Diamandis writes, my friend Neil Jacobstein is the “go-to expert” when it comes to AI. With his over 25 years of technical consulting experience in the field he holds the AI and Robotics Chair at Singularity University, and is a Distinguished Visiting Scholar at Stanford’s MediaX Program, a Henry Crown Fellow, an Aspen Institute moderator, and sits as a member of the National Academy of Sciences Earth and Life Studies Committee.
The predictions that follow cover the period from this year to 2024. Here are Jacobstein’s prognostications.
AlphaGo Zero, a machine learning computer program trained to play the complex game of Go, defeated the Go world champion in 2016 by 100 games to zero. But instead of learning from human play, AlphaGo Zero trained by playing against itself — a method known as reinforcement learning. Building its own knowledge from scratch, AlphaGo Zero demonstrates a novel form of creativity, free of human bias. Even more groundbreaking, this type of AI pattern recognition allows machines to accumulate thousands of years of knowledge in a matter of hours.
While these systems can’t answer the question, “What is orange juice?” or compete with the intelligence of a fifth grader, they are growing more strategically complex, merging with other forms of narrow AI. Within five years, who knows what successors of AlphaGo Zero will emerge, augmenting both your business functions and day-to-day life.
A group of Chinese and American researchers has recently created an AI system that diagnosis common childhood illnesses, ranging from influenza to meningitis. The system trained on electronic health records compiled 600,000 patients, and 1.3 million outpatient visits, produced diagnoses with unprecedented accuracy. While U.S. health systems do not tout the same level of accessible, universal health data as do Chinese systems, the country is making progress implementing AI to assist in medical diagnoses. Dr. Kang Zhang, Chief of Ophthalmic Genetics at the University of California, San Diego, has created a system to detect signs of diabetic blindness, relying on medical histories and images.
Jacobstein states “we will soon see an inflection point where doctors will feel it’s a risk to not use machine learning and AI in everyday practice because they don’t want to be called out for missing an important diagnostic signal.”
Researchers estimate that there are 1060 possible drug-like molecules which are more than the total number of atoms found in the Solar System. But today, chemists make drug predictions based on properties influenced by molecular structure and then synthesize variants to test their hypotheses. Using quantum computing could transform this time-consuming, high-cost process into an efficient, life-changing, drug discovery protocol. States Jacobstein, “Quantum computing is going to have a major industrial impact… not by breaking encryption, but by making inroads into design through massive parallel processing that can exploit superposition and quantum interference and entanglement, and that can wildly outperform classical computing.”
The incorporation of AI into almost every aspect of our lives leaves us vulnerable to “Deep Attacks” which use AI-generated content to avoid human and AI controls. We have seen examples of this in fake videos of former President Obama speaking fabricated sentences, and an adversarial AI fooling another algorithm into categorizing a stop sign as a 45 mph speed limit sign. Without appropriate cyber protections, AI systems can be manipulated to conduct any number of destructive acts whether it be ruining reputations or changing the performance of an autonomous vehicle.
Jacobstein states, “We all have security systems in our buildings, homes, around the healthcare system, air traffic control, financial organizations, the military, and intelligence communities. But we all know that these systems have been hacked periodically and we’re going to see that accelerate. So, there are major business opportunities … to get ahead of that curve before it bites you.”
Just as modern computing transformed our relationship with bits and information, AI will redefine and revolutionize our relationship with molecules and materials. Today AI is used to discover new materials for clean-tech energy innovations such as solar panels, batteries, and artificial photosynthesis. But using traditional computing hampers our ability to innovate in a world where we face the climate change issue and the need to accelerate a transition to a lower carbon future. For example, it takes about 15 to 20 years to create a single new material based on current design and development methods. But as AI design systems grow they will cut down the timeline needed for new materials discovery allowing us to accelerate the transition. With companies like Kebotix already on their way to streamlining the creation of chemistries and materials at the click of a button, we are getting to the point where atomically precise manufacturing will enable us to produce what was previously unimaginable.
