Thank you. "The best that individuals can do is make small steps towards a solution." I like this !! Good Luck
Best regards, Mohammadreza Alidoust On Fri, Aug 2, 2019 at 7:25 AM Matt Mahoney <[email protected]> wrote: > The obvious application of AGI is automating $80 trillion per year that > we have to pay people for work that machines aren't smart enough to do. > That means solving hard problems in language, vision, robotics, art, and > modeling human behavior. I listed the requirements in more detail in my > paper. The solution is going to require decades of global effort. The best > that individuals can do is make small steps towards a solution. > http://mattmahoney.net/costofai.pdf > > On Thu, Aug 1, 2019, 9:14 PM Mohammadreza Alidoust < > [email protected]> wrote: > >> Thank you. I really enjoy and appreciate your comments. >> >> There is no universal problem solver. So for the purpose of building a >> real AGI, how many problems should our model be able to solve? How big is >> our problem space? >> >> >> On Thu, Aug 1, 2019, 8:22 AM Matt Mahoney <[email protected]> >> wrote: >> >>> The human brain cannot solve every problem. There is no requirement for >>> AGI to do so either. Hutter and Legg proved that there is no such thing as >>> a universal problem solver or predictor. >>> >>> It feels like you could solve any problem given enough effort, but that >>> is an illusion. In reality you can't read a 20 digit number and recite it >>> back. The human brain is good at solving problems that improve reproductive >>> fitness, and that's only because it is very complex with thousands of >>> specialized structures and a billion bits of inherited knowledge. >>> >>> On Wed, Jul 31, 2019, 10:58 PM Mohammadreza Alidoust < >>> [email protected]> wrote: >>> >>>> I may not call the model "a reinforcement learning neural network", >>>> because nothing is going to be reinforced there. I would rather call it >>>> "model based decision making" where the model of the world will be >>>> incrementally completed and more accurate, which then helps in better >>>> decision making. >>>> >>>> The model is in its early stages and must be tested in heavier tasks >>>> like the ones you mentioned. However, I believe that AGI is an infinite >>>> problem-space and a real AGI must be able to solve everything. This >>>> requires further implementations, modifications, time, teamwork, financial >>>> support, etc. >>>> >>>> On Thu, Aug 1, 2019 at 1:34 AM Matt Mahoney <[email protected]> >>>> wrote: >>>> >>>>> Not understanding the math is the reader's problem. It is necessary to >>>>> describe the theory and the experiments and shouldn't be omitted. >>>>> >>>>> The paper describes 3 phases of training a reinforcement learning >>>>> neural network. The first phase is experimenting with random actions. The >>>>> next two phases choose the action estimated to maximize reward. They >>>>> differ >>>>> in that they use explicit and then implicit memory, although the paper >>>>> didn't explain these or other details of the learner. >>>>> >>>>> I like that the paper has an experimental results section, which most >>>>> papers on AGI lack. But I think calling it a "AGI brain" is a stretch. It >>>>> learns in highly abstract models of chemical manufacturing or cattle >>>>> grazing. It doesn't demonstrate actual AGI or solve any major components >>>>> like language or vision. >>>>> >>>>> On Wed, Jul 31, 2019, 8:01 AM Manuel Korfmann <[email protected]> >>>>> wrote: >>>>> >>>>>> I guess he meant: It’s difficult to understand all these mathematical >>>>>> equations. Visualizations are better at transporting ideas in a way that >>>>>> almost everyone can understand easily. >>>>>> >>>>>> On 31. Jul 2019, at 13:46, Mohammadreza Alidoust < >>>>>> [email protected]> wrote: >>>>>> >>>>>> Thank you for reading my paper. I wish you success too. >>>>>> >>>>>> Could you please explain more about the readership? I am afraid I did >>>>>> not get the point. >>>>>> >>>>>> Best regards, >>>>>> Mohammadreza Alidoust >>>>>> >>>>>> >>>>>> On Tue, Jul 30, 2019, 2:14 PM Stefan Reich via AGI < >>>>>> [email protected]> wrote: >>>>>> >>>>>>> If someone paid me to go, I'd go... :-) >>>>>>> >>>>>>> > http://agi-conf.org/2019/wp-content/uploads/2019/07/paper_21.pdf >>>>>>> >>>>>>> I like the stages you define in your paper (infancy, decision >>>>>>> making, expert). Sounds reasonable. >>>>>>> >>>>>>> I pretty much erased mathematical formulas from my brain though, >>>>>>> even though I have studied those things. These days I prefer to think in >>>>>>> natural language or code. Increases the readership exponentially too. >>>>>>> :-) >>>>>>> >>>>>>> Many greetings and best wishes to you >>>>>>> >>>>>>> >>>>>>> On Tue, 30 Jul 2019 at 02:13, Mohammadreza Alidoust < >>>>>>> [email protected]> wrote: >>>>>>> >>>>>>>> Dear Stefan Reich, >>>>>>>> >>>>>>>> Thank you. I do not know whether submitting my paper before >>>>>>>> official publication by Springer is against their copyrights or not. I >>>>>>>> am >>>>>>>> not sure about their rules. I will ask the authorities when I arrived >>>>>>>> Shenzhen and inform you. >>>>>>>> >>>>>>>> However I recommend not to miss the AGI-19. >>>>>>>> http://agi-conf.org/2019/ >>>>>>>> >>>>>>>> >>>>>>>> Best regards, >>>>>>>> Mohammadreza Alidoust >>>>>>>> >>>>>>> >>>>>>> >>>>>>> -- >>>>>>> Stefan Reich >>>>>>> BotCompany.de // Java-based operating systems >>>>>>> >>>>>> >>>>>> *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/Tf27122c71ce3b240-M89bff8225374adb7c0040aa1> > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Tf27122c71ce3b240-Md2e73d2ed98befbeb7a1b4af Delivery options: https://agi.topicbox.com/groups/agi/subscription
