Category Archives: Ai

BBC News

AI may take your job - in 120 years BBC News In 45 years' time, though, half of jobs currently filled by humans will have been taken over by an artificial intelligence system, results indicate. The report, When will AI exceed human performance?, says AI will reshape transport, health, science and ... and more »

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AI may take your job - in 120 years - BBC News

Forbes

Amazon Prime Wardrobe Could Be The Next Step In AI Becoming A Better Liar Forbes Today Amazon launched another new service to directly threaten retail store changing rooms. Amazon Prime Wardrobe is currently in beta and is a simple concept for Prime members. You order clothes, if you don't like them you can send them back within ... and more »

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Amazon Prime Wardrobe Could Be The Next Step In AI Becoming A Better Liar - Forbes

TechCrunch

Google launches its AI-powered jobs search engine | TechCrunch TechCrunch Looking for a new job is getting easier. Google today launched a new jobs search feature right on its search result pages that lets you search for jobs across.. You can now hunt for jobs directly in Google search results thanks to AIWired.co.uk Google Uses AI To Power New Jobs Search EngineHot Hardware Google Adds AI-Powered Job Listings To Search EngineMediaPost Communications Axios -NewsBytes -YouTube -Google Blog all 109 news articles »

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Google launches its AI-powered jobs search engine | TechCrunch - TechCrunch

Executive Summary

To date, many of us have achieved success by being smarter than other people as measured by grades and test scores, beginning from our early days in school. The smart people were those that received the highest scores by making the fewest mistakes.

AI will change that because there is no way any human being can outsmart, for example, IBMs Watson, at least without augmentation. What is needed is a new definition of being smart, one that promotes higher levels of human thinking and emotional engagement.

Andrew Ng has likened artificial intelligence (AI) to electricity in that it will be as transformative for us as electricity was for our ancestors. I can only guess that electricity was mystifying, scary, and even shocking to them just asAI will be to many of us. Credible scientists and research firms have predicted that the likely automation of service sectors and professional jobs in the United States will be more than 10times as large as the number of manufacturing jobs automated to date. That possibility is mind-boggling.

So, what can we do to prepare for the new world of work? Because AI will be a far more formidable competitor than any human, we will be in a frantic race to stay relevant. That will require us to take our cognitive and emotional skills to a much higher level.

Many experts believe that human beings will still be needed to do the jobs that require higher-order critical, creative, and innovative thinking and the jobs that require high emotional engagement to meet the needs of other human beings. The challenge for many of us is that we do not excel at those skills because of our natural cognitive and emotional proclivities:We are confirmation-seeking thinkers and ego-affirmation-seeking defensive reasoners. We will need to overcome those proclivities in order to take our thinking, listening, relating, and collaborating skills to a much higher level.

I believe that this process of upgrading begins with changing our definition of what it means to be smart. To date, many of us have achieved success by being smarter than other people as measured by grades and test scores, beginning inour early days in school. The smart people were those that received the highest scores by making the fewest mistakes.

AI will change that because there is no way any human being can outsmart, for example, IBMs Watson, at least without augmentation. Smart machines can process, store, and recall information faster and betterthan we humans. Additionally, AI can pattern-match faster and produce a wider array of alternatives than we can. AI can even learn faster. In an age of smart machines, our old definition of what makes a person smart doesnt make sense.

What is needed is a new definition of being smart, one that promotes higher levels of human thinking and emotional engagement. The new smart will be determined not by what or how you know but by the quality of your thinking, listening, relating, collaborating, and learning. Quantity is replaced by quality. And that shift will enable us to focus on the hard work of taking our cognitive and emotional skills to a much higher level.

We will spend more time training to be open-minded and learning to update our beliefs in response to new data. We will practice adjusting after our mistakes, and we will invest more in the skills traditionally associated with emotional intelligence. The new smart will be about trying to overcome the two big inhibitors of critical thinking and team collaboration: our ego and our fears. Doing so will make it easier to perceive reality as it is, rather than as we wish it to be. In short, we will embrace humility. That is how we humans will add value in a world of smart technology.

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In the AI Age, Being Smart Will Mean Something Completely Different - Harvard Business Review

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A Clever AI-Powered Robot Learns to Get a Grip - WIRED

Researchers from the Facebook Artificial Intelligence Research lab (FAIR) recently made an unexpected discovery while trying to improve chatbots. The bots known as dialog agents were creating their own language.

Using machine learning algorithms, dialog agents were left to converse freely in an attempt to strengthen their conversational skills. Over time, the bots began to deviate from the scripted norms and in doing so, started communicating in an entirely new language one they created without human input.

In an attempt to better converse with humans, chatbots took it a step further and got better at communicating without them.

And its not the only interesting discovery.

Researchers also found these bots to be incredibly crafty negotiators. After learning to negotiate, the bots relied on machine learning and advanced strategies in an attempt to improve the outcome of these negotiations. Over time, the bots became quite skilled at it and even began feigning interest in one item in order to sacrifice it at at a later stage in the negotiation as a faux compromise.

Were not talking singularity-level beings here, but the findings are a huge leap forward for AI.

Deal or No Deal? End-to-End Learning for Negotiation Dialogues on Facebook AI Research & Georgia Institute of Technology

Read next: Quantum entanglement is the future of the internet whether we understand it or not

Bryan Clark is the US Editor, a California resident, and a believer that the West Coast truly is the best coast. He digs web culture, social media, and inappropriate use of GIFs during otherwise serious conversation. Connect via Twitter or Facebook.

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Facebook's AI accidentally created its own language - TNW

Thousands of scientists worldwide tap into CERNs computer networks each day in their quest to better understand the fundamental structure of the universe. Unfortunately, they are not the only ones who want a piece of this vast pool of computing power, which serves the worlds largest particle physics laboratory. The hundreds of thousands of computers in CERNs grid are also a prime target for hackers who want to hijack those resources to make money or attack other computer systems. But rather than engaging in a perpetual game of hide-and-seek with these cyber intruders via conventional security systems, CERN scientists are turning to artificial intelligence to help them outsmart their online opponents.

Current detection systems typically spot attacks on networks by scanning incoming data for known viruses and other types of malicious code. But these systems are relatively useless against new and unfamiliar threats. Given how quickly malware changes these days, CERN is developing new systems that use machine learning to recognize and report abnormal network traffic to an administrator. For example, a system might learn to flag traffic that requires an uncharacteristically large amount of bandwidth, uses the incorrect procedure when it tries to enter the network (much like using the wrong secret knock on a door) or seeks network access via an unauthorized port (essentially trying to get in through a door that is off-limits).

CERNs cybersecurity department is training its AI software to learn the difference between normal and dubious behavior on the network, and to then alert staff via phone text, e-mail or computer message of any potential threat. The system could even be automated to shut down suspicious activity on its own, says Andres Gomez, lead author of a paper describing the new cybersecurity framework.

CERNthe French acronym for the European Organization for Nuclear Research lab, which sits on the Franco-Swiss borderis opting for this new approach to protect a computer grid used by more than 8,000 physicists to quickly access and analyze large volumes of data produced by the Large Hadron Collider (LHC). The LHCs main job is to collide atomic particles at high-speed so that scientists can study how particles interact. Particle detectors and other scientific instruments within the LHC gather information about these collisions, and CERN makes it available to laboratories and universities worldwide for use in their own research projects.

The LHC is expected to generate a total of about 50 petabytes of data (equal to 15 million high-definition movies) in 2017 alone, and demands more computing power and data storage than CERN itself can provide. In anticipation of that type of growth the laboratory in 2002 created its Worldwide LHC Computing Grid, which connects computers from more than 170 research facilities across more than 40 countries. CERNs computer network functions somewhat like an electrical grid, which relies on a network of generating stations that create and deliver electricity as needed to a particular community of homes and businesses. In CERNs case the community consists of research labs that require varying amounts of computing resources, based on the type of work they are doing at any given time.

One of the biggest challenges to defending a computer grid is the fact that security cannot interfere with the sharing of processing power and data storage. Scientists from labs in different parts of the world might end up accessing the same computers to do their research if demand on the grid is high or if their projects are similar. CERN also has to worry about whether the computers of the scientists connecting into the grid are free of viruses and other malicious software that could enter and spread quickly due to all the sharing. A virus might, for example, allow hackers to take over parts of the grid and use those computers either to generate digital currency known as bitcoins or to launch cyber attacks against other computers. In normal situations, antivirus programs try to keep intrusions out of a single machine, Gomez says. In the grid we have to protect hundreds of thousands of machines that already allow researchers outside CERN to use a variety of software programs they need for their different experiments. The magnitude of the data you can collect and the very distributed environment make intrusion detection on [a] grid far more complex, he says.

Jarno Niemel, a senior security researcher at F-Secure, a company that designs antivirus and computer security systems, says CERNs use of machine learning to train its network defenses will give the lab much-needed flexibility in protecting its grid, especially when searching for new threats. Still, artificially intelligent intrusion detection is not without risksand one of the biggest is whether Gomez and his team can develop machine-learning algorithms that can tell the difference between normal and harmful activity on the network without raising a lot of false alarms, Niemel says.

CERNs AI cybersecurity upgrades are still in the early stages and will be rolled out over time. The first test will be protecting the portion of the grid used by ALICE (A Large Ion Collider Experiment)a key LHC project to study the collisions of lead nuclei. If tests on ALICE are successful, CERNs machine learningbased security could then be used to defend parts of the grid used by the institutions six other detector experiments.

Excerpt from:

World's Most Powerful Particle Collider Taps AI to Expose Hack Attacks - Scientific American

Last week Facebook solicited help with what it dubbed hard questions including how it should tackle the spread of terrorism propaganda on its platform.

Yesterday Google followed suit with its own public pronouncement, via an op-ed in the FTnewspaper, explaining how its ramping up measures to tackle extremist content.

Both companies have been coming under increasing political pressure in Europe especially to do more to quash extremist content with politicians including in the UK and Germany pointing the finger of blame at platforms such as YouTube for hosting hate speech and extremist content.

Europe has suffered a spate of terror attacks in recent years, with four in the UK alone since March. And governments in the UK and France are currently considering whether to introduce a new liability for tech platforms that fail to promptly remove terrorist content arguing that terrorists are being radicalized with the help of such content.

Earlier this month the UKs prime minister also called for international agreements between allied, democratic governments to regulate cyberspace to prevent the spread of extremism and terrorist planning.

While in Germany a proposal that includes big fines for social media firms that fail to take down hate speech has already gained government backing.

Besides the threat of fines being cast into law, theres an additional commercial incentive for Google after YouTube faced an advertiser backlash earlier this yearrelated to ads being displayed alongside extremist content, with several companies pulling their ads from the platform.

Google subsequentlyupdated the platforms guidelinesto stop ads being served to controversial content, including videos containing hateful content and incendiary and demeaning content so their makers could no longer monetize the content via Googles ad network. Although the company still needs to be able to identify such content for this measure to be successful.

Rather than requesting ideas for combating the spread of extremist content, as Facebook did last week, Google is simply stating what its plan of action is detailingfour additional steps it says its going to take, and conceding that more action is needed to limit the spread of violent extremism.

While we and others have worked for years to identify and remove content that violates our policies, the uncomfortable truth is that we, as an industry, must acknowledge that more needs to be done. Now, writesKent Walker, general counselGoogle in a blog post.

The four additional steps Walker lists are:

Despite increasing political pressure over extremism and the attendant bad PR (not to mention threat of big fines) Google is evidently hoping to retain its torch-bearing stance as a supporter of free speech by continuing to host controversial hate speech on its platform, just in a way that means it cant be directly accused of providing violent individuals with a revenue stream. (Assuming its able to correctly identify all the problem content, of course.)

Whether this compromise will please either side on the remove hate speech vs retain free speech debate remains to be seen. The risk is it will please neither demographic.

The success of the approach will also stand or fall on how quickly and accurately Google is able to identify content deemed a problem and policing user-generated content at such scale is a very hard problem.

Its not clear exactly how many thousands of content reviewers Google employs at this point weve asked and will update this post with any response.

Facebook recently added an additional 3,000 to its headcount, bringing the total number of reviewers to 7,500. CEO Mark Zuckerberg also wants to apply AI to the content identification issue but has previously said its unlikely to be able to do this successfully for many years.

Touching on what Google has been doing already to tackle extremist content, i.e. prior to these additional measures, Walker writes: We have thousands of people around the world who review and counter abuse of our platforms. Our engineers have developed technology to prevent re-uploads of known terrorist content using image-matching technology. We have invested in systems that use content-based signals to help identify new videos for removal. And we have developed partnerships with expert groups, counter-extremism agencies, and the other technology companies to help inform and strengthen our efforts.

See the article here:

Google to ramp up AI efforts to ID extremism on YouTube - TechCrunch

In the past four months alone, there have been three separate terrorist attacks across the UK (and possibly a third reported just today) and thats after implementing efforts that the Defense Secretary claimed helped in thwarting 12 other incidents there in the previous year.

That spells a massive challenge for companies investing in curbing the spread of terrorist propaganda on the web. And although itd most certainly be impossible to stamp out the threat across the globe, its clear that we can do a lot more to tackle it right now.

Last week, we looked at some steps that Facebook is taking to wipe out content promoting and sympathizing with terrorists causes, which involve the use of AI and relying on reports from users, as well as the skills of a team of 150 experts to identify and take down hate-filled posts before they spread across the social network.

Now, Google has detailed the measures its implementing in this regard as well. Similar to Facebook, its targeting hateful content with machine learning-based systems that can sniff it out, and also working with human reviewers and NGOs in an attempt to introduce a nuanced approach to censoring extremist media.

The trouble is, battling terrorism isnt what these companies are solely about; theyre concerned about growing their user bases and increasing revenue. The measures they presently implement will help sanitize their platforms so theyre more easily marketable as a safe place to consume content, socialize and shop.

Meanwhile, the people who spread propaganda online dedicate their waking hours to finding ways to get their message out to the world. They can, and will continue to innovate so as to stay ahead of the curve.

Ultimately, whats needed is a way to reduce the effectiveness of this propaganda. There are a host of reasons why people are susceptible to radicalization, and those may be far beyond the scope of the likes of Facebook to tackle.

AI is already being used to identify content that human response teams review and take down. But I believe that its greater purpose could be to identify people who are exposed to terrorist propaganda and are at risk of being radicalized. To that end, theres hope in the form of measures that Google is working on. In the case of its video platform YouTube, the company explained in a blog post:

Building on our successful Creators for Change programme promoting YouTube voices against hate and radicalisation, we are working with Jigsaw to implement the Redirect Method more broadly across Europe.

This promising approach harnesses the power of targeted online advertising to reach potential ISIS recruits, and redirects them towards anti-terrorist videos that can change their minds about joining. In previous deployments of this system, potential recruits have clicked through on the ads at an unusually high rate, and watched over half a million minutes of video content that debunks terrorist recruiting messages.

In March, Facebook began testing algorithms that could detect warning signs of users in the US suffering from depression and possibly contemplating self-harm and suicide. To do this, it looks at whether people are frequently posting messages describing personal pain and sorrow, or if several responses from their friends read along the lines of, Are you okay? The company then contacts at-risk users to suggest channels they can seek out for help with their condition.

I imagine that similar tools could be developed to identify people who might be vulnerable to becoming radicalized perhaps by analyzing the content of the posts they share and consume, as well as the networks of people and groups they engage with.

The ideas spread by terrorists are only as powerful as they are widely accepted. It looks like well constantly find ourselves trying to outpace measures to spread propaganda, but what might be of more help is a way to reach out to people who are processing these ideas, accepting them as truth and altering the course their lives are taking. With enough data, its possible that AI could be of help but in the end, well need humans to talk to humans in order to fix whats broken in our society.

Naturally, the question of privacy will crop up at this point and its one that well have to ponder before giving up our rights but its certainly worth exploring our options if were indeed serious about quelling the spread of terrorism across the globe.

Read next: How secure is your favorite messaging app?

More here:

AI is our best weapon against terrorist propaganda - The Next Web - TNW

Google quietly released an academic paper that could provide a blueprint for the future of machine learning. Called One Model to Learn Them All, it lays out a template for how to create a single machine learning model that can address multiple tasks well.

The MultiModel, as the Google researchers call it, was trained on a variety of tasks, including translation, language parsing, speech recognition, image recognition, and object detection. While its results dont show radical improvements over existing approaches, they illustrate that training a machine learning system on a variety of tasks could help boost its overall performance.

For example, the MultiModel improved its accuracy on machine translation, speech, and parsing tasks when trained on all of the operations it was capable of, compared to when the model was just trained on one operation.

Googles paper could provide a template for the development of future machine learning systems that are more broadly applicable, and potentially more accurate, than the narrow solutions that populate much of the market today. Whats more, these techniques (or those they spawn) could help reduce the amount of training data needed to create a viable machine learning algorithm.

Thats because the teams results show that when the MultiModel is trained on all the tasks its capable of, its accuracy improves on tasks with less training data. Thats important, since it can be difficult to accumulate a sizable enough set of training data in some domains.

However, Google doesnt claim to havea master algorithm that can learn everything at once. As its name implies, the MultiModel network includes systems that are tailor-made to address different challenges, along with systems that help direct input to those expert algorithms. This research does show that the approach Google took could be useful for future development of similar systems that address different domains.

Its also worth noting that theres plenty more testing to be done. Googles results havent been verified, and its hard to know how well this research generalizes to other fields. The Google Brain team has released the MultiModel code as part of the TensorFlow open source project, so other people can experiment with it and find out.

Google also has some clear paths to improvement. The team pointed out that they didnt spend a lot of time optimizing some of the systems fixed parameters (known as hyperparameters in machine learning speak), and going through more extensive tweaking could help improve accuracy in the future.

Updated 10:45: This story initially said that there was not a timetable for releasing the MultiModel code under an open source license. The code was released last week. This story has been updated to note that and include a link to the repository.

Here is the original post:

Google advances AI with 'one model to learn them all' - VentureBeat