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Machine learning algorithms are starting to exceed human performance in many narrow and specific domains, such as image recognition and certain types of medical diagnoses. They’re also rapidly improving in more complex domains such as generating eerily human-like text. We increasingly rely on machine learning algorithms to make decisions on a wide range of topics, from what we collectively spend billions of hours watching to who gets the job.
But machine learning algorithms cannot explain the decisions they make.
How can we justify putting these systems in charge of decisions that affect people’s lives if we don’t understand how they’re arriving at those decisions?
This desire to get more than raw numbers from machine learning algorithms has led to a renewed focus on explainable AI: algorithms that can make a decision or take an action, and tell you the reasons behind it.
What Makes You Say That?
In some circumstances, you can see a road to explainable AI already. Take OpenAI’s GTP-2 model, or IBM’s Project Debater. Both of these generate text based on a large corpus of training data, and try to make it as relevant as possible to the prompt that’s given. If these models were also able to provide a quick run-down of the top few sources in that corpus of training data they were drawing information from, it may be easier to understand where the “argument” (or poetic essay about unicorns) was coming from.
This is similar to the approach Google is now looking at for its image classifiers. Many algorithms are more sensitive to textures and the relationship between adjacent pixels in an image, rather than recognizing objects by their outlines as humans do. This leads to strange results: some algorithms can happily identify a totally scrambled image of a polar bear, but not a polar bear silhouette.
Previous attempts to make image classifiers explainable relied on significance mapping. In this method, the algorithm would highlight the areas of the image that contributed the most statistical weight to making the decision. This is usually determined by changing groups of pixels in the image and seeing which contribute to the biggest change in the algorithm’s impression of what the image is. For example, if the algorithm is trying to recognize a stop sign, changing the background is unlikely to be as important as changing the sign.
Google’s new approach changes the way that its algorithm recognizes objects, by examining them at several different resolutions and searching for matches to different “sub-objects” within the main object. You or I might recognize an ambulance from its flashing lights, its tires, and its logo; we might zoom in on the basketball held by an NBA player to deduce their occupation, and so on. By linking the overall categorization of an image to these “concepts,” the algorithm can explain its decision: I categorized this as a cat because of its tail and whiskers.
Even in this experiment, though, the “psychology” of the algorithm in decision-making is counter-intuitive. For example, in the basketball case, the most important factor in making the decision was actually the player’s jerseys rather than the basketball.
Can You Explain What You Don’t Understand?
While it may seem trivial, the conflict here is a fundamental one in approaches to artificial intelligence. Namely, how far can you get with mere statistical associations between huge sets of data, and how much do you need to introduce abstract concepts for real intelligence to arise?
At one end of the spectrum, Good Old-Fashioned AI or GOFAI dreamed up machines that would be entirely based on symbolic logic. The machine would be hard-coded with the concept of a dog, a flower, cars, and so forth, alongside all of the symbolic “rules” which we internalize, allowing us to distinguish between dogs, flowers, and cars. (You can imagine a similar approach to a conversational AI would teach it words and strict grammatical structures from the top down, rather than “learning” languages from statistical associations between letters and words in training data, as GPT-2 broadly does.)
Such a system would be able to explain itself, because it would deal in high-level, human-understandable concepts. The equation is closer to: “ball” + “stitches” + “white” = “baseball”, rather than a set of millions of numbers linking various pathways together. There are elements of GOFAI in Google’s new approach to explaining its image recognition: the new algorithm can recognize objects based on the sub-objects they contain. To do this, it requires at least a rudimentary understanding of what those sub-objects look like, and the rules that link objects to sub-objects, such as “cats have whiskers.”
The issue, of course, is the—maybe impossible—labor-intensive task of defining all these symbolic concepts and every conceivable rule that could possibly link them together by hand. The difficulty of creating systems like this, which could handle the “combinatorial explosion” present in reality, helped to lead to the first AI winter.
Meanwhile, neural networks rely on training themselves on vast sets of data. Without the “labeling” of supervised learning, this process might bear no relation to any concepts a human could understand (and therefore be utterly inexplicable).
Somewhere between these two, hope explainable AI enthusiasts, is a happy medium that can crunch colossal amounts of data, giving us all of the benefits that recent, neural-network AI has bestowed, while showing its working in terms that humans can understand.
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MIT researchers have demonstrated a new kind of teleoperation system that allows a two-legged robot to “borrow” a human operator’s physical skills to move with greater agility. The system works a bit like those haptic suits from the Spielberg movie “Ready Player One.” But while the suits in the film were used to connect humans to their VR avatars, the MIT suit connects the operator to a real robot.
The robot is called Little HERMES, and it’s currently just a pair of little legs, about a third the size of an average adult. It can step and jump in place or walk a short distance while supported by a gantry. While that in itself is not very impressive, the researchers say their approach could help bring capable disaster robots closer to reality. They explain that, despite recent advances, building fully autonomous robots with motor and decision-making skills comparable to those of humans remains a challenge. That’s where a more advanced teleoperation system could help.
The researchers, João Ramos, now an assistant professor at the University of Illinois at Urbana-Champaign, and Sangbae Kim, director of MIT’s Biomimetic Robotics Lab, describe the project in this week’s issue of Science Robotics. In the paper, they argue that existing teleoperation systems often can’t effectively match the operator’s motions to that of a robot. In addition, conventional systems provide no physical feedback to the human teleoperator about what the robot is doing. Their new approach addresses these two limitations, and to see how it would work in practice, they built Little HERMES.
Image: Science Robotics
The main components of MIT’s bipedal robot Little HERMES: (A) Custom actuators designed to withstand impact and capable of producing high torque. (B) Lightweight limbs with low inertia and fast leg swing. (C) Impact-robust and lightweight foot sensors with three-axis contact force sensor. (D) Ruggedized IMU to estimates the robot’s torso posture, angular rate, and linear acceleration. (E) Real-time computer sbRIO 9606 from National Instruments for robot control. (F) Two three-cell lithium-polymer batteries in series. (G) Rigid and lightweight frame to minimize the robot mass.
Early this year, the MIT researchers wrote an in-depth article for IEEE Spectrum about the project, which includes Little HERMES and also its big brother, HERMES (for Highly Efficient Robotic Mechanisms and Electromechanical System). In that article, they describe the two main components of the system:
[…] We are building a telerobotic system that has two parts: a humanoid capable of nimble, dynamic behaviors, and a new kind of two-way human-machine interface that sends your motions to the robot and the robot’s motions to you. So if the robot steps on debris and starts to lose its balance, the operator feels the same instability and instinctively reacts to avoid falling. We then capture that physical response and send it back to the robot, which helps it avoid falling, too. Through this human-robot link, the robot can harness the operator’s innate motor skills and split-second reflexes to keep its footing.
You could say we’re putting a human brain inside the machine.
Image: Science Robotics
The human-machine interface built by the MIT researchers for controlling Little HERMES is different from conventional ones in that it relies on the operator’s reflexes to improve the robot’s stability. The researchers call it the balance-feedback interface, or BFI. The main modules of the BFI include: (A) Custom interface attachments for torso and feet designed to capture human motion data at high speed (1 kHz). (B) Two underactuated modules to track the position and orientation of the torso and apply forces to the operator. (C) Each actuation module has three DoFs, one of which is a push/pull rod actuated by a DC brushless motor. (D) A series of linkages with passive joints connected to the operator’s feet and track their spatial translation. (E) Real-time controller cRIO 9082 from National Instruments to close the BFI control loop. (F) Force plate to estimated the operator’s center of pressure position and measure the shear and normal components of the operator’s net contact force.
Here’s more footage of the experiments, showing Little HERMES stepping and jumping in place, walking a few steps forward and backward, and balancing. Watch until the end to see a compilation of unsuccessful stepping experiments. Poor Little HERMES!
In the new Science Robotics paper, the MIT researchers explain how they solved one of the key challenges in making their teleoperation system effective:
The challenge of this strategy lies in properly mapping human body motion to the machine while simultaneously informing the operator how closely the robot is reproducing the movement. Therefore, we propose a solution for this bilateral feedback policy to control a bipedal robot to take steps, jump, and walk in synchrony with a human operator. Such dynamic synchronization was achieved by (i) scaling the core components of human locomotion data to robot proportions in real time and (ii) applying feedback forces to the operator that are proportional to the relative velocity between human and robot.
Little HERMES is now taking its first steps, quite literally, but the researchers say they hope to use robotic legs with similar design as part of a more advanced humanoid. One possibility they’ve envisioned is a fast-moving quadruped robot that could run through various kinds of terrain and then transform into a bipedal robot that would use its hands to perform dexterous manipulations. This could involve merging some of the robots the MIT researchers have built in their lab, possibly creating hybrids between Cheetah and HERMES, or Mini Cheetah and Little HERMES. We can’t wait to see what the resulting robots will look like.
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Food. What we eat, and how we grow it, will be fundamentally transformed in the next decade.
Already, indoor farming is projected to be a US$40.25 billion industry by 2022, with a compound annual growth rate of 9.65 percent. Meanwhile, the food 3D printing industry is expected to grow at an even higher rate, averaging 50 percent annual growth.
And converging exponential technologies—from materials science to AI-driven digital agriculture—are not slowing down. Today’s breakthroughs will soon allow our planet to boost its food production by nearly 70 percent, using a fraction of the real estate and resources, to feed 9 billion by mid-century.
What you consume, how it was grown, and how it will end up in your stomach will all ride the wave of converging exponentials, revolutionizing the most basic of human needs.
3D printing has already had a profound impact on the manufacturing sector. We are now able to print in hundreds of different materials, making anything from toys to houses to organs. However, we are finally seeing the emergence of 3D printers that can print food itself.
Redefine Meat, an Israeli startup, wants to tackle industrial meat production using 3D printers that can generate meat, no animals required. The printer takes in fat, water, and three different plant protein sources, using these ingredients to print a meat fiber matrix with trapped fat and water, thus mimicking the texture and flavor of real meat.
Slated for release in 2020 at a cost of $100,000, their machines are rapidly demonetizing and will begin by targeting clients in industrial-scale meat production.
Anrich3D aims to take this process a step further, 3D printing meals that are customized to your medical records, heath data from your smart wearables, and patterns detected by your sleep trackers. The company plans to use multiple extruders for multi-material printing, allowing them to dispense each ingredient precisely for nutritionally optimized meals. Currently in an R&D phase at the Nanyang Technological University in Singapore, the company hopes to have its first taste tests in 2020.
These are only a few of the many 3D food printing startups springing into existence. The benefits from such innovations are boundless.
Not only will food 3D printing grant consumers control over the ingredients and mixtures they consume, but it is already beginning to enable new innovations in flavor itself, democratizing far healthier meal options in newly customizable cuisine categories.
Vertical farming, whereby food is grown in vertical stacks (in skyscrapers and buildings rather than outside in fields), marks a classic case of converging exponential technologies. Over just the past decade, the technology has surged from a handful of early-stage pilots to a full-grown industry.
Today, the average American meal travels 1,500-2,500 miles to get to your plate. As summed up by Worldwatch Institute researcher Brian Halweil, “We are spending far more energy to get food to the table than the energy we get from eating the food.” Additionally, the longer foods are out of the soil, the less nutritious they become, losing on average 45 percent of their nutrition before being consumed.
Yet beyond cutting down on time and transportation losses, vertical farming eliminates a whole host of issues in food production. Relying on hydroponics and aeroponics, vertical farms allows us to grow crops with 90 percent less water than traditional agriculture—which is critical for our increasingly thirsty planet.
Currently, the largest player around is Bay Area-based Plenty Inc. With over $200 million in funding from Softbank, Plenty is taking a smart tech approach to indoor agriculture. Plants grow on 20-foot-high towers, monitored by tens of thousands of cameras and sensors, optimized by big data and machine learning.
This allows the company to pack 40 plants in the space previously occupied by 1. The process also produces yields 350 times greater than outdoor farmland, using less than 1 percent as much water.
And rather than bespoke veggies for the wealthy few, Plenty’s processes allow them to knock 20-35 percent off the costs of traditional grocery stores. To date, Plenty has their home base in South San Francisco, a 100,000 square-foot farm in Kent, Washington, an indoor farm in the United Arab Emirates, and recently started construction on over 300 farms in China.
Another major player is New Jersey-based Aerofarms, which can now grow two million pounds of leafy greens without sunlight or soil.
To do this, Aerofarms leverages AI-controlled LEDs to provide optimized wavelengths of light for each plant. Using aeroponics, the company delivers nutrients by misting them directly onto the plants’ roots—no soil required. Rather, plants are suspended in a growth mesh fabric made from recycled water bottles. And here too, sensors, cameras, and machine learning govern the entire process.
While 50-80 percent of the cost of vertical farming is human labor, autonomous robotics promises to solve that problem. Enter contenders like Iron Ox, a firm that has developed the Angus robot, capable of moving around plant-growing containers.
The writing is on the wall, and traditional agriculture is fast being turned on its head.
In an era where materials science, nanotechnology, and biotechnology are rapidly becoming the same field of study, key advances are enabling us to create healthier, more nutritious, more efficient, and longer-lasting food.
For starters, we are now able to boost the photosynthetic abilities of plants. Using novel techniques to improve a micro-step in the photosynthesis process chain, researchers at UCLA were able to boost tobacco crop yield by 14-20 percent. Meanwhile, the RIPE Project, backed by Bill Gates and run out of the University of Illinois, has matched and improved those numbers.
And to top things off, The University of Essex was even able to improve tobacco yield by 27-47 percent by increasing the levels of protein involved in photo-respiration.
In yet another win for food-related materials science, Santa Barbara-based Apeel Sciences is further tackling the vexing challenge of food waste. Now approaching commercialization, Apeel uses lipids and glycerolipids found in the peels, seeds, and pulps of all fruits and vegetables to create “cutin”—the fatty substance that composes the skin of fruits and prevents them from rapidly spoiling by trapping moisture.
By then spraying fruits with this generated substance, Apeel can preserve foods 60 percent longer using an odorless, tasteless, colorless organic substance.
And stores across the US are already using this method. By leveraging our advancing knowledge of plants and chemistry, materials science is allowing us to produce more food with far longer-lasting freshness and more nutritious value than ever before.
With advances in 3D printing, vertical farming, and materials sciences, we can now make food smarter, more productive, and far more resilient.
By the end of the next decade, you should be able to 3D print a fusion cuisine dish from the comfort of your home, using ingredients harvested from vertical farms, with nutritional value optimized by AI and materials science. However, even this picture doesn’t account for all the rapid changes underway in the food industry.
Join me next week for Part 2 of the Future of Food for a discussion on how food production will be transformed, quite literally, from the bottom up.
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Image Credit: Vanessa Bates Ramirez Continue reading