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#433288 The New AI Tech Turning Heads in Video ...

A new technique using artificial intelligence to manipulate video content gives new meaning to the expression “talking head.”

An international team of researchers showcased the latest advancement in synthesizing facial expressions—including mouth, eyes, eyebrows, and even head position—in video at this month’s 2018 SIGGRAPH, a conference on innovations in computer graphics, animation, virtual reality, and other forms of digital wizardry.

The project is called Deep Video Portraits. It relies on a type of AI called generative adversarial networks (GANs) to modify a “target” actor based on the facial and head movement of a “source” actor. As the name implies, GANs pit two opposing neural networks against one another to create a realistic talking head, right down to the sneer or raised eyebrow.

In this case, the adversaries are actually working together: One neural network generates content, while the other rejects or approves each effort. The back-and-forth interplay between the two eventually produces a realistic result that can easily fool the human eye, including reproducing a static scene behind the head as it bobs back and forth.

The researchers say the technique can be used by the film industry for a variety of purposes, from editing facial expressions of actors for matching dubbed voices to repositioning an actor’s head in post-production. AI can not only produce highly realistic results, but much quicker ones compared to the manual processes used today, according to the researchers. You can read the full paper of their work here.

“Deep Video Portraits shows how such a visual effect could be created with less effort in the future,” said Christian Richardt, from the University of Bath’s motion capture research center CAMERA, in a press release. “With our approach, even the positioning of an actor’s head and their facial expression could be easily edited to change camera angles or subtly change the framing of a scene to tell the story better.”

AI Tech Different Than So-Called “Deepfakes”
The work is far from the first to employ AI to manipulate video and audio. At last year’s SIGGRAPH conference, researchers from the University of Washington showcased their work using algorithms that inserted audio recordings from a person in one instance into a separate video of the same person in a different context.

In this case, they “faked” a video using a speech from former President Barack Obama addressing a mass shooting incident during his presidency. The AI-doctored video injects the audio into an unrelated video of the president while also blending the facial and mouth movements, creating a pretty credible job of lip synching.

A previous paper by many of the same scientists on the Deep Video Portraits project detailed how they were first able to manipulate a video in real time of a talking head (in this case, actor and former California governor Arnold Schwarzenegger). The Face2Face system pulled off this bit of digital trickery using a depth-sensing camera that tracked the facial expressions of an Asian female source actor.

A less sophisticated method of swapping faces using a machine learning software dubbed FakeApp emerged earlier this year. Predictably, the tech—requiring numerous photos of the source actor in order to train the neural network—was used for more juvenile pursuits, such as injecting a person’s face onto a porn star.

The application gave rise to the term “deepfakes,” which is now used somewhat ubiquitously to describe all such instances of AI-manipulated video—much to the chagrin of some of the researchers involved in more legitimate uses.

Fighting AI-Created Video Forgeries
However, the researchers are keenly aware that their work—intended for benign uses such as in the film industry or even to correct gaze and head positions for more natural interactions through video teleconferencing—could be used for nefarious purposes. Fake news is the most obvious concern.

“With ever-improving video editing technology, we must also start being more critical about the video content we consume every day, especially if there is no proof of origin,” said Michael Zollhöfer, a visiting assistant professor at Stanford University and member of the Deep Video Portraits team, in the press release.

Toward that end, the research team is training the same adversarial neural networks to spot video forgeries. They also strongly recommend that developers clearly watermark videos that are edited through AI or otherwise, and denote clearly what part and element of the scene was modified.

To catch less ethical users, the US Department of Defense, through the Defense Advanced Research Projects Agency (DARPA), is supporting a program called Media Forensics. This latest DARPA challenge enlists researchers to develop technologies to automatically assess the integrity of an image or video, as part of an end-to-end media forensics platform.

The DARPA official in charge of the program, Matthew Turek, did tell MIT Technology Review that so far the program has “discovered subtle cues in current GAN-manipulated images and videos that allow us to detect the presence of alterations.” In one reported example, researchers have targeted eyes, which rarely blink in the case of “deepfakes” like those created by FakeApp, because the AI is trained on still pictures. That method would seem to be less effective to spot the sort of forgeries created by Deep Video Portraits, which appears to flawlessly match the entire facial and head movements between the source and target actors.

“We believe that the field of digital forensics should and will receive a lot more attention in the future to develop approaches that can automatically prove the authenticity of a video clip,” Zollhöfer said. “This will lead to ever-better approaches that can spot such modifications even if we humans might not be able to spot them with our own eyes.

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Posted in Human Robots

#433282 The 4 Waves of AI: Who Will Own the ...

Recently, I picked up Kai-Fu Lee’s newest book, AI Superpowers.

Kai-Fu Lee is one of the most plugged-in AI investors on the planet, managing over $2 billion between six funds and over 300 portfolio companies in the US and China.

Drawing from his pioneering work in AI, executive leadership at Microsoft, Apple, and Google (where he served as founding president of Google China), and his founding of VC fund Sinovation Ventures, Lee shares invaluable insights about:

The four factors driving today’s AI ecosystems;
China’s extraordinary inroads in AI implementation;
Where autonomous systems are headed;
How we’ll need to adapt.

With a foothold in both Beijing and Silicon Valley, Lee looks at the power balance between Chinese and US tech behemoths—each turbocharging new applications of deep learning and sweeping up global markets in the process.

In this post, I’ll be discussing Lee’s “Four Waves of AI,” an excellent framework for discussing where AI is today and where it’s going. I’ll also be featuring some of the hottest Chinese tech companies leading the charge, worth watching right now.

I’m super excited that this Tuesday, I’ve scored the opportunity to sit down with Kai-Fu Lee to discuss his book in detail via a webinar.

With Sino-US competition heating up, who will own the future of technology?

Let’s dive in.

The First Wave: Internet AI
In this first stage of AI deployment, we’re dealing primarily with recommendation engines—algorithmic systems that learn from masses of user data to curate online content personalized to each one of us.

Think Amazon’s spot-on product recommendations, or that “Up Next” YouTube video you just have to watch before getting back to work, or Facebook ads that seem to know what you’ll buy before you do.

Powered by the data flowing through our networks, internet AI leverages the fact that users automatically label data as we browse. Clicking versus not clicking; lingering on a web page longer than we did on another; hovering over a Facebook video to see what happens at the end.

These cascades of labeled data build a detailed picture of our personalities, habits, demands, and desires: the perfect recipe for more tailored content to keep us on a given platform.

Currently, Lee estimates that Chinese and American companies stand head-to-head when it comes to deployment of internet AI. But given China’s data advantage, he predicts that Chinese tech giants will have a slight lead (60-40) over their US counterparts in the next five years.

While you’ve most definitely heard of Alibaba and Baidu, you’ve probably never stumbled upon Toutiao.

Starting out as a copycat of America’s wildly popular Buzzfeed, Toutiao reached a valuation of $20 billion by 2017, dwarfing Buzzfeed’s valuation by more than a factor of 10. But with almost 120 million daily active users, Toutiao doesn’t just stop at creating viral content.

Equipped with natural-language processing and computer vision, Toutiao’s AI engines survey a vast network of different sites and contributors, rewriting headlines to optimize for user engagement, and processing each user’s online behavior—clicks, comments, engagement time—to curate individualized news feeds for millions of consumers.

And as users grow more engaged with Toutiao’s content, the company’s algorithms get better and better at recommending content, optimizing headlines, and delivering a truly personalized feed.

It’s this kind of positive feedback loop that fuels today’s AI giants surfing the wave of internet AI.

The Second Wave: Business AI
While internet AI takes advantage of the fact that netizens are constantly labeling data via clicks and other engagement metrics, business AI jumps on the data that traditional companies have already labeled in the past.

Think banks issuing loans and recording repayment rates; hospitals archiving diagnoses, imaging data, and subsequent health outcomes; or courts noting conviction history, recidivism, and flight.

While we humans make predictions based on obvious root causes (strong features), AI algorithms can process thousands of weakly correlated variables (weak features) that may have much more to do with a given outcome than the usual suspects.

By scouting out hidden correlations that escape our linear cause-and-effect logic, business AI leverages labeled data to train algorithms that outperform even the most veteran of experts.

Apply these data-trained AI engines to banking, insurance, and legal sentencing, and you get minimized default rates, optimized premiums, and plummeting recidivism rates.

While Lee confidently places America in the lead (90-10) for business AI, China’s substantial lag in structured industry data could actually work in its favor going forward.

In industries where Chinese startups can leapfrog over legacy systems, China has a major advantage.

Take Chinese app Smart Finance, for instance.

While Americans embraced credit and debit cards in the 1970s, China was still in the throes of its Cultural Revolution, largely missing the bus on this technology.

Fast forward to 2017, and China’s mobile payment spending outnumbered that of Americans’ by a ratio of 50 to 1. Without the competition of deeply entrenched credit cards, mobile payments were an obvious upgrade to China’s cash-heavy economy, embraced by 70 percent of China’s 753 million smartphone users by the end of 2017.

But by leapfrogging over credit cards and into mobile payments, China largely left behind the notion of credit.

And here’s where Smart Finance comes in.

An AI-powered app for microfinance, Smart Finance depends almost exclusively on its algorithms to make millions of microloans. For each potential borrower, the app simply requests access to a portion of the user’s phone data.

On the basis of variables as subtle as your typing speed and battery percentage, Smart Finance can predict with astounding accuracy your likelihood of repaying a $300 loan.

Such deployments of business AI and internet AI are already revolutionizing our industries and individual lifestyles. But still on the horizon lie two even more monumental waves— perception AI and autonomous AI.

The Third Wave: Perception AI
In this wave, AI gets an upgrade with eyes, ears, and myriad other senses, merging the digital world with our physical environments.

As sensors and smart devices proliferate through our homes and cities, we are on the verge of entering a trillion-sensor economy.

Companies like China’s Xiaomi are putting out millions of IoT-connected devices, and teams of researchers have already begun prototyping smart dust—solar cell- and sensor-geared particulates that can store and communicate troves of data anywhere, anytime.

As Kai-Fu explains, perception AI “will bring the convenience and abundance of the online world into our offline reality.” Sensor-enabled hardware devices will turn everything from hospitals to cars to schools into online-merge-offline (OMO) environments.

Imagine walking into a grocery store, scanning your face to pull up your most common purchases, and then picking up a virtual assistant (VA) shopping cart. Having pre-loaded your data, the cart adjusts your usual grocery list with voice input, reminds you to get your spouse’s favorite wine for an upcoming anniversary, and guides you through a personalized store route.

While we haven’t yet leveraged the full potential of perception AI, China and the US are already making incredible strides. Given China’s hardware advantage, Lee predicts China currently has a 60-40 edge over its American tech counterparts.

Now the go-to city for startups building robots, drones, wearable technology, and IoT infrastructure, Shenzhen has turned into a powerhouse for intelligent hardware, as I discussed last week. Turbocharging output of sensors and electronic parts via thousands of factories, Shenzhen’s skilled engineers can prototype and iterate new products at unprecedented scale and speed.

With the added fuel of Chinese government support and a relaxed Chinese attitude toward data privacy, China’s lead may even reach 80-20 in the next five years.

Jumping on this wave are companies like Xiaomi, which aims to turn bathrooms, kitchens, and living rooms into smart OMO environments. Having invested in 220 companies and incubated 29 startups that produce its products, Xiaomi surpassed 85 million intelligent home devices by the end of 2017, making it the world’s largest network of these connected products.

One KFC restaurant in China has even teamed up with Alipay (Alibaba’s mobile payments platform) to pioneer a ‘pay-with-your-face’ feature. Forget cash, cards, and cell phones, and let OMO do the work.

The Fourth Wave: Autonomous AI
But the most monumental—and unpredictable—wave is the fourth and final: autonomous AI.

Integrating all previous waves, autonomous AI gives machines the ability to sense and respond to the world around them, enabling AI to move and act productively.

While today’s machines can outperform us on repetitive tasks in structured and even unstructured environments (think Boston Dynamics’ humanoid Atlas or oncoming autonomous vehicles), machines with the power to see, hear, touch and optimize data will be a whole new ballgame.

Think: swarms of drones that can selectively spray and harvest entire farms with computer vision and remarkable dexterity, heat-resistant drones that can put out forest fires 100X more efficiently, or Level 5 autonomous vehicles that navigate smart roads and traffic systems all on their own.

While autonomous AI will first involve robots that create direct economic value—automating tasks on a one-to-one replacement basis—these intelligent machines will ultimately revamp entire industries from the ground up.

Kai-Fu Lee currently puts America in a commanding lead of 90-10 in autonomous AI, especially when it comes to self-driving vehicles. But Chinese government efforts are quickly ramping up the competition.

Already in China’s Zhejiang province, highway regulators and government officials have plans to build China’s first intelligent superhighway, outfitted with sensors, road-embedded solar panels and wireless communication between cars, roads and drivers.

Aimed at increasing transit efficiency by up to 30 percent while minimizing fatalities, the project may one day allow autonomous electric vehicles to continuously charge as they drive.

A similar government-fueled project involves Beijing’s new neighbor Xiong’an. Projected to take in over $580 billion in infrastructure spending over the next 20 years, Xiong’an New Area could one day become the world’s first city built around autonomous vehicles.

Baidu is already working with Xiong’an’s local government to build out this AI city with an environmental focus. Possibilities include sensor-geared cement, computer vision-enabled traffic lights, intersections with facial recognition, and parking lots-turned parks.

Lastly, Lee predicts China will almost certainly lead the charge in autonomous drones. Already, Shenzhen is home to premier drone maker DJI—a company I’ll be visiting with 24 top executives later this month as part of my annual China Platinum Trip.

Named “the best company I have ever encountered” by Chris Anderson, DJI owns an estimated 50 percent of the North American drone market, supercharged by Shenzhen’s extraordinary maker movement.

While the long-term Sino-US competitive balance in fourth wave AI remains to be seen, one thing is certain: in a matter of decades, we will witness the rise of AI-embedded cityscapes and autonomous machines that can interact with the real world and help solve today’s most pressing grand challenges.

Join Me
Webinar with Dr. Kai-Fu Lee: Dr. Kai-Fu Lee — one of the world’s most respected experts on AI — and I will discuss his latest book AI Superpowers: China, Silicon Valley, and the New World Order. Artificial Intelligence is reshaping the world as we know it. With U.S.-Sino competition heating up, who will own the future of technology? Register here for the free webinar on September 4th, 2018 from 11:00am–12:30pm PST.

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Posted in Human Robots

#432549 Your Next Pilot Could Be Drone Software

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 ...

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.

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Posted in Human Robots

#432487 Can We Make a Musical Turing Test?

As artificial intelligence advances, we’re encountering the same old questions. How much of what we consider to be fundamentally human can be reduced to an algorithm? Can we create something sufficiently advanced that people can no longer distinguish between the two? This, after all, is the idea behind the Turing Test, which has yet to be passed.

At first glance, you might think music is beyond the realm of algorithms. Birds can sing, and people can compose symphonies. Music is evocative; it makes us feel. Very often, our intense personal and emotional attachments to music are because it reminds us of our shared humanity. We are told that creative jobs are the least likely to be automated. Creativity seems fundamentally human.

But I think above all, we view it as reductionist sacrilege: to dissect beautiful things. “If you try to strangle a skylark / to cut it up, see how it works / you will stop its heart from beating / you will stop its mouth from singing.” A human musician wrote that; a machine might be able to string words together that are happy or sad; it might even be able to conjure up a decent metaphor from the depths of some neural network—but could it understand humanity enough to produce art that speaks to humans?

Then, of course, there’s the other side of the debate. Music, after all, has a deeply mathematical structure; you can train a machine to produce harmonics. “In the teachings of Pythagoras and his followers, music was inseparable from numbers, which were thought to be the key to the whole spiritual and physical universe,” according to Grout in A History of Western Music. You might argue that the process of musical composition cannot be reduced to a simple algorithm, yet musicians have often done so. Mozart, with his “Dice Music,” used the roll of a dice to decide how to order musical fragments; creativity through an 18th-century random number generator. Algorithmic music goes back a very long way, with the first papers on the subject from the 1960s.

Then there’s the techno-enthusiast side of the argument. iTunes has 26 million songs, easily more than a century of music. A human could never listen to and learn from them all, but a machine could. It could also memorize every note of Beethoven. Music can be converted into MIDI files, a nice chewable data format that allows even a character-by-character neural net you can run on your computer to generate music. (Seriously, even I could get this thing working.)

Indeed, generating music in the style of Bach has long been a test for AI, and you can see neural networks gradually learn to imitate classical composers while trying to avoid overfitting. When an algorithm overfits, it essentially starts copying the existing music, rather than being inspired by it but creating something similar: a tightrope the best human artists learn to walk. Creativity doesn’t spring from nowhere; even maverick musical geniuses have their influences.

Does a machine have to be truly ‘creative’ to produce something that someone would find valuable? To what extent would listeners’ attitudes change if they thought they were hearing a human vs. an AI composition? This all suggests a musical Turing Test. Of course, it already exists. In fact, it’s run out of Dartmouth, the school that hosted that first, seminal AI summer conference. This year, the contest is bigger than ever: alongside the PoetiX, LimeriX and LyriX competitions for poetry and lyrics, there’s a DigiKidLit competition for children’s literature (although you may have reservations about exposing your children to neural-net generated content… it can get a bit surreal).

There’s also a pair of musical competitions, including one for original compositions in different genres. Key genres and styles are represented by Charlie Parker for Jazz and the Bach chorales for classical music. There’s also a free composition, and a contest where a human and an AI try to improvise together—the AI must respond to a human spontaneously, in real time, and in a musically pleasing way. Quite a challenge! In all cases, if any of the generated work is indistinguishable from human performers, the neural net has passed the Turing Test.

Did they? Here’s part of 2017’s winning sonnet from Charese Smiley and Hiroko Bretz:

The large cabin was in total darkness.
Come marching up the eastern hill afar.
When is the clock on the stairs dangerous?
Everything seemed so near and yet so far.
Behind the wall silence alone replied.
Was, then, even the staircase occupied?
Generating the rhymes is easy enough, the sentence structure a little trickier, but what’s impressive about this sonnet is that it sticks to a single topic and appears to be a more coherent whole. I’d guess they used associated “lexical fields” of similar words to help generate something coherent. In a similar way, most of the more famous examples of AI-generated music still involve some amount of human control, even if it’s editorial; a human will build a song around an AI-generated riff, or select the most convincing Bach chorale from amidst many different samples.

We are seeing strides forward in the ability of AI to generate human voices and human likenesses. As the latter example shows, in the fake news era people have focused on the dangers of this tech– but might it also be possible to create a virtual performer, trained on a dataset of their original music? Did you ever want to hear another Beatles album, or jam with Miles Davis? Of course, these things are impossible—but could we create a similar experience that people would genuinely value? Even, to the untrained eye, something indistinguishable from the real thing?

And if it did measure up to the real thing, what would this mean? Jaron Lanier is a fascinating technology writer, a critic of strong AI, and a believer in the power of virtual reality to change the world and provide truly meaningful experiences. He’s also a composer and a musical aficionado. He pointed out in a recent interview that translation algorithms, by reducing the amount of work translators are commissioned to do, have, in some sense, profited from stolen expertise. They were trained on huge datasets purloined from human linguists and translators. If you can train an AI on someone’s creative output and it produces new music, who “owns” it?

Although companies that offer AI music tools are starting to proliferate, and some groups will argue that the musical Turing test has been passed already, AI-generated music is hardly racing to the top of the pop charts just yet. Even as the line between human-composed and AI-generated music starts to blur, there’s still a gulf between the average human and musical genius. In the next few years, we’ll see how far the current techniques can take us. It may be the case that there’s something in the skylark’s song that can’t be generated by machines. But maybe not, and then this song might need an extra verse.

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