Neurotechnologies are based on the principles of the human nervous system and modeled on the human brain. NeuroTech can help researchers understand brain function and disfunction, and can help doctors treat neurological disorders. Some NeuroTech applications are focused on enhancing cognitive performance, improving sleep, and improving brain health for Longevity. Advances in AI could revolutionize NeuroTech over the next decade. NeuroTech has unprecedented prospects for growth both in terms of technology and as an industry.

Over the past 100 years the financial industry has largely excluded people in retirement. Even today tech entrepreneurs are ignoring financial inclusion for people over 60, who make up the wealthiest part of the financial system, and instead, are developing financial products for younger people. The most valuable and capable client demographic in terms of purchasing power are the citizens of the 7th Continent which is made up of 1 billion people over 60. The global spending power of this demographic is expected to be $15 trillion this year. Who will serve this market?

Dmitry Kaminskiy speaks as though he were trying to unload everything he knows about the science and economics of longevity--from senolytics research that seeks to stop aging cells from spewing inflammatory proteins and other molecules to the trillion-dollar life extension industry that he and his colleagues are trying to foster--in one sitting. At the heart of the discussion with Singularity Hub is the idea that artificial intelligence will be the engine that drives breakthroughs in how we approach healthcare and healthy aging--a concept with little traction even just five years ago. "At that time, it was considered too futuristic that artificial intelligence and data science … might be more accurate compared to any hypothesis of human doctors," said Kaminskiy, co-founder and managing partner at Deep Knowledge Ventures, an investment firm that is betting big on AI and longevity. Artificial intelligence in healthcare is attracting more investments and deals than just about any sector of the economy, according to data research firm CB Insights. In the most recent third quarter, AI healthcare startups raised nearly $1.6 billion, buoyed by a $550 million mega-round from London-based Babylon Health, which uses AI to collect data from patients, analyze the information, find comparable matches, then make recommendations.

There's been significant growth in the AI for Drug Discovery sector this year. In the last quarter, the number of R&D centers increased by 5, companies increased by 20, collaborations increased by 30, and investors increased by 50. There are now 400 investors investing in AI for Drug Discovery including Google Ventures, Tencent, WuXi, Andreessen Horowitz, Khosla Ventures, and Sequoia Ventures. This week, Deep Knowledge Analytics Pharma Division published Landscape of AI for Drug Discovery and Advanced R&D Q2 2019. This report is the most comprehensive analytical report on the AI for Drug Discovery sector to date.

In our new book Longevity Industry 1.0 - Defining the Biggest and Most Complex Industry in Human History, Dmitry Kaminskiy and I distill the complex assembly of deep market intelligence and industry knowledge that we have developed over the past five years. We present the full-scope of the Global Longevity Industry showing exactly how we managed to define this complex and multidimensional industry and how we created a tangible framework for systematization and forecasting. This article includes the preface and the foreword from our book. Every cause of death is, in principle, preventable. I became aware of this in the 1990s and since then I have been following aging research very closely.

The many advances in deep learning reinforcement learning and generative adversarial learning made since 2014 are rapidly transforming multiple industries including search, translation, video games, retail, transportation, and many others. It is relatively easy to validate the performance of the AI systems in imaging, voice, text and other areas where human sensory systems can be used to rapidly verify the validity of the experimental results. However, in the pharmaceutical industry, the validation cycles take decades and cost billions of dollars. Most of the common questions asked by the pharmaceutical industry executives to all of the leading artificial intelligence groups worldwide deal with the novelty of the algorithms and experimental validation of the results in mice or even in humans. There is a grave disconnect between the leaders in AI focusing on the novelty of the algorithms and drug discovery and development experts focusing only on experimental data.

This is Pharma's AlphaGo moment when the potential for AI to radically transform the normal operating procedures and business models of the entire industry becomes tangibly obvious to the public. In the case of the AI industry, this happened in 2015, when AI company DeepMind succeeded in developing the first AI capable of beating a human Go champion in Go. This study by Insilico Medicine may be an analogous game-changing moment for Pharma. While it typically takes 2-3 years to go from initial drug discovery to preclinical validation, Insilico Medicine has done this in less than 2 months end-to-end. This is 15 times faster than Pharma companies capable of conducting the most efficient R&D processes. In a landmark study published in Nature Biotechnology on September 2, 2019, Insilico Medicine showed that they generated and validated a novel small molecule in just 46 days, and designed the drug from scratch based on specified molecular properties in just 21 days.

As Margaretta Colangelo, Partner at Deep Knowledge Ventures, mentions in her recent article'Although pharmaceutical companies spend over $172 billion on research and development annually, over 90% of molecules discovered using traditional techniques fail in human clinical trials. Moreover, 75% of newly approved drugs are unable to cover the cost of development and some analysts predict that ROI in pharmaceutical R&D may hit zero by 2020.' If we accept that this prediction has some good chances to become reality, then we should see the AI as the Deus ex Machina in the field of pharmaceutical R&D to keep a business alive, but also give the invaluable hope of choice to some patients. In this race for life, some firms set the pace trying to test their limits in uncharted waters. Deep Knowledge Analytics, a subsidiary of Deep Knowledge Ventures, an investment fund focused on DeepTech, separates the companies that are solely dedicated to core scientific R&D out of a total of 1000 AI Healthcare companies globally. According to DKA'The barriers to entry in the AI Healthcare industry are lower than for AI in drug discovery and these companies can achieve real results with much less funding and fewer highly specialized employees.'

As Margaretta Colangelo, Partner at Deep Knowledge Ventures, mentions in her recent article'Although pharmaceutical companies spend over $172 billion on research and development annually, over 90% of molecules discovered using traditional techniques fail in human clinical trials. Moreover, 75% of newly approved drugs are unable to cover the cost of development and some analysts predict that ROI in pharmaceutical R&D may hit zero by 2020.' If we accept that this prediction has some good chances to become reality, then we should see the AI as the Deus ex Machina in the field of pharmaceutical R&D to keep a business alive, but also give the invaluable hope of choice to some patients. In this race for life, some firms set the pace trying to test their limits in uncharted waters. Deep Knowledge Analytics, a subsidiary of Deep Knowledge Ventures, an investment fund focused on DeepTech, separates the companies that are solely dedicated to core scientific R&D out of a total of 1000 AI Healthcare companies globally. According to DKA'The barriers to entry in the AI Healthcare industry are lower than for AI in drug discovery and these companies can achieve real results with much less funding and fewer highly specialized employees.'

The Stanford Institute for Human-Centered AI officially launched today. Stanford HAI seeks to become an interdisciplinary global AI hub and to fundamentally change the field of AI by integrating a wide range of disciplines and prioritizing true diversity of thought. Researchers in Korea analyzed literature evaluating 516 AI algorithms for medical image analysis and found that only 6% validated their AI and 0% were ready for clinical use. This lack of appropriate clinical validation is referred to as digital exceptionalism. An analysis of 47 biomedical unicorns found that most of the highest valued startups in healthcare have a limited or non‐existent participation in the publicly available scientific literature.