Improving the decision-making capabilities of agents is a key challenge on the road to artificial intelligence. To improve the planning skills needed to make good decisions, MuZero's agent combines prediction by a network model and planning by a tree search using the predictions. MuZero's learning process can fail when predictions are poor but planning requires them. We use this as an impetus to get the agent to explore parts of the decision tree in the environment that it otherwise would not explore. The agent achieves this, first by normal planning to come up with an improved policy. Second, it randomly deviates from this policy at the beginning of each training episode. And third, it switches back to the improved policy at a random time step to experience the rewards from the environment associated with the improved policy, which is the basis for learning the correct value expectation. The simple board game Tic-Tac-Toe is used to illustrate how this approach can improve the agent's decision-making ability. The source code, written entirely in Java, is available at https://github.com/enpasos/muzero.

In a car outside a convenience store in Flint, Michigan, in late 2016, Kemo Parks handed his cousin Dequavion Harris a gun. Things happened quickly after that: Witnesses saw Harris "with his arm up and extended" toward a red truck. The wounded driver sped off but crashed into a tree. EMTs rushed him to the hospital. He was dead on arrival.

Penn State researchers are looking for a safer and more efficient way to use machine learning in the real world. Using a simulated high-rise office building, they developed and tested a new reinforcement learning algorithm aimed at improving energy consumption and occupant comfort in a real-world setting. Greg Pavlak, assistant professor of architectural engineering at Penn State, presented the results from the paper he co-authored, "Constrained Differentiable Cross-Entropy Method for Safe Model-Based Reinforcement Learning," at the Association for Computing Machinery International Conference on Systems for Energy-Efficient Built Environments (BuildSys) Conference, which was held Nov. 9-10 in Boston. "Reinforcement learning agents explore their environments to learn optimal actions through trial and error," Pavlak said. "Due to challenges in simulating the complexities of the real world, there is a growing trend to train reinforcement learning agents directly in the real world instead of in simulation."

When Leonardo DiCaprio's relationship with model/actress Camila Morrone ended three months after she celebrated her 25th birthday, the lifestyle site YourTango turned to neuroscience. DiCaprio has a well-documented history of dating women under 25. "Given that DiCaprio's cut-off point is exactly around the time that neuroscientists say our brains are finished developing, there is certainly a case to be made that a desire to date younger partners comes from a desire to have control," the article said. It quotes a couples therapist, who says that at 25, people's "brains are fully formed and that presents a more elevated and conscious level of connection"--the type of connection, YourTango suggests, that DiCaprio wants to avoid. YourTango was parroting a factoid that's gained a chokehold over pop science in the past decade: that 25 marks the age at which our brains become "fully developed" or "mature." This assertion has been used as an explanation for a vast range of phenomena.

Gartner has anointed "Hyperautomation" one of the top 10 trends for 2022. Is it a real trend, or just a collection of buzzwords? As a trend, it's not performing well on Google; it shows little long-term growth, if any, and gets nowhere near as many searches as terms like "Observability" and "Generative Adversarial Networks." And it's never bubbled up far enough into our consciousness to make it into our monthly Trends piece. However, that skeptical conclusion is too simplistic. Hyperautomation may just be another ploy in the game of buzzword bingo, but we need to look behind the game to discover what's important. There seems to be broad agreement that hyperautomation is the combination of Robotic Process Automation with AI. Natural language generation and natural language understanding are frequently mentioned, too, but they're subsumed under AI. So is optical character recognition (OCR)–something that's old hat now, but is one of the first successful applications of AI. Using AI to discover tasks that can be automated also comes up frequently. While we don't find the multiplication of buzzwords endearing, it's hard to argue that adding AI to anything is uninteresting–and specifically adding AI to automation. Get a free trial today and find answers on the fly, or master something new and useful.

In a statement to The Washington Post addressing reports of the town hall, EA Vice President of Communication, John Reseburg, characterized it as "an in-depth and very humble internal conversation about the recent Battlefield launch. It was about key learnings and actions we are taking, not blaming external factors." It is good that EA and Dice are assessing what went wrong around "Battlefield 2042" and that developers are working to improve it. But hopefully no one loses site of the biggest problem of all. The initial decision to patch up a flawed game after release -- particularly over the holiday season when developers traditionally take a needed respite -- shows zero respect for consumers. "Sell it, then fix it" is a recipe for disaster for any product, and that's exactly what was cooked up with "Battlefield 2042."

Back in 2015, Google was called out for its photo app that mistakenly labeled pictures of people with darker skin as gorillas. As you can imagine, it was a PR disaster. Of course, the company publicly apologized, said that such a result is unacceptable, and promised to fix the mistake. But apparently – as Wired uncovered three and a half years later – Google somehow never got to fixing the underlying issue. Instead, it implemented a workaround, blocking its AI from identifying gorillas (and other primates) altogether to prevent another miscategorization.

Decisions, decisions…we all want to make the right ones. So, in an era of artificial intelligence, how can technology help employees make challenging choices that can delight customers while improving efficiency and ultimately the bottom line? And worryingly, a survey by McKinsey revealed that 60% of executives thought that bad decisions were as frequent as good ones. Often it was attributed to cognitive biases. You only have to look at mistakes made during the recent pandemic when the rush to digitize processes and assist remote teams led to bad decisions on introducing automation.

FICO known for the "Credit score/FICO Score," an indicator used to predict credit issues, has released a report titled "The State of Responsible AI." The document is the results of a survey conducted with the help of business intelligence firm Corinium around responsible AI. The two organizations tried to understand the aspects that enable a company to adopt more responsible, ethical, transparent and secure AI. As part of an initiative led by Corinium and FICO, a survey was conducted on companies exploiting artificial intelligence on a daily basis. The objective was to better understand how companies are using AI and whether the issues of ethics, responsibility, and respect for the interests of customers are assimilated by these groups.