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#438762 When Robots Enter the World, Who Is ...
Over the last half decade or so, the commercialization of autonomous robots that can operate outside of structured environments has dramatically increased. But this relatively new transition of robotic technologies from research projects to commercial products comes with its share of challenges, many of which relate to the rapidly increasing visibility that these robots have in society.
Whether it's because of their appearance of agency, or because of their history in popular culture, robots frequently inspire people’s imagination. Sometimes this is a good thing, like when it leads to innovative new use cases. And sometimes this is a bad thing, like when it leads to use cases that could be classified as irresponsible or unethical. Can the people selling robots do anything about the latter? And even if they can, should they?
Roboticists understand that robots, fundamentally, are tools. We build them, we program them, and even the autonomous ones are just following the instructions that we’ve coded into them. However, that same appearance of agency that makes robots so compelling means that it may not be clear to people without much experience with or exposure to real robots that a robot itself isn’t inherently good or bad—rather, as a tool, a robot is a reflection of its designers and users.
This can put robotics companies into a difficult position. When they sell a robot to someone, that person can, hypothetically, use the robot in any way they want. Of course, this is the case with every tool, but it’s the autonomous aspect that makes robots unique. I would argue that autonomy brings with it an implied association between a robot and its maker, or in this case, the company that develops and sells it. I’m not saying that this association is necessarily a reasonable one, but I think that it exists, even if that robot has been sold to someone else who has assumed full control over everything it does.
“All of our buyers, without exception, must agree that Spot will not be used to harm or intimidate people or animals, as a weapon or configured to hold a weapon”
—Robert Playter, Boston Dynamics
Robotics companies are certainly aware of this, because many of them are very careful about who they sell their robots to, and very explicit about what they want their robots to be doing. But once a robot is out in the wild, as it were, how far should that responsibility extend? And realistically, how far can it extend? Should robotics companies be held accountable for what their robots do in the world, or should we accept that once a robot is sold to someone else, responsibility is transferred as well? And what can be done if a robot is being used in an irresponsible or unethical way that could have a negative impact on the robotics community?
For perspective on this, we contacted folks from three different robotics companies, each of which has experience selling distinctive mobile robots to commercial end users. We asked them the same five questions about the responsibility that robotics companies have regarding the robots that they sell, and here’s what they had to say:
Do you have any restrictions on what people can do with your robots? If so, what are they, and if not, why not?
Péter Fankhauser, CEO, ANYbotics:
We closely work together with our customers to make sure that our solution provides the right approach for their problem. Thereby, the target use case is clear from the beginning and we do not work with customers interested in using our robot ANYmal outside the intended target applications. Specifically, we strictly exclude any military or weaponized uses and since the foundation of ANYbotics it is close to our heart to make human work easier, safer, and more enjoyable.
Robert Playter, CEO, Boston Dynamics:
Yes, we have restrictions on what people can do with our robots, which are outlined in our Terms and Conditions of Sale. All of our buyers, without exception, must agree that Spot will not be used to harm or intimidate people or animals, as a weapon or configured to hold a weapon. Spot, just like any product, must be used in compliance with the law.
Ryan Gariepy, CTO, Clearpath Robotics:
We do have strict restrictions and KYC processes which are based primarily on Canadian export control regulations. They depend on the type of equipment sold as well as where it is going. More generally, we also will not sell or support a robot if we know that it will create an uncontrolled safety hazard or if we have reason to believe that the buyer is unqualified to use the product. And, as always, we do not support using our products for the development of fully autonomous weapons systems.
More broadly, if you sell someone a robot, why should they be restricted in what they can do with it?
Péter Fankhauser, ANYbotics: We see the robot less as a simple object but more as an artificial workforce. This implies to us that the usage is closely coupled with the transfer of the robot and both the customer and the provider agree what the robot is expected to do. This approach is supported by what we hear from our customers with an increasing interest to pay for the robots as a service or per use.
Robert Playter, Boston Dynamics: We’re offering a product for sale. We’re going to do the best we can to stop bad actors from using our technology for harm, but we don’t have the control to regulate every use. That said, we believe that our business will be best served if our technology is used for peaceful purposes—to work alongside people as trusted assistants and remove them from harm’s way. We do not want to see our technology used to cause harm or promote violence. Our restrictions are similar to those of other manufacturers or technology companies that take steps to reduce or eliminate the violent or unlawful use of their products.
Ryan Gariepy, Clearpath Robotics: Assuming the organization doing the restricting is a private organization and the robot and its software is sold vs. leased or “managed,” there aren't strong legal reasons to restrict use. That being said, the manufacturer likewise has no obligation to continue supporting that specific robot or customer going forward. However, given that we are only at the very edge of how robots will reshape a great deal of society, it is in the best interest for the manufacturer and user to be honest with each other about their respective goals. Right now, you're not only investing in the initial purchase and relationship, you're investing in the promise of how you can help each other succeed in the future.
“If a robot is being used in a way that is irresponsible due to safety: intervene! If it’s unethical: speak up!”
—Péter Fankhauser, ANYbotics
What can you realistically do to make sure that people who buy your robots use them in the ways that you intend?
Péter Fankhauser, ANYbotics: We maintain a close collaboration with our customers to ensure their success with our solution. So for us, we have refrained from technical solutions to block unintended use.
Robert Playter, Boston Dynamics: We vet our customers to make sure that their desired applications are things that Spot can support, and are in alignment with our Terms and Conditions of Sale. We’ve turned away customers whose applications aren’t a good match with our technology. If customers misuse our technology, we’re clear in our Terms of Sale that their violations may void our warranty and prevent their robots from being updated, serviced, repaired, or replaced. We may also repossess robots that are not purchased, but leased. Finally, we will refuse future sales to customers that violate our Terms of Sale.
Ryan Gariepy, Clearpath Robotics: We typically work with our clients ahead of the purchase to make sure their expectations match reality, in particular on aspects like safety, supervisory requirements, and usability. It's far worse to sell a robot that'll sit on a shelf or worse, cause harm, then to not sell a robot at all, so we prefer to reduce the risk of this situation in advance of receiving an order or shipping a robot.
How do you evaluate the merit of edge cases, for example if someone wants to use your robot in research or art that may push the boundaries of what you personally think is responsible or ethical?
Péter Fankhauser, ANYbotics: It’s about the dialog, understanding, and figuring out alternatives that work for all involved parties and the earlier you can have this dialog the better.
Robert Playter, Boston Dynamics: There’s a clear line between exploring robots in research and art, and using the robot for violent or illegal purposes.
Ryan Gariepy, Clearpath Robotics: We have sold thousands of robots to hundreds of clients, and I do not recall the last situation that was not covered by a combination of export control and a general evaluation of the client's goals and expectations. I'm sure this will change as robots continue to drop in price and increase in flexibility and usability.
“You're not only investing in the initial purchase and relationship, you're investing in the promise of how you can help each other succeed in the future.”
—Ryan Gariepy, Clearpath Robotics
What should roboticists do if we see a robot being used in a way that we feel is unethical or irresponsible?
Péter Fankhauser, ANYbotics: If it’s irresponsible due to safety: intervene! If it’s unethical: speak up!
Robert Playter, Boston Dynamics: We want robots to be beneficial for humanity, which includes the notion of not causing harm. As an industry, we think robots will achieve long-term commercial viability only if people see robots as helpful, beneficial tools without worrying if they’re going to cause harm.
Ryan Gariepy, Clearpath Robotics: On a one off basis, they should speak to a combination of the user, the supplier or suppliers, the media, and, if safety is an immediate concern, regulatory or government agencies. If the situation in question risks becoming commonplace and is not being taken seriously, they should speak up more generally in appropriate forums—conferences, industry groups, standards bodies, and the like.
As more and more robots representing different capabilities become commercially available, these issues are likely to come up more frequently. The three companies we talked to certainly don’t represent every viewpoint, and we did reach out to other companies who declined to comment. But I would think (I would hope?) that everyone in the robotics community can agree that robots should be used in a way that makes people’s lives better. What “better” means in the context of art and research and even robots in the military may not always be easy to define, and inevitably there’ll be disagreement as to what is ethical and responsible, and what isn’t.
We’ll keep on talking about it, though, and do our best to help the robotics community to continue growing and evolving in a positive way. Let us know what you think in the comments. Continue reading
Posted in Human Robots
#437978 How Mirroring the Architecture of the ...
While AI can carry out some impressive feats when trained on millions of data points, the human brain can often learn from a tiny number of examples. New research shows that borrowing architectural principles from the brain can help AI get closer to our visual prowess.
The prevailing wisdom in deep learning research is that the more data you throw at an algorithm, the better it will learn. And in the era of Big Data, that’s easier than ever, particularly for the large data-centric tech companies carrying out a lot of the cutting-edge AI research.
Today’s largest deep learning models, like OpenAI’s GPT-3 and Google’s BERT, are trained on billions of data points, and even more modest models require large amounts of data. Collecting these datasets and investing the computational resources to crunch through them is a major bottleneck, particularly for less well-resourced academic labs.
It also means today’s AI is far less flexible than natural intelligence. While a human only needs to see a handful of examples of an animal, a tool, or some other category of object to be able pick it out again, most AI need to be trained on many examples of an object in order to be able to recognize it.
There is an active sub-discipline of AI research aimed at what is known as “one-shot” or “few-shot” learning, where algorithms are designed to be able to learn from very few examples. But these approaches are still largely experimental, and they can’t come close to matching the fastest learner we know—the human brain.
This prompted a pair of neuroscientists to see if they could design an AI that could learn from few data points by borrowing principles from how we think the brain solves this problem. In a paper in Frontiers in Computational Neuroscience, they explained that the approach significantly boosts AI’s ability to learn new visual concepts from few examples.
“Our model provides a biologically plausible way for artificial neural networks to learn new visual concepts from a small number of examples,” Maximilian Riesenhuber, from Georgetown University Medical Center, said in a press release. “We can get computers to learn much better from few examples by leveraging prior learning in a way that we think mirrors what the brain is doing.”
Several decades of neuroscience research suggest that the brain’s ability to learn so quickly depends on its ability to use prior knowledge to understand new concepts based on little data. When it comes to visual understanding, this can rely on similarities of shape, structure, or color, but the brain can also leverage abstract visual concepts thought to be encoded in a brain region called the anterior temporal lobe (ATL).
“It is like saying that a platypus looks a bit like a duck, a beaver, and a sea otter,” said paper co-author Joshua Rule, from the University of California Berkeley.
The researchers decided to try and recreate this capability by using similar high-level concepts learned by an AI to help it quickly learn previously unseen categories of images.
Deep learning algorithms work by getting layers of artificial neurons to learn increasingly complex features of an image or other data type, which are then used to categorize new data. For instance, early layers will look for simple features like edges, while later ones might look for more complex ones like noses, faces, or even more high-level characteristics.
First they trained the AI on 2.5 million images across 2,000 different categories from the popular ImageNet dataset. They then extracted features from various layers of the network, including the very last layer before the output layer. They refer to these as “conceptual features” because they are the highest-level features learned, and most similar to the abstract concepts that might be encoded in the ATL.
They then used these different sets of features to train the AI to learn new concepts based on 2, 4, 8, 16, 32, 64, and 128 examples. They found that the AI that used the conceptual features yielded much better performance than ones trained using lower-level features on lower numbers of examples, but the gap shrunk as they were fed more training examples.
While the researchers admit the challenge they set their AI was relatively simple and only covers one aspect of the complex process of visual reasoning, they said that using a biologically plausible approach to solving the few-shot problem opens up promising new avenues in both neuroscience and AI.
“Our findings not only suggest techniques that could help computers learn more quickly and efficiently, they can also lead to improved neuroscience experiments aimed at understanding how people learn so quickly, which is not yet well understood,” Riesenhuber said.
As the researchers note, the human visual system is still the gold standard when it comes to understanding the world around us. Borrowing from its design principles might turn out to be a profitable direction for future research.
Image Credit: Gerd Altmann from Pixabay Continue reading
Posted in Human Robots
#437974 China Wants to Be the World’s AI ...
China’s star has been steadily rising for decades. Besides slashing extreme poverty rates from 88 percent to under 2 percent in just 30 years, the country has become a global powerhouse in manufacturing and technology. Its pace of growth may slow due to an aging population, but China is nonetheless one of the world’s biggest players in multiple cutting-edge tech fields.
One of these fields, and perhaps the most significant, is artificial intelligence. The Chinese government announced a plan in 2017 to become the world leader in AI by 2030, and has since poured billions of dollars into AI projects and research across academia, government, and private industry. The government’s venture capital fund is investing over $30 billion in AI; the northeastern city of Tianjin budgeted $16 billion for advancing AI; and a $2 billion AI research park is being built in Beijing.
On top of these huge investments, the government and private companies in China have access to an unprecedented quantity of data, on everything from citizens’ health to their smartphone use. WeChat, a multi-functional app where people can chat, date, send payments, hail rides, read news, and more, gives the CCP full access to user data upon request; as one BBC journalist put it, WeChat “was ahead of the game on the global stage and it has found its way into all corners of people’s existence. It could deliver to the Communist Party a life map of pretty much everybody in this country, citizens and foreigners alike.” And that’s just one (albeit big) source of data.
Many believe these factors are giving China a serious leg up in AI development, even providing enough of a boost that its progress will surpass that of the US.
But there’s more to AI than data, and there’s more to progress than investing billions of dollars. Analyzing China’s potential to become a world leader in AI—or in any technology that requires consistent innovation—from multiple angles provides a more nuanced picture of its strengths and limitations. In a June 2020 article in Foreign Affairs, Oxford fellows Carl Benedikt Frey and Michael Osborne argued that China’s big advantages may not actually be that advantageous in the long run—and its limitations may be very limiting.
Moving the AI Needle
To get an idea of who’s likely to take the lead in AI, it could help to first consider how the technology will advance beyond its current state.
To put it plainly, AI is somewhat stuck at the moment. Algorithms and neural networks continue to achieve new and impressive feats—like DeepMind’s AlphaFold accurately predicting protein structures or OpenAI’s GPT-3 writing convincing articles based on short prompts—but for the most part these systems’ capabilities are still defined as narrow intelligence: completing a specific task for which the system was painstakingly trained on loads of data.
(It’s worth noting here that some have speculated OpenAI’s GPT-3 may be an exception, the first example of machine intelligence that, while not “general,” has surpassed the definition of “narrow”; the algorithm was trained to write text, but ended up being able to translate between languages, write code, autocomplete images, do math, and perform other language-related tasks it wasn’t specifically trained for. However, all of GPT-3’s capabilities are limited to skills it learned in the language domain, whether spoken, written, or programming language).
Both AlphaFold’s and GPT-3’s success was due largely to the massive datasets they were trained on; no revolutionary new training methods or architectures were involved. If all it was going to take to advance AI was a continuation or scaling-up of this paradigm—more input data yields increased capability—China could well have an advantage.
But one of the biggest hurdles AI needs to clear to advance in leaps and bounds rather than baby steps is precisely this reliance on extensive, task-specific data. Other significant challenges include the technology’s fast approach to the limits of current computing power and its immense energy consumption.
Thus, while China’s trove of data may give it an advantage now, it may not be much of a long-term foothold on the climb to AI dominance. It’s useful for building products that incorporate or rely on today’s AI, but not for pushing the needle on how artificially intelligent systems learn. WeChat data on users’ spending habits, for example, would be valuable in building an AI that helps people save money or suggests items they might want to purchase. It will enable (and already has enabled) highly tailored products that will earn their creators and the companies that use them a lot of money.
But data quantity isn’t what’s going to advance AI. As Frey and Osborne put it, “Data efficiency is the holy grail of further progress in artificial intelligence.”
To that end, research teams in academia and private industry are working on ways to make AI less data-hungry. New training methods like one-shot learning and less-than-one-shot learning have begun to emerge, along with myriad efforts to make AI that learns more like the human brain.
While not insignificant, these advancements still fall into the “baby steps” category. No one knows how AI is going to progress beyond these small steps—and that uncertainty, in Frey and Osborne’s opinion, is a major speed bump on China’s fast-track to AI dominance.
How Innovation Happens
A lot of great inventions have happened by accident, and some of the world’s most successful companies started in garages, dorm rooms, or similarly low-budget, nondescript circumstances (including Google, Facebook, Amazon, and Apple, to name a few). Innovation, the authors point out, often happens “through serendipity and recombination, as inventors and entrepreneurs interact and exchange ideas.”
Frey and Osborne argue that although China has great reserves of talent and a history of building on technologies conceived elsewhere, it doesn’t yet have a glowing track record in terms of innovation. They note that of the 100 most-cited patents from 2003 to present, none came from China. Giants Tencent, Alibaba, and Baidu are all wildly successful in the Chinese market, but they’re rooted in technologies or business models that came out of the US and were tweaked for the Chinese population.
“The most innovative societies have always been those that allowed people to pursue controversial ideas,” Frey and Osborne write. China’s heavy censorship of the internet and surveillance of citizens don’t quite encourage the pursuit of controversial ideas. The country’s social credit system rewards people who follow the rules and punishes those who step out of line. Frey adds that top-down execution of problem-solving is effective when the problem at hand is clearly defined—and the next big leaps in AI are not.
It’s debatable how strongly a culture of social conformism can impact technological innovation, and of course there can be exceptions. But a relevant historical example is the Soviet Union, which, despite heavy investment in science and technology that briefly rivaled the US in fields like nuclear energy and space exploration, ended up lagging far behind primarily due to political and cultural factors.
Similarly, China’s focus on computer science in its education system could give it an edge—but, as Frey told me in an email, “The best students are not necessarily the best researchers. Being a good researcher also requires coming up with new ideas.”
Winner Take All?
Beyond the question of whether China will achieve AI dominance is the issue of how it will use the powerful technology. Several of the ways China has already implemented AI could be considered morally questionable, from facial recognition systems used aggressively against ethnic minorities to smart glasses for policemen that can pull up information about whoever the wearer looks at.
This isn’t to say the US would use AI for purely ethical purposes. The military’s Project Maven, for example, used artificially intelligent algorithms to identify insurgent targets in Iraq and Syria, and American law enforcement agencies are also using (mostly unregulated) facial recognition systems.
It’s conceivable that “dominance” in AI won’t go to one country; each nation could meet milestones in different ways, or meet different milestones. Researchers from both countries, at least in the academic sphere, could (and likely will) continue to collaborate and share their work, as they’ve done on many projects to date.
If one country does take the lead, it will certainly see some major advantages as a result. Brookings Institute fellow Indermit Gill goes so far as to say that whoever leads in AI in 2030 will “rule the world” until 2100. But Gill points out that in addition to considering each country’s strengths, we should consider how willing they are to improve upon their weaknesses.
While China leads in investment and the US in innovation, both nations are grappling with huge economic inequalities that could negatively impact technological uptake. “Attitudes toward the social change that accompanies new technologies matter as much as the technologies, pointing to the need for complementary policies that shape the economy and society,” Gill writes.
Will China’s leadership be willing to relax its grip to foster innovation? Will the US business environment be enough to compete with China’s data, investment, and education advantages? And can both countries find a way to distribute technology’s economic benefits more equitably?
Time will tell, but it seems we’ve got our work cut out for us—and China does too.
Image Credit: Adam Birkett on Unsplash Continue reading
Posted in Human Robots
#437683 iRobot Remembers That Robots Are ...
iRobot has released several new robots over the last few years, including the i7 and s9 vacuums. Both of these models are very fancy and very capable, packed with innovative and useful features that we’ve been impressed by. They’re both also quite expensive—with dirt docks included, you’re looking at US $800 for the i7+, and a whopping $1,100 for the s9+. You can knock a couple hundred bucks off of those prices if you don’t want the docks, but still, these vacuums are absolutely luxury items.
If you just want something that’ll do some vacuuming so that you don’t have to, iRobot has recently announced a new Roomba option. The Roomba i3 is iRobot’s new low to midrange vacuum, starting at $400. It’s not nearly as smart as the i7 or the s9, but it can navigate (sort of) and make maps (sort of) and do some basic smart home integration. If that sounds like all you need, the i3 could be the robot vacuum for you.
iRobot calls the i3 “stylish,” and it does look pretty neat with that fabric top. Underneath, you get dual rubber primary brushes plus a side brush. There’s limited compatibility with the iRobot Home app and IFTTT, along with Alexa and Google Home. The i3 is also compatible with iRobot’s Clean Base, but that’ll cost you an extra $200, and iRobot refers to this bundle as the i3+.
The reason that the i3 only offers limited compatibility with iRobot’s app is that the i3 is missing the top-mounted camera that you’ll find in more expensive models. Instead, it relies on a downward-looking optical sensor to help it navigate, and it builds up a map as it’s cleaning by keeping track of when it bumps into obstacles and paying attention to internal sensors like a gyro and wheel odometers. The i3 can localize directly on its charging station or Clean Base (which have beacons on them that the robot can see if it’s close enough), which allows it to resume cleaning after emptying it’s bin or recharging. You’ll get a map of the area that the i3 has cleaned once it’s finished, but that map won’t persist between cleaning sessions, meaning that you can’t do things like set keep-out zones or identify specific rooms for the robot to clean. Many of the more useful features that iRobot’s app offers are based on persistent maps, and this is probably the biggest gap in functionality between the i3 and its more expensive siblings.
According to iRobot senior global product manager Sarah Wang, the kind of augmented dead-reckoning-based mapping that the i3 uses actually works really well: “Based on our internal and external testing, the performance is equivalent with our products that have cameras, like the Roomba 960,” she says. To get this level of performance, though, you do have to be careful, Wang adds. “If you kidnap i3, then it will be very confused, because it doesn’t have a reference to know where it is.” “Kidnapping” is a term that’s used often in robotics to refer to a situation in which an autonomous robot gets moved to an unmapped location, and in the context of a home robot, the best example of this is if you decide that you want your robot to vacuum a different room instead, so you pick it up and move it there.
iRobot used to make this easy by giving all of its robots carrying handles, but not anymore, because getting moved around makes things really difficult for any robot trying to keep track of where it is. While robots like the i7 can recover using their cameras to look for unique features that they recognize, the only permanent, unique landmark that the i3 can for sure identify is the beacon on its dock. What this means is that when it comes to the i3, even more than other Roomba models, the best strategy, is to just “let it do its thing,” says iRobot senior principal system engineer Landon Unninayar.
Photo: iRobot
The Roomba i3 is iRobot’s new low to midrange vacuum, starting at $400.
If you’re looking to spend a bit less than the $400 starting price of the i3, there are other options to be aware of as well. The Roomba 614, for example, does a totally decent job and costs $250. It’s scheduling isn’t very clever, it doesn’t make maps, and it won’t empty itself, but it will absolutely help keep your floors clean as long as you don’t mind being a little bit more hands-on. (And there’s also Neato’s D4, which offers basic persistent maps—and lasers!—for $330.)
The other thing to consider if you’re trying to decide between the i3 and a more expensive Roomba is that without the camera, the i3 likely won’t be able to take advantage of nearly as many of the future improvements that iRobot has said it’s working on. Spending more money on a robot with additional sensors isn’t just buying what it can do now, but also investing in what it may be able to do later on, with its more sophisticated localization and ability to recognize objects. iRobot has promised major app updates every six months, and our guess is that most of the cool new stuff is going to show in the i7 and s9. So, if your top priority is just cleaner floors, the i3 is a solid choice. But if you want a part of what iRobot is working on next, the i3 might end up holding you back. Continue reading
Posted in Human Robots
#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.
Image Credit: McDonald’s Continue reading
Posted in Human Robots