Category Archives: Artificial Intelligence

Financial Times

Vector Institute is just the latest in Canada's AI expansion BBC News Canadian researchers have been behind some recent major breakthroughs in artificial intelligence. Now, the country is betting on becoming a big player in one of the hottest fields in technology, with help from the likes of Google and RBC. In an ... Canada aims to lead world in artificial intelligenceFinancial Times Canadian government, businesses back $150 million artificial ...Reuters Canada 'lost the lead' on artificial intelligence. Here's how Toronto will get it backCBC.ca all 14 news articles »

Read the rest here:

Vector Institute is just the latest in Canada's AI expansion - BBC News

Autonomous cars arent smarter than this.

Every day countless headlines emerge from myriad sources across the globe, both warning of dire consequences and promising utopian futures all thanks to artificial intelligence. AI is transforming the workplace, writes the Wall Street Journal, while Fortune magazine tells us that we are facing an AI revolution that will change our lives. But we dont really understand what interacting with AI will be like or what it should be like.

It turns out, though, that we already have a concept we can use when we think about AI: Its how we think about animals. As a former animal trainer (albeit briefly) who now studies how people use AI, I know that animals and animal training can teach us quite a lot about how we ought to think about, approach and interact with artificial intelligence, both now and in the future.

Using animal analogies can help regular people understand many of the complex aspects of artificial intelligence. It can also help us think about how best to teach these systems new skills and, perhaps most importantly, how we can properly conceive of their limitations, even as we celebrate AIs new possibilities.

As AI expert Maggie Boden explains, Artificial intelligence seeks to make computers do the sorts of things that minds can do. AI researchers are working on teaching computers to reason, perceive, plan, move and make associations. AI can see patterns in large data sets, predict the likelihood of an event occurring, plan a route, manage a persons meeting schedule and even play war-game scenarios.

Many of these capabilities are, in themselves, unsurprising: Of course a robot can roll around a space and not collide with anything. But somehow AI seems more magical when the computer starts to put these skills together to accomplish tasks.

Take, for instance, autonomous cars. The origins of the driverless car are in a 1980s-era Defense Advanced Research Project Agency project called the Autonomous Land Vehicle. The projects goals were to encourage research into computer vision, perception, planning and robotic control. In 2004, the ALV effort became the first Grand Challenge for self-driving cars. Now, more than 30 years since the effort began, we are on the precipice of autonomous or self-driving cars in the civilian market. In the early years, few people thought such a feat was impossible: Computers couldnt drive!

Yet, as we have seen, they can. Autonomous cars capabilities are relatively easy for us to understand. But we struggle to comprehend their limitations. After the 2015 fatal Tesla crash, where the cars autopilot function failed to sense a tractor-trailer crossing into its lane, few still seem to grasp the gravity of how limited Teslas autopilot really is. While the company and its software were cleared of negligence by the National Highway Traffic Safety Administration, it remains unclear whether customers really understand what the car can and cannot do.

What if Tesla owners were told not that they were driving a beta version of an autopilot but rather a semi-autonomous car with the mental equivalence of a worm? The so-called intelligence that provides full self-driving capability is really a giant computer that is pretty good at sensing objects and avoiding them, recognizing items in images and limited planning. That might change owners perspectives about how much the car could really do without human input or oversight.

Technologists often try to explain AI in terms of how it is built. Take, for instance, advancements made in deep learning. This is a technique that uses multi-layered networks to learn how to do a task. The networks need to process vast amounts of information. But because of the volume of the data they require, the complexity of the associations and algorithms in the networks, it is often unclear to humans how they learn what they do. These systems may become very good at one particular task, but we do not really understand them.

Instead of thinking about AI as something superhuman or alien, its easier to analogize them to animals, intelligent nonhumans we have experience training.

For example, if I were to use reinforcement learning to train a dog to sit, I would praise the dog and give him treats when he sits on command. Over time, he would learn to associate the command with the behavior with the treat.

Training an AI system can be very much the same. In reinforcement deep learning, human designers set up a system, envision what they want it to learn, give it information, watch its actions and give it feedback (such as praise) when they see what they want. In essence, we can treat the AI system like we treat animals we are training.

The analogy works at a deeper level too. Im not expecting the sitting dog to understand complex concepts like love or good. Im expecting him to learn a behavior. Just as we can get dogs to sit, stay and roll over, we can get AI systems to move cars around public roads. But its too much to expect the car to solve the ethical problems that can arise in driving emergencies.

Thinking of AI as a trainable animal isnt just useful for explaining it to the general public. It is also helpful for the researchers and engineers building the technology. If an AI scholar is trying to teach a system a new skill, thinking of the process from the perspective of an animal trainer could help identify potential problems or complications.

For instance, if I try to train my dog to sit, and every time I say sit the buzzer to the oven goes off, then my dog will begin to associate sitting not only with my command, but also with the sound of the ovens buzzer. In essence, the buzzer becomes another signal telling the dog to sit, which is called an accidental reinforcement. If we look for accidental reinforcements or signals in AI systems that are not working properly, then well know better not only whats going wrong, but also what specific retraining will be most effective.

This requires us to understand what messages we are giving during AI training, as well as what the AI might be observing in the surrounding environment. The oven buzzer is a simple example; in the real world it will be far more complicated.

Before we welcome our AI overlords and hand over our lives and jobs to robots, we ought to pause and think about the kind of intelligences we are creating. They will be very good at doing particular actions or tasks, but they cannot understand concepts, and do not know anything. So when you are thinking about shelling out thousands for a new Tesla car, remember its autopilot function is really just a very fast and sexy worm. Do you really want to give control over your life and your loved ones lives to a worm? Probably not, so keep your hands on the wheel and dont fall asleep.

Originally posted here:

How understanding animals can help us make the most of artificial intelligence - The Conversation US

Not long ago, I wrote about artificial intelligence (AI), its capabilities and its future.

In that article, the chief technology officer of Advanced Micro Devices Inc. AMD, +2.48% one of the largest makers of microprocessors, provided intriguing insights into the topic.

Today, I want to take it a step further: I got my hands on an interesting study by InsideSales.com, an AI-powered predictive sales acceleration platform. The study includes responses from nearly 2,000 Americans from all walks of life about the perceived dangers or opportunities brought on by AI.

Is 2017 the year when artificial intelligence will finally reach the mainstream, and if so, is the general population aware of it? Lets dive into the document and find out.

When asked if theyve ever used AI, almost 55% of respondents answered affirmatively. The survey uncovers an interesting correlation between income levels and AI adoption: Those that report the most frequent use of AI are from the lowest income bracket (less than $25,000 a year) and the highest (above $200,000).

Navigation apps (60.3%), video (55.2%) and music streaming (47.4%) are the most common ways AI-enhanced content is consumed. Thats understandable, since those technologies had more than a decade to win over the average person.

But what about the more innovative ways in which AI can be used, such as Amazon.com Inc.s AMZN, +0.23% Alexa or Alphabet Inc.s GOOG, +0.01% Google Assistant? Only 12% of respondents find those devices useful, indicating theyre still deemed a novelty. The survey uncovers two more areas that have failed to woo users enough to warrant greater adoption in their daily lives: home automation (5.5% of respondents report regular AI usage) and bots in the workplace (only 1%).

The biggest hurdle seems to be lack of trust. People dont have enough faith in AI to allow it to work for them. General lack of trust (42% of consumers said they dont trust AI) varies between the East Coast and the West Coast as well as central regions. The most skeptical were respondents in New York, Pennsylvania and New Jersey (49.2% of them couldnt name a single AI product they trusted), while users from the Pacific, West, South Central and New England regions were far more ready to rely on AI.

Trust levels also varied among different industries. Only 9.3% of consumers would allow AI to run their finances, and 4% would trust it with HR-related work. On the other hand, 35.6% of respondents rely on AI for various entertainment-related recommendations, 30.1% would let it produce goods made by automated machinery, while almost 19% believe it could enhance automated sales procedures. The percentage of those who would trust medical diagnostics made by learning and decision-making algorithms is smaller.

The most trusted company for AI-related products and services is Google (54.3%), followed by Apple Inc. AAPL, -0.13% (46.3%), Microsoft Corp. MSFT, +0.37% (40.05%) and Amazon (39.6%).

What about the future of learning and decision-making algorithms? Heres what respondents said:

Almost 49% of consumers believe AI will lead to medical advancements.

46.7% of consumers believe AI will take over dangerous jobs.

41.7% of consumers believe AI will automate mundane tasks in their personal life.

Almost 40% of consumers believe AI will lead to advancements in transportation and travel.

35.1 % of consumers believe AI will automate mundane tasks in their work life.

Although Americans may be cautious about AI now, many expect it to keep evolving until its capable of performing tasks that are currently beyond its capabilities. This conclusion correlates pretty well with the reality of AI and what it can currently do. It also means that the market in general has great, yet realistic, expectations of AI.

Finally, lets address the cyber elephant in the room: Will AI take your job? When asked that same question (click here to read what I think about the topic), 35.4% of respondents acknowledged being concerned about their job safety. Millennials and members of Generation Z are generally more worried than older generations, with almost 41% of them saying AI could be coming after their jobs in the future.

A further examination of the survey data leads to more interesting correlations: The more respondents believe AI is useful, the more they fear being replaced by it:

42% of Generation Z has a positive view of AI in the workplace; 36% believe AI will decrease the number of jobs available.

35% of millennials have a positive view of AI in the workplace; 37% believe AI will decrease the number of jobs available.

24% of Generation X have a positive view of AI in the workplace; 37% believe AI will decrease the number of jobs available.

21% of baby boomers have a positive view of AI in the workplace; 26% believe AI will decrease the number of jobs available.

When it comes to income levels of those interviewed:

More than 42% of consumers making under $25,000 a year believe AI will decrease the number of job opportunities.

Fewer than 26% of consumers making over $175,000 a year believe AI will decrease the number of job opportunities.

So, the more someone earns per year, the less he or she feels threatened by artificial intelligence.

AI is not a passing trend. Its been with us for decades and is here to stay. As technology and science improve, so will the algorithms behind AI and the hardware thats running it. However, I still believe it must improve before it can become an inseparable and integral part of our lives.

If you were asked the same questions as in the survey, what would you say about AI? Let me know in the comment section below.

Read more:

Opinion: What's holding back artificial intelligence? Americans don't trust it - MarketWatch

Executive Summary

The effective deployment of AI in the enterprise requires a focus on achieving business goals. Rushing towards an AI strategy and hiring someone with technical skills in AI to lead the charge might seem in tune with the current trends, but it ignores the reality that innovation initiatives only succeed when there is a solid understanding of actual business problems and goals. For AI to work in the enterprise, the goals of the enterprise must be the driving force.

Every serious technology company now has an Artificial Intelligence team in place. These companies are investing millions into intelligent systems for situation assessment, prediction analysis, learning-based recognition systems, conversational interfaces, and recommendation engines. Companies such as Google, Facebook, and Amazon arent just employing AI, but have made it a central part of their core intellectual property.

As the market has matured, AI is beginning to move into enterprises that will use it but not develop it on their own. They see intelligent systems as solutions for sales, logistics, manufacturing, and business intelligence challenges. They hope AI can improve productivity, automate existing process, provide predictive analysis, and extract meaning from massive data sets. For them, AI is a competitive advantage, but not part of their core product. For these companies, investment in AI may help solve real business problems but will not become part of customer facing products. Pepsi, Wal-Mart and McDonalds might be interested in AI to help with marketing, logistics or even flipping burgers but that doesnt mean that we should expect to see intelligent sodas, snow shovels, or Big Macs showing up anytime soon.

How it will impact business, industry, and society.

As with earlier technologies, we are now hearing advice about AI strategies and how companies should hire Chief AI Officers. In much the same way that the rise of Big Dataledto the Data Scientist craze, the argument is that every organization now needs to hire a C-Level officer who will drive the companys AI strategy.

I am here to ask you not to do this. Really, dont do this.

Its not that I doubt AIs usefulness. I have spent my entire professional life working in the field. Far from being a skeptic, I am a rabid true believer.

However, I also believe that the effective deployment of AI in the enterprise requires a focus on achieving business goals. Rushing towards an AI strategy and hiring someone with technical skills in AI to lead the charge might seem in tune with the current trends, but it ignores the reality that innovation initiatives only succeed when there is a solid understanding of actual business problems and goals. For AI to work in the enterprise, the goals of the enterprise must be the driving force.

This is not what youll get if you hire a Chief AI Officer. The very nature of the role aims at bringing the hammer of AI to the nails of whatever problems are lying around. This well-educated, well-paid, and highly motivated individual will comb your organization looking for places to apply AI technologies, effectively making the goal to use AI rather than to solve real problems.

This is not to say that you dont need people who understand AI technologies. Of course you do. But understanding the technologies and understanding what they can do for your enterprise strategically are completely different. And hiring a Chief of AI is no substitute for effective communication between the people in your organization with technical chops and those with strategic savvy.

One alternative to hiring a Chief AI Officer is start with the problems. Move consideration of AI solutions into the hands of the people who are addressing the problems directly. If these people are equipped with a framework for thinking about when AI solutions might be applicable, they can suggest where those solutions are actually applicable. Fortunately, the framework for this flows directly from the nature of the technologies themselves. We have already seen where AI works and where its application might be premature.

The question comes down to data and the task.

For example, highly structured data found in conventional databases with well-understood schemata tend to support traditional, highly analytical machine learning approaches. If you have 10 years of transactional data, then you should use machine learning to find correlations between customer demographics and products.

In cases where you have high volume, low feature data sets (such as images or audio), deep learning technologies are most applicable. So a deep learning approach that uses equipment sounds to anticipate failures on your factory floor might make sense.

If all you have is text, the technologies of data extraction, sentiment analysis and Watson-like approaches to evidence-based reasoning will be useful. Automating intelligent advice based on HR best practice manuals could fit into this model.

And if you have data that is used to support reporting on the status or performance of your business, then natural language generation is the best option. It makes no sense to have an analysts valuable time dedicated to analyzing and summarizing all your sales data when you can have perfectly readable English language reports automatically generated by a machine and delivered by email.

If decision-makers throughout your organization understand this, they can look at the business problems they have and the data theyre collecting and recognize the types of cognitive technologies that might be most applicable.

The point here is simple. AI isnt magic. Specific technologies provide specific functions and have specific data requirements. Understanding them does not require that you hire a wizard or unicorn to deal with them. It does not require a Chief of AI. It requires teams that know how to communicate the reality of business problems with those who understand the details of technical solutions.

The AI technologies of today are astoundingly powerful. As they enter the enterprise, they will change everything. If we focus on applying them to solve real, pervasive problems, we will build a new kind of man-machine partnership that empowers us all to work at the top of our game and realize our greatest potential.

See the original post here:

Please Don't Hire a Chief Artificial Intelligence Officer - Harvard Business Review

Apple CEO Tim Cook.Photo by Justin Sullivan Getty Images

Artificial intelligence has made great progress in helping computers recognize images in photos and recommending products online that you're more likely to buy. But the technology still faces many challenges, especially when it comes to computers remembering things like humans do.

On Tuesday, Apples director of AI research, Ruslan Salakhutdinov, discussed some of those limitations. However, he steered clear during his talk at an MIT Technology Review conference of how his secretive company incorporates AI into its products like Siri.

Get Data Sheet , Fortunes technology newsletter .

Salakhutdinov, who joined Apple in October, said he is particularly interested in a type of AI known as reinforcement learning, which researchers use to teach computers to repeatedly take different actions to figure out the best possible result . Google ( goog ) , for example, used reinforcement learning to help its computers find the best possible cooling and operating configurations in its data centers, thus making them more energy efficient.

Researchers at Carnegie Mellon, where Salakhutdinov is also an associate professor, recently used reinforcement learning to train computers to play the 1990's era video game Doom, Salakhutdinov explained. Computers learned to quickly and accurately shoot aliens while also discovering that ducking helps with avoiding enemy fire. However, these expert Doom computer systems are not very good at remembering things like the maze's layouts, which keeps them from planning and building strategies, he said.

Part of Salakhutdinovs research involves creating AI-powered software that memorizes the layouts of virtual mazes in Doom and points of references in order to locate specific towers. During the game, the software first spots what's either a red or green torch, with the color of the torch corresponding to the color of the tower it needs to locate.

Eventually, the software learned to navigate the maze to reach the correct tower. When it discovered the wrong tower, the software backtracked through the maze to find the right one. What was especially noteworthy was that the software was able to recall the color of the torch each time it spotted a tower, he explained.

For more about technology and finance, watch:

However, Salakhutdinov said this type of AI software takes a long time to train and that it requires enormous amounts of computing power, which makes it difficult to build at large scale. Right now its very brittle, Salakhutdinov said.

Another area Salakhutdinov wants to explore is teaching AI software to learn more quickly from few examples and few experiences. Although he did not mention it, his idea would benefit Apple in its race to create better products in less time.

Some AI experts and analysts believe Apple's AI technologies are inferior to competitors like Google or Microsoft because of the company's stricter user privacy rules, which limits the amount of data it can use to train its computers. If Apple used less data for computer training, it could perhaps satisfy its privacy requirements while still improving its software as quickly as rivals.

Visit link:

Apple's Artificial Intelligence Guru Talks About a Sci-Fi Future - Fortune

TORONTO The Canadian and Ontario governments plan to team up with a group of businesses to invest about C$200 million ($150 million) to fund an artificial intelligence institute at the University of Toronto, project organizers said on Tuesday.

Artificial intelligence, widely known as AI, has been touted as an emerging technology with potential to transform industries from healthcare and manufacturing to financial services. Those hopes have attracted Silicon Valley companies like Alphabet Inc's (GOOGL.O) Google and Facebook (FB.O), as well as banks and manufacturers to invest in AI research.

The center, to be known as the Vector Institute, will train large numbers of masters, doctoral and postdoctoral AI scientists who are needed by Canadian industry, said Ed Clark, who will head the institute.

It will also support research projects with potential to move from the laboratory to commercial success, Clark, a former chief executive of Toronto-Dominion Bank (TD.TO), told Reuters in an interview ahead of a government announcement this week about the new center.

"Clearly, the giants in Silicon Valley are going to be major players in this. But that doesn't mean that we can't find things and areas where we end up being best in the class," said Clark, now a business adviser to Ontario Premier Kathleen Wynne.

Clark serves on the board of directors of Thomson Reuters (TRI.TO), the parent of Reuters News.

A majority of the financial commitment will come from the federal and Ontario governments, organizers said. They did not specify when the institute would begin operation.

The federal government committed C$125 million to develop AI industry in its budget last week. A Toronto-based Google spokesman said the company had committed C$5 million to the Vector Institute.

Geoffrey Hinton, an AI scholar known for his work with neural networks, will be the institute's chief scientific adviser.

"This initiative came from the industry. They all know they need to have lots of very skilled people. This is a very fast-moving field and you want the people to be educated by people doing basic research," Hinton said.

He said that government support for AI research and training would encourage large corporations to expand their research labs to Canada.

(Reporting by Denny Thomas; Editing by Jim Finkle and Peter Cooney)

ANKARA A Turkish court halted the activities of online travel agent Booking.com in a court case alleging the website had violated Turkish competition law, the Association of Turkish Travel Agencies (TURSAB) said on Wednesday.

BERLIN Labor leaders at Volkswagen's luxury Audi brand have asked top management to assign production of an all-electric model to the carmaker's main plant in Germany, concerned they might lose out as electric cars gain in importance.

Read the original:

Canadian government, businesses back $150 million artificial ... - Reuters

Published on March 28th, 2017 | by Steve Hanley

March 28th, 2017 by Steve Hanley

Elon Musk sleeps only 6 hours a day. He runs Tesla, which builds automobiles and makes batteries. He also runs SpaceX, which is about to break all the rules of space travel by using a recycled rocket to lift a satellite into space. He is the person who first envisioned the Hyperloop. He wants to build space colonies on Mars so when human beings destroy the earth, a few lucky (and extremely wealthy) souls will have a life boat available. Just last year he decided to start boring tunnels underground in an effort to finally solve the insane traffic snarl that is Los Angeles. In his spare time, he thinks about artificial intelligence.

Musk is pushing hard to make Tesla automobiles the first production cars that can operate without any input from a human driver, a process that requires a supercomputer to make the billions of calculations a second required to drive around without bumping into things. He is the one of the founders, along with Y Combinators Sam Altman, of OpenAI, a project designed to explore how to develop artificial intelligence that doesnt turn on its masters as happened in the movie I, Robot.

At last years Code Conference sponsored by ReCode, Musk had an earful for those in attendance as he talked about the interface between computers and the human brain. Here are part of his remarks as reported by TechCrunch.

The fundamental limitation is input/output. Were already a cyborg, I mean you have a digital or partial version of yourself in the form of your emails and your social media and all the things that you do and you have basically superpowers with your computer and your phone and the applications that are there.

You have more power than the president of the united states had 20 years ago. you can answer any question, you can video conference with anyone anywhere, you can send a message to anyone instantly, you can just do incredible things. But the constraint is input/output. Were I/O bound particularly output bound.

Your output level is so low, particularly on a phone, your two thumbs sort of tapping away. This is ridiculously slow. Our input is much better because we have a high-bandwidth visual interface to the brain, our eyes take in a lot of data. So theres many orders of magnitude difference between input and output. Effectively merging in a symbiotic way with digital intelligence revolves around eliminating the I/O constraint, which would be some sort of direct cortical interface [] a neural lace.

Yesterday, Musk announced a new venture called Neuralink, a California medical research company that will explore how to physically interface computers and the human brain, presumably to speed up the output speed of the brain far beyond the few bytes a second it is capable of now. The new startup company will develop neural lace technology, which is sci-fi shorthand forlinkages that permit humans to seek self improvement through technology connections. A neural lace would involve electrodes that move thought messages from the brain to a computer and back again faster than ever before possible.

In February, Musk told the World Government Summit in Dubai thathumans need to avoid becoming redundant as artificial intelligence becomes more commonplace in our world. Neuralink is Musks first step toward merging humans with software to keep abreast of artificial intelligence innovations. Musk hasnt said so explicitly but he has to be thinking that lots of creative people with enhanced cognitive capabilities could solve many of the global challenges that confront mankind like global warming much more quickly and efficiently.

Musk is not the only one involved in such research. Braintree co-founder Bryan Johnson has created his own startup called Kernel that is also looking into way to improve human cognition. The point, claims The Verge, is not to prevent AI bots from taking over the world but rather to take the first steps toward hacking the brain, so to speak, so that human beings can in the future stay healthier for longer and potentially enjoy the benefits of treating the human brain like a computing platform.

We are a long way from implanting computer chips inside our skulls. Neuroscience still has only the most rudimentary understanding of how the human brain works.People are only going to be amenable to the idea [of an implant] if they have a very serious medical condition they might get help with, Blake Richards, a neuroscientist and assistant professor at the University of Toronto, told The Verge in an interview earlier this year. Most healthy individuals are uncomfortable with the idea of having a doctor crack open their skull.

But Musk is always years, if not decades, ahead of the curve. He can see the day coming where medical science could advance to the point where it would be possible to wire up our brains so they could process information more quickly. A man who thinks about building space colonies on Mars can truly be said to have his eyes on a very distant horizon.

He freely admits that space colonies may not even happen in his lifetime, but that hasnt stopped him from taking the first steps toward getting there. (Musk says the way to warm up Mars to make it habitable for humans is to explode a few hydrogen bombs above the planets surface. After all, he points out, the sun is just a really, really big hydrogen bomb 93 million miles away.)

According to The Verge, Neuralink and Kernel are trying to accelerate progress in this field using a mix of financial resources and a kind of brain trust approach to innovation. The idea is that if you put enough talented people with enough money in one place, you can achieve breakthroughs that otherwise would take years for traditional research organizations. If those people could just learn to turbocharge their own brains by interfacing with computers, those breakthroughs would come all that much faster.

Graphic image credit: TechCrunch

Buy a cool T-shirt or mug in the CleanTechnica store! Keep up to date with all the hottest cleantech news by subscribing to our (free) cleantech daily newsletter or weekly newsletter, or keep an eye on sector-specific news by getting our (also free) solar energy newsletter, electric vehicle newsletter, or wind energy newsletter.

Tags: artificial intelligence, brain research, Bryan Johnson Kernel founder, Code Conference, Elon Musk, human to computer interface, neural lace

Steve Hanley writes about the interface between technology and sustainability from his home in Rhode Island. You can follow him onGoogle +and onTwitter.

Read more from the original source:

Elon Musk Invests In Neuralink, A New Artificial Intelligence Company - CleanTechnica

The rise of mobile and online transactions and online-only challenger banks and startups, has introduced ravenous new competition and turned existing business models upside down.

Today, everything is commoditized, and with businesses struggling to compete on price and product, delivering a better customer experience has become the key differentiator. Next-generation technologies will become crucial in this battle for consumer loyalty. For example, consumers are frustrated by needing to remember numerous passwords, or having to go through multiple identification steps when they reach a brands customer service team.

Automation, particularly AI-driven automation, is already helping reduce costs and improve the customer experience. Very soon, we will also see artificial intelligence (AI) driven voice activation taking over a range of customer interactions. Voice activated services such as Siri and Alexa are also becoming commonplace, which is normalizing the use of voice as a means of communicating with machines.

In payments and financial services, the natural evolution for this is voice biometrics, which is set to become one of the most crucial tools in customer experience in the years ahead, providing customers with a convenient way to interact without the need for lengthy and inconvenient login processes, and with no additional customer authentication required.

For instance, a large bank headquartered on the west coast has developed a voice-driven payments system that combines biometric authentication, natural language processing and artificial intelligence technologies, to allow more complex actions such as transferring funds without having to type a single character. This means that transactions are secure, as they can only be verified by the users unique voice, whilst remaining convenient for the customer. The banks customer hub can give more time with agents to handle more complex interactions, such as a mortgage application.

Eventually, this kind of technology will even be able to upsell to customers, and offer highly personalized financial advice without the need for a human agent to be present. For customers, these technologies result in faster issue resolution, lower wait times, reduced customer effort and improved customer satisfaction.

The cost-savings are compelling, since live customer support agents normally charge by the hour, even a 30 second reduction in call time results in significant cost savings. Completing an entire customer service interaction via virtual means is even more cost effective.

The industry is advancing in leaps and bounds in an attempt to meet the needs of younger generations, and voice recognition is just the tip of the iceberg. The near future will see augmented and virtual reality, and even holograms in use, as companies look to meet the rising expectations of consumers. Its up to all organizations to ensure they are on the right side of the curve.

Ashish Koul is senior vice president and general manager at Servion Global Solutions.

See the rest here:

PayThink A slow embrace of artificial intelligence and voice loses the next generation - PaymentsSource

The future of artificial intelligence: The machines are taking over Phoenix Business Journal Ah, I was researching some companies there and it thought I might visit so it is offering hotels. This happens all the time online. It also happens behind the scenes with business software, engineering tools, and industrial machines. It is artificial ...

More:

The future of artificial intelligence: The machines are taking over - Phoenix Business Journal

Treasury Secretary Steven Mnuchin last week made a dangerously ignorant prediction. When asked about the future of artificial intelligence, automation and the workforce, this was his reply: "It's not even on our radar screen, he said at a media event, adding that significant workforce disruption due to AI is 50 to 100" years away. "I'm not worried at all" about robots displacing humans in the near future, Mnuchin said. "In fact I'm optimistic."

The Trump administration has repeatedly rejected evidence-based research and objective analysis on issues that include climate and human biology. When confronted with a complicated technology, like machine learning, administration officials now appear to be rejecting curiosity, too.

If Mnuchin was speaking in earnest, then we apparently have a Treasury secretary who, like the president he serves, does not read. To argue that were 50 to 100 years away from AI and automation tells us that Mnuchin took office without bothering to look over any of the economic studies or policy papers about the future of technology and the American workforce written during the past four decades. It means that, somehow, hes missed thousands of news stories published in the American Spectator, the Economist, the Washington Post, the Wall Street Journal, the National Review, this newspaper and elsewhere. It means that Mnuchin hasnt even been paying attention to the future of his own industry otherwise hed know that JPMorgan Chase CEO Jamie Dimon, in his 2016 annual letter to shareholders, cited AI as a significant threat to jobs in the banking sector.

Mnuchins statements also reveal that he is willfully and actively ignoring critical signals about our future. He suffers from the paradox of the present the delusion that the way things are now will continue indefinitely. Because he lacks direct exposure to the enormously complicated AI ecosystem, he outright rejects its reality.

Like his buddies in the Cabinet, he is obsessed with making America great again with trying to rebuild the past. He is foolishly optimistic that we can all return to whats familiar and comfortable.

And yet heres what we already know is on the horizon. The same week that President Trump climbed into the seat of a big rig wearing an I TRUCKS lapel pin, two autonomous trucking companies, Otto and Embark, were readying their driverless vehicles for the open road. The machine learning algorithms and sensors that power the autopilot capabilities in cars and trucks already exist today. In the near future, humans may still be needed to navigate local roads and parking lots, but it wont take long certainly not 50 to 100 years before American truck drivers need to start looking for other work. And, yes, there are many possible roadblocks along the way to full autonomy for example, government restrictions seeking to protect truckers jobs but were speeding toward a scenario in which Americas 3.5 million professional truck drivers wont be needed anymore.

As an AI-denier, Mnuchin is also disregarding the millions of other jobs in adjacent industries that are at risk. Once we no longer have truck drivers, well have no need for truck stops, which will hurt the bottom line of fast food chains, soda and water companies, vending machine operators, and even the restroom supply chain (toilet paper, soap, paper towels). With autonomous cars and trucks, we wont need the Highway Patrol, either. Companies that supply radar guns will go under unless they find another product to produce. There will be a sharp decline in speeding and parking tickets issued, which will cut into city and state budgets. If we no longer have speeding tickets, well have no need for traffic court and all of the lawyers, judges, magistrates and clerks who currently earn their livings there.

But AI wont just affect driving, of course, it will soon affect every worker who processes transactions, and that includes white collar jobs. Already, machines are out-performing humans at storied investment banks like Goldman Sachs, where four traders can be replaced by one computer engineer and a handful of complex trading algorithms with machine-learning capabilities. According to a January study by the McKinsey Global Institute, nearly 23% of a lawyers job can be automated right now. Legal startup Ross, which leverages IBM Watsons AI technology, takes just seconds to process a research request that would require a high-paid human lawyer 10 hours.

AI will not obviate all jobs. As the ecosystem matures we will create millions of new jobs with titles that dont yet exist: Well need medical data specialists, engineers who can securely encrypt the cloud, congressional staffers with technical expertise, skilled workers who know how to prevent and fix glitches in the system, even ethicists who will help machines learn to make decisions.

Its plausible that Mnuchin already knows all of this and was optimistic about our American grit and adaptability. Or maybe he is disconnected from reality. Or perhaps he was simply employing the Trump administrations default position: placating the masses with a blanket assurance that everything will be OK. Regardless, you should be concerned.

Amy Webb is the author of The Signals Are Talking: Why Todays Fringe Is Tomorrows Mainstream and is the chief executive of the Future Today Institute.

Follow the Opinion section on Twitter @latimesopinion or Facebook

MORE FROM OPINION

Note to Republicans: Drop the crusade against Planned Parenthood

California was once a bastion of xenophobia and racism. If we can change, so can the rest of the country

Who's to blame for Trump's failures? Must be Paul Ryan

See the original post here:

Trump's Treasury secretary is an Artificial Intelligence denier - LA ... - Los Angeles Times