My email is mindpixel at proton male daht Kahm. Hopefully all but the minimally intelligent bots out there don’t pick up on my address.
But I’ll let you all know if I can find the source or not. It wouldn’t take long to rewrite it, though. I’ll share the github link when I can. Sent from ProtonMail Mobile On Tue, Jun 12, 2018 at 5:52 PM, Jeremy Owen Turner via AGI <[email protected]> wrote: > Hello everyone, > > I have been passively reading the correspondence between all of the authors. > I do not have anything to add to this group at this point and I am not taking > any sides. I am just taking in all of your ideas into consideration for now. > I personally think that some kind of hybrid approach between all of these > cognitive engineering paradigms will eventually unlock AGI capabilities. > > Apologies to posting this email to the whole group. My intention was to > contact MP directly but I did not see a direct email for MP. > > MP, I would like to read more about MINT (papers, proposals, documentation, > code etc.)...Have there been any toy or real implementations of a MINT-agent > yet - virtual or otherwise? Is this something like MC-AIXI but even more > diluted in its intellectual capabilities and reach? > > Many thanks, > Jeremy > > On Sat, Jun 9, 2018 at 10:56 AM, MP via AGI <[email protected]> wrote: > >> 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](https://agi.topicbox.com/latest) / AGI > / see [discussions](https://agi.topicbox.com/groups/agi) + > [participants](https://agi.topicbox.com/groups/agi/members) + [delivery > options](https://agi.topicbox.com/groups) > [Permalink](https://agi.topicbox.com/groups/agi/T943a4c6b8d2be614-M05c9958a8642ed0691cc670f) ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T943a4c6b8d2be614-M95e27e631956118babd94900 Delivery options: https://agi.topicbox.com/groups
