Www.hslabs.org - Harik Shazeer Labs Creativity – The Last Human Stronghold? - TFOT. Creativity – The Last Human Stronghold? Monday, December 24, 2007 - Israel Beniaminy Home >> Personal Column >> Israel Beniaminy Tags: A.I. artificial-Intelligence computers technology chess art Creativity poet israel Humans are better than machines. However, the advantages of humans over machines have been in steady retreat – we gave up on physical strength first, then on some mental capacities such as computation, and now we’re in the process of conceding machine superiority in mental tasks that used to be considered as evidence of high intelligence.
Is there a last stronghold of capabilities machines will never have? This column considers the possibility that this last stronghold is human creativity. 1600 : Some machines are stronger than humans, but no machine can perform numerical computations. 1623 : Wilhelm Schickard builds the first automatic calculator. 1941 : Machines perform complex computations under the control of algorithms developed by humans. AI Overview. Big data is suddenly everywhere. Everyone seems to be collecting it, analyzing it, making money from it and celebrating (or fearing) its powers. Whether we’re talking about analyzing zillions of Google search queries to predict flu outbreaks, or zillions of phone records to detect signs of terrorist activity, or zillions of airline stats to find the best time to buy plane tickets, big data is on the case. By combining the power of modern computing with the plentiful data of the digital era, it promises to solve virtually any problem — crime, public health, the evolution of grammar, the perils of dating — just by crunching the numbers.
Or so its champions allege. Artificial Intelligence authors/titles recent submissions. /assets/_pubs/what-woult-they -thin. Home Page for Professor Michael Kearns, University of Pennsylvan. Most of this site is organized as a single flat html file. The links below let you navigate directly to the various subsections. Publications Research Group Members Teaching and Tutorial Material Professional Bio Educational Background Editorial and Professional Service Press My research interests include topics in machine learning, algorithmic game theory, social networks, computational finance, and artificial intelligence. I often examine problems in these areas using methods and models from theoretical computer science and related disciplines.
While the majority of my work is mathematical in nature, I have also participated in a variety of empirical and experimental projects, including applications of machine learning to finance, spoken dialogue systems, and other areas. Most recently, I have been conducting human-subject experiments on strategic and economic interaction in social networks. Current: The Past: In the past I have been program chair of NIPS, AAAI, COLT, and ACM EC.
Netflix Prize Results and Source Code. Overview When I heard about the Netflix Prize, I have to admit that I couldn't resist joining. The stated goal of this contest is to help Netflix improve their movie recommendation system. The team that can beat Netflix's own home-grown collaborative filtering system by 10% will win a million dollars. Like many others, I have doubts as to whether this feat is possible given the sparsity of data and inherent noise. Some speculate that the real goal is to fend off several software patents by showing prior works. I joined this contest purely as a learning experience and to test out several new tools using this new wealth of demo data. The Basics After you join the contest, you are presented with over 2 gigs of csv files containing 100 million ratings.
While 100 million ratings may sound like a lot, the more data the better. The charts below show some of the interesting aspects of the source data. Overal Rating Distribution Average Rating Distribution Rating Count Distribution The First Attempt. Decision-Theoretic Planning with non-Markovian Rewards. Journal of Artificial Intelligence Research, 25 (2006) 17-74. Submitted 12/04; published 01/06 © 2006 AI Access Foundation. All rights reserved. Abstract: A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decision-theoretic planning, where many desirable behaviours are more naturally expressed as properties of execution sequences rather than as properties of states, NMRDPs form a more natural model than the commonly adopted fully Markovian decision process (MDP) model.
Mark Watson, Java Consultant and Author. Edge. Simbrain. Natural Language Processing. Latest Essays - Artificial Intelligence De. Ian Clarkes blog. Kevin Warwick - Home Page. Gnod - The global network of dream. Self Organizing Map AI for P. Introduction this article is about creating an app to cluster and search for related pictures. i got the basic idea from a Longhorn demo in which they showed similar functionality. in the demo, they selected an image of the sunset, and the program was able to search the other images on the hard drive and return similar images. there are other photo library applications that offer similar functionality. honestly ... i thought that was pretty cool, and wanted to have some idea how they might be doing that. internally, i do not know how they actually operate ... but this article will show one possibility. also writing this article to continue my AI training Kohonen SOM luckily there is a type of NN that works with unsupervised training. i'm guessing that it is the 2nd or 3rd most popular type of NN?
Anyways, that is my current understanding; here are some other articles i recommend 1) Grid Layout 2) Color Grouping 3) Blog Community (OUCH!) 4) Picture Similarity. Generation5 - At the forefront of Artifici. ConceptNet. What is ConceptNet? [top] ConceptNet is a freely available commonsense knowledgebase and natural-language-processing toolkit which supports many practical textual-reasoning tasks over real-world documents right out-of-the-box (without additional statistical training) including topic-jisting (e.g. a news article containing the concepts, “gun,” “convenience store,” “demand money” and “make getaway” might suggest the topics “robbery” and “crime”), affect-sensing (e.g. this email is sad and angry), analogy-making (e.g.
“scissors,” “razor,” “nail clipper,” and “sword” are perhaps like a “knife” because they are all “sharp,” and can be used to “cut something”), text summarization contextual expansion causal projection cold document classification and other context-oriented inferences The ConceptNet knowledgebase is a semantic network presently available in two versions: concise (200,000 assertions) and full (1.6 million assertions). Papers about ConceptNet [top]: Download ConceptNet [top] S. CCT Map 3-Can the elements of thinking be. Agent. An agent is an animate entity that is capable of doing something on purpose. That definition is broad enough to include humans and other animals, the subjects of verbs that express actions, and the computerized robots and softbots. But it depends on other words whose meanings are just as problematical: animate, capable, doing, and purpose. The task of defining those words raises questions that involve almost every other aspect of ontology. Animate. Literally, an animate entity is one that has an anima or soul.
Psychology of Agents Linguistically, an agent is an animate being that can perform some action, and an action is an event that is initiated or carried out by some animate being. The word animate comes from the Latin anima, which means breath or soul. We must inquire for each kind of living thing, what is its psyche; what is that of a plant, and what is that of a human or a beast. Competence Levels Avoiding. The behavior of the lower levels depends primarily on immediate inputs.