Our AI-minded readers should find this NYT interview with Gary Marcus and Ernest Davis to be illuminating: #Bookofwhy
“Artificial intelligence has a trust problem. We are relying on A.I. more and more, but it hasn’t yet earned our confidence.“
Powerful op-Ed by my collaborator and Ernest Davis on the mindlessness of current AI and why that’s a problem.
We need to "shift our approach to #AI in the hope of developing machines that have a rich enough conceptual understanding of the world that we need not fear their operation"— and Ernest Davis
"We need to start building computer systems that innately grasp three basic concepts: time, space and causality." and Ernest Davis via #artificialintelligence #machinelearning
How to Build Artificial Intelligence We Can Trust Computer systems need to understand time, space and causality. Right now they don’t. by and Ernest Davis #AI #MachineLearning #2MA
A refreshingly contrarian perspective on #AI. argues that we need to start over with first principles like time, space, and causality if we want AI we can trust.
We need to stop building A.I. that merely gets better and better at detecting statistical patterns in data sets, and start building A.I. that grasps three basic concepts: time, space and causality, argue and Ernest Davis
“Without the concepts of time, space and causality, much of common sense is impossible.” Great op-ed on the limitations of #AI from and Ernest Davis. Looking forward to reading more in the book #RebootingAI.
"Amazon’s facial recognition system works great much of the time, but when asked to compare the faces of all 535 members of Congress with 25,000 public arrest photos, it found 28 matches" Love it. Rebel bringing punk rock AI to the masses
Great piece by : "dystopian speculation arises in large part from thinking about today’s mindless #AI systems and extrapolating from them. If all you can calculate is statistical #correlation, you can’t conceptualize harm" #causality #AIethics
Towards a shared reality for humans and AI: “we need to... start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space and causality” by #ml #ai
"Today's #AI systems know surprisingly little about the concepts of time, space & causality... Without these concepts, much of common sense is impossible." , Ernest Davis
This is an important op-ed by and Ernest Davis. We need more informed discussion on trust and explainability in #AI. I'm looking forward to reading more in #RebootingAI next week.
How to build complex #AI that we can trust 🤖 #ArtificialIntelligence #MachineLearning #MondayMotivaton #MondayMorning & Ernest Davis via RT @2morrowknight
Common sense is knowledge that is commonly held! Awesome piece by .
How to Build Artificial Intelligence We Can Trust
Building #AIWeCanTrust, a sneak preview of
“The problem is not that today’s A.I. needs to get better at what it does. The problem is that today’s A.I. needs to try to do something completely different.” Much needed AI Skepticism via and Ernie Davis.
Every month new weaknesses in A.I. are uncovered. The problem is not that today’s A.I. needs to get better at what it does. The problem is that today’s A.I. needs to try to do something completely different
Opinion | How to Build Artificial Intelligence We Can Trust - The New York Times
How to Build Artificial Intelligence We Can Trust. Computer systems need to grasp three basic concepts: time, space and causality, by + via #digitalhealth #AI
How to build AI we can trust. Computer systems need to understand time, space and causality. Right now they don’t — Gary Marcus and Ernest Davis
Love this AI piece from - Two follow-ups. Need AI to understand social cog, not just physics. Also, can this be coded into computers? Does real understanding require consciousness? I think it might.
Build AI We Can Trust: Computer systems need to understand time, space & causality. ’we can shift our approach to A.I. in the hope of developing machines that have a rich enough conceptual understanding of the world that we need not fear their operation’.
"We need to stop building computer systems that merely get better & better at detecting statistical patterns in data sets—often using #DL—& start building systems that from the moment of their assembly innately grasp time, space and causality."
I really like this op-ed. I've personally converted to becoming a bit of a deep learning critic. It's an incredibly powerful technique, but it's time to acknowledge its limitations