--- Russell Wallace <[EMAIL PROTECTED]> wrote: > On Wed, Apr 30, 2008 at 5:29 PM, Matt Mahoney <[EMAIL PROTECTED]> > wrote: > > By modeling symbolic knowledge in a neural network. I realize it > is > > horribly inefficient, but at least we have a working model to > > start from. > > Inefficient is reasonable, but how do you propose to do it at all?
By distributing the problem across the internet. AGI can be divided into lots of specialized experts and a network for getting messages to the right experts. http://www.mattmahoney.net/agi.html I estimate the cost of AGI will be a substantial fraction of the value of the human labor it replaces, worth about US $2 to 5 quadrillion over the next 30 years worldwide. Since there is no funding source this big, AGI must be a decentralized network of autonomous peers that have an incentive to cooperate. In an economy where information has negative value on average (e.g. advertising), peers must compete for reputation and audience by providing the most useful information. This provides an incentive to intelligently filter incoming messages and route them to the appropriate experts by understanding their content. "Understanding" can be as simple as matching terms in two documents, or something more complex, such as matching a video clip to a text or audio description. However, there is an incentive to develop sophisticated solutions (e.g. distinguish TV programming from commercials). This is the S part of the problem, essentially a hierarchical adaptive pattern recognition problem that could be implemented as a neural network or something similar on each peer. For language, the pattern hierarchy is letters -> words -> semantic categories -> grammatical structures. The task is divided by pattern. A peer whose expertise is recognizing when a picture contains an animal could route the message to peers that recognize cats or dogs. I believe that extremely narrow domains are practical in a network with billions of peers. The D part is "old school" AI, calculators, databases, theorem provers, programs that play chess, etc. Interfacing these to natural language is a job for the S peers, matching the most common expressions to their formal equivalents. This is not a hard problem in narrow domains. The AGI is "friendly" as long as humans make the bulk of decisions about what information is valuable. However, as hardware gets more powerful, this may not always be the case. I don't pretend that this architecture is ultimately safe. Two long term risks: - Slow takeoff. The protocol evolves from natural language to something incomprehensible as the attention of peers competing for computing resources for recursive self improvement overrides the need for human attention. - Fast takeoff. Peers with language and programming skills use human trickery and also discover thousands of security vulnerabilities in thousands of software applications and take over every computer on the internet. It continues to appear to function normally. -- Matt Mahoney, [EMAIL PROTECTED] ------------------------------------------- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244&id_secret=101455710-f059c4 Powered by Listbox: http://www.listbox.com
