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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.
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.
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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On a dark night, away from city lights, the stars of the Milky Way can seem uncountable. Yet from any given location no more than 4,500 are visible to the naked eye. Meanwhile, our galaxy has 100–400 billion stars, and there are even more galaxies in the universe.
The numbers of the night sky are humbling. And they give us a deep perspective…on drugs.
Yes, this includes wow-the-stars-are-freaking-amazing-tonight drugs, but also the kinds of drugs that make us well again when we’re sick. The number of possible organic compounds with “drug-like” properties dwarfs the number of stars in the universe by over 30 orders of magnitude.
Next to this multiverse of possibility, the chemical configurations scientists have made into actual medicines are like the smattering of stars you’d glimpse downtown.
But for good reason.
Exploring all that potential drug-space is as humanly impossible as exploring all of physical space, and even if we could, most of what we’d find wouldn’t fit our purposes. Still, the idea that wonder drugs must surely lurk amid the multitudes is too tantalizing to ignore.
Which is why, Alex Zhavoronkov said at Singularity University’s Exponential Medicine in San Diego last week, we should use artificial intelligence to do more of the legwork and speed discovery. This, he said, could be one of the next big medical applications for AI.
Dogs, Diagnosis, and Drugs
Zhavoronkov is CEO of Insilico Medicine and CSO of the Biogerontology Research Foundation. Insilico is one of a number of AI startups aiming to accelerate drug discovery with AI.
In recent years, Zhavoronkov said, the now-famous machine learning technique, deep learning, has made progress on a number of fronts. Algorithms that can teach themselves to play games—like DeepMind’s AlphaGo Zero or Carnegie Mellon’s poker playing AI—are perhaps the most headline-grabbing of the bunch. But pattern recognition was the thing that kicked deep learning into overdrive early on, when machine learning algorithms went from struggling to tell dogs and cats apart to outperforming their peers and then their makers in quick succession.
[Watch this video for an AI update from Neil Jacobstein, chair of Artificial Intelligence and Robotics at Singularity University.]
In medicine, deep learning algorithms trained on databases of medical images can spot life-threatening disease with equal or greater accuracy than human professionals. There’s even speculation that AI, if we learn to trust it, could be invaluable in diagnosing disease. And, as Zhavoronkov noted, with more applications and a longer track record that trust is coming.
“Tesla is already putting cars on the street,” Zhavoronkov said. “Three-year, four-year-old technology is already carrying passengers from point A to point B, at 100 miles an hour, and one mistake and you’re dead. But people are trusting their lives to this technology.”
“So, why don’t we do it in pharma?”
Trial and Error and Try Again
AI wouldn’t drive the car in pharmaceutical research. It’d be an assistant that, when paired with a chemist or two, could fast-track discovery by screening more possibilities for better candidates.
There’s plenty of room to make things more efficient, according to Zhavoronkov.
Drug discovery is arduous and expensive. Chemists sift tens of thousands of candidate compounds for the most promising to synthesize. Of these, a handful will go on to further research, fewer will make it to human clinical trials, and a fraction of those will be approved.
The whole process can take many years and cost hundreds of millions of dollars.
This is a big data problem if ever there was one, and deep learning thrives on big data. Early applications have shown their worth unearthing subtle patterns in huge training databases. Although drug-makers already use software to sift compounds, such software requires explicit rules written by chemists. AI’s allure is its ability to learn and improve on its own.
“There are two strategies for AI-driven innovation in pharma to ensure you get better molecules and much faster approvals,” Zhavoronkov said. “One is looking for the needle in the haystack, and another one is creating a new needle.”
To find the needle in the haystack, algorithms are trained on large databases of molecules. Then they go looking for molecules with attractive properties. But creating a new needle? That’s a possibility enabled by the generative adversarial networks Zhavoronkov specializes in.
Such algorithms pit two neural networks against each other. One generates meaningful output while the other judges whether this output is true or false, Zhavoronkov said. Together, the networks generate new objects like text, images, or in this case, molecular structures.
“We started employing this particular technology to make deep neural networks imagine new molecules, to make it perfect right from the start. So, to come up with really perfect needles,” Zhavoronkov said. “[You] can essentially go to this [generative adversarial network] and ask it to create molecules that inhibit protein X at concentration Y, with the highest viability, specific characteristics, and minimal side effects.”
Zhavoronkov believes AI can find or fabricate more needles from the array of molecular possibilities, freeing human chemists to focus on synthesizing only the most promising. If it works, he hopes we can increase hits, minimize misses, and generally speed the process up.
Proof’s in the Pudding
Insilico isn’t alone on its drug-discovery quest, nor is it a brand new area of interest.
Last year, a Harvard group published a paper on an AI that similarly suggests drug candidates. The software trained on 250,000 drug-like molecules and used its experience to generate new molecules that blended existing drugs and made suggestions based on desired properties.
An MIT Technology Review article on the subject highlighted a few of the challenges such systems may still face. The results returned aren’t always meaningful or easy to synthesize in the lab, and the quality of these results, as always, is only as good as the data dined upon.
Stanford chemistry professor and Andreesen Horowitz partner, Vijay Pande, said that images, speech, and text—three of the areas deep learning’s made quick strides in—have better, cleaner data. Chemical data, on the other hand, is still being optimized for deep learning. Also, while there are public databases, much data still lives behind closed doors at private companies.
To overcome the challenges and prove their worth, Zhavoronkov said, his company is very focused on validating the tech. But this year, skepticism in the pharmaceutical industry seems to be easing into interest and investment.
AI drug discovery startup Exscientia inked a deal with Sanofi for $280 million and GlaxoSmithKline for $42 million. Insilico is also partnering with GlaxoSmithKline, and Numerate is working with Takeda Pharmaceutical. Even Google may jump in. According to an article in Nature outlining the field, the firm’s deep learning project, Google Brain, is growing its biosciences team, and industry watchers wouldn’t be surprised to see them target drug discovery.
With AI and the hardware running it advancing rapidly, the greatest potential may yet be ahead. Perhaps, one day, all 1060 molecules in drug-space will be at our disposal. “You should take all the data you have, build n new models, and search as much of that 1060 as possible” before every decision you make, Brandon Allgood, CTO at Numerate, told Nature.
Today’s projects need to live up to their promises, of course, but Zhavoronkov believes AI will have a big impact in the coming years, and now’s the time to integrate it. “If you are working for a pharma company, and you’re still thinking, ‘Okay, where is the proof?’ Once there is a proof, and once you can see it to believe it—it’s going to be too late,” he said.
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Technological progress has radically transformed our concept of privacy. How we share information and display our identities has changed as we’ve migrated to the digital world.
As the Guardian states, “We now carry with us everywhere devices that give us access to all the world’s information, but they can also offer almost all the world vast quantities of information about us.” We are all leaving digital footprints as we navigate through the internet. While sometimes this information can be harmless, it’s often valuable to various stakeholders, including governments, corporations, marketers, and criminals.
The ethical debate around privacy is complex. The reality is that our definition and standards for privacy have evolved over time, and will continue to do so in the next few decades.
Implications of Emerging Technologies
Protecting privacy will only become more challenging as we experience the emergence of technologies such as virtual reality, the Internet of Things, brain-machine interfaces, and much more.
Virtual reality headsets are already gathering information about users’ locations and physical movements. In the future all of our emotional experiences, reactions, and interactions in the virtual world will be able to be accessed and analyzed. As virtual reality becomes more immersive and indistinguishable from physical reality, technology companies will be able to gather an unprecedented amount of data.
It doesn’t end there. The Internet of Things will be able to gather live data from our homes, cities and institutions. Drones may be able to spy on us as we live our everyday lives. As the amount of genetic data gathered increases, the privacy of our genes, too, may be compromised.
It gets even more concerning when we look farther into the future. As companies like Neuralink attempt to merge the human brain with machines, we are left with powerful implications for privacy. Brain-machine interfaces by nature operate by extracting information from the brain and manipulating it in order to accomplish goals. There are many parties that can benefit and take advantage of the information from the interface.
Marketing companies, for instance, would take an interest in better understanding how consumers think and consequently have their thoughts modified. Employers could use the information to find new ways to improve productivity or even monitor their employees. There will notably be risks of “brain hacking,” which we must take extreme precaution against. However, it is important to note that lesser versions of these risks currently exist, i.e., by phone hacking, identify fraud, and the like.
A New Much-Needed Definition of Privacy
In many ways we are already cyborgs interfacing with technology. According to theories like the extended mind hypothesis, our technological devices are an extension of our identities. We use our phones to store memories, retrieve information, and communicate. We use powerful tools like the Hubble Telescope to extend our sense of sight. In parallel, one can argue that the digital world has become an extension of the physical world.
These technological tools are a part of who we are. This has led to many ethical and societal implications. Our Facebook profiles can be processed to infer secondary information about us, such as sexual orientation, political and religious views, race, substance use, intelligence, and personality. Some argue that many of our devices may be mapping our every move. Your browsing history could be spied on and even sold in the open market.
While the argument to protect privacy and individuals’ information is valid to a certain extent, we may also have to accept the possibility that privacy will become obsolete in the future. We have inherently become more open as a society in the digital world, voluntarily sharing our identities, interests, views, and personalities.
“The question we are left with is, at what point does the tradeoff between transparency and privacy become detrimental?”
There also seems to be a contradiction with the positive trend towards mass transparency and the need to protect privacy. Many advocate for a massive decentralization and openness of information through mechanisms like blockchain.
The question we are left with is, at what point does the tradeoff between transparency and privacy become detrimental? We want to live in a world of fewer secrets, but also don’t want to live in a world where our every move is followed (not to mention our every feeling, thought and interaction). So, how do we find a balance?
Traditionally, privacy is used synonymously with secrecy. Many are led to believe that if you keep your personal information secret, then you’ve accomplished privacy. Danny Weitzner, director of the MIT Internet Policy Research Initiative, rejects this notion and argues that this old definition of privacy is dead.
From Witzner’s perspective, protecting privacy in the digital age means creating rules that require governments and businesses to be transparent about how they use our information. In other terms, we can’t bring the business of data to an end, but we can do a better job of controlling it. If these stakeholders spy on our personal information, then we should have the right to spy on how they spy on us.
The Role of Policy and Discourse
Almost always, policy has been too slow to adapt to the societal and ethical implications of technological progress. And sometimes the wrong laws can do more harm than good. For instance, in March, the US House of Representatives voted to allow internet service providers to sell your web browsing history on the open market.
More often than not, the bureaucratic nature of governance can’t keep up with exponential growth. New technologies are emerging every day and transforming society. Can we confidently claim that our world leaders, politicians, and local representatives are having these conversations and debates? Are they putting a focus on the ethical and societal implications of emerging technologies? Probably not.
We also can’t underestimate the role of public awareness and digital activism. There needs to be an emphasis on educating and engaging the general public about the complexities of these issues and the potential solutions available. The current solution may not be robust or clear, but having these discussions will get us there.
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