Category Archives: Artificial Intelligence

During a recent visit to IBMs digs in the chic Astor Place section of Manhattan, I got a peek at how Watson the famous (and increasingly useful) artificial intelligence machine is being taught to look for signs of improper trading, fraudulent account openings and other employee misdeeds.

"We take all of traders' emails and chats and run them through our personality insights and tone analyzer and identify whether theres anger, are they happy, are they sad?" said Marc Andrews, vice president of Watson Financial Services Solutions. Were analyzing the behavioral patterns that are associated with misconduct: How do people start behaving right before they get involved in misconduct?

One thing Watson has discovered, according to Andrews, is that U.S. traders stop using profanity and angry language just before doing something they shouldnt.

It might have been because they were trying to hide things, Andrews said.

But in the U.K., traders use of profanity rises when they go rogue.

They were being proper beforehand, but then they let go of their emotions, he said.

Along with the communications, Watson is analyzing trading behaviors, volumes and frequencies, looking for suspicious trading sequences, abnormal order sizes or significant price changes. Watson will weigh it against other recent events and communication patterns for signs something might be off.

Seeing a suspicious trade sequence or price alert alone might not indicate a problem, because good traders are good at timing the market, Andrews said. What would be telling would be a communication with a company insider just beforehand.

One trader received a note that said, Hey man, I think its going to rain here in Seattle, youd better cover up before you get drenched. Watson recognized that it was from a company insider and that it had a warning tone and therefore flagged it.

Watson will also look to see if traders had other compliance violations in their history or if they had made angry remarks.

By observing such patterns, it is hoped, Watson can start to alert banks to possible insider trading, pump-and-dump schemes, collusion and other forms of misconduct.

To catch phony accounts, Watson starts off looking for an unusually large number of complaints, which might indicate something is awry. It also looks for dormant accounts, accounts where notifications have been suppressed, mismatched contact information, suspicious logins, enrollment reversal and odd login times.

Watson will look to see if an employee suddenly had a spike in sales or unusual customer distribution, such as targeting elderly customers. It looks for management emails that express undue sales pressure.

IBM is beginning to apply this to voice communications, too, to identify ethics violations and changes in tone and speed of speech, as well as language, Andrews said.

The staff of Promontory Financial Group, the compliance consulting firm IBM bought last fall, provides color around motives, culture and conduct risk.

One of the largest global banks IBM would not say which one is using a cloud-based version of Watson for employee surveillance.

The gift of hindsight

Andrews acknowledges that when it comes to rogue trading and fake accounts, IBM is training Watson with histories of known prior misconduct and hindsight is 20-20. In fact, if you know exactly what you are looking for and someone violates a policy or law, a rules-based system could catch it; you do not even need artificial intelligence. It is when you do not know what to look for that trade surveillance gets tough.

Human beings are never static; theyre never doing the same thing today that they did previously, said Marten Den Haring, chief product officer at Digital Reasoning, whose artificial intelligence technology has been analyzing trader activity and communications on exchanges that use Nasdaq technology for a year.

Den Haring takes Watsons conclusions about British and American traders use of profanity with a grain of salt.

There are cultural differences to any type of communication patterns, he pointed out. I would be cautious to think you could identify the types of patterns you just described.

Better signals of wrongdoing come through tracking behaviors over time across multiple channels and seeing people try to conceal their behavior, he said.

In trying to cover up, people make more mistakes and leave a lot more clues, Den Haring said. A good example is boasting. You completed something nefarious, youre happy, youre done, you dont realize that high-fiving each other digitally is leaving just as many clues behind as planning to do something together, he said.

Digital Reasoning also pays close attention to networks among people and sudden changes in behavior. Those are far more interesting and less based on emotion and cultural differences, he said.

Someone has to care

In addition to the technological difficulties of identifying patterns of bad behavior, there is the question of the culture and will of a company and its management.

In most banking scandals, the underlying bad behavior was visible to the human eye for some time. Seven hundred whistleblower complaints had been lodged about fake accounts at Wells Fargo by 2010, along with hundreds of employee and customer complaints. In the JPMorgan Chase "London Whale" case, the trader Bruno Iksil has said his dangerously large credit swap positions were part of a trading strategy that had been initiated, approved, mandated and monitored by the CIOs senior management.

In such cases, it is not that no one knows what is going on, and technology is needed to bring it to light. Management knows and may even be directing the bad behavior, through emails and calls pressuring employees to cross-sell more aggressively or by ordering traders to execute a high-risk strategy. No amount of software, no matter how intelligent, can force leaders to make ethical decisions.

What technology can do is help speed a compliance investigation when foul play is suspected.

Once you have put your finger on an individual youre putting on a watchlist, were making the investigation capability far richer, more interesting for the financial institutions, Den Haring said. That quick 360-degree look-back gives you more clues into what seems out of the norm for a trader.

Andrews also describes the value of IBMs Watson this way.

Were providing augmented intelligence to banks to help them identify things more quickly, earlier on, and with less resources, he said. Were not making the decision, [but] were providing evidence to support a decision.

Valerie Bannert-Thurner, senior vice president and head of risk and surveillance at Nasdaq, says some of Nasdaqs bank clients have started integrating voice and electronic communications together with its SMARTS trade surveillance software and the Digital Reasoning AI engine, in order to watch everything traders say and do in all channels at once.

Customers want to know if traders are changing language, location or communication channels, or suddenly starting to communicate more rapidly or often, she said.

All that metadata around communications, overlaid with trading will flag unusual trade activity and any intent to manipulate markets, Bannert-Thurner said.

Artificial intelligence software could also uncover collusion. In late April, the Federal Reserve fined Deutsche Bank $156.6 million for, among other things, "using electronic chatrooms to communicate with competitors about their trading positions."

Technology from IBM, Digital Reasoning and Sybenetix could easily catch such known violations. A rules-based system probably could as well.

AI can help compliance officers do their jobs better and make traders more aware they are being watched.

Sybenetix teaches its AI engine the specifics of each job, so it can create a model of normal behavior. This is used to create intelligent alerts for compliance officers, which lets them ask smarter questions. In some cases, its replacing an Excel sheet and trade sampling.

Weve seen in a number of cases that traders are now coming to compliance officers before they trade and checking with them, said Wendy Jephson, co-founder and chief behavior scientist at Sybenetix. One of our clients said this is unheard-of behavioral change, for the front office to come in and talk to compliance. Theyre basically saying, Look, I know youre going to tap me on the shoulder, youre going to ask me questions, let me just tell you about it upfront and make sure youre fine.

And it helps with the classic problem of compliance: false positives.

If you go to large banks, their systems that are processing trades produce tens of thousands of alerts, and 99% will be false positives, said Richard Maton, chief marketing and strategy officer at Sybenetix.

At the same time, regulators are requiring surveillance on more asset classes and instruments. Software can help sift through the large amounts of data faster than humans, and layer in related communications and behavior, to isolate activity that is truly suspicious.

Editor at Large Penny Crosman welcomes feedback at penny.crosman@sourcemedia.com

Penny Crosman is Editor at Large at American Banker.

Original post:

Have rogue employees met their match in AI? - American Banker

Human character animation has gotten much better over the years, but its still one of the most recognizable issues when enjoying video games. Animations are normally a predetermined set of canned motions, and while real enough looking in the right setting, can totally break the immersive experience when they stray out of bounds. The uncanny valley is a particularly hard one to escape.

Now, a research team from the University of Edinburgh has developed a new way to animate game characters using neural net computing, which could help developers make more fluid and realistic animations, while decreasing the system resources and time involved. The video demonstrating the new technology is legit:

The minds behind this video, and an accompanying paper published in ACM Transactions, are Daniel Holden, Taku Komura and Jun Saito. When I caught the video, I immediately reached out to Holden for more info. What exactly makes this different from other animation methods, and whats happening in the neural net to make the magic happen?

Neural networks (or NNs) are a way to train a computer with millions of differently weighted data points. Using algorithms, a NN can create completely new outputs on its own, based on the data it has to reference. The emerging technology is commonly used in facial recognition, image processing, and stock market prediction applications.

The workings of the neural network are themselves quite abstract and hard to understand, Holden admitted in an email. But basically, what he and his colleagues have done is employ a system called a phase-functioned neural net, in which the variables controlling a characters movement and interaction within the environment can change on the fly. His aim was to develop intricate, human-like cyclical movement that reacts appropriately to user inputs. As players mash on their gamepads or move a mouse, the neural net learns and evolves over time to create a seamless animation.

We change the weights of the neural network depending on what point in time in the locomotion cycle the character is, explained Holden, those weights being the data that influences what the animation will be. For example, when the character puts their left foot down the weights of the neural network are different to when the character puts their right foot down.

The result? A drastic reduction in the amount of time and effort animators need to achieve that perfect gait. The video above only used about 1.5GB, or 2 hours, of motion capture data, and the animation doesnt need as much on-board processing power to render. Holden thinks it will take some of the burden off animation programmers maintaining the hugely complex animation systems.

It took 30 hours of NN training and 4 million data points to create the animation in the video and, to me, it looks pretty good. The character deftly navigates raised obstacles, appropriately speeds up and slows down based on the input and reacts accordingly to the walls and bridges. Obviously, third-person perspective games could benefit from the new approach, but VR experiences are also mediums where, for maximum immersion, were going to need hyper-realistic motion.

Neural networks have been used in gaming before, in developing opponent AIs, but this is a great example of how the technology can make developers lives easier. Holden just started a new R&D job at Ubisoft, and while unable to say if the tech is going to be used there, he looks forward to seeing more implementation in the future.

I hope that this technique does change gameplayhopefully these sort of technologies will allow game designers to be more adventurous with the kind of environments they create, said Holden.

Bryson is a freelance storyteller who wants to explore the universe with you.

Read more:

Artificial Intelligence Could Prevent the Next Video Game Animation ... - Gizmodo

Siri and Alexa already help you find your keys and remember friends' birthdays. So maybe its not such a stretch that they could also also manage a few hundred cattle.

Computer scientists and dairy experts at the University of Wisconsin-Madison and the UW Extension are collaborating to create a suite of computer programs that can help with dairy farming a virtual brain that uses artificial intelligence to help farmers with day-to-day decision-making.

According to Victor Cabrera, a UW-Madison dairy science professor and the projects principal investigator, modern farms are already equipped with sophisticated data-collecting technology. Cows wear sensors that can track heat and motion, giving farmers insight into whether a cow may be in heat or ill. Soil and crop monitoring systems influence feeding decisions. Milking robots keep track of which cows are fidgety, whether a cows udders are healthy, the composition of the milk a cow produces.

All of this information exists, said Cabrera. A big leap we want to do in this project is connect different sources of information.

Integrating the different streams of data would be the first step. Then, the challenge would be to fuse prior research on agricultural data with machine learning in other words, computer programs that have been taught to adapt based on new information to create artificial intelligence systems that can assess that data in real time.

That AI could provide insights to help farmers in all kinds of ways, said Cabrera. Consider reproductive decisionmaking, he said. When it comes to selecting animals to breed, farmers have to use their old-fashioned human brains to consider multiple sources of data, from semen quality to a cows market value to a heifers fertility.

In a farm brain, it should be able to do that on the fly, he said.

Same for milking parlor decisions, said Cabrera. A dairy farm AI could help farmers weed out cows that for whatever reason end up taking a longer time to milk, and adjust milking schedules accordingly.

Its simple, but its actually a great help to the farmers, said Cabrera. If you have a group being milked, that one cow can delay the whole group of animals.

Cabrera noted that this would ideally be a real-time system of applications that would ping farmers with prescriptive and predictive suggestions in real time.

When the farm comes into the office, they should be told by the system, this cow should not be bred anymore, he said. And for this cow, they should be bred with better semen.

The three-year research project to create the brain is still in its nascent stages. Cabrera said that the core group of scientists working on the brain are still putting together a team and looking at potential grants, but that the work should begin in earnest this summer. He said that they have found a progressive farm located in south-central Wisconsin that has volunteered to be the projects guinea pig.

The project is among 21 proposals that recently received money through the UW2020 awards initiative, which looks to give a boost to high-risk, innovative research ventures.

Read the original post:

'Open the barn doors, Hal!': Artificial intelligence could one day run a dairy farm - Madison.com

Since a group of scientists coined the term at Dartmouth College (my alma mater) in 1956, artificial intelligence (AI) has taken the world by storm, with a market expected to be valued at $152 billion by 2020. While over 80% of executives believe this pioneering technology improves worker performance and creates jobs, others arent convinced. Any disruptive technology introduces new pros and cons, but the fear lies in the potential AI has to eliminate jobs across various industries, from manufacturing to telecom to retail.

AI is defined as intelligence exhibited by machines. Its potential for disruption is limitless. For marketers, AI technology represents an opportunity to harness and analyze data with speed, depth, and breadth that were previously unimaginable. But the benefits can only be seized if marketers have the full confidence of their superiors. Today, the role of the CMO has shifted and they have more money to spend and greater impact on the bottom line than ever before. CIOs are no longer the only members of the C-suite investing in tech. As we speak, marketers are embracing the potential of AI-driven data analytics to enhance brand reputation while still relying on their human instinct to reap the full rewards.

advertisement

advertisement

Increasing data visibility and use

One of the biggest priorities for marketers is gaining visibility into the customer journey beginning with uncovering a problem to executing on a solution and all of the steps in between. Using artificial intelligence, CMOs can identify patterns and better understand who is converting, why, and how to optimize each interaction based on this insight. AI offers the ability to process data with unprecedented speed; even Stephen Hawking couldnt come close to sifting through the amounts of data that AI can process in mere seconds. Combined with the ability to adapt quickly and drive more velocity in the sales funnel, the end result is everybodys ultimate goal: generating more revenue.

To fully capitalize on AI tools, marketers shift their thinking to work more like analysts in order to process data effectively. The combination of the left brain to work with data and the right brain to imagine what comes next is key to taking full advantage of this technology. CMOs especially need to be aware of all of the AI-powered tools out there, like just-in-time A/B testing and personalization that optimizes for customer acquisition and retention. That real-time learning and feedback is an incredible advantage when addressing fickle, distracted buyers and test-driving new tactics.

The shifting role of the CMO

Understanding the critical combination of todays technology and human instinct, the question still remains: how many marketers will actually embrace AI in 2017? Historically, the CMOs role relied minimally on technology, with greater emphasis on traditional forms of marketing and advertising and data processing. This is a pivotal time for technology, and for the first time in the history of marketing, analysts predict that CMOs will outspend CIOs on technology this year.

While the power of AI technology is pushing the boundaries of whats possible in marketing, human instinct remains as critical as ever to ensure that customer interaction is still tailored to meet brand and customer standards. To stay relevant, CMOs must invest in AI technology ensuring that the data used to make key decisions is generated and maintained in the most efficient way possible. It is imperative that the friendship between marketers and their AI-powered analytical capabilities remains in balance. Without AI, todays CMOs and their brands are at risk of falling behind competitors who are willing to take the leap and embrace this monumental change.

Visit link:

Artificial Intelligence Is A CMO's Best Friend 05/04/2017 - MediaPost - MediaPost Communications

Forbes

Does Artificial Intelligence Discriminate? Forbes As the old joke goes, on the internet nobody knows you're a dog. But thanks to the rise of artificial intelligence, not only do today's machines know you're a canine, they can tell what color dog you are -- and may treat you differently as a result. AI ...

See the original post here:

Does Artificial Intelligence Discriminate? - Forbes

Artificial intelligence can predict Supreme Court decisions better than some experts.

davidevison/iStockphoto

By Matthew HutsonMay. 2, 2017 , 1:45 PM

See you in the Supreme Court! President Donald Trump tweeted last week, responding to lower court holds on his national security policies. But is taking cases all the way to the highest court in the land a good idea? Artificial intelligence may soon have the answer. A new study shows that computers can do a better job than legal scholarsat predicting Supreme Court decisions, even with less information.

Several other studies have guessed at justices behavior with algorithms. A 2011 project, for example, used the votes of any eight justices from 1953 to 2004 to predict the vote of the ninth in those same cases, with 83% accuracy. A 2004 paper tried seeing into the future, by using decisions from the nine justices whod been on the court since 1994 to predict the outcomes of cases in the 2002 term. That method had an accuracy of 75%.

The new study draws on a much richer set of data to predict the behavior of any set of justices at any time. Researchers used the Supreme Court Database, which contains information on cases dating back to 1791, to build a general algorithm for predicting any justices vote at any time. They drew on 16 features of each vote, including the justice, the term, the issue, and the court of origin. Researchers also added other factors, such as whether oral arguments were heard.

For each year from 1816 to 2015, the team created a machine-learning statistical model called a random forest. It looked at all prior years and found associations between case features and decision outcomes. Decision outcomes included whether the court reversed a lower courts decision and how each justice voted. The model then looked at the features of each case for that year and predicted decision outcomes. Finally, the algorithm was fed information about the outcomes, which allowed it to update its strategy and move on to the next year.

From 1816 until 2015, the algorithm correctly predicted 70.2% of the courts 28,000 decisions and 71.9% of the justices 240,000 votes, the authors report in PLOS ONE. That bests the popular betting strategy of always guess reverse, which has been the case in 63% of Supreme Court cases over the last 35 terms. Its also better than another strategy that uses rulings from the previous 10 years to automatically go with a reverse or an affirm prediction. Even knowledgeable legal experts are only about 66% accurate at predicting cases, the 2004 study found. Every time weve kept score, it hasnt been a terribly pretty picture for humans, says the studys lead author, Daniel Katz, a law professor at Illinois Institute of Technology in Chicago.

Roger Guimer, a physicist at Rovira i Virgili University in Tarragona, Spain, and lead author of the 2011 study, says the new algorithm is rigorous and well done. Andrew Martin, a political scientist at the University of Michigan in Ann Arbor and an author of the 2004 study, commends the new team for producing an algorithm that works well over 2 centuries. Theyre curating really large data sets and using state-of-the-art methods, he says. Thats scientifically really important.

Outside the lab, bankers and lawyers might put the new algorithm to practical use. Investors could bet on companies that might benefit from a likely ruling. And appellants could decide whether to take a case to the Supreme Court based on their chances of winning. The lawyers who typically argue these cases are not exactly bargain basement priced, Katz says.

Attorneys might also plug different variables into the model to forge their best path to a Supreme Court victory, including which lower court circuits are likely to rule in their favor, or the best type of plaintiff for a case. Michael Bommarito, a researcher at Chicago-Kent College of Law and study co-author, offers a real example in National Federation of Independent Business v. Sebelius, in which the Affordable Care Act was on the line: One of the things that made that really interesting was: Was it about free speech, was it about taxation, was it about some kind of health rights issues? The algorithm might have helped the plaintiffs decide which issue to highlight.

Future extensions of the algorithm could include the full text of oral arguments or even expert predictions. Says Katz: We believe the blend of experts, crowds, and algorithms is the secret sauce for the whole thing.

Read more here:

Artificial intelligence prevails at predicting Supreme Court decisions - Science Magazine

If your company is not embracing artificial intelligence, it is going to suffer. That's the message from all corners of the tech and business media world these days. CIO Dive is no exception. We've reported on the growing reliance on artificial intelligence everywhere from call centers to cloud computing to the airline industry.

Yet, the state of technology is still a long way from imbuing machines with human-level intelligence. It's even further from the merging of machine and humans that fans of the technological singularity believe will unlock our true potential and, eventually, immortality.

Despite the remarkable victory that Google's AI-powered Alpha computer scored against the world's top Go player, there is healthy debate around when machines will be able to truly attain human-like intelligence. That would mean a machine that could do more than just recognizing patterns and learning from mistakes, but also accurately responding to new information and understanding unstructured data.

Plus, it is no easy task to transfer a given type of AI from one application to another. For example, The Nature Conservancy wants to fight illegal fishing by using facial recognition software, running on cameras mounted over haul-in decks on fishing boats, to mark whenever an endangered or non-target species is brought aboard and not thrown back.

But it's not as simple as uploading a catalog of fish faces and pressing enter. Constantly changing light, variations in the orientation of the fish to the camera, and the movement of the boat all complicate matters. Kaggle, a code crowdsourcing platform, recently held a contest to incentivize coders to write software that addressed those variables.

Yet, the more pressing question around AI is not whether it has truly arrived, but whether the AI features vendors are trying to sell your company actually, and consistently, work as advertised. And if they'll meet your objectives.

The healthcare industry stands to benefit significantly from AI. Wearable devices are being developed to track changes or patterns in a patient's vital signs that could signal an approaching cardiac event. When one is detected, a physician can be alerted automatically. Of course, that's very different than relying on technology to make an actual clinical decision.

But a number of companies are starting to sell digital health assistants. This technology accesses a patient's medical records and analyzes input from the user to assess symptoms and provide a possible diagnosis. But why should we trust these platforms?

That's a question that Zoubin Ghahramani, professor of Information Engineering at the University of Cambridge, has spent a lot of time pondering.

We know that machine learning improves over time, as the software accrues more data and essentially learns from past events. So what if, Ghahramani and his team posit in a University of Cambridge research brief, we designed artificial intelligence with training wheels of sorts? Vehicles with autopilot mode, for instance, might ping a driver for help in unfamiliar territory,if the car's cameras or sensors are not capturing adequate data for processing.

But unless you've actually written the algorithms that power that autopilot, or any other piece of AI technology, it is not clear how the system reached a decision, or the soundness of that decision.

"We really view the whole mathematics of machine learning as sitting inside a framework of understanding uncertainty. Before you see data whether you are a baby learning a language or a scientist analyzing some data you start with a lot of uncertainty and then as you have more and more data you have more and more certainty," Ghahramani said.

"When machines make decisions, we want them to be clear on what stage they have reached in this process," he said. "And when they are unsure, we want them to tell us."

Last year, and with collaborators from the University of Oxford, Imperial College London, and at the University of California, Berkeley, Ghahramani helped launch the Leverhulme Centre for the Future of Intelligence.

One of the center's area of study is trust and transparency around AI, while other areas of focus include policy, security and the impacts that AI could have on personhood.

Adrian Weller, a senior researcher on Ghahramani's team and the trust and transparency research leader at the Leverhulme Centre for the Future of Intelligence, explained that AI systems based on machine learning use processes to arrive at decisions that do not mimic the "rational decision-making pathways" that humans comprehend. Using visualization and other approaches, the center is creating tools that can put AI decision processes in a human context.

The goal is not just to provide tools for cognitive scientists, but also for policy makers, social scientists, and even philosophers, because they will also take roles in integrating AI into society.

But by providing a means for making AI functions more transparent, commercial users of AI tools and their consumers could better understand how it works, determine its trustworthiness, and decide whether it is likely to meet the company's or its customer's needs.

The tech industry has begun collaborating around guiding principles to help ensure AI is deployed in an ethical, equitable, and secure manner. Representatives from Amazon, Apple, Facebook, IBM, Google and Microsoft have joined with academics as well as groups including the ACLU and MacArthur Foundation to form the Partnership on AI.

It seeks to explore the influence of AI on society and is organized around themes that include safety, transparency, labor and social good.

But a system for rating AI features and ensuring compliance with basic quality metrics similar to how Underwriters Lab ensures appliances meet basic safety or performance measures could also go a long way toward helping end users evaluate AI products.

Read the original:

The skeptic's guide to artificial intelligence - CIO Dive

Twitter hasn't done anything interesting with AI lately.

I know this because whatever machine learning they use to stop online harassment is more like an email filter to weed out some political fluff from your inbox or kill spam. Users are still able to create fake accounts, send harassing tweets, criticize you over and over again, remain completely anonymous, and come up with a variety of unflattering slams against celebrities that are never caught by the filters and go completely ignored for days or weeks on end.

That's what makes Mark Cuban's comments today about investing in Twitter because of their foray into AI a bit perplexing. What AI? The one that still lets trolls do whatever they want? The one that allows a tweet through that tells me to stick a fork into my front lobe?

There might be some confusion on this topic.

Recently, the company did implement new algorithms that can limit the accounts of users who show a pattern of abuse, something that is not exactly an AI. And, they've talked about using IBM Watson to help, but that's not exactly developing the AI in house.

An AI--designed and developed by Twitter--would identify abusive content based on context and be able to warn the offending user before he or she ever posts it in real time--say, when the user types a message that is obviously hurtful and tries to post it. Facebook has the same issue, because today you can post a revenge porn image or say something derogatory, and it's only when another user identifies the harmful comments or images that any pattern recognition kicks in.

The problem, of course, is that Twitter wants to appear intelligent. They haven't fully addressed the problem, and have let the issue slide since a blog post way back in March. If they are making progress on actual machine learning, they haven't let any of us know. Today, you can still tell someone to commit suicide or send other abusive comments--any real form of artificial intelligence would spot that and block it.

Twitter is walking on a tightrope here. To block very hurtful comments that do not use hate speech (something like "why don't you step in front of a truck") could be perceived as limiting free speech. An AI is not able to tell the difference quite yet. Context--e.g., who said what and why, when they said it, who they know, how often they engage in conversation--is difficult even for humans at times. I've received multiple tweets this last week that I took as hurtful and harmful, and I'd rather not see them, but it's all part of living in the age of trolls. If Twitter made a better AI, I could simply choose to block these tweets. If someone keeps sending death threats, or I report that person, or they use abusive words or hate speech, then Twitter's machine learning might kick in--or it might not. The problem is that, if the AI is only partially effective, is it really effective at all?

Meanwhile, an entire generation of people under 30 have moved over to Instagram and tend to avoid Twitter, which is widely known as a bastion of trolls.

There's often a serious misunderstanding about how an AI works. It's not just a filter or an algorithm. There has to be "intelligence" and understanding, a way to make a decision about context. That's the really hard part. Filters have existed for decades, but computer scientists know that an AI has to be able to deal with fuzzy logic and even moral quagmires. Is someone I know just joking around and telling me to jump off of a bridge? Is it someone who has a bone to pick with me because they invest in the company I'm criticizing? When I've tweeted before about email overload, the people who often send abusive comments just happen to be email marketers. An AI would have to understand that, and it's not exactly humming along nicely at Twitter.

There's no reason to think Twitter won't solve this, but for now--the machine learning is more like a spam filter. That doesn't seem like rocket science to me.

Read more:

Why Mark Cuban is Dead Wrong About Twitter and Artificial Intelligence - Inc.com

Forbes

Emerging Artificial Intelligence (AI) Leaders: Richard Socher, Salesforce Forbes AI teaches us who we are, says Richard Socher. The recent rapid progress in the field of artificial intelligence is the result of successfully processing a large amount of known training data, doing things [the computer] has seen before, he says.

The rest is here:

Emerging Artificial Intelligence (AI) Leaders: Richard Socher, Salesforce - Forbes

OXFORD Artificial Intelligence is the next technological frontier, and it has the potential to make or break the world order. The AI revolution could pull the bottom billion out of poverty and transform dysfunctional institutions, or it could entrench injustice and increase inequality. The outcome will depend on how we manage the coming changes.

Unfortunately, when it comes to managing technological revolutions, humanity has a rather poor track record. Consider the Internet, which has had an enormous impact on societies worldwide, changing how we communicate, work, and occupy ourselves. And it has disrupted some economic sectors, forced changes to long-established business models, and created a few entirely new industries.

But the Internet has not brought the kind of comprehensive transformation that many anticipated. It certainly didnt resolve the big problems, such as eradicating poverty or enabling us to reach Mars. As PayPal co-founder Peter Thiel once noted: We wanted flying cars; instead, we got 140 characters.

In fact, in some ways, the Internet has exacerbated our problems. While it has created opportunities for ordinary people, it has created even more opportunities for the wealthiest and most powerful. A recent study by researchers at the LSE reveals that the Internet has increased inequality, with educated, high-income people deriving the greatest benefits online and multinational corporations able to grow massively while evading accountability.

Perhaps, though, the AI revolution can deliver the change we need. Already, AI which focuses on advancing the cognitive functions of machines so that they can learn on their own is reshaping our lives. It has delivered self-driving (though still not flying) cars, as well as virtual personal assistants and even autonomous weapons.

But this barely scratches the surface of AIs potential, which is likely to produce societal, economic, and political transformations that we cannot yet fully comprehend. AI will not become a new industry; it will penetrate and permanently alter every industry in existence. AI will not change human life; it will change the boundaries and meaning of being human.

How and when this transformation will happen and how to manage its far-reaching effects are questions that keep scholars and policymakers up at night. Expectations for the AI era range from visions of paradise, in which all of humanitys problems have been solved, to fears of dystopia, in which our creation becomes an existential threat.

Making predictions about scientific breakthroughs is notoriously difficult. On September 11, 1933, the famed nuclear physicist Lord Rutherford told a large audience, Anyone who looks for a source of power in the transformation of the atoms is talking moonshine. The next morning, Leo Szilard hypothesized the idea of a neutron-induced nuclear chain reaction; soon thereafter, he patented the nuclear reactor.

The problem, for some, is the assumption that new technological breakthroughs are incomparable to those in the past. Many scholars, pundits, and practitioners would agree with Alphabet Executive Chairman Eric Schmidt that technological phenomena have their own intrinsic properties, which humans dont understand and should not mess with.

Others may be making the opposite mistake, placing too much stock in historical analogies. The technology writer and researcher Evgeny Morozov, among others, expects some degree of path dependence, with current discourses shaping our thinking about the future of technology, thereby influencing technologys development. Future technologies could subsequently impact our narratives, creating a sort of self-reinforcing loop.

To think about a technological breakthrough like AI, we must find a balance between these approaches. We must adopt an interdisciplinary perspective, underpinned by an agreed vocabulary and a common conceptual framework. We also need policies that address the interconnections among technology, governance, and ethics. Recent initiatives, such as Partnership on AI or Ethics and Governance of AI Fund are a step in the right direction, but lack the necessary government involvement.

These steps are necessary to answer some fundamental questions: What makes humans human? Is it the pursuit of hyper-efficiency the Silicon Valley mindset? Or is it irrationality, imperfection, and doubt traits beyond the reach of any non-biological entity?

Only by answering such questions can we determine which values we must protect and preserve in the coming AI age, as we rethink the basic concepts and terms of our social contracts, including the national and international institutions that have allowed inequality and insecurity to proliferate. In a context of far-reaching transformation, brought about by the rise of AI, we may be able to reshape the status quo, so that it ensures greater security and fairness.

One of the keys to creating a more egalitarian future relates to data. Progress in AI relies on the availability and analysis of large sets of data on human activity, online and offline, to distinguish patterns of behavior that can be used to guide machine behavior and cognition. Empowering all people in the age of AI will require each individual not major companies to own the data they create.

With the right approach, we could ensure that AI empowers people on an unprecedented scale. Though abundant historical evidence casts doubt on such an outcome, perhaps doubt is the key. As the late sociologist Zygmunt Bauman put it, questioning the ostensibly unquestionable premises of our way of life is arguably the most urgent of services we owe our fellow humans and ourselves.

See the rest here:

Democratizing Artificial Intelligence - Project Syndicate