> Many commentators here agreed (over time) how agi development requires a radically-different approach to all other computational endeavors to date.
Not sure what that means. A really good NLU will go a very long way, and then we'll have to find a new "magic learner" module that replaces neural networks, both for image/audio recognition and learning logic. I suggest evolutionary algorithms. On Mon, 4 Feb 2019 at 05:45, Nanograte Knowledge Technologies < [email protected]> wrote: > Perhaps it's because, for its exponential complexity, agi defies > theoretical science. If no executable, framework of computational > intelligence exists, what's the use of being able to run at the speed of > light? > > Many commentators here agreed (over time) how agi development requires a > radically-different approach to all other computational endeavors to date. > As evidenced, developing a feasible approach (in the sense of a platform) > would require at least 10 years of R&D. In my opinion, that is correct. In > my case it took more than 22 years - part-time. Towards an agi prototype > then, with 10-years' concentrated effort, perhaps another additional 5-7 > years? > > Perhaps we should start pooling our research and resources with those who > offer the best 10-year result to date? I'm beginning to think this would be > the best way forward. Imagine a safe, inclusive, collaborative environment > where R&D parties could post real problems they needed solving and tangible > credit was given to the authors of such solutions? We're talking sharing in > the pot of gold at the end of the rainbow off course. > > Except for those sticky-finger, big boys who do not play well with others > at all. I'm quite certain they monitor this list trying to farm it yet > never contributing one bit of usefulness to others. Those we should weed > out from any "collaborative" setup at every opportunity. They are only in > it for themselves, not for the industry, or the benefit of the world. Yes, > you know who you are! > > This is the extent of my professional opinion. > > Robert Benjamin > > ------------------------------ > *From:* Linas Vepstas <[email protected]> > *Sent:* Monday, 04 February 2019 6:16 AM > *To:* AGI > *Subject:* Re: [agi] The future of AGI > > I have no clue what Peter is actually thinking because he's coy and > secretive. But I'm not pessimistic. I'm just perplexed why no one ever > seems to try the obvious things. Or why I can never seem to explain obvious > things to anyone and have them understand it. I am quite certain that one > can do better than neural nets and more easily, too, an have explained > exactly how more times than I can count, but my words are not connecting > with anyone who understands them. So, whatever. Day at a time. > > --linas > > On Sun, Feb 3, 2019 at 5:28 PM <[email protected]> wrote: > > I’m not that pessimistic at all. > > > > Our own AGI project has made steady progress over the past 17 years in > spite of only spending about $10 million – about 150 man-years of focused > effort. We’ve managed to successfully commercialize an early version of > our proto-AGI engine in a company that now employs about 100 people > www.smartaction.com . For the last 5 years my full-time team of about 10 > people has been working on the next generation engine > www.AGIinnovations.com / www.Aigo.ai . We are now ready to commercialize > this more advanced platform. > > > > Our focus has been limited to natural language comprehension/ learning, > question answering/ inference, and conversation management. > > I think that $100 million could go a long way towards functional, > demonstrable proto AGI. It seems to me that DeepMind hasn’t made good use > of the $200 or $300million spend so far – they lack a proper theory of > intelligence. I don’t know why Vicarious, the other well-funded AGI > company, hasn’t made better progress in perception/ action – my guess, for > the same reason…. > > I think all of the theoretical calculations of processing power are widely > off the mark – we’re not trying to reverse-engineer a bird – just need to > build a flying machine. > > > > My articles are here: > https://medium.com/@petervoss/my-ai-articles-f154c5adfd37 > > > > Peter Voss > > > > *From:* Linas Vepstas <[email protected]> > *Sent:* Friday, February 1, 2019 10:26 PM > *To:* AGI <[email protected]> > *Subject:* Re: [agi] The future of AGI > > > > 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…. > > > > > -- > 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-M05c2b08f0a3333cd04828eef> > -- Stefan Reich BotCompany.de // Java-based operating systems ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Ta6fce6a7b640886a-M65d6bc85d90203eeef1e1075 Delivery options: https://agi.topicbox.com/groups/agi/subscription
