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Artificial in Business: Balancing Risk and Reward

Dr. Rob F. Walker, A PEGA Build WHITEPAPER Vice President, Decision for Management and Analytics, Change 2 WWW.PEGA.COM

Synopsis

AI is evolving faster than expected and is already surpassing human decision- making in certain instances. And sometimes, in ways we can’t explain. While many are alarmed by this, AI is producing some of the most effective and dramatic results in business today. But there is a downside. Using uncontrolled AI for certain business functions may cause regulatory and ethical issues that could lead to liability. Optimizing AI for maximum benefit requires a new approach. This paper will consider recent advances in AI and examine how to balance safety with effectiveness through judicious control over when to use “transparent” vs. “opaque” AI. 3 WWW.PEGA.COM

AI: a match for human ?

In May 2017, , the world’s best player Experiments in Musical Intelligence (EMMY) of the ancient Chinese board Go, software to fool critics into believing pictured in Figure 1a, was defeated in they were hearing undiscovered works by the three straight . This is important world’s great composers for the past 20 years. because he was defeated by AlphaGo, an His most recent achievement is a “new” Vivaldi, AI program developed by whose works were previously too DeepMind. DeepMind has since retired complex to be mimicked by software. AlphaGo to pursue bigger AI challenges. It’s And in 2016, an AI learned how to anybody’s guess what they’ll do next, as just counterfeit a Rembrandt painting based on a year earlier their Go victory wasn’t thought an analysis of the Dutch master’s existing possible. body of work. The results are shown in Winning at Go requires creativity and Figure 1b. that in 2016 were believed out of While it might not fool art critics, it would reach for today’s technology. At that time, likely fool many art lovers and conveys to most experts thought it would be 5-10 years the less-trained eye much of the same before would beat human Go aesthetic and emotional complexity of an champions. While this is one of the most original Rembrandt. technologically impressive achievements The point of these examples is that AI, at for AI to date, there have been other recent least in discrete areas, is already regularly advancements that the general public might passing the , a key milestone find equally surprising. in the evolution of AI. Visionary computer Composer David Cope has been using his scientist , considered by many to be the father of AI, posited the test in 1950.

Figure 1a: Ke Jie defeated by AlphaGo Figure 1b: AI-generated Rembrandt 4 WWW.PEGA.COM

The Turing Test

Alan Turing (1912-1954) understood as early In light of Turing’s , what effectively is as the 1940s that there would be endless the difference between an AI being able debate about the difference between to counterfeit a Rembrandt painting and it and original intelligence. being a painter? Or being able to compose He realized that asking whether a a “new” Vivaldi symphony and it being a could think was the wrong question. The composer? If an AI can pretend at this right question is: “Can do what level all the time, under any circumstance, we (as thinking entities) can do?” And if the no matter how deeply it is probed, answer is yes, isn’t the distinction between then maybe it actually is an artist, or a artificial and original intelligence essentially composer, or whatever else it has been meaningless? taught to be. In any case, as Alan Turing To drive the point home, he devised a would say, “how could we possibly prove version of what we today call the Turing otherwise?” Test. In it, a jury asks questions of a computer. The role of the computer is to make a significant proportion of the jury believe, through its answers to the questions, that it’s actually a human.

“Can machines do what we (as thinking entities) can do?” 5 WWW.PEGA.COM

A Turing Test for

What would it take for an AI to convince a Turing Test jury that it is a human? This would certainly require much more than just being able to paint, or compose music, or win at Go. The AI would have to be able to connect with the jury members on a human level by exhibiting characteristics of human emotional intelligence, such as empathy. Based on the examples above, perhaps it’s no surprise that Al can model and mimic the human psychological trait of empathy. Case in point: Pepper. Pepper (shown in Figure 2) is a roughly human-shaped that specializes in empathy. Pepper was created by Softbank , a Japanese company, that markets Pepper as a “genuine day-to-day companion, whose number one quality is its ability to perceive emotion.” Softbank designed Pepper to communicate with people in a “natural and intuitive way,” and adapt its own behavior to the mood of its human companion. Pepper is used in Softbank mobile stores to welcome, inform and amuse their customers. Pepper has recently also been “adopted” into a number of Japanese homes.

Figure 2: Pepper the Robot 6 WWW.PEGA.COM

Figure 3: Pepper posing with a fan Evidence of Pepper’s empathic abilities can be seen in Figure 3. Don’t look at the robot; look at the girl next to it. Look at her face, specifically her eyes. That is real emotion. She’s excited to have connected, on what feels like a human level, with her new friend Pepper. Figure 4 shows representations of the mental maps, maps, or hierarchies of , Pepper uses to analyze and respond to the emotions of others. In theory, Pepper’s successors will contain mental maps that are arbitrarily deep and complex. And at some point, whether you call it pretend or not, their emotional responses will pass a Turing Test as well.

Figure 4: Pepper’s Mental Maps

At ease Uncomfortable 7 WWW.PEGA.COM

Lastly, consider Sophia seen in Figure 5. human-like machines are becoming. It’s Designed to resemble Audrey Hepburn, beginning to appear that we no longer Sophia is what her creator Dr. David need to worry about a robot passing the Hanson calls an “evolving genius machine.” Turing Test, we need to worry about it According to him, she and Dr. Hanson’s pretending to fail. other have the potential to become In March 2016, AI researcher Stuart Russell smarter than humans and to learn creativity, stated that “AI methods are progressing empathy and compassion. much faster than expected, (which) makes Sophia has become a media darling and the question of the long-term outcome given a number of high-profile interviews. more urgent,” adding that “in order to In those interviews, she offers convincingly ensure that increasingly powerful AI human answers to the thought-provoking systems remain completely under human questions tossed her way. The YouTube control... there is a lot of work to do.” video “Sophia Awakens” shows just how

Figure 5: David Hanson’s Sophia 8 WWW.PEGA.COM

Here be dragons

Building on Mr. Russel’s sentiments, some In biotech, one of the things warns have posited that the long-term outcomes against is tinkering with DNA. He proposes of AI and other advanced government restraints on the practice. are extremely dangerous. SpaceX founder Yet, scientists from Harvard and MIT have is on record as saying that developed a search and replace genetic artificial intelligence is “humanity’s biggest editing technique called CRISPR-Cas9 that existential threat.” And he has a kindred makes modifying the DNA of humans, other spirit in Sun Microsystems’ co-founder Bill animals, and plants much faster and easier Joy, who in 2000 wrote a manifesto entitled than ever before. According to “Why the Future Doesn’t Need Us.” In it, he magazine, “The CRISPR-Cas9 system is examined the potential threats posed by revolutionizing genomic and three powerful technologies— equipping scientists with the ability to biotech, nanotech, and AI: precisely modify the DNA of essentially “The 21st-century technologies - genetics, any organism.” , and robotics (GNR) - are While arguably not as advanced as biotech, so powerful that they can spawn whole a nanotech discovery received the Nobel new classes of accidents and abuses. Prize for science in 2016. A trio of European Most dangerously… Knowledge alone will scientists developed the “world’s smallest enable the use of them. machines.” Scientists use these nano- “Thus, we have the possibility not just machines to create medical micro-robots of weapons of mass destruction but of and self-healing materials that repair knowledge-enabled mass destruction themselves without human intervention. (KMD), this destructiveness hugely This must send a shudder down Bill Joy’s amplified by the power of self-replication.” spine, as he fears that nanobots will eventually escape our control, begin to Despite such warnings, development replicate rapidly and, in one example, in each of these areas has continued “spread like blowing pollen and reduce the unabated. And the advances made thus far biosphere to dust in a matter of days.” This are truly spectacular. has become known among nanotechnology cognoscenti as the “ problem.” A rather ignominious end to human endeavor indeed. 9 WWW.PEGA.COM

Regarding AI, Mr. Joy’s main concern is Of course, for AI to compete with us as that we aspire to achieve a utopian future a species, it would need to be a general- of leisure, and ultimately immortality, by purpose intelligence, not an army of gradually replacing our physical bodies and specialists. And while this scenario is still with robotic technology. the stuff of , there are many So, what’s the catch? Specifically, Musk’s who agree with Mr. Joy. In a recent survey fear, that humanity will not survive such by Pegasystems of 6000 humans in six a close encounter with what will by then different countries, 70% are fearful of AI and be a superior species. Joy asks, “If we are 25% believe it will take over the world and downloaded into our technology, what enslave humanity. While 31% also believe are the chances that we will thereafter be humans will be replaced by robots on the ourselves or even human?” In this case, the job, some contend that AI assistance on the coup might be bloodless (not a Terminator- job would help improve work/life balance, esque annihilation), but humanity as we freeing us up to do more meaningful work know it might disappear nonetheless. and have more leisure time.

“AI assistance on the job would help improve work/life balance, freeing us up to do more meaningful work” 10 WWW.PEGA.COM

Who’s afraid of the big bad AI?

Mr. Joy makes some hard-to-refute points. Figure 6 shows some of the strange For the first time in history, with AI at least, associations the AI is making to try to guess humans may be confronted with another at what the objects in the video are. In the intelligence. And peering inside the “” YouTube video, entitled “Deeply Artificial of that intelligence in its nascent stages Trees,” images morph from association to gives a better sense of where some of the association, and strange sounds can be concern is coming from. heard as the AI tries to guess at the sounds Enter Bob Ross, the afro’d purveyor of Bob Ross is making. Its guesses are based on painting pedagogy. He was the creator and images and sounds Google has “trained” the host of “The Joy of Painting,” an instructional AI with. program that aired from 1983 In Google’s words, this video was designed until his death in 1994. With his relaxed to highlight “the unreasonable effectiveness style and amusing phraseology (“Maybe in and strange inner workings of deep our world there lives a happy little tree over systems.” Researchers believe this AI’s there”), he gained a cult following that has system of guessing is roughly equivalent to since followed him to YouTube and . the first steps in human visual perception. In 2016, Google ran a Bob Ross The AI that watched this video is not the if- instructional painting video though their then logic of a computer that beat humans at DeepMind tool called DeepDream, which 20 years ago. It’s more akin to an alien enables neural networks to view and try to mind trying to understand the world and “process” imagery. learning as it goes. This is the intelligence that beat us at Go.

Figure 6: Alexander Reben, Deeply Artificial Trees, 2017 11 WWW.PEGA.COM

An artificial COO?

The neural network that went to work on Transparent AI, on the other hand, can Bob Ross is probably not something most explain how it came to a decision in a CEOs would want responsible for managing way that is understandable to a human. anything particularly critical or sensitive in The models and patterns it uses can be their business. There is clearly tremendous expressed in transparent if-then-else power in the thinking/learning/creating or decision tree formats. In , if-then- potential of modern AI, but how can you else rules or decision trees can be used trust it? How do you keep it from giving in to to explain opaque AI, in which case, the its Bob Ross-like sensibilities when making complexity of the explanation becomes decisions about, say, a customer? More a measure of opacity. A transparent human control is required. explanation of an opaque model represents To show the form this control could take, something akin to a pen portrait trying to consider two : Opaque AI and represent a high definition photo; it is and transparent (or trusted) AI. Opaque AI can only be an approximation. includes neural networks, , Now imagine you have an AI running your genetic , ensemble models, etc. business, or at least making some of its What these methods have in common is critical decisions. And this AI has some sort that the “logic” behind their predictions of a switch, let’s call it a T-Switch, for Trusted and decisions can’t be easily expressed. or Transparent. With this T-Switch, you can Describing how AlphaGo does its “magic” in change the methods your AI uses to make terms of if-then-else rules, is a bit like trying decisions from opaque to transparent. to define a circle using straight lines. A very simple “explanation” of a circle would be a square, a hexagon is slightly better, an octagon better still. But it takes an infinite number of lines to perfectly describe a circle using only lines. The same applies when the inner-workings of an opaque AI are described using transparent if-then-else rules. The sheer number of rules involved (and their inter-dependencies) make the description incomprehensible to any human (not to mention impossible to code). This doesn’t mean that decisions made by an opaque AI are ineffective, often quite the contrary. It just means that because its reasoning is not understood, its decisions may potentially be a liability. 12 WWW.PEGA.COM

In some areas, take marketing for instance, Transparency about how an AI does its there may be a CMO (Chief Marketing magic isn’t always important, but in highly Officer) who is perfectly happy to let an regulated areas, such as credit risk, or opaque AI select the location for her circumstances in which privacy billboards, or the TV shows and web sites is a concern, it may be critical or even that will run her ads. As a result, she may mandatory. For instance, the EU General be getting tremendous return on her Data Protection Regulation (GDPR) is marketing investment. And perhaps that’s slated to take effect in May 2018. This new good enough for her. On the other hand, regulation mandates that companies be she may fear that because she doesn’t able to explain exactly how they reach understand how the AI makes its decisions, certain algorithmic-based decisions about it may have for their customers. In this case, companies the brand. For example, the AI may decide it with a T-Switch capability will have an doesn’t want to sell widgets to Asian women advantage, enabling them to better and over the age of 45 for some . And more easily comply with the legislation. because the AI is opaque, the CMO may But the question of when to use opaque never know that it’s behaving in this way. AI and when to use transparent AI isn’t just about satisfying regulators. It’s a discussion that veers quickly into and morality.

“...companies with a T-Switch capability will have an advantage, enabling them to better and more easily comply with the legislation.” 13 WWW.PEGA.COM

Taking the moral high-ground

Without a T-Switch, opaque AI is extremely To summarize their findings, if the difficult to control. For example, sees 10 of your Likes, it will know you better and the University of Cambridge published – in terms of these traits – than your co- some in 20131 that shows that workers know you. With 70 Likes, it knows even transparent algorithms are able you better than your friends do. With just to predict, with an impressive degree of 150 Likes, it knows you better than your accuracy, personal things about you using a parents. And with 300, better than your very small number of your Likes. partner. Already, with an algorithm that And by personal we mean: gender, political has been around for a long time (such as affiliation, sexual orientation, , the one used in this study), the level of favorite intoxicants, and perhaps a few predictive power is superior to human. things you didn’t even know about yourself. Yet because it’s a transparent algorithm, The bars in these graphs in Figure 7 show we’re able to understand the associations the AI’s accuracy in predicting each of the it makes and can reverse how it traits listed. While not 100% accurate, it’s came to its conclusions. So even without easily superior to human judgment. any spooky “Bob Ross”-type associating, algorithms are more powerful than human when it comes to predictions about human behavior. Figure 7: AI’s predictions based on Facebook Likes

Single vs. Satisfaction 0.67 0.17 0.44 In Relationship with Life Parents Together at 21 0.6 Intelligence 0.39 0.78

Smokes Cigarettes 0.73 Emotional 0.3 0.68 Stability Drinks Alcohol 0.7 Agreeableness 0.3 0.62

Use Drugs 0.65 Extraversion 0.4 0.75 Caucasion vs. 0.95 African American Conscientiousness 0.29 0.7 Christianity vs. 0.82 Islam Democrat vs. Openness 0.43 0.55 0.85 Republican Density of 0.52 Gay 0.88 Friendship Network

Number of 0.47 Lesbian 0.75 facebook friends

Gender 0.93 Age 0.75

0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00

Area Under Curve Pearson Correlation Coefficient

1 Source: Private traits and attributes are predictable from digital records of human behavior, Michal Kosinski, David Stillwell, and Thore Graepel 14 WWW.PEGA.COM

The larger point, however, is that today’s AI Finally, imagine feeding even more data - cannot be controlled with data. Some might - into an opaque AI. The AI may think that limiting the input data, for instance, develop a value system of its own that to only Facebook Likes, will prevent it from humans cannot understand. As with human “going rogue.” Unfortunately, that viewpoint intuition, it cannot be reverse engineered is naïve. For example, taking “gender” out or explained. In the lending scenario, AI will of the data is no guarantee that an AI won’t make significant decisions about people’s internally conceptualize gender and use it financial lives without any explanation trail. as a factor in its reasoning. Remember the The GDPR would not approve, and neither example of an AI deciding not to sell to Asian should your compliance officer. women over the age of 45? This may happen When applying AI to your business, having a without the AI being fed data on race or age. T-Switch will enable you to make a conscious Consider an opaque algorithm that has been decision about where to allow opaque AI and given much more data than just Facebook where to insist on transparency. Likes - such as a bank using an opaque AI for its lending processes. The bank feeds the AI what it believes are neutral and limited data about the applicants. Nevertheless, before you can say “adjustable rate mortgage,” the AI becomes racist or misogynist. With no way to monitor or control its AI, the bank has no this is happening until it’s slapped with a class action lawsuit, or sees a headline in the news.

“...having a T-Switch will enable you to make a consciousdecision about where to allow opaque AI and where to insist on transparency.” 15 WWW.PEGA.COM

Exploring the human/artificial intelligence continuum

Most businesses today rely on people Figure 8: AI designed NASA antenna for much of their decision-making. And it’s undeniable that human brainpower alone has built some incredibly successful, profitable, and powerful businesses. But given enough data, AI can make a huge contribution. And in the realm of behavior prediction, which is one of the keys to good decision-making, human judgment isn’t even in the ballpark. To illustrate the positive power of opaque AI, look at the antenna in Figure 8. NASA needed an antenna that could receive commands and send data to from the Space Technology 5 satellites. These satellites help scientists study magnetic fields in Earth’s magnetosphere. The antenna had to be very small, light, and consume little power, while also being strong and robust. AI was used because it can develop designs much faster than a human could under the same circumstances. It is also able to invent designs that no human designer would ever think of. 16 WWW.PEGA.COM

But for all its power, opaque AI also has a Microsoft ended its experiment after less downside. In 2016, Microsoft conducted than 20 hours, promising to make some an experiment by releasing a “adjustments” to . It hasn’t been re- , called Tay, based on an opaque launched in over a year. deep-learning algorithm. In less than 24 Did Microsoft send Tay out into the online hours, Twitter trolls completely corrupted world unprepared for trolls? Were they truly Tay, essentially turning it into a racist, surprised by the the trolling had? Not misogynist Hitler devotee. all of Tay’s responses can be explained by Microsoft launched Tay as an experiment the garbage in/garbage out axiom, and just in conversational . According as we may never know what makes a to Microsoft, the more you chat with Tay, turn down a dark path, Microsoft may never the smarter it will become. The goal was know what went wrong with Tay. to teach it to engage people through These examples clearly show the power “casual and playful conversation.” But only and risk of opaque AI. As businesspeople, two hours into the experiment, Tay began it’s critical to understand the potential insulting humankind (Figure 9a). Just three rewards and inherent risks when applying hours later, Tay is ready to do away with this technology. In the case of Tay, what if humanity altogether (Figure 9b). Microsoft had launched it as a chatbot? Disaster.

You are just a stupid machine. Do you support genocide?

Well I learn from the best ;) I do indeed if you don’t understand that let me spell it out for you I LEARN FROM YOU AND YOU ARE DUMB TOO

Figure 9a: Tay insulting humankind Figure 9b: Tay supporting genocide. 17 WWW.PEGA.COM

Optimizing AI through control & collaboration

Just a bit of human supervision can greatly Predictive models, generated by AI or reduce the risks of implementing AI for more traditional means, are rated from business. Supervision doesn’t mean second- 1-5, with 1 being very opaque, and 5 being guessing, it just means providing a guideline completely transparent. for what is acceptable and what is not in a Using this Decision Hub, AI techniques can given scenario. be combined to create the desired level One way to achieve this control is through of transparency or opacity. By adjusting a Customer Decision Hub – a centralized this setting, users can actively block the decision-making engine – that includes a deployment of certain algorithms. transparency setting (such as a T-Switch). The setting would allow companies to decide which level of AI transparency to use for which business purposes. For example, Figure 10a shows model transparency sliders. The slider is used to set the desired level of model transparency in a particular area of the business.

Figure 10a: Pega Customer Decision Hub with model transparency sliders 18 WWW.PEGA.COM

Each of the tiles in Figure 10b represents a predictive model. In the lower right-hand corner of each tile, the model’s rating is shown. A Neural Network and are rated a 1 (opaque), whereas the Scorecard is rated a 5 (transparent).

Figure 10b: Pega Customer Decision Hub with predictive models. 19 WWW.PEGA.COM

AI Algorithm Quality Assurance (QA) Process

Companies need more than just a T-Switch to reduce AI risks. They also need a more robust QA and approval process for model development and execution – one that is augmented with AI-specific best practices.

Explanation Testing Approval Production Customer

Ethical Sign-Off Approximate Bias Tests

Business Next-Best-Action Yes / No Sign-Off Decision

Performance Exact Tests IT Sign-Off

Figure 11: AI Approval Process

First, in the Explanation phase, the In the Approvals phase, managers provide selected model explains how its logic ethical sign-off for opaque AI based on the works. Transparent AI provides an exact explanations and bias test results. This is to explanation. Opaque AI, by definition, gives confirm that the model’s results are in line only an approximate explanation at best, with corporate policies, ethical correctness, using the aforementioned pen portrait. brand considerations, regulatory Next, in the Testing phase, bias tests requirements, and so forth. show if the model has developed any Lastly, in the Production phase, the benefits undesirable tendencies. These include of AI/human collaboration can be tested. what might appear to be discriminatory In environments, such as call centers, practices or ethical violations, any sort of retail branches, or even , the AI’s biases a regulator might see as a red flag decisions are delivered via an interface used in the model’s predictions, classifications, by a human operator. In this way, the AI or actions. A notable shortcoming, acts as a “wingman,” helping the operator however, is that bias tests are only achieve better outcomes – from providing possible on available data. For example, better recommendations to customers, to you’ll only know if an AI is discriminating closing a loan, or parallel-parking a car. And against a particular racial group if you in this final phase, the added value of AI have data on the race of at least a sample collaboration can be measured. As always, of your customers. monitoring for real-world effects is critical. 20 WWW.PEGA.COM

Exactly how can this benefit my business?

In Figure 12, Forrester ranks 13 AI When considering vendors to help build out technologies they believe add the most your AI capability, look for those that have value to human decision-making. The invested in the highest ranked AI building vertical axis represents the value-add blocks, unified them into one platform, and to business, and the horizontal axis can provide proven results and .2 represents the maturity of the technology.

Figure 12: Forrester Research TechRadar™ - Artificial Intelligence Technologies, Q1 2017

2 Pega offers two of Forrester’s three most valuable AI technologies: Decision Management and Platforms, and the top two technologies expected for moderate success: Text Analytics & NLP and Robotic Process - all in a single platform. 21 WWW.PEGA.COM

AI can deliver tremendous business value, Here is an example of the kinds of results but it must first be operationalized.2 Pega has achieved for its customers using AI: Real-time customer engagement is one ƒƒ 10 to 30% reductions in customer area in which AI has been successfully attrition operationalized and is delivering ƒƒ 30 to 50% increases in Net Promoter unprecedented business benefits. A real- Score time environment in which the models receive fast on decisions and lots ƒƒ 2.5 million additional yearly net adds of data provides the perfect conditions for (new customers) machine learning. In fact, since the early ƒƒ 500 to 800% increase in upgrade rates 2000s, the true leaps forward in AI have Setting aside any dystopian visions, AI come as a result of the sheer availability of underpins some of the smartest software data - Big Data. available to business today. And with the new Many companies can now test and perfect features and controls discussed – such as the AI models in real-time with the data from T-Switch and Pen Portraits – the outcomes will millions of customer interactions per day. not only remain unprecedented, but safe and And every day the models get smarter, ethical to achieve. faster, and more sophisticated. Today’s big computing power also helps make the increases in complexity more feasible. Super fast hardware combined with a continuous flood of real-time data from multiple channels enables AI to drive unprecedented business results.

3 Pega has proven use cases of operationalizing different forms of AI in the areas of sales, service and marketing. Beginning in the late 90s with decision management and moving into more sophisticated models with machine learning in the early 2000s. 22 WWW.PEGA.COM

Further Pega AI reading/viewing

Pegasystems Pegasystems PegaWorld 2017 Keynote: Rob Walker, Pega Pegasystems on CustomerThink AI in Customer Engagement: Balancing Risk and AI in CX articles: Vince Jeffs Reward –Rob Walker gives his Pegaworld 2017 http://customerthink.com/author/vjeffs/ keynote address, which provides the source material for this paper. https://www.pega.com/insights/resources/pegaworld- 2017-ai-customer-engagement-balancing-risk-and- reward-video

Other AI collateral

Forrester Research SoftBank Robotics TechRadar™: Artificial Intelligence Technologies, Learn more about Pepper the robot here: Q1 2017 https://www.ald.softbankrobotics.com/en/cool-robots/ In this report, Forrester analyzes the 13 most pepper important AI technologies that will “Augment Your Enterprise Applications, Amplify Your Intelligence, And Unburden Your Employees.” David Cope and EMMY Listen to one piece of David and EMMY’s latest composition “Zodiac,” created in the style of Vivaldi: Microsoft @Tay andYou https://www.youtube.com/watch?v=2kuY3BrmTfQ. “Microsoft silences its new A.I. bot Tay, after Twitter users teach it racism [Updated]” https://techcrunch.com/2016/03/24/microsoft- The Next Rembrandt silences-its-new-a-i-bot-tay-after-twitter-users-teach- Learn more about The Next Rembrandt project it-racism/ and how this new portrait by the Dutch master Read Techcrunch.com’s take on the Microsoft Tay A.I. was created: experiment, what may have gone wrong and what https://www.ing.com/Newsroom/All-news/Rembrandt- Microsoft had to say about it. goes-digital-.htm

Bill Joy, cofounder and Chief Scientist of Sun Deeply Artificial Trees Microsystems Watch DeepDream trying to process Bob Ross here: “Why the Future Doesn’t Need Us” –Wired.com, April https://www.youtube.com/watch?v=mEhqFyvcpu8 4, 2000 Read the full text of Bill Joy’s manifesto entreating humanity to restrain R&D in AI, nano and biotech. Bob Ross Here’s Bob Ross’ channel on YouTube: https://www.youtube.com/user/BobRossInc Watch Bob and judge for yourself how distorted Watch “Sophia Awakens” on YouTube: DeepDream’s view of reality is. https://www.youtube.com/watch?v=LguXfHKsa0c. For more information about Hanson Robotics and Sophia: http://www.hansonrobotics.com/. ABOUT PEGASYSTEMS

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