Cloud Hosting

Science fiction has helped shaped societys understanding and expectation of advanced AI technology in the future. However, Shadow Robot Company Director Rich Walker argued artificial intelligence technology could be used in industries we would not expect. While speaking to Express.co.uk, he explained that a new A.I tech could be introduced to be used in sectors such as the estate agency or booking services.

He added massive leaps in AI capabilities in recent years had raised expectations of what people believe artificial intelligence can be used for.

He said: AI technology has really been promising a lot for a very long time.

In the last few years we have really started to see some very impressive and surprising successes.

Self-driving cars are starting to be something that has gone from a complete fairy pipe-dream to the question of when are we going to see a self-driving car, because surely we can get one now.

DON'T MISS:AI WILL lead to human extinction if one crucial change isnt made

I think what will happen in the next couple of year is we will see some areas that we werent expecting suddenly being done by AI

Everyone will be like, yes, of course, we could have artificial intelligence in this industry.

Maybe it will be an estate agency or train booking.

Something that is a complicated annoying problem.

More:

Shocking ways AI technology will revolutionise every day industries in YOUR lifetime - Express.co.uk

By Vivek Wadhwa

AI has the potential to be as transformative to the world as electricity, by helping us understand the patterns of information around us. But it is not close to living up to the hype. The super-intelligent machines and runaway AI that we fear are far from reality; what we have today is a rudimentary technology that requires lots of training. Whats more, the phrase artificial intelligence might be a misnomer because human intelligence and spirit amount to much more than what bits and bytes can encapsulate.

I encourage readers to go back to the ancient wisdoms of their faith to understand the role of the soul and the deeper self. This is what shapes our consciousness and makes us human, what we are always striving to evolve and perfect. Can this be uploaded to the cloud or duplicated with computer algorithms? I dont think so.

What about the predictions that AI will enable machines to have human-like feeling and emotions? This, too, is hype. Love, hate and compassion arent things that can be codified. Not to say that a machine interaction cant seem human we humans are gullible, after all. According to Amazon, more than 1 million people had asked their Alexa-powered devices to marry them in 2017 alone. I doubt those marriages, should Alexa agree, would last very long!

Todays AI systems do their best to replicate the functioning of the human brains neural networks, but their emulations are very limited. They use a technique called Deep Learning. After you tell a machine exactly what you want it to learn and provide it with clearly labelled examples, it analyses the patterns in those data and stores them for future application. The accuracy of its patterns depends on completeness of data. So the more examples you give it, the more useful it becomes.

Herein lies a problem, though an AI system is only as good as the data it receives. It is able to interpret them only within the narrow confines of the supplied context. It doesnt understand what it has analysed so it is unable to apply its analysis to other scenarios. And it cant distinguish causation from correlation.

AI shines in performing tasks that match patterns in order to obtain objective outcomes. Examples of what it does well include playing chess, driving a car on a street and identifying a cancer lesion in a mammogram. These systems can be incredibly helpful extensions of how humans work, and with more data, the systems will keep improving. Although an AI machine may best a human radiologist in spotting cancer, it will not, for many years to come, replicate the wisdom and perspective of the best human radiologists. And it wont be able to empathise with a patient in the way that a doctor does. This is where AI presents its greatest risk and what we really need to worry about use of AI in tasks that may have objective outcomes but incorporate what we would normally call judgement. Some such tasks exercise much influence over peoples lives. Granting a loan, admitting a student to a university, or deciding whether children should be separated from their birth parents due to suspicions of abuse falls into this category. Such judgements are highly susceptible to human biases but they are biases that only humans themselves have the ability to detect.

And AI throws up many ethical dilemmas around how we use technology. It is being used to create killing machines for the battlefield with drones which can recognise faces and attack people. China is using AI for mass surveillance, and wielding its analytical capabilities to assign each citizen a social credit based on their behaviour. In America, AI is mostly being built by white people and Asians. So, it amplifies their inbuilt biases and misreads African Americans. It can lead to outcomes that prefer males over females for jobs and give men higher loan amount than women. One of the biggest problems we are facing with Facebook and YouTube is that you are shown more and more of the same thing based on your past views, which creates filter bubbles and a hotbed of misinformation. Thats all thanks to AI.

Rather than worrying about super-intelligence, we need to focus on the ethical issues about how we should be using this technology. Should it be used to recognise the faces of students who are protesting against the Citizenship (Amendment) Act? Should India install cameras and systems like China has? These are the types of questions the country needs to be asking.The writer is a distinguished fellow and professor, Carnegie Mellon Universitys College of Engineering, Silicon Valley.

This story is part of the 'Tech that can change your life in the next decade' package.

Link:

Quantum leap: Why we first need to focus on the ethical challenges of artificial intelligence - Economic Times

We Counter Hate

One of the most fascinating examples of social innovation Ive been tracking recently was the We Counter Hate platform, by Seattle-based agency Wunderman Thompson Seattle (formerly POSSIBLE) that sought to reduce hate speech on Twitter by turning retweets of these hateful messages into donations for a good cause.

Heres how it worked: Using machine learning, it first identified hateful speech on the platform. A human moderator then selected the most offensive and most dangerous tweets and attached an undeletable reply, which informed recipients that if they retweet the message, a donation will be committed to an anti-hate group. In a beautiful twist this non-profit wasLife After Hate, a group that helps members of extremist groups leave and transition to mainstream life.

Unfortunately (and ironically) on the very day I reached out to the team, Twitter decided to allow users to hide replies in their feeds in an effort to empower people faced with bullying and harassment, eliminating the reply function which was the main mechanism that gave #WeCounterHate its power and led to it being able to remove more than 20M potentialhatespeech impressions.

Undeterred, I caught up with some members of the core teamShawn Herron, Jason Carmel and Matt Gilmoreto find out more about their journey.

(From left to right)Shawn Herron, Experience Technology Director @ Wunderman ThompsonMatt ... [+] Gilmore, Creative Director @ Wunderman ThompsonJason Carmel, Chief Data Officer @ Wunderman Thompson

Afdhel Aziz: Gentlemen, welcome. How did the idea for WeCounterHate come about?

Shawn Herron: It started when we caught wind of what the citizens of the town of Wunsiedel, Germany were doing to combat the annual extremists that were descending on their town every year to hold rally and march through the streets. The towns people had devised a peaceful way to upend the extremists efforts by turning their hateful march into an involuntary walk-a-thon that benefitted EXIT Deutschland, an organization that helps people escape extremist groups. For every meter the neo Nazis marched 10 euro would be donated to Exit Deutschland. The question became, how can we scale something like that so anyone, anywhere, could have the ability to fight against hate in a meaningful way?

Jason Carmel: We knew that, to create scale, it had to be digital in nature and Twitter seemed like the perfect problem in need of a solution. We figured if we could reduce hate on a platform of that magnitude, even a small percentage, it could have a big impact. We started by developing an innovative machine-learning and natural-language processing technology that could identify and classify hate speech.

Matt Gilmore: But we still needed the mechanic, a catch 22, that would present those looking to spread hate on the platform with a no-win decision to make. Thats when we stumbled onto the fact that Twitter didnt allow people to delete comments on their tweets. The only way to remove a comment was to delete the post entirely. That mechanic is what gave us a way put a permanent marker, in the form of an image and message, on tweets containing hate speech. Its that permanent marker that let those looking to retweet, and spread hate, know that doing so would benefit an organization theyre opposed to, Life After Hate. No matter what they chose to do, love wins.

Aziz: Fascinating. So, what led you to the partnership with Life After Hate and how did that work?

Carmel: Staffed and founded by former hate group members and violent extremists, Life After Hate is a non-profit that helps people in extremist groups break from that hate-filled lifestyle. They offer a welcoming way out thats free of judgement.We collaborated with them in training the AI thats used to identify hate speech in near real time on Twitter. With the benefit of their knowledge our AI can even find hidden forms of hate speech (coded language, secret emoji combinations) in a vast sea of tweets. Their expertise was crucial to align the language we used when countering hate, making it more compassionate and matter-of-fact, rather than confrontational.

Herron: Additionally, their partnership just made perfect sense on a conceptual level as the beneficiary of the effort. If youre one of those people looking to spread hate on Twitter, youre much less likely to hit retweet knowing that youll be benefiting an organization youre opposed to.

Aziz: Was it hard to wade through that much hate speech? What surprised you?

Herron: Being exposed to all the hate filled tweets was easily the most difficult part of the whole thing. The human brain is not wired to read and see the kinds of messages we encountered for long periods of time. At the end of the countering process, after the AI identified hate, we always relied on a human moderator to validate it before countering/tagging it. We broke up the shifts between many volunteers, but it was always quite difficult when it was your shift.

Carmel: We learned that the identification of hate speech was much easier than categorizing it. Or initial understanding of hate speech, especially before Life After Hate helped us, was really just the movie version of hate speech and missed a lot of hidden context. We were also surprised at how much the language would evolve relative to current events. It was definitely something we had to stay on top of.

We were surprised by how broad a spectrum of people the hate was coming from. We went in thinking wed just encounter a bunch of thugs, but many of these people held themselves out as academics, comedians, or historians. The brands of hate some of them shared were nuanced and, in an insidious way, very compelling.

We were caught off guard by the amount of time and effort those who disliked our platform would take to slam or discredit it. A lot of these people are quite savvy and would go to great lengths to attempt to undermine our efforts. Outside of the things we dealt with in Twitter, one YouTube hate-fluencer made a video, close to an hour long, that wove all sorts of intricate theories and conspiracies about our platform.

Gilmore: We were also surprised by how wrong our instincts were. When we first started, the things we were seeing made us angry and frustrated. We wanted to come after these hateful people in an aggressive way. We wanted to fight back. Life After Hate was essential in helping course-correct our tone and message. They helped us understand (and wed like more people to know) the power of empathy combined with education, and its ability to remove walls rather than build them between people. It can be difficult to take this approach, but it ultimately gets everyone to a better place.

Aziz: I love that idea empathy with education. What were the results of the work youve done so far? How did you measure success?

Carmel: The WeCounterHate platform radically outperformed expectations of identifying hate speech (91% success) relative to a human moderator, as we continued to improve the model over the course of the project.

When @WeCounterHatereplied to a tweet containing hate, it reduces the spread of that hate by an average of 54%. Furthermore, 19% of the "hatefluencers" deleted their original tweet outright once it had been countered.

By our estimates, the Hate Tweets we countered were shared roughly 20 million fewer times compared to similar Hate Tweets by the same authors that werent countered.

Matt: It was a pretty mind-bending exercise for people working in an ad agency, that have spent our entire careers trying to gain exposure for the work we do on behalf of clients, to suddenly be trying to reduce impressions. We even began referring to WCH as the worlds first reverse-media plan, designed to reduce impressions by stopping retweets.

Aziz: So now that the project has ended, how do you hope to take this idea forward in an open source way?

Herron: Our hope was to counter hate speech online, while collecting insightful data about how hate speech online propagates. Going forward, hopefully this data will allow experts in the field to address the hate speech problem at a more systemic level. Our goal is to publicly open source archived data that has been gathered, hopefully next quarter (Q1 2020)

I love this idea on so many different levels. The ingenuity of finding a way to counteract hate speech without resorting to censorship. The partnership with Life After Hate to improve the sophistication of the detection. And the potential for this same model to be applied to so many different problems in the world (*anyone want to build a version for climate change deniers?). It proves that the creativity of the advertising world can truly be turned into a force for good, and for that I salute the team for showing us this powerful act of moral imagination.

Read this article:

The Power Of Purpose: How We Counter Hate Used Artificial Intelligence To Battle Hate Speech Online - Forbes

The past decade has seen the rise of remarkably human personal assistants, increasing automation in transportation and industrial environments, and even the alleged passing of Alan Turings famous robot consciousness test. Such innovations have taken artificial intelligence out labs and into our hands.

A.I. programs have become painters, drivers, doctors assistants, and even friends. But with these new benefits have also come increasing dangers. This ending decade saw the first, and likely not the last, death caused by a self-driving car.

This is #20 on Inverses 20 predictions for the 2020s.

And as we head toward another decade of machine learning and robotics research, questions surrounding the moral programming of A.I. and the limits of their autonomy will no longer be just thought-experiments but time-sensitive problem.

One such area to keep on eye on going forward into a new decade will be partially defined by this question: what kind of legal status will A.I. be granted as their capabilities and intelligence continues to scale closer to that of humans? This is a conversation the archipelago nation Malta started in 2018 when its leaders proposed that it should prepare to grant or deny citizenship to A.I.s just as they would humans.

The logic behind this being that A.I.s of the future could have just as much agency and potential to cause disruption as any other non-robotic being. Francois Piccione, policy advisor for the Maltese government, told Inverse in 2019 that not taking such measures would be irresponsible.

Artificial Intelligence is being seen in many quarters as the most transformative technology since the invention of electricity, said Piccione. To realize that such a revolution is taking place and not do ones best to prepare for it would be irresponsible.

While the 2020s might not see fully fledged citizenship for A.I.s, Inverse predicts that there will be increasing legal scrutiny in coming years over who is legally responsible over the actions of A.I., whether it be their owners or the companies designing them. Instead of citizenship or visas for A.I., this could lead to further restrictions on the humans who travel with them and the ways in which A.I. can be used in different settings.

Another critical point of increasing scrutiny in the coming years will be how to ensure A.I. programmers continue to think critically about the algorithms they design.

This past decade saw racism and death as the result of poorly designed algorithms and even poorer introspection. Inverse predicts that as A.I. continues to scale labs will increasingly call upon outside experts, such as ethicists and moral psychologists, to make sure these human-like machines are not doomed to repeat our same, dehumanizing, mistakes.

As 2019 draws to a close, Inverse is looking to the future. These are our 20 predictions for science and technology for the 2020s. Some are terrifying, some are fascinating, and others we can barely wait for. This has been #20. Read a related story here.

See the article here:

In the 2020s, human-level A.I. will arrive, and finally ace the Turing test - Inverse