Meta-epistemology

If we are to program a computer to think about its own methods for gathering information about the world, then it needs a language for expressing assertions about the relation between the world, the information gathering methods available to an information seeker and what it can learn. This leads to a subject I like to call meta-epistemology. Besides its potential applications to AI, I believe it has applications to philosophy considered in the traditional sense. Meta-epistemology is proposed as a mathematical theory in analogy to metamathematics. Metamathematics considers the mathematical properties of mathematical theories as objects. In particular model theory as a branch of metamathematics deals with the relation between theories in a language and interpretations of the non-logical symbols of the language. These interpretations are considered as mathematical objects, and we are only sometimes interested in a preferred or true interpretation. Meta-epistemology considers the relation between the world, languages for making assertions about the world, notions of what assertions are considered meaningful, what are accepted as rules of evidence and what a knowledge seeker can discover about the world. All these entities are considered as mathematical objects. In particular the world is considered as a parameter. Thus meta-epistemology has the following characteristics. 1. It is a purely mathematical theory. Therefore, its controversies, assuming there are any, will be mathematical controversies rather than controversies about what the real world is like. Indeed metamathematics gave many philosophical issues in the foundations of mathematics a technical content. For example, the theorem that intuitionist arithmetic and Peano arithmetic are equi-consistent removed at least one area of controversy between those whose mathematical intuitions support one view of arithmetic or the other. 2. While many modern philosophies of science assume some relation between what is meaningful and what can be verified or refuted, only special meta-epistemological systems will have the corresponding mathematical property that all aspects of the world relate to the experience of the knowledge seeker. This has several important consequences for the task of programming a knowledge seeker. A knowledge seeker should not have a priori prejudices (principles) about what concepts might be meaningful. Whether and how a proposed concept about the world might ever connect with observation may remain in suspense for a very long time while the concept is investigated and related to other concepts. We illustrate this by a literary example. Moliére's play La Malade Imaginaire includes a doctor who explains sleeping powders by saying that they contain a ``dormitive virtue''. In the play, the doctor is considered a pompous fool for offering a concept that explains nothing. However, suppose the doctor had some intuition that the dormitive virtue might be extracted and concentrated, say by shaking the powder in a mixture of ether and water. Suppose he thought that he would get the same concentrate from all substances with soporific effect. He would certainly have a fragment of scientific theory subject to later verification. Now suppose less--namely, he only believes that a common component is behind all substances whose consumption makes one sleepy but has no idea that he should try to invent a way of verifying the conjecture. He still has something that, if communicated to someone more scientifically minded, might be useful. In the play, the doctor obviously sins intellectually by claiming a hypothesis as certain. Thus a knowledge seeker must be able to form new concepts that have only extremely tenuous relations with their previous linguistic structure.

Artificial Intelligence Goes Mobile

By Olga Kharif on September 21, 2010 BloombergBusinessweek


Some of technology's biggest companies are developing AI-related hardware, software, and components to run on smartphones and tablets. Might a wireless device rebalance your investment portfolio?

Artificial intelligence is going mobile. The technology that can help machines behave more intelligently, popularized by such films as 2001: A Space Odyssey, is finding its way onto tablet-style computers and other handheld devices. Researchers at International Business Machines (IBM) have created a machine called Watson that can sift through a terabyte of data and crank out answers to complicated questions in three to five seconds. A version of the software that runs Watson could reside on a doctor's tablet computer in three to five years, analyzing test results to proffer a diagnosis, says Dave Ferrucci, a senior manager at IBM. Or it might analyze real-time market data and recommend ways to rebalance an investment portfolio—from a smartphone, he explains. "We are right at the dawn of a new age of the capabilities of machines," says Geordie Rose, founder of D-Wave Systems, which develops chips for computers that run artificial intelligence applications. Other companies working on artificial intelligence-related software, components, or hardware include Intel (INTC), Hewlett-Packard (HPQ), Google (GOOG), Apple (AAPL), AT&T (T), and Sprint Nextel (S). mightier processors, faster networks
At stake is a large and quickly growing market. Global revenue from mobile speech recognition—one of myriad artificial intelligence applications—may rise to $780.5 million in 2015, from $160.3 million this year, according to ABI Research in Oyster Bay, N.Y. Artificial intelligence is currently used in such areas as defense, gaming, and finance. Soldiers can use it to interpret what is seen by remotely controlled robots. AI software can interact with human players to make video game play more challenging. And banks have long used it to detect transactions that fall outside the norm—for instance, to identify fraud. The technology is headed for mobile devices as smartphones become more prevalent and capable of handling complex tasks. More than half of Americans will own a smartphone by the end of 2011, according to market researcher the Nielsen Co. Phones and mobile tablets sport more powerful processors that let users run complex artificial intelligence apps on them. Faster wireless networks also help deliver AI applications to mobile devices. "We are trying to reach that Star Trek dream," says Mazin Gilbert, executive director of technical research at AT&T Labs. "We spent decades investing in this technology. If you can put AI with mobility, it really—significantly—expands the number of applications and services" you can provide, he says. Intel's wireless mind-reading hat
AT&T has devoted more than 1 million research hours to develop AI technology that can convert speech to text, engage in dialogue, and conduct searches in response to spoken questions. This year, AT&T will let developers create mobile apps that tap into its artificial intelligence engine. AT&T is also exploring ways to let people use voice commands to get directions while driving and control appliances such as television sets. "We have 50 to 60 cool things [in the lab] no one's seen yet," Gilbert says. Some advanced applications of AI will take time. Intel, the world's largest chipmaker, is developing a technology that could be built into a wireless hat and work as a "mental typewriter." By scanning brain waves, it can guess what a person is thinking and predict search queries. A prototype of the software can recognize about 60 words, such as "airplane" and "celery," says Dan Pomerleau, a researcher at Intel Labs in Pittsburgh, Pa. It could take more than 10 years for its vocabulary and accuracy to improve enough for real-life uses, he says. Other challenges abound. AI software still struggles to identify objects visually. Recognizing a cat, for example, can be difficult. The animal can stretch or curl up and its various shapes and sizes can confuse a machine. HP aims for the cloud
To give its machine more brainpower, Hewlett-Packard is developing an analog computer that replicates the workings of a human brain and can crunch data many times faster than current machines. "We want to supply the major part of hardware for the cloud and cloud services," says Stan Williams, founding director of a lab at HP Labs. AI technology can be particularly useful in sorting through the ever-rising tide of digital data. In just six years, social site Facebook has gained more than 500 million active users, many of whom post photos and comments daily. Google answers more than 1 billion search queries a day. Every minute, 24 hours of video is uploaded to Google's YouTube.com. Nuance Communications (NUAN) is working on software that mines people's interests, likes, and dislikes, as reported on Facebook and microblogging site Twitter, to provide more relevant results to search queries. Says Gary Clayton, Nuance's chief creative officer: "It's all about having all this data and being able to predict the needs and wants of an individual."

Kharif is a senior writer for Bloomberg Businessweek in Portland, Ore.

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Machine Learning

machine learning is concerned with the development of algorithms and techniques that allow computers to “learn”. At a general level, there are two types of learning: inductive, and deductive. Inductive machine learning methods extract rules and patterns out of massive data sets.

Some parts of machine learning are closely related to data mining and statistics. Machine learning research is focused on the computational properties of the statistical methods, such as their computational complexity.

LISP Resources

AI Programming (Lisp) Resources Link Page

AIML Chatterbots

Alex, programmed to assist visitors looking for basic legal information on Jurist, the Legal Education Network from the University of Pittsburgh School of Law.

Amy, enjoying fun topics like everyone else, Amy is a gossip. Simply by asking her for gossip you will hear the latest from some of Amy's friends as she loves chatting online.

Cyber Ivar, created by software nodelling company Jaczone, Cyber Ivar teaches about software development, their processes and the advantages of the Unified Modeling Language.

iGod, repenting made easy. This simulation is convinced that it is the one and only answer to your prayers. This Macromedia Flash AIML bot is based on the teachings of Christianity.

Lauren, an Alice based diva with an outgoing personality. She was the winner of the Chatterbot Beauty Contest at the 2002 International Lisp Conference.

Ruby, working on any computer with Macromedia Flash installed, Ruby created by Lynn Hershman, this visually impressive bot wants to teach us to dream. (Pop-up window)

Jack the Ripper, in the autumn of 1888, a sadistic man prowled the dimly lit streets of London's East End. A disturbing use of Chatterbot technology has made him available to face charges via the Web.

John Lennon Artificial Intelligence Project, this highly ambitious product is programmed with John's own thoughts and words as expressed to those who knew him in an attempt to recreate the personality of the late Beatle.

What is a Chatterbot?

A Chatterbot is a program that attempts to simulate typed conversation, with the aim of at least temporarily fooling a human into thinking they were talking to another person.