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) …
Since the 2013 Target breach, it's been clear that companies need to respond better to security alerts even as volumes have gone up. With this year's fast-spreading ransomware attacks and ever-tightening compliance requirements, response must be much faster. Adding staff is tough with the cybersecurity hiring crunch, so companies are turning to machine learning and artificial intelligence (AI) to automate tasks and better detect bad behavior. In a cybersecurity context, AI is software that perceives its environment well enough to identify events and take action against a predefined purpose. AI is particularly good at recognizing patterns and anomalies within them, which makes it an excellent tool to detect threats.
It may have been the first bit of fake news in the history of the Internet: in 1984, someone posted on Usenet that the Soviet Union was joining the network. It was a harmless April's Fools Day prank, a far cry from today's weaponized disinformation campaigns and unscrupulous fabrications designed to turn a quick profit. In 2017, misleading and maliciously false online content is so prolific that we humans have little hope of digging ourselves out of the mire. Instead, it looks increasingly likely that the machines will have to save us. One algorithm meant to shine a light in the darkness is AdVerif.ai,
Cyber criminals are constantly seeking new ways to perpetrate a breach but thanks to artificial intelligence (AI) and its subset machine learning, it's becoming possible to fight off these attacks automatically. The secret is in machine learning's ability to monitor network traffic and learn what's normal within a system, using this information to flag up any suspicious activity. As the technology's name suggests, it's able to use the vast amounts of security data collected by businesses every day to become more effective over time. At the moment, when the machine spots an anomaly, it sends an alert to a human – usually a security analyst – to decide if an action needs to be taken. But some machine learning systems are already able to respond themselves, by restricting access for certain users, for example.
The workshop focused on possible solutions to both technical and organizational challenges to mobility and manipulation research. This article presents the highlights of that discussion along with the content of the accompanying exhibits. Fortunately, these applications can be successful through simple repetitive behaviors or remote human operation. However, useful autonomy needed for operation in general situations requires advanced mobility and manipulation. Opening doors, retrieving specific items, and maneuvering in cluttered environments are required for useful deployment in anything but the most controlled environment. The mobile manipulation skills necessary to perform tasks in arbitrary environments may not result from current approaches to robotics and AI. Moving toward true robot autonomy may require new paradigms, hardware, and ways of thinking. The goal of the AAAI 2008 Workshop on Mobility and Manipulation was not only to demonstrate current research successes to the AAAI community but also to road-map future mobility and manipulation challenges that create synergies between artificial intelligence and robotics. The half-day workshop included both a session on the exhibits and a panel discussion. The panel consisted of five prominent researchers who led a discussion of future directions for mobility and manipulation research.
Robots in the Robot Host competition, part of the Eighteenth National Conference on Artificial Intelligence (AAAI-2002) Mobile Robot Competition faced two challenges: (1) a serving task that was similar to the Hors d'Oeuvres, Anyone? Both tasks required moving carefully among people, politely offering them information or hors d'oeuvres, recognizing when the people are making a request, and answering the request. Both tasks required moving carefully among people, politely offering them information or hors d'oeuvres, recognizing when the people are making a request, and answering the request. Celebrating the sixth year for the Robot Host competition, a new task, the robot information kiosk, was added. Three entries took on the challenge of creating host robots who can both offer hors d'oeuvres to attendees of the robot exhibition and can serve as a source of information to attendees during breaks in the program.
The Eighteenth National Conference on Artificial Intelligence (AAAI-2002) Robot Challenge is part of an annual series of robot challenges and competitions. It is intended to promote the development of robot systems that interact intelligently with humans in natural environments. The Challenge task calls for a robot to attend the AAAI conference, which includes registering for the conference and giving a talk about itself. In this article, we review the task requirements, introduce the robots that participated at AAAI-2002 and describe the strengths and weaknesses of their performance. The purpose of the challenge is to promote the development of robot systems that interact intelligently with humans in natural environments.
WE NEED BETTER STANDARDS FOR AI RESEARCH The state of the art in any science includes the criteria for evaluating research. Like every other aspect of the science, it has to be developed. In my previous message I grumbled about there being insufficient basic research, but one of the reasons for this is the difficulty of evaluating whether a piece of research has made basic progress. It seems that evaluation should be based on the kind of advance the research purports to be. I haven't been able to develop a complete set of criteria, but here are some considerations.
From an AI perspective, finding effective treatments for cancer is a high-dimensional search problem characterized by many molecularly distinct cancer subtypes, many potential targets and drug combinations, and a dearth of highquality data to connect molecular subtypes and treatments to responses. The broadening availability of molecular diagnostics and electronic medical records presents both opportunities and challenges to apply AI techniques to personalize and improve cancer treatment. We discuss these in the context of Cancer Commons, a "rapid learning" community where patients, physicians, and researchers collect and analyze the molecular and clinical data from every cancer patient and use these results to individualize therapies. Research opportunities include adaptively planning and executing individual treatment experiments across the whole patient population, inferring the causal mechanisms of tumors, predicting drug response in individuals, and generalizing these findings to new cases. The goal is to treat each patient in accord with the best available knowledge and to continually update that knowledge to benefit subsequent patients.
Picture tactile feedback and situated computing. That's the year when a revolving cadre of scientists began work on the problem of predicting the outcome of the spin of a roulette wheel. Although lacking the societal import of, say, predicting cancer in a patient, or even poison in a mushroom, predicting roulette seems on the face of it of even greater difficulty. The game itself is designed in every way for unpredictability. The problem is at its core a machine learning problem with a direct physical basis.
I view the World Wide Web as an information food chain. The maze of pages and hyperlinks that comprise the Web are at the very bottom of the chain. The maze of pages and hyperlinks that comprise the Web are at the very bottom of the chain. Today's Web is populated by a panoply of primitive but popular information services. Is the Web challenge a distraction from our long-term goal of understanding intelligence and building intelligent agents?