Category Archives: Artificial Intelligence

Facebook has started using artificial intelligence to identify users who are potentially at risk of taking their own lives.

The social network has developed algorithms capable of scanning posts and comments for warning signs.

These could be phrases such as Are you okay? or Im worried about you, or more general talk of sadness and pain.

The AI tool would send such posts to a human review team, which would get in touch with the user thought to be at risk and offer help, in the form of contact details for support services or a chat with a member of staff through Facebook Messenger.

The site had previously relied on other users reporting worrying updates.

The AI is actually more accurate than the reports that we get from people that are flagged as suicide and self injury, Facebook product manager Vanessa Callison-Burch told BuzzFeed. The people who have posted that content [that AI reports] are more likely to be sent resources of support versus people reporting to us.

The system is currently being tested in the US.

The site has also announced new safety features for Facebook Live, which has been used to live stream several suicides.

Users can now flag up concerning Facebook Live behaviour with the site, which will display advice and highlight the video to staff for immediate review.

The goal is to provide help as quickly as possible, mid-broadcast rather than post-broadcast.

Some might say we should cut off the stream of the video the moment there is a hint of somebody talking about suicide, said Jennifer Guadagno, the projects lead researcher.

But what the experts emphasised was that cutting off the stream too early would remove the opportunity for people to reach out and offer support. So, this opens up the ability for friends and family to reach out to a person in distress at the time they may really need it the most.

Facebook CEO Mark Zuckerberg described plans to use AI to identify worrying content in a recently published manifesto.

Looking ahead, one of our greatest opportunities to keep people safe is building artificial intelligence to understand more quickly and accurately what is happening across our community, it read.

An earlier version of the piece said that it would take many years to develop AI systems capable of identifying issues such as bullying and terrorism risks online, but the section was removed before the manifesto was publicly issued.

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Facebook using artificial intelligence to help suicidal users - The Independent

Gone are the days of the hobbyist hackermodern malware is a trillion-euro business.

Dr Eva Maia at VisionTechLab, a young cybersecurity firm in Matosinhos, Portugal, said that attacks on computer networks are not only multiplying, they are also growing sneakier.

Malwares typically go unnoticed for months by remaining dormant on infected computers, said Dr Maia. This was recently the case in the Panama Papers attack, where no one knew that the network had been compromised until long after the damage was done.

In the EU-funded SecTrap project, VisionTechLab has been studying the market for a new line of defence that could rob malicious software of its current hiding places.

Conventional antiviruses and firewalls are trained like nightclub bouncers to block known suspects from entering the system. But new threats can be added to the wanted list only after causing trouble. If computers could instead be trained as detectives, snooping around their own circuits and identifying suspicious behaviour, hackers would have a harder time camouflaging their attacks.

The challenge is that machines have traditionally been built to follow orders, not recognise patterns or draw conclusions. Dr Maia is working on advances in artificial intelligence (AI) to change that.

Bootcamp

We are seeing a boom in AI techniques, said Dr Maia. Research that was previously theoretical is now moving from academic laboratories to industry at an unprecedented pace.

Research that was previously theoretical is now moving from academic laboratories to industry at an unprecedented pace.

Dr Eva Maia, VisionTechLab, Portugal

Over the past few years, computers have started driving passenger cars,following voice orders and outmatching humans at identifying faces on photographs. These breakthroughs are the fruit of a new trend in AI based on mimicking living neural networks.

In the same way as our brains sort new information based on past experiences, enough practice data can teach computers to learn, categorise and generalise for themselves.

How many examples are needed to identify a trend can run into astronomical numbers. Fortunately, vast hoards of behavioural data are strewn everyday across the internet by heedless bloggers, commentators and social media users.

Computers learnt their first cognitive functions by devouring terabytesof this online text, sound and images. With the help of recent computing power and the kind of algorithms developed by Dr Maia, they have become so good at identifying content that they now label some of it for us.

The challenge in cybersecurity is to use this ability to distinguish between innocent and malicious behaviour on a computer. For this, Alberto Pelliccione, chief executive of ReaQta, a cybersecurity venture in Valletta, Malta, has found an analogous way of educating by experience.

ReaQta breeds millions of malware programs in a virtual testing environment known as a sandbox, so that algorithms can inspect their antics at leisure and in safety. It is not always necessary to know what they are trying to steal. Just to record the applications that they open and their patterns of operation can be enough.

So that the algorithms can learn about business as usual, they then monitor the behaviour of legal software, healthy computers, and ultimately the servers of each new client. Their lesson never ends. The algorithms continue to learn from their users even after being put into operation.

In doing so, ReaQtas algorithms can assess whether programs or computers are behaving unusually. If they are, they inform human operators, who can either shut them down or study the tactics of the malware infecting them. The objective of the artificial intelligence is not to teach computers what we define as good or bad data, but to spot anomalies, said Pelliccione.

Nowhere to hide

This is welcome news for IT administrators. Cyber criminals typically attack the computer networks of large organisations by compromising the machines of less security-savvy users on their periphery and working their way through to the centre. A few weak links in a sprawling network are difficult to spot and can progressively put an entire company at risk.

To make matters worse, hackers install dormant access points on each machine that they compromise. If security analysts manage to block one, hackers return through another. Dormant access points are notoriously difficult to spot because they do nothing until hackers activate them.

As part of the European ProBOS project, ReaQta has developed software that can be nested at the very core of machines, between their operating system and hardware. Its role is to monitor daily operations in every corner of the system, allowing AI algorithms to sift through ubiquitous data and spot any malicious installation.

ReaQta is licensing the security platform across European and Southeast Asian markets this month. Its first clients are companies that operate over 500 computers simultaneously.

Next year, VisionTechLab plans to release its first AI security services for banks and governments. In the longer term, Dr Maia sees applications for individuals.

For all the benefits of mobile devices, social networking and cloud computing, these technologies are placing more private data at risk. While AI may not yet be capable of guaranteeing its safety, it can now shine a powerful search light on any attempts to steal it.

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Cybersecurity is at the heart of the EU's strategy for the Digital Single Market.

The EU's cybersecurity strategy was created to embed cybersecurity into new policies in areas such as automated driving, make the EU a strong player in the cybersecurity market, and ensure that all Member States have similar capabilities to fight cyber crime.

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Artificial intelligence being turned against spyware - Horizon magazine

You have to know when to hold em and when to fold em.

Now a program developed by computer scientist at the University of Alberta can do both and do it much better than an entire cohort of professional poker players.

The achievement marks a new milestone for artificial intelligence (AI) involving deep learning, a style of programming that mimics certain aspects of how human brains acquire expertise. But while the program, dubbed DeepStack, represents a significant step, a rival U.S. team said that the method by which it was tested against humans was insufficient to reveal the true extent of its capabilities.

Games provide an important testbed for artificial intelligence because they offer a well-defined arena where programming approaches can be evaluated and compared. Last year, another deep learning system developed by Google DeepMind managed to beat the world champion at Go, a board game that is fiendishly complex despite its simple rules because of the number of possible decisions a player can make.

Poker specifically the version known as Heads-Up No-Limit Texas Holdem presents a different kind of challenge. Unlike Go or chess, where both players can assess the state of the game simply by looking at the board, poker players must deal with incomplete knowledge because of cards that are hidden from view.

The essence of poker is being able to make decisions when you dont have all of the information that you need, said Michael Bowling, who leads the universitys computer poker research group. Dr. Bowling added that the same kind of reasoning is often required when computers have to solve real-world problems, which makes poker an attractive hurdle for designers of intelligent systems.

The Alberta group has been working for 20 years on programs that try to solve poker. In 2008, it developed an algorithm that could defeat top human players at the heads-up limit version of the game, in which all bets are of fixed size. There are one thousand billion different decision points than can arise in such a game, a numerical challenge that Dr. Bowling compares to checkers. While not trivial, its a game that a computer can be hardwired to win.

In comparison, the no-limit version of the game is astronomically more complicated because players can choose to bet any amount up to the number of chips in their possession. A winning strategy often involves betting high when the opponent believes incorrectly that his or her hand is the stronger one.

In designing DeepStack, Dr. Bowlings team, together with colleagues at the Czech Technical University in Prague, had to create a system that not only understood the strength of its own hand and make an informed guess about its opponents, but also weigh what its opponent might be thinking in order to bluff and conceal its own intentions.

People think of bluffing as this very human, psychological thing, but it pretty much falls out of the mathematics of the game, Dr. Bowling said. He added that DeepStack had to be able to learn how to bluff, otherwise it would be a terrible player.

The team developed a deep-learning system that tried to make the best choice by looking only a few actions ahead, otherwise it would be overwhelmed by the mathematical possibilities. The system was trained using an army of lesser computers who played through a multitude of game scenarios, gradually building up DeepStacks intuition for what to do. An overview of how the program works along with the results of its matchups against human players were published Thursday in the journal Science.

To test the system, the team recruited 33 professional poker players from 17 countries to go toe-to-toe with DeepStack, with an offer of cash prizes up to $5,000 awarded to the top three players. The players were each given four weeks in late 2016 to complete 3,000 games against the program. Only a third of the human players went the full distance. Of those, all but one were beaten by a significant enough margin to rule out luck.

Tuomas Sandholm, who leads the Alberta groups chief competitor at Carnegie Mellon University in Pittsburgh, said that DeepStack featured a new combination of programming methods that made it a potent player.

However, he cited several weaknesses in the way the system was tested, including the fact that the players DeepStack faced were not the worlds best and the prizes they were offered were likely not sufficient to motivate the players to perform at their sharpest. He also said that 3,000 matches would not provide humans with enough experience to learn to adjust and potentially outsmart the program.

Dr. Sandholms team has been working with a different system called Libratus that runs on a supercomputer and does not employ deep learning. but instead uses a trial-and-error approach called reinforcement learning. In a sign of how close the competition has become, last month Libratus beat a team of four top, human Heads-Up No-Limit Texas Holdem players. There are no plans as yet for a tournament that would pit the two systems against each other.

In the meantime, Dr. Bowling said there was plenty of scope to beef up DeepStacks capabilities and also to try it out on different variations of no-limit poker that more closely resemble a human championship game.

Follow Ivan Semeniuk on Twitter: @ivansemeniuk

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Artificial intelligence crosses the poker barrier - The Globe and Mail

Forbes

The Future Of Work: The Intersection Of Artificial Intelligence And Human Resources Forbes Artificial intelligence is transforming our lives at home and at work. At home, you may be one of the 1.8 million people who use Amazon's Alexa to control the lights, unlock your car, and receive the latest stock quotes for the companies in your ...

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The Future Of Work: The Intersection Of Artificial Intelligence And Human Resources - Forbes

Facebook will begin using artificial intelligence and pattern recognition technology in an effort to automatically identify users likely to commit suicide, the company said Wednesday.

The social network has expanded its decade-old suicide prevention tools by making it easier for users both actual and automated to flag account holders at risk of self-harm, Facebook said in a Wednesday press release.

In addition to integrating its current suicide prevention tools across its chat and live-streaming platforms Messenger and Facebook Live, respectively the announcement said the social network is experimenting with artificial intelligence and its potential to identify users likely to attempt suicide.

Based on feedback from experts, we are testing a streamlined reporting process using pattern recognition in posts previously reported for suicide, the announcement said. This artificial intelligence approach will make the option to report a post about suicide or self injury more prominent for potentially concerning posts like these.

The announcement, penned by Facebook product manager Vanessa Callison-Burch, researcher Jennifer Guadagno and Antigone Davis, the companys head of Global Safety, said the company is testing pattern recognition to identify posts as very likely to include thoughts of suicide.

Our Community Operations team will review these posts and, if appropriate, provide resources to the person who posted the content, even if someone on Facebook has not reported it yet, the announcement said.

Suicide claims an average of one life every 40 seconds, according to Facebook, making it the second-leading cause of death among individuals ages 15 through 29. The social network boasted 1.86 billion monthly active users as of the end of 2016, roughly a quarter of which are under the age of 25.

Facebook is in a unique position through friendships on the site to help connect a person in distress with people who can support them, the announcement said. Its part of our ongoing effort to help build a safe community on and off Facebook.

Artificial intelligence and pattern recognition technology will only be used on a limited basis in the U.S. for now, Facebook said. Elsewhere, however, users should expect to see integrated suicide prevention tools while using both Facebook Live and Messenger beginning with Wednesdays roll-out, according to the announcement.

Facebook Live platform was initially introduced to celebrity users in August 2015 before the service was broadened to the rest of its billion-plus account holders the following April. At least three Facebook users have broadcast their suicides using the live-streaming service since the start of 2017, including a 14-year-old girl who allegedly took her own life in January at the end of a two-hour broadcast, Variety reported this week.

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Facebook implements artificial intelligence to identify suicidal users - Washington Times

At the recent Davos 2017, Klaus Schwab, the founder of the World Economic Forum in conversation with Sergey Brin, co-Founder of Alphabet Google, described his book the 4th Industrial Revolution1 published just a year ago, as rapidly evolving with the rise of artificial intelligence2. While this revolution in fusion of physical, digital, and biological worlds from 3D printing, additive manufacturing, and net shape processing to nanotechnology, bioengineering to deep neural networks were. Underpinned by hyperscaling of infrastructure and advances in consumerization and embedding technology. Most commentators and governments are looking at the positive and negative consequences of these changes both in terms of direct human work impact and indirect associated activity that may be impact from automation and a society with AI.

Transition to an AI Society

The main driver for this revolution is not only the affordability and availability of reliable sensors and computing resources, but also the advancements made in software in particular within the realm of artificial intelligence (eg. machine learning). Andrew Ng recently said there was little if any industry that could not be impacted by AI in some form, and described AI as the new electricity.

While AI is projected to have a pervasive potential for disruption across most industry and business sectors, it is nevertheless important to distinguish the hype from real expectations for such a technology, particularly for practitioners and professionals within our society. Nevertheless, we must heed the cautions expressed by the preeminent minds of our times, never before in the history of humans has there been the availability of hardware and software that will enable humans to contrast intelligent machines. The interconnection of devices when coupled with the automation using these new kinds of technology will have a very serious impact on human employment, privacy, security, social and economic wellbeing that will have huge long-term consequences and implications for the society of human beings.

Earlier revolutions of industry, agrarian, political and social dislocations started in the West and East at different times, which have now become a global phenomenon. Have we reached a point in time where Adam Smith6 economics of the 18th century that defined the basis of the wealth of Nations as economic wellbeing, needs revising in order to accommodate changing value? Changes within our society and habitat, such as aging populations and increasing limitations of global resources and concerns over greenhouse gases, etc. will inevitably present us with new challenges and hurdles for humankind to overcome often in unrealistic timeframes.

What will be the new jobs in this new world and how will these kinds of new technology help to create new jobs, which will be balanced in some manner given that the distribution of work and wealth will change drastically in the next few decades. Pursuing laissez-faire economic policies with minimal intervention on peoples rights and wellbeing may need to be rethought as changes are brought upon the very mechanisms of wealth creation and sharing are integrated, and even controlled, by machine automation.

Professor Mark Skilton, Dr Felix Hovespian

Forthcoming book: The Fourth Industrial Revolution: An Executive Guide to Intelligent Systems, 2017 Palgrave Macmillan. Professor Mark Skilton. Dr Felix Hovespian.

1. Klaus Schwab, The 4th Industrial Revolution, World Economic Forum, 2016 ISBN-13 978-1-944835-00-2

3. Dr. Andrew Ng: Artificial Intelligence is the New Electricity, January 25 2017. Stanford MSx Future Forum https://www.youtube.com/watch?v=21EiKfQYZXc&feature=youtu.be&a (Dr. Andrew Ng, VP & Chief Scientist of Baidu; Co-Chairman and Co-Founder of Coursera; and an Adjunct Professor at Stanford University)

5. Adam Smith: The Father of Economics, Sept 7 2016 , Investopedia http://www.investopedia.com/updates/adam-smith-economics/

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How do we prepare for the Artificial Intelligence Society? - Huffington Post

TechCrunch

Baidu Ventures partners with Comet Labs as both double down on artificial intelligence TechCrunch Baidu Ventures exists on the front-lines of the Chinese search giant's push to brand itself as an artificial intelligence company. In an effort to bridge the AI ecosystems of China and the United States, Baidu Ventures is partnering with Comet Labs, a ... and more »

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Baidu Ventures partners with Comet Labs as both double down on artificial intelligence - TechCrunch

NVIDIA (NASDAQ:NVDA) was one of the hottest tech stocks of 2016, jumping 230% over the past 12 months. The company makes the vast majority of its revenue from gaming -- about 62% in the fiscal fourth quarter 2017 -- but NVIDIA is much more than just a gaming processor company.

The artificial intelligence (AI) market is quickly expanding, and NVIDIA is positioning itself to make big gains in the space. According to an investor note published by Goldman Sachs' Toshiya Haria couple of months ago, NVIDIA's total addressable market in AI and deep learning could be as big as $5 billion to $10 billion -- out of a total market of $40 billion.

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Data source: Goldman Sachs. Chart by author.

Hari mentioned that NVIDIA already has a lead in the AI space and that the company's competition "continues to face high barriers to entry." He went on to add that NVIDIA already holds nearly 90% of the market for chips that are used for training tasks in machine learning (referring to the company's GPUs).

NVIDIA's position in the burgeoning AI market market comes from several of the company's products. Its Drive PX 2 supercomputer uses deep learning to process image information for self-driving vehicles and help them decide how they should react in certain situations.

More than 80 automakers and Tier 1 automotive suppliers already use a version of Drive PX, including Tesla. The electric-auto maker is rapidly moving toward a self-driving car future and has already added key driverless car technologies -- including NVIDIA's computers -- to make it a reality.

But the company isn't satisfied just with using its GPU-based AI computers for autonomous driving. NVIDIA's DGX-1 supercomputer server uses machine learning systems to process information faster than previous deep learning machines of its kind, and is currently being used by SAP for enterprise solutions for 320,000 customers.

NVIDIA says that its latest Pascal chip architecture is"purpose-built for AI," and other major tech companies have already taken notice. BothIBM and Microsoft are using NVIDIA's GPUs for some of their AI services as well."

While NVIDIA's AI opportunity is huge, investors should know one thing: AI makes up only a small percentage of revenue right now.

In the fiscal fourth quarter 2017, NVIDIA brought in just $296 million from its data center division -- 13% of total revenue -- and even its automotive business (which includes the Drive PX 2 AI supercomputer) brings in less than 6% of total revenue.

That means the company will need to continue releasing new hardware and adding more customers to reach its full AI potential. But at this point, NVIDIA is already well on its way to fully tapping into the AI market, and it's doing so at the perfect time.

This year may be one of the best years to invest in AI, as tech companies and governments around the world are ramping up investments in artificial intelligence. NVIDIA's current position in AI, potential market share, and focus on new AI hardware should push the company to the top of the list for anyone looking to invest in AI over the long term.

Teresa Kersten is an employee of LinkedIn and is a member of The Motley Fools Board of Directors. LinkedIn is owned by Microsoft. Chris Neiger has no position in any stocks mentioned. The Motley Fool owns shares of and recommends Nvidia and Tesla. The Motley Fool has a disclosure policy.

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NVIDIA's Artificial Intelligence Opportunity in 1 Chart - Motley Fool

PMLiVE

Artificial intelligence: could pharma lead the way? PMLiVE Chris Cooper A rare opportunity now exists for the pharmaceutical industry to play its part in driving customer behaviour with a digital marketing approach that the world is fast becoming familiar with, and will soon be reliant on - artificial ...

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Artificial intelligence: could pharma lead the way? - PMLiVE

Forbes

Can Artificial Intelligence Solve Today's Big Data Dilemma? Forbes By now, we know artificial intelligence will be big. The question is: What else will it bring with it? In the last four years, deals with AI startups jumped from 160 in 2012 to 658 in 2016. Companies are already using it for everything from self ... 4 challenges Artificial Intelligence must addressTNW Sanlam to launch artificial intelligence investment serviceInternational Adviser How Can Artificial Intelligence Be Leveraged In AdTech Industry?Techstory International Business Times, India Edition all 10 news articles »

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Can Artificial Intelligence Solve Today's Big Data Dilemma? - Forbes