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#432671 Stuff 3.0: The Era of Programmable ...

Posted on April 30, 2018 by Android

It’s the end of a long day in your apartment in the early 2040s. You decide your work is done for the day, stand up from your desk, and yawn. “Time for a film!” you say. The house responds to your cues. The desk splits into hundreds of tiny pieces, which flow behind you and take on shape again as a couch. The computer screen you were working on flows up the wall and expands into a flat projection screen. You relax into the couch and, after a few seconds, a remote control surfaces from one of its arms.

In a few seconds flat, you’ve gone from a neatly-equipped office to a home cinema…all within the same four walls. Who needs more than one room?

This is the dream of those who work on “programmable matter.”

In his recent book about AI, Max Tegmark makes a distinction between three different levels of computational sophistication for organisms. Life 1.0 is single-celled organisms like bacteria; here, hardware is indistinguishable from software. The behavior of the bacteria is encoded into its DNA; it cannot learn new things.

Life 2.0 is where humans live on the spectrum. We are more or less stuck with our hardware, but we can change our software by choosing to learn different things, say, Spanish instead of Italian. Much like managing space on your smartphone, your brain’s hardware will allow you to download only a certain number of packages, but, at least theoretically, you can learn new behaviors without changing your underlying genetic code.

Life 3.0 marks a step-change from this: creatures that can change both their hardware and software in something like a feedback loop. This is what Tegmark views as a true artificial intelligence—one that can learn to change its own base code, leading to an explosion in intelligence. Perhaps, with CRISPR and other gene-editing techniques, we could be using our “software” to doctor our “hardware” before too long.

Programmable matter extends this analogy to the things in our world: what if your sofa could “learn” how to become a writing desk? What if, instead of a Swiss Army knife with dozens of tool attachments, you just had a single tool that “knew” how to become any other tool you could require, on command? In the crowded cities of the future, could houses be replaced by single, OmniRoom apartments? It would save space, and perhaps resources too.

Such are the dreams, anyway.

But when engineering and manufacturing individual gadgets is such a complex process, you can imagine that making stuff that can turn into many different items can be extremely complicated. Professor Skylar Tibbits at MIT referred to it as 4D printing in a TED Talk, and the website for his research group, the Self-Assembly Lab, excitedly claims, “We have also identified the key ingredients for self-assembly as a simple set of responsive building blocks, energy and interactions that can be designed within nearly every material and machining process available. Self-assembly promises to enable breakthroughs across many disciplines, from biology to material science, software, robotics, manufacturing, transportation, infrastructure, construction, the arts, and even space exploration.”

Naturally, their projects are still in the early stages, but the Self-Assembly Lab and others are genuinely exploring just the kind of science fiction applications we mooted.

For example, there’s the cell-phone self-assembly project, which brings to mind eerie, 24/7 factories where mobile phones assemble themselves from 3D printed kits without human or robotic intervention. Okay, so the phones they’re making are hardly going to fly off the shelves as fashion items, but if all you want is something that works, it could cut manufacturing costs substantially and automate even more of the process.

One of the major hurdles to overcome in making programmable matter a reality is choosing the right fundamental building blocks. There’s a very important balance to strike. To create fine details, you need to have things that aren’t too big, so as to keep your rearranged matter from being too lumpy. This might make the building blocks useless for certain applications—for example, if you wanted to make tools for fine manipulation. With big pieces, it might be difficult to simulate a range of textures. On the other hand, if the pieces are too small, different problems can arise.

Imagine a setup where each piece is a small robot. You have to contain the robot’s power source and its brain, or at least some kind of signal-generator and signal-processor, all in the same compact unit. Perhaps you can imagine that one might be able to simulate a range of textures and strengths by changing the strength of the “bond” between individual units—your desk might need to be a little bit more firm than your bed, which might be nicer with a little more give.

Early steps toward creating this kind of matter have been taken by those who are developing modular robots. There are plenty of different groups working on this, including MIT, Lausanne, and the University of Brussels.

In the latter configuration, one individual robot acts as a centralized decision-maker, referred to as the brain unit, but additional robots can autonomously join the brain unit as and when needed to change the shape and structure of the overall system. Although the system is only ten units at present, it’s a proof-of-concept that control can be orchestrated over a modular system of robots; perhaps in the future, smaller versions of the same thing could be the components of Stuff 3.0.

You can imagine that with machine learning algorithms, such swarms of robots might be able to negotiate obstacles and respond to a changing environment more easily than an individual robot (those of you with techno-fear may read “respond to a changing environment” and imagine a robot seamlessly rearranging itself to allow a bullet to pass straight through without harm).

Speaking of robotics, the form of an ideal robot has been a subject of much debate. In fact, one of the major recent robotics competitions—DARPA’s Robotics Challenge—was won by a robot that could adapt, beating Boston Dynamics’ infamous ATLAS humanoid with the simple addition of a wheel that allowed it to drive as well as walk.

Rather than building robots into a humanoid shape (only sometimes useful), allowing them to evolve and discover the ideal form for performing whatever you’ve tasked them to do could prove far more useful. This is particularly true in disaster response, where expensive robots can still be more valuable than humans, but conditions can be very unpredictable and adaptability is key.

Further afield, many futurists imagine “foglets” as the tiny nanobots that will be capable of constructing anything from raw materials, somewhat like the “Santa Claus machine.” But you don’t necessarily need anything quite so indistinguishable from magic to be useful. Programmable matter that can respond and adapt to its surroundings could be used in all kinds of industrial applications. How about a pipe that can strengthen or weaken at will, or divert its direction on command?

We’re some way off from being able to order our beds to turn into bicycles. As with many tech ideas, it may turn out that the traditional low-tech solution is far more practical and cost-effective, even as we can imagine alternatives. But as the march to put a chip in every conceivable object goes on, it seems certain that inanimate objects are about to get a lot more animated.

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Posted in Human Robots

#432646 How Fukushima Changed Japanese Robotics ...

Posted on April 26, 2018 by Android

In March 2011, Japan was hit by a catastrophic earthquake that triggered a terrible tsunami. Thousands were killed and billions of dollars of damage was done in one of the worst disasters of modern times. For a few perilous weeks, though, the eyes of the world were focused on the Fukushima Daiichi nuclear power plant. Its safety systems were unable to cope with the tsunami damage, and there were widespread fears of another catastrophic meltdown that could spread radiation over several countries, like the Chernobyl disaster in the 1980s. A heroic effort that included dumping seawater into the reactor core prevented an even bigger catastrophe. As it is, a hundred thousand people are still evacuated from the area, and it will likely take many years and hundreds of billions of dollars before the region is safe.

Because radiation is so dangerous to humans, the natural solution to the Fukushima disaster was to send in robots to monitor levels of radiation and attempt to begin the clean-up process. The techno-optimists in Japan had discovered a challenge, deep in the heart of that reactor core, that even their optimism could not solve. The radiation fried the circuits of the robots that were sent in, even those specifically designed and built to deal with the Fukushima catastrophe. The power plant slowly became a vast robot graveyard. While some robots initially saw success in measuring radiation levels around the plant—and, recently, a robot was able to identify the melted uranium fuel at the heart of the disaster—hopes of them playing a substantial role in the clean-up are starting to diminish.



In Tokyo’s neon Shibuya district, it can sometimes seem like it’s brighter at night than it is during the daytime. In karaoke booths on the twelfth floor—because everything is on the twelfth floor—overlooking the brightly-lit streets, businessmen unwind by blasting out pop hits. It can feel like the most artificial place on Earth; your senses are dazzled by the futuristic techno-optimism. Stock footage of the area has become symbolic of futurism and modernity.

Japan has had a reputation for being a nation of futurists for a long time. We’ve already described how tech giant Softbank, headed by visionary founder Masayoshi Son, is investing billions in a technological future, including plans for the world’s largest solar farm.

When Google sold pioneering robotics company Boston Dynamics in 2017, Softbank added it to their portfolio, alongside the famous Nao and Pepper robots. Some may think that Son is taking a gamble in pursuing a robotics project even Google couldn’t succeed in, but this is a man who lost nearly everything in the dot-com crash of 2000. The fact that even this reversal didn’t dent his optimism and faith in technology is telling. But how long can it last?

The failure of Japan’s robots to deal with the immense challenge of Fukushima has sparked something of a crisis of conscience within the industry. Disaster response is an obvious stepping-stone technology for robots. Initially, producing a humanoid robot will be very costly, and the robot will be less capable than a human; building a robot to wait tables might not be particularly economical yet. Building a robot to do jobs that are too dangerous for humans is far more viable. Yet, at Fukushima, in one of the most advanced nations in the world, many of the robots weren’t up to the task.

Nowhere was this crisis more felt than Honda; the company had developed ASIMO, which stunned the world in 2000 and continues to fascinate as an iconic humanoid robot. Despite all this technological advancement, however, Honda knew that ASIMO was still too unreliable for the real world.

It was Fukushima that triggered a sea-change in Honda’s approach to robotics. Two years after the disaster, there were rumblings that Honda was developing a disaster robot, and in October 2017, the prototype was revealed to the public for the first time. It’s not yet ready for deployment in disaster zones, however. Interestingly, the creators chose not to give it dexterous hands but instead to assume that remotely-operated tools fitted to the robot would be a better solution for the range of circumstances it might encounter.

This shift in focus for humanoid robots away from entertainment and amusement like ASIMO, and towards being practically useful, has been mirrored across the world.

In 2015, also inspired by the Fukushima disaster and the lack of disaster-ready robots, the DARPA Robotics Challenge tested humanoid robots with a range of tasks that might be needed in emergency response, such as driving cars, opening doors, and climbing stairs. The Terminator-like ATLAS robot from Boston Dynamics, alongside Korean robot HUBO, took many of the plaudits, and CHIMP also put in an impressive display by being able to right itself after falling.

Yet the DARPA Robotics Challenge showed us just how far the robots are from truly being as useful as we’d like, or maybe even as we would imagine. Many robots took hours to complete the tasks, which were highly idealized to suit them. Climbing stairs proved a particular challenge. Those who watched were more likely to see a robot that had fallen over, struggling to get up, rather than heroic superbots striding in to save the day. The “striding” proved a particular problem, with the fastest robot HUBO managing this by resorting to wheels in its knees when the legs weren’t necessary.

Fukushima may have brought a sea-change over futuristic Japan, but before robots will really begin to enter our everyday lives, they will need to prove their worth. In the interim, aerial drone robots designed to examine infrastructure damage after disasters may well see earlier deployment and more success.

It’s a considerable challenge.

Building a humanoid robot is expensive; if these multi-million-dollar machines can’t help in a crisis, people may begin to question the worth of investing in them in the first place (unless your aim is just to make viral videos). This could lead to a further crisis of confidence among the Japanese, who are starting to rely on humanoid robotics as a solution to the crisis of the aging population. The Japanese government, as part of its robots strategy, has already invested $44 million in their development.

But if they continue to fail when put to the test, that will raise serious concerns. In Tokyo’s Akihabara district, you can see all kinds of flash robotic toys for sale in the neon-lit superstores, and dancing, acting robots like Robothespian can entertain crowds all over the world. But if we want these machines to be anything more than toys—partners, helpers, even saviors—more work needs to be done.

At the same time, those who participated in the DARPA Robotics Challenge in 2015 won’t be too concerned if people were underwhelmed by the performance of their disaster relief robots. Back in 2004, nearly every participant in the DARPA Grand Challenge crashed, caught fire, or failed on the starting line. To an outside observer, the whole thing would have seemed like an unmitigated disaster, and a pointless investment. What was the task in 2004? Developing a self-driving car. A lot can change in a decade.

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Posted in Human Robots

#432568 Tech Optimists See a Golden ...

Posted on April 22, 2018 by Android

Technology evangelists dream about a future where we’re all liberated from the more mundane aspects of our jobs by artificial intelligence. Other futurists go further, imagining AI will enable us to become superhuman, enhancing our intelligence, abandoning our mortal bodies, and uploading ourselves to the cloud.

Paradise is all very well, although your mileage may vary on whether these scenarios are realistic or desirable. The real question is, how do we get there?

Economist John Maynard Keynes notably argued in favor of active intervention when an economic crisis hits, rather than waiting for the markets to settle down to a more healthy equilibrium in the long run. His rebuttal to critics was, “In the long run, we are all dead.” After all, if it takes 50 years of upheaval and economic chaos for things to return to normality, there has been an immense amount of human suffering first.

Similar problems arise with the transition to a world where AI is intimately involved in our lives. In the long term, automation of labor might benefit the human species immensely. But in the short term, it has all kinds of potential pitfalls, especially in exacerbating inequality within societies where AI takes on a larger role. A new report from the Institute for Public Policy Research has deep concerns about the future of work.

Uneven Distribution
While the report doesn’t foresee the same gloom and doom of mass unemployment that other commentators have considered, the concern is that the gains in productivity and economic benefits from AI will be unevenly distributed. In the UK, jobs that account for £290 billion worth of wages in today’s economy could potentially be automated with current technology. But these are disproportionately jobs held by people who are already suffering from social inequality.

Low-wage jobs are five times more likely to be automated than high-wage jobs. A greater proportion of jobs held by women are likely to be automated. The solution that’s often suggested is that people should simply “retrain”; but if no funding or assistance is provided, this burden is too much to bear. You can’t expect people to seamlessly transition from driving taxis to writing self-driving car software without help. As we have already seen, inequality is exacerbated when jobs that don’t require advanced education (even if they require a great deal of technical skill) are the first to go.

No Room for Beginners
Optimists say algorithms won’t replace humans, but will instead liberate us from the dull parts of our jobs. Lawyers used to have to spend hours trawling through case law to find legal precedents; now AI can identify the most relevant documents for them. Doctors no longer need to look through endless scans and perform diagnostic tests; machines can do this, leaving the decision-making to humans. This boosts productivity and provides invaluable tools for workers.

But there are issues with this rosy picture. If humans need to do less work, the economic incentive is for the boss to reduce their hours. Some of these “dull, routine” parts of the job were traditionally how people getting into the field learned the ropes: paralegals used to look through case law, but AI may render them obsolete. Even in the field of journalism, there’s now software that will rewrite press releases for publication, traditionally something close to an entry-level task. If there are no entry-level jobs, or if entry-level now requires years of training, the result is to exacerbate inequality and reduce social mobility.

Automating Our Biases
The adoption of algorithms into employment has already had negative impacts on equality. Cathy O’Neil, mathematics PhD from Harvard, raises these concerns in her excellent book Weapons of Math Destruction. She notes that algorithms designed by humans often encode the biases of that society, whether they’re racial or based on gender and sexuality.

Google’s search engine advertises more executive-level jobs to users it thinks are male. AI programs predict that black offenders are more likely to re-offend than white offenders; they receive correspondingly longer sentences. It needn’t necessarily be that bias has been actively programmed; perhaps the algorithms just learn from historical data, but this means they will perpetuate historical inequalities.

Take candidate-screening software HireVue, used by many major corporations to assess new employees. It analyzes “verbal and non-verbal cues” of candidates, comparing them to employees that historically did well. Either way, according to Cathy O’Neil, they are “using people’s fear and trust of mathematics to prevent them from asking questions.” With no transparency or understanding of how the algorithm generates its results, and no consensus over who’s responsible for the results, discrimination can occur automatically, on a massive scale.

Combine this with other demographic trends. In rich countries, people are living longer. An increasing burden will be placed on a shrinking tax base to support that elderly population. A recent study said that due to the accumulation of wealth in older generations, millennials stand to inherit more than any previous generation, but it won’t happen until they’re in their 60s. Meanwhile, those with savings and capital will benefit as the economy shifts: the stock market and GDP will grow, but wages and equality will fall, a situation that favors people who are already wealthy.

Even in the most dramatic AI scenarios, inequality is exacerbated. If someone develops a general intelligence that’s near-human or super-human, and they manage to control and monopolize it, they instantly become immensely wealthy and powerful. If the glorious technological future that Silicon Valley enthusiasts dream about is only going to serve to make the growing gaps wider and strengthen existing unfair power structures, is it something worth striving for?

What Makes a Utopia?
We urgently need to redefine our notion of progress. Philosophers worry about an AI that is misaligned—the things it seeks to maximize are not the things we want maximized. At the same time, we measure the development of our countries by GDP, not the quality of life of workers or the equality of opportunity in the society. Growing wealth with increased inequality is not progress.

Some people will take the position that there are always winners and losers in society, and that any attempt to redress the inequalities of our society will stifle economic growth and leave everyone worse off. Some will see this as an argument for a new economic model, based around universal basic income. Any moves towards this will need to take care that it’s affordable, sustainable, and doesn’t lead towards an entrenched two-tier society.

Walter Schiedel’s book The Great Leveller is a huge survey of inequality across all of human history, from the 21st century to prehistoric cave-dwellers. He argues that only revolutions, wars, and other catastrophes have historically reduced inequality: a perfect example is the Black Death in Europe, which (by reducing the population and therefore the labor supply that was available) increased wages and reduced inequality. Meanwhile, our solution to the financial crisis of 2007-8 may have only made the problem worse.

But in a world of nuclear weapons, of biowarfare, of cyberwarfare—a world of unprecedented, complex, distributed threats—the consequences of these “safety valves” could be worse than ever before. Inequality increases the risk of global catastrophe, and global catastrophes could scupper any progress towards the techno-utopia that the utopians dream of. And a society with entrenched inequality is no utopia at all.

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Posted in Human Robots

#432549 Your Next Pilot Could Be Drone Software

Posted on April 20, 2018 by Android

Would you get on a plane that didn’t have a human pilot in the cockpit? Half of air travelers surveyed in 2017 said they would not, even if the ticket was cheaper. Modern pilots do such a good job that almost any air accident is big news, such as the Southwest engine disintegration on April 17.

But stories of pilot drunkenness, rants, fights and distraction, however rare, are reminders that pilots are only human. Not every plane can be flown by a disaster-averting pilot, like Southwest Capt. Tammie Jo Shults or Capt. Chesley “Sully” Sullenberger. But software could change that, equipping every plane with an extremely experienced guidance system that is always learning more.

In fact, on many flights, autopilot systems already control the plane for basically all of the flight. And software handles the most harrowing landings—when there is no visibility and the pilot can’t see anything to even know where he or she is. But human pilots are still on hand as backups.

A new generation of software pilots, developed for self-flying vehicles, or drones, will soon have logged more flying hours than all humans have—ever. By combining their enormous amounts of flight data and experience, drone-control software applications are poised to quickly become the world’s most experienced pilots.

Drones That Fly Themselves
Drones come in many forms, from tiny quad-rotor copter toys to missile-firing winged planes, or even 7-ton aircraft that can stay aloft for 34 hours at a stretch.

When drones were first introduced, they were flown remotely by human operators. However, this merely substitutes a pilot on the ground for one aloft. And it requires significant communications bandwidth between the drone and control center, to carry real-time video from the drone and to transmit the operator’s commands.

Many newer drones no longer need pilots; some drones for hobbyists and photographers can now fly themselves along human-defined routes, leaving the human free to sightsee—or control the camera to get the best view.

University researchers, businesses, and military agencies are now testing larger and more capable drones that will operate autonomously. Swarms of drones can fly without needing tens or hundreds of humans to control them. And they can perform coordinated maneuvers that human controllers could never handle.

Could humans control these 1,218 drones all together?

Whether flying in swarms or alone, the software that controls these drones is rapidly gaining flight experience.

Importance of Pilot Experience
Experience is the main qualification for pilots. Even a person who wants to fly a small plane for personal and noncommercial use needs 40 hours of flying instruction before getting a private pilot’s license. Commercial airline pilots must have at least 1,000 hours before even serving as a co-pilot.

On-the-ground training and in-flight experience prepare pilots for unusual and emergency scenarios, ideally to help save lives in situations like the “Miracle on the Hudson.” But many pilots are less experienced than “Sully” Sullenberger, who saved his planeload of people with quick and creative thinking. With software, though, every plane can have on board a pilot with as much experience—if not more. A popular software pilot system, in use in many aircraft at once, could gain more flight time each day than a single human might accumulate in a year.

As someone who studies technology policy as well as the use of artificial intelligence for drones, cars, robots, and other uses, I don’t lightly suggest handing over the controls for those additional tasks. But giving software pilots more control would maximize computers’ advantages over humans in training, testing, and reliability.

Training and Testing Software Pilots
Unlike people, computers will follow sets of instructions in software the same way every time. That lets developers create instructions, test reactions, and refine aircraft responses. Testing could make it far less likely, for example, that a computer would mistake the planet Venus for an oncoming jet and throw the plane into a steep dive to avoid it.

The most significant advantage is scale: Rather than teaching thousands of individual pilots new skills, updating thousands of aircraft would require only downloading updated software.

These systems would also need to be thoroughly tested—in both real-life situations and in simulations—to handle a wide range of aviation situations and to withstand cyberattacks. But once they’re working well, software pilots are not susceptible to distraction, disorientation, fatigue, or other human impairments that can create problems or cause errors even in common situations.

Rapid Response and Adaptation
Already, aircraft regulators are concerned that human pilots are forgetting how to fly on their own and may have trouble taking over from an autopilot in an emergency.

In the “Miracle on the Hudson” event, for example, a key factor in what happened was how long it took for the human pilots to figure out what had happened—that the plane had flown through a flock of birds, which had damaged both engines—and how to respond. Rather than the approximately one minute it took the humans, a computer could have assessed the situation in seconds, potentially saving enough time that the plane could have landed on a runway instead of a river.

Aircraft damage can pose another particularly difficult challenge for human pilots: It can change what effects the controls have on its flight. In cases where damage renders a plane uncontrollable, the result is often tragedy. A sufficiently advanced automated system could make minute changes to the aircraft’s steering and use its sensors to quickly evaluate the effects of those movements—essentially learning how to fly all over again with a damaged plane.

Boosting Public Confidence
The biggest barrier to fully automated flight is psychological, not technical. Many people may not want to trust their lives to computer systems. But they might come around when reassured that the software pilot has tens, hundreds, or thousands more hours of flight experience than any human pilot.

Other autonomous technologies, too, are progressing despite public concerns. Regulators and lawmakers are allowing self-driving cars on the roads in many states. But more than half of Americans don’t want to ride in one, largely because they don’t trust the technology. And only 17 percent of travelers around the world are willing to board a plane without a pilot. However, as more people experience self-driving cars on the road and have drones deliver them packages, it is likely that software pilots will gain in acceptance.

The airline industry will certainly be pushing people to trust the new systems: Automating pilots could save tens of billions of dollars a year. And the current pilot shortage means software pilots may be the key to having any airline service to smaller destinations.

Both Boeing and Airbus have made significant investments in automated flight technology, which would remove or reduce the need for human pilots. Boeing has actually bought a drone manufacturer and is looking to add software pilot capabilities to the next generation of its passenger aircraft. (Other tests have tried to retrofit existing aircraft with robotic pilots.)

One way to help regular passengers become comfortable with software pilots—while also helping to both train and test the systems—could be to introduce them as co-pilots working alongside human pilots. Planes would be operated by software from gate to gate, with the pilots instructed to touch the controls only if the system fails. Eventually pilots could be removed from the aircraft altogether, just like they eventually were from the driverless trains that we routinely ride in airports around the world.

This article was originally published on The Conversation. Read the original article.

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Posted in Human Robots

#432539 10 Amazing Things You Can Learn From ...

Posted on April 18, 2018 by Android

Hardly a day goes by without a research study or article published talking sh*t—or more precisely, talking about the gut microbiome. When it comes to cutting-edge innovations in medicine, all signs point to the microbiome. Maybe we should have listened to Hippocrates: “All disease begins in the gut.”

Your microbiome is mostly located in your gut and contains trillions of little guys and gals called microbes. If you want to optimize your health, biohack your body, make progress against chronic disease, or know which foods are right for you—almost all of this information can be found in your microbiome.

My company, Viome, offers technology to measure your microscopic organisms and their behavior at a molecular level. Think of it as the Instagram of your inner world. A snapshot of what’s happening inside your body. New research about the microbiome is changing our understanding of who we are as humans and how the human body functions.

It turns out the microbiome may be mission control for your body and mind. Your healthy microbiome is part best friend, part power converter, part engine, and part pharmacist. At Viome, we’re working to analyze these microbial functions and recommend a list of personalized food and supplements to keep these internal complex machines in a finely tuned balance.

We now have more information than ever before about what your microbiome is doing, and it’s going to help you and the rest of the world do a whole lot better. The new insights emerging from microbiome research are changing our perception of what keeps us healthy and what makes us sick. This new understanding of the microbiome activities may put an end to conflicting food advice and make fad diets a thing of the past.

What are these new insights showing us? The information is nothing short of mind-blowing. The value of your poop just got an upgrade.

Here are some of the amazing things we’ve learned from our work at Viome.

1. Was Popeye wrong? Why “health food” isn’t necessarily healthy.
Each week there is a new fad diet released, discussed, and followed. The newest “research” shows that this is now the superfood to eat for everyone. But, too often, the fad diet is just a regurgitation of what worked for one person and shouldn’t be followed by everyone else.

For example, we’ve been told to eat our greens and that greens and nuts are “anti-inflammatory,” but this is actually not always true. Spinach, bran, rhubarb, beets, nuts, and nut butters all contain oxalate. We now know that oxalate-containing food can be harmful, unless you have the microbes present that can metabolize it into a non-harmful substance.

30% of Viome customers do not have the microbes to metabolize oxalates properly. In other words, “healthy foods” like spinach are actually not healthy for these people.

Looks like not everyone should follow Popeye’s food plan.

2. Aren’t foods containing “antioxidants” always good for everyone?
Just like oxalates, polyphenols in foods are usually considered very healthy, but unless you have microbes that utilize specific polyphenols, you may not get full benefit from them. One example is a substance found in these foods called ellagic acid. We can detect if your microbiome is metabolizing ellagic acid and converting it into urolithin A. It is only the urolithin A that has anti-inflammatory and antioxidant effects. Without the microbes to do this conversion you will not benefit from the ellagic acid in foods.

Examples: Walnuts, raspberries, pomegranate, blackberries, pecans, and cranberries all contain ellagic acid.

We have analyzed tens of thousands of people, and only about 50% of the people actually benefit from eating more foods containing ellagic acid.

3. You’re probably eating too much protein (and it may be causing inflammation).
When you think high-protein diet, you think paleo, keto, and high-performance diets.

Protein is considered good for you. It helps build muscle and provide energy—but if you eat too much, it can cause inflammation and decrease longevity.

We can analyze the activity of your microbiome to determine if you are eating too much protein that feeds protein-fermenting bacteria like Alistipes putredinis and Tannerella forsythia, and if these organisms are producing harmful substances such as ammonia, hydrogen sulfide, p-cresol, or putrescine. These substances can damage your gut lining and lead to things like leaky gut.

4. Something’s fishy. Are “healthy foods” causing heart disease?
Choline in certain foods can get converted by bacteria into a substance called trimethylamine (TMA) that is associated with heart disease when it gets absorbed into your body and converted to TMAO. However, TMA conversion doesn’t happen in individuals without these types of bacteria in their microbiome.

We can see the TMA production pathways and many of the gammaproteobacteria that do this conversion.

What foods contain choline? Liver, salmon, chickpeas, split peas, eggs, navy beans, peanuts, and many others.

Before you decide to go full-on pescatarian or paleo, you may want to check if your microbiome is producing TMA with that salmon or steak.

5. Hold up, Iron Man. We can see inflammation from too much iron.
Minerals like iron in your food can, in certain inflammatory microbial environments, promote growth of pathogens like Esherichia, Shigella, and Salmonella.

Maybe it wasn’t just that raw chicken that gave you food poisoning, but your toxic microbiome that made you sick.

On the other hand, when you don’t have enough iron, you could become anemic leading to weakness and shortness of breath.

So, just like Iron Man, it’s about finding your balance so that you can fly.

6. Are you anxious or stressed? Your poop will tell you.
Our gut and brain are connected via the vagus nerve. A large majority of neurotransmitters are either produced or consumed by our microbiome. In fact, some 90% of all serotonin (a feel-good neurotransmitter) is produced by your gut microbiome and not by your brain.

When you have a toxic microbiome that’s producing a large amount of toxins like hydrogen sulfide, the lining of your gut starts to deteriorate into what’s known as leaky gut. Think of leaky gut as your gut not having healthy borders or boundaries. And when this happens, all kinds of disease can emerge. When the barrier of the gut breaks down, it starts a chain reaction causing low-grade chronic inflammation—which has been identified as a potential source of depression and higher levels of anxiety, in addition to many other chronic diseases.

We’re not saying you shouldn’t meditate, but if you want to get the most out of your meditation and really reduce your stress levels, make sure you are eating the right food that promotes a healthy microbiome.

7. Your microbiome is better than Red Bull.
If you want more energy, get your microbiome back into balance.

No you don’t need three pots of coffee to keep you going, you just need a balanced microbiome.

Your microbiome is responsible for calorie extraction, or creating energy, through pathways such as the Tricarboxylic acid cycle. Our bodies depend on the energy that our microbiome produces.

How much energy we get from our food is dependent on how efficient our microbiome is at converting the food into energy. High-performing microbiomes are excellent at converting food into energy. This is great when you are an athlete and need the extra energy, but if you don’t use up the energy it may be the source of some of those unwanted pounds.

If the microbes can’t or won’t metabolize the glucose (sugar) that you eat, it will be stored as fat. If the microbes are extracting too many calories from your food or producing lipopolysaccharides (LPS) and causing metabolic endotoxemia leading to activation of toll-like receptors and insulin resistance you may end up storing what you eat as fat.

Think of your microbiome as Doc Brown’s car from the future—it can take pretty much anything and turn it into fuel if it’s strong and resilient enough.

8. We can see your joint pain in your poop.
Got joint pain? Your microbiome can tell you why.

Lipopolysaccharide (LPS) is a key pro-inflammatory molecule made by some of your microbes. If your microbes are making too much LPS, it can wreak havoc on your immune system by putting it into overdrive. When your immune system goes on the warpath there is often collateral damage to your joints and other body parts.

Perhaps balancing your microbiome is a better solution than reaching for the glucosamine. Think of your microbiome as the top general of your immune army. It puts your immune system through basic training and determines when it goes to war.

Ideally, your immune system wins the quick battle and gets some rest, but sometimes if your microbiome keeps it on constant high alert it becomes a long, drawn-out war resulting in chronic inflammation and chronic diseases.

Are you really “getting older” or is your microbiome just making you “feel” older because it keeps giving warnings to your immune system ultimately leading to chronic pain?

Before you throw in the towel on your favorite activities, check your microbiome. And, if you have anything with “itis” in it, it’s possible that when you balance your microbiome the inflammation from your “itis” will be reduced.

9. Your gut is doing the talking for your mouth.
When you have low stomach acid, your mouth bacteria makes it down to your GI tract.

Stomach acid is there to protect you from the bacteria in your mouth and the parasites and fungi that are in your food. If you don’t have enough of it, the bacteria in your mouth will invade your gut. This invasion is associated with and a risk factor for autoimmune disease and inflammation in the gut.

We are learning that low stomach acid is perhaps one of the major causes of chronic disease. This stomach acid is essential to kill mouth bacteria and help us digest our food.

What kinds of things cause low stomach acid? Stress and antacids like Nexium, Zantac, and Prilosec.

10. Carbs can be protein precursors.
Rejoice! Perhaps carbs aren’t as bad as we thought (as long as your microbiome is up to the task). We can see if some of the starches you eat can be made into amino acids by the microbiome.

Our microbiome makes 20% of our branched-chain amino acids (BCAAs) for us, and it will adapt to make these vital BCAAs for us in almost any way it can.

Essentially, your microbiome is hooking up carbons and hydrogens into different formulations of BCAAs, depending on what you feed it. The microbiome is excellent at adapting and pivoting based on the food you feed it and the environment that it’s in.

So, good news: Carbs are protein precursors, as long as you have the right microbiome.

Stop Talking Sh*t Now
Your microbiome is a world class entrepreneur that can take low-grade sources of food and turn them into valuable and useable energy.

You have a best friend and confidant within you that is working wonders to make sure you have energy and that all of your needs are met.

And, just like a best friend, if you take great care of your microbiome, it will take great care of you.

Given the research emerging daily about the microbiome and its importance on your quality of life, prioritizing the health of your microbiome is essential.

When you have a healthy microbiome, you’ll have a healthy life.

It’s now clear that some of the greatest insights for your health will come from your poop.

It’s time to stop talking sh*t and get your sh*t together. Your life may depend on it.

Viome can help you identify what your microbiome is actually doing. The combination of Viome’s metatranscriptomic technology and cutting-edge artificial intelligence is paving a brand new path forward for microbiome health.

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