what is deep learningimproved text-to-speech conversion digital assistants such as google now and amazon alexa near-human-level autonomous driving improved ad targetingas used by googlebaiduand bing improved search results on the web ability to answer natural-language questions superhuman go playing we're still exploring the full extent of what deep learning can do we've started applying it to wide variety of problems outside of machine perception and natural-language understandingsuch as formal reasoning if successfulthis may herald an age where deep learning assists humans in sciencesoftware developmentand more don' believe the short-term hype although deep learning has led to remarkable achievements in recent yearsexpectations for what the field will be able to achieve in the next decade tend to run much higher than what will likely be possible although some world-changing applications like autonomous cars are already within reachmany more are likely to remain elusive for long timesuch as believable dialogue systemshuman-level machine translation across arbitrary languagesand human-level natural-language understanding in particulartalk of human-level general intelligence shouldn' be taken too seriously the risk with high expectations for the short term is thatas technology fails to deliverresearch investment will dry upslowing progress for long time this has happened before twice in the pastai went through cycle of intense optimism followed by disappointment and skepticismwith dearth of funding as result it started with symbolic ai in the in those early daysprojections about ai were flying high one of the best-known pioneers and proponents of the symbolic ai approach was marvin minskywho claimed in "within generation the problem of creating 'artificial intelligencewill substantially be solved three years laterin he made more precisely quantified prediction"in from three to eight years we will have machine with the general intelligence of an average human being in such an achievement still appears to be far in the future--so far that we have no way to predict how long it will take--but in the and early sseveral experts believed it to be right around the corner (as do many people todaya few years lateras these high expectations failed to materializeresearchers and government funds turned away from the fieldmarking the start of the first ai winter ( reference to nuclear winterbecause this was shortly after the height of the cold warit wouldn' be the last one in the sa new take on symbolic aiexpert systemsstarted gathering steam among large companies few initial success stories triggered wave of investmentwith corporations around the world starting their own in-house ai departments to develop expert systems around companies were spending over $ billion each year on the technologybut by the early sthese systems had proven expensive to maintaindifficult to scaleand limited in scopeand interest died down thus began the second ai winter licensed to