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, mattmahone...@gmail.com ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T731509cdd81e3f5f-Mb45fd1f76d6ed4014a05cc4f Delivery options: https://agi.topicbox.com/groups
