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Big data, personalized medicine, artificial intelligence. String these three buzzphrases together, and what do you have?
A system that may revolutionize the future of healthcare, by bringing sophisticated health data directly to patients for them to ponder, digest, and act upon—and potentially stop diseases in their tracks.
At Singularity University’s Exponential Medicine conference in San Diego this week, Dr. Ran Balicer, director of the Clalit Research Institute in Israel, painted a futuristic picture of how big data can merge with personalized healthcare into an app-based system in which the patient is in control.
Dr. Ran Balicer at Exponential Medicine
Picture this: instead of going to a physician with your ailments, your doctor calls you with some bad news: “Within six hours, you’re going to have a heart attack. So why don’t you come into the clinic and we can fix that.” Crisis averted.
Following the treatment, you’re at home monitoring your biomarkers, lab test results, and other health information through an app with a clean, beautiful user interface. Within the app, you can observe how various health-influencing life habits—smoking, drinking, insufficient sleep—influence your chance of future cardiovascular disease risks by toggling their levels up or down.
There’s more: you can also set a health goal within the app—for example, stop smoking—which automatically informs your physician. The app will then suggest pharmaceuticals to help you ditch the nicotine and automatically sends the prescription to your local drug store. You’ll also immediately find a list of nearby support groups that can help you reach your health goal.
With this hefty dose of AI, you’re in charge of your health—in fact, probably more so than under current healthcare systems.
Sound fantastical? In fact, this type of preemptive care is already being provided in some countries, including Israel, at a massive scale, said Balicer. By mining datasets with deep learning and other powerful AI tools, we can predict the future—and put it into the hands of patients.
The Israeli Advantage
In order to apply big data approaches to medicine, you first need a giant database.
Israel is ahead of the game in this regard. With decades of electronic health records aggregated within a central warehouse, Israel offers a wealth of health-related data on the scale of millions of people and billions of data points. The data is incredibly multiplex, covering lab tests, drugs, hospital admissions, medical procedures, and more.
One of Balicer’s early successes was an algorithm that predicts diabetes, which allowed the team to notify physicians to target their care. Clalit has also been busy digging into data that predicts winter pneumonia, osteoporosis, and a long list of other preventable diseases.
So far, Balicer’s predictive health system has only been tested on a pilot group of patients, but he is expecting to roll out the platform to all patients in the database in the next few months.
Truly Personalized Medicine
To Balicer, whatever a machine can do better, it should be welcomed to do. AI diagnosticians have already enjoyed plenty of successes—but their collaboration remains mostly with physicians, at a point in time when the patient is already ill.
A particularly powerful use of AI in medicine is to bring insights and trends directly to the patient, such that they can take control over their own health and medical care.
For example, take the problem of tailored drug dosing. Current drug doses are based on average results conducted during clinical trials—the dosing is not tailored for any specific patient’s genetic and health makeup. But what if a doctor had already seen millions of other patients similar to your case, and could generate dosing recommendations more relevant to you based on that particular group of patients?
Such personalized recommendations are beyond the ability of any single human doctor. But with the help of AI, which can quickly process massive datasets to find similarities, doctors may soon be able to prescribe individually-tailored medications.
Tailored treatment doesn’t stop there. Another issue with pharmaceuticals and treatment regimes is that they often come with side effects: potentially health-threatening reactions that may, or may not, happen to you based on your biometrics.
Back in 2017, the New England Journal of Medicine launched the SPRINT Data Analysis Challenge, which urged physicians and data analysts to identify novel clinical findings using shared clinical trial data.
Working with Dr. Noa Dagan at the Clalit Research Institute, Balicer and team developed an algorithm that recommends whether or not a patient receives a particularly intensive treatment regime for hypertension.
Rather than simply looking at one outcome—normalized blood pressure—the algorithm takes into account an individual’s specific characteristics, laying out the treatment’s predicted benefits and harms for a particular patient.
“We built thousands of models for each patient to comprehensively understand the impact of the treatment for the individual; for example, a reduced risk for stroke and cardiovascular-related deaths could be accompanied by an increase in serious renal failure,” said Balicer. “This approach allows a truly personalized balance—allowing patients and their physicians to ultimately decide if the risks of the treatment are worth the benefits.”
This is already personalized medicine at its finest. But Balicer didn’t stop there.
We are not the sum of our biologics and medical stats, he said. A truly personalized approach needs to take a patient’s needs and goals and the sacrifices and tradeoffs they’re willing to make into account, rather than having the physician make decisions for them.
Balicer’s preventative system adds this layer of complexity by giving weights to different outcomes based on patients’ input of their own health goals. Rather than blindly following big data, the system holistically integrates the patient’s opinion to make recommendations.
Balicer’s system is just one example of how AI can truly transform personalized health care. The next big challenge is to work with physicians to further optimize these systems, in a way that doctors can easily integrate them into their workflow and embrace the technology.
“Health systems will not be replaced by algorithms, rest assured,” concluded Balicer, “but health systems that don’t use algorithms will be replaced by those that do.”
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It’s common to hear phrases like ‘machine learning’ and ‘artificial intelligence’ and believe that somehow, someone has managed to replicate a human mind inside a computer. This, of course, is untrue—but part of the reason this idea is so pervasive is because the metaphor of human learning and intelligence has been quite useful in explaining machine learning and artificial intelligence.
Indeed, some AI researchers maintain a close link with the neuroscience community, and inspiration runs in both directions. But the metaphor can be a hindrance to people trying to explain machine learning to those less familiar with it. One of the biggest risks of conflating human and machine intelligence is that we start to hand over too much agency to machines. For those of us working with software, it’s essential that we remember the agency is human—it’s humans who build these systems, after all.
It’s worth unpacking the key differences between machine and human intelligence. While there are certainly similarities, it’s by looking at what makes them different that we can better grasp how artificial intelligence works, and how we can build and use it effectively.
Central to the metaphor that links human and machine learning is the concept of a neural network. The biggest difference between a human brain and an artificial neural net is the sheer scale of the brain’s neural network. What’s crucial is that it’s not simply the number of neurons in the brain (which reach into the billions), but more precisely, the mind-boggling number of connections between them.
But the issue runs deeper than questions of scale. The human brain is qualitatively different from an artificial neural network for two other important reasons: the connections that power it are analogue, not digital, and the neurons themselves aren’t uniform (as they are in an artificial neural network).
This is why the brain is such a complex thing. Even the most complex artificial neural network, while often difficult to interpret and unpack, has an underlying architecture and principles guiding it (this is what we’re trying to do, so let’s construct the network like this…).
Intricate as they may be, neural networks in AIs are engineered with a specific outcome in mind. The human mind, however, doesn’t have the same degree of intentionality in its engineering. Yes, it should help us do all the things we need to do to stay alive, but it also allows us to think critically and creatively in a way that doesn’t need to be programmed.
The Beautiful Simplicity of AI
The fact that artificial intelligence systems are so much simpler than the human brain is, ironically, what enables AIs to deal with far greater computational complexity than we can.
Artificial neural networks can hold much more information and data than the human brain, largely due to the type of data that is stored and processed in a neural network. It is discrete and specific, like an entry on an excel spreadsheet.
In the human brain, data doesn’t have this same discrete quality. So while an artificial neural network can process very specific data at an incredible scale, it isn’t able to process information in the rich and multidimensional manner a human brain can. This is the key difference between an engineered system and the human mind.
Despite years of research, the human mind still remains somewhat opaque. This is because the analog synaptic connections between neurons are almost impenetrable to the digital connections within an artificial neural network.
Speed and Scale
Consider what this means in practice. The relative simplicity of an AI allows it to do a very complex task very well, and very quickly. A human brain simply can’t process data at scale and speed in the way AIs need to if they’re, say, translating speech to text, or processing a huge set of oncology reports.
Essential to the way AI works in both these contexts is that it breaks data and information down into tiny constituent parts. For example, it could break sounds down into phonetic text, which could then be translated into full sentences, or break images into pieces to understand the rules of how a huge set of them is composed.
Humans often do a similar thing, and this is the point at which machine learning is most like human learning; like algorithms, humans break data or information into smaller chunks in order to process it.
But there’s a reason for this similarity. This breakdown process is engineered into every neural network by a human engineer. What’s more, the way this process is designed will be down to the problem at hand. How an artificial intelligence system breaks down a data set is its own way of ‘understanding’ it.
Even while running a highly complex algorithm unsupervised, the parameters of how an AI learns—how it breaks data down in order to process it—are always set from the start.
Human Intelligence: Defining Problems
Human intelligence doesn’t have this set of limitations, which is what makes us so much more effective at problem-solving. It’s the human ability to ‘create’ problems that makes us so good at solving them. There’s an element of contextual understanding and decision-making in the way humans approach problems.
AIs might be able to unpack problems or find new ways into them, but they can’t define the problem they’re trying to solve.
Algorithmic insensitivity has come into focus in recent years, with an increasing number of scandals around bias in AI systems. Of course, this is caused by the biases of those making the algorithms, but underlines the point that algorithmic biases can only be identified by human intelligence.
Human and Artificial Intelligence Should Complement Each Other
We must remember that artificial intelligence and machine learning aren’t simply things that ‘exist’ that we can no longer control. They are built, engineered, and designed by us. This mindset puts us in control of the future, and makes algorithms even more elegant and remarkable.
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Shopping is becoming less and less of a consumer experience—or, for many, less of a chore—as the list of things that can be bought online and delivered to our homes grows to include, well, almost anything you can think of. An Israeli startup is working to make shopping and deliveries even faster and cheaper—and they’re succeeding.
Last week, CommonSense Robotics announced the launch of its first autonomous micro-fulfillment center in Tel Aviv. The company claims the facility is the smallest of its type in the world at 6,000 square feet. For comparison’s sake—most fulfillment hubs that incorporate robotics are at least 120,000 square feet. Amazon’s upcoming facility in Bessemer, Alabama will be a massive 855,000 square feet.
The thing about a building whose square footage is in the hundred-thousands is, you can fit a lot of stuff inside it, but there aren’t many places you can fit the building itself, especially not in major urban areas. So most fulfillment centers are outside cities, which means more time and more money to get your Moroccan oil shampoo, or your vegetable garden starter kit, or your 100-pack of organic protein bars from that fulfillment center to your front door.
CommonSense Robotics built the Tel Aviv center in an area that was previously thought too small for warehouse infrastructure. “In order to fit our site into small, tight urban spaces, we’ve designed every single element of it to optimize for space efficiency,” said Avital Sterngold, VP of operations. Using a robotic sorting system that includes hundreds of robots, plus AI software that assigns them specific tasks, the facility can prepare orders in less than five minutes end-to-end.
It’s not all automated, though—there’s still some human labor in the mix. The robots fetch goods and bring them to a team of people, who then pack the individual orders.
CommonSense raised $20 million this year in a funding round led by Palo Alto-based Playground Global. The company hopes to expand its operations to the US and UK in 2019. Its business model is to charge retailers a fee for each order fulfilled, while maintaining ownership and operation of the fulfillment centers. The first retailers to jump on the bandwagon were Super-Pharm, a drugstore chain, and Rami Levy, a retail supermarket chain.
“Staying competitive in today’s market is anchored by delivering orders quickly and determining how to fulfill and deliver orders efficiently, which are always the most complex aspects of any ecommerce operation. With robotics, we will be able to fulfill and deliver orders in under one hour, all while saving costs on said fulfillment and delivery,” said Super-Pharm VP Yossi Cohen. “Before CommonSense Robotics, we offered our customers next-day home delivery. With this partnership, we are now able to offer our customers same-day delivery and will very soon be offering them one-hour delivery.”
Long live the instant gratification economy—and the increasingly sophisticated technology that’s enabling it.
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“You really can’t justify tuna in Chicago as a source of sustenance.” That’s according to Dr. Sylvia Earle, a National Geographic Society Explorer who was the first female chief scientist at NOAA. She came to the Good Food Institute’s Good Food Conference to deliver a call to action around global food security, agriculture, environmental protection, and the future of consumer choice.
It seems like all options should be on the table to feed an exploding population threatened by climate change. But Dr. Earle, who is faculty at Singularity University, drew a sharp distinction between seafood for sustenance versus seafood as a choice. “There is this widespread claim that we must take large numbers of wildlife from the sea in order to have food security.”
A few minutes later, Dr. Earle directly addressed those of us in the audience. “We know the value of a dead fish,” she said. That’s market price. “But what is the value of a live fish in the ocean?”
That’s when my mind blew open. What is the value—or put another way, the cost—of using the ocean as a major source of protein for humans? How do you put a number on that? Are we talking about dollars and cents, or about something far larger?
Dr. Liz Specht of the Good Food Institute drew the audience’s attention to a strange imbalance. Currently, about half of the yearly global catch of seafood comes from aquaculture. That means that the other half is wild caught. It’s hard to imagine half of your meat coming directly from the forests and the plains, isn’t it? And yet half of the world’s seafood comes from direct harvesting of the oceans, by way of massive overfishing, a terrible toll from bycatch, a widespread lack of regulation and enforcement, and even human rights violations such as slavery.
The search for solutions is on, from both within the fishing industry and from external agencies such as governments and philanthropists. Could there be another way?
Makers of plant-based seafood and clean seafood think they know how to feed the global demand for seafood without harming the ocean. These companies are part of a larger movement harnessing technology to reduce our reliance on wild and domesticated animals—and all the environmental, economic, and ethical issues that come with it.
Producers of plant-based seafood (20 or so currently) are working to capture the taste, texture, and nutrition of conventional seafood without the limitations of geography or the health of a local marine population. Like with plant-based meat, makers of plant-based seafood are harnessing food science and advances in chemistry, biology, and engineering to make great food. The industry’s strategy? Start with what the consumer wants, and then figure out how to achieve that great taste through technology.
So how does plant-based seafood taste? Pretty good, as it turns out. (The biggest benefit of a food-oriented conference is that your mouth is always full!)
I sampled “tuna” salad made from Good Catch Food’s fish-free tuna, which is sourced from legumes; the texture was nearly indistinguishable from that of flaked albacore tuna, and there was no lingering fishy taste to overpower my next bite. In a blind taste test, I probably wouldn’t have known that I was eating a plant-based seafood alternative. Next I reached for Ocean Hugger Food’s Ahimi, a tomato-based alternative to raw tuna. I adore Hawaiian poke, so I was pleasantly surprised when my Ahimi-based poke captured the bite of ahi tuna. It wasn’t quite as delightfully fatty as raw tuna, but with wild tuna populations struggling to recover from a 97% decline in numbers from 40 years ago, Ahimi is a giant stride in the right direction.
These plant-based alternatives aren’t the only game in town, however.
The clean meat industry, which has also been called “cultured meat” or “cellular agriculture,” isn’t seeking to lure consumers away from animal protein. Instead, cells are sampled from live animals and grown in bioreactors—meaning that no animal is slaughtered to produce real meat.
Clean seafood is poised to piggyback off platforms developed for clean meat; growing fish cells in the lab should rely on the same processes as growing meat cells. I know of four companies currently focusing on seafood (Finless Foods, Wild Type, BlueNalu, and Seafuture Sustainable Biotech), and a few more are likely to emerge from stealth mode soon.
Importantly, there’s likely not much difference between growing clean seafood from the top or the bottom of the food chain. Tuna, for example, are top predators that must grow for at least 10 years before they’re suitable as food. Each year, a tuna consumes thousands of pounds of other fish, shellfish, and plankton. That “long tail of groceries,” said Dr. Earle, “is a pretty expensive choice.” Excitingly, clean tuna would “level the trophic playing field,” as Dr. Specht pointed out.
All this is only the beginning of what might be possible.
Combining synthetic biology with clean meat and seafood means that future products could be personalized for individual taste preferences or health needs, by reprogramming the DNA of the cells in the lab. Industries such as bioremediation and biofuels likely have a lot to teach us about sourcing new ingredients and flavors from algae and marine plants. By harnessing rapid advances in automation, robotics, sensors, machine vision, and other big-data analytics, the manufacturing and supply chains for clean seafood could be remarkably safe and robust. Clean seafood would be just that: clean, without pathogens, parasites, or the plastic threatening to fill our oceans, meaning that you could enjoy it raw.
What about price? Dr. Mark Post, a pioneer in clean meat who is also faculty at Singularity University, estimated that 80% of clean-meat production costs come from the expensive medium in which cells are grown—and some ingredients in the medium are themselves sourced from animals, which misses the point of clean meat. Plus, to grow a whole cut of food, like a fish fillet, the cells need to be coaxed into a complex 3D structure with various cell types like muscle cells and fat cells. These two technical challenges must be solved before clean meat and seafood give consumers the experience they want, at the price they want.
In this respect clean seafood has an unusual edge. Most of what we know about growing animal cells in the lab comes from the research and biomedical industries (from tissue engineering, for example)—but growing cells to replace an organ has different constraints than growing cells for food. The link between clean seafood and biomedicine is less direct, empowering innovators to throw out dogma and find novel reagents, protocols, and equipment to grow seafood that captures the tastes, textures, smells, and overall experience of dining by the ocean.
Asked to predict when we’ll be seeing clean seafood in the grocery store, Lou Cooperhouse the CEO of BlueNalu, explained that the challenges aren’t only in the lab: marketing, sales, distribution, and communication with consumers are all critical. As Niya Gupta, the founder of Fork & Goode, said, “The question isn’t ‘can we do it’, but ‘can we sell it’?”
The good news is that the clean meat and seafood industry is highly collaborative; there are at least two dozen companies in the space, and they’re all talking to each other. “This is an ecosystem,” said Dr. Uma Valeti, the co-founder of Memphis Meats. “We’re not competing with each other.” It will likely be at least a decade before science, business, and regulation enable clean meat and seafood to routinely appear on restaurant menus, let alone market shelves.
Until then, think carefully about your food choices. Meditate on Dr. Earle’s question: “What is the real cost of that piece of halibut?” Or chew on this from Dr. Ricardo San Martin, of the Sutardja Center at the University of California, Berkeley: “Food is a system of meanings, not an object.” What are you saying when you choose your food, about your priorities and your values and how you want the future to look? Do you think about animal welfare? Most ethical regulations don’t extend to marine life, and if you don’t think that ocean creatures feel pain, consider the lobster.
Seafood is largely an acquired taste, since most of us don’t live near the water. Imagine a future in which children grow up loving the taste of delicious seafood but without hurting a living animal, the ocean, or the global environment.
Do more than imagine. As Dr. Earle urged us, “Convince the public at large that this is a really cool idea.”
Ahimi (Ocean Hugger)
New Wave Foods
Heritage Health Food
The Vegetarian Butcher
Table based on Figure 5 of the report “An Ocean of Opportunity: Plant-based and clean seafood for sustainable oceans without sacrifice,” from The Good Food Institute.
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