Enterprise Artificial Intelligence (AI) Market by Deployment Type (Cloud and On-Premise), Technology (Machine Learning, Natural Language Processing, Image Processing, and Speech Recognition), Organization Size (Large Enterprises and Small & Medium Enterprises), and Industry Vertical (Media & Advertising, BFSI, IT & Telecom, Retail, Healthcare, Automotive & Transportation, and Others): Global Opportunity Analysis And Industry Forecast, 2019-2026

New York, Jan. 17, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Enterprise Artificial Intelligence Market by Deployment Type, Technology, Organization Size, and Industry Vertical : Global Opportunity Analysis And Industry Forecast, 2019-2026" - https://www.reportlinker.com/p05828825/?utm_source=GNW

Artificial intelligence has been one of the fastest-growing technologies in recent years. AI is associated to human intelligence with similar characteristics, such as language understanding, reasoning, learning, problem solving, and others. Manufacturers in the market witness enormous underlying intellectual challenges in the development and revision of such technology. AI is positioned at the core of the next-gen software technologies in the market. Companies, such as Google, IBM, Microsoft, and other leading players, have actively implemented AI as a crucial part of their technologies. The increase in number of innovative start-ups and advancements in technology have led to rise in investment in artificial intelligence technologies. Moreover, escalating demand for analyzing and interpreting large amount of data boosts the requirement of artificial intelligence industry solutions. Moreover, development of more reliable cloud computing infrastructures and improvements in dynamic artificial intelligence solutions have a strong impact on the growth potential of the AI market. However, lack of trained and experienced staff hinders the growth of the enterprise Artificial Intelligence (AI) market. Furthermore, increase in adoption of AI in developing economies, such as China, and India are expected to provide major opportunities for the market growth in the upcoming years. Also, on-going developments in smart virtual assistants and robots are anticipated to be opportunistic for the growth of the enterprise artificial intelligence (AI) market. The global enterprise artificial intelligence (AI) market is segmented on the basis of deployment type, technology, organization size, industry vertical, and region. Based on deployment type, the market is bifurcated into cloud and on-premise. Based on technology, the market is divided into machine learning, natural language processing, image processing, and speech recognition. Based on organization size, the market is classified into large enterprises and small & medium enterprises. Depending on industry vertical, the market is segmented into media & advertising, BFSI, IT & telecom, retail, healthcare, automotive & transportation, and others. Based on region, the market is analyzed across North America, Europe, Asia-Pacific, and LAMEA. The report includes the profiles of key players operating in the market analysis. These include Alphabet Inc. (Google Inc.), Apple Inc., Amazon Web Services, Inc., International Business Machines Corporation, IPsoft Inc., MicroStrategy Incorporated, NVIDIA Corporation, SAP, Verint, and Wipro Limited.

KEY BENEFITS The report provides an in-depth analysis of the global enterprise artificial intelligence (AI) market trends, key driving factors, and potential areas for product investments. Key players are analyzed with respect to their primary offerings, recent investments, and future development strategies. Porters five forces analysis illustrates the potency of buyers and suppliers operating in the industry. The quantitative analysis of the global enterprise artificial intelligence (AI) market share from 2018 to 2026 is provided to determine the market potential.

KEY MARKET SEGMENTS

BY DEPLOYMENT TYPE Cloud On-premise

BY TECHNOLOGY Machine Learning Natural Language Processing Image Processing Speech Recognition

BY ORGANIZATION SIZE Large Enterprises Small & Medium Enterprises

BY INDUSTRY VERTICAL Media & Advertising BFSI IT & Telecom Retail Healthcare Automotive & Transportation Others

BY REGION North America o U.S. o Canada

Europe o UK o Germany o France o Russia o Rest of Europe

Asia-Pacific o China o Japan o India o Australia o Rest of Asia-Pacific

LAMEA o Latin America o Middle East o Africa

KEY MARKET PLAYERS PROFILED IN THE REPORT Alphabet Inc. (Google Inc.) Apple Inc. Amazon Web Services, Inc. International Business Machines Corporation IPsoft Inc. MicroStrategy Incorporated NVIDIA Corporation SAP Verint Wipro Limited

Read the full report: https://www.reportlinker.com/p05828825/?utm_source=GNW

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Enterprise Artificial Intelligence Market by Deployment Type, Technology, Organization Size, and Industry Vertical : Global Opportunity Analysis And...

The #4 company on our Franchise 500 list is learning from its customers -- and serving up exactly what they want.

January14, 20202 min read

This story appears in the January 2020 issue ofEntrepreneur. Subscribe

On November 20, 1953, pilot Scott Crossfield took a Douglas Aircraft Skyrocket up over the dusty California desert, blasting through the sound barrier and setting a world record that helped usher in the space age. That same year, the first Sonic Drive-In opened in Shawnee, Okla. Its slogan: Service at the Speed of Sound.

Today, nearly seven decades and 3,600 restaurants later, the company is sonically booming in another direction -- this time, into the era of machine learning. Weve heard from our guests, says Sonic president Claudia San Pedro. They still want us to be fast, but they also want their favorite customized food.

So through a partnership with Mastercard, Sonic is piloting an AI-powered car-side menu board that updates its offering in real time based on the customer, weather, and time of day. Fueled in part by the robust analytics Sonic gathers from its new order-ahead app, the restaurants offerings will grow increasingly personalized, with the goal of creating a tailored experience thats unrivaled in fast food.

But the upward trajectory hasnt been without hiccups.In 2018, Sonics incomerose from $63.7 million to$71.2 million, yet the restaurant saw overall revenuedrop. It was then acquired by Inspire Brands in late 2018 for $2.3 billion, meaning it now shares a corporate parent with the likes of Arbys and Buffalo Wild Wings. The new ownership could be a boon to franchisees: System-wide sales are moving upward, and the partnership provided improved access to delivery services like DoorDash and Uber Eats.

Related:3 Surprising Ways That Video Game Companies Leverage AI

Even as Sonics menu continues to rotate in creations like deep-fried Oreos la Mode and Red Bull Slush, the restaurant isnt sitting on its laurels. In August, it announced that it was using a new creative agency, Mother, and it promised to deliver a revamped brand logo in 2020.

So what can we expect? Something Googie? A rocket? A Footlong Coney dressed in a space suit? Its a little bit of making sure we are grounded in our roots, but with a much more forward-looking view, says San Pedro. Thats all Im going to say.

Check out our complete Franchise 500 rankings, or view more stories here.

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#4 on the Franchise 500: How Sonic Drive-In Uses Artificial Intelligence to Improve Customer Service - Entrepreneur

Artificial intelligence (AI) is the ability of a computer program or a machine to think and learn. It is also a field of study which tries to make computers "smart". They work on their own without being encoded with commands.John McCarthy came up with the name "artificial intelligence" in 1955.

In general use, the term "artificial intelligence" means a programme which mimics human cognition. At least some of the things we associate with other minds, such as learning and problem solving can be done by computers, though not in the same way as we do.[1] Andreas Kaplan and Michael Haenlein define AI as a systems ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.[2]

An ideal (perfect) intelligent machine is a flexible agent which perceives its environment and takes actions to maximize its chance of success at some goal or objective.[3] As machines become increasingly capable, mental faculties once thought to require intelligence are removed from the definition. For example, optical character recognition is no longer perceived as an exemplar of "artificial intelligence": it is just a routine technology.

At present we use the term AI for successfully understanding human speech,[1] competing at a high level in strategic game systems (such as Chess and Go), self-driving cars, and interpreting complex data.[4] Some people also consider AI a danger to humanity if it continues to progress at its current pace.[5]

An extreme goal of AI research is to create computer programs that can learn, solve problems, and think logically.[6][7] In practice, however, most applications have picked on problems which computers can do well. Searching data bases and doing calculations are things computers do better than people. On the other hand, "perceiving its environment" in any real sense is way beyond present-day computing.

AI involves many different fields like computer science, mathematics, linguistics, psychology, neuroscience, and philosophy. Eventually researchers hope to create a "general artificial intelligence" which can solve many problems instead of focusing on just one. Researchers are also trying to create creative and emotional AI which can possibly empathize or create art. Many approaches and tools have been tried.

Borrowing from the management literature, Kaplan and Haenlein classify artificial intelligence into three different types of AI systems: analytical, human-inspired, and humanized artificial intelligence.[8] Analytical AI has only characteristics consistent with cognitive intelligence generating cognitive representation of the world and using learning based on past experience to inform future decisions. Human-inspired AI has elements from cognitive as well as emotional intelligence, understanding, in addition to cognitive elements, also human emotions considering them in their decision making. Humanized AI shows characteristics of all types of competencies (i.e., cognitive, emotional, and social intelligence), able to be self-conscious and self-aware in interactions with others.

The first appearance of artificial intelligence is in Greek myths, like Talos of Crete or the bronze robot of Hephaestus. Humanoid robots were built by Yan Shi, Hero of Alexandria, and Al-Jazari. Sentient machines became popular in fiction during the 19th and 20th centuries with the stories of Frankenstein and Rossum's Universal Robots.

Formal logic was developed by ancient Greek philosophers and mathematicians. This study of logic produced the idea of a computer in the 19th and 20th century. Mathematician Alan Turing's theory of computation said that any mathematical problem could be solved by processing 1's and 0's. Advances in neurology, information theory, and cybernetics convinced a small group of researchers that an electronic brain was possible.

AI research really started with a conference at Dartmouth College in 1956. It was a month long brainstorming session attended by many people with interests in AI. At the conference they wrote programs that were amazing at the time, beating people at checkers or solving word problems. The Department of Defense started giving a lot of money to AI research and labs were created all over the world.

Unfortunately, researchers really underestimated just how hard some problems were. The tools they had used still did not give computers things like emotions or common sense. Mathematician James Lighthill wrote a report on AI saying that "in no part of the field have discoveries made so far produced the major impact that was then promised".[9] The U.S and British governments wanted to fund more productive projects. Funding for AI research was cut, starting an "AI winter" where little research was done.

AI research revived in the 1980s because of the popularity of expert systems, which simulated the knowledge of a human expert. By 1985, 1 billion dollars were spent on AI. New, faster computers convinced U.S. and British governments to start funding AI research again. However, the market for Lisp machines collapsed in 1987 and funding was pulled again, starting an even longer AI winter.

AI revived again in the 90s and early 2000s with its use in data mining and medical diagnosis. This was possible because of faster computers and focusing on solving more specific problems. In 1997, Deep Blue became the first computer program to beat chess world champion Garry Kasparov. Faster computers, advances in deep learning, and access to more data have made AI popular throughout the world.[10] In 2011 IBM Watson beat the top two Jeopardy! players Brad Rutter and Ken Jennings, and in 2016 Google's AlphaGo beat top Go player Lee Sedol 4 out of 5 times.

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Artificial intelligence - Simple English Wikipedia, the free ...

Apr 15, 2013

Damn it Spielberg you did it again! I thought you wouldn't get me but once again you made me cry whilst watching one of your films, sheesh!. Right...'A.I.', batten down the hatches mateys, this could be a big one.From the collective minds of Kubrick and Spielberg comes this lavish epic about a little robot boy who is brought into a young couples life. Based on a short story by a writer I admit I've never heard of, yet the idea could easily be mistaken for work from the brains of Arthur C. Clarke, Isaac Asimov or Philip K. Dick.Lets begin, this film gave me a headache, not a bad headache, more of a problematic headache. I was stuck and didn't know what to think. The film is a massive story betwixt two ideas or genres almost, on one hand you have the first half of a film that centres around the human angst and emotion of trying to adapt to adopting a robot child. The pain of a mother who's child is at deaths door from disease, and the decision by her husband to offer her a brand new state of the art robot child that for the first time can learn and express love for its owner.The second half of the film then changes completely, gone is the sentiment and powerful family bound plot as we enter into a more seedy grim world. One could almost say the film adopts many visual concepts from other sci-fi films/genres, which do work on their own, but maybe not together with this story. The story is enthralling and draws you in...but oh so many questions arise Mr Spielberg, where to begin!. Once we leave the comfort of the family orientated first part of the film we pretty much straight away hit the Flesh Fair. Now this really did seem too harsh for me, a completely disjoined idea that harks back to a 'Mad Max' type world. Why would people of the future act like this towards simple machines? the whole sequence looked like some freaky red neck carnival. It also seemed like a huge setup for not very much, just a few minutes of carnage, was all that fan fair really required?.This lead me to the question of why do this to old, lost, outdated Mecha's? (the term for robots in this film which sounds a bit Japanese to me). Now surely these robots cost a lot to make, much time, effort, design etc...went into creating them, so surely destroying them is a complete waste. Wouldn't fixing them up for simple labour tasks like cleaning or whatever, be more useful? maybe selling them on? and even if you did have to shut them down, just do it more humanly, why the need for all the violence?. The whole sequence just didn't seem sensible really, and it was thought up by Spielberg!.Eventually we get to Rouge City, where is this suppose to be? why not use a real city?. Again the whole concept seemed out of place, the city seemed much more futuristic than everything else we have seen, plus the architecture was truly odd. The huge tunnel bridges with a woman's gaping open mouth as the opening? it seemed very 'Giger-esq' to me, quite sexual too, kids film anyone?. Then you had buildings shaped like women's boobs and legs etc...geez!. Its here we meet 'Gigolo Joe' who is superbly played by Jude Law I can't deny, but really at the end of the day, was he needed at all?. He is a nice character, very likeable but virtually bordering on a cartoon character, and why the need for the tap dancing?. The makeup was very good for the Mecha characters, simple yet effective for both Law and Osment. Kudos to Osment of course for his portrayal of the robot 'David', I honestly can say its probably the best performance for a robot I've ever seen. Brilliant casting too I might add, Osment can act but his looks are half the battle won right there, he has this almost perfect plastic looking young face, its all in the eyes I think.Speaking of characters how can I not mention the star of the film, 'Teddy'. Now this little guy was adorable, I still find myself wanting my own Teddy *whimpers*. Every scene this little fellow was in I loved, I loved to see him waddle around and assist David in his simple electronic voice. I found myself caring for all the characters in this film but especially Teddy, he was just awesome. Sure he seemed to have some kind of infinite power source but that made him even cooler damn it!. What really broke my heart was we don't know what happens to lill Teddy, we see him at the end but what becomes of him?? what Steven WHAT??!!. I loved that lill guy *sniff*.As you near the end of the film and its multiple ongoing finales you literately get submerged in questions. 2000 years pass from the time David is trapped under the sea and his rescue (the ferris wheel didn't crush the helicopter/sub thingy??), in that time the planet has gone from global warming jungles to a MASSIVE ice age? I mean a REALLY HEAVY ice age. Now I'm no scientist but that doesn't seem right. I might quickly add, in the future why are all the skyscrapers in New York in tatters? as if they've been burnt out?. Sure the bottom of them has been flooded but they look like skeletons! as if a nuke hit them, eh?.The we get to the evolved Mecha's (or 'Close Encounter' aliens). How would these robots evolve into these angelic liquid-like creatures?? I don't get it, if the human race became extinct tomorrow would computers evolve into alien-like creatures?. Sure these robots can fix themselves and update themselves but that far? really?. Then you gotta ask yourself why would they be digging up old human remains? they know humans created them, OK they might not understand why but does that matter?. They clearly have highly advanced technology so why don't they travel space and look for new similar intelligent life?. Why bother with the human race, of which many despised them anyway, treated them like crap.This then leads onto the resurrection part of the story. I still can't quite work out why David's mother would only live for one day when brought back. There is an explanation from the advanced Mecha's but I couldn't follow it. Again we then have all manner of plot issues...why his mother doesn't recall her husband or son when she wakes, she doesn't question why David is there, she's disorientated but doesn't question anything. She doesn't seem to remember anything like the fact she was probably an old lady when she was last awake, and she doesn't ask to go outside! they stay inside the whole time. You could say the advanced Mecha fixed it so she wouldn't recall anything so not to jeopardize the situation, but when she wakes she acts as if nothing happened and its just a new day.Where the plot really gets silly is the fact this is all possible simply because Teddy kept some strands of cut hair from David's mother about 2000 years prior. Where on earth did he keep these hairs? its not like he has pockets, and what's more...why did he keep the strands of hair??!!. On top of that, and again I'm no scientist, but surely you'd need the roots of human hair for the DNA, not just cut strands, no?.Now there are a lot of whines in there but unfortunately there are a lot of plot issues in the film. I won't and can't say its a bad film, its a truly fantastic bit of sci-fi with some lovely design work and visuals, but there are problems along the way. First half is a decent sci-fi story similar to 'Bicentennial Man', second half is really a rehashed rip off of the classic 'Pinocchio' tale set in the future.The film garnered a lot of interest due to the involvement of Kubrick and Spielberg admittedly but its still a wonderful bit of work. Part sci-fi but all fairytale in the end, the film slowly becomes more of a children's tale the deeper you go, the narration nails that home if you think about it. The very end is kinda tacked on and doesn't feel correct, true, you can see they had trouble ending the film and a weepy ending was required so they made one. But god damn it works *sniff*.The final sequence of David lying besides his motionless mother still brings a lump to my throat as I type this now. We then see Teddy join them on the bed and just sit down to watch over them both, like a guardian. Does David actually die here? does he voluntarily switch himself off somehow? again...what happens to Teddy? I'm not sure. But as the score swells and the lights dim, you can't help but wipe away a tear.

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A.I. Artificial Intelligence (2001) - Rotten Tomatoes

BRYAN, Tex. (KBTX) - Almost everyone knows what a smartphone is, but there are also new generations of smart machines being built that will be used in agriculture to help manage a crop. Shannon Pickering is a market development manager for Blue River Technology.

We are working on several projects but primarily focused on spraying. So precision spraying using computer vision systems and artificial intelligence in order to be able to identify every plant in the field and determine what is the crop versus the weeds and only spray the weeds.

John Deere acquired Blue River Technology in 2017 to help make its Ag equipment smarter.

We hope to do several things all at once basically. Number one is become more efficient. Utilize resources wisely. Be able to spray less pesticides on crops. If we can identify the weeds in the field and only spray the weeds instead of spraying the entire field then thats a big deal. Thats a lot of chemical savings thats not going into the soil or onto the plant. So being able to provide a more sustainable solution for our farmers going forward is really a big deal for us.

One of their early conceptual sprayers showed up to 95 percent fewer chemicals being sprayed in the field where they were being very precise and applying it only where it needed to go which was on the weeds.

It has to provide value to the grower. It has to provide efficiency to pay for itself, and so thats a must and it will do that for sure. The technology is here. We definitely have the capability of doing this today. Its just a matter of integrating it into the machinery. Were a few years away yet probably from seeing it in a go-to-market form but the potential is there. The technology works and its coming for sure.

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From the Ground Up: Using Artificial Intelligence for Weed Control - KBTX

What happens when you take cells from frog embryos and grow them into new organisms that were "evolved" by algorithms? You get something that researchers are calling the world's first "living machine."

Though the original stem cells came from frogs the African clawed frog, Xenopus laevis these so-called xenobots don't resemble any known amphibians. The tiny blobs measure only 0.04 inches (1 millimeter) wide and are made of living tissue that biologists assembled into bodies designed by computer models, according to a new study.

These mobile organisms can move independently and collectively, can self-heal wounds and survive for weeks at a time, and could potentially be used to transport medicines inside a patient's body, scientists recently reported.

Related: The 6 Strangest Robots Ever Created

"They're neither a traditional robot nor a known species of animal," study co-author Joshua Bongard, a computer scientist and robotics expert at the University of Vermont, said in a statement. "It's a new class of artifact: a living, programmable organism."

Algorithms shaped the evolution of the xenobots. They grew from skin and heart stem cells into tissue clumps of several hundred cells that moved in pulses generated by heart muscle tissue, said lead study author Sam Kriegman, a doctoral candidate studying evolutionary robotics in the University of Vermont's Department of Computer Science, in Burlington.

"There's no external control from a remote control or bioelectricity. This is an autonomous agent it's almost like a wind-up toy," Kriegman told Live Science.

Biologists fed a computer constraints for the autonomous xenobots, such as the maximum muscle power of their tissues, and how they might move through a watery environment. Then, the algorithm produced generations of the tiny organisms. The best-performing bots would "reproduce" inside the algorithm. And just as evolution works in the natural world, the least successful forms would be deleted by the computer program.

"Eventually, it was able to give us designs that actually were transferable to real cells. That was a breakthrough," Kriegman said.

The study authors then brought these designs to life, piecing stem cells together to form self-powered 3D shapes designed by the evolution algorithm. Skin cells held the xenobots together, and the beating of heart tissue in specific parts of their "bodies" propelled the 'bots through water in a petri dish for days, and even weeks at a stretch, without needing additional nutrients, according to the study. The 'bots were even able to repair significant damage, said Kriegman.

"We cut the living robot almost in half, and its cells automatically zippered its body back up," he said.

"We can imagine many useful applications of these living robots that other machines can't do," said study co-author Michael Levin, director of theCenter for Regenerative and Developmental Biologyat Tufts University in Massachusetts. These might include targeting toxic spills or radioactive contamination, collecting marine microplastics or even excavating plaque from human arteries, Levin said in a statement.

Creations that blur the line between robots and living organisms are popular subjects in science fiction; think of the killer machines in the "Terminator" movies or the replicants from the world of "Blade Runner." The prospect of so-called living robots and using technology to create living organisms understandably raises concerns for some, said Levin.

"That fear is not unreasonable," Levin said. "When we start to mess around with complex systems that we don't understand, we're going to get unintended consequences."

Nevertheless, building on simple organic forms like the xenobots could also lead to beneficial discoveries, he added.

"If humanity is going to survive into the future, we need to better understand how complex properties, somehow, emerge from simple rules," Levin said.

The findings were published online Jan. 13 in the journal Proceedings of the National Academy of Sciences.

Originally published on Live Science.

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World's First 'Living Machine' Created Using Frog Cells and Artificial Intelligence - Livescience.com

The reality of artificial intelligences role in media buying may be turning out very different from the dream.

Automation and AI could be used, so the theory went, for forecasting, analyzing data and ultimately improving campaign performance, so that marketers could change and reallocate budgets quickly. But despite advancements being made, AIs adoption for media buying is ending up with slightly different use cases.

The AI is there, said Jason Harrison, CEO of North America for WPPs Essence. Youre just not seeing it in the ways you would have expected.

The expectation was that AIs use for media buying otherwise known as automated decision-making would enable machines to tap data about specific audiences so as to create automated campaigns across different digital channels. And this would enable agencies to stop worrying about the minute details of media planning and buying so they could instead spend more time on strategic work and insight delivery for their clients. But so far, that hasnt been the case, as AI has led media buyers, as previously reported by Digiday, to spend added time on campaign reporting and the more difficult aspects of the job.

And AIs role in media buying hasnt been nearly as sexy as pitched: While media agencies have been able to use AI to automate campaigns (mostly for paid search advertising), it has been delivering better targeted audiences for the same amount of money as marketers previously spent and clarifying the gaps in a media plan rather than handling all the minute details.

AIs use for media buying hasnt lived up to the dream for a few reasons. For one, the effects of AIs use on media buying are generally still found in a biddable, programmatic environment where marketers have come to expect automation. And agencies, marketers and platform providers are all still testing the best ways to use AI for media buying. Without a set of standards in place, it difficult to compare marketers use of AI for media buying.

Outside of the current state of programmatic, adoption of AI isnt widespread in a systemic and systematic way across the industry, wrote William Restrepo, svp of business intelligence for Publicis Media, in an email. Different agencies and different vendors (and vendor types) are still in a trial-and-error phase determining what works and doesnt work for them.

At the same time, Facebook and Google have made advancements in the AI media buying capabilities for their platforms, making it appealing for marketers to use those platforms AI rather than continuing to build out their own.

An exclusive, inside look at whats actually happening in the video industry, including original reporting, analysis of important stories and interviews with interesting executives and other newsmakers.

At least, that has been case for Orangetheory Fitness. Just slightly more than two years ago, the high-end gym chainlaunched its own AI platform, enabling the company to slice its cost per lead from $20 to $8 and end up with better leads. With those results, the chain quadrupled its media spend and focused most of those dollars on AI. But although the company once was bullish about its own AI platform, Orangetheory has since pulled the plug on it, opting instead to have its internal teams and its media agency, the Tombras Group, manage more of its media buying and planning decisions.

We were heavily relying our digital media efforts on AI two years ago, said Tammie DeGrasse-Cabrera, the global marketing director for Orangetheory Fitness. Weve shifted back and really made sure the humans on our team, [those on] our media agency, are really doing that for us. Were also using AI thats already being developed in media platforms like Facebook and Google and connecting that and marrying that to the art and science of media buying, she added.

Orangetheory Fitnesss AI journey might be a microcosm of what midlevel marketers have been experiencing when using AI for media buying. Now that Facebooks and Googles AI for media buying has become more advanced, relying on those platforms has become attractive for marketers rather than spending significant resources on building out their own. Thats especially true at this point since the promise that AI would making the job of media buying simpler has not come to fruition.

But thats not the case for larger marketers with the resources to build their own AI solution, according to media executives; they said that major marketers are still vying for custom solutions that tap AI for media buying across a variety of platforms.

We rely on automation, but we dont set it and forget it, said Doug Rozen, chief media officer for 360i, who noted that his company has made significant advancements in use of AI for media buying over the last six months although work still remains. Its the human and the robot working together almost like sometimes the automation is taking a blunt object to something thats more nuanced than just applying the overall algorithmic automation to it.

When it comes to AIs use for media buying, the complexity of whats being accomplished is at times difficult to convey to marketers. And for someone not using the platforms each day, its easy to miss the ways that AI is already changing media buying in a biddable environment. Publishers and platform providers have done a good job of externalizing the technology and interfaces to make it easy for someone to place a media buy and enter constraints, Harrison said. It becomes akin to indicating this is how much Im willing to pay; these are my bid thresholds; this is the outcome I expect; heres how much I have to spend and hitting go, he said.

Added Harrison: Behind the scenes, the work of those platforms has gotten a lot smarter; and the return, the value that advertisers get for that money, is a lot more because in theory its being targeted to the right people; its more precise and all of that is powered by AI decisioning. He said, Its not fair to [say] it hasnt gone anywhere. It has. Youre just not as explicitly seeing it.

As Luke Lambert, OMD USAs head of programmatic advertising, observed, What were really seeing is not the change in output that we were always dreaming about, that we thought AI would produce for us. He added, Instead, its taught us that theres a better way of doing things before we even give the AI a dollar to spend, which is a positive thing. Its a good thing to find process efficiencies. We just expected them to be on the other side.

Even though AIs use for media buying has not to date delivered quite what was expected, media executives are still bullish on its potential and the need for marketers to enlist it. Weve really only scratched the surface, Harrison said. The more complexity you have in the media ecosystem with the number of players, platforms and opportunities, [and] the more complexity you have in the content ecosystem, with options in the way people consume and see content, the harder it is to train machines to anticipate where the next best impression should go. He added, Thats really the challenge now to build AI that can accommodate and contemplate all of that complexity.

And despite the challenges associated with contending with all that complexity, he said, the need for AI to help media buyers manage those players, platforms and channels is clear. Humans are not going to be able to do that decision game much longer and arguably were doing it at a suboptimal way today, Harrison said. The sooner we build AI to do that job the better marketers outcomes will be.

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Dont set it and forget it: Artificial intelligences role in media buying is taking shape - Digiday

Artificial Intelligence in BFSI Market by Offerings (Hardware, Software, and Services), Solution (Chatbots, Fraud Detection & Prevention, Anti-Money Laundering, Customer Relationship Management, Data Analytics & Prediction, and Others), Technology (Deep Learning, Querying Method, Natural Language Processing, and Context Aware Processing) : Global Opportunity Analysis and Industry Forecast, 20192026

New York, Jan. 16, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Artificial Intelligence in BFSI Market by Offerings, Solution, Technology : Global Opportunity Analysis and Industry Forecast, 20192026" - https://www.reportlinker.com/p05836997/?utm_source=GNW

The global artificial intelligence (AI) in BFSI market was valued at $17,765.2 million in 2018 and is expected to reach $247,366.7 million by 2026, registering a CAGR of 38.0% from 2019 to 2026. Artificial intelligence is the recreation of human intelligence that perform tasks like humans. In financial institutions and other major finance industries, AI has become a core adaption and is expected to change the overall scenario of product and service offerings. For instance, insurance companies are improving risk models to maintain customer loyalty and client satisfaction with the help of advanced AI technological platforms.

Various fraud detection, risk mitigation, back-end office works with thousands of people processing customer requests are improved with the help of AI enabled technologies such as chatbots, machine learning, and other such technologies, which boosts the growth of the market. In addition, the reduction in the tendency of human errors by automation of backend processes and enhancement in proactive customer experience is expected to drive the growth of the AI in the BFSI market. However, rise in security concerns, inadequacy of trust while issuing customer data, and higher cost for implementation of AI technologies is expected to restrain the market growth. New entrants like FinTech (Financial Technology) with advance features in the market, new initiatives in government regulations, and existing traditional banking system provides lucrative opportunities for the market growth.

The global artificial intelligence (AI) in BFSI market is segmented on the basis of offerings, solution, technology, and region. On the basis of offerings, it is segmented into hardware, software, and services. By service providers, it is segmented into chatbots, fraud detection & prevention, anti-money laundering, customer relationship management, data analytics & prediction, and others. By technology, it is classified into deep learning, querying method, natural language processing, and context aware processing. Region wise, the market is analyzed across North America, Europe, Asia-Pacific, and LAMEA.

KEY BENEFITS FOR STAKEHOLDERS ? The study provides an in-depth analysis of the global artificial intelligence (AI) in BFSI market along with the current trends and future estimations to elucidate the imminent investment pockets. ? Comprehensive analysis of the factors that drive and restrict the market growth is provided in the report. ? Comprehensive quantitative analysis of the industry from 2019 to 2026 is provided to enable the stakeholders to capitalize on the prevailing market opportunities. ? Extensive analysis of the key segments of the industry helps in understanding the offerings, solution, and technology across the globe. ? Key market players and their strategies have been analyzed to understand the competitive outlook of the market.

KEY MARKET SEGMENTS By Offerings o Hardware o Software o Services By Solution o Chatbots o Fraud Detection & Prevention o Anti-Money Laundering o Customer Relationship Management o Data Analytics & Prediction o Others By Technology o Deep Learning o Querying Method o Natural Language Processing o Context Aware Processing By Region o North America U.S. Canada Mexico o Europe o UK o Germany o France o Rest of Europe o Asia-Pacific o Japan o India o China o Australia o Rest of Asia-Pacific o LAMEA o Middle East o Latin America o Africa

KEY PLAYERS PROFILED Alphabet Inc. (Google) Baidu, Inc. Inbenta Technologies, Inc. Intel Corporation International Business Machines Corporation (IBM) Microsoft Corporation Oracle Corporation Palantir Technologies Inc. SAP SE salesforce.com, inc.

The other players in the market include (profiles not included in the report) the following: Lexalytics Inc. Digital Reasoning Inc. Interaction LLC, Inc. Ipsoft Inc. Zest FinanceRead the full report: https://www.reportlinker.com/p05836997/?utm_source=GNW

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The global artificial intelligence (AI) in BFSI market was valued at $17,765.2 million in 2018 and is expected to reach $247,366.7 million by 2026,...

Artificial intelligence is supposed to free the hiring process from prejudices and biases. We can have a totally neutral system that evaluates candidates and selects the best possible one, regardless of race, gender, or any other characteristic.

It sounds fantastic, but it's been an abysmal failure in that matter. Artificial intelligence is only as good as the programmers, who, of course, are actual humans with flaws. Amazon, which, of course, has gobs of money to pour into development, had to scrap its A.I. recruiting process because the bot didn't like women.

HireVue faces pressure from rights groups over its hiring systems, which, according to TheWashington Post,

use video interviews to analyze hundreds of thousands of data points related to a person's speaking voice, word selection and facial movements. The system then creates a computer-generated estimate of the candidates' skills and behaviors, including their "willingness to learn" and "personal stability."

This model of gaming the system has been in place for as long as people have applied for jobs. There are thousands of articles on the internet that tell you how to answer standard interview questions ("Where do you see yourself in five years?") or extol the virtues of a firm handshake. This is really no different than the training these consultants give. Except, instead of trying to convince a human, you're trying to convince a machine.

And that makes this training so much more valuable. I can tell you "firm handshakes are important!" and then you interview with someone who prefers the dead-fish version of shaking hands and my advice harms instead of helps. Butif two companies use the same software, the information from these consultants will help you shine regardless of who the hiring manager is.

That's the goal, of course, to take the human biases out of interviews. But the biases still exist in A.I.--it's just that every job requires you to overcome the same preferences. Which means it will be easier to beat the system. Once the consultants figure out what the algorithms want, they can train you to respond the right way.

While it potentially levels the playing field, people who can afford training will do better in the interviews. Interviewers already discriminate on class, so this doesn't solve that problem at all.

Can artificial intelligence potentially make hiring better? Probably. But, as these consultants understand--anytime there is a system, there is a way to beat it. While humans are fallible, at least we all know they are. Artificial intelligence allows you to think the process is bias-free, but it's not. It just makes for consistent bias.

Published on: Jan 15, 2020

The opinions expressed here by Inc.com columnists are their own, not those of Inc.com.

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Companies Use Artificial Intelligence to Help With Hiring. Korean Consultants Teach You How to Beat It - Inc.

The Asia Pacific artificial intelligence in fashion market accounted for US$ 55. 1 Mn in 2018 and is expected to grow at a CAGR of 39. 0% over the forecast period 2019-2027, to account for US$ 1015. 8 Mn in 2027.

New York, Jan. 15, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Asia Pacific Artificial Intelligence in Fashion Market to 2027 - Regional Analysis and Forecasts by Offerings; Deployment; Application; End-User Industry" - https://www.reportlinker.com/p05833586/?utm_source=GNW Real-time consumer behavior insights and increased operational efficiency are driving the adoption of artificial intelligence in fashion industry. Moreover, the availability of a large amount of data originating from different data sources is one of the key factors driving the growth of AI technology across the fashion industry. Artificial Intelligence has already disrupted several industries, including the retail and fashion industry. The fashion industry so far has been one of the primary adopters of the technology. The fashion retailers these days are leveraging several revolutionary technologies, including machine learning, like augmented reality (AR) and artificial intelligence (AI), to make seamless shopping experiences across the channels, from online models to brick and mortar stores. Fashion retailers are progressively moving towards the AI integration within their supply chain, where more focus is being on customer-facing AI initiatives. Further, an AI integrated search engine is expected to reshape the way fashion designers develop new product designs. Store operations and in-store services will also be greatly benefited from AI integration in the fashion industry.The artificial intelligence in fashion market is fragmented in nature due to the presence of several end-user industries, and the competitive dynamics in the market are anticipated to change during the coming years.In addition to this, various initiatives are undertaken by governmental bodies to accelerate the artificial intelligence in fashion market further.

The governments of various countries in this region are trying to attract FDIs in the technology sector with the increasing need for enhanced technology-related services.For instance, Chinas government relaxed the restrictions on new entries with an objective to encourage overseas and private capital to invest in its economy.

This factor is anticipated to drive the demand for artificial intelligence in fashion market in this region.The artificial intelligence in fashion market by deployment type is segmented into on-premise and cloud.During the forecast period of 2019 to 2027, the cloud-based segment is anticipated to be the largest contributor in artificial intelligence in fashion market.

The artificial intelligence in fashion market is experiencing a paradigm shift from traditional on-premise deployment to cloud-based deployments in the current scenario. This trend is predominantly driven by the presence of a new category of cloud-only solutions, which help in minimizing integration complexities and installation costs with quick setup.The overall artificial intelligence in fashion market size has been derived using both primary and secondary source.The research process begins with exhaustive secondary research using internal and external sources to obtain qualitative and quantitative information related to the artificial intelligence in fashion market.

It also provides an overview and forecast for the artificial intelligence in fashion market based on all the segmentation provided with respect to the Asia Pacifica region.Also, primary interviews were conducted with industry participants and commentators to validate data and analysis.

The participants who typically take part in such a process include industry expert such as VPs, business development managers, market intelligence managers, and national sales managers, and external consultants such as valuation experts, research analysts, and key opinion leaders specializing in the artificial intelligence in fashion market. Some of the players present in artificial intelligence in fashion market are Adobe Inc., Amazon Web Services, Inc., Catchoom Technologies S.L., Facebook, Inc., Google LLC, Huawei Technologies Co., Ltd., IBM Corporation, Microsoft Corporation, Oracle Corporation, and SAP SE among others.Read the full report: https://www.reportlinker.com/p05833586/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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Asia Pacific Artificial Intelligence in Fashion Market to 2027 - Regional Analysis and Forecasts by Offerings; Deployment; Application; End-User...