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] ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Ta6fce6a7b640886a-M370f3f46bd2983789caad831 Delivery options: https://agi.topicbox.com/groups/agi/subscription
