Wait, hold your brains... https://www.health.harvard.edu/mind-and-mood/does-a-virus-cause-alzheimers
On 20.06.2019 02:51, Matt Mahoney wrote:
I derive most of the numbers in my paper on the cost of AI. In particular I compared the compressed size of the human genome with the compressed size of source code, and it comes to 300M lines. http://mattmahoney.net/costofai.pdf The size of the genome is comparable to Landauer's estimate of human long term memory for words, pictures, and sounds, about 10^9 bits. That's pretty consistent with twin studies of personality traits and intelligence. It's half innate, half learned. There are 10^37 DNA bases in the biosphere, mostly in 10^30 bacteria. I estimate the replication rate to be every 10^6 seconds (days or weeks) for 10^17 seconds (3 billion years). In addition, there have been 10^50 RNA and amino acid transcription operations. Evolution is slow because each generation of N offspring adds at most log N bits to the genome. Writing code is faster, about 10 lines = 160 bits per day. Some archaea evolved rotating propellers, but no living things evolved wheels. Evolution is not a universal learning algorithm because the search operations on DNA are limited to SNPs, cuts, and pastes. Hutter proved that AIXI is optimal for reinforcement learning and not computable. Legg extended Godel's incompleteness theorem to show that above a certain level of complexity, all theorems are unprovable. https://arxiv.org/abs/cs/0606070 The simple proof that there is no universal learner is as follows: suppose your program can learn any computable bit sequence by some easy criteria you choose, say anything less than a 100% error rate. Then I can compute a sequence that your program cannot predict. My program runs your program and outputs the opposite bit. AGI is a really hard problem, but most people don't realize just how incredibly hard it is. It's going to take decades of global effort, whole new technologies like molecular computing, and a willingness to give up privacy to produce computational models of your mind. If your life's work produces no AGI, it's only because you underestimated the cost by a factor of a billion. On Wed, Jun 19, 2019, 1:14 PM martin biehl <[email protected] <mailto:[email protected]>> wrote: Hi Matt, I am always intrigued by those numbers, do you have a paper on this or another source? I may have missed it at some point in the past. Also, didn't evolution evolve wheels? Why and where would you draw a line? Best, Martin On Thu, Jun 20, 2019 at 12:02 AM Matt Mahoney <[email protected] <mailto:[email protected]>> wrote: Not impressed. The paper lacks an experimental results section. The paper proposes learning how to learn AI algorithms. Since Legg and Hutter proved that there is no such thing as a simple, universal learning algorithm, something more than someone's idea is needed. Half of human knowledge is learned and half is inherited (10^9 bits each). The fastest way to code the inherited half is to write on the order of 100 million lines of code at a cost of $100 per line. The alternative, evolution, is often cited as a simple, universal learner but it is not universal (we did not evolve wheels), nor is it computationally feasible. It cost 10^48 DNA base copy operations to write our own source code. On Wed, Jun 19, 2019, 2:51 AM Junyan Xu <[email protected] <mailto:[email protected]>> wrote: Jeff Clune: https://twitter.com/jeffclune/status/1128327656401850369 *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/agi/subscription> Permalink <https://agi.topicbox.com/groups/agi/Tf33072618c7254e4-M04542b10d6ac77ac7424180a>
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