When learning tasks over time, artificial neural networks suffer from a problem known as Catastrophic Forgetting (CF). This happens when the weights of a network are overwritten during the training of a new task causing forgetting of old information. To address this issue, we propose MetA Reusable Knowledge or MARK, a new method that fosters weight reusability instead of overwriting when learning a new task. Specifically, MARK keeps a set of shared weights among tasks. We envision these shared weights as a common Knowledge Base (KB) that is not only used to learn new tasks, but also enriched with new knowledge as the model learns new tasks.

As Tina walks onto the stage in front of hundreds of people she is beaming. She's collecting her British Sign Language (BSL) certificate which is the culmination of a journey that began with tragedy. Learning BSL has helped me say words that I cannot speak, she says. In 2018, while returning from a holiday, Tina fell down a flight of stairs and was in a coma for six weeks. The accident caused a traumatic brain injury that dramatically changed her life, leaving her struggling to speak.

We are going to do everything in our power to fight this,

Speaking ahead of the event in London, US Vice President Kamala Harris said that world leaders "must address the full spectrum of AI risks to humanity" and listed examples of faulty algorithms in healthcare, the use of AI in making "deepfakes", misinformation and biased facial recognition.

Yuxi (Hayden) Liu is a Software Engineer, Machine Learning at Google. Previously he worked as a machine learning scientist in a variety of data-driven domains and applied his ML expertise in computational advertising, marketing and cybersecurity. He is now developing and improving the machine learning models and systems for ads optimization on the largest search engine in the world. He is an author of a series of machine learning books and an education enthusiast. His first book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon in 2017 and 2018, and was translated into many different languages.

A general game playing system is one that can accept a formal description of a game and play the game effectively without human intervention. Unlike specialized game players, such as Deep Blue, general game players do not rely on algorithms designed in advance for specific games; and, unlike Deep Blue, they are able to play different kinds of games. In order to promote work in this area, the AAAI is sponsoring an open competition at this summer's Twentieth National Conference on Artificial Intelligence. This article is an overview of the technical issues and logistics associated with this summer's competition, as well as the relevance of general game playing to the long range-goals of artificial intelligence. We can exercise and improve our intellectual skills by playing such games.

DITORIAL AI Magazine Volume 11 Number 2 (1990) ( AAAI) In this issue, Luc Steels takes a new and insightful look at knowledgebased systems and provides a synthesis of several different approaches to analyzing expertise. It's a long article but, in my opinion, an important one. I recommend it to anyone with an interest in knowledge-level analysis of expert systems. On the same general topic of expert systems but from a different perspective is the article by Rob Weitz, who proposes a methodology for forecasting the impact of expert systems on the workplace over the near term. Finally, James Hendler, Austin Tate, and Mark Drummond present an extensive survey of AI systems and techniques for plan generation.

This Fall issue marks the first time we have devoted the AI Magazine to a single theme. The idea originated a couple of years ago, and I'm pleased to see the actual implementation. Mark Fox, Special Editor for this issue, is to be congratulated for a fine job of selecting some of the best authorities in the field and working with them to produce an excellent survey of the current state of the art in AI for manufacturing. In fact, Mark exceeded our expectations and solicited more articles than we could reasonably fit in one issue. The quality of all the articles was so high that we didn't want to exclude any of them.

That intelligence is a form of information processing and that the framework of modern digital computers provides pretty much all that is needed for representing and processing information for doing AI are two of the most foundational of such assumptions. Turing (1950) explicitly articulated this idea in the late 1940s, and later Newell and Simon (1976) proposed the physical symbol system hypothesis (PSSH) as a newer form of the same set of intuitions about the relation between computation and thinking. In this tradition, the computational approach is not just one way of making intelligent systems, but representing and processing information within the computational framework is necessary for intelligence as a process, wherever it is implemented. The language of thought (LOT) hypothesis, of which Fodor (1975) has given the most well-known exposition, is a variant of the computational hypothesis in AI. LOT holds that underlying thinking is a medium that has the properties of formal symbolic languages that we are familiar with in computer science.

Bylaws of the American Association for Artificial Intelligence, 13(1): Spring 1992, A2-A9 Adler, Mark see Rewari, Anil. Anick, Peter see Rewari, Anil. Architecture for Real-Time Distributed Scheduling, An, 13(3): Fall 1992, 46-56. Billmers, Meyer see Rewari, Anil. Bylaws of the American Association for Artificial Intelligence, 13(1): Spring 1992, A2-A9 Cambridge Center for Behavioral Studies see Weintraub, Joseph.