DUBLIN--(BUSINESS WIRE)--Research and Markets has announced the addition of the "Artificial Intelligence Impact on Public Safety, Security and Privacy" report to their offering.

Artificial Intelligence (AI) is undergoing a transformation from silo implementations to a utility function across many industry verticals as a form of Artificial General Intelligence (AGI) capability. This capability is becoming embedded and/or associated with many applications, services, products, and solutions. We see AI innovation in a variety of areas including personalized AI to both support and protect end-users.

This transformation will have a profound effect upon public safety, security, and private for consumers, enterprise, and governments. This research evaluates the growth of AI, its application across diverse sectors, and the associated impact upon Public Safety, Security, and Privacy.

Topics Addressed in the Report Include:

Target Audience:

Companies Mentioned:

Key Topics Covered:

1 Introduction

2 Executive Summary

3 Overview

4 The Global Artificial Intelligence Marketplace

5 AI Industry Analysis

For more information about this report visit http://www.researchandmarkets.com/research/sh73x4/artificial

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Artificial Intelligence Impact on Public Safety, Security and Privacy - Personal AI Will Act as a Form of AI ... - Business Wire (press release)

CREDIT: AP Photo/Thibault Camus

Facebook unveiled a new plan for preventing advertisers from discriminating Wednesday that emphasizes education and artificial intelligence.

The company announced a three-fold plan Wednesday in response to a ProPublica report in October that found advertisers could limit which Facebook users saw their ads based on race or ethnicity. Facebook immediately turned off the feature after receiving public criticism.

In a blog post announcing its latest changes, Facebook said it has strengthened language in its anti-discrimination policy, created a new section for advertisers to learn about federal anti-discrimination laws, and implemented an AI-powered enforcement tool that picks out problematic ads.

When an advertiser attempts to show an ad that we identify as offering a housing, employment or credit opportunity and either includes or excludes our multicultural advertising segmentswhich consist of people interested in seeing content related to the African American, Asian American and US Hispanic communitieswe will disapprove the ad, Facebook wrote.

Additionally, Facebook will require advertisers posting ads for housing, employment, or credit to certify they are complying with the companys anti-discrimination policies.

The new plan targets key points of criticism, namely that Facebook advertisers may have been running afoul of the federal Fair Housing and Civil Rights Acts, and overly relied on harmful stereotypes by excluding ad recipients based on demographics.

Facebook said it collaborated with advocacy groups, members of governmentincluding the Congressional Black Caucusand New York State Attorney General Eric Schneiderman to shape the new policies.

We believe in the power of our advertising products to create opportunities for people from all backgrounds, so we are committed to working with these groups toward that goal, the company wrote.

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Facebook turns to artificial intelligence to prevent advertisers from discriminating - ThinkProgress

Discerning luxury shoppers can buy bras and undies from Cosabella in many places Nordstrom, Neiman Marcus, Bloomingdales and Amazon. But it also wants to sell more in its stores and on its own Web site, and it felt stuck.

So back in October, it left its ad agency and signed on with a new artificial-intelligence platform, and increased email-led sales by more than 60% compared to the same quarter in the previous year. The move also doubled its subscription base, and powered large gains in its social-media sales, especially on Facebook.

Before this, weve struggled with how to use the data we had, how to turn it into insights that could shape not just our marketing, but our products, says Guido Campello, Cosabellas CEO. He says the AI-enabled marketing automation platform -- from Emarsys, the Austrian-based cloud-marketing B2C software company -- has done that.

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One of its biggest collections, for example, is the Never Say Never line, which can run from $26 thongs to $80 bras to $120 baby-dolls, including dozens of colors, sizes and pricing variations. We often wondered, 'Are we doing too much? Introducing too many new colors? Too many prints? Should we use this many photos? It turns out customers love to see the new colors, and it helps keep them in love with the whole collection.

The insights, which combine AI with raw data into a highly personalized customer experiences, are also delivered much faster than its previously data-scraping methods, he tells Marketing Daily. The fashion calendar is about 18 months, which is a long time. But Millennials want looks that are on trend, and they share them quickly through social media. So we need to be able to do things in three months, and this helps.

Cosabella is hardly alone, as retailers scramble to find new ways to use AI to attract and keep customers. Gartner Research predicts that by 2020, 50% of retail customer service requests will be conducted at least partially through conversational AI applications, for instance, and that 85% of customer interactions will be managed without a human.

The new technology has also given us some important marketing insights, says marketing director, Courtney Connell. For example, it became clear that if a customer hasnt bought something from us in 62 days, were losing them. And the data is showing us trends about what products are most engaging, and that our high-spending customers react better to more emotional appeals, while lower-spenders are more involved in whats promotional.

And while the gains in email have been considerable, social is also growing fast, ahead of the Florida-based companys predictions. Weve seen exponential growth in social commerce, Connell says. Its almost matching revenues gained from search and display spending.

In January alone, she says Facebook accounts for 30% of revenues from all its paid marketing efforts. If it had been 15%, we would have been happy.

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Cosabella Goes All-In On Artificial Intelligence - MediaPost Communications

Artificial intelligence is already well-established in the world of targeted advertising and recommendations. But AIis also rapidly evolving on social media as a way to help brands quickly and efficiently discover, engage with, and learn from their followers.

Although there is no one definition for it, we can summarize social artificial intelligence as a form of collecting and sifting through customer history, user-generated content, and data from social media channels to generate more relevant content and, as a result, a more meaningful experience for followers.

Social AI has the ability to provide a better social experience overall. For an example of what social AI can do, we just have to look at Facebook. The social network has already incorporated artificial intelligence as part of the platform in many innovative ways. From automatic face tagging to the stories that appear in News Feeds, Facebook has been at the forefront of what AI can do for social media by incorporating a variety of AI technologies that help continuously improve the Facebook user experience.

Were now seeing more and more social networks investing in social AI technologies, and although the technology is still relatively young, many remarkable new ways to surface content to audiences have emerged. Yet despite the groundbreaking opportunities social AI presents, many brands have yet to turn to social AI to help engage their audiences, target new customers, and analyze the enormous volumes of social data that is now accessible.

So to help uncover what social artificial intelligence can do, heres a look at some of the exciting opportunities it brings to the table for those in the social media marketing world and how marketers can keep an eye on this trend.

Rather than viewing social AI as a potential threat to the jobs of social media marketers everywhere, John Hagel of Deloitte suggests the new wave of technology could actually be an exciting opportunity for brands to free up their time for more real, creative work. If we allow machines to take care of all of the picayune, everyday tasks that machines can take care of (such as recommendations and customer support), then marketers can have more time to focus on the creative side of their campaigns.

The technology that seems so threatening now may actually become our ally, amplifying our performance improvement by freeing us from the tasks that today keep us tightly locked into the routines of the past and providing us with the data we need to spark even more imagination and creativity, says John Hagel, co-chairman for Deloittes Center for the Edge.

For brands publishing multiple new stories or posts per day, automating a significant portion of those messages can free up time for creating more substantial content and monitoring responses. The New York Times did just this with achatbot that automates some of the 300 messages it posts to its social media pages daily.

The intelligent bot helps predict how stories will perform on social media, as well as suggests which stories editors should boost or promote. An analysis of the campaign found that the posts generated by the chatbot received almost 380 percent more clicks. For marketers seeking to keep engagement levels up while keeping the numbers of hours spent creating content down, this can be a good way to do so.

There are a number of facial recognition technologies, but Facebook took its algorithm to the next level with AI. With its enormous database of images,Facebooks algorithm is constantly improving through machine learning. Every time someone tags a photo, it is added to a huge, user-driven wealth of knowledge that helps advance the entire facial recognition algorithm. According to Facebook, it is able to accurately identify a person 98 percentof the time.

Such facial recognition on a wider scale could have many applications for a brands social strategy. Andy Pringle, head of performance media at digital marketing agencyPerformics, points out just how brands will be able to target followers with facial recognition technology:

You can imagine brands asking people to give permission to be recognised in return for offers while theyre out and about. Say, theres a guy waiting for a bus for ages in front of digital screen running a beer campaign. If that person likes that brand on Facebook you can foresee either the screen saying hi and giving him or her a voucher code for a free beer or triggering a voucher to be delivered to their Facebook inbox.

Its highly unlikely that AI will ever replace all engagements on social media after all, the point of social media is human interaction. But it does give brands the ability to automatically surface the most valuable, important conversations to respond to or engage with.

According to Eli Israel, the founder of Meshfire, a platform that uses AI to assist with social media, the workloads of social media managers have hit an all-time high. Social media teams have been assigned an overwhelming number of tasks that go beyond simple content creation they are required to perform a certain level of customer service as well. Unfortunately, customer support has become a major time suck. He suggests a number of ways social AI can help social media teams alleviate the pressures of providing instant support in order to spend their time much more effectively, including:

Increased investments and resources are being allocated to the advancement of social AI technology to revolutionize social media and a brands role in it. The intersection of social media and AI also presents many new opportunities for social media marketers to shine. To prepare for this new age, Forrester discussed a number of recommendations on how marketers can adapt. And while they mostly refer to the surge in chatbots, the advice can also be applied to adapting to social AI.

As Forresterput it, being human, helpful, and handy is key. The traditional marketer role of pushing content must be readjusted to focusing more on two-way conversations. AI will guide the conversations in the beginning, but humans must step in for the actual engagements.

Marketers must also accept that they will need to serve customers in real time. Instant responses are now expected on social media, and these expectations will only solidify over the next year. Making sure your team is set up internally to handle rapid turnarounds on social media, and implementing automated response technology if needed, will ensure your brand is prepared to deal with these customer expectations in both the short and long term.

There are a number of ethical dilemmas that surround artificial intelligence. Questionable trending algorithms and fake news are just two examples of the side effects weve seen so far. Even though these have created problems more for publishers than for actual brands on social media, its still important to follow these stories as artificial intelligence applications carry over into the marketing world.

The amount of research being put out is still limited, so following the top AI thought leaders who are discussing the intersection of AI and social media is a good way to stay on top of this trend. IBM omnichannel marketer Amber Armstrong, speaker and brand consultant Tamara McCleary, and Marshall Kirkpatrick are just a few people social discovery platformLittle Bird identifies as the best social media thought leaders to follow in this space.

Social AI will constantly change as it further develops, but keeping a close eye on this trend is a good place for marketers to start. There wont ever be a complete substitute for human engagement, but social AI definitely has the potential to be a means to the end goal of social media marketing, which is to truly understand your followers.

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What 'social artificial intelligence' means for marketers - VentureBeat

Things are going insanely well for people in computer science. I mean, our work is everywhere. Nearly every process imaginable is powered by a machine at the middle. The computer has transformed communication, retail, how we access information and how we navigate around the world. Computers are winning.

Things are even better in A.I. We are at the beginning of a renaissance of interest and utilization of intelligent systems in an ever-widening sphere of influence. It started with the big tech companies (Google, Apple, IBM, Facebook). Now it is expanding every day as advancements in machine learning, voice and image recognition, and intelligent analytics move into the enterprise -- at an increasingly faster pace, driven by over half of the Fortune 500 believing that A.I. is crucial to their future.

As I have conversations with many of these companies that are looking to adopt emerging A.I. technologies (most based on machine learning), there are two issues for them that keep popping up: "How do we integrate these technologies into our workflow and decision-making?" And even more crucial: "How do we integrate them into existing best practices, business rules and methodologies?"

That is, how do you integrate analytic systems that can learn to recognize entities in images, categorize activity as fraudulent or even predict the sales of different products based on weather information, with the business thinking on how to deal with these situations? How do you integrate systems that "think fast" with the deeper reasoning of the business strategies that require you to "think slow"? How are companies going to integrate the machine's ability to recognize with the human ability to reason?

My concern is the potential of companies to become disappointed and disenchanted with A.I. -- just as it is re-emerging -- because these questions go unanswered. (Narrative Science has its own take on the solution, but that is a discussion for a different time.)

I am finally a little relieved about this issue because Elemental Cognition, the company founded by David Ferrucci (the man who drove the work on IBM Watson), has come out of stealth mode and revealed that its mission is to solve this problem.

On its website, the company defines the following challenge:

Today's AI systems can help you locate the closest restaurants or find answers to simple questions.

But, these systems cannot grasp the underlying meaning of language or provide rich explanations behind their answers.

The next grand challenge in AI is to build systems that truly understand.

Elemental Cognition's technology seems to be aimed at the problem of going beyond the simple recognition/response models that dominate the A.I. landscape right now. I suspectFerrucci is building out a technology that will combine outputs of deep learning and other machine-learning systems with the ability to draw inferences from, reason about and support decisions using the facts that they generate.

I have faith in Ferrucci for a very specific reason: He is less a scientist and more an engineer. His history at IBM was one of building toward the solution rather than in service of a theory. He succeeded in building a system that leveraged existing technologies, but was built in such a way that it did the job at hand and did it exceedingly well.

There are other companies that claim to be building out general machine intelligence, but most of them are trying to prove a point. As far as I can tell, Ferrucci is trying to solve a problem.

There was an inflection point with machine learning a few years ago. We had the data, the machines and the raw compute power to learn at a level we had never seen before. We gave the machine the ability to look at the world and recognize what it saw. While the science related to this inflection point wasn't new, the engineering was.

In order for A.I. to make it to the next stage of development, we need a similar inflection point for the reasoning part of the equation. We need to empower the machine with a deeper ability to think about the words it hears and the situations it observes. I believe that the science is already in our hands. Now, we just need the right engineers to decide that they want to see it happen.

So join me in welcoming Elemental Cognition to the party. We're all in this together.

This article is published as part of the IDG Contributor Network. Want to Join?

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Why this company will help change the future of artificial intelligence - Computerworld

Tech giant rolls out new software which crams its virtual assistant inside the LG Watch Style and Watch Sport

Google has unveiled the second generation of its smartwatch software, which will place artificial intelligence (AI) in the companys wearables for the first time.

Android Wear 2.0, which will begin rolling out to all current Android Wear smartwatch users in the coming weeks as an update, will include Google Assistant, the tech giants AI virtual helper which responds to voice commands.

PA:Press Association

Google also revealed the first two new devices that would run the software the LG Watch Style and Watch Sport as the tech giant looks to take on the Apple Watch.

While traditional watches tell the time, Android Wear watches make the most of your time, Android Wears engineering chief David Singleton said.

In an instant, you can check when and where youre meeting a friend, whether youll need an umbrella tonight, or how many minutes youve been active today-all without reaching for your phone.

Today, were launching Android Wear 2.0 to give you watch faces that do more, better ways to work out, more ways to stay in touch, new ways to use apps, and on-the-go help from Google Assistant.

As part of the Google Assistant integration, users will be able to add items to their shopping lists, set reminders and make restaurant reservations directly from their watches.

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Google Android Wear 2.0 update puts artificial intelligence inside your wristwatch - The Sun

It has become common practice for attackers to use Artificial Intelligence (AI) and Machine Learning (ML) to link tools together so that they can be run in parallel when conducting an attack.

Attackers use AI and ML to take the results from one tool and then allow the other tools to "learn" about the finding and use it against other systems. As an example, if a one tool finds a password, that tool can feed the information to another tool or bot that may conduct the exploitation of one or many systems using the discovered password.

AI and ML allows for an attacker to program a toolset or bot to act like a "real" attacker. As an example, the tool or bot may launch a phishing attack against an organization and then take the results of the phishing tool and conduct other types of attacks just as a human would.

Attackers are building toolsets and bots that use AI and ML techniques to evade detection and blocking the methods already in place within most organizations. Many of these tools (typically open source) can be easily obtained from the Internet. This gives anyone the ability to run the tools against target organizations.

In an article in Wired President Obama expressed his concerns about AI-enabled bots attacking nuclear weapon silos and causing a launch. This intimates that the threat of AI and ML enhanced attacks are a major concern even at the highest level of government.

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As the Chief Information Officer of Digital Defense, Tom DeSot is charged with developing and maintaining relationships with key industry and market regulators; functioning as the "face of DDI" through public speaking initiatives, identifying key integration and service partnerships, and serving as the prime regulatory compliance resource for external and internal contacts. Tom also serves as the companys internal auditor on security-related matters.

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How criminals use Artificial Intelligence and Machine Learning - BetaNews

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how its being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey, insideHPC / insideBIGDATA AI/Deep Learning Survey 2016, to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains. The complete insideBIGDATA Guide to Deep Learning & Artificial Intelligence is availablefor download from the insideBIGDATA White Paper Library.

Deep Learning and AI An Overview

This is the epoch of artificial intelligence (AI), when the technology came into its own for the mainstream enterprise. AI-based tools are pouring into the marketplace, and many wellknown names have committed to adding AI solutions to their product mixGeneral Electric is pushing its AI business called Predix, IBM runs ads featuring its Watson technology talking with Bob Dylan, and CRM giant Salesforce released an AI addition to their products, a system called Einstein that provides insights into what sales leads to follow and what products to make next.

These moves represent years of collective development effort and billions of dollars in terms of investment. There are big pushes for AI in manufacturing, transportation, consumer finance, precision agriculture, healthcare & medicine, and many other industries including the public sector.

AI is becoming important as an enabling technology, and as a result the U.S. federal government recently issued a policy statement, Preparing for the Future of AI from the Subcommittee on Machine Learning and Artificial Intelligence, to provide technical and policy advice on topics related to AI.

Perhaps the biggest question surrounding this new-found momentum is Why now? The answer centers on both the opportunity that AI represents as well as the reality of how many companies are afraid to miss out on potential benefit. Two key drivers of AI progress today are: (i) scale of data, and (ii) scale of computation. It was only recently that technologists have figured out how to scale computation to build deep learning algorithms that can take effective advantage of voluminous amounts of data.

One of the big reasons why AI is on its upward trajectory is the rise of relatively inexpensive compute resources. Machine learning techniques like artificial neural networks were widely used in the 1980s and early 1990s, but for various reasons their popularity diminished in the late 1990s. More recently, neural networks have had a major resurgence. A central factor for why their popularity waned is because a neural network is a computationally expensive algorithm. Today, computers have become fast enough to run large scale neural networks. Since 2006, advanced neural networks have been used to realize methods referred to as Deep Learning. Now, with the adoption of GPUs (the graphics processing unit originally designed 10 years ago for gaming), neural network developers can now run deep learning with compute power required to bring AI to life quickly. Cloud and GPUs are merging as well, with AWS, Azure and Google now offering GPU access in the cloud.

There are many flavors of AI: neural networks, long short-term memories (LSTM), Bayesian belief networks, etc. Neural networks for AI are currently split between two distinct workloads, training and inference. Commonly, training takes much more compute performance and uses more power, and inference (formerly known as scoring) is the opposite. Generally speaking, leading edge training compute is dominated by NVIDIA GPUs, whereas legacy training compute (before the use of GPUs) by traditional CPUs. Inference compute is divided across the Intel CPU, Xilinx/Altera FPGA, NVIDIA GPU, ASICs like Google TPU and even DSPs.

Over the next few weeks we will explore these deep learning & artificial intelligence topics:

If you prefer, thecomplete insideBIGDATA Guide to Deep Learning & Artificial Intelligence isavailablefordownload in PDF from theinsideBIGDATA White Paper Library, courtesy of NVIDIA.

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insideBIGDATA Guide to Deep Learning and Artificial Intelligence - insideBIGDATA

Forbes

Actress Kristen Stewart's Research Paper On Artificial Intelligence: A Critical Evaluation Forbes There are perhaps two different questions to answer here: (1) What do we think of the paper? (2) What do we think of the headlines that the paper generated? Let me address the second question first, because I think that is the root of the (possible ...

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Actress Kristen Stewart's Research Paper On Artificial Intelligence: A Critical Evaluation - Forbes

"The acceptance of AI in the business is going to involve an evolution."

There's plenty of excitement around artificial intelligence: analyst Gartner places it at the top of its top 10 strategic technology trends for 2017. The analyst says the technology has reached a tipping point and AI is beginning to extend its tentacles into every service, thing, or application, and that it will become the primary battleground for technology vendors looking to make money through 2020.

Interim CIO Christian McMahon, who is managing director at transformation specialist three25, acknowledges interest in AI has exploded recently, but he also voices a word of caution.

AI and the Future of Business

Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of them.

"All the major corporates, accelerators and venture capitalists are desperate to find a foothold," he says. "However, I don't think the current AI market is at a stage where breakthrough technologies are about to be unveiled. Rather, it's a vibrant market which seems more conceptual than one of tangible substance."

It is a sentiment that chimes with Omid Shiraji, interim CIO at Camden Council. His organisation holds a huge amount of data and aims to use its knowledge to help people with complex needs. AI could provide a breakthrough in data insight, yet Shiraji says CIOs must focus on value creation.

"The business case for these projects is not easy -- you can take a step into the unknown," says Shiraji. "You sometimes have to rely on intuition rather than ROI to place your investments in these types of projects."

Gartner suggests executives who take a risk on AI projects will be rewarded and should consider experiments in one or two high-impact scenarios. So how will pioneering organisations build a business case for AI? Two IT leaders -- one each from the private and public sectors -- give us their take.

Sizing up the opportunity

Matt Peers, CIO of global law firm Linklaters, draws a parallel between the use of big data and the growing importance of AI. Peers says success in big data is all about being able to make the best use of the information you possess -- and Linklaters, a 175-year-old firm, is a business with more knowledge than most.

Peers says his organisation should be able to turn its history into a competitive advantage. Lawyers need knowledge about legal precedent, previous projects, and internal skills specialisms. He believes advances in AI will help his firm to create more sophisticated approaches to search.

"The key to success is getting the right information to people quickly," he says. "Some of the tools that are being developed for AI will help us search big data. Most of the technologies on the market today are good at clustering and reading contracts, and enabling you to search vast volumes of data for legal themes."

He expects the ability to digitise and search contracts for key legal themes to become commonplace very quickly. Linklaters has already created an AI working group to help analyse services in the marketplace and to work out how these technologies might impact the business.

"Firms in some key sectors are already making a move," says Peers. "We've spent a lot of time in the past 18 months sizing up the opportunities by talking to people, seeing demonstrations, and running proof of concept studies."

Peers recognises AI could also help change the way lawyers work, yet he also expects a cultural challenge. Senior partners trust their associates to spend hours considering the details of legal documents. Trusting computers to undertake the same task in seconds presents a different form of dependence.

"It's a big shift because the reputation of that lawyer and firm is on the line," he says. "The acceptance of AI in the business is going to involve an evolution. It's important to remember that there are many matters in the legal world where AI is not going to be useful for quite a long time. It's going to take a while for computers to provide trusted advice and opinion."

Using data to save lives

Toby Clarke, interim head of IT at Moorfields Eye Hospital NHS Foundation Trust, says AI will have a huge impact on the work of publicly-funded organisations. Moorfields has been working closely with DeepMind Research, a project that involves the Trust sharing a set of one million anonymised eye scans.

The project between Moorfields and DeepMind relies on historic scans, meaning that while the results of the research might be used to improve future care, they will not affect patients today. However, the hope is that discoveries through the initiative will lead to earlier detection and help reduce preventable eye disease.

"What they're doing with that information is truly amazing," says Clarke, referring to the DeepMind project. "It's cutting edge and will make a significant difference."

He says the key to long-term change through AI is being able to use information to inform patient care. And that use presents challenges, particularly in terms of data security and confidentiality. "The real value will come from using non-anonymised data," he says.

"If you have a large repository of information, and you can add big data from demographics, you can start to take make predictions about patient healthcare. You could potentially say when people should be coming in for tests in terms of early warnings."

The current project uses anonymous data. "It has to be that way," says Clarke. "In terms of healthcare, there will always be issues around how you commercialise data, and how you deliver value back to the host organisation and its patients."

Clarke, however, is keen to point out that similar projects could sponsor significant change. "It's difficult for humans to understand the impact of AI right now but the potential is huge," he says. "The technology self-learns and I find it exceptionally exciting. AI is different and new, and it's something everyone involved in IT should be investigating."

In contrast to reports that automation simply leads to job cuts, Clarke says AI - particularly in the role of predictive medicine - could lead to a whole new range of data science roles. "It's not about removing jobs but it is potentially about saving lives," he says.

Continued here:

Artificial intelligence: How to build the business case - ZDNet

Forbes

Artificial Intelligence-Driven Robots: More Brains Than Brawn Forbes Automation and robots for manufacturing have come a long way since Unimate was introduced in the 1960's. The machines that manufacturers are using today are smaller, safer and able to perform more than a single task without expensive programming.

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Artificial Intelligence-Driven Robots: More Brains Than Brawn - Forbes

Mashable

Forget lessons, these smart skis are loaded with artificial intelligence Mashable Forget lessons, these smart skis are loaded with artificial intelligence. 833. Shares. Share. Tweet. Share. What's This? Image: Piq. 2016%2f09%2f16%2f8f%2fhttpsd2mhye01h4nj2n.cloudfront.netmediazgkymde1lza3.c1888 By Karissa Bell 2017-02-07 ... and more »

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Forget lessons, these smart skis are loaded with artificial intelligence - Mashable

SIPA Asia via ZUMA Wire

Atrios today:

Self-Checkouts

Those still a thing? I mean, I know they are, but around me the 3 major supermarkets within walking distance got rid of them....Anyway, I know they still exist, but I do think our robot future is not quite as inevitable as people think. Worrying about the impact of future automation on jobs seems to be a cool tech away of ignoring the current fucked and bullshit jobs situation. And, yes, automation has been going on for decades, which is actually my point. There's nothing new about it, and I don't know why people think there will be this sudden automation discontinuity. The robots have been here for awhile, and they aren't really going away, but that doesn't mean the sci-fi dystopian workless future is just around the corner. Shit is fucked up and bullshit enough without worrying about things which haven't happened yet, and likely won't.

It really doesn't matter if artificial intelligence is distracting us from whatever you think the "real" problem is. It's coming anyway. The speed of the AI revolution depends solely on fundamental factors (mostly continued reductions in the cost of parallel computing power) and the level of interest in AI software development. The fundamental factors are obviously still barreling ahead, and it sure looks like the free market has a ton of interest too:

Besides, AI is the real problem. As we all know (don't we?), the decline of manufacturing in the US has far more to do with automation than with trade or globalization. That decline set up the conditions for an angry working class in three Midwestern states that finally decided it had found a savior in a guy who claimed it was all the fault of a bunch of foreigners. So now Donald Trump is president. How much more real can you get?

And that was just old-fashioned dumb automation. Smart automation is going to have a far bigger and far faster effect. We're not very far off from the first real destruction of an industry (probably long-haul trucking) thanks to smart automation, and after that it's going to come thick and fast.

So what are we going to do? Will our future be in the hands of demagogues who gain power by lashing out at scapegoats while they work hard to make sure that rich people get all the benefits of AI? Or will it be in the hands of people who actually give a damn about the working class and understand that a world of increasing automation requires a dramatic rethink of basic economics? I would sure like it to be the latter.

Unfortunately, like global warming, the effects of AI are slow and invisibleon a human timescale anyway. So it's easy to pretendno matter how idiotic this isthat AI is just a rerun of the Industrial Revolution. It's easy to pretend that each new advance isn't really a step toward true AI. It's easy to pretend that each individual industry to fall is just a special case. It's easy to pretend that something else is always more important.

Is AI coming soon? I find this question too boring to spend much time on anymore. Of course it's coming soon. The only question I'm interested in is what we're going to do about it. I keep pondering this, and I keep failing to come up with any likely answers that are very optimistic in the medium term. Maybe I'm not thinking outside the box enough. But it sure looks like we're determined to keep our collective heads in the sand for a long time. At best, the result is going to be a grim future of plutocracy for some and the dole for everyone else. At worst, it's going to be a future of global genocide (do you think there's enough aid in the world to keep Bangladesh afloat when there's no longer any work there?).

Eventually everything will work out, probably after a lot of suffering and a popular revolt. But wouldn't it be nice to avoid all that?

Oh, and those self-checkout machines? I don't know about Philly, but there's hardly a supermarket within ten miles of me that doesn't have them. Not only are they still a thing, but they're only going to get better. So sorry about all those nice union jobs as checkers and baggers.

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Artificial Intelligence Is Coming Whether You Like It Or Not - Mother Jones

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Swarm AI correctly predicted the outcome of Super Bowl LI, right down to the final score - Digital Trends

Forbes

Why C-Levels Need To Think About eLearning And Artificial Intelligence Forbes ... proprietary Artificial Intelligence to analyze each learner's behavior, cognition, engagement and performance to predict learning and future performance, optimize learning content and to create a deep personalized individual and social learning ...

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Why C-Levels Need To Think About eLearning And Artificial Intelligence - Forbes

The Quebecois city of Montreal has long been known as a hotbed of creativity -- home of Cirque du Soleil and a hub for companies in the online gaming and special effects industries, not to mention its place as a financial and trade capital.

Creativity played a key role when the city of 2 million (with 4 million regionally) competed against other municipalities globally to win the 2016 title of Intelligent Community of the Year.

And now that commitment to creativity is spurring the city to explore a range of unique new smartphone apps and other startup-generated initiatives that leverage sensors, data collection and analysis, and machine learning to deal with snow removal, ever-increasing traffic and other municipal challenges.

Public Wi-Fi, smart mobility and digital public services are just some of the 70 municipal projects detailed in the city's Smart and Digital City Action Plan, begun in 2015. More than half of the projects are expected to be finished by 2018, though some will take longer.

"Montreal is known as the place 'where Shakespeare meets Moliere.' It's a creativity hub," says Harout Chitilian, the elected official in charge of the city's smart city initiatives and technology. "All these things meshing together make Montreal one of the greatest startup digital ecosystems."

By intent, the government has made that startup ecosystem a key compontent of its smart city push, says Chitilian, who serves as vice president of the city's executive committee, the executive branch of the municipal government that includes Mayor Denis Coderre.

Of the dozens of initiatives currently underway in Montreal, several involve partnerships with the private sector in which the city, Quebec Province and businesses share costs. Those projects range from a high-speed, fiber-optic Scientific Information Network to eight different smart mobility and parking projects.

The principal driver of this partnership is InnoCit MTL, an independent, non-profit tech accelerator that receives both city and business financial support. Housed in the historic Notman House in downtown Montreal, InnoCit MTL has already fostered more than 15 startups in just over a year.

Notman House was alive with activity when Computerworld visited during a cold snap in mid-December, 2016 as part of a three-day tour of this smart city. Here's what we found.

The city government, along with the Province of Quebec and members of the academic community, have put special focus on artificial intelligence. Those efforts meld well with private sector startups that likewise are tapping the power of AI.

One such startup is Infra.AI, which intends to use machine learning and artificial intelligence to scan high resolution images of the city's streets and buildings."The benefits of AI are numerous," says co-founder Franois Maillet. "The fact that Montreal is serious about smart city and investing in it, there's a direct and positive impact in the startup community and the R&D. For the city itself, it provides better services to the citizens."

LIDAR images can help municipalities like Montreal monitor city infrastructure to identify such changes in status as detoriating bridges, broken windows or building code violations.

With digital image information from satellites, low-flying planes and LIDAR-equipped city vehicles, technology under development at Infra.AI will make it possible for Montreal and other cities to provide almost-real-time data on street conditions or the safety of roads and bridges.

That data can be combined with information from traffic video sensors and sensors on buildings, says Maillet, who also co-founded a related startup, MLDB.AI, that is working on a machine-learning database.

The potential applications are far-ranging. A firetruck speeding to a fire might be automatically advised that there's an obstruction in the roadway, allowing it to take another pathway. Or a pothole larger than a foot could be spotted, automatically dispatching a road crew to patch it. AI can even help identify a sagging highway bridge span, noticing a small drop when compared with the previous scans from days or weeks earlier.

Montreal-based Infra.AI is employing pattern recognition intelligence to distinguish a group of pedestrians from vehicles. The software could be used to identify problem locations and develop systems for improved pedestrian safety.

Infra.AI is currently piloting a program that helps identify ailing trees on city streets, a problem plaguing Montreal right now. When the startup's AI system is shown images of healthy trees, it can compare those with recent imagery to identify less-healthy trees with patches and browning leaves that need to be maintained or replaced.

"When you think of the kind of data [already] coming in from LIDAR and cameras, it's huge. The applications are now becoming possible with AI," says Jean-Franois Gagn, CEO of Element AI, a Montreal-based incubator dedicated to matching AI startups with larger companies and with government agencies.

Through its Canada First Research Excellence Fund, the Canadian government last year provided about $200 (US) million to three Montreal-based universities for research that Gagn believes will yield sophisticated AI spinoff companies in 2017.

In addtion, both Google and Microsoft have recently made investments in Montreal-based AI.

On a more personal level, another InnoCit MTL startup, Key2Access, is getting ready to test an app to make it safer for disabled people to cross city streets, according to CEO Sophie Aladas. Key2Access's tech is already being piloted in Ottawa, and has been successfully tested there by Richard Marsolais, a man with a vision impairment who is a specialist in independent living for the Canadian National Institute for the Blind.

Marsolais and his guide dog, Ashland, along with Motaz Aladas, head engineer for Key2Access (and CEO Sophie's father), demonstrated for Computerworld at a Montreal intersection how a small handheld device or a smartphone could be used to activate a Bluetooth-enabled crosswalk signal, making it safe for a vision-impaired or disabled person to cross. (See Smart Cities: Montreal for video footage of that demonstration.)

Marsolais says it would be helpful to have a handheld activation device to change the signal, instead of relying only on his guide dog or an audible crossing signal, which isn't always easy to hear. In addition, it isn't always clear in which direction it's safe to cross; Key2Access aims to solve that problem by using audible commands or vibrations to direct the user onto the crosswalk in the proper direction.

For Key2Access to function, traffic engineers in Montreal will need to install a receiver at each intersection to receive the wireless signal from the handheld device, Aladas says. The cost will be comparable to enabling a traditional crosswalk button on a pole, Sophie Aladas says. The city is expected to install the gear on at least one intersection in the spring as part of the testing phase.

A number of initiatives are in the works to help reduce traffic in Montreal in the next two years, including a tripling of the number of intelligent traffic signals to reach 2,200 units.

Data from the 700 existing smart signals installed over the last two years and from 500 surveillance cameras and Bluetooth sensors already helps prioritize buses traveling the streets to lessen commute times by 15% to 20%, the city's Chitilian says, with more improvements expected. Montreal is also in partnership with Waze, Google's crowdsourcing traffic app, to help syphon off driver data for greater intelligence.

In addition to its efforts to lessen traffic congestion and improve the efficiency of public transportation, Montreal heavily promotes bicycle riding. It's not uncommon to see bicyclists pedaling through downtown streets even in the dead of winter.

Bixi, a bike-sharing system, got its start in Montreal in 2009; as of 2015, there were 3.5 million Bixi rides each year in the city, and the service has grown to 45,000 bikes in 15 cities. The Bixi mobile apps for iOS and Android, along with other Bixi add-ins developed by Montreal startups, allow everything from online payments to personal fitness tracking for the bikes.

Separately, Montreal startup SmartHalo is testing technology to turn any bike into a smart bike using a rider's smartphone and its GPS connection.

"We know for a fact that adding preferential lights and dedicated bus lanes increases the speed of going from point A to B and makes the service much more efficient. You can have the same amount of buses and workable hours with better service," Chitilian says.

Sensor data from traffic signals is already being sent to a recently created central command post -- a "decision center," Chitilian calls it -- where technicians pore over dozens of desktop monitors and large wall displays. "The center gives us the ability to have an overall view" of the city, helping if there is an accident or other public safety need, he says.

Montreal also has designated $76 million US to replace 100,000 streetlights in the next five years with more efficient LED lighting that will be equipped with sensor and communications technology to expand the city's ability to manage congestion, pedestrian crowds, accidents and more, according to Chitilian.

With its combination of AI-focused startup innovation, sensor-driven traffic-improvement initiatives and data-driven apps for citizen empowerment, Montreal seems well on its way to furthering its designation as an intelligent city.

"We are trying to build a smart city from the ground up, and are putting in the pillars to do it," Chitilian says. "As politicians, we have to show immediate results, but some of our decisions will have lasting impact beyond our political mandates," he muses.

"We have to make decisions that will look good down the road," Chitilian says. "What we have in Montreal is more than optimism. It is a generational transformation."

Montreal and the Quebec Province have committed to sharingpublicly available data, which private enterpreneurs have put to innovative use via smartphone apps. Here are a few of locals' favorites:

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Montreal sees its future in smart sensors, artificial intelligence (with ... - Computerworld

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Harmony AI, which is set to be released on April 15, will be a smartphone app andis reported tofeature a range of traits for customers to choose fortheir sex dolls,while the dolls will also be able to learn about their ownersand respond in different ways accordingly.

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We are developing the Harmony AI system to add a new layer to the relationships people can have with a RealDoll, said CEO Matt McMullen to Digital Trends. Many of our clients rely on their imaginations to a great degree to impose imagined personalities on their dolls. With the Harmony AI, they will be able to actually create these personalities instead of having to imagine them.

They will be able to talk to their dolls, and the AI will learn about them over time through these interactions, thus creating an alternative form of relationship, he continued. The scope of conversations possible with the AI is quite diverse, and not limited to sexual subject matter. We feel that this system, and this technology, will appeal to a segment of the population that struggles with forming intimate connections with other people, whether by choice or circumstance. Furthermore, it will likely attract those who seek to explore uncharted and new territory where relationships and sex are concerned.

Harmony AI will be the first product in a range of next-generation technologies coming from RealDoll over the next few years.

Other planned releases include robotic head systems, which are set to be released by the end of the year, followed by a virtual reality platform in 2018.

RealDoll isnt the first company to recognize the potential connection between sex and AI. This happens because people are lonely and bored It is a symptom of our society, said Robin Labs chief executive Ilya Eckstein, who claims that his companys virtual assistant Robin is used by teenagers and truckers without girlfriends for up to 300 conversations a day.

As well as the people who want to talk dirty, there are men who want a deeper sort of relationship or companionship, hecontinued, adding that some people wanted to talk for no particular reason and were just lonely or bored.

In an interview with Breitbart Tech last year, Futurologist Dr. Ian Pearson also predicted that sex with robots would be fully emotional in the future, addingthat people will eventually spendabout the same as they do today on a decent family-size car.

Artificial intelligence is reaching human levels and also becoming emotional as well, claimed Dr. Pearson. So people will actually have quite strong emotional relationships with their own robots. In many cases that will develop into a sexual one because theyll already think that the appearance of the robot matches their preference anyway, so if it looks nice and it has a superb personality too its inevitable that people will form very strong emotional bonds with their robots and in many cases that will lead to sex.

Charlie Nash is a reporterforBreitbart Tech. You can follow himon Twitter@MrNashingtonorlike his page at Facebook.

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RealDoll Creating Artificial Intelligence System, Robotic Sex Dolls - Breitbart News

Photograph by Mehau Kulyk/SPL Getty Images/Science Photo Library RF

Self-driving cars and robots that can zoom on their own around warehouses are just some of what's possible because of artificial intelligence. But expect unforeseen consequences if researchers ignore the inherent ethical dilemmas in the emerging technology.

Thats one of the takeaways from a panel about AI ethics and education in San Francisco that was hosted by the Future of Life Institute , a research group focused on preventing societal problems created by the technology.

Although humans typically program AI-powered robots to accomplish a particular goal, these robots will typically make decisions on their own to reach the goal, explained Benjamin Kuipers, a computer science professor and AI researcher at the University of Michigan.

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Its these smaller decisions that robots make on their own that can cause trouble because human programmers may fail to take all of a robot's possible choices into account, Kuipers said.

This is not the robot apocalypse, said Kuipers. What were seeing here are robots pursuing human-generated goals in unconstrained ways.

Kuipers did not cite a specific example of a robot making a harmful decision that its human programmers overlooked. Instead, he cited the Disney animated film Fantasia as an example of what technologists need to take into account when building their robots.

In Fantasia , Mickey Mouse, as a young wizard apprentice, magically commands a broom to fill a cauldron with water. When Mickey falls asleep, however, the broom ends up flooding the room because the untrained wizard failed to take in account that the broom would continue to fill the cauldron even after it was full.

Illah Nourbakhsh, a robotics professor at Carnegie Mellon University, said that educators need to teach computer science and robotics students a basic understanding of ethics, because the technologies they are creating are so powerful that they are actually changing society. He cited the examples of drones being used in warfare and AI technologies being used in advertising as ways cutting-edge technology is being used on a global scale and changing consumer behavior.

Having a basic understanding of ethics can help technologists better understand the potential ramifications of the AI-powered software and robotics they are creating, he explained. One ethical dilemma he cited is how robotics can increase factory productivity; while this may lead to a boost in a nation's GDP, it can also increase the wage gap between the poor and the rich.

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Nourbakhsh does not believe that technology is neutral, and that it is ultimately up to other people to determine how it should be used, for better or worse.

Technologists should think about how their creations will impact society and even the choice of words they use to describe them. For example, calling the technology that powers self-driving cars either a safety-enhancing system or a labor-saving system has big consequences for how society perceives the technology, he explained.

Continued here:

How Powerful AI Technology Can Lead to Unforeseen Disasters - Fortune

Babak Hodjat believes humans are too emotional for thestock market. So he's started one of the first hedge funds run completely by artificial intelligence.

"Humans have bias and sensitivities, conscious and unconscious," says Hodjat, a computer scientist who helped laythe groundwork for Apple's Siri. "It's well documented we humans make mistakes. For me, it's scarier to be relying on those human-based intuitions and justifications than relying on purely what the data and statistics are telling you."

Babak Hodjat

Photographer: David Paul Morris/Bloomberg

Hodjat, with 21 patents to his name, is co-founder and top scientist of Sentient Technologies Inc., a startup that has spent nearly a decadelargely in secrettraining an AI system that can scour billions of pieces of data, spot trends, adapt as it learns and make money trading stocks. The team of technology-industry vets is betting that softwareresponsible forteaching computers to drive cars, beat the world's best poker players and translate languages will give their hedge fund an edge on Wall Street pros.

The walls of Sentient's San Francisco office are dotted with posters for robots-come-alive movies such as "Terminator." Inside a small windowless trading room, the only light emanates fromcomputer screens and a virtual fire on a big-screen TV. Two guys are quietly monitoring the machine's tradesjust in case the system needs to be shut down.

If all hell breaks loose," Hodjat says, "there is a red button."

Sentient won't disclose its performance or many details about the technology, and the jury is out on the wisdom of handing off trading to a machine. While traditional hedge funds including Bridgewater Associates, Point72 and Renaissance Technologies have poured money into advanced technology, many use artificial intelligence to generate ideasnot to control their entire trading operations.

All the same, Sentient, which currently trades only its own money, is being closely watched by the finance and AIcommunities. The venture capital firm owned by Hong Kong's richest man, Li Ka-shing, and India's biggest conglomerate, Tata Group, are among backers who have given the company $143 million. (Beyond trading, Sentient's AI system is being applied to a separate e-commerce product.)

Trading is "one of the top 10 places that AI can make a difference," says Nello Cristianini, a professor of artificial intelligence at the University of Bristol who has been advising Sentient. "A trading algorithm can look at the data, make a decision, act and repeatyou can have full autonomy."

Sentient's team includes veterans of Amazon, Apple, Google, Microsoft and other technology companies. They're part of a small group in Silicon Valley using expertise in data science and the field of artificial intelligence known as machine learning to try and disrupt financial markets.

AI scientists typically have no interest in working for a hedge fund, says Richard Craib, who started the AI hedge fund Numerai. "But they may want to mess around with data sets." Numerai's system makes trades by aggregating trading algorithms submitted by anonymous contributors who participate in a weekly tournament where prizes are awarded in Bitcoin. It recently raised $6 million from investors including Howard Morgan, the co-founder of the quant investment management firm Renaissance Technologies. "It's entirely a data science problem," Craib says.

Another company, called Emma, started a hedge fund last year based on an artificial intelligence system that can write news articles.

Employees of Sentient Technologies in San Francisco.

Photographer: David Paul Morris/Bloomberg

Hodjat of Sentient spent much of his career focused on the language-detection technology behind smartphone digital assistants. Several employees from his previous company, Dejima, went on to create Apple's Siri. Rather than join, he chose to focus on advances in artificial intelligence. His career goals didn't include finance, but he sees markets as one of the most promising applications for the technology. The vast amounts of publicly available data, along with stronger computers to analyse it for patterns, make the field an ideal fit. "That is the fuel for AI," he says.

Sentient's system is inspired by evolution. According to patents, Sentient has thousands of machines running simultaneously around the world, algorithmically creating what are essentially trillions of virtual traders that it calls "genes." These genes are tested by giving them hypothetical sums of money to trade in simulated situations created from historical data. The genes that are unsuccessful die off, while those that make money are spliced together with others to create the next generation. Thanks to increases in computing power, Sentient can squeeze 1,800 simulated trading days into a few minutes.

An acceptable trading gene takes a few days and then is used inlive trading. Employees set goals such as returns to achieve, risk level and time horizon, and then let the machines go to work. The AI system evolves autonomously as it gains more experiences.

Sentient typically owns a wide-ranging batch of U.S. stocks, trading hundreds of times per day and holding positions for days or weeks. "We didn't impose that on the system," says Jeff Holman, the company's chief investment officer. "The artificial intelligence seems to agree with what you get from human intelligence that it's better to spread your bets and have a more diversified portfolio."

As impressive as Sentient's technology appears, it's hard to know if it works. The company says the AI system is beating internal benchmarks, but won't disclose what those are. It shares little about the data used for the AI's decision-makingand isn't profitable. The company plans to bring in outside investors later this year. Holman, a Wall Street veteran who joined last year, said thecompany is limited on what it can say by U.S. Securities Exchange Commission rules restricting marketing by hedge funds that are raising money. "The platform is solid," he says. "It doesn't look like any other strategy I've seen."

Anthony Ledford, the chief scientist at the $19 billion hedge fund Man AHL in London, warns of putting too much faith in this branch of artificial intelligence without more evidence. Man AHL uses machine learning for a portion of its clients money, and Ledford is encouraged by the results. While the company is exploring a standalone machine-learning strategy, he says it's too early to declare success."There's a lot of hype and promise," Ledford says. "But when you actually ask people how many hundreds of millions dollars they are trading, many of them don't come back with much at all."

Little performance data is available about AI-focused hedge funds. One index that tracks 12 pools that utilize AI as part of its core strategies, called Eurekahedge AI Hedge Fund Index, returned 5 percent last year. That's slightly better than the average hedge fund, but trailed the S&P 500.

Tristan Fletcher, who wrote his doctoral thesis on machine learning in financial markets and works for a hedge fund, says investors may be reluctant to turn over their money completely to a machine. "I know how conservative investors are and I know of no one who would put their money in asystem that's fully systematic," says Fletcher. "Machine learning isn't a panacea for everything. You need people who have literal thinking."

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Deep Blue was one hell of a chess player.

It was February 1996 and the machine developed by IBM was locked in battle with Gary Kasparov. Chess was big news as the computer system project originally begun in 1985 at Carnegie Mellon University attempted to do something other chess-playing devices had been unable to do beat a reigning world champion.

Even those with only a passing interest in chess like myself were intrigued by the matchup. Deep Blues designer said the machine could evaluate 200 million positions per second, and at the time, it was the fastest computer to match up with a world chess champion. Reports on the days progress were published in newspapers all across the globe.

Ultimately, the first match of six games was a victory for humanitywith Kasparov notching a 4-2 victory. However, in May the following year, and after some additional re-engineering, it was Deep Blue coming out on top.

The Deep Blue phenomenon has been in my head for the last couple weeks as four top poker players (Jason Les, Daniel McAulay, Jimmy Chou and Dong Kim) squared off against artificial intelligence software at the Rivers Casino in Pittsburgh.

This time the AI came out on top.

As Reuters noted, Libratus [Latin for balance], an AI built by Carnegie Mellon University racked up over $1.7 million worth of chips against four of the top professional poker players in the world in a 20-day marathon poker tournament that ended on Tuesday.

Headlines have trumpeted Libratus accomplishment around the world. Here are just a few examples:

Machine beats humans for the first time in poker (Reuters) Computer manages to beat 4 of world's best poker players (FOX News) A Computer Just Clobbered Four Pros At Poker (FiveThirtyEight) A Mystery AI Just Crushed the Best Human Players at Poker (Wired magazine) Artificial Intelligence Goes All-in on Texas Holdem (Wall Street Journal)

Developers compared the victory to that of Deep Blue 20 years ago. The team certainly faced a challenge in engineering their AI to adjust to betting differences, imperfect information, unorthodox play, and that unique aspect of poker that differs it from most other games,bluffing.

Players were given a certain amount of play money and Libratus would go on to notch a computer's first victory in the no limit variety of Texas Hold'em (a previous computer had already mastered Limit Hold'em).

Yes, poker is just a game," University of Michigan professor Michael Wellman, who specializes in game theory and closely follows AI poker, said to Wired magazine. "But the game theory exhibited by Libratus could help with everything from financial trading to political negotiations to auctions.

Some have hailed the entire spectacle as great for the game of poker and no doubt there is some nice PR benefit that comes with it. But from a simple poker-playing perspective and in regards to its relevance among poker fans, the whole thing seems a bit too much. As a massive fan of the game of poker, this whole spectacle lacks the impact of Deep Blues win.

To me, this matchup of man versus droid/computer/software/techno-gizmo lacks the one aspect of poker that makes it so unique:risk. Its the reason that playing poker online for free or playing with your grandmother for matchsticks (or cheerios or whatever) is so lame;there is no risk of losing ones own money.

Chess is a game with merely risk of losing one individual match itself. The two combatants may have some kind of extrinsic monetary motivation, such as tournament payouts, appearance fees, etc., but there is not an inherent expected loss of ones own personal earnings.

In poker, players must square off against each other with their (usually) hard-earned money and that risk of ones own cash is a huge part of pokers appeal. Financial risk is inherently about losing money, and if youre not playing with risk in the game, youre not really playing poker.

If youre afraid to lose your money, you cant play to win, said Johnny Moss, a Texas poker legend and winner of the first two WSOP Main Events.

That attitude is something inherently flawed in making so much hoopla about Libratus' accomplishment;a machine/software/robot has no real inherent sense of loss or risk.

And when it comes to the art of the bluff, it seems engineering a machine to make these kinds of moves misses the key component of the risk involved in doing this: the pulse-racing feel of having all your chips in on a pot when you know your hand is squadoosh as ESPN WSOP analyst Norman Chad likes to put it. A highly-engineered AI topped four poker sharks with no real money on the line.

As a poker fan, this whole event doesnt even seem like real poker and just left me asking: So what? Poker is a game that is extremely dependent on human emotion and temperament.

Artificial intelligence has no fears about losing the mortgage payment in a pot.

Artificial intelligence has no fears about losing the mortgage payment in a pot or being down to that last bit of the poker bankroll and having to look for a real job to build it back.

Another aspect of this matchup with Libratus that is really missing for me, and I think for many poker fans, is that the self-reliant, mano-a-mano, battle of minds that takes place at the poker table. Sure I can concede a machine can get the better of humans in this type of setup, but pokers appeal for me is seeing players squaring off against each other and matching skills.

A battle against a computer lacks the panache of seeing real-life humans battling it out for their own cash. Libratus may have massive amounts of computing power, but it lacks the humanity that makes poker great and now watchable on television.

Many poker insiders and those with deep roots in the game may forget that, to casual fans, seeing thousands of dollars won and lost on a single game of cards is extremely bizarre yet extremely appealing. That appeal, along with the games unique characters and history, is the reason poker has grown into the international game it is today.

Poker is great because the human aspect is so important to excelling; it is not simply a series of moves on a game board or your old Commodore 64. Players who master the game can read other players and keep their own emotions in check.

They must master the subtleties and games within the game to excel. They benefit themselves by timing their actions correctly based on other players tendencies, outlooks and general gameplay. Players like Jason Mercier and Daniel Negreanu have mastered these nuances.

Dont read my hand wrong here, I am not a poker pessimist who thinks the game is moving in the wrong direction. Quite the contrary: I think the game is moving in the right direction in general after massive growth in the 2000s.

Libratus is not the next Big Blue and these four players were not Gary Kasparov.

Actual growth of the game depends on continuing presentations of the game in its real context on the felt and focusing on the players.

Some of those include: continued growth of the WSOP and live ESPN broadcasts; the World Poker Tours continued success and international growth; great broadcasts like Poker Centrals Super High Roller Bowl (with great commentary catering to fans and hard-core players alike); progress (thought slow) of state-by-state legalized online poker; the growth of the game by appealing younger players via Twitch; and the success of middle-tier tours catering to average Joe poker players (which are still needed to grow the game) like the Heartland Poker Tour and Mid-States Poker Tour.

The AI win seems like a minute footnote in comparison. Libratus may have won the battle against mankind, but was there ever really a war? Im not sure this is a battle that means a whole lot in the big picture of modern poker.

Libratus may have won the battle against mankind, but was there ever really a war?

Libratus is not the next Big Blue and these four players were not Gary Kasparov. It may have been an interesting technological endeavor, but Im sure these players in the "Brains vs. Artificial Intelligence, as the event came to be known, would much rather bring home a WSOP gold bracelet or WPT title if they had to pick. That hardware (not software) would be tangible and real and it would certainly be a nice real-life check to cash.

Sean Chaffin is a freelance writer in Crandall, Texas, and writes frequently about gambling and poker. If you have any story ideas, please email him at seanchaffin@sbcglobal.net or follow him @PokerTraditions. His poker book is RAISING THE STAKES: True Tales of Gambling, Wagering & Poker Faces and available on amazon.com.

The opinions expressed here are those of the authors and do not necessarily reflect the positions PokerNews

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Artificial Intelligence Tops Humans in Poker Battle What's the Big Deal? - PokerNews.com