Daily Archives: April 3, 2017

Eighty percent of marketing leaders say thatAI will revolutionize marketingby 2020, but many CMOs remain paralyzed on the sidelines, questioning how this kind of bleeding-edge tech should be used and if it will provide a marked return on investment.

Chris Neff

Ironically enough, at the same time we talk about the uncertainty that an artificial intelligence-powered future could bring, we are increasingly adopting these experiences into our day-to-day lives. From Siri and self-driving cars to connected devices like Amazon Echo and customer service chatbots, experiences powered by artificial intelligence will soon be the norm. People might not fully comprehend the growing role of AI in their lives, but theyre adopting it regardless, sometimes even unbeknownst to them.

Even though marketers generally accept AIs growing influence, they hesitate to adopt it (though they may not realize that if their brand invests in programmatic media buying, for example, theyre already deeply immersed in the world of AI). But although marketers are liberally leaning on AI to drive media buying and automate customer service, they struggle when it comes to its role within the creative puzzle. One wayof thinking is to consider how AI could enhanceexistingprograms and processesthose tried-and-true elements that have proven impactand gofrom there.Here are a few ways to start thinking about AI within a more relatable context.

Copybots, robot journalism and tools likeWordsmithhave emerged as an efficient way to crankout tedious copy updates and iterations. Moreover, when machine learning can be used to speed up time-intensive yet simple tasks, brands and their partners can free up creative resources to focus on more strategic storytelling efforts, saving both time and money in the long run.

Were building an AI copywriter for one of our partners, for example, to understand and process users interests andcreate hundreds of lines of copy using natural language processing. This tool will be used to streamline the time and cost associated with producing copy variants, allowing the brand to invest more instrategic resources or media.

One of marketers biggest challenges iskeeping up with the demands of content generation without breaking the bankperAccenture,90 percent of marketers say their content needs will grow over the next two years. AI is already helping marketers fill the content pipeline by taking on and speeding up existing processes, but what about the secondary challenge of ensuring thecontent is relevant and contextual for those who experience it? In addition to generating an arsenal of content at scale, AI can be used to optimize these assets based on users interests or geography.

We recently we teamed up with Saatchi LA for Toyota to create an AI-powered system designed to recognize more than 700 activities that exist in the world and then create opposite activities based on properties across the images. Dubbed the RAVtivity Machinefor the Toyota RAV4the technology spit out 300 versions of the content automatically. Those were then served through specified interest layers within key distribution channels like Facebook, Instagram and Snapchat.

Automating creative processes is one thing, but what about leveraging automated processes to drive creativity itself? Perhaps more powerful than the RAVtivity Machines ability to expedite manual workflows and recut content based on users interests and other inputs was its ability to draw novel correlations among the data sets humans alone may not have been able to do. In essence, a new AI-inspired creativity was born from an automated, pragmatic process.

Lastfall, 20thCentury Fox promoted the movie Morgan by debuting the worlds first AI-produced trailer. The studiopartnered with IBMs Watsonto identify the factors that comprise the scariest possible video and then incorporate specific elements into the teaser. Similar approaches have been used to test the production of other creative outputs, such asa music video.From a creative marketing standpoint, these types of use cases reflect the true prowess of artificial intelligence.

If thePokemon Go phenomenon of 2016has taught us anything, its that augmented reality canpenetrate the mainstream. In fact,Forrester predictsthat by 2021, AR will drop its status as an emerging technology and become a basic part of peoples everyday lives.

In concert, AI and AR technology can be used to create powered layered experiences that connect people with brands as they experience things in the real world. At CES, Lenovodebuted a pair of smart glassesthat combined AI and AR in this way. Lenovos New Glass C200 works by capturing a wearers field of vision and then offering instructions to repair or troubleshoot issues as they are detected.

Another example is Chevy, which weve helped utilize AI and image recognition to refresh the traditional car brochure into an AI-powered mobile experience. Prospective buyers who scan a Chevy Bolt brochure at their local dealership, for example, will automatically be served video content to better explain the cars main features and differentiators.Unlike traditional barcode scanning or QR code reading, you dont need a downloaded app for thisits all done through the mobile browser using the camera of the phone as the scanner and relying on the AI brain to connect the dots.

The best way for brands to dive into AI is to view the technology within the context of theirexisting marketing objectives and determine how AI drives both practical andcreative returns. Pragmatically speaking, AI can be used to optimize existing processes that have grown more challenging with theproliferation of content channels (and therefore formats). It can also be used to deliver more relevant content to consumers with fewer resources, saving time and money.

Yet, below these surface use cases lies a more powerful one: the capacity of AI to surface previously untapped data correlations to drive unprecedented creativity and storytelling. It is this facet of AI that holds the promise to propel our industry forwardin 2017 and beyond.

Chris Neff is executive producer, director of digital andexperiential, with Tool of North America.

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3 Practical Ways Artificial Intelligence Can Enhance Marketing Creativity Right Now - Adweek

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Editor's Note:This sponsored post was created by StackCommerce. The products featured do not reflect endorsements by the Live Science editorial team.

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Become a Master of Artificial Intelligence with Online Course Bundle (Sponsored) - Live Science

Mashable

Google's plan to root out hateful videos with artificial intelligence Mashable Google has an answer for all the brands worried that its automated systems are placing ads on offensive YouTube videos: smarter machines. The search giant said Monday that it's made progress towards implementing the artificial intelligence safeguards ... Google will use artificial intelligence to identify objectionable content and sell 'brand-safe' adsBusiness Insider Google Is Using Artificial Intelligence to Make Sure YouTube Content Is Safe for BrandsAdweek all 59 news articles »

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Google's plan to root out hateful videos with artificial intelligence - Mashable

Home > Cool Tech > Demystifying artificial intelligence: Heres

Crazy singularities, robot rebellions, falling in love with computers artificialintelligence conjures up a multitude of wild what-ifs. But in the real world, AI involves machine learning, deep learning, and many other programmable capabilities that were just beginning to explore. Lets put the fantasy stuff on hold at least for now and talk about this real-world AI. Heres how it works, and where its going.

More:Is the AI apocalypse a tired Hollywood trope, or human destiny?

Todays AI systems seek to process or respond to data in human-like ways. Its a broaddefinition, but it needs to be as broadas possible, because there are a lot of different AI projects currently in existence. If you want a little more classification, there are two types of AI to consider.

AI can also be classified by how it operates, which is particularly important when considering how complex an AI system is and the ultimate costs of that software. If a company is creating an AI solution, the first question must be, Will it learn through training or inference?

There have been books and books written about what specific features AI must include to be truly AI, and unsurprisingly, no one really agrees on what these features are; every description of AI is a little different. But there are several examples of successful AIs in our current landscape worth looking at.

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Demystifying artificial intelligence: Here's everything you need to know about AI - Digital Trends

special feature

How to Implement AI and Machine Learning

The next wave of IT innovation will be powered by artificial intelligence and machine learning. We look at the ways companies can take advantage of it and how to get started.

Revenue for cognitive and artificial intelligence systems will hit $12.5 billion in 2017, up 59.3 percent from a year ago, according to IDC. Through 2020, these AI systems will top $46 billion, up 54.4 percent on a compound annual growth rate.

The biggest portion of that spending in 2017 will go to cognitive applications. IDC projects 2017 spending of $4.5 billion for the year. Cognitive and AI software platforms with tools to organize, access and analyze data will see spending of $2.5 billion.

Meanwhile, services attached to rolling out cognitive and AI services will top $3.5 billion in 2017, said IDC, which also noted that $1.9 billion will be spent on hardware for these systems.

As for use cases, quality management and recommendations, diagnosis and treatment, customer service, security and fraud investigations will lead. Those use cases will account for half of all AI system spending in 2017. Between 2015 and 2020, public safety and emergency response and pharmaceutical research and discovery will grow at the fastest clip.

The U.S. will spend the most--nearly $9.7 billion--on AI in 2017 with EMEA No. 2. By 2020, Asia/Pacific will be No. 2, said IDC.

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Cognitive and artificial intelligence spending expected to surge through 2020, says IDC - ZDNet

BERKELEY Former US Treasury Secretary Larry Summers recently took exception to current US Treasury Secretary Steve Mnuchins views on artificial intelligence (AI) and related topics. The difference between the two seems to be, more than anything else, a matter of priorities and emphasis.

Mnuchin takes a narrow approach. He thinks that the problem of particular technologies called artificial intelligence taking over American jobs lies far in the future. And he seems to question the high stock-market valuations for unicorns companies valued at or above $1 billion that have no record of producing revenues that would justify their supposed worth and no clear plan to do so.

Summers takes a broader view. He looks at the impact of technology on jobs generally, and considers the stock-market valuation for highly profitable technology companies such as Google and Apple to be more than fair.

I think that Summers is right about the optics of Mnuchins statements. A US treasury secretary should not answer questions narrowly, because people will extrapolate broader conclusions even from limited answers. The impact of information technology on employment is undoubtedly a major issue, but it is also not in societys interest to discourage investment in high-tech companies.

On the other hand, I sympathize with Mnuchins effort to warn non-experts against routinely investing in castles in the sky. Although great technologies are worth the investment from a societal point of view, it is not so easy for a company to achieve sustained profitability. Presumably, a treasury secretary already has enough on his plate to have to worry about the rise of the machines.

In fact, it is profoundly unhelpful to stoke fears about robots, and to frame the issue as artificial intelligence taking American jobs. There are far more constructive areas for policymakers to direct their focus. If the government is properly fulfilling its duty to prevent a demand-shortfall depression, technological progress in a market economy need not impoverish unskilled workers.

This is especially true when value is derived from the work of human hands, or the work of things that human hands have made, rather than from scarce natural resources, as in the Middle Ages. Karl Marx was one of the smartest and most dedicated theorists on this topic, and even he could not consistently show that technological progress necessarily impoverishes unskilled workers.

Technological innovations make whatever is produced primarily by machines more useful, albeit with relatively fewer contributions from unskilled labor. But that by itself does not impoverish anyone. To do that, technological advances also have to make whatever is produced primarily by unskilled workers less useful. But this is rarely the case, because there is nothing keeping the relatively cheap machines used by unskilled workers in labor-intensive occupations from becoming more powerful. With more advanced tools, these workers can then produce more useful things.

Historically, there are relatively few cases in which technological progress, occurring within the context of a market economy, has directly impoverished unskilled workers. In these instances, machines caused the value of a good that was produced in a labor-intensive sector to fall sharply, by increasing the production of that good so much as to satisfy all potential consumers.

The canonical example of this phenomenon is textiles in eighteenth- and nineteenth-century India and Britain. New machines made the exact same products that handloom weavers had been making, but they did so on a massive scale. Owing to limited demand, consumers were no longer willing to pay for what handloom weavers were producing. The value of wares produced by this form of unskilled labor plummeted, but the prices of commodities that unskilled laborers bought did not.

The lesson from history is not that the robots should be stopped; it is that we will need to confront the social-engineering and political problem of maintaining a fair balance of relative incomes across society. Toward that end, our task becomes threefold.

First, we need to make sure that governments carry out their proper macroeconomic role, by maintaining a stable, low-unemployment economy so that markets can function properly. Second, we need to redistribute wealth to maintain a proper distribution of income. Our market economy should promote, rather than undermine, societal goals that correspond to our values and morals. Finally, workers must be educated and trained to use increasingly high-tech tools (especially in labor-intensive industries), so that they can make useful things for which there is still demand.

Sounding the alarm about artificial intelligence taking American jobs does nothing to bring such policies about. Mnuchin is right: the rise of the robots should not be on a treasury secretarys radar.

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Artificial Intelligence and Artificial Problems by J. Bradford DeLong ... - Project Syndicate

NEW DELHI: London-headquartered professional services firm EY is set to open its first artificial intelligence centre in Mumbai to help its clients figure out the best way to use these emerging technologies.

The centre will bring together teams of multi-disciplinary practitioners, combining expertise in areas such as AI, robotics, machine learning and cognitive technology, along with domain experience in sectors, according to the firm.

"The premise is that when any technology comes in, there is a lot of hype. Everyone talks about it but most dont know how to adopt it," said Milan Sheth, partner, advisory services and technology sector leader, EY India.

He said that artificial intelligence is already being deployed across industries such as automotive, telecom and technology, but there is a need to help enterprises understand how they can best use these new technologies to their advantage.

"The idea was to demystify AI since everyone talks about the concept but nobody talks about how to apply it. The B2C (business to consumer) industry adopts this very quickly; B2B (business to business) is the slowest adopter," Sheth said.

The idea to set up such a centre first came up six months ago, after which EY developed training content, got trainers and conducted a few beta sessions with some clients over the past three months.

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EY to open first artificial intelligence centre in Mumbai - Economic Times