I don't even think it's a question of computational capacity - look at self driving cars, which took years of development with powerful servers processing terabytes of data. Yet they still crash into wall barriers. Look at any old high school dropout who wouldn't be able to understand predicate calculus, on the other hand: he'll drive at over 100 miles an hour in the pouring rain at two in the morning to go angrily figure out why his girlfriend is sleeping with his best friend!
My point is, I don't think it's a question of computational speed or power. I moreso am under the impression that most of our theories and efforts taken to produce an artificial mind aren't at all how the mind really works. Sure, we CAN theoretically take the route of a recursive self improving AI. Or rely on waiting for a Quantum computer like D-wave to have enough qubits to implement AIXI, but I think we can get there quicker with radically new theories of mind. Because nothing so far has worked. All the endearing promises over the last fifty years have fallen flat on their face. So, let's forget the overblown search algorithms. Forget about rigid data structures. For the love of God, forget about deep learning! We need something new. Sent from ProtonMail mobile -------- Original Message -------- On Feb 2, 2019, 12:25 AM, Linas Vepstas wrote: > 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](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/Ta6fce6a7b640886a-M074e47437b4dda937bf4a3e2) ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Ta6fce6a7b640886a-M14fde9b9b1b2456dd49aed13 Delivery options: https://agi.topicbox.com/groups/agi/subscription
