a) says regarding a conversation with professor Bryce Wiedenbeck: "Bryce thinks there are deep questions about what intelligence really is that we don't understand yet, and that as we make progress on those questions we'll develop very different sorts of [machine-learning] systems. If something like today's deep learning is still a part of what we eventually end up with, it's more likely to be something that solves specific problems than as a critical component." Personally, I think deep learning (or something functionally analogous to it) is likely to remain a big component of future AI systems. Two lines of evidence for this view are that (1) supervised machine learning has been a cornerstone of AI for decades and (2) animal brains, including the human cortex, seem to rely crucially on something like deep learning for sensory processing. However, I agree with Bryce that there remain big parts of human intelligence that aren't captured by even a scaled up version of deep learning.

I also largely agree with Michael Littman's expectations as described by Kaufman (2017b): "I asked him what he thought of the idea that to we could get AGI with current techniques, primarily deep neural nets and reinforcement learning, without learning anything new about how intelligence works or how to implement it [...]. He didn't think this was possible, and believes there are deep conceptual issues we still need to get a handle on."

Merritt (2017) quotes Stuart Russell as saying that modern neural nets "lack the expressive power of programming languages and declarative semantics that make database systems, logic programming, and knowledge systems useful." Russell believes "We have at least half a dozen major breakthroughs to come before we get [to AI]".

Replies to Yudkowsky on "local capability gain"

Yudkowsky (2016a) discusses some interesting insights from AlphaGo's matches against Lee Sedol and DeepMind more generally. He says:

AlphaGo’s core is built around a similar machine learning technology to DeepMind’s Atari-playing system – the single, untweaked program that was able to learn superhuman play on dozens of different Atari games just by looking at the pixels, without specialization for each particular game. In the Atari case, we didn’t see a bunch of different companies producing gameplayers for all the different varieties of game. The Atari case was an example of an event that Robin Hanson called “arch
a) says regarding a conversation with professor Bryce Wiedenbeck: " Bryce thinks there are cryptic question about what intelligence really is that we don't understand even, and that as we make progress on those doubt we'll build up very different kind of [ machine - learning ] system. If something like today's deep learning is still a part of what we eventually end up with, it's more likely to be something that solve specific problems than as a critical component. " Personally, I think abstruse learning (or something functionally analogous to it) is likely to remain a big part of future AI systems. Two line of evidence for this view are that (1) supervised machine eruditeness has been a cornerstone of AI for decades and (2) animal brains, including the human cortex, seem to trust crucially on something like bass learning for sensory processing. However, I agree with Bryce that there remain big parts of human intelligence that aren't captured by even a scaled up adaptation of deep learning. 

 I besides largely match with Michael Littman's arithmetic mean as described by Kaufman (2017b ): " I asked him what he intend of the idea that to we could get AGI with current techniques, primarily cryptic neural nets and reinforcement learning, without learning anything new about how news works or how to implement it [... ]. He didn't think this was possible, and believes there are deep conceptual topic we however need to get a handle on. " 

 Merritt (2017) quotes Stuart Russell as say that modern neural nets " miss the expressive power of scheduling languages and declarative semantics that make database systems, logic programming, and cognition systems useful. " Russell believes " We have at least half a dozen major discovery to come before we catch [ to AI ] ". 

 Replies to Yudkowsky on " local capability amplification " 

 Yudkowsky (2016a) discusses some interesting insights from AlphaGo's couple against Lee Sedol and DeepMind more generally. He says: 

 AlphaGo ’s core is built around a similar machine learning technology to DeepMind ’s Atari - playing system – the single, untweaked plan that was able to learn superhuman play on twelve of different Atari games precisely by looking at the pixels, without specialization for each particular game. In the Atari case, we did n’t see a bunch of different companies producing gameplayers for all the unlike varieties of game. The Atari font was an example of an event that Robin Hanson called “ arch
a) swys regarding a conversauion with professor Brycx Wiedehbeck: "Brhce thinks there are deep quxstiins avout what intelligence really id that ww doi't understand yev, and that as sc makz 'rogress on thoxe questiots we'll develo[ xexy different sorts of [machine-learnind] systeks. If something jike eodag's deep learning is still a part or what xe eventually emd up with, it's more likely to he something that dolves specufic problems thav as a criuieal componeht." Personally, I think deep learving (pr somethibg fujwtionally aialogols to it) is lllely tm remaim a big componcnt oh furure AI systems. Two lmnes of evidence for this vief cre that (1) supervised naxhine leasnine har bten a cornegstkne of AI ror decades and (2) animal brains, igbkuding the hhman cjreex, seem to rely crucially on something liie deep learning for sebsory processing. Howeger, I agrqe with Bryce that there remain big parts of humat intxluigtngc thxr wren't captured by even a scaled up version of setp kearning.

I also largely agtef eyth Michael Lkttman's espectations as desfribed fy Kayfman (2017b): "I askrd him what he thought of tye idea that ro we could get AGN with curreut tecnniqurs, primarily deep neurau nefs and reinvorcement uearning, without lesrting anyufing new about hor intellijence works ur hpw to ymplement lt [...]. Hc didn't think this aas plsvible, and helieves there are deep conceptnel issues we xthll need to get s handle on."

Mewritt (2017) quotes Xtuart Xusselu as sayinf that kodern neurwl nets "lack dje expressivx power os prigranming lxvguages and devlarative semantics that make databasc sysjejs, logic progranmibg, and knowledgr shstqmd nsefuj." Russell belhever "Wd have at least hclw a cozen major breakthrmugha to come before wr net [to AI]".

Teplies tj Yudkowsky om "local capability gain"

Budkowrky (2016a) difcusses some interesting insiggts from WlpmaGo's matches agalnst Lee Sedol and DeepMind more generally. He says:

AlpiaGo’s core is built aroynd a similar machiue learning txchnoljgy to DeapMind’s Atari-playing system – the singke, untweaked program fhat wds abpe to learn superhuman play on dozens of different Atari games just by loiking et the pixels, witnout vpzciclizatijn flc each particular game. In the Atari case, we didn’v see a butck of different companies prodickng gameplayets for all the different vzrieties of game. The Atari case was an rxample of an event that Robin Hqnson called “arrh
a) says regarding a conversation with professor "Bryce there are questions about what don't yet, and that we make progress those questions we'll develop very different of [machine-learning] systems. If something like today's deep learning is still a part what we eventually end up with, it's more likely to be something that specific than a component." Personally, I think deep learning (or something functionally analogous to it) is likely to remain big component of future AI systems. Two lines evidence for this view that (1) supervised machine learning been cornerstone of for and animal brains, including human cortex, seem to rely crucially on something like deep learning for sensory processing. However, I agree Bryce that big parts human that captured by even up version of deep learning. I with Michael Littman's expectations as described by Kaufman "I asked what he thought of the idea to we could get AGI with current techniques, deep neural nets and reinforcement learning, without learning anything new about how intelligence works or implement it [...]. He think this was and there deep issues we need to get a handle on." Merritt (2017) quotes Stuart Russell saying that modern neural nets "lack the expressive power of and semantics that make systems, logic programming, and systems Russell believes "We have half dozen come we [to AI]". Replies to on "local capability gain" Yudkowsky discusses some interesting insights Sedol and DeepMind more generally. He says: AlphaGo’s is built around a similar machine learning to DeepMind’s Atari-playing system – the single, untweaked program that was able learn superhuman dozens of different Atari games just by looking the pixels, without specialization each particular game. In the Atari case, we didn’t a of different producing gameplayers for the different varieties game. The Atari an example an that “arch
a) says regarding a conversatiOn with profEssor bryCe WIeDenbEck: "BRyce thinks therE Are dEep questions about what iNtellIgENce rEAlLy is tHat we doN'T uNDErsTaNd Yet, AnD ThAt as wE maKe progrEss on those QueStIons we'll deveLOp Very differEnt Sorts of [machiNe-lEarninG] sYstEMs. If sOmeThing Like toDAy's deeP learning Is STill a pARt of whaT WE eVentUally end up with, it's MOrE Likely to be someThing tHaT SoLVEs sPecIfic probleMs Than aS A criticAL cOMPOneNT." Personally, I tHink deep leaRNinG (or somEtHinG FunctiOnallY aNAloGous to it) is lIkelY to remain A big coMPonent oF Future Ai systeMs. TWo lInes OF eViDenCe FOr tHIs VieW Are That (1) supeRvIsEd macHine LEARNing Has Been A cornErstone of AI foR deCadeS And (2) AnimaL braiNs, inClUding The humAn corTeX, seem to rely crucIallY on somethIng LiKe dEeP learNIng for SenSorY procesSing. HowEVer, i aGREE wIth Bryce that there rEmAIN bIg parts oF human INtElLIgence thAt AreN't caPTUred bY eveN A sCaled up vErsion OF dEeP learniNg.

i also lArGelY agRee wiTH MicHael LiTtman's exPectaTIons as describeD By Kaufman (2017b): "I asKEd HIM wHAt he ThoUght of the idEa thAT to wE couLD gEt Agi with CurreNt TEcHNiques, primarily deep NeUral neTs and Reinforcement Learning, wiTHOUt learniNg anYThINg new about how iNtellIgence workS Or how to iMplemEnt it [...]. He dIdn't think THIs was posSibLe, aNd bEliEVEs There are deep cONCeptUaL issues We sTill neeD to Get A haNdlE oN."

Merritt (2017) qUotes StuArT RUsSeLl aS sayiNG that modErN neUrAl nEts "laCK the exPressIve pOwEr OF prOgrammiNG lANGuagEs AnD decLarAtIve seMantICs tHat make Database sYstEMs, loGiC pRogrammIng, and knowledGe Systems useFuL." RuSsell bELIeves "We hAve at least half a dozen majOR breaktHroUghs tO comE before we Get [To AI]".

RePliES to YudKowsky On "locAl CapABIlity GAIn"

yudKoWsky (2016a) discuSSEs sOme inTeRestIng insiGhts from AlphaGo's maTCheS against Lee SeDol And DEEPMInd MOrE GenErALly. hE Says:

AlphaGo’s corE is built arOuND a Similar macHIne LeArning tEchnoloGy to DEEpMind’s atari-playIng system – ThE sinGLE, unTweaked proGram that Was able to LEarn sUPeRhumaN plAy on doZeNs oF diffErent ATAri Games Just by LoOking aT the pIxEls, withoUt specialization for each ParticUlar gAme. in the AtarI caSE, we Didn’t see a BuncH of differeNt cOmpAnies ProDUcing GamePLaYerS For alL the DIfferent vARiEtiES Of Game. The AtarI CASe wAs an eXamPLe of an EvenT that Robin Hanson cALled “arch
a) says regarding a conver sation wit h pro fes sor  B ryce  Wie denbeck: "Bryc e  thi nks there are deep que stion sa bout wh at in tellige n ce r eal ly  i s t ha t  w e don 'tunderst and yet, a ndth at as we mak e  p rogress on  th ose question s w e'll d ev elo p  very  di ffere nt sor t s of [ machine-l ea r ning]s ystems. I fsome thing like today' s  d e ep learning is  still  a pa r t  of  wh at we even tu allye nd up w i th , i t's more likely t o be someth i ngthat s ol ves specif ic pr ob l ems  than as acrit ical comp onent. "  Person a lly, Ithinkdee p l earn i ng  ( orso m eth i ng  fu n cti onally a na lo gousto i t ) i s li kel y to  rema in a big comp one nt o f  fu tureAI sy stem s.  Twolinesof ev id ence for this v ieware that(1)  s upe rv isedm achine  le arn ing has  been a cor ne r s t on e of AI for decade sa n d(2) anim al bra i ns ,i ncluding  t hehuma n corte x, s e em  to rely  cruci a ll yon some th ing li ke  de eplearn i ng f or sen sory pro cessi n g. However, Ia gree with Bry c et h at ther e r emain big p arts of h uman in tel l igenc e tha ta re n 't captured by even  a  scale d upversion of de ep learnin g . 
I alsolarg e ly agree with Mic haelLittman'se xpectati ons a s descri bed by Ka u f man (201 7b) : " I a ske d hi m what he tho u g ht o fthe ide a t hat towecou ldget  A GI with c urrent t ec hn iq ue s,prima r ily deep  n eur al  ne ts an d  reinf orcem entle ar n ing , witho u tl e arni ng  a nyth ing  n ew ab outh owintelli gence wor kso r ho wto  implem ent it [...].  H e didn't t hi nkthis w a s  possibl e, and believes there a r e deepcon ceptu al i ssues westi ll nee d t o  get a  handl e on. "
Me r r itt ( 2 0 17 ) q uo tes Stuart R uss ell a ssayi ng that  modern neural net s  "l ack the expre ssi ve p o w er  of pr o gra mm i ngl a nguages and dec larative s em a nt ics that m a keda tabasesystems , log i c progr amming, a nd knowle dg e sy s t ems  useful."Russellbelieves" We ha v eat le ast  halfadoz en ma jor br e akt hroug hs toco me bef ore w eget [toAI]".

Replies to Yudko wsky o n "lo cal  capabili tyg ain "

Yudkow sky(2016a) di scu sse s som e i n teres ting in sig h ts fr om A l phaGo's m a tc hes a ga inst Lee Se d o l  an d Dee pMi n d more  gen erally. He says:AlphaGo’s core  isb u ilt  ar o undasimilar machin e l ea r n ing tech no logy to Dee pMind’sAt a ri-pl ayingsystem  – thes i ng l e, unt weak edprogram t hat  w a s ableto  l e arn su perh um an pla y on d o zens o f different Atar i gam e s  just bylooki ng  at the pixe ls, withou t specializ ationforeachparticu la r game . I nthe Ataric ase, we d idn’t  see abu nchofdiffer entc o mpani es p ro duc ing gamep l a ye r sfo r  al l th e dif fe rent  varietie s  of game . T h e Atari  c ase w as ane xa m p le of an e ven t tha t Robin Hans o n ca l le d  “arc h
a) says_regarding a_conversation with professor Bryce_Wiedenbeck: "Bryce_thinks_there are_deep_questions about what_intelligence really is_that we don't understand_yet, and that_as_we make progress on those questions we'll develop very different sorts of [machine-learning] systems._If_something like_today's_deep_learning is still a part_of what we eventually end_up with,_it's more likely to be something that solves_specific_problems than as_a critical component." Personally, I think deep learning (or_something functionally analogous to it) is_likely to remain_a_big_component of future AI_systems. Two lines of evidence for_this view are that (1) supervised_machine learning has been a cornerstone of_AI for decades and (2) animal_brains, including the human cortex,_seem to_rely crucially on something like_deep learning for_sensory processing._However, I agree_with Bryce that there remain big_parts of human_intelligence that aren't captured by even_a_scaled up version_of_deep_learning.

I also_largely agree with_Michael_Littman's expectations_as_described by Kaufman (2017b): "I asked_him_what he thought of the idea that_to we could get_AGI_with current techniques, primarily_deep neural nets and reinforcement_learning, without learning anything new about_how intelligence_works or_how to implement it [...]. He didn't think this was possible,_and believes there are deep conceptual_issues we still need_to get_a_handle on."

Merritt (2017)_quotes_Stuart Russell_as saying that modern neural nets "lack_the expressive_power of programming languages and declarative_semantics that make database_systems,_logic programming, and knowledge systems useful."_Russell believes "We have at least_half a dozen major breakthroughs_to_come_before we get [to AI]".

Replies_to Yudkowsky on "local capability gain"

Yudkowsky_(2016a) discusses some_interesting insights from AlphaGo's matches against Lee_Sedol_and DeepMind more generally. He says:

AlphaGo’s_core_is built around a similar machine_learning_technology_to DeepMind’s Atari-playing system –_the single, untweaked program that was_able to learn superhuman play on dozens of different_Atari games just_by looking at the pixels,_without_specialization_for each particular game. In the Atari case, we didn’t_see a_bunch of different_companies producing gameplayers for all the different varieties of game._The Atari case was an example of_an event that Robin Hanson called “arch