Category Archives: Artificial Intelligence

The human brain is the most powerful supercomputer on Earth, and now researchers from the University of Southern California are taking inspiration from the structure of the human brain to make better artificial intelligence systems.

Artificial intelligence (or AI) is a system of computing that aims to mimic the power of the human brain.We have more than 100 trillion neurons, or electrically conducting cells in our brain, that give us the incredible computing power for which we are known.Computers can do things like multiply 134,341 by 989,999 really well, but they can't do things like recognize human faces or learn or change their understanding of the world.At least not yet, and that's the goal of AI:to devise a computer system that can learn, process images and otherwise be human-like.

Very good question! Part of this answer is:why not?AI istheholy grail for computerscientists who want to make a computer as powerful asthe human brain. Basically, they want to create a computer that doesn't need to be programmed with all the variables becauseit can learn them just like our brain does.

A six-foot-tall, 300-pound Valkyrie robot is seen at University of Massachusetts-Lowell's robotics center in Lowell, Mass. "Val," one of four sister robots built by NASA, could be the vanguard for the colonization of Mars by helping to set up a habitat for future human explorers. (Elise Amendola/Associated Press)

Another reason scientists are interested in AI is thatit could be used for things like surveillance and face recognition, and having computer systems that can learn new terrain or solve a new problem somewhat autonomously, which, in certain situations, could be very beneficial.

In order to fully mimic the power of our own cognitive capacity, we have to first understand how the brain works, which is a feat in and of itself.We have to re-engineer and re-envision the computer to be completely different from hardware to software and everything in between, and the reason we have to do this has to do with how our brains are powered.

"If we compare, for example, our brain to the super computers we have today,they run on megawatts, [which is] a huge amount of power that's equivalent to a few hundred households, while our brain only relies on water and sandwiches to function," saidartificial intelligence and computing expert Han Wang from the University of Southern California said."It consumes power that's equivalent to a light bulb."

So you see the incredible efficiency of millions of years of evolution on our brain means we have learned to work with limited resources and become so power-efficient that we can beat a supercomputer for complex processing without breaking the energy bank.

This is where the main difference between the brain and the computer lie.

"Our current computers, there's a very powerful corebut then you have a long queue of tasks [which]come in sequentially and are processed sequentially," Wang said. "While our brain, the computation of units, which are the neurons, are connected in highly parallel manner. It's this high level parallelism that has advantages in learning and recognition."

So it's the parallelism in the brain that allows us to use only what we need only when we need it, and to not waste energy on running background processes that we all know slow down our computing power.

It's this concept of running at low energy in parallel circuits. The key to this is to make computer circuits more complex in the messagesthey can send.

In a typical computer, we think that each node sends a one or a zero, and then there's a series of ones and zerosuntil a program is made.

In the brain, it's a very small circuit and they can send a onewhich means go, a zerowhich means no signal, and/ora two that says stop, or both a one and a twoat the same time.

Artificial intelligence could be beneficial in situations where robots need to make quick decisions, like how to maneuver unknown terrain. (Boston Dynamics)

In other words, our brains can send double the information in any given exchange compared to a computer, and that, coupled with smaller networks working in parallel, reduces the power strain.

What Wang and colleagues did was to create a system of wires that connect using tin selenite and black phosphate that can send, stop, go, do nothing, or do both signals, dependingon the voltage sent.

Nowthe plan is to re-engineer the computer from the ground up andbuild a computer that has the capacity for these low voltage decisions that aren't wired through these few cores that we see today, but instead with each circuit of messages working in parallel like the brain does.

Until recently, this was a theoretical concept because there was really no way to send as much information in a single transmission as we have now.

So, artificial intelligence is only a few incredible brilliant research careers away from a reality.

See the original post here:

Human brain structure inspires artificial intelligence - CBC.ca

Twice in the space of six weeks, the world has suffered major attacks of ransomware malicious software that locks up photos and other files stored on your computer, then demands money to release them.

It's clear that the world needs better defenses, and fortunately those are starting to emerge, if slowly and in patchwork fashion. When they arrive, we may have artificial intelligence to thank.

Ransomware isn't necessary trickier or more dangerous than other malware that sneaks onto your computer, but it can be much more aggravating, and at times devastating. Most such infections don't get in your face about taking your digital stuff away from you the way ransomware does, nor do they shake you down for hundreds of dollars or more.

Despite those risks, many people just aren't good at keeping up with security software updates. Both recent ransomware attacks walloped those who failed to install a Windows update released a few months earlier.

Watchdog security software has its problems, too. With this week's ransomware attack , only two of about 60 security services tested caught it at first, according to security researchers.

"A lot of normal applications, especially on Windows, behave like malware, and it's hard to tell them apart," said Ryan Kalember, an expert at the California security vendor Proofpoint.

HOW TO FIND MALWARE

In the early days, identifying malicious programs such as viruses involved matching their code against a database of known malware. But this technique was only as good as the database; new malware variants could easily slip through.

So security companies started characterizing malware by its behavior. In the case of ransomware, software could look for repeated attempts to lock files by encrypting them. But that can flag ordinary computer behavior such as file compression.

Newer techniques involve looking for combinations of behaviors. For instance, a program that starts encrypting files without showing a progress bar on the screen could be flagged for surreptitious activity, said Fabian Wosar, chief technology officer at the New Zealand security company Emsisoft. But that also risks identifying harmful software too late, after some files have already been locked up.

An even better approach identifies malware using observable characteristics usually associated with malicious intent for instance, by quarantining a program disguised with a PDF icon to hide its true nature.

This sort of malware profiling wouldn't rely on exact code matches, so it couldn't be easily evaded. And such checks could be made well before potentially dangerous programs start running.

MACHINE VS. MACHINE

Still, two or three characteristics might not properly distinguish malware from legitimate software. But how about dozens? Or hundreds? Or even thousands?

For that, security researchers turn to machine learning, a form of artificial intelligence. The security system analyzes samples of good and bad software and figures out what combination of factors is likely to be present in malware.

As it encounters new software, the system calculates the probability that it's malware, and rejects those that score above a certain threshold. When something gets through, it's a matter of tweaking the calculations or adjusting the threshold. Now and then, researchers see a new behavior to teach the machine.

AN ARMS RACE

On the flip side, malware writers can obtain these security tools and tweak their code to see if they can evade detection. Some websites already offer to test software against leading security systems. Eventually, malware authors may start creating their own machine-learning models to defeat security-focused artificial intelligence.

Dmitri Alperovitch, co-founder and chief technology officer at the California vendor CrowdStrike, said that even if a particular system offers 99 percent protection, "it's just a math problem of how many times you have to deviate your attack to get that 1 percent."

Still, security companies employing machine learning have claimed success in blocking most malware, not just ransomware. SentinelOne even offers a $1 million guarantee against ransomware; it hasn't had to pay it yet.

A FUNDAMENTAL CHALLENGE

So why was ransomware still able to spread in recent weeks?

Garden-variety anti-virus software even some of the free versions can help block new forms of malware, as many are also incorporating behavioral-detection and machine-learning techniques. But such software still relies on malware databases that users aren't typically good at keeping up to date.

Next-generation services such as CrowdStrike, SentinelOne and Cylance tend to ditch databases completely in favor of machine learning.

But these services focus on corporate customers, charging $40 to $50 a year per computer. Smaller businesses often don't have the budget or the focus on security for that kind of protection.

And forget consumers; these security companies aren't selling to them yet. Though Cylance plans to release a consumer version in July, it says it'll be a tough sell at least until someone gets attacked personally or knows a friend or family member who has.

As Cylance CEO Stuart McClure puts it: "When you haven't been hit with a tornado, why would you get tornado insurance?"

Read the rest here:

How artificial intelligence is taking on ransomware - ABC News

IBM is convinced that its Watson supercomputer is capable of doing a whole lot more than winning at Jeopardy--and the company wants to make sure it stays that way.

To that end, IBM is making a push this week to urge lawmakers not to fall victim to artificial intelligence fear mongering. David Kenny, IBM Watson's senior vice president, sent a letter to Congress on Tuesday stressing the importance of pushing A.I. forward instead of restricting it. According to Recode, he's meeting with a group of Representatives today to discuss the technology.

"When you actually do the science of machine intelligence," Kenny wrote in the letter, which IBM published Tuesday, "and when you actually apply it in the real world of business and society ... you understand that this technology does not support the fear-mongering commonly associated with the AI debate today."

Kenny argued that fears of "massive job loss, or even an eventual AI 'overlord' " are overblown. "I must disagree with these dystopian views," he wrote. "The real disaster would be abandoning or inhibiting cognitive technology before its full potential can be realized."

IBM has an interest in ensuring that the government chooses not to restrict the use of artificial intelligence. While the Watson system is perhaps most famous for beating Jeopardy champ Ken Jennings in 2011, it's since been applied to a variety of tasks. Watson is used to recommend treatments for patients in medical facilities including the Cleveland Clinic and New York's Sloan-Kettering Cancer Center. H&R Block has begun using Watson to prepare client's tax returns. In April, the software was applied to the Masters golf tournament, letting online viewers quickly see the most exciting highlights, which it selected automatically based on factors like crowd noise and player reactions.

Even so, in recent months, some in the A.I. world have expressed surprise that Watson isn't further along in its capabilities, given what it did six years ago. The company's ambitions for more widespread applications of its tech mean the company has a lot at stake.

As A.I.'s abilities expand to tasks like driving, reading X-rays, diagnosing illnesses, and performing paralegal work--all of which it's already capable of doing on some level--millions of jobs could be lost. Recent expert predictions on the number of jobs lost have ranged from from 6 percent by 2021 to 50 percent by 2035.

Yet IBM is the latest A.I. company to assure the public that its fears of the technology are overblown. Adam Cheyer, co-founder of Apple's Siri and virtual assistant A.I. startup Viv, compared the fears that A.I. will become too smart to worrying about overpopulation on Mars. "We're barely at the beginning of A.I.," he said. "There's nothing to even be done yet."

Last month, Jeff Bezos, whose popular Amazon Alexa relies heavily on A.I., said during a chat at the Internet Association that the problem with artificial intelligence is that we don't have more of it. "Basically," he said, "there's no institution in the world that cannot be improved with machine learning."

Google co-founders Larry Page and Sergey Brin have also spoken out in defense of A.I.

Meanwhile, there's also a vocal group within the tech industry that errs on the fear-mongering side. A recent survey of academics and industry leaders found they believe, on average, that A.I. will be capable of performing any task--from driving trucks to writing novels--better than humans by 2060.

Elon Musk, whose Tesla vehicles rely on artificial intelligence, soon chimed in with the notion that this would happen closer to 2030. "I hope I'm wrong," he tweeted.

Other Silicon Valley giants have warned against the technology. Peter Thiel co-founded OpenAI, a non-profit to ensure A.I.'s safe use, along with Musk. Earlier this year, Bill Gates suggested that robot taxes could help slow the loss of jobs to automation. And Tim Berners-Lee, inventor of the world wide web, recently warned that A.I. could one day replace financial institutions and control the world economy.

The lobbying push from IBM comes about a month after the formation of the Congressional Artificial Intelligence Caucus, a group of Representatives that will study A.I. and seek to create policies related to its use and implementation. Congressman John K. Delaney of Maryland, one of the group's co-founders, recently met with Amazon and Google, according to CNBC. The meeting with IBM on Wednesday is the group's next step.

See original here:

IBM Watson Heads to Washington to Argue That Artificial Intelligence Isn't Really That Bad - Inc.com

From Alexa and self-driving cars to job applicant screening processes, artificial intelligence is fast becoming the norm in business. But it also could start playing far bigger roles in security, helping law enforcement and other protective agents figure out who's up to no good. As Fredrick Kunkle of The Washington Post reports, there's now an AI-based kiosk designed to detect whether travelers are fibbing.

Designed by Aaron Elkins, assistant professor of the Fowler College of Business Administration at San Diego State University, the new AI lie detector goes by the name Automated Virtual Agent for Truth Assessments in Real Time, or AVATAR for short. Once you've scanned your ID or passport, the kiosk asks you a bunch of questions. The inquiries are a good mix of inquiries you could practice (e.g., when were you born) and questions that might throw you if you're faking it (e.g., describe what you did today). You can see some of the process in the video below:

If everything goes well, security personnel should let you go on your way. If the results suggest you're being dishonest, security personnel might detain you for questioning or a search.

As you answer questions from AVATAR, the system uses sensors to gather data your body gives off. More specifically, the system looks at factors like voice (tone, pronoun use, etc.), pupil dilation and eye movement, facial expression (e.g., engagement of muscles around the corners of the eyes and mouth in a Duchenne smile) and posture. The theory is that it takes less effort to tell the truth than to maintain a faade. You subconsciously reveal that effort through physical cues, many of which researchers are still studying and pinning down. The AI is a big step forward from traditional polygraphs, which aren't practical for general, large-scale screening, use more limited physiological data (e.g, heart rate) and generally aren't considered very reliable.

In theory, AVATAR could become a widely applied staple in local law enforcement agencies around the world, helping police sort out a variety of conflicts. But its main intent is for border security checkpoints and airports. These facilities are of concern in part because of the high traffic they receive. But they are also worry points because of the current worldwide focus on terrorism. Although these types of attacks can come from many different individuals or groups and can be domestic or foreign in nature, the increasing activity of the Islamic State of Iraq and Syria (ISIS) has been particularly alarming for leaders around the globe. Attacks have led U.S. President Donald Trump, for instance, to call for a controversial travel ban against travelers from six majority-Muslim countries. AVATAR might one day help screen out individuals associated with ISIS or similar groups.

Right now, AVATAR is still in its infant stages. It's only collecting research data at border crossings in Mexico and Romania. But even at this point, it's a beautiful demonstration of how science and technology can blend toward a practical social good.

More:

This Artificial Intelligence Kiosk Will Spot Liars at Airports, Keep You Safer - Inc.com

The Curiosity Mars Rover is now smart enough to pick its own targets for exploration, according to a new study.

The secret to Curiosity's better brain was a software update sent from the ground in October 2015, called the Autonomous Exploration for Gathering Increased Science (AEGIS). This was the first time artificial intelligence had been tried on a remote probe, and the results have shown that similar AI techniques could be applied to future missions, according to the NASA scientists working on the project.

AEGIS allows the rover to be "trained" to identify rocks with certain characteristics that scientists on the ground want to investigate. This is valuable because Curiosity's human controllers can't be in direct contact with the rover all the time. Instead of waiting for instructions to "go there and sample that piece of rock," Curiosity can now look for targets even when it isn't in contact with its human controllers, according to a new study that describes Curiosity's use of the software. [Amazing Mars Rover Curiosity's Latest Photos]

"We can't be in constant contact with the rover Mars rotates and when [Curiosity is] on the far side we can't get in touch with it," Raymond Francis, lead system engineer for the deployment of AEGIS, told Space.com.

According to the study, once the AEGIS system was deployed, it was used 54 times between May 13, 2016, and April 7, 2017. Without intelligent targeting, Curiosity could be expected to hit a target the scientists were interested in about 24 percent of the time; with AEGIS, the rover managed 93 percent, according to the study.

Even when the rover is in contact, the signals from Earth to Mars take time to get there and back. In May 2016, Mars was the closest it had been to Earth in 11 years 46.8 million miles. A radio signal would take just over 4 minutes to get there and four more to get back. So if there is something planetary scientists want a closer look at, it can take a while to send the commands.

Idle time is often lost science time for the rover mission, and because sending a robot to Mars is expensive and difficult, it's not ideal. A few hours hanging around each day may not seem like much, but it adds up over the course of an entire mission. With AEGIS, the rover could drive to a location, choose targets for investigation and gather data while it waits for radio contact with Earth again. That means Earth-bound scientists are free to choose a new target once they re-establish contact with the rover.

For the study, the NASA team trained Curiosity, with the AEGIS software, to analyze bedrock in a feature called the Murray formation after each drive. The Murray formation is a rocky outcrop with characteristic bands of mudstone, possibly laid down by lakes of liquid water. One question the scientists wanted to answer was whether the chemical composition of the Murray formation changed over time, because that could reveal changes in the water chemistry, divulging more about the history of water on Mars.

This analysis of the Murray formation required taking many samples of the mudstone, but doing them would take time away from other experiments and observations. With AEGIS, Curiosity took care of these repetitive observations when it was out of touch with Earth, and researchers would not be using it for more advanced tasks. One could use AEGIS to train Curiosity to look for other types of rock, Francis said.

The AEGIS system works by using two of the rover's cameras, the Chemistry and Camera instrument (ChemCam) and the navigation cameras. The software uses images captured by the cameras, and tries to recognize edges of objects in the frame, and looks for edges that connect to create a "loop."

"If you find edges that close into a loop you've found an object and on Mars that's usually a rock," Francis said. AEGIS can also look at the relative brightness of the various objects in the frame (the navigation cameras don't have color vision). The combination of edges and brightness allows AEGIS to identify objects.

The science team will have criteria for what kinds of things count as interesting for example, brightly colored bedrock and the rover can then use the cameras to "choose" a target. The ChemCam can then use a powerful instrument called a laser spectrometer that uses light to find out what a target is made of.

There are limitations to AEGIS' abilities; for example, the rover sometimes identified a rock's shadow as part of the rock's outline. Even so, the software has proved a useful tool, the study said.

Francis notes that the autonomy will likely become a fixture for many future robotic missions.

"The farther you go in the solar system, the longer the light time delay, the more decisions need to be made on the spot," he said.

The study appears in the June 21 issue of the journal Science Robotics.

Follow us @Spacedotcom , Facebook and Google+ . Original article on Space.com .

Read more here:

Curiosity Mars Rover is now using artificial intelligence to pick its own targets - Fox News

A look at TurboPatents new RoboReview service. (TurboPatent Photo)

Seattle startup TurboPatent is releasing a pair of new products designed to improve the patent application process, with help from artificial intelligence.

TurboPatent, which raised $1.4 millionin funding earlier this year, focuses on corporations and law firms, automating taskslike formatting or document preparation, for example, freeing up people to work on more complex, high-value work. The service is designed to cut costs, save time and lead to more accurate patent documentation.

The new products are called RoboReview and RapidResponse. RoboReview uses AI and predictive analytics to automatically analyze and review draft patent applications. The company says this will reduce to seconds a process that can normally take several people multiple days to complete. RapidResponse helps speed upoffice actions,written correspondence between an applicant and patent examiner during the application process.

Like any procedure that involves people, the review process is subject to human error, said TurboPatent CEO James Billmaier. TurboPatent automates the most tedious and time-consuming parts of the process, which drastically cuts down on the likelihood of potential issues going unnoticed. That leaves humans to decide whether or not to submit a patent and if so, what alterations need to be made.

Formerly known as Patent Navigation, TurboPatent has an experienced team led by co-foundersBillmaier and Charles Mirho. Billmaier was previously CEO of Melodeo, a cloud-based media platform company that sold to HP in 2010. He also teamed up with Paul Allen in 1999 to launchhome-entertainment technology company Digeo, which was eventually sold in 2009 to ARRIS Group Inc.

Mirho, meanwhile, is a patent law veteran, having worked as a patent counsel at Intel and later as a managing partner of a patent law firm. He also has a computer science degree from Rutgers.

Go here to see the original:

TurboPatent aims to improve the patent process with new artificial ... - GeekWire

Faculty and staff from several schools at Carnegie Mellon University are joining forces in an effort to accelerate the science of Artificial Intelligence.

University leaders said they hope that by pulling together more than 100 faculty through the creation of CMU AI, it will maintain the universitys role as a leader in the field.

CMU School of Computer Sciencedean Andrew Moore said the confederation of faculty and students from various disciplines, which will allow the school to offer what he calls full stack education and research.

That means [the students] need to be able to hang out and work on projects in labs not just with the technology experts on specific parts of AI, like machine learning or computer vision, but they have seen examples of putting everything together, Moore said.

Moore said the university has been able to build great AI systems that combine technologies from several different disciplines. However, they have been dependent on individual faculty members and students with a special vision.

Were relying on the fact tat weve got a few smart faculty and students whove got the way to do this sitting in their head, Moore said. Its almost an art rather than a technology. By creating CMU AI, were going to turn this into a technology stack so we can educate eventually hundreds of thousands of people to be able to assemble these large robot systems.

The idea was born about a year ago when a group of potential students told him they were impressed by CMU but they wanted to learn about artificial intelligence, not just its parts.

Artificial Intelligence is slowly becoming a part of our every day lives, and Moore said the nation needs this type of confederation to lead the way. He said the mix of educators, researchers and commercial endeavors, such as Uber, in Pittsburgh puts the city in a unique position to be the worlds leader.

View post:

New Artificial Intelligence Hub At CMU Aims To Make Pittsburgh A World Leader In AI - 90.5 WESA

Machines capable of carrying out tasks normally reserved for humans will boost global GDP by as much as 14 percent by 2030, according to PwC.

In a report, the global auditing and consulting firm argued that the widespread adoption of artificial intelligence (AI) can contribute $15.7 million to the world economy over the next decade, the equivalent of the current combined output of China and India, as it would vastly increase productivity and spur shoppers to spend more. (See also: Artificial Intelligence.)

According to the firm's calculations, the bulk of these gains, $9.1 trillion, will be generated by consumption-side effects. Shoppers, driven to work by autonomous cars, are expected to use their extra time and resources to buy personalized and higher-quality goods. The U.S. is forecast to be a major beneficiary of this trend PwC reckons that consumption patterns triggered by AI will add $3.7 trillion to the North American economy. (See also: Self-Driving Vehicles Will Create 'Passenger Economy' Worth $7 Trillion: Study.)

China is predicted to be an even bigger beneficiary, particularly as its heavily dependent on manufacturing, an industry that is expected to receive a huge economic boost from the introduction of automated robotic workforces.

The mindset today is man versus machine, Anand Rao, an AI researcher at PwC in Boston, said following the release of the report, according to Bloomberg. What we see as the future is man and machine together can be better than the human.

One of the biggest controversies surrounding AI, aside from fears that robots might one day malfunction and turn on humans, is the risks they pose to jobs. While automated manufacturing processes might save companies plenty over the long-run, it is also true that this process could come at the expense of employees, potentially leaving billions of people out of work.

Some 42 percent of the $15.7 trillion that PwC claims AI can pump into the global economy is expected to be generated by automated machinery in the workplace. If this phenomenon leads to mass job losses, as many predict, the reports argument of stronger consumption patterns suddenly appears less convincing. (See also: Buffett Slams Wealth Inequality, Calls GOP Health Bill 'Relief for the Rich'.)

PwC thinks otherwise, arguing that new jobs will be created to coincide with the rising adoption of AI. The adoption of no-human-in-the-loop technologies will mean that some posts will inevitably become redundant, but others will be created by the shifts in productivity and consumer demand emanating from AI, and through the value chain of AI itself, said the report. In addition to new types of workers who will focus on thinking creatively about how AI can be developed and applied, a new set of personnel will be required to build, maintain, operate, and regulate these emerging technologies.

Original post:

Artificial Intelligence Will Add $15.7 Trillion to the Global Economy: PwC - Investopedia

Carnegie Mellon University is revamping the way it teaches artificial intelligence.

The universitys computer science department debuted Tuesday its CMU AI initiative intended to better prepare students for entering the workforce.

The goal is to train students to build complex software systems or powerful robots that utilize multiple different AI technologies, whether it be machine learning tech to help those systems learn from data or technology that helps robots see and perceive the world similar to humans.

Get Data Sheet , Fortunes technology newsletter.

There is a real science to building these things, said CMU dean of computer science Andrew Moore.

While its common for students or faculty members to concentrate on one specific subset of AI, like machine learning, those subsets represent only one portion of a finalized product, like a robot or digital assistant like Apples ( aapl ) Siri.

However, there hasnt been a standardized way to develop these complex projects that require multiple AI technologies to function together. Similar to how building a skyscraper requires people with expertise in diverse fields like structural engineering and concrete mixing, building powerful software like Siri or robots requires people with expertise in many different areas of AI.

We have done a good job of covering all the component parts, Moore said of teaching different subsets of AI like machine learning and computer vision. We need to be thinking very seriously about the science of putting it all together.

Students who know how to piece together all the various AI components into functioning software will have better chances at landing high-caliber jobs. Moore said that when he worked at Google ( goog ) where he was once a vice president of engineering and a director in Google's Pittsburgh officehe learned how rare and how desperate it was to find people who have these skill sets.

Moore says he hopes the new AI initiative will better train students to fill the labor pipeline.

Continue reading here:

Carnegie Mellon's AI Program Aims to Better Prepare Students for the Changing Workforce - Fortune

By

June 27, 2017, 7:01 PM EDT

Stop worrying that robots will steal your job. Artificial intelligence is actually set to boost to the British economy, according to PricewaterhouseCoopers LLP.

The majority of the U.K.s economic gains over the period to 2030 will come from increasing consumer demand thanks to AI driving a greater choice of products, increasing personalization and making them more affordable over time, PwC research published Wednesday shows.

QuickTake: Artificial Intelligence

Exclusive insights on technology around the world.

Get Fully Charged, from Bloomberg Technology.

While we expect that the nature of jobs will change and that some will be susceptible to automation, our research shows that the boost to U.K. GDP that AI-driven products and services will bring will also generate significant offsetting job gains, PwC economist Jonathan Gillham said. Automating the more mundane and repetitive aspects of peoples jobs will also increase the U.K.s productivity and boost real wages.

While Britain could see a 10 percent increase in gross domestic product through 2030, the nations likely to see the biggest upswings are China and North America, which will be boosted 26 percent and 14.5 percent, PwC said. It sees the biggest gains in the retail, financial services, and health care sectors.

Read the original here:

Artificial Intelligence Could Add 10% to UK GDP, PwC Says - Bloomberg