Bots removed opponents' tools from the game space, and launched themselves into the air… Two teams of AI agents tasked with playing a game (or million) of hide and seek in a virtual environment developed complex strategies and counterstrategies – and exploited holes in their environment that even its creators didn't even know that it had. The game was part of an experiment by OpenAI designed to test the AI skills that emerge from multi-agent competition and standard reinforcement learning algorithms at scale. OpenAI described the outcome in a striking paper published this week. The organisation, now heavily backed by Microsoft, described the outcome as further proof that "skills, far more complex than the seed game dynamics and environment, can emerge" (from such experiments/training exercises). Some of its findings are neatly captured in the video below.
A common refrain in the media is that people don't like their boss and people are scared of robots. So I wondered about the truth and nuance to these emotions: how many people would prefer a robot to their boss? The old saying goes, "People join a company, but they leave a bad boss." As Gallup research demonstrates, 70% of how we feel about work--our emotional commitment--is driven by who our manager is. The ongoing employee engagement crisis is largely about managers who know how to manage tasks, but don't know how to lead people.
Humans are a species that can adapt to environmental challenges, and over eons this has enabled us to biologically evolve -- an essential characteristic found in animals but absent in AI. Although machine learning has made remarkable progress in complex games such as Go and Dota 2, the skills mastered in these arenas do not necessarily generalize to practical applications in real-world scenarios. The goal for a growing number of researchers is to build a machine intelligence that behaves, learns and evolves more like humans. A new paper from San Francisco-based OpenAI proposes that training models in the children's game of hide-and-seek and pitting them against each other in tens of millions of contests results in the models automatically developing humanlike behaviors that increase their intelligence and improve subsequent performance. Hide-and-seek was selected as a fun starting point mostly due to its simple rules, says the paper's first author, OpenAI Researcher Bowen Baker.
Pitting two artificial intelligences against each other in games such as DeepMind's Go has led to some of the biggest breakthroughs in AI in recent years, as the machines learn skills through trial and error that eventually lead to them beating humans. But can the same technique produce a more useful AI capable of operating in the real word? OpenAI, a San Francisco-based AI research group, published research on Tuesday showing what it claimed was a method for training increasingly powerful smart systems that could prepare them for tackling more ordinary human problems. Set in increasingly realistic environments, the technique points to a way for the AI to "evolve" in a simulated world until it is ready to be used, it said. The researchers used several intelligent "agents" in a game of hide-and-seek played in a simulated physical environment.
We've observed agents discovering progressively more complex tool use while playing a simple game of hide-and-seek. Through training in our new simulated hide-and-seek environment, agents build a series of six distinct strategies and counterstrategies, some of which we did not know our environment supported. The self-supervised emergent complexity in this simple environment further suggests that multi-agent co-adaptation may one day produce extremely complex and intelligent behavior.
Today we are joined by Gregg Willcox, Director of Research and Development at Unanimous AI. Starting out with a general interest in robotics, Gregg found himself in the world of machine learning and AI, inspired specifically by the idea of humans as smart data processors, instead of data points. With the team at Unanimous AI, Gregg uncovered a secret that many creatures in nature have been doing for centuries: using the collective intelligence of a group produces more accurate results, in a more efficient way, (also known as swarming), than an individual alone. From this research, 'Swarm' was born, a game-like collaboration platform that channels the beliefs and convictions of individuals to come to a consensus. Going one step further, using a behavioral neural network trained on people's behavior called'Conviction', the precision of the results is further amplified, leading to significant increases in detailed accuracy.
How did our agents perform as well as they did? First, we noticed that the agents had very fast reaction times and were very accurate taggers, which might explain their performance (tagging is a tactical action that sends opponents back to their starting point). Humans are comparatively slow to process and act on sensory input, due to our slower biological signalling. Here's an example of a reaction time test you can try yourself. Thus, our agents' superior performance might be a result of their faster visual processing and motor control.
Recent years have seen a rising interest in developing AI algorithms for real world big data domains ranging from autonomous cars to personalized assistants. At the core of these algorithms are architectures that combine deep neural networks, for approximating the underlying multidimensional state-spaces, with reinforcement learning, for controlling agents that learn to operate in said state-spaces towards achieving a given objective. The talk will first outline notable past and future efforts in deep reinforcement learning as well as identify fundamental problems that this technology has been struggling to overcome. Towards mitigating these problems (and open up an alternative path to general artificial intelligence), I will then summarize a brain computing model of intelligence, rooted in the latest findings in neuroscience. The talk will conclude with an overview of the recent research efforts in the field of multi-agent systems, to provide the future teams of humans and agents with the necessary tools that allow them to safely co-exist.
Customer support teams use swivlStudio to get the most out of AI using Human-in-the-Loop systems. The IA, or Intelligent Agent, can handle the up front interactions and common problems and FAQs on a large scale when trained and fed relevant data. For customer interactions and issues that are more complex or require an empathetic human touch, the IA can quickly bring in a human to continue the customer interaction.
If AI gains legal personhood via the corporate loophole, laws granting equal rights to artificially intelligent agents may result, as a matter of equal treatment. That would lead to a number of indignities for the human population. Because software can reproduce itself almost indefinitely, if given civil rights, it would quickly make human suffrage inconsequential 14 leading to the loss of self-determination for human beings. Such loss of power would likely lead to the redistribution of resources from humanity to machines as well as the possibility of AIs serving as leaders, presidents, judges, jurors, and even executioners. We might see military AIs targeting human populations and deciding on their own targets and acceptable collateral damage.