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#432487 Can We Make a Musical Turing Test?

Posted on April 8, 2018 by Android

As artificial intelligence advances, we’re encountering the same old questions. How much of what we consider to be fundamentally human can be reduced to an algorithm? Can we create something sufficiently advanced that people can no longer distinguish between the two? This, after all, is the idea behind the Turing Test, which has yet to be passed.

At first glance, you might think music is beyond the realm of algorithms. Birds can sing, and people can compose symphonies. Music is evocative; it makes us feel. Very often, our intense personal and emotional attachments to music are because it reminds us of our shared humanity. We are told that creative jobs are the least likely to be automated. Creativity seems fundamentally human.

But I think above all, we view it as reductionist sacrilege: to dissect beautiful things. “If you try to strangle a skylark / to cut it up, see how it works / you will stop its heart from beating / you will stop its mouth from singing.” A human musician wrote that; a machine might be able to string words together that are happy or sad; it might even be able to conjure up a decent metaphor from the depths of some neural network—but could it understand humanity enough to produce art that speaks to humans?

Then, of course, there’s the other side of the debate. Music, after all, has a deeply mathematical structure; you can train a machine to produce harmonics. “In the teachings of Pythagoras and his followers, music was inseparable from numbers, which were thought to be the key to the whole spiritual and physical universe,” according to Grout in A History of Western Music. You might argue that the process of musical composition cannot be reduced to a simple algorithm, yet musicians have often done so. Mozart, with his “Dice Music,” used the roll of a dice to decide how to order musical fragments; creativity through an 18th-century random number generator. Algorithmic music goes back a very long way, with the first papers on the subject from the 1960s.

Then there’s the techno-enthusiast side of the argument. iTunes has 26 million songs, easily more than a century of music. A human could never listen to and learn from them all, but a machine could. It could also memorize every note of Beethoven. Music can be converted into MIDI files, a nice chewable data format that allows even a character-by-character neural net you can run on your computer to generate music. (Seriously, even I could get this thing working.)

Indeed, generating music in the style of Bach has long been a test for AI, and you can see neural networks gradually learn to imitate classical composers while trying to avoid overfitting. When an algorithm overfits, it essentially starts copying the existing music, rather than being inspired by it but creating something similar: a tightrope the best human artists learn to walk. Creativity doesn’t spring from nowhere; even maverick musical geniuses have their influences.

Does a machine have to be truly ‘creative’ to produce something that someone would find valuable? To what extent would listeners’ attitudes change if they thought they were hearing a human vs. an AI composition? This all suggests a musical Turing Test. Of course, it already exists. In fact, it’s run out of Dartmouth, the school that hosted that first, seminal AI summer conference. This year, the contest is bigger than ever: alongside the PoetiX, LimeriX and LyriX competitions for poetry and lyrics, there’s a DigiKidLit competition for children’s literature (although you may have reservations about exposing your children to neural-net generated content… it can get a bit surreal).

There’s also a pair of musical competitions, including one for original compositions in different genres. Key genres and styles are represented by Charlie Parker for Jazz and the Bach chorales for classical music. There’s also a free composition, and a contest where a human and an AI try to improvise together—the AI must respond to a human spontaneously, in real time, and in a musically pleasing way. Quite a challenge! In all cases, if any of the generated work is indistinguishable from human performers, the neural net has passed the Turing Test.

Did they? Here’s part of 2017’s winning sonnet from Charese Smiley and Hiroko Bretz:

The large cabin was in total darkness.
Come marching up the eastern hill afar.
When is the clock on the stairs dangerous?
Everything seemed so near and yet so far.
Behind the wall silence alone replied.
Was, then, even the staircase occupied?
Generating the rhymes is easy enough, the sentence structure a little trickier, but what’s impressive about this sonnet is that it sticks to a single topic and appears to be a more coherent whole. I’d guess they used associated “lexical fields” of similar words to help generate something coherent. In a similar way, most of the more famous examples of AI-generated music still involve some amount of human control, even if it’s editorial; a human will build a song around an AI-generated riff, or select the most convincing Bach chorale from amidst many different samples.

We are seeing strides forward in the ability of AI to generate human voices and human likenesses. As the latter example shows, in the fake news era people have focused on the dangers of this tech– but might it also be possible to create a virtual performer, trained on a dataset of their original music? Did you ever want to hear another Beatles album, or jam with Miles Davis? Of course, these things are impossible—but could we create a similar experience that people would genuinely value? Even, to the untrained eye, something indistinguishable from the real thing?

And if it did measure up to the real thing, what would this mean? Jaron Lanier is a fascinating technology writer, a critic of strong AI, and a believer in the power of virtual reality to change the world and provide truly meaningful experiences. He’s also a composer and a musical aficionado. He pointed out in a recent interview that translation algorithms, by reducing the amount of work translators are commissioned to do, have, in some sense, profited from stolen expertise. They were trained on huge datasets purloined from human linguists and translators. If you can train an AI on someone’s creative output and it produces new music, who “owns” it?

Although companies that offer AI music tools are starting to proliferate, and some groups will argue that the musical Turing test has been passed already, AI-generated music is hardly racing to the top of the pop charts just yet. Even as the line between human-composed and AI-generated music starts to blur, there’s still a gulf between the average human and musical genius. In the next few years, we’ll see how far the current techniques can take us. It may be the case that there’s something in the skylark’s song that can’t be generated by machines. But maybe not, and then this song might need an extra verse.

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Posted in Human Robots

#432431 Why Slowing Down Can Actually Help Us ...

Posted on March 30, 2018 by Android

Leah Weiss believes that when we pay attention to how we do our work—our thoughts and feelings about what we do and why we do it—we can tap into a much deeper reservoir of courage, creativity, meaning, and resilience.

As a researcher, educator, and author, Weiss teaches a course called “Leading with Compassion and Mindfulness” at the Stanford Graduate School of Business, one of the most competitive MBA programs in the world, and runs programs at HopeLab.

Weiss is the author of the new book How We Work: Live Your Purpose, Reclaim your Sanity and Embrace the Daily Grind, endorsed by the Dalai Lama, among others. I caught up with Leah to learn more about how the practice of mindfulness can deepen our individual and collective purpose and passion.

Lisa Kay Solomon: We’re hearing a lot about mindfulness these days. What is mindfulness and why is it so important to bring into our work? Can you share some of the basic tenets of the practice?

Leah Weiss, PhD: Mindfulness is, in its most literal sense, “the attention to inattention.” It’s as simple as noticing when you’re not paying attention and then re-focusing. It is prioritizing what is happening right now over internal and external noise.

The ability to work well with difficult coworkers, handle constructive feedback and criticism, regulate emotions at work—all of these things can come from regular mindfulness practice.

Some additional benefits of mindfulness are a greater sense of compassion (both self-compassion and compassion for others) and a way to seek and find purpose in even mundane things (and especially at work). From the business standpoint, mindfulness at work leads to increased productivity and creativity, mostly because when we are focused on one task at a time (as opposed to multitasking), we produce better results.

We spend more time with our co-workers than we do with our families; if our work relationships are negative, we suffer both mentally and physically. Even worse, we take all of those negative feelings home with us at the end of the work day. The antidote to this prescription for unhappiness is to have clear, strong purpose (one third of people do not have purpose at work and this is a major problem in the modern workplace!). We can use mental training to grow as people and as employees.

LKS: What are some recommendations you would make to busy leaders who are working around the clock to change the world?

LW: I think the most important thing is to remember to tend to our relationship with ourselves while trying to change the world. If we’re beating up on ourselves all the time we’ll be depleted.

People passionate about improving the world can get into habits of believing self-care isn’t important. We demand a lot of ourselves. It’s okay to fail, to mess up, to make mistakes—what’s important is how we learn from those mistakes and what we tell ourselves about those instances. What is the “internal script” playing in your own head? Is it positive, supporting, and understanding? It should be. If it isn’t, you can work on it. And the changes you make won’t just improve your quality of life, they’ll make you more resilient to weather life’s inevitable setbacks.

A close second recommendation is to always consider where everyone in an organization fits and help everyone (including yourself) find purpose. When you know what your own purpose is and show others their purpose, you can motivate a team and help everyone on a team gain pride in and at work. To get at this, make sure to ask people on your team what really lights them up. What sucks their energy and depletes them? If we know our own answers to these questions and relate them to the people we work with, we can create more engaged organizations.

LKS: Can you envision a future where technology and mindfulness can work together?

LW: Technology and mindfulness are already starting to work together. Some artificial intelligence companies are considering things like mindfulness and compassion when building robots, and there are numerous apps that target spreading mindfulness meditations in a widely-accessible way.

LKS: Looking ahead at our future generations who seem more attached to their devices than ever, what advice do you have for them?

LW: It’s unrealistic to say “stop using your device so much,” so instead, my suggestion is to make time for doing things like scrolling social media and make the same amount of time for putting your phone down and watching a movie or talking to a friend. No matter what it is that you are doing, make sure you have meta-awareness or clarity about what you’re paying attention to. Be clear about where your attention is and recognize that you can be a steward of attention. Technology can support us in this or pull us away from this; it depends on how we use it.

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Posted in Human Robots

#432311 Everyone Is Talking About AI—But Do ...

Posted on March 15, 2018 by Android

In 2017, artificial intelligence attracted $12 billion of VC investment. We are only beginning to discover the usefulness of AI applications. Amazon recently unveiled a brick-and-mortar grocery store that has successfully supplanted cashiers and checkout lines with computer vision, sensors, and deep learning. Between the investment, the press coverage, and the dramatic innovation, “AI” has become a hot buzzword. But does it even exist yet?

At the World Economic Forum Dr. Kai-Fu Lee, a Taiwanese venture capitalist and the founding president of Google China, remarked, “I think it’s tempting for every entrepreneur to package his or her company as an AI company, and it’s tempting for every VC to want to say ‘I’m an AI investor.’” He then observed that some of these AI bubbles could burst by the end of 2018, referring specifically to “the startups that made up a story that isn’t fulfillable, and fooled VCs into investing because they don’t know better.”

However, Dr. Lee firmly believes AI will continue to progress and will take many jobs away from workers. So, what is the difference between legitimate AI, with all of its pros and cons, and a made-up story?

If you parse through just a few stories that are allegedly about AI, you’ll quickly discover significant variation in how people define it, with a blurred line between emulated intelligence and machine learning applications.

I spoke to experts in the field of AI to try to find consensus, but the very question opens up more questions. For instance, when is it important to be accurate to a term’s original definition, and when does that commitment to accuracy amount to the splitting of hairs? It isn’t obvious, and hype is oftentimes the enemy of nuance. Additionally, there is now a vested interest in that hype—$12 billion, to be precise.

This conversation is also relevant because world-renowned thought leaders have been publicly debating the dangers posed by AI. Facebook CEO Mark Zuckerberg suggested that naysayers who attempt to “drum up these doomsday scenarios” are being negative and irresponsible. On Twitter, business magnate and OpenAI co-founder Elon Musk countered that Zuckerberg’s understanding of the subject is limited. In February, Elon Musk engaged again in a similar exchange with Harvard professor Steven Pinker. Musk tweeted that Pinker doesn’t understand the difference between functional/narrow AI and general AI.

Given the fears surrounding this technology, it’s important for the public to clearly understand the distinctions between different levels of AI so that they can realistically assess the potential threats and benefits.

As Smart As a Human?
Erik Cambria, an expert in the field of natural language processing, told me, “Nobody is doing AI today and everybody is saying that they do AI because it’s a cool and sexy buzzword. It was the same with ‘big data’ a few years ago.”

Cambria mentioned that AI, as a term, originally referenced the emulation of human intelligence. “And there is nothing today that is even barely as intelligent as the most stupid human being on Earth. So, in a strict sense, no one is doing AI yet, for the simple fact that we don’t know how the human brain works,” he said.

He added that the term “AI” is often used in reference to powerful tools for data classification. These tools are impressive, but they’re on a totally different spectrum than human cognition. Additionally, Cambria has noticed people claiming that neural networks are part of the new wave of AI. This is bizarre to him because that technology already existed fifty years ago.

However, technologists no longer need to perform the feature extraction by themselves. They also have access to greater computing power. All of these advancements are welcomed, but it is perhaps dishonest to suggest that machines have emulated the intricacies of our cognitive processes.

“Companies are just looking at tricks to create a behavior that looks like intelligence but that is not real intelligence, it’s just a mirror of intelligence. These are expert systems that are maybe very good in a specific domain, but very stupid in other domains,” he said.

This mimicry of intelligence has inspired the public imagination. Domain-specific systems have delivered value in a wide range of industries. But those benefits have not lifted the cloud of confusion.

Assisted, Augmented, or Autonomous
When it comes to matters of scientific integrity, the issue of accurate definitions isn’t a peripheral matter. In a 1974 commencement address at the California Institute of Technology, Richard Feynman famously said, “The first principle is that you must not fool yourself—and you are the easiest person to fool.” In that same speech, Feynman also said, “You should not fool the layman when you’re talking as a scientist.” He opined that scientists should bend over backwards to show how they could be wrong. “If you’re representing yourself as a scientist, then you should explain to the layman what you’re doing—and if they don’t want to support you under those circumstances, then that’s their decision.”

In the case of AI, this might mean that professional scientists have an obligation to clearly state that they are developing extremely powerful, controversial, profitable, and even dangerous tools, which do not constitute intelligence in any familiar or comprehensive sense.

The term “AI” may have become overhyped and confused, but there are already some efforts underway to provide clarity. A recent PwC report drew a distinction between “assisted intelligence,” “augmented intelligence,” and “autonomous intelligence.” Assisted intelligence is demonstrated by the GPS navigation programs prevalent in cars today. Augmented intelligence “enables people and organizations to do things they couldn’t otherwise do.” And autonomous intelligence “establishes machines that act on their own,” such as autonomous vehicles.

Roman Yampolskiy is an AI safety researcher who wrote the book “Artificial Superintelligence: A Futuristic Approach.” I asked him whether the broad and differing meanings might present difficulties for legislators attempting to regulate AI.

Yampolskiy explained, “Intelligence (artificial or natural) comes on a continuum and so do potential problems with such technology. We typically refer to AI which one day will have the full spectrum of human capabilities as artificial general intelligence (AGI) to avoid some confusion. Beyond that point it becomes superintelligence. What we have today and what is frequently used in business is narrow AI. Regulating anything is hard, technology is no exception. The problem is not with terminology but with complexity of such systems even at the current level.”

When asked if people should fear AI systems, Dr. Yampolskiy commented, “Since capability comes on a continuum, so do problems associated with each level of capability.” He mentioned that accidents are already reported with AI-enabled products, and as the technology advances further, the impact could spread beyond privacy concerns or technological unemployment. These concerns about the real-world effects of AI will likely take precedence over dictionary-minded quibbles. However, the issue is also about honesty versus deception.

Is This Buzzword All Buzzed Out?
Finally, I directed my questions towards a company that is actively marketing an “AI Virtual Assistant.” Carl Landers, the CMO at Conversica, acknowledged that there are a multitude of explanations for what AI is and isn’t.

He said, “My definition of AI is technology innovation that helps solve a business problem. I’m really not interested in talking about the theoretical ‘can we get machines to think like humans?’ It’s a nice conversation, but I’m trying to solve a practical business problem.”

I asked him if AI is a buzzword that inspires publicity and attracts clients. According to Landers, this was certainly true three years ago, but those effects have already started to wane. Many companies now claim to have AI in their products, so it’s less of a differentiator. However, there is still a specific intention behind the word. Landers hopes to convey that previously impossible things are now possible. “There’s something new here that you haven’t seen before, that you haven’t heard of before,” he said.

According to Brian Decker, founder of Encom Lab, machine learning algorithms only work to satisfy their preexisting programming, not out of an interior drive for better understanding. Therefore, he views AI as an entirely semantic argument.

Decker stated, “A marketing exec will claim a photodiode controlled porch light has AI because it ‘knows when it is dark outside,’ while a good hardware engineer will point out that not one bit in a register in the entire history of computing has ever changed unless directed to do so according to the logic of preexisting programming.”

Although it’s important for everyone to be on the same page regarding specifics and underlying meaning, AI-powered products are already powering past these debates by creating immediate value for humans. And ultimately, humans care more about value than they do about semantic distinctions. In an interview with Quartz, Kai-Fu Lee revealed that algorithmic trading systems have already given him an 8X return over his private banking investments. “I don’t trade with humans anymore,” he said.

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Posted in Human Robots

#432303 What If the AI Revolution Is Neither ...

Posted on March 14, 2018 by Android

Why does everyone assume that the AI revolution will either lead to a fiery apocalypse or a glorious utopia, and not something in between? Of course, part of this is down to the fact that you get more attention by saying “The end is nigh!” or “Utopia is coming!”

But part of it is down to how humans think about change, especially unprecedented change. Millenarianism doesn’t have anything to do with being a “millennial,” being born in the 90s and remembering Buffy the Vampire Slayer. It is a way of thinking about the future that involves a deeply ingrained sense of destiny. A definition might be: “Millenarianism is the expectation that the world as it is will be destroyed and replaced with a perfect world, that a redeemer will come to cast down the evil and raise up the righteous.”

Millenarian beliefs, then, intimately link together the ideas of destruction and creation. They involve the idea of a huge, apocalyptic, seismic shift that will destroy the fabric of the old world and create something entirely new. Similar belief systems exist in many of the world’s major religions, and also the unspoken religion of some atheists and agnostics, which is a belief in technology.

Look at some futurist beliefs around the technological Singularity. In Ray Kurzweil’s vision, the Singularity is the establishment of paradise. Everyone is rendered immortal by biotechnology that can cure our ills; our brains can be uploaded to the cloud; inequality and suffering wash away under the wave of these technologies. The “destruction of the world” is replaced by a Silicon Valley buzzword favorite: disruption. And, as with many millenarian beliefs, your mileage varies on whether this destruction paves the way for a new utopia—or simply ends the world.

There are good reasons to be skeptical and interrogative towards this way of thinking. The most compelling reason is probably that millenarian beliefs seem to be a default mode of how humans think about change; just look at how many variants of this belief have cropped up all over the world.

These beliefs are present in aspects of Christian theology, although they only really became mainstream in their modern form in the 19th and 20th centuries. Ideas like the Tribulations—many years of hardship and suffering—before the Rapture, when the righteous will be raised up and the evil punished. After this destruction, the world will be made anew, or humans will ascend to paradise.

Despite being dogmatically atheist, Marxism has many of the same beliefs. It is all about a deterministic view of history that builds to a crescendo. In the same way as Rapture-believers look for signs that prophecies are beginning to be fulfilled, so Marxists look for evidence that we’re in the late stages of capitalism. They believe that, inevitably, society will degrade and degenerate to a breaking point—just as some millenarian Christians do.

In Marxism, this is when the exploitation of the working class by the rich becomes unsustainable, and the working class bands together and overthrows the oppressors. The “tribulation” is replaced by a “revolution.” Sometimes revolutionary figures, like Lenin, or Marx himself, are heralded as messiahs who accelerate the onset of the Millennium; and their rhetoric involves utterly smashing the old system such that a new world can be built. Of course, there is judgment, when the righteous workers take what’s theirs and the evil bourgeoisie are destroyed.

Even Norse mythology has an element of this, as James Hughes points out in his essay in Nick Bostrom’s book Global Catastrophic Risks. Ragnarok involves men and gods being defeated in a final, apocalyptic battle—but because that was a little bleak, they add in the idea that a new earth will arise where the survivors will live in harmony.

Judgement day is a cultural trope, too. Take the ancient Egyptians and their beliefs around the afterlife; the Lord of the underworld, Osiris, weighs the mortal’s heart against a feather. “Should the heart of the deceased prove to be heavy with wrongdoing, it would be eaten by a demon, and the hope of an afterlife vanished.”

Perhaps in the Singularity, something similar goes on. As our technology and hence our power improve, a final reckoning approaches: our hearts, as humans, will be weighed against a feather. If they prove too heavy with wrongdoing—with misguided stupidity, with arrogance and hubris, with evil—then we will fail the test, and we will destroy ourselves. But if we pass, and emerge from the Singularity and all of its threats and promises unscathed, then we will have paradise. And, like the other belief systems, there’s no room for non-believers; all of society is going to be radically altered, whether you want it to be or not, whether it benefits you or leaves you behind. A technological rapture.

It almost seems like every major development provokes this response. Nuclear weapons did, too. Either this would prove the final straw and we’d destroy ourselves, or the nuclear energy could be harnessed to build a better world. People talked at the dawn of the nuclear age about electricity that was “too cheap to meter.” The scientists who worked on the bomb often thought that with such destructive power in human hands, we’d be forced to cooperate and work together as a species.

When we see the same response over and over again to different circumstances, cropping up in different areas, whether it’s science, religion, or politics, we need to consider human biases. We like millenarian beliefs; and so when the idea of artificial intelligence outstripping human intelligence emerges, these beliefs spring up around it.

We don’t love facts. We don’t love information. We aren’t as rational as we’d like to think. We are creatures of narrative. Physicists observe the world and we weave our observations into narrative theories, stories about little billiard balls whizzing around and hitting each other, or space and time that bend and curve and expand. Historians try to make sense of an endless stream of events. We rely on stories: stories that make sense of the past, justify the present, and prepare us for the future.

And as stories go, the millenarian narrative is a brilliant and compelling one. It can lead you towards social change, as in the case of the Communists, or the Buddhist uprisings in China. It can justify your present-day suffering, if you’re in the tribulation. It gives you hope that your life is important and has meaning. It gives you a sense that things are evolving in a specific direction, according to rules—not just randomly sprawling outwards in a chaotic way. It promises that the righteous will be saved and the wrongdoers will be punished, even if there is suffering along the way. And, ultimately, a lot of the time, the millenarian narrative promises paradise.

We need to be wary of the millenarian narrative when we’re considering technological developments and the Singularity and existential risks in general. Maybe this time is different, but we’ve cried wolf many times before. There is a more likely, less appealing story. Something along the lines of: there are many possibilities, none of them are inevitable, and lots of the outcomes are less extreme than you might think—or they might take far longer than you think to arrive. On the surface, it’s not satisfying. It’s so much easier to think of things as either signaling the end of the world or the dawn of a utopia—or possibly both at once. It’s a narrative we can get behind, a good story, and maybe, a nice dream.

But dig a little below the surface, and you’ll find that the millenarian beliefs aren’t always the most promising ones, because they remove human agency from the equation. If you think that, say, the malicious use of algorithms, or the control of superintelligent AI, are serious and urgent problems that are worth solving, you can’t be wedded to a belief system that insists utopia or dystopia are inevitable. You have to believe in the shades of grey—and in your own ability to influence where we might end up. As we move into an uncertain technological future, we need to be aware of the power—and the limitations—of dreams.

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Posted in Human Robots

#432271 Your Shopping Experience Is on the Verge ...

Posted on March 9, 2018 by Android

Exponential technologies (AI, VR, 3D printing, and networks) are radically reshaping traditional retail.

E-commerce giants (Amazon, Walmart, Alibaba) are digitizing the retail industry, riding the exponential growth of computation.

Many brick-and-mortar stores have already gone bankrupt, or migrated their operations online.

Massive change is occurring in this arena.

For those “real-life stores” that survive, an evolution is taking place from a product-centric mentality to an experience-based business model by leveraging AI, VR/AR, and 3D printing.

Let’s dive in.

E-Commerce Trends
Last year, 3.8 billion people were connected online. By 2024, thanks to 5G, stratospheric and space-based satellites, we will grow to 8 billion people online, each with megabit to gigabit connection speeds.

These 4.2 billion new digital consumers will begin buying things online, a potential bonanza for the e-commerce world.

At the same time, entrepreneurs seeking to service these four-billion-plus new consumers can now skip the costly steps of procuring retail space and hiring sales clerks.

Today, thanks to global connectivity, contract production, and turnkey pack-and-ship logistics, an entrepreneur can go from an idea to building and scaling a multimillion-dollar business from anywhere in the world in record time.

And while e-commerce sales have been exploding (growing from $34 billion in Q1 2009 to $115 billion in Q3 2017), e-commerce only accounted for about 10 percent of total retail sales in 2017.

In 2016, global online sales totaled $1.8 trillion. Remarkably, this $1.8 trillion was spent by only 1.5 billion people — a mere 20 percent of Earth’s global population that year.

There’s plenty more room for digital disruption.

AI and the Retail Experience
For the business owner, AI will demonetize e-commerce operations with automated customer service, ultra-accurate supply chain modeling, marketing content generation, and advertising.

In the case of customer service, imagine an AI that is trained by every customer interaction, learns how to answer any consumer question perfectly, and offers feedback to product designers and company owners as a result.

Facebook’s handover protocol allows live customer service representatives and language-learning bots to work within the same Facebook Messenger conversation.

Taking it one step further, imagine an AI that is empathic to a consumer’s frustration, that can take any amount of abuse and come back with a smile every time. As one example, meet Ava. “Ava is a virtual customer service agent, to bring a whole new level of personalization and brand experience to that customer experience on a day-to-day basis,” says Greg Cross, CEO of Ava’s creator, an Austrian company called Soul Machines.

Predictive modeling and machine learning are also optimizing product ordering and the supply chain process. For example, Skubana, a platform for online sellers, leverages data analytics to provide entrepreneurs constant product performance feedback and maintain optimal warehouse stock levels.

Blockchain is set to follow suit in the retail space. ShipChain and Ambrosus plan to introduce transparency and trust into shipping and production, further reducing costs for entrepreneurs and consumers.

Meanwhile, for consumers, personal shopping assistants are shifting the psychology of the standard shopping experience.

Amazon’s Alexa marks an important user interface moment in this regard.

Alexa is in her infancy with voice search and vocal controls for smart homes. Already, Amazon’s Alexa users, on average, spent more on Amazon.com when purchasing than standard Amazon Prime customers — $1,700 versus $1,400.

As I’ve discussed in previous posts, the future combination of virtual reality shopping, coupled with a personalized, AI-enabled fashion advisor will make finding, selecting, and ordering products fast and painless for consumers.

But let’s take it one step further.

Imagine a future in which your personal AI shopper knows your desires better than you do. Possible? I think so. After all, our future AIs will follow us, watch us, and observe our interactions — including how long we glance at objects, our facial expressions, and much more.

In this future, shopping might be as easy as saying, “Buy me a new outfit for Saturday night’s dinner party,” followed by a surprise-and-delight moment in which the outfit that arrives is perfect.

In this future world of AI-enabled shopping, one of the most disruptive implications is that advertising is now dead.

In a world where an AI is buying my stuff, and I’m no longer in the decision loop, why would a big brand ever waste money on a Super Bowl advertisement?

The dematerialization, demonetization, and democratization of personalized shopping has only just begun.

The In-Store Experience: Experiential Retailing
In 2017, over 6,700 brick-and-mortar retail stores closed their doors, surpassing the former record year for store closures set in 2008 during the financial crisis. Regardless, business is still booming.

As shoppers seek the convenience of online shopping, brick-and-mortar stores are tapping into the power of the experience economy.

Rather than focusing on the practicality of the products they buy, consumers are instead seeking out the experience of going shopping.

The Internet of Things, artificial intelligence, and computation are exponentially improving the in-person consumer experience.

As AI dominates curated online shopping, AI and data analytics tools are also empowering real-life store owners to optimize staffing, marketing strategies, customer relationship management, and inventory logistics.

In the short term,retail store locations will serve as the next big user interface for production 3D printing (custom 3D printed clothes at the Ministry of Supply), virtual and augmented reality (DIY skills clinics), and the Internet of Things (checkout-less shopping).

In the long term,we’ll see how our desire for enhanced productivity and seamless consumption balances with our preference for enjoyable real-life consumer experiences — all of which will be driven by exponential technologies.

One thing is certain: the nominal shopping experience is on the verge of a major transformation.

Implications
The convergence of exponential technologies has already revamped how and where we shop, how we use our time, and how much we pay.

Twenty years ago, Amazon showed us how the web could offer each of us the long tail of available reading material, and since then, the world of e-commerce has exploded.

And yet we still haven’t experienced the cost savings coming our way from drone delivery, the Internet of Things, tokenized ecosystems, the impact of truly powerful AI, or even the other major applications for 3D printing and AR/VR.

Perhaps nothing will be more transformed than today’s $20 trillion retail sector.

Hold on, stay tuned, and get your AI-enabled cryptocurrency ready.

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Abundance Digital Online Community: I’ve created a digital/online community of bold, abundance-minded entrepreneurs called Abundance Digital.

Abundance Digital is my ‘onramp’ for exponential entrepreneurs — those who want to get involved and play at a higher level. Click here to learn more.

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