Thanks Matt, very nice post! We're on the same wavelength, it seems. -- Linas
On Thu, Jan 31, 2019 at 3:17 PM Matt Mahoney <[email protected]> wrote: > When I asked Linas Vepstas, one of the original developers of OpenCog > led by Ben Goertzel, about its future, he responded with a blog post. > He compared research in AGI to astronomy. Anyone can do amateur > astronomy with a pair of binoculars. But to make important > discoveries, you need expensive equipment like the Hubble telescope. > https://blog.opencog.org/2019/01/27/the-status-of-agi-and-opencog/ > > Opencog began 10 years ago in 2009 with high hopes of solving AGI, > building on the lessons learned from the prior 12 years of experience > with WebMind and Novamente. At the time, its major components were > DeStin, a neural vision system that could recognize handwritten > digits, MOSES, an evolutionary learner that output simple programs to > fit its training data, RelEx, a rule based language model, and > AtomSpace, a hypergraph based knowledge representation for both > structured knowledge and neural networks, intended to tie together the > other components. Initial progress was rapid. There were chatbots, > virtual environments for training AI agents, and dabbling in robotics. > The timeline in 2011 had OpenCog progressing through a series of > developmental stages leading up to "full-on human level AGI" in > 2019-2021, and consulting with the Singularity Institute for AI (now > MIRI) on the safety and ethics of recursive self improvement. > > Of course this did not happen. DeStin and MOSES never ran on hardware > powerful enough to solve anything beyond toy problems. ReLex had all > the usual problems of rule based systems like brittleness, parse > ambiguity, and the lack of an effective learning mechanism from > unstructured text. AtomSpace scaled poorly across distributed systems > and was never integrated. There is no knowledge base. Investors and > developers lost interest. > > Meanwhile the last decade transformed our lives with smart phones, > social networks, and online maps. Big companies like Apple, Google, > Facebook, and Amazon, powered it with AI: voice recognition, face > recognition, natural language understanding, and language translation > that actually works. It is easy to forget that none of this existed 10 > years ago. Just those four companies now have a combined market cap of > USD $3 trillion, enough to launch hundreds of Hubble telescopes if > they wanted to. > > Of course we have not yet solved AGI. We still do not have vision > systems as good as the human eye and brain. We do not have systems > that can tell when a song sounds good or what makes a video funny. We > still pay people $87 trillion per year worldwide to do work that > machines are not smart enough to do. And in spite of dire predictions > that AGI will take our jobs, that figure is increasing at 3-4% per > year, continuing a trend that has lasted centuries. > > Over a lifetime your brain processes 10^19 bits of input, performing > 10^25 operations on 10^14 synapses at a cost of 10^-15 joule per > operation. This level of efficiency is a million times better than we > can do with transistors, and Moore's Law is not going to help. Clock > speeds stalled at 2-3 GHz a decade ago. We can't make transistors > smaller than about 10 nm, the spacing between P or N dopant atoms, and > we are almost there now. If you want to solve AGI, then figure out how > to compute by moving atoms instead of electrons. Otherwise Moore's Law > is dead. > > Even if we can extend Moore's Law using nanotechnology and biological > computing (and I believe we will), there are other obstacles to the > coming Singularity. > > First, the threshold for recursive self improvement is not human level > intelligence, but human civilization level intelligence. That's higher > by a factor of 7 billion. But that's already happening. It's the > reason our economy and population are both growing at a faster than > exponential rate. > > Second is Eroom's Law. The price of new drugs doubles every 9 years. > Global life expectancy has been increasing 0.2 years per year since > the early 1900's, but that rate has slowed a bit since 1990. Testing > new medical treatment is expensive because testing requires human > subjects and the value of human life is increasing as the economy > grows. > > Third, Moore's Law doesn't cover software or knowledge collection, two > of the three components of AGI (the other being hardware). Human > knowledge collection is limited to how fast you can communicate, about > 150 words per minute per person. Software productivity has remained > constant at 10 lines per day since 1950. If you were hoping for an > automated method to develop software, keep in mind that the 6 x 10^9 > bits of DNA that is you (equivalent to 300 million lines of code) > required 10^50 copy and transcription operations on 10^37 bits of DNA > to write over the last 3.5 billion years. > > Comments? > > -- > -- Matt Mahoney, [email protected] -- cassette tapes - analog TV - film cameras - you ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Ta6fce6a7b640886a-M074e47437b4dda937bf4a3e2 Delivery options: https://agi.topicbox.com/groups/agi/subscription
