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The project might seem broad in its goals, but the researchers believe that ultimately, the system will benefit from multiple technologies working together. Consider the meeting-transcription function, says William Mark, vice president of the information and computer-science division at SRI. Even the best speech-recognition systems would have trouble producing an accurate transcript of a meeting unassisted, he says, but "in our context, because of information management, CALO has deep and rich knowledge about who are the people in the room, and what are the documents and phrases and slang used in context."
Since CALO has many learning systems, one challenge is integrating them so that CALO has a consistent structure for information that it can use to make decisions based on the noisy, uncertain data that it extracts from its various interactions. Domingos and others have been working on a probability consistency engine, which unifies two traditional approaches to artificial intelligence: logic and probability.
Alan Qi, an assistant professor of computer science at Purdue University, who is not involved with CALO, says that the unification of logic and probability is an important endeavor for the field of artificial intelligence. Combining these two approaches, Qi says, is far better than using either alone. Probabilistic approaches can handle noise and uncertainty well, while a logical structure is best for handling meaning.
Although CALO's approach is very far-reaching, SRI has made a version, called CALO Express, that boils down some of the features of CALO that are almost ready for deployment. CALO Express is a lightweight version of the real deal that integrates with Microsoft products such as Outlook and PowerPoint. Cheyer says that it includes parts of the three main features of information management, meeting assistance, and task management. He says that CALO Express is now being evaluated for use at DARPA. While it's uncertain whether CALO Express will become a commercial product available outside of the military, there is still hope that the average person may get access to technologies of this type. The research has already produced a few products, such as Smart Desktop, which is an information-management system that spun off of the task-tracer project done by Oregon State University as part of CALO. Radar Networks, makers of the Semantic Web product Twine, has also worked on some of CALO's semantic underpinnings. (See "The Semantic Web Goes Mainstream.")
A startup has licensed heavyweight artificial-intelligence technology for its online recommendation engine.
Software lets a hybrid car learn your habits and optimize its power usage.
Great attempt. I wish you every success further.
But any approach which tries to simulate any functionality of the brain (learning) should take in to account the nature of forgetting. The way brain forgets over time is one of the main reason for the brain's speed. Its remembering capacity is due to the basis of the frequency of the data usage. Also forgetting is due to not using the data over long time. This helps to throw away or exclude the irrelevent data for the fact at hand.
Probably we can make intelligent system better by associating the forgetting threshold to each and every fact & data and modifying it over time based on the usage frequency.
www.browsetoknow.blogspot.com
I disagree. Though forgetting is an essential operation to human intelligence it is merely an evolutionary design constraint begging to be expanded upon.
Sure, to start with a working model such emphasis should be considered....but after making it work; to make it work _well_ such constraints should be solved.
futureprogress.net
Actually, forgetting is a very practical issue with CALO for two reasons. First, it is easy to fill the disks on current desktop and laptop computers with the CALO knowledge base, data base, event history, and harvested documents. Second, the user's "work world" changes over time as projects come and go, so it is important for the learning algorithms to adapt to these changes (e.g., by downweighting older data or by explicitly modeling the change process). So while in principle there is nothing to gain by forgetting things, in practice there is a practical need and a scientific justification for forgetting old data.
actually the human brain and computers are very similar already in the forgetting process....however in forgetting we are not disposing the information but instead like a computer telling us that there are unused icons on the desktop would you like to cleanup the desktop in which it compresses and archives the data
I agree for the point you made. It is crucial in the beigning to make the models work properly.
The point I wolud like to emphasis is that we can assign some weights to the facts/data and imporve the data access decisions. These weights are dynamically modified based on the frequency of the data references. As the weights vary for each data unit in the knowledge base, accessiing the frequently reffered data should be made faster than rarely accessed data(The way brain recalls). I do not know whether this is a relevant and required future for your system.
Thank You.
sman
Why is everyone so giddy about a machine that learns? Lest we forget - what we dream of, can (and usually does) happen eventually. Given our imagination (ie, movies/TV show/etc.) we should be asking ourselves 'Is this going to lead somewhere that we do not want to go?' This should not be considered part of evolution on human terms, but on machine terms. Look at our past to look at our future. Could it be better with our roles reversed? Only you can decide...
From what I understand our brain is FAR more capable of remembering things than pretty much anybody on the planet uses it. I don't think is so much HAS to throw out information, but that through lack of use the connections die. Which is very important as logic is not an instinct, without killing off old connections I think we would be EXTREMELY prejudice and never be able to get over addictions and habits we had.
A lifebyte is 151.3 quadrabytes
Lifetime of experience
In neurophysiology it has been observed that with direct electrical stimulation to the brain anything from your past is available and nothing is forgotten. A lot of old knowledge is waiting for an association to a stimulation or need to be recalled. I hope computers can store everything I have ever experienced or thought and associate and recall it when needed. I made a rough calculation that if you had a video camera in HD recording everything you saw, heard or said for 100 years it would require 151.3 Quadrabytes to store it all, perhaps called a lifebyte. Add the sum total of human knowledge the AI system can access, I think we would have a much faster development of new knowledge.
Re: A lifebyte is 151.3 quadrabytes
Wouldn't that be the data only? Interpretation of the data (knowledge) would require additional overhead. But a good approximation none the less. I wish I could see and remember in high def though :)
Re: A lifebyte is 151.3 quadrabytes
I don't think the brain saves data, especially visual data, in the linear approach we use in computers. I think our brains use a very complex association sparking neurons that combine create patterns that help us kind of reverse engineer the visual scene from our memory. And that is why witness testimony is so unreliable and subject to the emotions and other input while the memory is being created. All in all with the (off the top of my head) something like 7 billion neurons in our brain. Assuming that each neuron is capable of multiple equivalent bits of information, I think it would still be considerably smaller than 151.3 quadrabytes.
so I'm just wondering how this compares with cyc?
http://www.opencyc.com
This sort of technology would be a perfect fit for a business technology strategy called BPM, or Business Process Management.
Just like a living cell has millions of chemical processes which are routinely executed and orchestrated to achieve the purposes and goals of the organism, so too do large corporate organizations have work "processes" in which many people engage in patterns of email, phone calls, paper forms, software usage, and face-to-face communications.
These interactions stream together into processes, which are now being targeted for identification and automation through the "BPM" methodology of IT system design, and execution in BPM software platforms.
The task of identifying and designing these work processes in corporations is the current bottleneck in this billion dollar, emerging industry.
Automatically mining process data from organizations is a holy grail in the BPM industry, and it would be great if this technology were applied to this specific business problem.
Manufacturing in the United States is in trouble. That's bad news not just for the country's economy but for the future of innovation.
This document is part of the “How-To Guide for Most Common Measurements” centralized resource portal. This tutorial provides a detailed guide for measurement and device considerations to take temperature measurements using thermocouples. Get an introduction to thermocouples, which are inexpensive sensing devices widely used with PC-based data acquisition systems. Also review some specific thermocouple examples and learn how thermocouples work and ways to integrate them into a data acquisition measurement system.
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fiberman
186 Comments
AI!
When MIT started looking at AI sometime in the last century, I used to say "I won't believe in 'artificial intelligence' until someone proves to me that 'natural intelligence' really exists. As the IBM instructor who taught me programming 45 years ago (IBM 650s with tubes) used to say: "Computers do what you tell them to do, not what you want them to do." Not much has changed, eh?
Heck, I'd be happy if MS Word would simply leave me alone to write what I want instead of guessing and suggesting!
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Watcher
2 Comments
Re: AI!
"Computers do what you tell them to do, not what you want them to do."
No, not much has changed - for now...
Natural intelligence is almost absent due to 'less-than-desirable' factors inherent in modern society.
In the absense of intelligenge comes ignorance and arrogance, both are bad enough on their own, but together they can be absolutely deadly.
And, Brother Bill won't leave us alone. MS is intergrated into almost every system on the planet. We all have the MS blues now. The question now is: Should we do without MS, Computers, or anything associated with them and the oncoming emergence of AI? Can we?
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