The volume and complexity of available biological data vastly exceeds the scope of any individual human brain, one consequence of which is the likelihood that the vast majority of interesting, relevant patterns in already-published data remain undiscovered. The obvious solution to this issue is the deployment of AI technology for biological data analysis, interpretation and simulation; but AI itself is a complex area of R&D, and application of AI to bioinformatics and systems biology is currently far from "plug and play." In this talk I will discuss recent work using Biomind LLC's machine learning tools (a kind of AI) to analyze genomic data (SNP, gene expression, and mitochondrial mutation) from various organisms including humans. This work has been carried out together with biologists from various organizations including the CDC, the NIH, the University of Virginia, and Genescient Corp. I will detail how AI tools have led us to novel and important discoveries regarding age-associated diseases like Parkinsons and Alzheimers Disease, and regarding the mechanisms underlying calorie restriction and healthspan extension. The use of these tools to uncover the mechanisms allowing Genescient's Methuselah Flies to live 5x as long as control flies will be detailed. I will also mention some very recent work incorporating drug databases into the machine learning process, so as to enable automated drug target discovery (with a current focus on neuro, cardio and immune diseases). At the end of the talk I will touch on the future of the AI/bioinformatics connection, which goes far beyond deployment of machine learning as a data analysis tool. Ultimately, I believe that the only sound route to fully understanding molecular biology and the human organism is to feed the totality of biological data into a robust Artificial General Intelligence (AGI) system and allow its reasoning to assist us in designing new experiments, therapeutics, etc. There is strong potential for AI and biomedicine to advance in synergy.