 would take at least another 10 years before a machine could beat a human in Go.

What’s most remarkable is that AlphaGo turns out to be a machine that can improve its performance every day, without the direct supervision of a human programmer. That’s like an aircraft that can fly faster and faster without the help of an engineer. How can that be possible?

The past, present and future of machine learning

Structured data. When machine learning first took off, it was used to predict how we click, buy, lie, or die. Machines improved the way companies email, call, offer a discount, recommend a product, show an ad, inspect flaws, and approve loans. Under the hood of machine learning is statistical data mining that uncovers previously unknown patterns and recommends real-time actions. The downside to this approach is that it is context dependent. This is why most algorithms were built for a single purpose, like Deep Blue, which beat former chess grandmaster Garry Kasparov, but wasn’t useful for anything else. For these first-generation machines, learning was made possible only through constant monitoring by computer scientists and statisticians. Data had to be labeled, and goals set. The same program design couldn’t be used for different problems, and the algorithm couldn’t understand unstructured data expressed in natural human language.

Natural languages. When IBM Watson beat former champions Ken Jennings and Brad Rutter in the game show Jeopardy! in February 2011, it became clear that machine learning could go beyond this single-minded focus and handle unstructured, ambiguous data. In addition to factual knowledge on a wide variety of topics, competing on Jeopardy! requires the ability to understand subtle meaning, irony, riddles, slang, metaphors, jokes, puns, and other language complexities. Meaning is dependent on what has been said before, the topic itself, and how it is being discussed. At the end of the two-day Jeopardy! tournament, Watson amassed $77,147 in prize money, more than three times that of its human opponents. Jennings, who came in second, later said, “Just as factory jobs were eliminated in the 20th century by new assembly line robots, Brad and I were the first knowledge-industry workers put out of work by the new generation of ‘thinking’ machines.”

Currently, rather than replacing experts, Watson enhances their work. For example, the algorithm provides research and clinical recommendations to
would take at least another 10 years before a machine could beat a homo in Go. 

 What ’s about noteworthy is that AlphaGo turns out to be a machine that can improve its operation every day, without the direct supervision of a human programmer. That ’s like an aircraft that can fly quicker and quicker without the help of an engineer. How can that be possible? 

 The past, present and future of machine learning 

 Structured data. When car learning first took off, it was used to bode how we click, buy, lie, or fail. Machines better the manner companies email, call, propose a discount, recommend a product, show an ad, inspect defect, and approve loans. Under the hood of machine learning is statistical data mining that uncovers previously unknown traffic pattern and recommends real - time actions. The downside to this approach is that it is context dependent. This is why most algorithms were built for a single purpose, like Deep Blue, which beat former chess grandmaster Garry Kasparov, but was n’t useful for anything else. For these first - generation machines, learning was make possible only through ceaseless monitoring by calculator scientist and statisticians. Data had to be labeled, and goals set. The same program invention could n’t be used for different problems, and the algorithm could n’t understand unstructured data press out in natural human language. 

 Natural languages. When IBM Watson tick erstwhile champions Ken Jennings and Brad Rutter in the game show Jeopardy! in February 2011, it became clear that car learning could go beyond this single - minded focus and handle unstructured, ambiguous data. In summation to factual cognition on a wide variety of topics, competing on Jeopardy! requires the ability to sympathize subtle meaning, irony, riddles, slang, metaphors, joke, puns, and other language complexities. Meaning is dependent on what has been say before, the topic itself, and how it is being discussed. At the end of the two - day Jeopardy! tournament, Watson amassed $ 77,147 in prize money, more than three times that of its human opposition. Jennings, who came in second, later state, “ Just as factory jobs were eliminated in the 20th century by new forum line robots, Brad and I were the first knowledge - industry workers frame out of workplace by the fresh generation of ‘ thinking ’ machines. ” 

 Currently, rather than replacing expert, Watson enhances their work. For example, the algorithm provides research and clinical recommendations to
 wokld take at least anothev 10 years before c machiie couls beat a human in Go.

What’s most remarnavle iw that AlphaGo turns ojt to be w machinw thet can improve iva perfoviancs evexy day, without tme direct sgpervision of d fulan programmer. That’s like an aircrast that cwn fly faster wnd gwsted without the help of an engineer. Gow can that be possoble?

The past, present and fkturf of machine learnlng

Structurgs dwra. When machkne learning first tooi off, it was used to predict hod we elick, buy, lue, or gie. Machinew impgoved the way companias emaik, call, offer a divcoynt, recommend a produrt, show an ad, inspecj flaws, ang cpprove loans. Under tye hood of kachkbe ueadnmng is stwtiatical datz mining thqt uncovers previouxlr unknown pattsrns agd recommends real-time actions. The downsige fo this approach is thar it is context depenfent. This is why most algorithms were built for a single pgrposx, uikt Eeep Couf, which beat former chess grandmaster Garry Kwapsrpv, but wasn’t ufeful for amyhhogg else. For tfese fndsf-generation machinfs, learging qas made kossinle only through constant minitoring by xomputer scientistd and statirticoans. Cata had to be labeled, cnd gozls set. The same proffam design couldv’t ne gsed for different problemf, and the algprithm zoulcn’t unqerstand ujstrugdured data expressfd in ndtural humwn language.

Natural languages. Whxi IBM Watson nedt xormer ckampioks Ken Jennings and Brad Ruttgr in the game show Jeopzrdy! in February 2011, yt became cledt that machinx learnind coyld to beyova this single-monded focls and handle unstructurcd, amcjguous data. In cbdution to factuak kvowjefgx on w wide varietf of tooocs, cumpeting on Geopsrdy! requires the abhlitg to understand suntje meanibg, irony, riddles, slanb, metaphors, jokes, kuns, aid othxr lanbuade complexities. Meaning is depsndent on whwt has been faid before, the copic itself, and how it is being discussxd. At the end of the twi-day Jeopardy! tourncmtnt, Watson amessed $77,147 in prize money, more than thrwe times that of lts human opponents. Jehnings, who fame in second, later said, “Just as factory jobs were eliminated in the 20th xenturb fy new assejbly line rjyots, Brwd aid I were the firsn knowledge-industry workers put uut of wmrh by the new generation of ‘tnivking’ machiner.”

Currently, rather than rsplacing experts, Watson enhances their eork. For example, the algprithm peovidss resvzrrh and clinical recokmendacions ro
would take at least another 10 years machine beat a in Go. What’s turns to be a that can improve performance every day, without the direct of a human programmer. That’s like an aircraft that can fly faster and without the help of an engineer. How can that be possible? The past, and of learning data. When machine learning first took off, it was used to predict how we click, buy, or die. Machines improved the way companies email, offer a discount, recommend product, show an ad, inspect and loans. Under hood machine is statistical data that uncovers previously unknown patterns and recommends real-time actions. The downside to this approach is that it context dependent. why most were for single purpose, like which beat former chess grandmaster Garry useful for anything else. For these first-generation machines, was made only through constant monitoring by computer and statisticians. Data had to be labeled, and set. The same program design couldn’t be used for different problems, and the algorithm couldn’t data expressed in natural language. Natural languages. IBM beat champions Jennings and Rutter in the game show Jeopardy! in February 2011, it became that machine learning could go beyond this single-minded focus and ambiguous In addition to knowledge on a wide of competing on Jeopardy! requires to subtle slang, jokes, and other language complexities. is dependent on what has said before, the topic being discussed. At the end of the two-day tournament, Watson amassed $77,147 in prize money, than three times that of its human opponents. Jennings, who came in later said, factory jobs were eliminated in the 20th century new assembly line robots, and I were the first knowledge-industry workers put out work the new of ‘thinking’ machines.” rather than replacing Watson enhances their example, the provides and
 would take at least another 10 yeArs before a MachiNe cOulD bEat a HumaN in Go.

What’s most REmarKable is that AlphaGo turnS out tO bE A macHInE that Can imprOVe ITS peRfOrManCe EVeRy day, WitHout the Direct supeRviSiOn of a human prOGrAmmer. That’s LikE an aircraft tHat Can fly FaSteR And faSteR withOut the HElp of aN engineer. hoW Can thaT Be possiBLE?

THe paSt, present and futurE Of MAchine learning

structUrED dATA. WhEn mAchine learNiNg firST took ofF, It WAS UseD To predict how wE click, buy, liE, Or dIe. MachInEs iMProved The waY cOMpaNies email, caLl, ofFer a discoUnt, recOMmend a pROduct, shOw an ad, InsPecT flaWS, aNd AppRoVE loANs. undER thE hood of mAcHiNe leaRninG IS STatiStiCal dAta miNing that uncovErs PrevIOusLy unkNown pAtteRnS and rEcommeNds reAl-Time actions. The dOwnsIde to this AppRoAch Is That iT Is contExt DepEndent. THis is whY MosT aLGORiThms were built for a sInGLE pUrpose, liKe Deep bLuE, wHIch beat fOrMer ChesS GRandmAsteR gaRry KaspaRov, but WAsN’t Useful fOr AnythiNg ElsE. FoR thesE FirsT-generAtion macHines, LEarning was made POssible only thROuGH CoNStanT moNitoring by cOmpuTEr scIentIStS anD StatiSticiAnS. daTA had to be labeled, and gOaLs set. THe samE program desigN couldn’t be USED for diffErenT PrOBlems, and the algOrithM couldn’t unDErstand uNstruCtured daTa expressED In naturaL huMan LanGuaGE.

naTural languageS. wHen IbM watson bEat Former cHamPioNs KEn JEnNings and BRad RutteR iN tHe GaMe sHow JeOPardy! in FEbRuaRy 2011, It bEcame CLear thAt macHine LeArNIng Could go BEyOND thiS sInGle-mIndEd Focus And hANdlE unstruCtured, ambIguOUs daTa. in AdditioN to factual knoWlEdge on a widE vAriEty of tOPIcs, compeTing on Jeopardy! requires tHE abilitY to UnderStanD subtle meAniNg, ironY, riDDles, slAng, metAphorS, jOkeS, PUns, anD OThEr lAnGuage complEXItiEs. MeaNiNg is DependeNt on what has been saiD BefOre, the topic itSelF, and HOW iT is BEiNG diScUSseD. aT the end of the two-Day JeopardY! tOUrNament, WatsON amAsSed $77,147 in prIze moneY, more THan threE times thaT of its humAn OppoNENts. jennings, whO came in sEcond, lateR Said, “JUSt As facTorY jobs wErE elIminaTed in tHE 20th CentuRy by neW aSsemblY line RoBots, Brad And I were the first knowledGe-induStry wOrkErs put out Of wORk bY the new geNeraTion of ‘thinKinG’ maChineS.”

CuRRentlY, ratHEr ThaN ReplaCing EXperts, WatSOn EnhANCeS their work. FOR EXamPle, thE alGOrithm ProvIdes research and clINical recommendAtioNS To
 would take at least anoth er 10 year s bef ore  ama chin e co uld beat a hum a n in  Go.

What’s most rema rkabl ei s th a tAlpha Go turn s  o u t  to  b ea m ac h in e tha t c an impr ove its pe rfo rm ance every d a y,  without t hedirect super vis ion of  a  hu m an pr ogr ammer . That ’ s like  an aircr af t  thatc an flyf a st er a nd faster without th e  help of an en gineer .H ow c antha t be possi bl e?

T h e past, pr e s e nta nd future ofmachine lea r nin g

Str uc tur e d data . Whe nm ach ine learnin g fi rst tookoff, i t  was us e d to pr edicthow  we  cli c k,  b uy,  l i e,o rdie .  Ma chines i mp ro ved t he w a y c ompa nie s em ail,call, offer a  di scou n t,recom menda pr od uct,show a n ad,  i nspect flaws, a nd a pprove lo ans .Und er  theh ood of  ma chi ne lear ning is sta ti s t i ca l data mining that  u n c ov ers prev iously un kn o wn patte rn s a nd r e c ommen ds r e al -time ac tions. Th edownsid eto thi sapp roa ch is that  it is  context  depe n dent. This isw hy most algor i th m s  w e re b uil t for a sin glep urpo se,l ik e D e ep Bl ue, w hi c hb eat former chess gr an dmaste r Gar ry Kasparov,but wasn’t u s eful for  any t hi n g else. For th ese f irst-gener a tion mac hines , learni ng was ma d e  possibl e o nly  th rou g h  c onstant monit o r ingby  comput erscienti sts  an d s tat is ticians.Data had  t obe  l abe led,a nd goals  s et.  T hesamep rogram  desi gn c ou ld n ’tbe used fo r diff er en t pr obl em s, an d th e  al gorithm  couldn’t  un d erst an dunstruc tured data ex pr essed in n at ura l huma n language .

Natural languages. W h en IBMWat son b eatformer ch amp ions K enJ enning s andBradRu tte r in th e ga mesh ow Jeopard y !  in  Febr ua ry 2 011, it  became clear that mac hine learning  co uldg o  b eyo n dt his  s i ngl e - minded focus an d handle u ns t ru ctured, am b igu ou s data.  In add ition to fact ual knowl edge on a  w idev a rie ty of topi cs, comp eting onJ eopar d y!  requ ire s theab ili ty to  under s tan d sub tle me an ing, i rony,  r iddles,slang, metaphors, jokes , puns , and  ot her langu age com plexities . Me aning is d epe nde nt on  wh a t has  bee n  s aid befor e, t h e topic i t se lf, a nd  how it isb e i ngdiscu sse d . At t he e nd of the two-day Jeopardy! tour name n t , W ats o n am as sed $77,147 in  pr iz e money, m or e than thre e timesth a t ofits hu man op ponents . Je n nings,  who  ca me in sec ond ,l ater sa id ,“ Just a s fa ct ory jo bs wer e  eli m i nated in the 20t h cen t u ry by new  asse mb ly line robo ts, Brad a nd I were t he fir st k nowle dge-ind us try wo rke rs  put out o f  work bythe n ew gene ra tion  of  ‘thin king ’ machi nes. ”
Cu rrently,r a th e rth a n r epla cingex pert s, Watson enhances  th e ir work .For e xample ,  t h e  algorithm  pr ovide s research a n d cl i ni c al re commen dation s to
 would_take at_least another 10 years_before a_machine_could beat_a_human in Go.

What’s_most remarkable is_that AlphaGo turns out_to be a_machine_that can improve its performance every day, without the direct supervision of a human_programmer._That’s like_an_aircraft_that can fly faster and_faster without the help of_an engineer._How can that be possible?

The past, present and_future_of machine learning

Structured_data. When machine learning first took off, it was_used to predict how we click,_buy, lie, or_die._Machines_improved the way companies_email, call, offer a discount, recommend_a product, show an ad, inspect_flaws, and approve loans. Under the hood_of machine learning is statistical data_mining that uncovers previously unknown_patterns and_recommends real-time actions. The downside_to this approach_is that_it is context_dependent. This is why most algorithms_were built for_a single purpose, like Deep Blue,_which_beat former chess_grandmaster_Garry_Kasparov, but_wasn’t useful for_anything_else. For_these_first-generation machines, learning was made possible_only_through constant monitoring by computer scientists and_statisticians. Data had to_be_labeled, and goals set._The same program design couldn’t_be used for different problems, and_the algorithm_couldn’t understand_unstructured data expressed in natural human language.

Natural languages. When IBM Watson_beat former champions Ken Jennings and_Brad Rutter in the_game show_Jeopardy!_in February 2011,_it_became clear_that machine learning could go beyond this_single-minded focus_and handle unstructured, ambiguous data. In_addition to factual knowledge_on_a wide variety of topics, competing_on Jeopardy! requires the ability to_understand subtle meaning, irony, riddles,_slang,_metaphors,_jokes, puns, and other language_complexities. Meaning is dependent on what_has been said_before, the topic itself, and how it_is_being discussed. At the end of_the_two-day Jeopardy! tournament, Watson amassed $77,147_in_prize_money, more than three times_that of its human opponents. Jennings,_who came in second, later said, “Just as factory_jobs were eliminated_in the 20th century by_new_assembly_line robots, Brad and I were the first knowledge-industry workers_put out_of work by_the new generation of ‘thinking’ machines.”

Currently, rather than replacing experts,_Watson enhances their work. For example, the_algorithm provides research and clinical recommendations to