Author: omohundro

Cable TV Future Talk: “The AI Revolution”

On February 26, 2020 Steve Omohundro was interviewed by Marty Wasserman for the Palo Alto Cable TV program “Future Talk” about “The AI Revolution”:

Long time artificial intelligence researcher Steve Omohundro, Chief Scientist at the AI company AIBrain, discusses the exponential growth of AI, how it’s affecting every aspect of our lives, and the tradeoffs between the benefits and the dangers.

FXPAL Talk: The AI Platform Business Revolution, Matchmaking, Empathetic Technology, and AI Gamification

On October 15, Steve Omohundro spoke at FXPAL (FX Palo Alto Laboratory) about “The AI Platform Business Revolution, Matchmaking, Empathetic Technology, and AI Gamification”:


Popular media is full of stories about self-driving cars, video deepfakes, and robot citizens. But this kind of popular artificial intelligence is having very little business impact. The actual impact of AI on business is in automating business processes and in creating the “AI Platform Business Revolution”. Platform companies create value by facilitating exchanges between two or more groups. AI is central to these businesses for matchmaking between producers and consumers, organizing massive data flows, eliminating malicious content, providing empathetic personalization, and generating engagement through gamification. The platform structure creates moats which generate outsized sustainable profits. This is why platform businesses are now dominating the world economy. The top five companies by market cap, half of the unicorn startups, and most of the biggest IPOs and acquisitions are platforms. For example, the platform startup Bytedance is now worth $75 billion based on three simple AI technologies.

In this talk we survey the current state of AI and show how it will generate massive business value in coming years. A recent McKinsey study estimates that AI will likely create over 70 trillion dollars of value by 2030. Every business must carefully choose its AI strategy now in order to thrive over coming decades. We discuss the limitations of today’s deep learning based systems and the “Software 2.0” infrastructure which has arisen to support it. We discuss the likely next steps in natural language, machine vision, machine learning, and robotic systems. We argue that the biggest impact will be created by systems which serve to engage, connect, and help individuals. There is an enormous opportunity to use this technology to create both social and business value.

Cooperation is the Central Issue of our Time

Cooperation is the most important issue of our time. It is the key to understanding biology, the success of humans, effective business models, social media, and future society based on beneficial AI.

The challenge is that many interactions have the character of the “Prisoner’s Dilemma” or “Tragedy of the Commons” where selfish actors do better for themselves while arming the group benefit and cooperative actors help the group but can lose out in individual competition.

A variety of mechanisms that lead to cooperation have been invented and studied in biology, economics, political science, business, analysis of social technologies, and increasingly in analyzing AI.

All of these subjects are grounded in biology and today’s biology exhibits cooperation at every level of the “Major Transitions in Evolution”:

The Major Transitions in Evolution

From Smith and Szathmary’s book “The Major Transitions in Evolution”:


Biology has to explain how independent biological molecules work cooperatively inside of cellular compartments, how separate genes cooperate in a genome, how mitochondria and other organelles cooperate in eukaryotic cells, how the cells in multicellular organisms cooperate, how two or more sexes cooperate in creating offspring, how social insects and other animals cooperate in hives, how mutualisms between different species happen, how humans cooperated in creating and using language, how humans created cooperative societies.

Biological cooperation contains all the abstract elements of general cooperation studied by economics. But biological cooperation has the extra element of “relatedness” between organisms that share genes. Hamilton’s notion of “inclusive fitness” has been a central insight in understanding cooperation in many of these biological systems.

But it’s looking to me like “partner choice”, “partner switching”, and “cheater punishment” are the fundamental mechanisms underlying many of these cooperative interactions and they apply as well to economic interactions, business interactions, political interactions, and increasingly technological and AI interactions.

I therefore think it is very important to have a clear and mathematically precise theory of these mechanisms. And would love to see detailed simulation modelling and eventually AI models both for understanding and for mechanism design and policy design.

Those preliminary thoughts are meant to motivate the study of this excellent review article which tries to systematize the different explanations for cooperation in biology:

Evolutionary Explanations for Cooperation

Stuart A.West Ashleigh S.Griffin AndyGardner

Natural selection favours genes that increase an organism’s ability to survive and reproduce. This would appear to lead to a world dominated by selfish behaviour. However, cooperation can be found at all levels of biological organisation: genes cooperate in genomes, organelles cooperate to form eukaryotic cells, cells cooperate to make multicellular organisms, bacterial parasites cooperate to overcome host defences, animals breed cooperatively, and humans and insects cooperate to build societies. Over the last 40 years, biologists have developed a theoretical framework that can explain cooperation at all these levels. Here, we summarise this theory, illustrate how it may be applied to real organisms and discuss future directions.

Here is the pdf of the paper:

Here is the key figure which tries to categorize all of the biological cooperation mechanisms:



Interview for the Argentinian El Cronista: “Do presidents dream of electric ministers?”

On August 26, 2019, Sebastiande de Toma published an article in the Argentinian business newspaper El Cronista based in part on an interview with Steve Omohundro. His article is titled “Suenan los presidentes con ministros electricos?” or “Do presidents dream of electric ministers?”:

He explores whether AI will help politicians make better economic decisions.

Steve suggested 4 levels of AI support for politicians:

  1. AI’s can build much better economic models from a much wider range of data than traditional econometric data. For example, an AI model might include video feeds from TV news, social media posts, video feeds from commerce hubs, audio from radio shows, etc. All of the data can inform much richer economic models. Monte Carlo simulations could then make much better predictions about the impact of policy interventions and repeated simulations can reveal how robust the response to an intervention might be.
  2. AI’s can help politicians recognize their cognitive biases and counteract them. The field of “behavioral economics” has identified a large number of biases, especially around small probability events and the different perceptions of gains and losses. AI’s can model the correct Bayesian responses and help a politician to counteract his intuitive biases.
  3. In addition to helping a politician simulate the effects of a policy intervention, AI’s can help to create policies with a desired impact. Economic models with policy knobs can be automatically optimized for the best predicted outcomes.
  4. Recently there have been advances in using AI to solve complex game theoretic problems (eg. the Libratus and Pluribus AI’s which recently beat expert human poker players). This kind of AI could be applied to the problem of new policy causing other parties to change their behavior. Well-designed policy should account for these responses and lead to desirable outcomes taking account of all participant’s likely behaviors.

Sebastiande’s wrote (as translated by Google Translate):


How Researchers Changed the World Podcast: “The Ethical Implications of Artificial Intelligence”

On June 18, 2019, the podcast “How Researchers Changed the World” supported by the Taylor & Francis Group featured Steve Omohundro on “The ethical implications of artificial intelligence”.


Steve’s paper “Autonomous technology and the greater human good” was the most read paper in the history of the Journal of Experimental & Theoretical Artificial Intelligence. It’s available here:

The podcast explores the origins of that work and is available here along with a transcript:

Steve Omohundro – The ethical implications of artificial intelligence

The press release for the episode is available here:


Linghacks Keynote: “Language and AI: Hacking Humanity’s Greatest Invention”

On March 30-31 the wonderful “Linghack” organization supporting computational linguistics held their “Linghacks II” event in Silicon Valley:

Steve Omohundro was invited to give the opening Keynote Address on “AI and Language: Hacking Humanity’s Greatest Invention”. His talk is available here starting at 14:20:

The slides are available here:

Autopiloto Podcast from a Self-Driving Car

On November 15, 2018 Steve Omohundro was interviewed live for the Autopiloto Podcast from a self-driving car which was exploring places in Silicon Valley of interest for self-driving. Here is the 12 hour podcast:

The interview with Steve begins at the timestamp 3:45:20.

Autopiloto Podcast Thursday

AUTOPILOTO is a 24-hour live online radio broadcast about all
things self-driving hosted from a semi-autonomous vehicle looping the
Bay Area. This broadcast takes up questions of how autonomy and
automatic movement will shape Bay Area geographies, societies, and
cultures. Considering  self-driving as technology, psychological
state, anthropological condition and systems, what will our cities
sound like in a driverless future? How will society and infrastructure
systems adapt? What might humans do during newfound transit time? In
what ways do machines imitate human auto-pilot modes, and vice versa?
How can we build equitable, planetary, intelligent transit for all?