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#431377 The Farms of the Future Will Be ...

Swarms of drones buzz overhead, while robotic vehicles crawl across the landscape. Orbiting satellites snap high-resolution images of the scene far below. Not one human being can be seen in the pre-dawn glow spreading across the land.
This isn’t some post-apocalyptic vision of the future à la The Terminator. This is a snapshot of the farm of the future. Every phase of the operation—from seed to harvest—may someday be automated, without the need to ever get one’s fingernails dirty.
In fact, it’s science fiction already being engineered into reality. Today, robots empowered with artificial intelligence can zap weeds with preternatural precision, while autonomous tractors move with tireless efficiency across the farmland. Satellites can assess crop health from outer space, providing gobs of data to help produce the sort of business intelligence once accessible only to Fortune 500 companies.
“Precision agriculture is on the brink of a new phase of development involving smart machines that can operate by themselves, which will allow production agriculture to become significantly more efficient. Precision agriculture is becoming robotic agriculture,” said professor Simon Blackmore last year during a conference in Asia on the latest developments in robotic agriculture. Blackmore is head of engineering at Harper Adams University and head of the National Centre for Precision Farming in the UK.
It’s Blackmore’s university that recently showcased what may someday be possible. The project, dubbed Hands Free Hectare and led by researchers from Harper Adams and private industry, farmed one hectare (about 2.5 acres) of spring barley without one person ever setting foot in the field.
The team re-purposed, re-wired and roboticized farm equipment ranging from a Japanese tractor to a 25-year-old combine. Drones served as scouts to survey the operation and collect samples to help the team monitor the progress of the barley. At the end of the season, the robo farmers harvested about 4.5 tons of barley at a price tag of £200,000.

“This project aimed to prove that there’s no technological reason why a field can’t be farmed without humans working the land directly now, and we’ve done that,” said Martin Abell, mechatronics researcher for Precision Decisions, which partnered with Harper Adams, in a press release.
I, Robot Farmer
The Harper Adams experiment is the latest example of how machines are disrupting the agricultural industry. Around the same time that the Hands Free Hectare combine was harvesting barley, Deere & Company announced it would acquire a startup called Blue River Technology for a reported $305 million.
Blue River has developed a “see-and-spray” system that combines computer vision and artificial intelligence to discriminate between crops and weeds. It hits the former with fertilizer and blasts the latter with herbicides with such precision that it can eliminate 90 percent of the chemicals used in conventional agriculture.
It’s not just farmland that’s getting a helping hand from robots. A California company called Abundant Robotics, spun out of the nonprofit research institute SRI International, is developing robots capable of picking apples with vacuum-like arms that suck the fruit straight off the trees in the orchards.
“Traditional robots were designed to perform very specific tasks over and over again. But the robots that will be used in food and agricultural applications will have to be much more flexible than what we’ve seen in automotive manufacturing plants in order to deal with natural variation in food products or the outdoor environment,” Dan Harburg, an associate at venture capital firm Anterra Capital who previously worked at a Massachusetts-based startup making a robotic arm capable of grabbing fruit, told AgFunder News.
“This means ag-focused robotics startups have to design systems from the ground up, which can take time and money, and their robots have to be able to complete multiple tasks to avoid sitting on the shelf for a significant portion of the year,” he noted.
Eyes in the Sky
It will take more than an army of robotic tractors to grow a successful crop. The farm of the future will rely on drones, satellites, and other airborne instruments to provide data about their crops on the ground.
Companies like Descartes Labs, for instance, employ machine learning to analyze satellite imagery to forecast soy and corn yields. The Los Alamos, New Mexico startup collects five terabytes of data every day from multiple satellite constellations, including NASA and the European Space Agency. Combined with weather readings and other real-time inputs, Descartes Labs can predict cornfield yields with 99 percent accuracy. Its AI platform can even assess crop health from infrared readings.
The US agency DARPA recently granted Descartes Labs $1.5 million to monitor and analyze wheat yields in the Middle East and Africa. The idea is that accurate forecasts may help identify regions at risk of crop failure, which could lead to famine and political unrest. Another company called TellusLabs out of Somerville, Massachusetts also employs machine learning algorithms to predict corn and soy yields with similar accuracy from satellite imagery.
Farmers don’t have to reach orbit to get insights on their cropland. A startup in Oakland, Ceres Imaging, produces high-resolution imagery from multispectral cameras flown across fields aboard small planes. The snapshots capture the landscape at different wavelengths, identifying insights into problems like water stress, as well as providing estimates of chlorophyll and nitrogen levels. The geo-tagged images mean farmers can easily locate areas that need to be addressed.
Growing From the Inside
Even the best intelligence—whether from drones, satellites, or machine learning algorithms—will be challenged to predict the unpredictable issues posed by climate change. That’s one reason more and more companies are betting the farm on what’s called controlled environment agriculture. Today, that doesn’t just mean fancy greenhouses, but everything from warehouse-sized, automated vertical farms to grow rooms run by robots, located not in the emptiness of Kansas or Nebraska but smack dab in the middle of the main streets of America.
Proponents of these new concepts argue these high-tech indoor farms can produce much higher yields while drastically reducing water usage and synthetic inputs like fertilizer and herbicides.
Iron Ox, out of San Francisco, is developing one-acre urban greenhouses that will be operated by robots and reportedly capable of producing the equivalent of 30 acres of farmland. Powered by artificial intelligence, a team of three robots will run the entire operation of planting, nurturing, and harvesting the crops.
Vertical farming startup Plenty, also based in San Francisco, uses AI to automate its operations, and got a $200 million vote of confidence from the SoftBank Vision Fund earlier this year. The company claims its system uses only 1 percent of the water consumed in conventional agriculture while producing 350 times as much produce. Plenty is part of a new crop of urban-oriented farms, including Bowery Farming and AeroFarms.
“What I can envision is locating a larger scale indoor farm in the economically disadvantaged food desert, in order to stimulate a broader economic impact that could create jobs and generate income for that area,” said Dr. Gary Stutte, an expert in space agriculture and controlled environment agriculture, in an interview with AgFunder News. “The indoor agriculture model is adaptable to becoming an engine for economic growth and food security in both rural and urban food deserts.”
Still, the model is not without its own challenges and criticisms. Most of what these farms can produce falls into the “leafy greens” category and often comes with a premium price, which seems antithetical to the proposed mission of creating oases in the food deserts of cities. While water usage may be minimized, the electricity required to power the operation, especially the LEDs (which played a huge part in revolutionizing indoor agriculture), are not cheap.
Still, all of these advances, from robo farmers to automated greenhouses, may need to be part of a future where nearly 10 billion people will inhabit the planet by 2050. An oft-quoted statistic from the Food and Agriculture Organization of the United Nations says the world must boost food production by 70 percent to meet the needs of the population. Technology may not save the world, but it will help feed it.
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#431362 Does Regulating Artificial Intelligence ...

Some people are afraid that heavily armed artificially intelligent robots might take over the world, enslaving humanity—or perhaps exterminating us. These people, including tech-industry billionaire Elon Musk and eminent physicist Stephen Hawking, say artificial intelligence technology needs to be regulated to manage the risks. But Microsoft founder Bill Gates and Facebook’s Mark Zuckerberg disagree, saying the technology is not nearly advanced enough for those worries to be realistic.
As someone who researches how AI works in robotic decision-making, drones and self-driving vehicles, I’ve seen how beneficial it can be. I’ve developed AI software that lets robots working in teams make individual decisions as part of collective efforts to explore and solve problems. Researchers are already subject to existing rules, regulations and laws designed to protect public safety. Imposing further limitations risks reducing the potential for innovation with AI systems.
How is AI regulated now?
While the term “artificial intelligence” may conjure fantastical images of human-like robots, most people have encountered AI before. It helps us find similar products while shopping, offers movie and TV recommendations, and helps us search for websites. It grades student writing, provides personalized tutoring, and even recognizes objects carried through airport scanners.
In each case, the AI makes things easier for humans. For example, the AI software I developed could be used to plan and execute a search of a field for a plant or animal as part of a science experiment. But even as the AI frees people from doing this work, it is still basing its actions on human decisions and goals about where to search and what to look for.
In areas like these and many others, AI has the potential to do far more good than harm—if used properly. But I don’t believe additional regulations are currently needed. There are already laws on the books of nations, states, and towns governing civil and criminal liabilities for harmful actions. Our drones, for example, must obey FAA regulations, while the self-driving car AI must obey regular traffic laws to operate on public roadways.
Existing laws also cover what happens if a robot injures or kills a person, even if the injury is accidental and the robot’s programmer or operator isn’t criminally responsible. While lawmakers and regulators may need to refine responsibility for AI systems’ actions as technology advances, creating regulations beyond those that already exist could prohibit or slow the development of capabilities that would be overwhelmingly beneficial.
Potential risks from artificial intelligence
It may seem reasonable to worry about researchers developing very advanced artificial intelligence systems that can operate entirely outside human control. A common thought experiment deals with a self-driving car forced to make a decision about whether to run over a child who just stepped into the road or veer off into a guardrail, injuring the car’s occupants and perhaps even those in another vehicle.
Musk and Hawking, among others, worry that a hyper-capable AI system, no longer limited to a single set of tasks like controlling a self-driving car, might decide it doesn’t need humans anymore. It might even look at human stewardship of the planet, the interpersonal conflicts, theft, fraud, and frequent wars, and decide that the world would be better without people.
Science fiction author Isaac Asimov tried to address this potential by proposing three laws limiting robot decision-making: Robots cannot injure humans or allow them “to come to harm.” They must also obey humans—unless this would harm humans—and protect themselves, as long as this doesn’t harm humans or ignore an order.
But Asimov himself knew the three laws were not enough. And they don’t reflect the complexity of human values. What constitutes “harm” is an example: Should a robot protect humanity from suffering related to overpopulation, or should it protect individuals’ freedoms to make personal reproductive decisions?
We humans have already wrestled with these questions in our own, non-artificial intelligences. Researchers have proposed restrictions on human freedoms, including reducing reproduction, to control people’s behavior, population growth, and environmental damage. In general, society has decided against using those methods, even if their goals seem reasonable. Similarly, rather than regulating what AI systems can and can’t do, in my view it would be better to teach them human ethics and values—like parents do with human children.
Artificial intelligence benefits
People already benefit from AI every day—but this is just the beginning. AI-controlled robots could assist law enforcement in responding to human gunmen. Current police efforts must focus on preventing officers from being injured, but robots could step into harm’s way, potentially changing the outcomes of cases like the recent shooting of an armed college student at Georgia Tech and an unarmed high school student in Austin.
Intelligent robots can help humans in other ways, too. They can perform repetitive tasks, like processing sensor data, where human boredom may cause mistakes. They can limit human exposure to dangerous materials and dangerous situations, such as when decontaminating a nuclear reactor, working in areas humans can’t go. In general, AI robots can provide humans with more time to pursue whatever they define as happiness by freeing them from having to do other work.
Achieving most of these benefits will require a lot more research and development. Regulations that make it more expensive to develop AIs or prevent certain uses may delay or forestall those efforts. This is particularly true for small businesses and individuals—key drivers of new technologies—who are not as well equipped to deal with regulation compliance as larger companies. In fact, the biggest beneficiary of AI regulation may be large companies that are used to dealing with it, because startups will have a harder time competing in a regulated environment.
The need for innovation
Humanity faced a similar set of issues in the early days of the internet. But the United States actively avoided regulating the internet to avoid stunting its early growth. Musk’s PayPal and numerous other businesses helped build the modern online world while subject only to regular human-scale rules, like those preventing theft and fraud.
Artificial intelligence systems have the potential to change how humans do just about everything. Scientists, engineers, programmers, and entrepreneurs need time to develop the technologies—and deliver their benefits. Their work should be free from concern that some AIs might be banned, and from the delays and costs associated with new AI-specific regulations.
This article was originally published on The Conversation. Read the original article.
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#431301 Collective Intelligence Is the Root of ...

Many of us intuitively think about intelligence as an individual trait. As a society, we have a tendency to praise individual game-changers for accomplishments that would not be possible without their teams, often tens of thousands of people that work behind the scenes to make extraordinary things happen.
Matt Ridley, best-selling author of multiple books, including The Rational Optimist: How Prosperity Evolves, challenges this view. He argues that human achievement and intelligence are entirely “networking phenomena.” In other words, intelligence is collective and emergent as opposed to individual.
When asked what scientific concept would improve everybody’s cognitive toolkit, Ridley highlights collective intelligence: “It is by putting brains together through the division of labor— through trade and specialization—that human society stumbled upon a way to raise the living standards, carrying capacity, technological virtuosity, and knowledge base of the species.”
Ridley has spent a lifetime exploring human prosperity and the factors that contribute to it. In a conversation with Singularity Hub, he redefined how we perceive intelligence and human progress.
Raya Bidshahri: The common perspective seems to be that competition is what drives innovation and, consequently, human progress. Why do you think collaboration trumps competition when it comes to human progress?
Matt Ridley: There is a tendency to think that competition is an animal instinct that is natural and collaboration is a human instinct we have to learn. I think there is no evidence for that. Both are deeply rooted in us as a species. The evidence from evolutionary biology tells us that collaboration is just as important as competition. Yet, at the end, the Darwinian perspective is quite correct: it’s usually cooperation for the purpose of competition, wherein a given group tries to achieve something more effectively than another group. But the point is that the capacity to co-operate is very deep in our psyche.
RB: You write that “human achievement is entirely a networking phenomenon,” and we need to stop thinking about intelligence as an individual trait, and that instead we should look at what you refer to as collective intelligence. Why is that?
MR: The best way to think about it is that IQ doesn’t matter, because a hundred stupid people who are talking to each other will accomplish more than a hundred intelligent people who aren’t. It’s absolutely vital to see that everything from the manufacturing of a pencil to the manufacturing of a nuclear power station can’t be done by an individual human brain. You can’t possibly hold in your head all the knowledge you need to do these things. For the last 200,000 years we’ve been exchanging and specializing, which enables us to achieve much greater intelligence than we can as individuals.
RB: We often think of achievement and intelligence on individual terms. Why do you think it’s so counter-intuitive for us to think about collective intelligence?
MR: People are surprisingly myopic to the extent they understand the nature of intelligence. I think it goes back to a pre-human tendency to think in terms of individual stories and actors. For example, we love to read about the famous inventor or scientist who invented or discovered something. We never tell these stories as network stories. We tell them as individual hero stories.

“It’s absolutely vital to see that everything from the manufacturing of a pencil to the manufacturing of a nuclear power station can’t be done by an individual human brain.”

This idea of a brilliant hero who saves the world in the face of every obstacle seems to speak to tribal hunter-gatherer societies, where the alpha male leads and wins. But it doesn’t resonate with how human beings have structured modern society in the last 100,000 years or so. We modern-day humans haven’t internalized a way of thinking that incorporates this definition of distributed and collective intelligence.
RB: One of the books you’re best known for is The Rational Optimist. What does it mean to be a rational optimist?
MR: My optimism is rational because it’s not based on a feeling, it’s based on evidence. If you look at the data on human living standards over the last 200 years and compare it with the way that most people actually perceive our progress during that time, you’ll see an extraordinary gap. On the whole, people seem to think that things are getting worse, but things are actually getting better.
We’ve seen the most astonishing improvements in human living standards: we’ve brought the number of people living in extreme poverty to 9 percent from about 70 percent when I was born. The human lifespan is expanding by five hours a day, child mortality has gone down by two thirds in half a century, and much more. These feats dwarf the things that are going wrong. Yet most people are quite pessimistic about the future despite the things we’ve achieved in the past.
RB: Where does this idea of collective intelligence fit in rational optimism?
MR: Underlying the idea of rational optimism was understanding what prosperity is, and why it happens to us and not to rabbits or rocks. Why are we the only species in the world that has concepts like a GDP, growth rate, or living standard? My answer is that it comes back to this phenomena of collective intelligence. The reason for a rise in living standards is innovation, and the cause of that innovation is our ability to collaborate.
The grand theme of human history is exchange of ideas, collaborating through specialization and the division of labor. Throughout history, it’s in places where there is a lot of open exchange and trade where you get a lot of innovation. And indeed, there are some extraordinary episodes in human history when societies get cut off from exchange and their innovation slows down and they start moving backwards. One example of this is Tasmania, which was isolated and lost a lot of the technologies it started off with.
RB: Lots of people like to point out that just because the world has been getting better doesn’t guarantee it will continue to do so. How do you respond to that line of argumentation?
MR: There is a quote by Thomas Babington Macaulay from 1830, where he was fed up with the pessimists of the time saying things will only get worse. He says, “On what principle is it that with nothing but improvement behind us, we are to expect nothing but deterioration before us?” And this was back in the 1830s, where in Britain and a few other parts of the world, we were only seeing the beginning of the rise of living standards. It’s perverse to argue that because things were getting better in the past, now they are about to get worse.

“I think it’s worth remembering that good news tends to be gradual, and bad news tends to be sudden. Hence, the good stuff is rarely going to make the news.”

Another thing to point out is that people have always said this. Every generation thought they were at the peak looking downhill. If you think about the opportunities technology is about to give us, whether it’s through blockchain, gene editing, or artificial intelligence, there is every reason to believe that 2017 is going to look like a time of absolute misery compared to what our children and grandchildren are going to experience.
RB: There seems to be a fair amount of mayhem in today’s world, and lots of valid problems to pay attention to in the news. What would you say to empower our readers that we will push through it and continue to grow and improve as a species?
MR: I think it’s worth remembering that good news tends to be gradual, and bad news tends to be sudden. Hence, the good stuff is rarely going to make the news. It’s happening in an inexorable way, as a result of ordinary people exchanging, specializing, collaborating, and innovating, and it’s surprisingly hard to stop it.
Even if you look back to the 1940s, at the end of a world war, there was still a lot of innovation happening. In some ways it feels like we are going through a bad period now. I do worry a lot about the anti-enlightenment values that I see spreading in various parts of the world. But then I remind myself that people are working on innovative projects in the background, and these things are going to come through and push us forward.
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#431189 Researchers Develop New Tech to Predict ...

It is one of the top 10 deadliest diseases in the United States, and it cannot be cured or prevented. But new studies are finding ways to diagnose Alzheimer’s disease in its earliest stages, while some of the latest research says technologies like artificial intelligence can detect dementia years before the first symptoms occur.
These advances, in turn, will help bolster clinical trials seeking a cure or therapies to slow or prevent the disease. Catching Alzheimer’s disease or other forms of dementia early in their progression can help ease symptoms in some cases.
“Often neurodegeneration is diagnosed late when massive brain damage has already occurred,” says professor Francis L Martin at the University of Central Lancashire in the UK, in an email to Singularity Hub. “As we know more about the molecular basis of the disease, there is the possibility of clinical interventions that might slow or halt the progress of the disease, i.e., before brain damage. Extending cognitive ability for even a number of years would have huge benefit.”
Blood Diamond
Martin is the principal investigator on a project that has developed a technique to analyze blood samples to diagnose Alzheimer’s disease and distinguish between other forms of dementia.
The researchers used sensor-based technology with a diamond core to analyze about 550 blood samples. They identified specific chemical bonds within the blood after passing light through the diamond core and recording its interaction with the sample. The results were then compared against blood samples from cases of Alzheimer’s disease and other neurodegenerative diseases, along with those from healthy individuals.
“From a small drop of blood, we derive a fingerprint spectrum. That fingerprint spectrum contains numerical data, which can be inputted into a computational algorithm we have developed,” Martin explains. “This algorithm is validated for prediction of unknown samples. From this we determine sensitivity and specificity. Although not perfect, my clinical colleagues reliably tell me our results are far better than anything else they have seen.”
Martin says the breakthrough is the result of more than 10 years developing sensor-based technologies for routine screening, monitoring, or diagnosing neurodegenerative diseases and cancers.
“My vision was to develop something low-cost that could be readily applied in a typical clinical setting to handle thousands of samples potentially per day or per week,” he says, adding that the technology also has applications in environmental science and food security.
The new test can also distinguish accurately between Alzheimer’s disease and other forms of neurodegeneration, such as Lewy body dementia, which is one of the most common causes of dementia after Alzheimer’s.
“To this point, other than at post-mortem, there has been no single approach towards classifying these pathologies,” Martin notes. “MRI scanning is often used but is labor-intensive, costly, difficult to apply to dementia patients, and not a routine point-of-care test.”
Crystal Ball
Canadian researchers at McGill University believe they can predict Alzheimer’s disease up to two years before its onset using big data and artificial intelligence. They developed an algorithm capable of recognizing the signatures of dementia using a single amyloid PET scan of the brain of patients at risk of developing the disease.
Alzheimer’s is caused by the accumulation of two proteins—amyloid beta and tau. The latest research suggests that amyloid beta leads to the buildup of tau, which is responsible for damaging nerve cells and connections between cells called synapses.
The work was recently published in the journal Neurobiology of Aging.
“Despite the availability of biomarkers capable of identifying the proteins causative of Alzheimer’s disease in living individuals, the current technologies cannot predict whether carriers of AD pathology in the brain will progress to dementia,” Sulantha Mathotaarachchi, lead author on the paper and an expert in artificial neural networks, tells Singularity Hub by email.
The algorithm, trained on a population with amnestic mild cognitive impairment observed over 24 months, proved accurate 84.5 percent of the time. Mathotaarachchi says the algorithm can be trained on different populations for different observational periods, meaning the system can grow more comprehensive with more data.
“The more biomarkers we incorporate, the more accurate the prediction could be,” Mathotaarachchi adds. “However, right now, acquiring [the] required amount of training data is the biggest challenge. … In Alzheimer’s disease, it is known that the amyloid protein deposition occurs decades before symptoms onset.”
Unfortunately, the same process occurs in normal aging as well. “The challenge is to identify the abnormal patterns of deposition that lead to the disease later on,” he says
One of the key goals of the project is to improve the research in Alzheimer’s disease by ensuring those patients with the highest probability to develop dementia are enrolled in clinical trials. That will increase the efficiency of clinical programs, according to Mathotaarachchi.
“One of the most important outcomes from our study was the pilot, online, real-time prediction tool,” he says. “This can be used as a framework for patient screening before recruiting for clinical trials. … If a disease-modifying therapy becomes available for patients, a predictive tool might have clinical applications as well, by providing to the physician information regarding clinical progression.”
Pixel by Pixel Prediction
Private industry is also working toward improving science’s predictive powers when it comes to detecting dementia early. One startup called Darmiyan out of San Francisco claims its proprietary software can pick up signals before the onset of Alzheimer’s disease by up to 15 years.
Darmiyan didn’t respond to a request for comment for this article. Venture Beat reported that the company’s MRI-analyzing software “detects cell abnormalities at a microscopic level to reveal what a standard MRI scan cannot” and that the “software measures and highlights subtle microscopic changes in the brain tissue represented in every pixel of the MRI image long before any symptoms arise.”
Darmiyan claims to have a 90 percent accuracy rate and says its software has been vetted by top academic institutions like New York University, Rockefeller University, and Stanford, according to Venture Beat. The startup is awaiting FDA approval to proceed further but is reportedly working with pharmaceutical companies like Amgen, Johnson & Johnson, and Pfizer on pilot programs.
“Our technology enables smarter drug selection in preclinical animal studies, better patient selection for clinical trials, and much better drug-effect monitoring,” Darmiyan cofounder and CEO Padideh Kamali-Zare told Venture Beat.
An estimated 5.5 million Americans have Alzheimer’s, and one in 10 people over age 65 have been diagnosed with the disease. By mid-century, the number of Alzheimer’s patients could rise to 16 million. Health care costs in 2017 alone are estimated to be $259 billion, and by 2050 the annual price tag could be more than $1 trillion.
In sum, it’s a disease that cripples people and the economy.
Researchers are always after more data as they look to improve outcomes, with the hope of one day developing a cure or preventing the onset of neurodegeneration altogether. If interested in seeing this medical research progress, you can help by signing up on the Brain Health Registry to improve the quality of clinical trials.
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#431155 What It Will Take for Quantum Computers ...

Quantum computers could give the machine learning algorithms at the heart of modern artificial intelligence a dramatic speed up, but how far off are we? An international group of researchers has outlined the barriers that still need to be overcome.
This year has seen a surge of interest in quantum computing, driven in part by Google’s announcement that it will demonstrate “quantum supremacy” by the end of 2017. That means solving a problem beyond the capabilities of normal computers, which the company predicts will take 49 qubits—the quantum computing equivalent of bits.
As impressive as such a feat would be, the demonstration is likely to be on an esoteric problem that stacks the odds heavily in the quantum processor’s favor, and getting quantum computers to carry out practically useful calculations will take a lot more work.
But these devices hold great promise for solving problems in fields as diverse as cryptography or weather forecasting. One application people are particularly excited about is whether they could be used to supercharge the machine learning algorithms already transforming the modern world.
The potential is summarized in a recent review paper in the journal Nature written by a group of experts from the emerging field of quantum machine learning.
“Classical machine learning methods such as deep neural networks frequently have the feature that they can both recognize statistical patterns in data and produce data that possess the same statistical patterns: they recognize the patterns that they produce,” they write.
“This observation suggests the following hope. If small quantum information processors can produce statistical patterns that are computationally difficult for a classical computer to produce, then perhaps they can also recognize patterns that are equally difficult to recognize classically.”
Because of the way quantum computers work—taking advantage of strange quantum mechanical effects like entanglement and superposition—algorithms running on them should in principle be able to solve problems much faster than the best known classical algorithms, a phenomenon known as quantum speedup.
Designing these algorithms is tricky work, but the authors of the review note that there has been significant progress in recent years. They highlight multiple quantum algorithms exhibiting quantum speedup that could act as subroutines, or building blocks, for quantum machine learning programs.
We still don’t have the hardware to implement these algorithms, but according to the researchers the challenge is a technical one and clear paths to overcoming them exist. More challenging, they say, are four fundamental conceptual problems that could limit the applicability of quantum machine learning.
The first two are the input and output problems. Quantum computers, unsurprisingly, deal with quantum data, but the majority of the problems humans want to solve relate to the classical world. Translating significant amounts of classical data into the quantum systems can take so much time it can cancel out the benefits of the faster processing speeds, and the same is true of reading out the solution at the end.
The input problem could be mitigated to some extent by the development of quantum random access memory (qRAM)—the equivalent to RAM in a conventional computer used to provide the machine with quick access to its working memory. A qRAM can be configured to store classical data but allow the quantum computers to access all that information simultaneously as a superposition, which is required for a variety of quantum algorithms. But the authors note this is still a considerable engineering challenge and may not be sustainable for big data problems.
Closely related to the input/output problem is the costing problem. At present, the authors say very little is known about how many gates—or operations—a quantum machine learning algorithm will require to solve a given problem when operated on real-world devices. It’s expected that on highly complex problems they will offer considerable improvements over classical computers, but it’s not clear how big problems have to be before this becomes apparent.
Finally, whether or when these advantages kick in may be hard to prove, something the authors call the benchmarking problem. Claiming that a quantum algorithm can outperform any classical machine learning approach requires extensive testing against these other techniques that may not be feasible.
They suggest that this could be sidestepped by lowering the standards quantum machine learning algorithms are currently held to. This makes sense, as it doesn’t really matter whether an algorithm is intrinsically faster than all possible classical ones, as long as it’s faster than all the existing ones.
Another way of avoiding some of these problems is to apply these techniques directly to quantum data, the actual states generated by quantum systems and processes. The authors say this is probably the most promising near-term application for quantum machine learning and has the added benefit that any insights can be fed back into the design of better hardware.
“This would enable a virtuous cycle of innovation similar to that which occurred in classical computing, wherein each generation of processors is then leveraged to design the next-generation processors,” they conclude.
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