True Artificial Intelligence Really Is - Futurism

Cisco CEO Chuck Robbins at Fortune Brainstorm Tech 2016 in Aspen, Colo.Stuart IsettFortune

Ciscos appetite for startups continues.

The networking technology company said Thursday that it plans to buy MindMeld, a startup specializing in artificial intelligence, for $125 million. The deal is expected to close in early 2017.

MindMeld, founded in 2011, builds software tools for coders to create chat bots that can recognize and respond to human voices. Music-streaming service Spotify, for example, has experimented with MindMelds technology for people to search and play songs by talking to their smartphones.

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Considering MindMeld has roughly 20 employees based on the companys website, its likely Cisco is buying the startup for its workers.

Cisco said that the MindMeld staff will report to the companys collaboration business unit, which sells workplace chat software and other business productivity tools like a 55-inch digital whiteboard for people to conduct meetings with.

Rowan Trollope, a Cisco senior vice president, said in a blog post that MindMelds technology and staff would help improve the companys various chat and related workplace software.

We realized that to really enable our customers to have more natural, conversational interactions in our enterprise collaboration tools, wed have to do more for them, and do more of the heavy lifting, said Trollope.

The popularity of voice-activated digital assistants like Apples ( aapl ) Siri and Microsofts ( msft ) Cortana has led to a lot of hype in companies creating their own voice-powered software. However, Trollope admitted that there are a tremendous number of bots which are impressively bad at natural language conversation.

Still, Trollope seems hopeful.

Bringing the MindMeld team to Cisco is a giant leap forward in helping our customers experience the next generation of interactive, conversational interfaces, he said.

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Ciscos latest acquisition follows its $610 million planned acquisition of networking startup Viptela, announced in early May. In January, Cisco said it would buy AppDynamics, a startup that helps companies monitor the performance of their apps, for $3.7 billion .

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Cisco Pays $125 Million For This Artificial Intelligence Startup - Fortune

Im willing to bet you didnt know that artificial intelligence can help sort cucumbers.

It can, and in fact it does. And while AI has gotten massive amounts of attention recently due to its role in making cars autonomous, doing facial recognition, and automatically translating languages, theres one man in Silicon Valley who really wants everyone developing any kind of technology-based tool to know that AI has something to offer them as well.

Last year, Frank Chen, a partner at the A-list venture capital firm Andreessen Horowitz (a16z), published a primer on artificial intelligence. The 45-minute video took viewers through a history of the technology, from its birthday in the summer of 1956 through its years in the wilderness of technology and straight through current-daySilicon Valley, where it is dominating conversations at most of the largest tech companies there.

In fact, if the mobile cloud was computings previous major era, the next will be the era of AI, Jen-Hsun Huang, the CEO of Nvidia, one of the worlds largest makers of the kinds of graphics processors that power the computers behind todays AI applications, told me last year. It is the most important computing development in the last 20 years, and [every major technology company is]going to have to race to make sure that AIs a core competency.

Chens primervideo went unexpectedly viral, hetold Fast Company yesterday, becoming one of a16zs most viewed pieces of content ever. He began getting hundreds of inbound calls about AI, with everyone from policy makers to startup founders wanting him to help them understand this white-hot ecosystem. The editor of Fashion Week called me, Chen said, and said, Oh, will robots take all the fashion designer jobs?'

Having been interested in AI since his days studying the technology at Stanford in the late 1980s and early 90s, Chen knew that it has now become mature enough that its applicable to a far wider range of people and companies than ever before. Indeed, his thinking on the matter has centered on the notion that, today, AI can help even an average product manager at Delta Airlinesthe kind of role few would have imagined could benefit from artificial intelligence or machine learningor a cucumber farmer.

Thats why Chen has now published both an AI playbook that helps just about anyoneespecially non-technical audiencesunderstand how the technology can help them, as well as a second primer aimed at spelling out numerous ways AI has made its way into everyday life and spread well beyond the halls of the Facebooks, Microsofts, Amazons, and Googles of the world.

AI isnt some future thing, Chen said, pointing to the fact that hes seen demos of Star Trek-like language translators that you can pop in your ear and that should be on the market in a year or two.

In short, Chens explainers are offering the world his version of Everything you wanted to know about AI, but were afraid to ask.

Many people find thattrying to understand the technology underpinning AI can hurt the brain. Doing so requires digesting concepts like convolutional neural networks (CNNs), recurrent neural networks (RNNs), supervised learning, unsupervised learning, and so on. Chen is basically saying, relax, its okay, lets unpack these concepts without math so that anyone can grokthem.

Of course, for the more adventurous, he offers up more technical examples such as talking about what happens when you feed sentences into Baidus translator, or images into either Googles or IBM Watsons systems. He even walks people through writing a simple business card recognizer that uses an iPhones camera.

Yet he wants people to know that each companys approach to AI is a bit different, with wildly different results to similar experiments. The AI wars are the Wild West.

Chen seems a bit amused that hes ended up in the role of AI explainer, something hesort of fell into it by accident.

But having embracing that role, hes now got an agenda: He wants people, not just hard-core technologists, to be inspired to try new things. He wants folks to see that AI has something for anyone building an application, that for everyone, theres something AI can do to give their software a serious boost.

First, he says, its easier than ever to figure out how to make anyones software better, smarter, and more useful, and second, that it doesnt take a PhD to understand how to incorporate AI into tools. Anyone who can figure out how to use an API can take advantage of AI, he argues.

I want people to be excited [about AI] in the here and now, he said. I cant wait for people to see what they can do once their software has superpowers.

Yet Chen is also sensitive to how seriously confusingAI can be to some, and that many people assume the technology is controlled by the priesthood.

Its not, at least not anymore, he says. And every new technology platform has felt the same way before it was democratized.

Were just starting to get there, Chen believes, and when we do, AI will be everywhere, powering everything. And it wont be a specialized technology anymoreonce people allow themselves to really understand it and how it can work for them.

Right now, AIs kind of the hottest thing in Silicon Valley, Chen said. So every company I see represents themselves as an AI company. [In a few years] nobody will say theyre an AI company, because itll be assumed.

Daniel Terdiman is a San Francisco-based technology journalist with nearly 20 years of experience. A veteran of CNET and VentureBeat, Daniel has also written for Wired, The New York Times, Time, and many other publications.

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Here's The Unofficial Silicon Valley Explainer On Artificial Intelligence - Fast Company

In the last ten years, artificial intelligence has changed the world in subtle but sweeping ways, but its got nothing on the coming decade, if you look at whats being developed today. Voice recognition on every smartphone were simple proofs of concept. Over the next 10 years, artificial intelligence will make more progress than in the fifty before it, combined. With countless quickly oncoming applications to business, government, and personal life, its influence will soon touch absolutely every aspect of our lives.

Here are 25 surprising ways life and society that will be forever changed by artificial intelligence over the coming decade.

Cooking is perfectly suited to A.I., since it basically just requires knowledge of how a list of ingredients get combined in different ways, in different amounts. Products like the Hello Egg can not only help find and execute recipes more easily, but watch your eating and cooking habits to design meal plans that improve health. True, some past experiments in getting A.I. to design tasty meals havent gone so smoothly, but dont forget that IBM is on the case now, and Chef Watson likely has some inventive new ideas just around the corner.

It might not work quite right just yet, but Amazons quest to make physical shopping even less hassle than online is only possible at all thanks to the advanced A.I. technology behind it. Online shopping algorithms are a dime a dozen these days, but one fascinating project from Pinterest could expand the idea to the physical world. Prediction will also play a bigger role than ever, as everything from Etsy to Amazon itself begins using A.I. to suggest the perfect product to shoppers, and to make sure that product is stocked up, back at the warehouse.

Terribly named as the aforementioned Pay With Your Face technology may be, it could also save an incredible amount of time. Advanced A.I. face recognition algorithms will soon be quick enough and cheap enough to support millions of transactions per day, but machine learning can teach a computer to recognize more than faces. Wells Fargo and others plan to secure secure some financial transactions with equally advanced biometric analysis of a users voice.

Its admittedly a little difficult to trust anyone who genuinely thinks the phrase, Pay with your face is going to take off, but Alibabas Jack Ma has a compelling point in the above talk: in the coming decades, the worlds best CEOs might just be robots. If management is the process of noticing and properly assigning talent, then an A.I. might well be better able to do that. Then again, others like Singularity Universitys Nell Watson think that the rise of A.I. might make people so capable of organizing on their own that managers become less important, altogether.

Now, this is crucially different than landing a date, though with a good-enough algorithm that might be made easier, as well. The idea is that A.I. could get to know you well enough to essentially do the Tinder swiping for you. Tinder chairman Sean Rad was recently quoted as saying the company wants the app to notice that a user is looking for something to do, so it can pop up and say, Theres someone down the street you might be attracted to. Shes also attracted to you. Shes free tomorrow night. We know you both like the same band, and its playing would you like us to buy you tickets?

Its tough being out in the game, and some people just need practice. While an A.I. will (hopefully) never offer a real replacement for human interaction, it could soon offer a pretty good test run. A.I. chat-bots are starting to crop up, ostensibly to provide training in basic interaction with the opposite sex. Dont think it could work? Cleverbot has already managed to successfully chat up a fair number of ladies on OKCupid.

The news is in, straight from the website built specifically to cater to the future sex-doll market: truly advanced sex A.I. is on the horizon. The famous RealDoll is getting in on the sex-bot gold rush, and designing an A.I. partner that learns from the owner and evolves to create a real relationship. That might sound like were just headed toward sex-crazed male fantasy robots but A.I. could just as easily be used to make more realistic cyber-romance, too.

A.I. wont replace the experience of having a child any time soon, but what about a pet? The constant, loyal presence of a furry friend can be just what some people need to get through the day, and an A.I. companion could even play soothing music at the same time. Not only are household robots becoming cute, lovable, and even vulnerable, but their minds are starting to be able to replicate some of the most crucial aspects of pet intelligence. Its not just about loyalty soon, A.I. will be able to grow up with you, and form a lasting connection as its personality evolves before your eyes.

Pandora long ago sold the public on the idea that computers can break music down to its components, then analyze those components to find similar music in the future. Whats intriguing about the A.I.-powered future for this idea is that with new technologies available, curators no longer need to wait for a listeners active feedback on songs. A.I. can already analyze music well enough to create its own human-inspired songs, and that ability to dive into things like genre will be crucial to wider applications. In particular, Googles Magenta seems to be building the sort of robust understanding necessary to create A.I. music curators, and even A.I. music critics.

The rise of A.I. could completely upend one of mankinds oldest professions: bookies. Its one thing to ban card counting in casinos (either with a biological brain or an A.I. neural net) but quite another to restrict bets made remotely. How will an unassisted human being beat the odds, in a world where an A.I. analyst can take into account every shot a basketball player has ever made (or missed)? A.I. can come up with its own novel predictions based on data, or it can aggregate the predictions of experts. Either way, its questionable how much of a future human competitors will have in picking winning options, or even complex games of skill, like poker. In the future, the only time a human could beat a machine in a game of chance could be when playing a slot machine.

Right now, there are A.I. working to discern the inner workings of Donald Trumps mind, and to intelligently aggregate his many gaffes into a coherent news feed. With the public up in arms about the idea of bias in news, there could very easily be a swing toward a (seemingly) less biased alternative. As always, the Japanese are ahead of the robot game, when it comes to artificial newspersons.), when it comes to artificial newspersons.

Elon Musk loves to talk about self-driving road trips, and he should; by letting people sleep on the freeway, self-driving cars could make short-distance flights far less necessary. This is probably the most sought-after aspect of fully autonomous cars, more even than the elimination of driving during the daily commute to work. It could totally change the nature of mid-range travel, and allow the creation of a new class of mobile workers who literally never settle down, at all.

By now most people are aware that self-driving cars will dramatically reduce the amount of time spent in traffic, but when fully autonomous vehicles hit the roads and allow drivers to totally forget the road, a whole new portion of the day can become productive. What used to be a mandatory 45-minutes spent staring at the tarmac may soon be 20, spent reading a book or chatting with friends. In the video above, we can see how computer scientists are already thinking about how to take advantage of a self-driving revolution that hasnt even happened yet.

A.I. helpers like Siri and Googles new Assistant have always at least attempted to help their users schedule their various appointments, but its only with the very recent introduction of artificial intelligence that these projects have managed to do more than remember past appointments and repeat them. Now, and especially in the near future, A.I. can read and understand your conversations to actively pull out scheduling info. If someone says that you should hang out next Thursday after the show youre both attending, machine learning algorithms can now parse these references to pull out their real meaning, and suggest a scheduling point at the time you actually want.

Not long ago, an A.I. managed to perform as well as most humans do on a standard math SAT. That means it had to read and understand the questions on its own, including the diagrams, showing that A.I. is beginning to be able to not just solve but define problems, and some basic solutions are already making their way to regular app stores. In schools of the future, it might not make sense to assign that sort of problem-based homework, at all. ) as well as most humans do on a standard math SAT. That means it had to read and understand the questions on its own, including the diagrams, showing that A.I. is beginning to be able to not just solve but define problems, and some basic solutions are already making their way to regular app stores. In schools of the future, it might not make sense to assign that sort of problem-based homework, at all.

The strategy of mutli-player team sports is too complicated for A.I. that was always the conventional wisdom. Yet now, even given the nearly infinite variability of human behavior and ingenuity on the field and rink, it seems that artificial intelligence could soon design all-new strategies for even the worlds best-studied sports. The beautiful game has been successfully broken down and understood, in its basic principles, by a machine learning algorithm, and according the inventor of that algorithm it should apply quite well to other continuous sports like basketball and hockey. A whole lot of teams are taking notice.

A.I. might design the perfect offensive formation, but the human players enacting that plan have to have the necessary skills. Once again, modern machine learning algorithms can help. Partly, its just a matter of collecting data in wider and wider contexts, through smart golf clubs and basketballs and baseball bats (above). But A.I. could also provide more active feedback, like by watching your swing to offer corrections. And, of course, the granddaddy of all machine learning applications is fitness, which is basically just data given human form. Will we all become more healthy thanks to A.I.? Probably not. But with A.I. well have far fewer excuses left to hide behind.

Distributed botnets designed to attack and harass over the internet are nothing new, but the autonomous propaganda efforts of actors all over the world are starting to truly come into their own. Theres even an A.I. company selling electoral management (read: manipulation) to the highest bidder. Its approach is to use A.I.s power to crunch large volumes of data to design the most effective possible political campaigns. These days, that means setting A.I. to do careful political control of an individual or a partys messaging, AKA, propaganda.

Farming person-tracking out to A.I. is a no-brainer. Face recognition could take a huge proportion of the responsibility to stop incoming undesirables off of border agents, and could greatly reduce wait times at border crossings. The U.S. government is very interested in biometrics for border security, up to and including monitoring fenced stretches of border for illegal crossings. The Trump Administration has refused to embrace an A.I.-based digital wall with Mexico, which would track incursions with cameras rather than stopping them with concrete, but its notable that the proposition arose as a credible possibility, at all.

One of Edward Snowdens more shocking revelations was not that the NSA had begun using A.I. to augment its cyber-defenses (that should have been assumed), but that it was used its new A.I.-born capabilities to partially automate national security, and even counter-attacks. The MonsterMind platform can take automatic retaliatory action against the worlds many hackers, an ability that Snowden has called the worst thing he saw in his time at NSA. We dont have any information about how effective MonsterMind and similar programs are at present, but we know theyll play a major role in shaping global cyber-security in the future.

The novel Neuromancer predicted it: automated cyber defense intelligences. Everything in security in an arms race, and no matter how talented the security expert, their puny human finger-speed simply cannot hold a candle to the speedy attacks of an automated botnet. Over the next 10 years, and in fact over the next 10 months, we will see large proportions of cyber-security offloaded to automated techniques. As BlackRidge said at the opening of their recent study on the subject: Cyber defense automation is an imperative.

To an extent, real-time machine translation already exists from major tech companies like Skype and Microsoft. But other research bodies like Google and even DARPA are looking to take the idea even further. Machines can currently only even try to translate about 100 of the worlds more than 7000 languages it seems certain is that whether its the military or international corporations or just regular old academia, someone is going to use A.I. to push real-time translation forward and let us all talk to everyone, very soon.

Theres a certain enjoyment in watching bankers get automated into unemployment, but the real win for the little guy will come when the A.I. really take over. Right now, the financial services sector has a lot to do with why the rich get richer they can afford to hire more and better financial help to manage the money they have. With A.I., especially open source fintech solutions, it could be possible to change personal finance to put it on a much more even playing field.

This makes a lot of sense: laws are supposed to be totally mechanical, so why couldnt they be handled by a mechanical lawyer? The reality is that, for high-level lawyering, its precisely the ability to see around the rules that make a lawyer successful. Where A.I. could really change society, at a deep and fundamental level, is in providing half-decent defense to those who cannot afford adequate human representation. In many places, the public defense corps is an unmitigated disaster but A.I. dont get tired, or jaded, or immediately judgemental. They will provide an adequate legal defense to millions of people who currently do not receive one. Played out beyond the 10-year timeline, this could have some of the furthest-reaching implications of any entry on this list.

Complementing a doctors human intuition with the precision and completeness of A.I. could be one of the greatest revolutions in healthcare since hand-washing. Listen to doctors think about the possibilities. The simple fact is that the human race has produced more overall understanding of human health than any one human brain can usefully contain, and A.I. are quickly starting to out-perform even the best human docs. Even faced with a totally binary decision between all-human and all-A.I. care, in 10 years time, how many people will be willing to opt for the better bedside manner?

Graham is a freelance science and tech writer in Vancouver, Canada covering the interface between culture and bleeding edge research. His work has also been featured in MIT Technology Review, Motherboard, ExtremeTech, and elsewhere. He has a degree in biochemistry, takes really long showers, and makes documentaries about war and conflict for "fun." Email him at graham.templeton@inverse.com.

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25 Examples of A.I. That Will Seem Normal in 2027 - Inverse

Smart artificial intelligence platforms are now handling some media planning and buying work that is traditionally done by media agencies.

Maxus tested a few AI platforms over the past year and is now mainly using one called Lucy to process and rearrange structured data for better efficiency. For instance, a hotel chain recently wanted to understand how its visitor profiles overlap across its business, economy and high-end hotel offerings, so Maxus used Lucy to isolate different audience segments and their media exposure for the client. The agency also tested Lucy in Asia for a cosmetics brand to understand audience segmentation across its products (lipsticks and mascara, for example) and how that influenced media consumption acrossTV, digital and out of home by country.

David Gaines, chief planning officer for Maxus North American operations, thinks agencies should view AI platforms as an opportunity for collaboration, rather than competition.

AI is not going to rupture media agencies. Technology like this will be how we evolve, said Gaines. Too much manual labor gets in the way of tapping into the talent we have and making clients benefit from that value.

AI software helps marketers save time by combing through a plethora of data and distributing a solution fast. An extreme case is an AI platform called Albert that Dole Asia used for a digital campaign to handle all the media buying, optimization and placement autonomously. Technologies like Albert work well for a business that operates heavily in the direct response space with lots of call-to-action ads, but if a brand wants to create reasons for consumers to choose it over its competitors, its media strategy needs to be balanced with communications planning, Gaines said.

If five car brands use the same AI tool, the platform will make very similar media planning and buying suggestions. So what is the differentiator? he said. You still need a human touch.

It is also hard for platforms like Lucy to process unstructured data, including consumer surveys, media consumption data and a clients first-party data. Media planners need to formulate that data first in order for AI to restructure them. This is especially time-consuming when it comes to programmatic, Gaines said.

Its amazing how much time our media planners still spend on copying and pasting data in Excel so we can compare data sources to data sources anduse them as future-facing insight, rather than just a view on what has already happened, he said.

Lucy illustrates both the strengths and the limitations of AI platforms. For example, Lucy can suggest the best channels for a campaign, including search, social, magazine, outdoorand TV. But it cant make recommendations about which specific social network Facebook, Snapchat or Twitter, for instance to use becausethe industry doesnt have a consistent measurement across the different platforms. And the AI platform may get confused about questions like, What kind of people stay at these hotels? simply because the options and answers are currently broad, Gaines explained.

Lucy is excellent at delivering a collection of data that is effectively the observations, he said. But what AI cant do right now is then tell you what the insight is from those observations, which means we still have a job for a while.

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How media agency Maxus is testing artificial intelligence - Digiday

As consumers get more comfortable with AI and understand its ability to improve their lives, companies are making investments to improve key supporting technology, such as translation, algorithms and discovery.

In IBB Consultings work with media companies, weve identified three key areas where opportunity exists to integrate AI and machine learning to improve the customer experience, boost revenues, increase productivity and more. From chatbots and content creation to new levels of personalization, the following areas are opportunities to integrate AI into everything from consumer-facing properties to internal functions.

AI-driven chatbots can already interact with people on the web or on mobile to help find information, answer questions and sell services. Were early in the game when it comes to how they will be deployed, with the goal being to ultimately get to a place where communicating with a chatbot feels no different than chatting with a real person.

Most customer requests and issues are basic and can be handled by programming chatbots to understand questions and trigger words, then provide answers by querying specific data sources. From a customer care perspective, chatbots and virtual assistants can be a cost-effective way to meet the needs of customers any time of day, with no wait. If necessary, chatbots can be programmed to hand off to a human agent should the conversation become too complicated. Over time, this will not be necessary as machine learning drives continuous improvements and the chatbots understanding of how to address an issue it once could not.

Chatbots can also provide new customer experiences. For example, The Food Network built a Facebook chatbot that recommends meals to interested customers by searching its database of more than 60,000 recipes. HBO built a customized website for Westworld that included a chatbot named Aeden that interacted with visitors and helped them explore the park.

This is just the beginning of whats possible. Content owners can create destinations that feature chatbots modeled off of favorite characters. Fans can chat about their favorite topics, ask questions about the show, or really, just about anything. AI-driven chatbots will reduce OPEX as they become more effective and create better engagement with consumers by giving them another way to interact with their favorite content and characters. The added layer of engagement will then drive more tune-in to popular content and create more impression opportunities for lesser-known content.

AI can also serve the role of content creator. Its not going to start writing TV scripts, but today, AI is smart enough to pull data from multiple sources to generate financial reports, sports commentaries and brief event summaries, it can aid in research or even basic content creation. However, while AI can report facts, it cannot add emotional responses or opinions. It also struggles to create detailed storylines. As machine learning continues to evolve, media companies can lean on the technology to produce other kinds of content, such as series and movie reviews that pull from, and aggregate in a cohesive story, what consumers are saying on social media.

AI can also support video production efforts. Fuisz Video is an interactive video and technology company that allows marketers to simultaneously shoot videos from multiple perspectives. Integrated AI then automatically produces a seamlessly edited clip that is ready for distribution or finishing touches. In an age when getting content online as fast as possible is a key to driving social sharing and large audiences, media companies need to capitalize on every advantage available to them.

AI can also be used to accomplish the opposite task. Instead of providing a single edit, AI can create a range of edit choices. Maybe different cuts need to feature different actors more prevalently than others. Or different edits to fill different time requirements may be needed. This functionality will be critical as programmers aim to get hyper-surgical in targeting of audiences across different social networks and platforms.

Today, when users interact with companies, they can sometimes be made to feel like just another number. Theyre asked to enter account information and PINs. The rep they speak with typically does not have time to comb through their history or profile before a conversation starts. This couldnt be more at odds with the kind of experience that customers value most.

Today, personalization is a major differentiator as media properties compete for customers. Viewers are favoring tailored recommendations, like Spotifys Discover Weekly. Netflixs recommendation algorithm has been estimated to save the company $1B annually by keeping users engaged and reducing churn.

AI can be used to customize service interfaces or web homepage experiences, hiding content that would not appeal to a certain customer, while putting a spotlight on precisely what will keep them watching, listening or clicking for long periods of time. AI can also be trained over time to offer recommendations that are personalized, but not too personal (aka creepy). IRIS.TV has already started curating video libraries for media companies to align with each viewers preferences and behaviors, while Vidora is helping news companies customize communication to reduce churn.

Media companies should consider building more robust recommendation engines that leverage deep learning to create more personalized experiences. Having the capability in-house vs using a vendor solution provides content providers with more flexibility and gives them the option to use the technology in other parts of the business. As the technology continues to learn it will become an increasingly valuable asset for target marketing and other monetization opportunities.

Kicking off AI efforts in a smart way

AIs possibilities and support requirements can be overwhelming, but investments in machine learning and application of AI across the media business is a critical step toward staying ahead of the competition. After all, digital native businesses are building from the foundation with AI capabilities in mind.

Media companies should identify and prioritize AI business opportunities that either solve a current problem or transform the business in a visionary new direction. Developing rapid proof of concepts using proven vendors and technologies can return immediate insights. With so many AI companies emerging, potential acquisitions should be evaluated.

Weve only seen the beginning of whats possible with AI. While there is a long road ahead, moving forward with a strategy is the only way to address the challenges and opportunities media companies will face along the way.

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Three ways Artificial Intelligence will advance media businesses - Videonet

By Anna Irrera | NEW YORK

NEW YORK The Bank of New York Mellon Corp (BK.N) has developed and deployed automated computer programs, or more than 220 "bots", across its businesses over the past 15 months seeking more efficiency and lower costs, as the adoption of artificial intelligence technology in banking increases.

The 233-year-old custodian bank says its new army of robotics, or software created to carry out an often repetitive task that would normally be performed by humans, range from automated programs that respond to data requests from external auditors, to systems that correct formatting and data mistakes in requests for dollar funds transfers.

BNY Mellon said 20 bots have been placed in production since the start of the year. The robotics push comes as the banking sector ramps up the use of artificial intelligence (AI) and automation to save money and time on cumbersome and manual processes, ranging from back office tasks to customer service.

The bank estimates that its funds transfer bots alone are saving it $300,000 annually, by cutting down the time its employees need to spend on identifying and dealing with data mistakes and accelerating payments processing.

Bots are becoming particularly popular in banking for customer service, as banks are hopeful they can replace their costly large call centers.

More than three quarters of 600 top bankers surveyed by consultancy Accenture Plc (ACN.N) for a recent report said they believe AI will be the primary way banks interact with customers within the next three years.

One of the objectives of BNY Mellon's bot strategy is to help the organization "get rid of the mind numbing tasks" so that employees could focus on different activities, said Doug Shulman, senior executive vice president at BNY Mellon.

The bots also help provide a better client experience and reduce costs, Shulman added.

The bank said, for example, bots that reply to information requests on financial statements from auditors, enabled it to cut down its response time to 24 hours from 6 to 10 business days. The process previously required humans to manually sift through large swathes of data stored across six different IT systems.

Another bot in production allows the bank to process trades that fail to be automatically processed on its custody platform because of issues such as incorrect information. The bot investigates the reason for the failure and corrects the issue, cutting down processing times by 30 percent, the bank said.

But as AI advances, many banks face significant challenges with older systems as they have been around for decades and are not built for newer forms of computing.

(*This version of the story corrects spelling of last name to Shulman, not Schulman)

(Reporting by Anna Irrera; Editing by Bernard Orr)

BERLIN Germany's federal cyber agency said on Thursday that Yahoo Inc had not cooperated with its investigation into a series of hacks that compromised more than one billion of the U.S. company's email users between 2013 and 2016.

Twitter Inc said on Thursday it signed a multi-year deal with the U.S. National Football League to live-stream pre-game coverage as well as a 30-minute show on the microblogging website.

BRUSSELS/LUXEMBOURG Uber faces the biggest challenge yet to its European roll-out after the region's top court was advised to rule that the U.S. ride-hailing firm is actually a transport service not an app.

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BNY Mellon advances artificial intelligence tech across operations - Reuters

Want to feel old? Steven Spielbergs Minority Report was released fifteen years ago.

It casts a long shadow. For a decade after the films release, it was referenced at least once at every conference relating to human-computer interaction. Unsurprisingly, most of the focus has been on the technology in the film. The hardware and interfaces in Minority Report came out of a think tank assembled in pre-production. It provided plenty of fodder for technologists to mock and praise in subsequent years: gestural interfaces, autonomous cars, miniature drones, airpods, ubiquitous advertising and surveillance.

At the time of the films release, a lot of the discussion centered on picking apart the plot. The discussions had the same tone of time-travel paradoxes, the kind thrown up by films like Looper and Interstellar. But Minority Report isnt a film about time travel, its a film about prediction.

Or rather, the plot is about prediction. The filmlike so many great works of cinemais about seeing. Its packed with images of eyes, visions, fragments, and reflections.

The theme of prediction was rarely referenced by technologists in the subsequent years. After all, that aspect of the storyas opposed to the gadgets, gizmos, and interfaceswas one rooted in a fantastical conceit; the idea of people with precognitive abilities.

But if you replace that human element with machines, the central conceit starts to look all too plausible. Its suggested right there in the film:

To which the response is:

Suppose that Agatha, Arthur, and Dashiell werent people in a floatation tank, but banks of servers packed with neural nets: the kinds of machines that are already making predictions on trading stocks and shares, traffic flows, mortgage applicationsand, yes, crime.

Rewatching Minority Report now, it holds up very well indeed. Apart from the misstep of the final ten minutes, its a fast-paced twisty noir thriller. For all the attention to detail in its world-building and technology, the idea that may yet prove to be most prescient is the concept of Precrime, introduced in the original Philip K. Dick short story, The Minority Report.

Minority Report works today as a commentary on Artificial Intelligencewhich is ironic given that Spielberg directed a film one year earlier ostensibly about A.I.. In truth, that film has little to say about technologybut much to say about humanity.

Like Minority Report, A.I. was very loosely based on an existing short story: Super-Toys Last All Summer Long by Brian Aldiss. Its a perfectly-crafted short story that is deeply, almost unbearably, sad.

When I had the great privilege of interviewing Brian Aldiss, I tried to convey how much the story affected me.

At the time of its release, the general consensus was that A.I. was a mess. Its true. The film is a mess, but I think that, like Minority Report, its worth revisiting.

Watching now, A.I. feels like a horror film to me. The horror comes notas we first suspectfrom the artificial intelligence. The horror comes from the humans. I dont mean the cruelty of the flesh fairs. Im talking about the cruelty of Monica, who activates Davids unconditional love only to reject it (watching now, both scenesthe activation and the rejectionare equally horrific). Then theres the cruelty of the people of who created an artificial person capable of deep, never-ending love, without considering the implications.

There is no robot uprising in the film. The machines want only to fulfil their purpose. But by the end of the film, the human race is gone and the descendants of the machines remain. Based on the conduct of humanity that were shown, its hard to mourn our species extinction. For a film that was panned for being overly sentimental, it is a thoroughly bleak assessment of what makes us human.

The question of what makes us human underpins A.I., Minority Report, and the short stories that spawned them. With distance, it gets easier to brush aside the technological trappings and see the bigger questions beneath. As Al Robertson writes, its about leaving the future behind:

This was originally posted on my own site.

Link:

A Minority Report on Artificial Intelligence - NewCo Shift

Artificial intelligence (AI) is the ability of a program or machine to think and learn on its own. AI can come in many forms, such as Apples Siri virtual assistant, the way non-player characters react in a video game, self-driving cars and more. However, despite AIs prevalence in everyday life, its clear that many people arent quite sure what AI actually is.

According to a report published by Havas, Canadian awareness for AI is high, particularly amongst males (86 percent), millennials (86 percent), HH Income $100K+ (90 percent) and those with a university education (93 percent).

However, 86 percent of Canadians said that while they may have heard of AI, they dont actually know what the term implies.

In addition, 84 percent of Canadians said they think they know what AI is, but provided an incorrect definition nonetheless. Interestingly, a combined three percent of participants said that they thought AI referred to either aliens, people (specifically politicians) pretending to be intelligent, the 2001 science-fiction film of the same name, or brain-implanted microchips.

Canadians also reported that they werent sure how often they come across AI in their everyday lives. While 77 percent of Canadians said that they think theyve encountered the technology at some point in time, only 23 percent indicated that theyve done so on a daily basis.

And finally, 51 percent of Canadians said they were unsure how AI would affect jobs, with 29 percent saying it be a positive influence and 19 percent saying it would be a negative influence.

The report was based off an online survey that was conducted among 1,509 randomly selected Canadian adults across two days in April 2017.

This article was originally published on MobileSyrup

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Report: Most Canadians don't know what artificial intelligence is - BetaKit

Metro

Job losses due to artificial intelligence and robotics 'could reach 40% by 2030 Metro 'We use the term augmented intelligence [rather than artificial intelligence],' Paul Ryan, head of Watson Artificial Intelligence, IBM UK, tells Metro.co.uk at the AI Summit. 'We are not pursuing general AI, we're not building systems that are trying ...

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Job losses due to artificial intelligence and robotics 'could reach 40% by 2030 - Metro