For me, try to picture this situation. After a hot aerobic workout at your neighborhood gym, you head to the showers and stop by the smoothie bar to get the ideal recovery smoothie created especially for you. By using the capabilities of artificial intelligence and machine learning, your workout data is synchronized to your eating plan, and it develops the ideal mix of components for your recovery smoothie to maximize your weight loss and muscle recovery goals. In addition to taking into account your sleep, insulin, and stress levels, this magical intelligence can optimize your diet at every turn and propose the ideal combination of meals to enhance your health and quality of life.
No, according to Deliveroo’s “Snack to the Future: 2040” Report, which was published in early July, this is what 2040 food consumption will look like. The analysis estimates that the global market for personalized nutrition will be worth £14.5 billion in 2023 and £66 billion in 2040. That’s no easy task; there will need to be a substantial innovation in personalized nutrition solutions, and the brand is ready to coin the word “me-ganism” (you heard it from me first) to describe it.
Regarding physical assessments, we already have equipment that reads our insulin levels before and following meals to help us plan our meals going forward. Some businesses provide custom diet plans (which, admittedly, are questionably effective at the moment), the analysis of your feces to determine the health of your microbiome (if you’re willing to collect it and drop it off at your post office), and the award-winning personal toilet insert U-Scan, which analyses your urine for a variety of metrics to determine your health. This technology’s interconnection is boundless, and astounding, yet not as widespread as we assume. It doesn’t matter if it inspires dread or excitement—we are just a few curious brains away from a technological breakthrough—and it’s an extraordinary moment to be living.
Little information is available
The first thing to keep in mind is how little we as a society understand about nutrition and how it affects, heals, and biohacks our bodies. from humans have been eating from the beginning of time, this is a huge missed opportunity to learn more. The majority of modern doctors in wealthy nations have astonishingly little training in using food as a kind of preventative medicine, and the typical consumer depends on the industry’s marketing strategies to determine what is healthy for their body.
Having an impact is the food business
It is safe to say that we are still learning as a result. Research and testing are necessary for learning. Our scientific community, sadly, suffers from a basic defect where the excitement of discovery is frequently reserved for individuals who receive money and is not evenly distributed. For instance, how much research is needed on sugar or a brand-new superfood as opposed to how several foods combine to form a unique approach to health prevention? When objectively examining all of the available materials to create an effective learning platform, results may be contradictory because certain studies are supported by significant players with a stake in the results. To make this a reality, we need more financing and unbiased nutrition research.
There is no universal diet.
The third thing to keep in mind is that there is no one-size-fits-all diet since our bodies react to diets differently. The concept of “me-ganism” is intriguing, and a glucose monitor is an intriguing approach to documenting our unique relationship with food. But there are other things to think about besides insulin, and it can be difficult to traverse the intricacies that our biologies produce. How well can AI determine the ideal diet for you, given that some dietary recommendations may vary according to your circumstances if you have diabetes and a high risk of kidney stones?
Negative optimization
The universe of AI is now input-based, which means it is built on knowledge of previous decisions. Some people may find this retrospective study useful, find it intriguing to evaluate the data they collected from their devices, and then decide to alter their living choices. When all is said and done, it is still a reflection of their past choices rather than a true representation of their present-day choices, and this is a very difficult field to enter. In light of this, a young new AI technique known as “inverse optimization” has been developed at John Hopkins University. Your choices are taken into account as “inputs” through inverse optimization, which establishes the target. For instance, inverse optimization may examine your existing diet and find ways to reduce your salt intake if it finds that it contains a lot of salt.
Adherence
The problem, however, is that while machine learning can make suggestions for us, doing so will require us to completely forgo our ability to make independent decisions. to skip dinner when we know we should, or to choose a slice of cheesecake when we want one even when our “me-nu” suggests a salad. AI can only make suggestions; whether or not we follow them is up to us. You can gather all the information you want about your body, but if you lack the motivation and willpower to act on it, it will remain merely interesting information.
