Longevity, eternal youth or even immortality have been an aspiration in religion and culture throughout history. Efforts to delay aging are increasingly quantified with sensors, wearables, or even biohacking — interventions to influence body biology. The new hope for advancements in longevity is seen in artificial intelligence, which is becoming increasingly powerful.

Alex Zhavoronkov has been researching the use of AI in aging for years. He is the CEO of Insilico Medicine, a Baltimore-based leader in next-generation artificial intelligence technologies for drug discovery and aging biomarkers discovery. He truly is a well of knowledge — since 2012 he has published over 130 peer-reviewed research papers and 2 books, including “The Ageless Generation: How Biomedical Advances Will Transform the Global Economy” (Palgrave Macmillan, 2013). As he emphasizes, constant research is crucial in the longevity industry because the field has been plagued with deceit and fraud throughout history. This s why Insilico Medicine is staying on the brink of medicine, to have the credibility that opens doors for collaborations with medical experts.

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There are three areas of how AI can be applied to aging, notes Alex Zhavoronkov:

1. the construction of aging clocks - guessing age,

2. generation of novel chemistry — enables the design of interventions to test your hypothesis,

3. new data generation - creating models in which moving one feature in time shows changes in other data types.

Combined those three applications present a very powerful tool for AI, which is steadily gaining in sophistication. “Today, we can, when working with gene expression data and signaling pathways, we can generate new data just by using age as a generaton condition, and see how pathways and gene expression changes,” explains Zhavoronkov.

Several techniques fall under next-generation artificial intelligence:

Machine learning refers to algorithms that can learn from and make predictions on data by building a model from sample inputs.

Deep learning is a subset of machine learning and refers to modeling of complex relationships among layers of non-linear computational units — so-called neural networks.

Reinforcement learning solves the difficult problem of correlating immediate actions with the delayed returns they produce. The challenge here is that we know the inputs and outputs, but not quite how one led to another.

Generative Adversarial Networks are structured, probabilistic models for generating data and consist of two entities — the generator and the denominator. The denominator checks the authenticity of the data produced by the generator, whereas the generator tries to trick the denominator — it’s kind of like trying to learn to lie without getting caught.

Transfer learning is a machine learning method where the set of learned features of a model for a specific task is reused, or repurposed, as the targeting point for a model on a second task. In practice, it’s often used for optimization.

Generative Adversarial Networks and Transfer Learning bring the promise of faster progress in the field of aging. For example, algorithms can be trained in diseases with enough patients and a new understanding of biological processes could be applied to areas where data is harder to come by, such as rare diseases.

AI research is inspiring, but might be slowed down due to increasingly challenging data acquisition. Rising demands for privacy regulation in the West are making Asian countries more competitive since they aren’t yet as strict as the Western world regarding privacy protection laws.

Medical research and hunts for new cures are a business where you usually more often fail than succeed, says Alex Zhavoronkov. For these purposes, he believes, patient data should be universally available: “It should be a fundamental law, for all the medical data to be donated for medical purposes until we aren’t capable of curing diseases that kill people. People think about data privacy but they fail to remember the pain and suffering caused by diseases. We need data to find cures.”

Some questions addressed in the podcast:

• How well can people and computers predict and individuals age based on her looks?

• Next generation AI comprises of machine learning, deep learning, reinforcement learning, generative Adversarial Networks (GANs), transfer learning, and meta-learning. Why does meta-learning seem like the most promising in aging research?

• One of the impediments in aging research is the absence of biomarkers that may be targeted and measured to track the effectiveness of anti-aging therapeutic interventions because standard biomarkers are usually developed for measuring a strictly defined physiological process, and are not necessarily adapted for measuring the effects of a systemic process such as aging. How are then aging therapies hypothesis designed and tested?

• What is the latest on the connections between genes and healthy aging?

• Why is progress in Alzhemer’s disease research so slow?

• What is your opinion about the various rejuvenating techniques on the market such as blood transfusions from young to old?

To learn more about aging and AI, read the following:

Blood Biochemistry Analysis to Detect Smoking Status and Quantify Accelerated Aging in Smokers

Artificial intelligence for aging and longevity research: Recent advances and perspectives

Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry