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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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Neural networks are powerful things, but they need a lot of juice. Engineers at MIT have now developed a new chip that cuts neural nets’ power consumption by up to 95 percent, potentially allowing them to run on battery-powered mobile devices.
Smartphones these days are getting truly smart, with ever more AI-powered services like digital assistants and real-time translation. But typically the neural nets crunching the data for these services are in the cloud, with data from smartphones ferried back and forth.
That’s not ideal, as it requires a lot of communication bandwidth and means potentially sensitive data is being transmitted and stored on servers outside the user’s control. But the huge amounts of energy needed to power the GPUs neural networks run on make it impractical to implement them in devices that run on limited battery power.
Engineers at MIT have now designed a chip that cuts that power consumption by up to 95 percent by dramatically reducing the need to shuttle data back and forth between a chip’s memory and processors.
Neural nets consist of thousands of interconnected artificial neurons arranged in layers. Each neuron receives input from multiple neurons in the layer below it, and if the combined input passes a certain threshold it then transmits an output to multiple neurons above it. The strength of the connection between neurons is governed by a weight, which is set during training.
This means that for every neuron, the chip has to retrieve the input data for a particular connection and the connection weight from memory, multiply them, store the result, and then repeat the process for every input. That requires a lot of data to be moved around, expending a lot of energy.
The new MIT chip does away with that, instead computing all the inputs in parallel within the memory using analog circuits. That significantly reduces the amount of data that needs to be shoved around and results in major energy savings.
The approach requires the weights of the connections to be binary rather than a range of values, but previous theoretical work had suggested this wouldn’t dramatically impact accuracy, and the researchers found the chip’s results were generally within two to three percent of the conventional non-binary neural net running on a standard computer.
This isn’t the first time researchers have created chips that carry out processing in memory to reduce the power consumption of neural nets, but it’s the first time the approach has been used to run powerful convolutional neural networks popular for image-based AI applications.
“The results show impressive specifications for the energy-efficient implementation of convolution operations with memory arrays,” Dario Gil, vice president of artificial intelligence at IBM, said in a statement.
“It certainly will open the possibility to employ more complex convolutional neural networks for image and video classifications in IoT [the internet of things] in the future.”
It’s not just research groups working on this, though. The desire to get AI smarts into devices like smartphones, household appliances, and all kinds of IoT devices is driving the who’s who of Silicon Valley to pile into low-power AI chips.
Apple has already integrated its Neural Engine into the iPhone X to power things like its facial recognition technology, and Amazon is rumored to be developing its own custom AI chips for the next generation of its Echo digital assistant.
The big chip companies are also increasingly pivoting towards supporting advanced capabilities like machine learning, which has forced them to make their devices ever more energy-efficient. Earlier this year ARM unveiled two new chips: the Arm Machine Learning processor, aimed at general AI tasks from translation to facial recognition, and the Arm Object Detection processor for detecting things like faces in images.
Qualcomm’s latest mobile chip, the Snapdragon 845, features a GPU and is heavily focused on AI. The company has also released the Snapdragon 820E, which is aimed at drones, robots, and industrial devices.
Going a step further, IBM and Intel are developing neuromorphic chips whose architectures are inspired by the human brain and its incredible energy efficiency. That could theoretically allow IBM’s TrueNorth and Intel’s Loihi to run powerful machine learning on a fraction of the power of conventional chips, though they are both still highly experimental at this stage.
Getting these chips to run neural nets as powerful as those found in cloud services without burning through batteries too quickly will be a big challenge. But at the current pace of innovation, it doesn’t look like it will be too long before you’ll be packing some serious AI power in your pocket.
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Henry Ford didn’t invent the motor car. The late 1800s saw a flurry of innovation by hundreds of companies battling to deliver on the promise of fast, efficient and reasonably-priced mechanical transportation. Ford later came to dominate the industry thanks to the development of the moving assembly line.
Today, the sector is poised for another breakthrough with the advent of cars that drive themselves. But unlike the original wave of automobile innovation, the race for supremacy in autonomous vehicles is concentrated among a few corporate giants. So who is set to dominate this time?
I’ve analyzed six companies we think are leading the race to build the first truly driverless car. Three of these—General Motors, Ford, and Volkswagen—come from the existing car industry and need to integrate self-driving technology into their existing fleet of mass-produced vehicles. The other three—Tesla, Uber, and Waymo (owned by the same company as Google)—are newcomers from the digital technology world of Silicon Valley and have to build a mass manufacturing capability.
While it’s impossible to know all the developments at any given time, we have tracked investments, strategic partnerships, and official press releases to learn more about what’s happening behind the scenes. The car industry typically rates self-driving technology on a scale from Level 0 (no automation) to Level 5 (full automation). We’ve assessed where each company is now and estimated how far they are from reaching the top level. Here’s how we think each player is performing.
Volkswagen has invested in taxi-hailing app Gett and partnered with chip-maker Nvidia to develop an artificial intelligence co-pilot for its cars. In 2018, the VW Group is set to release the Audi A8, the first production vehicle that reaches Level 3 on the scale, “conditional driving automation.” This means the car’s computer will handle all driving functions, but a human has to be ready to take over if necessary.
Ford already sells cars with a Level 2 autopilot, “partial driving automation.” This means one or more aspects of driving are controlled by a computer based on information about the environment, for example combined cruise control and lane centering. Alongside other investments, the company has put $1 billion into Argo AI, an artificial intelligence company for self-driving vehicles. Following a trial to test pizza delivery using autonomous vehicles, Ford is now testing Level 4 cars on public roads. These feature “high automation,” where the car can drive entirely on its own but not in certain conditions such as when the road surface is poor or the weather is bad.
GM also sells vehicles with Level 2 automation but, after buying Silicon Valley startup Cruise Automation in 2016, now plans to launch the first mass-production-ready Level 5 autonomy vehicle that drives completely on its own by 2019. The Cruise AV will have no steering wheel or pedals to allow a human to take over and be part of a large fleet of driverless taxis the company plans to operate in big cities. But crucially the company hasn’t yet secured permission to test the car on public roads.
Waymo Level 5 testing. Image Credit: Waymo
Founded as a special project in 2009, Waymo separated from Google (though they’re both owned by the same parent firm, Alphabet) in 2016. Though it has never made, sold, or operated a car on a commercial basis, Waymo has created test vehicles that have clocked more than 4 million miles without human drivers as of November 2017. Waymo tested its Level 5 car, “Firefly,” between 2015 and 2017 but then decided to focus on hardware that could be installed in other manufacturers’ vehicles, starting with the Chrysler Pacifica.
The taxi-hailing app maker Uber has been testing autonomous cars on the streets of Pittsburgh since 2016, always with an employee behind the wheel ready to take over in case of a malfunction. After buying the self-driving truck company Otto in 2016 for a reported $680 million, Uber is now expanding its AI capabilities and plans to test NVIDIA’s latest chips in Otto’s vehicles. It has also partnered with Volvo to create a self-driving fleet of cars and with Toyota to co-create a ride-sharing autonomous vehicle.
The first major car manufacturer to come from Silicon Valley, Tesla was also the first to introduce Level 2 autopilot back in 2015. The following year, it announced that all new Teslas would have the hardware for full autonomy, meaning once the software is finished it can be deployed on existing cars with an instant upgrade. Some experts have challenged this approach, arguing that the company has merely added surround cameras to its production cars that aren’t as capable as the laser-based sensing systems that most other carmakers are using.
But the company has collected data from hundreds of thousands of cars, driving millions of miles across all terrains. So, we shouldn’t dismiss the firm’s founder, Elon Musk, when he claims a Level 4 Tesla will drive from LA to New York without any human interference within the first half of 2018.
Who’s leading the race? Image Credit: IMD
At the moment, the disruptors like Tesla, Waymo, and Uber seem to have the upper hand. While the traditional automakers are focusing on bringing Level 3 and 4 partial automation to market, the new companies are leapfrogging them by moving more directly towards Level 5 full automation. Waymo may have the least experience of dealing with consumers in this sector, but it has already clocked up a huge amount of time testing some of the most advanced technology on public roads.
The incumbent carmakers are also focused on the difficult process of integrating new technology and business models into their existing manufacturing operations by buying up small companies. The challengers, on the other hand, are easily partnering with other big players including manufacturers to get the scale and expertise they need more quickly.
Tesla is building its own manufacturing capability but also collecting vast amounts of critical data that will enable it to more easily upgrade its cars when ready for full automation. In particular, Waymo’s experience, technology capability, and ability to secure solid partnerships puts it at the head of the pack.
This article was originally published on The Conversation. Read the original article.
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