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#439070 Are Digital Humans the Next Step in ...

In the fictional worlds of film and TV, artificial intelligence has been depicted as so advanced that it is indistinguishable from humans. But what if we’re actually getting closer to a world where AI is capable of thinking and feeling?

Tech company UneeQ is embarking on that journey with its “digital humans.” These avatars act as visual interfaces for customer service chatbots, virtual assistants, and other applications. UneeQ’s digital humans appear lifelike not only in terms of language and tone of voice, but also because of facial movements: raised eyebrows, a tilt of the head, a smile, even a wink. They transform a transaction into an interaction: creepy yet astonishing, human, but not quite.

What lies beneath UneeQ’s digital humans? Their 3D faces are modeled on actual human features. Speech recognition enables the avatar to understand what a person is saying, and natural language processing is used to craft a response. Before the avatar utters a word, specific emotions and facial expressions are encoded within the response.

UneeQ may be part of a larger trend towards humanizing computing. ObEN’s digital avatars serve as virtual identities for celebrities, influencers, gaming characters, and other entities in the media and entertainment industry. Meanwhile, Soul Machines is taking a more biological approach, with a “digital brain” that simulates aspects of the human brain to modulate the emotions “felt” and “expressed” by its “digital people.” Amelia is employing a similar methodology in building its “digital employees.” It emulates parts of the brain involved with memory to respond to queries and, with each interaction, learns to deliver more engaging and personalized experiences.

Shiwali Mohan, an AI systems scientist at the Palo Alto Research Center, is skeptical of these digital beings. “They’re humanlike in their looks and the way they sound, but that in itself is not being human,” she says. “Being human is also how you think, how you approach problems, and how you break them down; and that takes a lot of algorithmic design. Designing for human-level intelligence is a different endeavor than designing graphics that behave like humans. If you think about the problems we’re trying to design these avatars for, we might not need something that looks like a human—it may not even be the right solution path.”

And even if these avatars appear near-human, they still evoke an uncanny valley feeling. “If something looks like a human, we have high expectations of them, but they might behave differently in ways that humans just instinctively know how other humans react. These differences give rise to the uncanny valley feeling,” says Mohan.

Yet the demand is there, with Amelia seeing high adoption of its digital employees across the financial, health care, and retail sectors. “We find that banks and insurance companies, which are so risk-averse, are leading the adoption of such disruptive technologies because they understand that the risk of non-adoption is much greater than the risk of early adoption,” says Chetan Dube, Amelia’s CEO. “Unless they innovate their business models and make them much more efficient digitally, they might be left behind.” Dube adds that the COVID-19 pandemic has accelerated adoption of digital employees in health care and retail as well.

Amelia, Soul Machines, and UneeQ are taking their digital beings a step further, enabling organizations to create avatars themselves using low-code or no-code platforms: Digital Employee Builder for Amelia, Creator for UneeQ, and Digital DNA Studio for Soul Machines. Unreal Engine, a game engine developed by Epic Games, is doing the same with MetaHuman Creator, a tool that allows anyone to create photorealistic digital humans. “The biggest motivation for Digital Employee Builder is to democratize AI,” Dube says.

Mohan is cautious about this approach. “AI has problems with bias creeping in from data sets and into the way it speaks. The AI community is still trying to figure out how to measure and counter that bias,” she says. “[Companies] have to have an AI expert on board that can recommend the right things to build for.”

Despite being wary of the technology, Mohan supports the purpose behind these virtual beings and is optimistic about where they’re headed. “We do need these tools that support humans in different kinds of things. I think the vision is the pro, and I’m behind that vision,” she says. “As we develop more sophisticated AI technology, we would then have to implement novel ways of interacting with that technology. Hopefully, all of that is designed to support humans in their goals.” Continue reading

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#437791 Is the Pandemic Spurring a Robot ...

“Are robots really destined to take over restaurant kitchens?” This was the headline of an article published by Eater four years ago. One of the experts interviewed was Siddhartha Srinivasa, at the time professor of the Robotics Institute at Carnegie Mellon University and currently director of Robotics and AI for Amazon. He said, “I’d love to make robots unsexy. It’s weird to say this, but when something becomes unsexy, it means that it works so well that you don’t have to think about it. You don’t stare at your dishwasher as it washes your dishes in fascination, because you know it’s gonna work every time… I want to get robots to that stage of reliability.”

Have we managed to get there over the last four years? Are robots unsexy yet? And how has the pandemic changed the trajectory of automation across industries?

The Covid Effect
The pandemic has had a massive economic impact all over the world, and one of the problems faced by many companies has been keeping their businesses running without putting employees at risk of infection. Many organizations are seeking to remain operational in the short term by automating tasks that would otherwise be carried out by humans. According to Digital Trends, since the start of the pandemic we have seen a significant increase in automation efforts in manufacturing, meat packing, grocery stores and more. In a June survey, 44 percent of corporate financial officers said they were considering more automation in response to coronavirus.

MIT economist David Autor described the economic crisis and the Covid-19 pandemic as “an event that forces automation.” But he added that Covid-19 created a kind of disruption that has forced automation in sectors and activities with a shortage of workers, while at the same time there has been no reduction in demand. This hasn’t taken place in hospitality, where demand has practically disappeared, but it is still present in agriculture and distribution. The latter is being altered by the rapid growth of e-commerce, with more efficient and automated warehouses that can provide better service.

China Leads the Way
China is currently in a unique position to lead the world’s automation economy. Although the country boasts a huge workforce, labor costs have multiplied by 10 over the past 20 years. As the world’s factory, China has a strong incentive to automate its manufacturing sector, which enjoys a solid leadership in high quality products. China is currently the largest and fastest-growing market in the world for industrial robotics, with a 21 percent increase up to $5.4 billion in 2019. This represents one third of global sales. As a result, Chinese companies are developing a significant advantage in terms of learning to work with metallic colleagues.

The reasons behind this Asian dominance are evident: the population has a greater capacity and need for tech adoption. A large percentage of the population will soon be of retirement age, without an equivalent younger demographic to replace it, leading to a pressing need to adopt automation in the short term.

China is well ahead of other countries in restaurant automation. As reported in Bloomberg, in early 2020 UBS Group AG conducted a survey of over 13,000 consumers in different countries and found that 64 percent of Chinese participants had ordered meals through their phones at least once a week, compared to a mere 17 percent in the US. As digital ordering gains ground, robot waiters and chefs are likely not far behind. The West harbors a mistrust towards non-humans that the East does not.

The Robot Evolution
The pandemic was a perfect excuse for robots to replace us. But despite the hype around this idea, robots have mostly disappointed during the pandemic.

Just over 66 different kinds of “social” robots have been piloted in hospitals, health centers, airports, office buildings, and other public and private spaces in response to the pandemic, according to a study from researchers at Pompeu Fabra University (Barcelona, Spain). Their survey looked at 195 robot deployments across 35 countries including China, the US, Thailand, and Hong Kong.

But if the “robot revolution” is a movement in which automation, robotics, and artificial intelligence proliferate through the value chain of various industries, bringing a paradigm shift in how we produce, consume, and distribute products—it hasn’t happened yet.

But there’s a more nuanced answer: rather than a revolution, we’re seeing an incremental robot evolution. It’s a trend that will likely accelerate over the next five years, particularly when 5G takes center stage and robotics as a field leaves behind imitation and evolves independently.

Automation Anxiety
Why don’t we finally welcome the long-promised robotic takeover? Despite progress in AI and increased adoption of industrial robots, consumer-facing robotic products are not nearly as ubiquitous as popular culture predicted decades ago. As Amara’s Law says: “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” It seems we are living through the Gartner hype cycle.

People have a complicated relationship with robots, torn between admiring them, fearing them, rejecting them, and even boycotting them, as has happened in the automobile industry.

Retail robot in a Walmart store. Credit: Bossa Nova Robotics
Walmart terminated its contract with Bossa Nova and withdrew its 1,000 inventory robots from its stores because the company was concerned about how shoppers were reacting to seeing the six-foot robots in the aisles.

With road blocks like this, will the World Economic Forum’s prediction of almost half of tasks being carried out by machines by 2025 come to pass?

At the rate we’re going, it seems unlikely, even with the boost in automation caused by the pandemic. Robotics will continue to advance its capabilities, and will take over more human jobs as it does so, but it’s unlikely we’ll hit a dramatic inflection point that could be described as a “revolution.” Instead, the robot evolution will happen the way most societal change does: incrementally, with time for people to adapt both practically and psychologically.

For now though, robots are still pretty sexy.

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#437620 The Trillion-Transistor Chip That Just ...

The history of computer chips is a thrilling tale of extreme miniaturization.

The smaller, the better is a trend that’s given birth to the digital world as we know it. So, why on earth would you want to reverse course and make chips a lot bigger? Well, while there’s no particularly good reason to have a chip the size of an iPad in an iPad, such a chip may prove to be genius for more specific uses, like artificial intelligence or simulations of the physical world.

At least, that’s what Cerebras, the maker of the biggest computer chip in the world, is hoping.

The Cerebras Wafer-Scale Engine is massive any way you slice it. The chip is 8.5 inches to a side and houses 1.2 trillion transistors. The next biggest chip, NVIDIA’s A100 GPU, measures an inch to a side and has a mere 54 billion transistors. The former is new, largely untested and, so far, one-of-a-kind. The latter is well-loved, mass-produced, and has taken over the world of AI and supercomputing in the last decade.

So can Goliath flip the script on David? Cerebras is on a mission to find out.

Big Chips Beyond AI
When Cerebras first came out of stealth last year, the company said it could significantly speed up the training of deep learning models.

Since then, the WSE has made its way into a handful of supercomputing labs, where the company’s customers are putting it through its paces. One of those labs, the National Energy Technology Laboratory, is looking to see what it can do beyond AI.

So, in a recent trial, researchers pitted the chip—which is housed in an all-in-one system about the size of a dorm room mini-fridge called the CS-1—against a supercomputer in a fluid dynamics simulation. Simulating the movement of fluids is a common supercomputer application useful for solving complex problems like weather forecasting and airplane wing design.

The trial was described in a preprint paper written by a team led by Cerebras’s Michael James and NETL’s Dirk Van Essendelft and presented at the supercomputing conference SC20 this week. The team said the CS-1 completed a simulation of combustion in a power plant roughly 200 times faster than it took the Joule 2.0 supercomputer to do a similar task.

The CS-1 was actually faster-than-real-time. As Cerebrus wrote in a blog post, “It can tell you what is going to happen in the future faster than the laws of physics produce the same result.”

The researchers said the CS-1’s performance couldn’t be matched by any number of CPUs and GPUs. And CEO and cofounder Andrew Feldman told VentureBeat that would be true “no matter how large the supercomputer is.” At a point, scaling a supercomputer like Joule no longer produces better results in this kind of problem. That’s why Joule’s simulation speed peaked at 16,384 cores, a fraction of its total 86,400 cores.

A comparison of the two machines drives the point home. Joule is the 81st fastest supercomputer in the world, takes up dozens of server racks, consumes up to 450 kilowatts of power, and required tens of millions of dollars to build. The CS-1, by comparison, fits in a third of a server rack, consumes 20 kilowatts of power, and sells for a few million dollars.

While the task is niche (but useful) and the problem well-suited to the CS-1, it’s still a pretty stunning result. So how’d they pull it off? It’s all in the design.

Cut the Commute
Computer chips begin life on a big piece of silicon called a wafer. Multiple chips are etched onto the same wafer and then the wafer is cut into individual chips. While the WSE is also etched onto a silicon wafer, the wafer is left intact as a single, operating unit. This wafer-scale chip contains almost 400,000 processing cores. Each core is connected to its own dedicated memory and its four neighboring cores.

Putting that many cores on a single chip and giving them their own memory is why the WSE is bigger; it’s also why, in this case, it’s better.

Most large-scale computing tasks depend on massively parallel processing. Researchers distribute the task among hundreds or thousands of chips. The chips need to work in concert, so they’re in constant communication, shuttling information back and forth. A similar process takes place within each chip, as information moves between processor cores, which are doing the calculations, and shared memory to store the results.

It’s a little like an old-timey company that does all its business on paper.

The company uses couriers to send and collect documents from other branches and archives across town. The couriers know the best routes through the city, but the trips take some minimum amount of time determined by the distance between the branches and archives, the courier’s top speed, and how many other couriers are on the road. In short, distance and traffic slow things down.

Now, imagine the company builds a brand new gleaming skyscraper. Every branch is moved into the new building and every worker gets a small filing cabinet in their office to store documents. Now any document they need can be stored and retrieved in the time it takes to step across the office or down the hall to their neighbor’s office. The information commute has all but disappeared. Everything’s in the same house.

Cerebras’s megachip is a bit like that skyscraper. The way it shuttles information—aided further by its specially tailored compiling software—is far more efficient compared to a traditional supercomputer that needs to network a ton of traditional chips.

Simulating the World as It Unfolds
It’s worth noting the chip can only handle problems small enough to fit on the wafer. But such problems may have quite practical applications because of the machine’s ability to do high-fidelity simulation in real-time. The authors note, for example, the machine should in theory be able to accurately simulate the air flow around a helicopter trying to land on a flight deck and semi-automate the process—something not possible with traditional chips.

Another opportunity, they note, would be to use a simulation as input to train a neural network also residing on the chip. In an intriguing and related example, a Caltech machine learning technique recently proved to be 1,000 times faster at solving the same kind of partial differential equations at play here to simulate fluid dynamics.

They also note that improvements in the chip (and others like it, should they arrive) will push back the limits of what can be accomplished. Already, Cerebras has teased the release of its next-generation chip, which will have 2.6 trillion transistors, 850,00 cores, and more than double the memory.

Of course, it still remains to be seen whether wafer-scale computing really takes off. The idea has been around for decades, but Cerebras is the first to pursue it seriously. Clearly, they believe they’ve solved the problem in a way that’s useful and economical.

Other new architectures are also being pursued in the lab. Memristor-based neuromorphic chips, for example, mimic the brain by putting processing and memory into individual transistor-like components. And of course, quantum computers are in a separate lane, but tackle similar problems.

It could be that one of these technologies eventually rises to rule them all. Or, and this seems just as likely, computing may splinter into a bizarre quilt of radical chips, all stitched together to make the most of each depending on the situation.

Image credit: Cerebras Continue reading

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#437550 McDonald’s Is Making a Plant-Based ...

Fast-food chains have been doing what they can in recent years to health-ify their menus. For better or worse, burgers, fries, fried chicken, roast beef sandwiches, and the like will never go out of style—this is America, after all—but consumers are increasingly gravitating towards healthier options.

One of those options is plant-based foods, and not just salads and veggie burgers, but “meat” made from plants. Burger King was one of the first big fast-food chains to jump on the plant-based meat bandwagon, introducing its Impossible Whopper in restaurants across the country last year after a successful pilot program. Dunkin’ (formerly Dunkin’ Donuts) uses plant-based patties in its Beyond Sausage breakfast sandwiches.

But there’s one big player in the fast food market that’s been oddly missing from the plant-based trend—until now. McDonald’s announced last week that it will debut a sandwich called the McPlant in key US markets next year. Unlike Dunkin’ and Burger King, who both worked with Impossible Foods to make their plant-based products, McDonald’s worked with Los Angeles-based Beyond Meat, which makes chicken, beef, and pork-like products from plants.

According to Bloomberg, though, McDonald’s decided to forego a partnership with Beyond Meat in favor of creating its own plant-based products. Imitation chicken nuggets and plant-based breakfast sandwiches are in its plans as well.

McDonald’s has bounced back impressively from its March low (when the coronavirus lockdowns first happened in the US). Last month the company’s stock reached a 52-week high of $231 per share (as compared to its low in March of $124 per share).

To keep those numbers high and make it as easy as possible for customers to get their hands on plant-based burgers and all the traditional menu items too, the fast food chain is investing in tech and integrating more digital offerings into its restaurants.

McDonald’s has acquired a couple artificial intelligence companies in the last year and a half; Dynamic Yield is an Israeli company that uses AI to personalize customers’ experiences, and McDonald’s is using Dynamic Yield’s tech on its smart menu boards, for example by customizing the items displayed on the drive-thru menu based on the weather and the time of day, and recommending additional items based on what a customer asks for first (i.e. “You know what would go great with that coffee? Some pancakes!”).

The fast food giant also bought Apprente, a startup that uses AI in voice-based ordering platforms. McDonald’s is using the tech to help automate its drive-throughs.

In addition to these investments, the company plans to launch a digital hub called MyMcDonald’s that will include a loyalty program, start doing deliveries of its food through its mobile app, and test different ways of streamlining the food order and pickup process—with many of the new ideas geared towards pandemic times, like express pickup lanes for people who placed digital orders and restaurants with drive-throughs for delivery and pickup orders only.

Plant-based meat patties appear to be just one small piece of McDonald’s modernization plans. Those of us who were wondering what they were waiting for should have known—one of the most-recognized fast food chains in the world wasn’t about to let itself get phased out. It seems it will only be a matter of time until you can pull out your phone, make a few selections, and have a burger made from plants—with a side of fries made from more plants—show up at your door a little while later. Drive-throughs, shouting your order into a fuzzy speaker with a confused teen on the other end, and burgers made from beef? So 2019.

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#437345 Moore’s Law Lives: Intel Says Chips ...

If you weren’t already convinced the digital world is taking over, you probably are now.

To keep the economy on life support as people stay home to stem the viral tide, we’ve been forced to digitize interactions at scale (for better and worse). Work, school, events, shopping, food, politics. The companies at the center of the digital universe are now powerhouses of the modern era—worth trillions and nearly impossible to avoid in daily life.

Six decades ago, this world didn’t exist.

A humble microchip in the early 1960s would have boasted a handful of transistors. Now, your laptop or smartphone runs on a chip with billions of transistors. As first described by Moore’s Law, this is possible because the number of transistors on a chip doubled with extreme predictability every two years for decades.

But now progress is faltering as the size of transistors approaches physical limits, and the money and time it takes to squeeze a few more onto a chip are growing. There’ve been many predictions that Moore’s Law is, finally, ending. But, perhaps also predictably, the company whose founder coined Moore’s Law begs to differ.

In a keynote presentation at this year’s Hot Chips conference, Intel’s chief architect, Raja Koduri, laid out a roadmap to increase transistor density—that is, the number of transistors you can fit on a chip—by a factor of 50.

“We firmly believe there is a lot more transistor density to come,” Koduri said. “The vision will play out over time—maybe a decade or more—but it will play out.”

Why the optimism?

Calling the end of Moore’s Law is a bit of a tradition. As Peter Lee, vice president at Microsoft Research, quipped to The Economist a few years ago, “The number of people predicting the death of Moore’s Law doubles every two years.” To date, prophets of doom have been premature, and though the pace is slowing, the industry continues to dodge death with creative engineering.

Koduri believes the trend will continue this decade and outlined the upcoming chip innovations Intel thinks can drive more gains in computing power.

Keeping It Traditional
First, engineers can further shrink today’s transistors. Fin field effect transistors (or FinFET) first hit the scene in the 2010s and have since pushed chip features past 14 and 10 nanometers (or nodes, as such size checkpoints are called). Korduri said FinFET will again triple chip density before it’s exhausted.

The Next Generation
FinFET will hand the torch off to nanowire transistors (also known as gate-all-around transistors).

Here’s how they’ll work. A transistor is made up of three basic components: the source, where current is introduced, the gate and channel, where current selectively flows, and the drain. The gate is like a light switch. It controls how much current flows through the channel. A transistor is “on” when the gate allows current to flow, and it’s off when no current flows. The smaller transistors get, the harder it is to control that current.

FinFET maintained fine control of current by surrounding the channel with a gate on three sides. Nanowire designs kick that up a notch by surrounding the channel with a gate on four sides (hence, gate-all-around). They’ve been in the works for years and are expected around 2025. Koduri said first-generation nanowire transistors will be followed by stacked nanowire transistors, and together, they’ll quadruple transistor density.

Building Up
Growing transistor density won’t only be about shrinking transistors, but also going 3D.

This is akin to how skyscrapers increase a city’s population density by adding more usable space on the same patch of land. Along those lines, Intel recently launched its Foveros chip design. Instead of laying a chip’s various “neighborhoods” next to each other in a 2D silicon sprawl, they’ve stacked them on top of each other like a layer cake. Chip stacking isn’t entirely new, but it’s advancing and being applied to general purpose CPUs, like the chips in your phone and laptop.

Koduri said 3D chip stacking will quadruple transistor density.

A Self-Fulfilling Prophecy
The technologies Koduri outlines are an evolution of the same general technology in use today. That is, we don’t need quantum computing or nanotube transistors to augment or replace silicon chips yet. Rather, as it’s done many times over the years, the chip industry will get creative with the design of its core product to realize gains for another decade.

Last year, veteran chip engineer Jim Keller, who at the time was Intel’s head of silicon engineering but has since left the company, told MIT Technology Review there are over a 100 variables driving Moore’s Law (including 3D architectures and new transistor designs). From the standpoint of pure performance, it’s also about how efficiently software uses all those transistors. Keller suggested that with some clever software tweaks “we could get chips that are a hundred times faster in 10 years.”

But whether Intel’s vision pans out as planned is far from certain.

Intel’s faced challenges recently, taking five years instead of two to move its chips from 14 nanometers to 10 nanometers. After a delay of six months for its 7-nanometer chips, it’s now a year behind schedule and lagging other makers who already offer 7-nanometer chips. This is a key point. Yes, chipmakers continue making progress, but it’s getting harder, more expensive, and timelines are stretching.

The question isn’t if Intel and competitors can cram more transistors onto a chip—which, Intel rival TSMC agrees is clearly possible—it’s how long will it take and at what cost?

That said, demand for more computing power isn’t going anywhere.

Amazon, Microsoft, Alphabet, Apple, and Facebook now make up a whopping 20 percent of the stock market’s total value. By that metric, tech is the most dominant industry in at least 70 years. And new technologies—from artificial intelligence and virtual reality to a proliferation of Internet of Things devices and self-driving cars—will demand better chips.

There’s ample motivation to push computing to its bitter limits and beyond. As is often said, Moore’s Law is a self-fulfilling prophecy, and likely whatever comes after it will be too.

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