In August, trolls took to Twitter to spout racist slurs at Paul Pogba of Manchester United for committing the unforgivable offense of missing a kick from 12 yards. They felt comfortable doing it because it's easy to be anonymous on social media since all it takes to set up a fake account is an email address, password and name. People can hide heinous actions behind these accounts, shielding themselves from taking responsibility for their actions and possible litigation.

Perhaps unbeknownst to Pogba, his home country of France has already been taking action to try to prevent things like this happening on social media, as Facebook founder Mark Zuckerberg met with French President Emmanuel Macron in May to discuss the platform's role in spreading hate speech and misinformation. Facebook has recognized that it needs to do more to stop extremist content on its platform and, as a result, has granted the French courts access to IP addresses to help them identify its proponents.

But even if Facebook and Twitter allowed this for all countries, does it go far enough? While social media provides a valuable service to billions of people worldwide by connecting the world and giving a voice to those who typically don't have one, such as with #MeToo movement, it also has its issues. It can potentially be used as a platform for misleading people on important topics such as the U.S. elections, European elections, Brexit and the Cambridge Analytica scandal.

There is increasing pressure on social media companies to protect their users from this content. In 2017, Germany passed a law that gives social media companies 24 hours to remove any "obviously illegal" posts or face stiff financial penalties. Now, France is looking to go a step further by gaining access to these IP addresses.

While knowing someone's IP address will certainly help, it's no panacea given the proliferation and consumerization of VPN software that can mask a computer's IP address. A better way would be to know what legal identity is connected with the social media account in question. This would make investigating and holding people accountable much easier and help the public regain trust in social media platforms. I believe more identity proofing will only elevate and enhance a social media platform's brand and reputation and make perpetrators think twice about committing such atrocities in the first place.

At Onfido, we're actively working with some social media companies to help them provide a more secure and trusted route to authenticating their users. For bots, which are essentially computer algorithms that mimic human behavior in online social networks to spread misinformation, we can periodically introduce "liveness" tests. These tests act as a CAPTCHA, where the account holder would need to carry out a selfie video in order to proceed to post an article to their account.

Facebook recently showed some signs of moving in this direction with its selfie CAPTCHA, but it doesn't take into account higher-value or riskier transactions. Although there are fears that Facebook may misuse face data, it has confirmed that this service is dedicated to motion only in order to stop bots and "does not use facial recognition."

We're not the only ones looking at this problem. There are startups like Digital Shadows, a cybersecurity firm that uses AI to identify fake websites, phony social media profiles and "counterfeit" company domains set up to spoof a brand's online identity.

LinkedIn also announced it is proactively taking a stance by using a mixture of machine learning and human moderation "to detect groups of accounts that look or act similarly, which implies they were created or controlled by the same bad actor."

Using methods like these can help social media networks stop the spread of misinformation via bots like the recent accusations surrounding the Hong Kong riots while making hate speech spreaders accountable for their actions and helping businesses create a foundation of trust.

Social media companies need only look at the sharing economy space to see how digital identity verification is taking off. Many scooter, car and apartment rental companies are using it to help build trust between service providers and consumers. It has become a necessity in the financial services industry, where fraudulent accounts have the potential for massive financial loses and large fines from regulators if they don't follow AML or KYC policies. Some new online banks have welcomed the arrival of online identity verification, which has helped make banks such as Revolut and Monzo successful at onboarding users quickly but safely.

From my experience, the leaders in these companies needed to figure out the extent to which identity verification had a strategic role within their organization whether it was more a tick-box or core to the integrity of their offerings. They also had to consider where their customers are and how common fraud is in those places before deciding their comfort level with respect to fraud, answering whether they wanted to prioritize the speed of onboarding or the ability to catch all bad actors.

According to research published by DataReportal, there are roughly 3.5 billion global social media users. I believe we have a responsibility to take action now before hate speech becomes the new norm and we find it so hard to distinguish real news from fake news that we no longer accept or consume information.

Here is the original post:
Identity Verification Is Critical To Combating Misinformation And Extremist Content - Forbes

First announced at a University Senate Meeting in the spring, Georgia State welcomed a group of researchers from both the University of New Mexico and the Mind Research Network, a non-profit imaging center. This fall, the two seperate research groups came together to work at Georgia State.

These new researchers now combined to work at the Center for Translational Research in Neuroimaging and Data Science (TReNDS), which is now located on the 18th floor of 55 Park Place.

All of the researchers were brought over by one person: Vince Calhoun, the founding director and visionary of TReNDS, who also made the trip across the states to come to Georgia State.

But why did the researchers travel across the country to come here?

According to Calhoun, the university was interested in making a mark and expanding their brain imaging portfolio.

The TReNDS center researches the brain in a more general manner, meaning that the center is looking at healthy and unhealthy brains, normal and disordered brains and everything else in between.

With the analysis of brain imaging comes complicated data, especially for the unhealthy and disordered brains. What the center has developed and continues to develop are the techniques for making sense of the complex brain imaging data.

On a deeper level, there are several other, more specific projects going on. One of them is a research project focusing on using tools to analyze brain-imaging data in order to better understand and find features relating to abnormal human behavior, specifically psychiatric disorders like dementia and schizophrenia.

Another project is international, involving the study and research of the effects of city lights and the greenness of the environment on brain patterns using satellite imaging data and brain imaging data.

Sergei Plis, associate professor of computer science and working member of TReNDS, compared the tools involved, to simplify, as being similar to how google translate functions.

Although the center is located at Georgia State, TReNDS is a tri-institutional center shared between Georgia State, Georgia Tech and Emory University. This means that each institution contributes resources in some way to the TReNDS center, such as faculty or support for postdoctorales. Emory, specifically, contributes to the patient population and clinical expertise within psychiatry and neurology.

As work transferred to the state of Georgia, so did ten of the graduate research students involved with the center. But Georgia State lacked one thing: a graduate engineering program.

Because of this, the ten students involved transferred to Georgia Tech. Since the move, there are some postdoctoral research assistants who have joined the team from Georgia State.

Reliable and accurate data needs large and diverse sample sizes, according to Plis. With help from the growing neuroimaging community, TReNDS is able to receive data from across the nation and around the world. Some collaborators are in India, China, and England.

How is this possible? Doesnt the Health Insurance Portability and Accountability Act, better known as HIPAA, protect patient data? What about ethical and legal issues? And why would researchers share data that they have worked years to develop?

As Plis explains it, TReNDS built a system that allows for the data to stay where it is and never leave the data center no matter the location.

We can connect online using algorithms we developed to run around and collect certain data here and there in different data sets. We are sharing minimal information, and we still get the results we need as if all the data were still together, Plis said. We are kind of sharing without sharing and solving this problem of data sharing.

Along with the data gathered around the world, the TReNDS center utilizes the Center for Advanced Brain Imaging, located near Georgia Tech. Here, you will find a 3-Tesla Siemens Prisma-Fit MRI system, which according to Calhoun is the most modern, cutting-edge scanner for research available.

The TReNDS center also developed some collaborative tools that are put into CABI so that other people can more easily get access to their data, share it, collaborate and anonymize it.

The data will all get archived and analyzed in standard pipeline, Calhoun said. We speed up the process and eventually were planning to have that enable us to compute scores for the different brain imaging markers that were developing.

Whats unique about the center, according to Jean Liu, an associate professor of computer science at Georgia State, is the strength of the team.

We have a group of extremely trained engineers that can use very sophisticated algorithms to study brain imaging, which is not very common within other brain centers, Liu said.

Other centers, Liu said, will have people with various backgrounds like neuroscience and psychology. Although this may be helpful in some respects, there are obstacles that present themselves in finding specific features when big data is presented, according to Liu.

This is where the trained engineers with sophisticated algorithms that the team relies on come in. Their job is to develop tools using the algorithms to help people in different professional backgrounds better understand and find the specific data they are looking for.

Read more here:
The reachers who travelled across the country: Why they came to Georgia State to study the brain - The Signal

Marrying up the law of entropy and the fact that the number of digital payments being made across the globe is increasing dramatically, by default means that fraud management processes need to be constantly refined.

Entropy as a scientific principle concerns the loss of energy from a system and describes how an ordered system moves towards disorder. The key point in understanding entropy is that it cannot be stopped, and to maintain a desired level of order, energy or performance, more of the same must be added into the system. A simple example is when you wear your coat on a cold day. When you take your coat off, entropy is the process that explains the loss of warmth, which can only be countered by putting it on again.

In the payment industry this is fully supported by the fact that fraud has reached the highest levels on record, affecting more organizations than ever. The scale of the problem was revealed in last years PWC Global Economic Crime and Fraud Survey. Nearly half (49%) of the 7,228 businesses across 123 territories that were interviewed reported that they had experienced fraud and economic crime over a two-year period.

Today, fraud management consists of several manual processes models and rules performance monitoring, fraud pattern discovery and fraud alert management, to name a few. While these manual processes may be manageable at first, as the number of payment types and channels increase, it can rapidly become untenable to add more and more staff to manage and monitor all processes. Managing fraud can become very expensive, which is why efficient management processes are so important.

The fact that entropy exists, and remains a factor that cannot be stopped, means that all aspects of the business need to be monitored. You may be most interested in product development or working with clients, but if you do not watch the other parts of the business, such as accounts payable or accounting, entropy will eventually cause problems. Management regarding entropy aims at small corrections to keep projects or departments on track, rather than letting those areas run in isolation, until there is a much larger breakdown or problem.

There is a lot of information on how machine learning is helping to understand human behavior and more specifically, false/positive detection. However, there is little available research on how this relates to the end-to-end process within fraud management.

This draws you back to the fact that the industry is focused, and rightly so, on detecting fraud, but is not focused on evaluating the impact to the whole end-to-end process. Clearly the interdependencies on these two activity streams are significant, so the question is why both factors arent being considered by the fraud prevention suppliers.

While things naturally move to disorder over time, we can position ourselves to create stability. There are two types of stability: active and passive. Consider an airplane, which, if designed well, should be able to fly without intervention this is passive stability. Conversely, a fighter jet requires active stability. With active stability, you are applying energy to a system, in order to bring about some advantage (keeping the plane from crashing). The plane cant fly for more than a few seconds, without having to adjust its wings and these adjustments happen so quickly that its controlled by software in modern airplanes.

Autopilot ML, in this analogy, is the fighter pilot for fraud defense. Reacting quickly to fraud pattern changes by creating new machine learning models to stop the threats; while continually optimizing the fraud detection strategy, so that it is equipped to counter the newest and most damaging threats, while maintaining high acceptance and low insult rates.

In summary, machine learning is having a huge impact on the entropy of fraud detection, it is helping to maintain order, providing the system with passive stability. However, as stated above, without constant refinement and active stability, effectiveness is likely to decay. This rate of entropic decay needs to be measured, understood and more importantly learned from and acted upon. In terms of the latter, it is the efficiency of the changes that are critical, essentially providing stability optimization.

Are the fraud prevention products and services you are currently deploying maintaining stability?

For reprint and licensing requests for this article, click here.

Link:
Business entropy is making the fraud problem worse - PaymentsSource

At the beginning of this millennium, just two months after 9/11, scientists in the Peruvian Amazon made one of the most unsettling discoveries ever to be reported in the pages of the prestigious journal Mammalian Biology.

It was a sloth, and it was hanging out inside of a toilet, and it was absolutely gorging itself on a potent liquid slurry of human waste by the handful. It apparently didn't want to be seen engaging in whatever esoteric sloth ritual this was.

"It was scooping with one hand from the semi-liquid manure composed of faeces, urine and toilet paper and then eating from the hand," the researchers reported in a 2011 research paper that made the rounds last week for some unknown reason, but which I absolutely could not resist clicking on because I am broken.

"When more persons gathered around the latrine to watch this bizarre behavior, the sloth emerged from the latrine and climbed into the nearest tree," the researchers wrote.

Over the next few years, until the latrine was fenced in in 2007, the researchers observed 25 more sloths heading to the poo pit for a midnight feast. As for why this is a thing, the researchers speculated that the sloths could be trying to glean some nutrients from human waste or possibly eating worms.

And yet, I have questions that demand answers:

1. Did we bother you?

I have seen this face before, in the mirror. This is how I look when I am forced to admit I ate an entire pizza after the box is discovered. I am weak, and I am ashamed, and I am fundamentally seen. This is Lenny from The Simpsons feebly pleading, "Please don't tell anyone how I live" after his wretched existence is exposed by a wall in his house comically falling down. I empathize deeply, and I want to apologize if we humans interrupted or embarrassed you, the poo sloth.

2. Where are you now?

It's been a long time since we found you eating crap. Are you OK? Have you moved on to trading cryptocurrencies or investing in cannabis? How long do sloths even live?

3. What is sloths' whole deal with poo?

It seems like poo is a whole thing for sloths. These creatures have one gigantic, probably-painful poo once a week. It is risky because they have to do it on the ground, exposing them to predators, and honestly does not sound all that fun. And yet, sloths desire to consume this same substance with unmatched ferocity, even desperation. What gives?

All I know is that, somehow, this sloth set the tone for our current shitworld which was just ramping up when we found the long-armed toilet hamster eating its first handful of poo, like it was pulling some huge cosmic lever.

Visit link:
We Have Questions for This Sloth Found Gorging on Human Poo in a Toilet - Free

On November 21 around 6 p.m., 18-year-old Kennedy Stokes said she was at Walmart with her sister and cousin when they ran into two men who tried to talk to them several times.

Stokes said they felt like they were being followed and texted their parents.

A day later, Stokes said she was driving to her apartment when her car started making a rattling noise. She said she made it to the entrance of her apartment complex on the east side, when she got out to check under the hood of her car.

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"When I closed the hood down when I'm walking back towards the driving side of my car this guy is approaching me he doesn't say anything, he legit just started grabbing on me," Stokes said.

She said she noticed he was wearing gloves and what she believes was a box cutter in his hand. She told us she strongly believes it was one of the men from Walmart the day before because she said she remembers the clothing one of the men was wearing.

"He was grabbing on me my jacket unzips and he's like cutting my chest I had all of these scratches on my chest," "Stokes said.

Her mother, Kana Stokes, not knowing what the man's intentions were has a lot of thoughts running through her mind. She said she is mainly worried that it could be linked to human trafficking, but she doesn't know for sure.

"It really is sickening, it really is taking over my mindset right now, Kana Stokes said.

Fourteen miles away, on the same day Stokes said she was attacked, another mother said she experienced a terrifying situation.

A mother, who wished to remain anonymous for safety, said her 12-year-old son stayed in the car while she paid for gas at a UDF gas station on Indianola.

"When I came outside, he was very upset he was visibly shaken and he said that someone had tried to get into the car," she said.

The young boy told his mother a man was yanking on the handle, not saying anything, just looking at him trying to get in. In the police report, Columbus police said the incident was caught on a security camera which they are reviewing.

Sgt. James Fuqua said the man had talked to several other people in the lot of this business before leaving in a white van. He said it now an investigation with the human trafficking task force.

Two different situations, two locations, but both mothers fearing "what if?"

The mother of the 12-year-old boy said, "Immediately and this is because I'm aware of what human trafficking is, I thought this could've been a situation where I never saw my son again."

Sgt. Fuqua said most, not all, but most human traffickers know the victim and try to build a relationship with them earning the victim's trust.

Regardless, Columbus police take every report, like these two situations, very seriously and look at every possible motive or intention of the stranger.

"You don't want to just assume that when someone is approaching someone they just want theft, you don't want to ever assume that maybe they're trying to make that person a victim of sexual assault, it could always be as extreme as someone trying to take someone away for the purposes of human trafficking," Sgt. James Fuqua said.

He said when it comes to the topic of human trafficking, human traffickers don't just target young females, a victim could be any gender or any age.

Sgt. Fuqua said if a victim is being human trafficked, they may show some signs of missing work, constantly fearing for their life but not explaining why, personality changes, financial changes, and signs of physical or mental abuse.

He said if someone finds themselves in a questionable situation, make a lot of noise and call attention to what is happening. He said to make it known to the stranger and loudly, that their behavior is unwanted.

In order to avoid certain situations, he suggests to park or walk in well-lit areas and make sure to be visible to others at all times.

Here is a website for the human trafficking hotline: https://humantrafficking.ohio.gov/index.html

Original post:
Columbus police address concerns of human trafficking after 2 recent attempted abduction reports - 10TV

Scientists studying cognition report that we are frequently overconfident when considering small possibilities we think they are more significant than they are, at least mathematically. Overconfidence is not necessarily good or bad; a belief in a slight chance of recovery is called hope. An abundance of caution may prompt us to be more prudent in the face of a small, but existentialrisk.

Overconfidence can be particularly problematic when two conditions prevail. First, when the information we are assessing is noisy, there is some signal of truth, but it is accompanied by a degree of doubt. Second, when this same noisy informational environment provides weak feedback, that is, feedback that comes after a significant delay or that is not overly convincing. Many of the scientific positions that are controversial and attract strident polarized views often meet those two criteria.

For an older example, consider the hundred-year history of smokings effect on our health. Lung cancer, long before it was the most common cause of cancer deaths, was so rare that physicians gathered around to see this odd pathology. And in the early days of the twentieth century, many other causes of death hid the rising tide of lung cancer. Additionally, smokers dont develop lung cancer for many years after they start smoking; the feedback that smoking is harmful is an excellent example of very delayed feedback. Over the next thirty or forty years, the persistent signal of lung cancer became more evident, there was less noise, and we had longitudinal data that made the feedback stronger. Tobacco companies facing financial peril did not and could not repress the growing evidence, but they cast doubt on the conclusions, by framing the evidence as not overly convincing. By casting doubt through every available media sources, they sought to enforce the truth of their claims by shouting louder and more frequently than their opponents.

One would hope that disseminating information more broadly and cheaply would serve as a corrective; the Internet could be counted upon to reduce the distortions of noise and weak feedback. But, if anything, it has proven to be a more effective, pliable way to continue to increase the noise and spread the doubt. Searching for information on the net has been likened to drinking water from a fire hose - our first precondition for overconfidence, little signal, much noise. To use an old meme, when you use the net, no one knows youre a dog everyone can present themselves as an expert. And knowing that, makes every report a little more doubtful, it further weakens the feedback.

Science is, at its heart, about discovery, but the media that communicates science to us is often about advocacy. Everything is a sales pitch. The study funded by Big Tobacco, Big Climate, or Big Natural is readily identified. Still, government-funded research is pitched to what is politically fundable, and journals and foundations are pitched attention-getting results. One consequence of such a system is what Steven Colbert characterized as truthiness, our belief in something because it feels right; another way we share our overconfidence. The error for us lies not in the overconfidence, after all, that is our human behavior, it is in confusing the science of discovery with the sales pitch, and it results in us talking past one another rather than engaging in the discussion that is science.

Read more here:
Often Wrong, Never In Doubt - American Council on Science and Health

Meet the singer-songwriter,Brigitte Mena! The Texas-native recently released a brand new single entitled Maniac. The track is based on the Netlix show Maniac that was released last September, and is inspired by Emma Stones character, Annie Landsberg (suffering from Borderline PD) who takes a series of pills in a pharmaceutical trial that is supposed to cure her struggles/disorder. There are many references to machines including various sound effects throughout the song as a way of recreating her experience of the trials.

Brigitte Mena was born to be a storyteller, and her vehicle is music. The singer-songwriters heartrending tunes tell harrowing tales enveloped in atmospheric, ambient melodies. But the artist has her sights set on much more than producing pop rock tracks. Armed with her versatile voice, a pen and a penchant for crafting compelling songs, Mena strives to strike a chord with audiences and tell relevant, resonant stories.

Mena started crafting original music in high school, and founded her first rock band as a freshman at Southern Methodist University, where she studied Music and Psychology. Menas music studies helped her hone her craft, while her work in psychology gave her an avenue to explore her interest in human behavior. Instead of choosing one passion over the other, the artist decided to merge the two roads ahead of her. By using her talents as a musician, Mena shines a light on topics like behavior, mental health, relationships and identity.

Connect With Brigitte Mena Online Here- Website Facebook Instagram

Learn more Brigitte Mena in the following All Access interview-

Thank you for your time! So what does a typical day look like for you lately?

Of course! Thanks for taking the time to chat with me! Ialways begin my day with a good cup of coffee and usually plan out my to-dolist for the day. I can easily get overwhelmed with everything I have toaccomplish, so this simple routine of making a daily task list really helps me.Lately Ive been in the studio finishing up my next record so a lot of my daysare filled with recording and mixing sessions.

Now that we are inthe latter half of the year, how has 2019 treated you? What are some goals thatyou have had for yourself this year? How close are you to reaching them or didyou already? What are you already looking forward to in 2020?

This year has been pretty amazing. I released three singlesthis year and Im almost finished recording my second album! One of my goals Iset at the end of last year was to write a full length record, so its beenexciting seeing it all come together. Im looking forward to the release nextyear hopefully next Spring or Summer! Keep an eye out!

Growing up, howimportant was music in your life? Can you recall the moment when you decidedthat you wanted to be a musician? Was it an easy or difficult choice to make?

Music has always been my saving grace. When I think back onmy most difficult times, its music that has literally saved me. Although Ivealways loved music, it was about two years ago where I finally realized that Iwanted to make a career as a musician. I left my job to officially pursue itfull time.

Was there ever atime when you thought about doing something else? If you werent a musiciantoday, what else could you see yourself doing? Would you be as fulfilled inlife?

Definitely. I changed my major like 4 times throughoutcollege, but I ended up finishing my music degree. As much as I wanted to be amusician, the whole making it work part always scared me. I had to learn howto become comfortable with the unknown and even more comfortable with theamount of work it takes to be successful. If I wasnt doing music, Im prettysure I would be a teacher. After college, I worked a few different teachingjobs, and although I loved teaching, I knew that music was my true passion, andhow unhappy I would be if I didnt just go for it.

What has been thebiggest surprise so far about making music your career? What has been anunexpected or welcome challenge to it all? What has been the best part about itall?

Honestly, how Ive never once regretted just going for it.There were a lot of times right before I quit my job where I was thinking okay, this is just for a few months or something, so you better enjoy it! Butthe everyday challenge of fighting for something you believe in has been sorewarding. There are days where the grind is overwhelming, but I love everysecond of it. For me Its like planting a seed and watching it grow intosomething beautiful. You have to provide that seed with its nutrients, love,attention, and PATIENCE. Making a career out of music is just like that for me.

What was theinspiration for your newest track, Maniac? What was it like having it bebased on the Netflix show also called Maniac?

Maniac is inspired by Emma Stones character, Annie.Its basically a song about her experience throughout the show and thechallenges she faces. The show definitely brought the song to life. A lot of mysongs are written from a psychological perspective, but this song was moreinfluenced by her character.

How would you saythat Maniac compares to anything else that you have released?

I think content-wise its probably the most differentcompared to other songs Ive released. Ive never written a song based on ashow, so it was definitely a different experience for me.

Do you have plansto release more new music soon and a full of collection of new songs?

YES! Be on the lookout for a new album from me next year!

How would you saythat your newest music compares to anything else that you have released in thepast?

I think the biggest difference between my last record andthe record I am currently recording is the content. Maslow, the album Ireleased last year, was a collection of songs primarily based on a reallydifficult breakup I went through. It was also a concept album based onMaslows Hierarchy of Needs. My newest material is still personal, butencompasses various experiences both myself and others close to me have hadthroughout a years timespan.

How do you thinkyou have grown as a musician since you first started making music? What, ifanything, has stayed the same about your music-making process?

I think my biggest area of growth has been findinginspiration out of literally anything. When I first started writing, I couldonly create when I was extremely sad or unhappy. But now, I feel like Im ableto look outside of those darker experiences a bit, and find inspirationelsewhere. Thats kind of how Maniac was formed looking for inspirationoutside of my personal experiences. Of course, I think we can all agree thatsome of the best writing comes out from painful times. What has stayedconsistent for me is that most of my songs start off with anacoustic/singer-songwriter feel.

How do you feel about social media? What do you think social media has done for your career?

Eh I have a love/hate relationship with it. While I thinkits a great way to get your name out there, I also think its A LOT to keep upwith. I feel like youre expected to constantly be posting about whats goingon in your life and keep up with various content. I will say that its helpedme share my music with people who would have never heard it!

What musicianswould you absolutely still love to work with in the future?

Anthony Green or Billie Eilish for sure. Also PhoebeBridgers or LIGHTS would be super cool.

If you coulddesign your dream music video right now, what would it look like?

I have something in mind for a song off my forthcomingalbum, so I dont want to give it away quite yet

At the end of theday, what do you hope people take away from your music?

I hope my music fills a void in peoples lives That ithelps get them through difficult times and brings a sense of security andfulfillment in the same way it has for me. I hope my music creates a connectionbetween what Ive personally experienced and what the listener is experiencing.

See original here:
An Interview With Singer-Songwriter BRIGITTE MENA On New Music and More! | All Access Music - All Access Music Group

DIRIYAH:The Saudi Ministry of Culture is to stage an international art exhibition showcasing the works of 27 artists from the Kingdom and Gulf Cooperation Council (GCC) countries.

Titled From Inside, the expo will be held at the industrial area in Diriyah and opens next Sunday through to Dec. 26.

The Diriyah Season event will form part of the Quality of Life program, a Saudi Vision 2030 initiative aimed at enriching the creative scene and supporting Saudi contemporary artists by exhibiting their work before an international audience in a prestigious platform from inside the Kingdom.

The ministry is also looking to project Saudi artistic talent onto an international stage to help strengthen the position of the Kingdom, and Diriyah, as an international art destination.

From Inside will reflect the cultural developments taking place in the Kingdom and is part of the ministrys comprehensive plan to transform Diriyah into a contemporary art area hosting works from all over the world.

The exhibition will include paintings, drawings, sculptures, videos and installation artworks raising questions about the relation between architecture, human behavior and the ways that human experiences and societal nature are shown in the development of civilization.

The event will also explore how feelings and emotions are influenced by architecture, construction methods and art.

See the original post:
Cabinet hails Saudi-UAE ties after crown prince visit to the Emirates - Arabnews

Image credit: Depositphotos

This article is part ofDemystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI.

Last week, Lee Se-dol, the South Korean Go champion who lost in a historical matchup against DeepMinds artificial intelligence algorithm AlphaGo in 2016, declared his retirement from professional play.

With the debut of AI in Go games, Ive realized that Im not at the top even if I become the number one through frantic efforts, Lee told theYonhap news agency. Even if I become the number one, there is an entity that cannot be defeated.

Predictably, Se-dols comments quickly made the rounds across prominent tech publications, some of them using sensational headlines with AI dominance themes.

Since the dawn of AI, games have been one of the main benchmarks to evaluate the efficiency of algorithms. And thanks to advances in deep learning and reinforcement learning, AI researchers are creating programs that can master very complicated games and beat the most seasoned players across the world. Uninformed analysts have been picking up on these successes to suggest that AI is becoming smarter than humans.

But at the same time, contemporary AI fails miserably at some of the most basic that every human can perform.

This begs the question, does mastering a game prove anything? And if not, how can you measure the level of intelligence of an AI system?

Take the following example. In the picture below, youre presented with three problems and their solution. Theres also a fourth task that hasnt been solved. Can you guess the solution?

Youre probably going to think that its very easy. Youll also be able to solve different variations of the same problem with multiple walls, and multiple lines, and lines of different colors, just by seeing these three examples. But currently, theres no AI system, including the ones being developed at the most prestigious research labs, that can learn to solve such a problem with so few examples.

The above example is from The Measure of Intelligence, a paper by Franois Chollet, the creator of Keras deep learning library. Chollet published this paper a few weeks before Le-sedol declared his retirement. In it, he provided many important guidelines on understanding and measuring intelligence.

Ironically, Chollets paper did not receive a fraction of the attention it needs. Unfortunately, the media is more interested in covering exciting AI news that gets more clicks. The 62-page paper contains a lot of invaluable information and is a must-read for anyone who wants to understand the state of AI beyond the hype and sensation.

But I will do my best to summarize the key recommendations Chollet makes on measuring AI systems and comparing their performance to that of human intelligence.

The contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks, such as board games and video games, Chollet writes, adding that solely measuring skill at any given task falls short of measuring intelligence.

In fact, the obsession with optimizing AI algorithms for specific tasks has entrenched the community in narrow AI. As a result, work in AI has drifted away from the original vision of developing thinking machines that possess intelligence comparable to that of humans.

Although we are able to engineer systems that perform extremely well on specific tasks, they have still stark limitations, being brittle, data-hungry, unable to make sense of situations that deviate slightly from their training data or the assumptions of their creators, and unable to repurpose themselves to deal with novel tasks without significant involvement from human researchers, Chollet notes in the paper.

Chollets observations are in line with those made by other scientists on the limitations and challenges of deep learning systems. These limitations manifest themselves in many ways:

Heres an example: OpenAIs Dota-playing neural networks needed 45,000 years worth of gameplay to reach a professional level. The AI is also limited in the number of characters it can play, and the slightest change to the game rules will result in a sudden drop in its performance.

The same can be seen in other fields, such as self-driving cars. Despite millions of hours of road experience, the AI algorithms that power autonomous vehicles can make stupid mistakes, such as crashing into lane dividers or parked firetrucks.

One of the key challenges that the AI community has struggled with is defining intelligence. Scientists have debated for decades on providing a clear definition that allows us to evaluate AI systems and determine what is intelligent or not.

Chollet borrows the definition by DeepMind cofounder Shane Legg and AI scientist Marcus Hutter: Intelligence measures an agents ability to achieve goals in a wide range of environments.

Key here is achieve goals and wide range of environments. Most current AI systems are pretty good at the first part, which is to achieve very specific goals, but bad at doing so in a wide range of environments. For instance, an AI system that can detect and classify objects in images will not be able to perform some other related task, such as drawing images of objects.

Chollet then examines the two dominant approaches in creating intelligence systems: symbolic AI and machine learning.

Early generations of AI research focused on symbolic AI, which involves creating an explicit representation of knowledge and behavior in computer programs. This approach requires human engineers to meticulously write the rules that define the behavior of an AI agent.

It was then widely accepted within the AI community that the problem of intelligence would be solved if only we could encode human skills into formal rules and encode human knowledge into explicit databases, Chollet observes.

But rather than being intelligent by themselves, these symbolic AI systems manifest the intelligence of their creators in creating complicated programs that can solve specific tasks.

The second approach, machine learning systems, is based on providing the AI model with data from the problem space and letting it develop its own behavior. The most successful machine learning structure so far is artificial neural networks, which are complex mathematical functions that can create complex mappings between inputs and outputs.

For instance, instead of manually coding the rules for detecting cancer in x-ray slides, you feed a neural network with many slides annotated with their outcomes, a process called training. The AI examines the data and develops a mathematical model that represents the common traits of cancer patterns. It can then process new slides and outputs how likely it is that the patients have cancer.

Advances in neural networks and deep learning have enabled AI scientists to tackle many tasks that were previously very difficult or impossible with classic AI, such as natural language processing, computer vision and speech recognition.

Neural networkbased models, also known as connectionist AI, are named after their biological counterparts. They are based on the idea that the mind is a blank slate (tabula rasa) that turns experience (data) into behavior. Therefore, the general trend in deep learning has become to solve problems by creating bigger neural networks and providing them with more training data to improve their accuracy.

Chollet rejects both approaches because none of them has been able to create generalized AI that is flexible and fluid like the human mind.

We see the world through the lens of the tools we are most familiar with. Today, it is increasingly apparent that both of these views of the nature of human intelligenceeither a collection of special-purpose programs or a general-purpose Tabula Rasaare likely incorrect, he writes.

Truly intelligent systems should be able to develop higher-level skills that can span across many tasks. For instance, an AI program that masters Quake 3 should be able to play other first-person shooter games at a decent level. Unfortunately, the best that current AI systems achieve is local generalization, a limited maneuver room within their own narrow domain.

In his paper, Chollet argues that the generalization or generalization power for any AI system is its ability to handle situations (or tasks) that differ from previously encountered situations.

Interestingly, this is a missing component of both symbolic and connectionist AI. The former requires engineers to explicitly define its behavioral boundary and the latter requires examples that outline its problem-solving domain.

Chollet also goes further and speaks of developer-aware generalization, which is the ability of an AI system to handle situations that neither the system nor the developer of the system have encountered before.

This is the kind of flexibility you would expect from a robo-butler that could perform various chores inside a home without having explicit instructions or training data on them. An example is Steve Wozniaks famous coffee test, in which a robot would enter a random house and make coffee without knowing in advance the layout of the home or the appliances it contains.

Elsewhere in the paper, Chollet makes it clear that AI systems that cheat their way toward their goal by leveraging priors (rules) and experience (data) are not intelligent. For instance, consider Stockfish, the best rule-base chess-playing program. Stockfish, an open-source project, is the result of contributions from thousands of developers who have created and fine-tuned tens of thousands of rules. A neural networkbased example is AlphaZero, the multi-purpose AI that has conquered several board games by playing them millions of times against itself.

Both systems have been optimized to perform a specific task by making use of resources that are beyond the capacity of the human mind. The brightest human cant memorize tens of thousands of chess rules. Likewise, no human can play millions of chess games in a lifetime.

Solving any given task with beyond-human level performance by leveraging either unlimited priors or unlimited data does not bring us any closer to broad AI or general AI, whether the task is chess, football, or any e-sport, Chollet notes.

This is why its totally wrong to compare Deep Blue, Alpha Zero, AlphaStar or any other game-playing AI with human intelligence.

Likewise, other AI models, such as Aristo, the program that can pass an eighth-grade science test, does not possess the same knowledge as a middle school student. It owes its supposed scientific abilities to the huge corpora of knowledge it was trained on, not its understanding of the world of science.

(Note: Some AI researchers, such as computer scientist Rich Sutton, believe that the true direction for artificial intelligence research should be methods that can scale with the availability of data and compute resources.)

In the paper, Chollet presents the Abstraction Reasoning Corpus (ARC), a dataset intended to evaluate the efficiency of AI systems and compare their performance with that of human intelligence. ARC is a set of problem-solving tasks that tailored for both AI and humans.

One of the key ideas behind ARC is to level the playing ground between humans and AI. It is designed so that humans cant take advantage of their vast background knowledge of the world to outmaneuver the AI. For instance, it doesnt involve language-related problems, which AI systems have historically struggled with.

On the other hand, its also designed in a way that prevents the AI (and its developers) from cheating their way to success. The system does not provide access to vast amounts of training data. As in the example shown at the beginning of this article, each concept is presented with a handful of examples.

The AI developers must build a system that can handle various concepts such as object cohesion, object persistence, and object influence. The AI system must also learn to perform tasks such as scaling, drawing, connecting points, rotating and translating.

Also, the test dataset, the problems that are meant to evaluate the intelligence of the developed system, are designed in a way that prevents developers from solving the tasks in advance and hard-coding their solution in the program. Optimizing for evaluation sets is a popular cheating method in data science and machine learning competitions.

According to Chollet, ARC only assesses a general form of fluid intelligence, with a focus on reasoning and abstraction. This means that the test favors program synthesis, the subfield of AI that involves generating programs that satisfy high-level specifications. This approach is in contrast with current trends in AI, which are inclined toward creating programs that are optimized for a limited set of tasks (e.g., playing a single game).

In his experiments with ARC, Chollet has found that humans can fully solve ARC tests. But current AI systems struggle with the same tasks. To the best of our knowledge, ARC does not appear to be approachable by any existing machine learning technique (including Deep Learning), due to its focus on broad generalization and few-shot learning, Chollet notes.

While ARC is a work in progress, it can become a promising benchmark to test the level of progress toward human-level AI. We posit that the existence of a human-level ARC solver would represent the ability to program an AI from demonstrations alone (only requiring a handful of demonstrations to specify a complex task) to do a wide range of human-relatable tasks of a kind that would normally require human-level, human-like fluid intelligence, Chollet observes.

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Artificial intelligence: How to measure the I in AI - TechTalks

SAN FRANCISCO (AP) Two opera singers who accused Placido Domingo of sexual misconduct said Tuesday that it was disappointing and disturbing that the opera legend recently claimed he has always behaved like a gentleman and never acted improperly toward women.

Angela Turner Wilson and Patricia Wulf were among more than 20 women who accused Domingo of sexual harassment or inappropriate sexually charged behavior in two Associated Press reports this summer.

Their new statement came in response to comments Domingo made in two recent interviews with European publications, in which he disputed the allegations against him and said he never abused his power. He said he always behaved like a gentleman but that gallant gestures are viewed differently nowadays.

There is nothing chivalrous or gallant about groping a woman in the workplace, in any country or era, Wilson and Wulf said in the statement issued through their attorney, Debra Katz.

The Grammy Award-winning singer is one of the most celebrated men in the opera world and regarded as one of the greatest opera singers of all time. The long-married, Spanish-born star also is a prolific conductor and longtime administrator, having served as the general director of both the Los Angeles Opera and Washington Opera.

In the AP stories, several singers, a dancer and backstage staff at opera companies accused Domingo of sexual harassment and other inappropriate, sexually charged behavior that included unwelcomed kisses, touching and late-night phone calls.

Many said Domingo tried to pressure them into sexual relationships and sometimes punished them professionally if they rejected him. The accusers and dozens of others interviewed said Domingos behavior was an open secret in the opera world.

Until recently, the 78-year-old had not spoken publicly about the allegations and had limited his reaction to statements from his lawyer and publicist. He had called the accusations in many ways, simply incorrect without elaborating.

Last week, Domingo gave an interview to Spanish online newspaper El Confidencial in which he again stopped short of flatly denying the womens allegations but insisted he had never behaved improperly. He added that Spaniards are by nature warm, affectionate and loving.

I have been gallant but always within the limits of gentlemanliness, respect and sensitivity, he said.

Domingo also spoke to Italian newspaper Corriere della Sera, denying he abused his power and saying casting decisions were not made by him but by a team of four or five people. He said that very offensive things were said about me as a human being.

Turner and Wulfs statement said Domingos continued failure to take responsibility for wrongdoing or to express any remorse is extremely disappointing and deeply disturbing.

He did not behave like a gentleman when he repeatedly propositioned women for sex in the workplace ... and when he groped them and kissed them over their objections, the statement said. He did not behave respectfully when he offered to assist with the careers of aspiring female opera singers if they came to his apartment and had sex with him.

The most serious allegation lodged against Domingo came from Turner, a soprano, who told the AP that he forcefully grabbed her breast in a makeup room at the Washington Opera in 1999 after she rejected his advances for weeks.

Wulf, a mezzo soprano, said Domingo persistently propositioned and harassed her during performances at the Washington Opera in 1998, when he was general director.

Another singer said when she worked with Domingo at the Los Angeles Opera in the mid-2000s, he stuck his hand down her skirt after asking her to sing for him at his apartment. Others said he forced wet kisses on their lips.

U.S. opera houses canceled Domingos upcoming performances following the accusations, and he resigned from the LA Opera, where he had been general director since 2003. Its investigating the allegations.

European theaters have supported Domingo and maintained his appearances.

It is deeply upsetting and unfair that Mr. Domingo can retreat to another world without having to come to terms with what he has done to many, many women here, Wulf said.

The womens statement said Domingos comments show an attempt to absolve his misconduct by blaming cultural differences and changing rules and standards.

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Domingo's accusers: Nothing 'chivalrous' about groping women - The Associated Press