Prime Rebooting AI. Building Artificial Intelligence We Can Trust By Gary Marcus –

Rebooting AI. Building Artificial Intelligence We Can Trust Two Leaders In The Field Offer A Compelling Analysis Of The Current State Of The Art And Reveal The Steps We Must Take To Achieve A Truly Robust Artificial IntelligenceDespite The Hype Surrounding AI, Creating An Intelligence That Rivals Or Exceeds Human Levels Is Far Complicated Than We Have Been Led To Believe Professors Gary Marcus And Ernest Davis Have Spent Their Careers At The Forefront Of AI Research And Have Witnessed Some Of The Greatest Milestones In The Field, But They Argue That A Computer Beating A Human In Jeopardy Does Not Signal That We Are On The Doorstep Of Fully Autonomous Cars Or Superintelligent Machines The Achievements In The Field Thus Far Have Occurred In Closed Systems With Fixed Sets Of Rules, And These Approaches Are Too Narrow To Achieve Genuine Intelligence The Real World, In Contrast, Is Wildly Complex And Open Ended How Can We Bridge This Gap What Will The Consequences Be When We Do Taking Inspiration From The Human Mind, Marcus And Davis Explain What We Need To Advance AI To The Next Level, And Suggest That If We Are Wise Along The Way, We Won T Need To Worry About A Future Of Machine Overlords If We Focus On Endowing Machines With Common Sense And Deep Understanding, Rather Than Simply Focusing On Statistical Analysis And Gatherine Ever Larger Collections Of Data, We Will Be Able To Create An AI We Can Trustin Our Homes, Our Cars, And Our Doctors Offices Rebooting AI Provides A Lucid, Clear Eyed Assessment Of The Current Science And Offers An Inspiring Vision Of How A New Generation Of AI Can Make Our Lives Better

About the Author: Gary Marcus

Is a well-known author, some of his books are a fascination for readers like in the Rebooting AI. Building Artificial Intelligence We Can Trust book, this is one of the most wanted Gary Marcus author readers around the world.

5 thoughts on “Rebooting AI. Building Artificial Intelligence We Can Trust

  1. Kenneth Forbus Kenneth Forbus says:

    While there has been significant and important progress in artificial intelligence, there has also been a tidal wave of hype Pundits hyperventilate about super intelligent systems and less discerning and in a few cases, less scrupulous researchers overstate progress in ways that result in misleading media stories Marcus Davis new book, Rebooting AI, is a perfect corrective to such hype They do an excellent job of descr

  2. T. Nield T. Nield says:

    I ve been waiting for this book for awhile Gary Marcus has always been on the forefront calling out the sensationalism and hype of modern AI trends Many of us have forgotten or were not born when this all happened before in the 1960 s and 1980 s This AI rhetoric and sensationalism has happened before, and was always followed by an AI winter.What Gary and Ernest do well is to not to leave readers without a possible soluti

  3. Chris Chris says:

    I currently work as an engineer in the autonomous vehicle space, and I find that this book sheds light on the current state of AI.Basically, I found this book because at work I ran in to a couple of the current fundamental problems of deep learning One, deep learning struggles to adapt with a low number of human labeled examples Two, deep learning also struggles to adapt when the distribution of the input shifts or the tas

  4. CHERYL CHERYL says:

    I have been waiting for the industry to finally get critical with itself Thanks for this thoughtful treatment of a subject that has had misleading and frothy treatment from the media, unrealistic and misguided expectation setting from software vendors, and too little serious self examination from practitioners The authors have set out a pretty good set of functional requirements for a generalized AI here They ve done a good

  5. A M A M says:

    Almost all the computer science researchers and programmers unanimously adopt machine learning in general and deep learning in particular as the algorithm that will bring AI and AGI artificial general intelligence This book begs to differ It bravely criticizes the prospects of deep learning to deliver AGI, by presenting cases where this approach fails and suggests some half baked ideas in regards to other approaches such as us

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