The A.I. Diet is an algorithm that tells you how to eat to live a longer and healthier life.  As you know, an algorithm is a process or set of rules followed by a computer used in calculations or problem-solving, so the A.I diet is all based on science. 

Cardiologist, Dr Eric Topol participated in a 2-week experiment using a smartphone app tracking: all consumption (food, drink and medication), sleep and exercise. A sensor monitoring blood-glucose levels was added and a stool sample was sent in for an assessment of his gut microbiome. His data was amassed with similar input from 1000+ others and analysed by AI to create a personalised diet algorithm.

The final verdict showed unexpected results: In the sweets category: Cheesecake was given an A grade, but whole-wheat fig bars were given a C grade. In fruits: Strawberries were an A+ for him, but grapefruit a C. For legumes: Mixed nuts were an A+, but veggie burgers a C.

The Results:

The results didn’t match with what he thought he knew about healthy eating. For the most part the highly recommended foods, given an A+/- grade like cheesecake, cheese Danish, Bratwurst, nuts and strawberries were ones he thought unhealthy or really disliked, while those rated C+/-, like oatmeal, melon, baked squash, veggie burgers, whole-wheat fig bars and grapefruit were typically among his favourites. He realised he had a big problem. To avoid glucose spikes he would have to make some changes and sacrifices in his diet.

General Conclusions:

• That despite decades of diet fads and government-issued food pyramids, we know surprisingly little about the science of nutrition.
• That it is difficult to attain reliable and accurate data because of the human factor.
• That the diet industry has been undermined by the food industry, which tries to exert influence over the research it funds.
• Studies have serially contradicted one another.
• That there is a central flaw in the whole premise of fad diets: the idea that there is one optimal diet for all people. It contradicts the remarkable heterogeneity of human metabolism, microbiome (the 40 trillion bacteria from about 1,000 species that reside in our guts) and environment (lifestyle, family history, medical conditions, medications) that make each of us unique.
• That in fact a good diet has to be individualised. And there is a need to factor in cost and convenience.

Break Throughs:

The first major development in this field occurred a few years ago at the Weizmann Institute of Science in Israel published in the journal Cell a landmark paper titled “Personalised Nutrition by Prediction of Glycaemic Responses.” The spikes in blood-glucose levels in response to eating are only one signature of our individualised response to food. But these represent the first objective proof that we do indeed respond quite differently to eating the same foods in the same amounts.

Using machine learning, a subtype of artificial intelligence, the billions of data points were analysed to see what drove the glucose response to specific foods for each individual. In that way, an algorithm was built without the biases of the scientists.

More than a hundred factors were found to be involved in glycaemic response, but notably food wasn’t the key determinant. Instead it was the gut bacteria. Here were two simultaneous firsts in nutritional science: one, the discovery that our gut microbiome plays such a big role in our unique response to food intake, and the other that this discovery was made possible by A.I. The journal ran an accompanying editorial titled “Siri, What Should I Eat?”

There is now a commercial version of this test though it is much more limited: It only analyses a gut microbiome sample, without monitoring glucose or what you eat.

There are other efforts underway in the field as well. In some continuing nutrition studies, smartphone photos of participants’ plates of food are being processed by deep learning, another subtype of A.I., to accurately determine what they are eating. This avoids the hassle of manually logging in the data and the use of unreliable food diaries (as long as participants remember to take the picture).

But that is a single type of data. Rather than just create a simple calorie calculator, processing all of our data: activity, sleep, level of stress, medications, genome, microbiome and glucose — from multiple devices, like skin patches and smartwatches is needed. With advanced algorithms, this is eminently doable taking all aspects of dieting into account including, body composition, adherence, weight change rate trends, hunger and fatigue. The key thing to note is that it is not just gathering this data. An algorithm is constantly assessing how these data points trend with each other and making informed adjustments.

In the next few years, you could have a virtual health coach that is deep learning about your relevant health metrics and providing you with customised dietary recommendations.

The Apps:

• Forget – My Fitness Pal and the tedium of searching for and the recording of foods and calories
• Calorie Mama AI or BiteSnap makes instant nutrition and calories estimates from your meals by just snapping a food photo
• Eat Right – by Amos Wong – is a personalised food recommendation platform which through AI and machine learning algorithms identifies the dishes on a menu and provides the user with the nutritional and calorific value that a dish would provide.

The interfaces: The next phase:

• Computer systems that transcribe words users ‘speak silently’.
• Bone-conduction headphones that transmit vibrations – pick up otherwise undetectable neuromuscular signals triggered by internal verbalisations. This lets the user undetectably pose and receive answers to even difficult computational problems.
• Recently Elon Musk entered the industry with a $27 million investment in Neuralink, a venture with the mission to develop a BCI (Brain Computer Interface) that improves human communication in the light of AI. Why: the combination of human and technology could be more powerful than AI.

Also, in the market: 

Relaunching Google Glass in design and construction that are small and cheap enough to be implemented in glasses that don’t look different from what’s commonplace now. Even contact lenses. A wearable computer as a hands-free smartphone, letting users access the mobile internet browser, camera, maps calendar and other apps by voice commands.

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