Development of artificial intelligence agents tends to frequently be measured by their performance in games, but there's a good reason for that: Games tend to offer a wide proficiency curve, in terms of being relatively simple to grasp the basics, but difficult to master, and they almost always have a built-in scoring system to evaluate performance. DeepMind's agents have tackled board game Go, as well as real-time strategy video game StarCraft. But the Alphabet company's most recent feat is Agent57, a learning agent that can beat the average human on each of 57 Atari games with a wide range of difficulty, characteristics and gameplay styles. Being better than humans at 57 Atari games may seem like an odd benchmark against which to measure the performance of a deep learning agent, but it's actually a standard that goes all the way back to 2012, with a selection of Atari classics including Pitfall, Solaris, Montezuma's Revenge and many others. Taken together, these games represent a broad range of difficulty levels, as well as requiring a range of different strategies in order to achieve success.
Canada may have just found a new icon, thanks to a group of enthusiasts pushing to adopt a national lichen. This all may seem a frivolous venture during the coronavirus pandemic. But it's come at a time when people who are weary of being cooped up in their homes are reconnecting with nature like never before. It's also a time when people, confronting a universal vulnerability that calls for global cooperation to beat it, are rethinking complex systems. More than 18,000 Canadians weighed in on a vote organized by the Canadian Museum of Nature in Ottawa.
In a preprint paper published this week by DeepMind, Google parent company Alphabet's U.K.-based research division, a team of scientists describe Agent57, which they say is the first system that outperforms humans on all 57 Atari games in the Arcade Learning Environment data set. Assuming the claim holds water, Agent57 could lay the groundwork for more capable AI decision-making models than have been previously released. This could be a boon for enterprises looking to boost productivity through workplace automation; imagine AI that automatically completes not only mundane, repetitive tasks like data entry, but which reasons about its environment. "With Agent57, we have succeeded in building a more generally intelligent agent that has above-human performance on all tasks in the Atari57 benchmark," wrote the study's coauthors. "Agent57 was able to scale with increasing amounts of computation: the longer it trained, the higher its score got."
Should I stay or should I go now? If I go there will be trouble And if I stay it will be double So ya gotta let me know Should I stay or should I go? -- The Clash Don't we all feel like that sometimes? Unhappy about your current job but the new job pays lower. Unsure whether to invest in the stocks market or in wealth management platform? Even Spider-Man had to make to a choice between saving Mary Jane or saving a cable car of people.
You can be addicted to your Artificial Intelligence (AI) software as much as your favored fortune. And you'll feel rewarding being addicted to your AI. Because they replace the extravagance, inefficiency, and endangerment associated with business operations. Tech Oracle if you ask? Employing AI will lessen human error, mundane tasks, in turn, more time for innovation. This means you print money while remaining effortless.
A paper published by researchers at Carnegie Mellon University, San Francisco research firm OpenAI, Facebook AI Research, the University of California at Berkeley, and Shanghai Jiao Tong University describes a paradigm that scales up multi-agent reinforcement learning, where AI models learn by having agents interact within an environment such that the agent population increases in size over time. By maintaining sets of agents in each training stage and performing mix-and-match and fine-tuning steps over these sets, the coauthors say the paradigm -- Evolutionary Population Curriculum -- is able to promote agents with the best adaptability to the next stage. In computer science, evolutionary computation is the family of algorithms for global optimization inspired by biological evolution. Instead of following explicit mathematical gradients, these models generate variants, test them, and retain the top performers. They've shown promise in early work by OpenAI, Google, Uber, and others, but they're somewhat tough to prototype because there's a dearth of tools targeting evolutionary algorithms and natural evolution strategies (NES).
We will be releasing a'playground', a simple simulation environment for intelligent agents based on the Unity platform3. This environment has basic physics rules and a set of objects such as food, walls, negative-reward zones, pushable blocks and more. The playground can be configured by the participants and they can spawn any combination of objects in preset or random positions (pictured). It will be important for the participants to design good environments for their agents to learn in. Configuration files for the playground can also be exchanged between participants should they wish to collaborate.
The US presidential election campaign is in its final days. Donald Trump is behind in the polls and the pundits are predicting a win for his Democrat challenger, former vice president Joe Biden. He boasts that he will win again. With two weeks to go, his campaign unleashes an offensive in the crucial swing states: adverts, Facebook posts, WhatsApp groups and tweets. They warn of violent crime and civil unrest driven by immigrants and gangs, playing up Trump's endorsement by evangelicals and smearing Biden as a closet atheist. The initiative works and Trump snatches another unlikely victory.
Artificial intelligence (AI) is getting a bad reputation. And while Forrester predicts that 1 out of 4 CX professionals will lose their jobs in 2020, it has little to do with employers implementing AI. The real reason is the growing business-criticality of CX. In fact, brands will spend $8 billion more on customer service agents in 2020 than 2019 due to heightened demand and competition for highly skilled agents. The truth is plain and simple: AI isn't replacing contact center agents -- it's helping them step up to be the best they can be and deliver more value than ever.
We consider the problem of using logged data to make predictions about what would happen if we changed the rules of the game' in a multi-agent system. This task is difficult because in many cases we observe actions individuals take but not their private information or their full reward functions. In addition, agents are strategic, so when the rules change, they will also change their actions. They make counterfactual predictions by using observed actions to learn the underlying utility function (a.k.a. This approach imposes heavy assumptions such as the rationality of the agents being observed and a correct model of the environment and agents' utility functions.