My model is a genetic algorithm system based on AIXI. It’s a really lame solution to AGI, being naive and brute force, but it’s something small and simple any computer can run.
I call it MINT - for minimal intelligence. I really should dig up the old java source... it’s a neat little system. Sent from ProtonMail Mobile On Sat, Jun 9, 2018 at 11:55 AM, Matt Mahoney via AGI <[email protected]> wrote: > Like everyone else on this list, I do not have a working AGI. It is easy to > underestimate the enormity of the problem. The most obvious application of > AGI is to automate human labor. Globally this is a USD $75 trillion per year > problem. A working solution would have a ROI of world GDP divided by market > interest rates, about $1 quadrillion. When investors won't even put in $1 > million into your project, they are effectively setting odds of a billion to > one against your success. When you count the number of false promises and > failures to solve AI since the 1950's, that's not unreasonable. It's not that > I haven't tried. Ten years ago I proposed an AGI composed of billions of > narrow AI specialists and a distributed index to route your requests to the > right agents. Building this would require a global effort over decades and an > economic infrastructure that rewards providing useful services in a hostile > and competitive distributed computing environment. Agents would compete for > attention and reputation in a world where information has negative value. The > distributed index provides a message posting service, where all messages are > public, signed and dated and cannot be edited or deleted once posted, and are > routed to anyone who might care. You can find the proposal at > http://mattmahoney.net/agi2.html Such a project is far beyond what one person > or even a large company could accomplishment. A human brain sized neural > network with 10^14 synapses and 10 ms clock would require 10 petaflops and > about 1 petabyte of RAM. Such computers exist but require over 1 megawatt of > electricity, compared to about 20 watts for the human brain. You would need 7 > billion of these computers to replace 7 billion workers, requiring 7000 > terawatts of power. Global energy production is currently 15 TW. This kind of > power reduction is not going to be achieved by further shrinking transistors, > which are already close to the limits of physics. It will require computing > by moving atoms and molecules rather than electrons, something that biology > has already figured out but is still a long way off for us. Perhaps there are > more efficient solutions to AGI. Sure, for some problems, like arithmetic. > But deep neural networks are still the best we have for vision and probably > language. The problem is that intelligence (measured by reward or prediction > accuracy) over a wide range of problems increases logarithmically with CPU > time and memory because the problems themselves have a power law distribution > over difficulty. This clearly shows as the economy grows linearly while > computing power grows exponentially. It shows in my own work in data > compression as a measure of natural language prediction accuracy. The two > graphs in http://mattmahoney.net/dc/text.html are 10 years old but the > relationship hasn't changed. Ambitious, decades long AGI projects like > OpenCog and NARS are knowledge representation frameworks with empty knowledge > bases and inadequate hardware to solve any useful problems even if they were > populated. Why? Because software is easy and human knowledge collection is > hard. The design complexity of the human body cannot exceed the compressed > information content of our DNA, which I estimated to be 300 million lines of > code. (These numbers from my 2013 paper "The Cost of AI" published in 2017 as > a chapter in "Philosophy of Mind: Contemporary Perspective" (Curado, Gouveia > eds). http://mattmahoney.net/costofai.pdf ). This is doable for $30 billion, > a tiny, insignificant fraction of the total cost. Knowledge collection is > much harder. Human long term memory capacity is 10^9 bits, of which 1% is > unique to you and can only be extracted by speech or typing at 7 bits per > second costing $5 per hour at global average wage rates. This is only > practical (and still costing hundreds of trillions to collect 10^17 bits) by > giving up privacy and publishing your personal data. Otherwise you are stuck > with endless surveys and repeating the same data over and over. How many > times today did you have to type in just basic info like your name or email? > Hoping to stimulate some intelligent discussion. -- -- Matt Mahoney, > [email protected] ------------------------------------------ Artificial > General Intelligence List: AGI Permalink: > https://agi.topicbox.com/groups/agi/T731509cdd81e3f5f-Mb45fd1f76d6ed4014a05cc4f > Delivery options: https://agi.topicbox.com/groups ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T731509cdd81e3f5f-Mb76c1d451cc21345a62488e4 Delivery options: https://agi.topicbox.com/groups
