# AI Still Needs New Scientific Breakthroughs by Rodney A. Brooks > “The first class of stories is about the science, where many researchers are now vocally pointing out that there is a lot more science to be done in order to come up with learning algorithms that mimic the broad capabilities of humans and animals. Deep learning by itself will not solve many of the learning problems that are necessary for general Artificial Intelligence, for instance where spatial or deductive reasoning is involved. Further, all the breakthrough results we have seen in AI have been years in the making, and there is no scientific reason to expect there to be a sudden and sustained series of them, despite the enthusiasm from young researchers who were not around of the last three waves of such predictions in the 1950's, 1960's, and 1980's.” > **— Rodney A. Brooks**, *2016, Edge Annual Question, “What Do You Consider The Most Interesting Recent [Scientific] News? What Makes It Important?”* ## Sources and Context - **Direct Edge response and surrounding essay:** [Artificial Intelligence](https://www.edge.org/response-detail/26678) — Edge published this exact passage in its 2016 Annual Question collection, *WHAT DO YOU CONSIDER THE MOST INTERESTING RECENT [SCIENTIFIC] NEWS? WHAT MAKES IT IMPORTANT?*. The response page preserves the surrounding argument and qualifications. - **Complete local collection index:** [[research/Edge 2016 Full Quotation Curation|Edge 2016 Full Quotation Curation]] — retained source-first record for the complete response cohort and this passage's promotion status. ## Related Articles and Collections - **Collection:** [[collections/Edge|Edge]] - **Collection:** [[collections/Machine Succession|Machine Succession]] - **Article:** [[articles/Modern Artificial Intelligence in the 1970s|AI in the 1970s]] - **Article:** [[articles/Christopher Nolan's Odyssey at the Threshold of Machine Succession|Nolan's Odyssey]] - **Article:** [[articles/Cyber-Physical Lessons from Imperial Beekeeping|Cyber-Physical Beekeeping]] - **Wiki map:** [[wiki/Artificial Intelligence|Artificial Intelligence]] ## Related Topics - [[wiki/Artificial Intelligence|Artificial Intelligence]] - [[wiki/Infrastructural Superintelligence|Infrastructural Superintelligence]] - [[wiki/Machine Learning|Machine Learning]] - [[wiki/Machine Succession|Machine Succession]] ## Share on Social Media ``` “The first class of stories is about the science, where many researchers are now vocally pointing out that there is a lot more science to be done in order to come up with learning algorithms that mimic the broad capabilities of humans and animals. Deep learning by itself will not solve many of the learning problems that are necessary for general Artificial Intelligence, for instance where spatial or deductive reasoning is involved. Further, all the breakthrough results we have seen in AI have been years in the making, and there is no scientific reason to expect there to be a sudden and sustained series of them, despite the enthusiasm from young researchers who were not around of the last three waves of such predictions in the 1950's, 1960's, and 1980's.” — Rodney A. Brooks, 2016, Edge Annual Question, “What Do You Consider The Most Interesting Recent [Scientific] News? What Makes It Important?” https://bryantmcgill.com/simple-reminders-rodney-brooks-ai-still-needs-new-scientific ```