Sure, AI can be creative, but it will never possess genius

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Sarah Bernhardt plays Hamlet, London 1899

“What’s Hecuba to him, or he to Hecuba, 
That he should weep for her?” 

The close of Act II Scene ii, and Hamlet questions how the performers in a play about the siege of Troy are able to convey such emotion – feel such empathy – for the stranger queen of an ancient city. 

The construct here is complex. A play within a play, sparking a key moment of introspection, and ultimately self doubt. It is no coincidence that in this same work we find perhaps the earliest use of the term “my mind’s eye,” heralding a shift in theatrical focus from traditions of enacted disputes, lovers passions, and farce, to more a more nuanced kind of drama that issues from psychological turmoil.

Hamlet is generally considered to be a work of creative genius. For many laboring in the creative arts, works like this and those in its broader category serve as aspirational benchmarks. Indelible reminders of the brilliant outlands of human creativity. 

Now, for the first time in our history, humans have a rival in deliberate acts of aesthetic creation. In the midst of the avalanche of artificial intelligence hype comes a new promise – creative AI; here to relieve us of burdensome tasks including musical, literary, and artistic composition.  

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Will Every Kid Get an Equal Shot at an ‘A’ In the Era of New Tech & AI?

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“One child, one teacher, one book, one pen can change the world.”

These are the inspirational words of activist Malala Yousafzai, best known as “the girl who was shot by the Taliban” for championing female education in her home country of Pakistan. This modest, pared-down idea of schooling is cherished by many. There is something noble about it, perhaps because harkens back to the very roots of intellectual enquiry. No tools and no distractions; just ideas and conversation. 

Traditionalists may be reminded of the largely bygone “chalk and talk” methods of teaching, rooted in the belief that students need little more than firm, directed pedagogical instruction to prepare them for the world. Many still reminisce about these relatively uncomplicated teaching techniques, but we should be careful not to misread Yousafzai’s words as prescribing simplicity as the optimal conditions for education. 

On the contrary, her comments describe a baseline. 

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Three Things I Learned: Living with AI (Experts)

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Credit: Tanisha Bassan

There is strong evidence to show that subject-specific experts frequently fall short on their informed judgments. Particularly when it comes to forecasting.

In fact, in 2005 the University of Pennsylvania’s Professor Philip E. Tetlock devised a test for seasoned and respected commentators that found as their level of expertise rose, their confidence also rose – but not their accuracy. Repeatedly, Tetlock’s experts attached high probability to low frequency events in error, relying upon intuitive casual reasoning rather than probabilistic reasoning. Their assertions were often no more reliable than, to quote the experimenter, “a dart throwing chimp.”

I was reminded of Tetlock’s ensuing book and other similar experiments at the Future Trends Forum in Madrid last month; an event that (valiantly) attempts to convene a room full of thought leaders and task them with predicting our future. Specifically, in this case, our AI future.

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Tech for Humans, Part 2: Designing a Human-Centered Future

YouTheData.com is delighted to feature a two-part guest post by Andrew Sears. Andrew is passionate about emerging technologies and the future we’re building with them. He’s driven innovation at companies like IBM, IDEO, and Genesis Mining with a focus on AI, cloud, and blockchain products. He serves as an Advisor at All Tech is Human and will complete his MBA at Duke University in 2020. You can keep up with his work at andrew-sears.com.

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In Part 1 of this series, we explored the paradox of human-centered design as it is commonly practiced today: well-intentioned product teams start with the goal of empathizing deeply with humanneeds and desires, only to end up with a product that is just plain bad for humans.

In many cases, this outcome represents a failure to appreciate the complex web of values, commitments, and needs that define human experience. By understanding their users in reductively economic terms, teams build products that deliver convenience and efficiency at the cost of privacy, intimacy, and emotional wellbeing. But times are changing. The growing popularity of companies like Light, Purism, Brave, and Duck Duck Go signifies a shift in consumer preferences towards tech products that respect their users’ time, attention, privacy, and values.

Product teams now face both a social and an economic imperative to think more critically about the products they put into the world. To change their outcomes, they should start by changing their processes. Fortunately, existing design methodologies can be adapted and augmented to build products that appreciate more fully the human complexity of their users. Here are three changes that product teams should make to put the “human” in human centered design:

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Tech for Humans, Part 1: The Paradox of Human Centered Design

YouTheData.com is delighted to feature a two-part guest post by Andrew Sears. Andrew is passionate about emerging technologies and the future we’re building with them. He’s driven innovation at companies like IBM, IDEO, and Genesis Mining with a focus on AI, cloud, and blockchain products. He serves as an Advisor at All Tech is Human and will complete his MBA at Duke University in 2020. You can keep up with his work at andrew-sears.com.

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“Ideate.” “Prototype.” “Iterate.” “Empathize.” These words have become mainstays in the modern tech product team’s vocabulary, thanks in part to human-centered design.

Human-centered design is an approach to product design that is based on building empathy with the people who will be using the product. The assumption is that, by understanding the needs, preferences, and desires of the end user, the product team can build a product that customers will find both useful and easy to use. Within this mindset, great products are those that integrate seamlessly into the user’s life and help them to accomplish tasks more efficiently.

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AI needs cooperation, not an arms race

This article by Fiona J McEvoy (YouTheData.com) was originally posted on All Turtles.

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Writing in the New York Times recently, venture capitalist Kai-Fu Lee signaled an important, oncoming change in the way we think about artificial intelligence. We are graduating, he cautioned, from an age of discovery and vision into a more practical era of implementation.

Lee is promoting his new book, titled A.I. Superpowers: China, Silicon Valley, and the New World Order, and he suggests that this transition from lab to launchpad may naturally privilege Chinese advantages—like data abundance and government investment—above the research capabilities and “freewheeling intellectual environment” of the U.S.

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Good Gadgets: The rise of socially conscious tech

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From algorithmic bias to killer robots, fake news, and the now almost daily prophesying about the dangers of AI, it’s fair to say that tech is under scrutiny.

Episodes like the Cambridge Analytica scandal opened our eyes to the fact that some of our nearest and dearest technologies had become fully socialized before we truly understood the full force of their influence. Consequently, new tools and gadgets coming down the line are being closely examined so that we can begin to uncover any damaging consequences that could manifest 10, 20, or even 100 years from now.

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Peer pressure: An unintended consequence of AI

This article by Fiona J McEvoy (YouTheData.com) was originally posted on All Turtles.

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Last winter, Kylie Jenner tweeted that she stopped using Snapchat, and almost immediately the company’s shares dropped six-percent, losing $1.3 billion in value. Her seemingly innocent comments had led investors to believe that the 20-year-old’s 25 million followers would do the same, and the knock-on effect would seal the social media apps fate as a “has been” among its key demographic of younger women.

This astonishing event demonstrates in technicolor how the notion of influence is evolving, latterly taking on a new significance. In the age of technology, though influence is still associated with power, it is no longer the limited reserve of “the Powerful”—i.e. those in recognized positions of authority, like bankers, lawyers, or politicians.

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Trying on wearables

This article by Fiona J McEvoy (YouTheData.com) was originally posted on All Turtles.

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The dramatic failures of Google Glass and Snapchat Spectacles demonstrated the countless challenges faced by wearable technologies. Beyond the ubiquitous activity trackers and smartwatches, wearable consumer products have yet to yield a mass-market success. Though the idea of wearable tech and human-technology synergy still gets marketers excited, product designers have yet to hit upon a breakout device that will prove as popular and indispensable as blue jeans. Still, the allure of developing such a product remains irresistible.

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AI and the future shape of product design

This article by Fiona J McEvoy (YouTheData.com) was originally posted on All Turtles.

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These days we talk so much about artificial intelligence and its creators that it’s easy to overlook the increasingly prolific role AI itself is playing in product creation and design. Across different industries, the technical and the creative are being drawn closely together to create a range of products that may otherwise never have been conceived.

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Take, for example, the new aerodynamic bicycle presented this month at the International Conference on Machine Learning, which was designed using Neural Concept software. By employing AI in the design phase, a small team from French college IUT Annecy were able to completely bypass the usual methods of testing for aerodynamism – a process that usually requires a great deal of time and computing power.

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