Framed in positive terms, this report examines how technical AI research might be steered in a manner that is more attentive to humanity's long-term prospects for survival as a species. In negative terms, we ask what existential risks humanity might face from AI development in the next century, and by what principles contemporary technical research might be directed to address those risks. A key property of hypothetical AI technologies is introduced, called \emph{prepotence}, which is useful for delineating a variety of potential existential risks from artificial intelligence, even as AI paradigms might shift. A set of \auxref{dirtot} contemporary research \directions are then examined for their potential benefit to existential safety. Each research direction is explained with a scenario-driven motivation, and examples of existing work from which to build. The research directions present their own risks and benefits to society that could occur at various scales of impact, and in particular are not guaranteed to benefit existential safety if major developments in them are deployed without adequate forethought and oversight. As such, each direction is accompanied by a consideration of potentially negative side effects.

Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning--and the need for it. In 2007 Fei-Fei Li, the head of Stanford's Artificial Intelligence Lab, gave up trying to program computers to recognize objects and began labeling the millions of raw images that a child might encounter by age three and feeding them to computers. By being shown thousands and thousands of labeled data sets with instances of, say, a cat, the machine could shape its own rules for deciding whether a particular set of digital pixels was, in fact, a cat.1 1.Fei-Fei Li, "How we're teaching computers to understand pictures," TED, March 2015, ted.com. Last November, Li's team unveiled a program that identifies the visual elements of any picture with a high degree of accuracy.

It's no longer the preserve of artificial-intelligence researchers and born-digital companies like Amazon, Google, and Netflix. Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning--and the need for it. In 2007 Fei-Fei Li, the head of Stanford's Artificial Intelligence Lab, gave up trying to program computers to recognize objects and began labeling the millions of raw images that a child might encounter by age three and feeding them to computers.

It's no longer the preserve of artificial-intelligence researchers and born-digital companies like Amazon, Google, and Netflix. Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning--and the need for it. In 2007 Fei-Fei Li, the head of Stanford's Artificial Intelligence Lab, gave up trying to program computers to recognize objects and began labeling the millions of raw images that a child might encounter by age three and feeding them to computers. By being shown thousands and thousands of labeled data sets with instances of, say, a cat, the machine could shape its own rules for deciding whether a particular set of digital pixels was, in fact, a cat.1 1.Fei-Fei Li, "How we're teaching computers to understand pictures," TED, March 2015, ted.com.

As a venture capitalist, my team and I try to foresee the major disruptive trends ahead of most and it has become clear to us that we are moving from a world of machine automation to a world of autonomous machines. Sometimes some of the biggest disruptions are happening in plain sight of everyone but go unnoticed for years and can only be fully appreciated with a rear view mirror perspective! At BootstrapLabs we feel that the Autonomous Corporation is one of these mega trends silently awakening in the networks of every corporations around the world... For this reason we felt it was important to bring this topic at the forefront and bring our our AI community together on November 15th. We stand in front of the 4th and largest wave of the industrial revolution, powered by AI and Data.

We stand in front of the 4th and largest wave of the industrial revolution, powered by AI and Data. This is the biggest opportunity so far for innovation and entrepreneurship, and every single industry will be disrupted and redefined by companies that are not even born yet. With the AI market projected to grow over 20 fold in the next 10 years to 3Tn annually, we believe Applied Artificial Intelligence represents one of the major wealth creation opportunities of this century. Any system that is not learning will soon die, and applying AI to new or existing systems will extend and accelerate societal improvement. AI-driven systems will soon be able to build better, more efficient products that scale and solve problems in ways that have not been possible before.

It's no longer the preserve of artificial-intelligence researchers and born-digital companies like Amazon, Google, and Netflix. Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning--and the need for it. In 2007 Fei-Fei Li, the head of Stanford's Artificial Intelligence Lab, gave up trying to program computers to recognize objects and began labeling the millions of raw images that a child might encounter by age three and feeding them to computers. By being shown thousands and thousands of labeled data sets with instances of, say, a cat, the machine could shape its own rules for deciding whether a particular set of digital pixels was, in fact, a cat.1 1.Fei-Fei Li, "How we're teaching computers to understand pictures," TED, March 2015, ted.com.