AI models have progressed rapidly in recent years and can already outperform humans in various tasks, from generating basic code to dominating games like chess and Go. But despite massive computing power and billions of dollars in investor funding, these advanced models still can't hold up to humans when it comes to truly understanding how real people interact with one another in the world. In other words, AI still fundamentally struggles at "reading the room." That's the claim made in a new paper by researchers from Johns Hopkins University. In the study, researchers asked a group of human volunteers to watch three-second video clips and rate the various ways individuals in those videos were interacting with one another.

Robots have progressed over the years from clunky hunks of metal to complex, AI-enabled machines capable of running, speaking, and even painting pictures. But even with all those advances humans still can't help but place robots in bizarre and uncomfortable situations. This year, researchers took advanced robots and had them clean up karate-chopped Coke cans, suck up cigarette butts, wear a fleshy, lab-grown face, and pick up dog poo. Two-legged, humanoid robots, which could one day work on factory floors, were gut-punched and forced to wear festive clothes while performing acrobatics. Here are just a few of the oddest things we did to robots this year.

Fox News contributor Dr. Marc Siegel weighs in on how artificial intelligence can change the patient-doctor relationship on'America's Newsroom.' Artificial intelligence has been able to beat masters at games like chess and poker and Go. AI has also been able to beat human competitors in various video games. While impressive nonetheless, there is one major capability that these games do not require of the AI: physical skill. CyberRunner is an AI tasked with learning how to play the popular labyrinth maze game.

ARTIFICIAL intelligence systems have mastered some of mankind's best creations and natural intuitions. These AI systems notched some of the first wins for the machines. Artificial intelligence and table games make a good pair because humans have been trying to develop perfect tactics for strategy games for decades or even centuries. Chess is "known as a game that requires strategy, foresight, logic--all sorts of qualities that make up human intelligence," IBM researcher Murray Campbell told Scientific American. Campbell and a team developed Deep Blue, a six-foot supercomputer that defeated chess grandmaster Garry Kasparov in a six-game series in 1997.

From SIRI to self-driving cars, artificial intelligence (AI) is advancing rapidly. While science fiction often depicts AI as robots with human-like features, AI can include anything from Google's search algorithms to IBM's Watson to autonomous weaponry. Artificial intelligence now is well known as narrow AI (or weak AI), in that it is intended to perform a small task like facial recognition or internet searches. However, the long-term goal of various researchers is to produce general AI. While narrow AI may beat humans at whatever its particular task is, like playing chess or answering equations, AGI would beat humans at approximately all cognitive tasks.

MIT researchers have developed a bot equipped with artificial intelligence that can beat human players in tricky online multiplayer games where player roles and motives are kept secret. Many gaming bots have been built to keep up with human players. Earlier this year, a team from Carnegie Mellon University developed the world's first bot that can beat professionals in multiplayer poker. DeepMind's AlphaGo made headlines in 2016 for besting a professional Go player. Several bots have also been built to beat professional chess players or join forces in cooperative games such as online capture the flag.

Although artificial neurons and perceptrons were inspired by the biological processes scientists were able to observe in the brain back in the 50s, they do differ from their biological counterparts in several ways. Birds have inspired flight and horses have inspired locomotives and cars, yet none of today's transportation vehicles resemble metal skeletons of living-breathing-self replicating animals. Still, our limited machines are even more powerful in their own domains (thus, more useful to us humans), than their animal "ancestors" could ever be. It is easy to draw the wrong conclusions from the possibilities in AI research by anthropomorphizing Deep Neural Networks, but artificial and biological neurons do differ in more ways than just the materials of their containers. The idea behind perceptrons (the predecessors to artificial neurons) is that it is possible to mimic certain parts of neurons, such as dendrites, cell bodies and axons using simplified mathematical models of what limited knowledge we have on their inner workings: signals can be received from dendrites, and sent down the axon once enough signals were received.

MIT researchers have developed a bot equipped with artificial intelligence that can beat human players in tricky online multiplayer games where player roles and motives are kept secret. Many gaming bots have been built to keep up with human players. Earlier this year, a team from Carnegie Mellon University developed the world's first bot that can beat professionals in multiplayer poker. DeepMind's AlphaGo made headlines in 2016 for besting a professional Go player. Several bots have also been built to beat professional chess players or join forces in cooperative games such as online capture the flag.

When IBM's Deep Blue chess machine defeated world chess champion Garry Kasparov in 1997, the world responded with surprise and angst at how far computers had come: "Be Afraid," read a Weekly Standard headline reacting to the news. Artificial intelligence has since made advancements that were unthinkable just 20 years ago -- in the past decade alone, robots have achieved dominance over humans in games far more complex than chess. While most of those advances can't be quantified with milestones like chess victories, programmers have continued the tradition of building machines designed to outsmart humans at our own games. Here's a comprehensive list of the competitions, games, and challenges that robots beat humans at in the past decade.

Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal, the domain of StarCraft has emerged by consensus as an important challenge for artificial intelligence research, owing to its iconic and enduring status among the most difficult professional esports and its relevance to the real world in terms of its raw complexity and multiagent challenges. Over the course of a decade and numerous competitions 1–3, the best results have been made possible by hand-crafting major elements of the system, simplifying important aspects of the game, or using superhuman capabilities 4. Even with these modifications, no previous system has come close to rivalling the skill of top players in the full game. We chose to address the challenge of StarCraft using general purpose learning methods that are in principle applicable to other complex domains: a multi-agent reinforcement learning algorithm that uses data from both human and agent games within a diverse league of continually adapting strategies and counterstrategies, each represented by deep neural networks5,6. We evaluated our agent, AlphaStar, in the full game of StarCraft II, through a series of online games against human players. AlphaStar was rated at Grandmaster level for all three StarCraft races and above 99.8% of officially ranked human players.