If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
If you could play god to an emerging artificial intelligence, would you? That's the moral dilemma at the heart of Agence, an interactive "dynamic film" that blends virtual reality, gaming, and cinematic storytelling to let audiences influence a handful of evolving, three-legged AI creatures, known as agents. The project, which recently debuted at the Venice International Film Festival, is a co-production between Toronto-based indie studio Transitional Forms and the National Film Board (NFB) of Canada. Think of it as Tamagotchi for the 2020s, but with real consequences on the development of digital life. "I think the core artistic vision of this is to cause people to question humans' role in artificial intelligence," says Pietro Gagliano, creator of Agence and founder of Transitional Forms, "both in its creation and interaction right now. These are virtually living creatures that are learning. This is a moment that I hope that we look back on in time as, you know, we made the right choices. And we decided to empathize with these creatures that didn't ask to be born."
Starfleet's star android, Lt. Commander Data, has been enlisted by his renegade android "brother" Lore to join a rebellion against humankind -- much to the consternation of Jean-Luc Picard, captain of the USS Enterprise. "The reign of biological life-forms is coming to an end," Lore tells Picard. "You, Picard, and those like you, are obsolete." In real life, the era of smart machines has already arrived. They haven't completely taken over the world yet, but they're off to a good start.
The "Curly" curling robots are capturing hearts around the world. A product of Korea University in Seoul and the Berlin Institute of Technology, the deep reinforcement learning powered bots slide stones along ice in a winter sport that dates to the 16th century. As much as their human-expert-bettering accuracy or technology impresses, a big part of the Curly appeal is how we see the little machines in the physical space: the determined manner in which the thrower advances in the arena, smartly raising its head-like cameras to survey the shiny white curling sheet, gently cradling and rotating a rock to begin delivery, releasing deftly at the hog line as a skip watches from the backline, with our hopes. Artificial intelligence (AI) today delivers everything from soup recipes to stock predictions, but most tech works out-of-sight. More visible are the physical robots of various shapes, sizes and functions that embody the latest AI technologies. These robots have generally been helpful, and now they are also becoming a more entertaining and enjoyable part of our lives.
I am Imtiaz Adam, and this article is an introduction to AI key terminologies and methodologies on behalf of myself and DLS (www.dls.ltd). This article has been updated in September 2020 to take into account advances in the field of AI with techniques such as NeuroSymbolic AI, Neuroevolution and Federated Learning. AI deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. Narrow AI: the field of AI where the machine is designed to perform a single task and the machine gets very good at performing that particular task. However, once the machine is trained, it does not generalise to unseen domains. This is the form of AI that we have today, for example Google Translate.
The main drawback to reinforcement learning is that it can't be used in some real-life applications. That's because in the process of training themselves, computers initially try just about anything and everything before eventually stumbling on the right path. This initial trial-and-error phase can be problematic for certain applications, such as climate-control systems where abrupt swings in temperature wouldn't be tolerated. The CSEM engineers have developed an approach that overcomes this problem. They showed that computers can first be trained on extremely simplified theoretical models before being set to learn on real-life systems.
Recent advancements in quantum computing have driven the scientific community's quest to solve a certain class of complex problems for which quantum computers would be better suited than traditional supercomputers. To improve the efficiency with which quantum computers can solve these problems, scientists are investigating the use of artificial intelligence approaches. In a new study, scientists at the U.S. Department of Energy's (DOE) Argonne National Laboratory have developed a new algorithm based on reinforcement learning to find the optimal parameters for the Quantum Approximate Optimization Algorithm (QAOA), which allows a quantum computer to solve certain combinatorial problems such as those that arise in materials design, chemistry and wireless communications. "Combinatorial optimization problems are those for which the solution space gets exponentially larger as you expand the number of decision variables," said Argonne computer scientist Prasanna Balaprakash. "In one traditional example, you can find the shortest route for a salesman who needs to visit a few cities once by enumerating all possible routes, but given a couple thousand cities, the number of possible routes far exceeds the number of stars in the universe; even the fastest supercomputers cannot find the shortest route in a reasonable time."
It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".
In recent years, researchers have been developing machine learning algorithms for an increasingly wide range of purposes. This includes algorithms that can be applied in healthcare settings, for instance helping clinicians to diagnose specific diseases or neuropsychiatric disorders or monitor the health of patients over time. Researchers at Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital have recently carried out a study investigating the possibility of using deep reinforcement learning to control the levels of unconsciousness of patients who require anesthesia for a medical procedure. Their paper, set to be published in the proceedings of the 2020 International Conference on Artificial Intelligence in Medicine, was voted the best paper presented at the conference. "Our lab has made significant progress in understanding how anesthetic medications affect neural activity and now has a multidisciplinary team studying how to accurately determine anesthetic doses from neural recordings," Gabriel Schamberg, one of the researchers who carried out the study, told TechXplore.
A robot equipped with artificial intelligence (AI) can excel at the Olympic sport of curling -- and even beat top-level human teams. Success requires precision and strategy, but the game is less complex than other real-world applications of robotics. That makes curling a useful test case for AI technologies, which often perform well in simulations but falter in real-world scenarios with changing conditions. Using a method called adaptive deep reinforcement learning, Seong-Whan Lee and his colleagues at Korea University in Seoul created an algorithm that learns through trial and error to adjust a robot's throws to account for changing conditions, such as the ice surface and the positions of stones. The team's robot, nicknamed Curly, needed a few test throws to calibrate itself to the curling rink where it was to compete.