Category Archives: Artificial Intelligence

In BriefThe line between human and artificial intelligence isincreasingly blurring. When AI software isn't too busy beatinghumans at their favorite games, they are also finding time tocompose music, write movies, and edit film trailers. Soon no onemay be able to point out AI artists amidst humans. Intelligence Challenge

Lets start with a little challenge: which of the following tunes was composed by an AI, and which by an HI (Human Intelligence)?

Ill tell you at the end of the answer which tune was composed by an AI and which by an HI. For now, if youre like most people, youre probably unsure. Both pieces of music are pleasing to the ear. Both have good rhythm. Both could be part of the soundtrack of a Hollywood film, and you would never know that one was composed by an AI.

And this is just the beginning.

In recent years, AI has managed to

Now, dont get me wrong: most of these achievements dont even come close to the level of an experienced human artist. But AI has something that humans dont: its capable of training itself on millions of samples, and constantly improve itself. Thats how Alpha Go, the AI that recently wiped the floor with Gos most proficient players, got so good at the game: it played a few million games against itself, and discovered new strategies and best moves. It acquired an intuition for the game, and kept rapidly evolving to improve itself.

And theres no reason that AI wont be able to do that in art as well.

In the next decade, well see AI composing music and even poems, drawing abstract paintings, and writing books and movie scripts. And itll get better at it all the time.

So what happens to art, when AI can create it just as easily as human beings do?

For starters, we all benefit. In the future, when youll upload your new YouTube clip, youll be able to have the AI add original music to it, which will fit the clip perfectly. The AI will also write your autobiography just by going over your Facebook and Gmail history, and if you want will turn it into a movie script and direct it too. Itll create new comic books easily and automatically both the script and the drawing and coloring part and whats more, itll fit each story to the themes that you like. You want to see Superman fighting the Furry Triple-Breasted Slot Machines of Pandora? You got it.

Thats what happens when you take a task that humans need to invest decades to become really good at, and let computers perform it quickly and efficiently. And as a result, even poor people will be able to have a flock of AI artists at their beck and call.

At this point you may ask yourselves what all the human artists will do at that future. Well, the bad news is that obviously, we wont need as many human artists. The good news is that those few human artists who are left, will make a fortune by leveraging their skills.

Let me explain what I mean by that. Homer is one of the earliest poets we know of. He was probably dirt poor. Why? Because he had to wander from inn to inn, and could only recite his work aloud for audiences of a few dozen people at the time, at most. Shakespeare was much more succesful: he could have his plays performed in front of hundreds of people at the same time. And Justin Bieber is a millionnaire, because he leverages his art with technology: once he produces a great song, everyone gets is immediately via YouTube or by paying for and downloading the song on iTunes.

Great composers will still exist in the future, and they will work at creating new kinds of music and then having the AI create variations on that theme, and earning revenue from it. Great painters will redefine drawing and painting, and they will teach the AI to paint accordingly. Great script writers will create new styles of stories, whereas the old AI could only produce the old style.

And of course, every time a new art style is invented, itll only take AI a few years or maybe just a few days to teach itself that new style. But the human creative, crazy, charismatic artists who created that new style, will have earned the status of artistic super-stars by then: the people who changed our definitions of what is beautiful, ugly, true or false. They will be the people who really create art, instead of just making boring variations on a theme.

The truly best artists, the ones who can change our outlook about life and impact our thinking in completely unexpected ways, will still be here even a hundred years into the future.

Oh, and as for the two tunes? The first one was composed by a human being and performed by Morten Faerestrand in his YouTube clip 3 JUICY jazz guitar improv tools. The second was composed by the Algorithmic Music Composer and demonstrated in the YouTube clip Computer-Generated Jazz Improvisation.

Did you get it right?

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Artificial Intelligences are Quickly Becoming Better Artists - Futurism

How AI is changing the nature of cybersecurity (iStock) How AI is changing the nature of cybersecurity (iStock)

Speed of Change

Published Friday, Jun. 09, 2017 1:08PM EDT

Last updated Friday, Jun. 09, 2017 1:08PM EDT

With the rise of cloud-based apps and the proliferation of mobile devices, information security is becoming a top priority for both the IT department and the C-Suite. Organizations enthusiastic about the Internet of Things (IoT) are equally guarded as global cyberattacks continue to dominate headlines.

Businesses ranging from startups to large corporations are increasingly looking to new technologies, like artificial intelligence (AI) and machine learning, to protect their consumers. For cybersecurity, AI can analyze vast amounts of data and help cybersecurity professionals identify more threats than would be possible if left to do it manually. But the same technology that can iimprove corporate defences can also be used to attack them.

On Wednesday, June 28 at 11:30 a.m. (ET), Aleksander Essex will join us for a live discussion on the impact of artificial intelligence on cybersecurity. Essex is the head of Whisper Lab, a cyber-security research group at Western University, and an associate professor of software engineering and a speciality in cryptography.

To leave a question or comment in advance of the discussion, please fill in the comment field below.

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Q&A: How artificial intelligence is changing the nature of cybersecurity - The Globe and Mail

Artificial intelligence researchers at Facebook have figured out how to train their AI models for image recognition at eye-popping speeds.

The company announced the results of the effort to speed up training time at the Data@Scale event in Seattle this morning. Using Facebooks custom GPU (graphics processing unit) hardware and some new algorithms, researchers were able to train their models on 40,000 images a second, making it possible to get through the ImageNet dataset in under an hour with no loss of accuracy, said Pieter Noordhuis, a software engineer at Facebook.

You dont need a proper supercomputer to replicate these results, Noordhuis said.

The system works to associate images with words, which is called supervised learning, he said. Thousands of images from a training set are assigned a description (say, a cat) and the system is shown all of the images with an associated classification. Then, researchers present the system with images of the same object (say, a cat) but without the description attached. If the system knows its looking at a cat, its learning how to associate imagery with descriptive words.

The breakthrough allows Facebook AI researchers to start working on even bigger datasets; like, say, the billions of things posted to its website every day. Its also a display of Facebooks hardware expertise; the company made sure to note that its hardware is open-source, this means that for others to reap these benefits, theres no need for incredibly advanced TPUs, it said in a statement throwing some shade at Googles recent TPU announcement at Google I/O.

Facebook plans to release more details about its AI training work in a research paper published to its Facebook Research page.

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Facebook's AI training models can now process 40000 images a second - GeekWire

June 8, 2017 by Joe Kullman ASU Professor Subbarao Kambhampati with one of the robots used in his lab teams research aimed at enabling effective collaboration between humans and intelligent robots. The wooden blocks spell out the name of the lab, Yochan, meaning thought or plan in the Sanskrit language. Credit: Marco-Alexis Chaira/ASU

Interest in artificial intelligence has exploded, with some predicting that machines will take over and others optimistically hoping that people will be freed up to explore creative pursuits.

According to Arizona State University Professor Subbarao Kambhampati, the reality will be more in the middlebut the technology will certainly bring about a restructuring of our society.

AI will accomplish a lot of good things, Kambhampati said, but we must also be vigilant about possible ramifications of the technology. And yes, some jobs will be lostbut maybe not the ones people most often think of.

The professor of computer science and engineering in ASU's Ira A. Fulton Schools of Engineering is well qualified to enter the debate. He has been doing work in the areacommonly called "AI"for more than three decades, and he is at the midpoint of a two-year term as president of the international Association for the Advancement of Artificial Intelligence (AAAI), the largest organization of scientists, engineers and others in the field.

Kambhampati, whose current research focuses on developing "human-aware" AI systems to enable people and intelligent machines to work collaboratively, is also on the board of trustees of the Partnership on Artificial Intelligence to Benefit People and Society (PAI), which aims to help establish industry-wide best practices and ethics guidelines.

The following interview is edited from a recent conversation with him.

Question: You became president of the AI association at a time when public awareness of these technologies and the issues they raise has exploded. What's sparking the widespread interest?

Answer: AI as a scientific field has actually been around since the 1950s and has made amazing, if fitful, progress in getting machines to show hallmarks of intelligence. The Deep Blue computer's win over the world chess champion in 1997 was a watershed moment, but even after that, AI remained a staid academic field. Most people didn't come into direct contact with AI technology until relatively recently.

With the recent advances of AI in perceptual intelligence, we all now have smartphones that can hear and talk back to us and recognize images. AI is now a very ubiquitous part of our everyday lives, so there's a visceral understanding of its impact.

Q: Plus, it's a big driver of major industries, right?

A: In 2008, for instance, few if any tech companies were mentioning investments and involvement in AI in their annual reports or quarterly earnings reports. Today you'll find about 300 major companies emphasizing their AI projects or ventures in those reports.

The members of the Partnership for Artificial Intelligence, which I am involved with, include Amazon, Facebook, Google's Deep Mind, IBM and Microsoft. So, yes, AI is now a very big deal.

Q: The big question about AI is what it means for not only business and the economy, but what it portends for society when AI machines are doing more jobs that people used to do. What's your perspective on that?

A: Elon Musk (the prominent engineer, inventor and tech entrepreneur) started this trend of AI fears by remarking that what keeps him up at night is the idea of super-intelligent machines that will become more powerful than humans. Then Stephen Hawking (renowned physicist and cosmologist) chimed in. Statements like that, coming from influential people, of course make the public worry.

I don't take such a pessimistic view. I think AI is going to do a lot of good things. But it is also going to be a very powerful technology that will shape and change our world. So we should remain vigilant of all the ramifications of this powerful technology and work to mitigate unintended consequences. Fortunately, this is a goal shared by both AAAI and PAI.

Q: Garry Kasparov, the former chess champion who was defeated by the Deep Blue computer, writes that we should embrace AI, that it will free people from work so that they can develop their intellectual and creative capabilities. Others are saying the same. Do you agree?

A: I think Kasparov and others who say this are maybe too optimistic. We see from the past that new technology has taken away certain jobs but also created new kinds of jobs. But it's not certain that will always be the case with the proliferation of AI.

It seems clear that some professions are going to disappear, and not just blue-collar jobs like trucking, but also high-paying white-collar jobs. There are going to be many fewer radiologists, because machines are already doing a better job of reading X-rays. Machines can also be much faster and better at doing the kind of information gathering and research now done by paralegals, for instance.

This is why we have to start thinking about how society is going to be restructured if AI technologies and systems are doing much of the work that people once did.

Q: What would such a restructuring look like?

A: This is quite an open question, and organizations like AAAI and PAI are trying to get ahead of the curve in answering it.

I do want to emphasize that I don't think it is solely the job of AI experts, or of industry, to think about these issues of long-term restructuring. This is something that society at large has to contend with. We also have to realize that AI consequences play into already existing social ills such as societal biases, wealth concentration and social alienation. We have to work to make sure that AI moderates rather than amplifies these trends.

Q: What can those in the AI field do proactively to produce the most positive outcomes from the expansion of the technology?

A: We can take potential impacts into consideration when deciding in what directions we want to take our research and development. Much research now, like mine, is focusing on systems that are not intended to replace humans but to augment and enhance what humans are doing. We want to enable humans and machines to work together to do things better than what humans can do alone.

For AI systems to work with humans, they need to acquire emotional and social intelligence, something humans expect from their co-workers. That's where human-aware AI comes into play.

Q: What keeps you excited about your research?

A: I've always thought that the biggest questions facing our age are about three fundamental things: the origin of the universe, the origin of life and the nature of intelligence.

AI research takes you to the heart of one of them. In developing AI systems, I get a window into the basic nature of intelligence. That's why I tell my students that it takes a particularly bad teacher to make AI uninteresting.

That is what hooked me into this work. And now I'm getting the opportunity to go beyond the technical aspects of the field and have a voice on issues of ethics and practices and societal outcomes. That is energizing me even more.

Explore further: AI 'good for the world'... says ultra-lifelike robot

Sophia smiles mischievously, bats her eyelids and tells a joke. Without the mess of cables that make up the back of her head, you could almost mistake her for a human.

Major technology firms have joined forces in a partnership on artificial intelligence, aiming to cooperate on "best practices" on using the technology "to benefit people and society."

Advances in artificial intelligence will soon lead to robots that are capable of nearly everything humans do, threatening tens of millions of jobs in the coming 30 years, experts warned Saturday.

A technology industry alliance devoted to making sure smart machines don't turn against humanity said Friday that Apple has signed on and will have a seat on the board.

The phrase "artificial intelligence" saturates Hollywood dramas from computers taking over spaceships, to sentient robots overpowering humans. Though the real world is perhaps more boring than Hollywood, artificial intelligence ...

Microsoft chief executive Satya Nadella said Wednesday tech developers have a responsibility to prevent a dystopian "1984" future as the US technology titan unveiled a fresh initiative to bring artificial intelligence into ...

An AI machine has taken the maths section of China's annual university entrance exam, finishing it faster than students but with a below average grade.

Globally, from China and Germany to the United States, electric vehicle (EV) subsidies have been championed as an effective strategy to boost production of renewable technology and reduce greenhouse gas emissions (GHG).

As global automakers compete to bring the first flying car to market, Czech pilot Pavel Brezina is trying a different tack: instead of creating a car that flies, he has made a "GyroDrive"a mini helicopter you can drive.

Apple's new HomePod speaker may be music to the ears of its loyal fans, but how much it can crank up volume in the smart speaker market remains to be heard.

Autonomous vehicles with no human backup will be put to the test on publicly traveled roads as early as next year in what may be the first attempt at unassisted autonomous piloting.

Using Earth-abundant materials, EPFL scientists have built the first low-cost system for splitting CO2 into CO, a reaction necessary for turning renewable energy into fuel.

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Artificial intelligence's potential impacts raise promising possibilities, societal challenges - Phys.Org

Quantitative analytical procedures are some of the most successful in the financial world, with an increasing number of money managers turning the grunt work of data processing over to computer algorithms and artificial intelligence (AI). One argument in favor of quantitative methods like these is that they remove the human element from the analytical process, thereby ensuring faster processing times, more thorough analysis, and the effective removal of emotions and potential bias from the process. Now, at least one company is looking to capitalize on the advantages that quantitative methods have over old-fashioned human ones, but in a new area: sports betting.

The new company, Stratagem, is based in London and was set up by an ex-hedge funder, Andreas Koukorinis. In an interview with Business Insider, Koukorinis described his initial efforts at harnessing the powers of quantitative analysis for the purposes of sports betting as "building these robots to let them run around on the floor." He and his team have been developing predictive analytics programs for sports betting procedures, using machine learning and AI to process vast data fields. With these computer systems in place, Koukorinis believes that he will gain an edge in the competitive and often-arbitrary world of sports betting.

Koukorinis has been developing Stratagem for several years, and the company now appears to be taking off. The fund has seen some success with its machine learning models, and Stratagem now has an internal syndicate which allows it to bet its own money and bring in a return. One of the next steps for the fund is to raise around 25 million in the next few months to allow for further growth. Investors will essentially be buying into a sports betting-focused hedge fund.

Charles McGarraugh, CEO of the fledgling company, believes that the model is a straightforward sell to potential investors. "Sports lend themselves well to this kind of predictive analytics because it's a large number of repeated events. And it's uncorrelated to the rest of the market. And the duration of the asset class is short."

Stratagem focuses on both data collection and processing. For the former, the company uses both public sources as well as its own data generation system. Once the data has been gathered, Stratagem uses its analytical tools to crunch the numbers in search of mispriced odds. The results so far have been promising.

Could this be the future of quant methods? Koukorinis and others with Stratagem believe so, seeing a strong connection between the world of sports betting and the hard data analysis that quant is specially designed for. Whether the company will beat the odds remains to be seen.

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Sports Betting: The Next Big Thing for Artificial Intelligence - Investopedia

BEIJING: An artificial intelligence (AI) machine has taken the maths section of China's annual university entrance exam, finishing it faster than students but with a below average grade.

The artificial intelligence machine -- a tall black box containing 11 servers placed in the centre of a test room -- took two versions of the exam on Wednesday in Chengdu, Sichuan province.

The machine, called AI-MATHS, scored 105 out of 150 in 22 minutes. Students have two hours to complete the test, the official Xinhua news agency reported.

It then spent 10 minutes on another version and scored 100.

Beijing liberal art students who took the maths exam last year scored an average of 109.

Exam questions and the AI machine's answers were both shown on a big screen while three people kept score.

The AI was developed in 2014 by a Chengdu-based company, Zhunxingyunxue Technology, using big data, artificial intelligence and natural language recognition technologies from Tsinghua University.

"I hope next year the machine can improve its performance on logical reasoning and computer algorithms and score over 130," Lin Hui, the company's CEO, was quoted as saying by Xinhua.

"This is not a make-or-break test for a robot. The aim is to train artificial intelligence to learn the way humans reason and deal with numbers," Lin said.

The machine took only one of the four subjects in the crucially important entrance examination, the other three being Chinese, a foreign language and one comprehensive test in either liberal arts or science.

While AI is faster with numbers than humans, it struggles with language.

"For example, the robot had a hard time understanding the words 'students' and 'teachers' on the test and failed to understand the question, so it scored zero for that question," Lin said.

The test was the latest attempt to show how AI technology can perform in comparison to the human brain.

Last year, the Google-owned computer algorithm AlphaGo became the first computer programme to beat an elite player in a full match of the ancient Chinese game of Go.

AlphaGo won again last month, crushing the world's top player, Ke Jie of China, in a three-game sweep.

AlphaGo's feats have fuelled visions of AI that can not only perform pre-programmed tasks, but help humanity look at complex scientific, technical and medical mysteries in new ways.

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Artificial Intelligence gets below average grade in Chinese university entrance exam - Economic Times

This AI will tell people when theyre likely to die -- and thats a good thing. Thats because scientists from the University of Adelaide in Australia have used deep learning technology to analyze the computerized tomography (CT) scans of patient organs, in what could one day serve as an early warning system to catch heart disease, cancer, and other diseases early so that intervention can take place.

Using a dataset of historical CT scans, and excluding other predictive factors like age, the system developed by the team was able to predict whether patients would die within five years around 70 percent of the time. The work was described in an article published in the journal Scientific Reports.

The goal of the research isn't really to predict death, but to produce a more accurate measurement of health, Dr. Luke Oakden-Rayner, a researcher on the project, told Digital Trends. A patient's risk of death is directly related to the health of their organs and tissues, but the changes of chronic diseases build up for decades before we get symptoms. By the time we recognize a disease is present it is often quite advanced. So we can take a known outcome, like death, and look back in time at the patient's medical scans to find patterns that relate to undetected disease. Our goal is to identify these changes earlier and more accurately so we can tailor our treatment to individuals.

The AI analyzes CT scans to make its decisions.

At present, this is still a proof-of-concept experiment, however, and Oakden-Rayner points out that theres a lot more work to be done before this becomes the transformative clinical tool it could be. For one thing, the AIs 70-percent predictive accuracy when looking at scans is in line with the manual predictions made by experts. That makes it a potential time-saving tool, or a good means of double-checking, but the hope is that it can be much more than that.

Our next major step is to expand our dataset, Oakden-Rayner continued. We used a very small cohort of 48 patients in this study to show that our approach can work, but in general deep learning works better if you can give it much more data. We are collecting and analyzing a dataset of tens of thousands of cases in the next stage of our project.

The team also aims to expand what the AI is looking for, to help spot things like strokes before they strike.

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Artificial intelligence can now predict if someone will die in the next 5 years - Fox News

Executive Summary

Management teams often assume they can leapfrog best practices for basic data analytics by going directly to adopting artificial intelligence and other advanced technologies. But companies that rush into sophisticated artificial intelligence before reaching a critical mass of automated processes and structured analytics end up paralyzed. So how can companies tell if they are really ready for AI and other advanced technologies? First, managers should ask themselves if they have automated processes in problem areas that cost significant money and slow down operations. Next, managers should ensure they have structured analytics as well as centralize data processes so that the way data is collected is standardized and can be entered only once. After these standard structured analytics are in place, they can integrated with artificial intelligence.

Management teams often assume they can leapfrog best practices for basic data analytics by going directly to adopting artificial intelligence and other advanced technologies. But companies that rush into sophisticated artificial intelligence before reaching a critical mass of automated processes and structured analytics can end up paralyzed. They can become saddled with expensive start-up partnerships, impenetrable black-box systems, cumbersome cloud computational clusters, and open-source toolkits without programmers to write code for them.

By contrast, companies with strong basic analytics such as sales data and market trends make breakthroughs in complex and critical areas after layering in artificial intelligence. For example, one telecommunications company we worked with can now predict with 75 times more accuracy whether its customers are about to bolt using machine learning. But the company could only achieve this because it had already automated the processes that made it possible to contact customers quickly and understood their preferences by using more standard analytical techniques.

So how can companies tell if they are really ready for AI and other advanced technologies?

First, managers should ask themselves if they have automated processes in problem areas that cost significant money and slow down operations. Companies need to automate repetitive processes involving substantial amounts of data especially in areas where intelligence from analytics or speed would be an advantage. Without automating such data feeds first, companies will discover their new AI systems are reaching the wrong conclusions because they are analyzing out-of-date data. For example, online retailers can adjust product prices daily because they have automated the collection of competitors prices. But those that still manually check what rivals are charging can require as much as a week to gather the same information. As a result, as one retailer discovered, they can end up with price adjustments perpetually running behind the competition even if they introduce AI because their data is obsolete.

Without basic automation, strategic visions of solving complex problems at the touch of a button remain elusive. Take fund managers. While the profession is a great candidate for artificial intelligence, many managers spend several weeks manually pulling together data and checking for human errors introduced through reams of excel spreadsheets. This makes them far from ready for artificial intelligence to predict the next risk to client investment portfolios or to model alternative scenarios in real-time.

Meanwhile, companies that automate basic data manipulation processes can be proactive. With automated pricing engines, insurers and banks can roll out new offers as fast as online competitors. One traditional insurer, for instance, shifted from updating its quotes every several days to every 15 minutes by simply automating the processes that collect benchmark pricing data. A utility company made its service more competitive by offering customized, real-time pricing and special deals based on automated smart meter readings instead of semi-annual in-person visits to homes.

Once processes critical to achieving an efficiency or goal are automated, managers need to develop structured analytics as well as centralize data processes so that the way data is collected is standardized and can be entered only once.

With more centralized information architectures, all systems refer back to the primary source of truth, updates propagate to the entire system, and decisions reflect a single view of a customer or issue. A set of structured analytics provides retail category managers, for instance, with a complete picture of historic customer data; shows them which products were popular with which customers; what sold where; which products customers switched between; and to which they remained loyal.

Armed with this information, managers can then allocate products better, and, see why choices are made. By understanding the drivers behind customer decisions, managers can also have much richer conversations about category management with their suppliers such as explaining that very similar products will be removed to make space for more unique alternatives.

After these standard structured analytics are integrated with artificial intelligence, its possible to comprehensively predict, explain, and prescribe customer behavior. In the earlier telecommunications company example, managers understood customer characteristics. But they needed artificial intelligence to analyze the wide set of data collected to predict if customers were at risk of leaving. After machine learning techniques identified the customers who presented a churn risk, managers then went back to their structured analytics to determine the best way to keep them and use automated processes to get an appropriate retention offer out fast.

Artificial intelligence systems make a huge difference when unstructured data such as social media, call center notes, images, or open-ended surveys are also required to reach a judgment. The reason Amazon, for instance, can recommend products to people before they even know they want them is because, using machine learning techniques, it can now layer in unstructured data on top of its strong, centralized collection of structured analytics like customers payment details, addresses, and product histories.

AI also helps with decisions not based on historic performance. Retailers with strong structured analytics in place can figure out how best to distribute products based on how they are selling. But it takes machine learning techniques to predict how products not yet available for sale will do partly because no structured data is available.

Finally, artificial intelligence systems can make more accurate forecasts based on disparate data sets. Fund managers with a strong base of automated and structured data analytics are predicting with greater accuracy how stocks will perform by applying AI to data sets involving everything from weather data to counting cars in different locations to analyzing supply chains. Some data pioneers are even starting to figure out if companies will gain or lose ground using artificial intelligence systems analyses of consumer sentiment data from unrelated social media feeds.

Companies are just beginning to discover the many different ways that AI technologies can potentially reinvent businesses. But one thing is already clear: they must invest time and money to be prepared with sufficiently automated and structured data analytics in order to take full advantage of the new technologies. Like it or not, you cant afford to skip the basics.

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If Your Company Isn't Good at Analytics, It's Not Ready for AI - Harvard Business Review

The performance of humans puny brains will be outmatched by computers within just 13 years, billionaire Elon Musk has claimed.

TheTesla and SpaceX foundersaid that artificial intelligence will beat us at just about everything by 2030.

He made the comments on Twitter, where he was responding to a new study which claims our race will be overtaken by 2060.

Probably closer to 2030 to 2040 in my opinion, he wrote.

According to the terrifying research from boffs at the University of Oxford, its not looking good for us humans.

Machines will be better than us at translating languages by 2024 and writingschool essays by 2026, they claimed.

Within ten years computers will be better at driving a truck than us and by 2031 they will be better atselling goods and will put millions of retail workers on the dole queue.

AI will write a bestselling book by 2049 and conduct surgery by 2053, the researchers suggested.

In fact, every single human jobwill be automated within the next 120 years,according to computer experts the university researchers quizzed.

It's unlikely to trouble the billionaire tech entrepreneur, however.

Musk already has plans to plug our brains into computers.

He recently launched a new neuroscience company which aims to develop cranial computers that can download thoughts and possibly even treat disorders such as epilepsy and depression,the New York Post reported.

Over the years, the 45-year-old hasconjured up new ideas for space rocketsand electric-cars, proven that they can work efficiently, and then rolled them out for public and private use.

He's even hoping to start a human colony on Mars by 2030.

He's not alone in his estimations for the great computer takeover, either.

Scientists reckon humans are on the brink of a new evolutionary shift and man as we know it "probably won't survive".

In a terrifying advance, some have warned that computers are so advanced, those developing the complex formulas that make them "tick" aren't even sure howthey work.

And because they cannot understand the mechanical brains they have built, they fear that wecould lose control of them altogether.

That means they could behave unexpectedly - potentially putting lives at risk.

Take the case of driverless cars, for example where an algorithm might behave differently to normal and cause a crash.

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Elon Musk says artificial intelligence will beat humans at 'everything' by 2030 - Fox News

As research teams at Google, Microsoft, Facebook, IBM, and even Amazon have broken new ground in artificial intelligence in recent years, Apple always seemed to be the odd man out. It was too closed off to meaningfully integrate AI into the companys softwareit wasnt a part of the research community, and didnt have developer tools available for others to bring AI to its systems.

Thats changing. Through a slew of updates and announcements today at its annual developer conference, Apple made it clear that the machine learning found everywhere else in Silicon Valley is foundational to its software as well, and its giving developers the power to use AI in their own iOS apps as well.

The biggest news today for developers looking to build AI into their iOS apps was barely mentioned on stage. Its a new set of machine learning models and application protocol interfaces (APIs) built by Apple, called Core ML. Developers can use these tools to build image recognition into their photo apps, or have a chatbot understand what youre telling it with natural language processing. Apple has initially released four of these models for image recognition, as well as an API for both computer vision and natural language processing. These tools run locally on the users device, meaning data stays private and never needs to process on the cloud. This idea isnt neweven data hoarders like Google have realized the value of letting users keep and process data on their own devices.

Apple also made it easy for AI developers to bring their own flavors of AI to Apple devices. Certain kinds of deep neural networks can be converted directly into Core ML. Apple now supports Caffe, an open-source software developed by the University of California-Berkeley for building and training neural networks, and Keras, a tool to make that process easier. It notably doesnt support TensorFlow, Googles open-source AI framework, which is by far the largest in the AI community. However, theres a loophole so creators can build their own converters. (I personally expect a TensorFlor converter in a matter of days, not weeks.)

Some of the pre-trained machine learning models that Apple offers are open-sourced Google code, primarily for image recognition.

Apple made it clear in the keynote today that every action taken on the phone is logged and analyzed by a symphony of machine-learning algorithms in the operating system, whether its predicting when you want to make a calendar appointment, call a friend, or make a better Live Photo.

The switch to machine learning can be seen in the voice of Siri. Rather than using the standard, pre-recorded answers that Apple has always relied on, Siris voice is now entirely generated by AI. It allows for more flexibility (four different kinds of inflection were demonstrated on stage), and, as the technology advances, it will sound exactly like a human anyway. (Apples competitors are not far off.)

Apple also rattled off a number of other little tweaks powered by ML, like the iPad distinguishing your palm from the tip of an Apple Pencil, or dynamically extending the battery life of the device by understanding which apps need to consume power.

Okay, so Apples really only published one paper. But it was a good one! And Ruslan Salakhutdinov, Apples new director of AI research, has been on the speaking circuit. He recently spoke at Nvidias GPU Technology Conference (although Apples latest computers use AMD chips), and will be speaking later this month in New York City, to name a few.

Apple also held a closed-door meeting with their competitors at a major AI conference late last year, shortly after Salakhutdinov was hired, to explain what it was working on in its labs. Quartz obtained some of those slides and published them here.

Is Apple a leader in AI research? Not according to most metrics. But many consider open research to be a way of recruiting top talent in AI, so we might see more papers and talks in the future.

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Apple is finally serious about artificial intelligence - Quartz