You're going to have to use the shape equations to make predictions about the color of certain pixel coordinates, find discriminative ones, and fetch the corresponding cubes from the drawers to make a conclusion. (One of several possible methods you could come up with!)
MACHINE LEARNING
-
Reasoning: Making Sense Beyond Simple Interpolation
By
–
Reasoning is what you use to make sense of things that aren't a simple interpolation of things you've seen before.
-
Reasoning versus pattern recognition in novel problem forms
By
–
You'd have to use reasoning if the problem comes in a form that you've never seen before, that renders your pattern recognition ability ineffective. Let's say the task specification comes in the form of shape equations in the 2D plane, and your images come in the form of…
-
Reasoning in Visual Perception: Distinguishing Squares from Circles
By
–
The former sounds perhaps stranger, so here's an example. Let's say you have to tell whether a given image contains a square or a circle — a canonical perception problem. Sounds easy enough if you have a well-trained visual system, right? How would reasoning come into play?
-
Perception vs Reasoning: Data Volume Determines Problem-Solving Approach
By
–
Any task, even those canonically considered to be perception problems, can be solved with reasoning (if working with very little data). Inversely any task, even those canonically considered to be reasoning problems, can be solved with pattern recognition (given sufficient data).
-
VR AR MR Gaming Integration Powered by Advanced Technology
By
–
😂❤️When VR, AR and MR are combined with gaming then powered by tech, this happens. #tech #VR #AR #MixedReality #programming #IoT #DevCommunity #Technology #ML #Coding #100DaysOfCode #AI #ML #CX #UX #UI #VirtualReality #AugmentedReality #3D #innovation pic.twitter.com/yIUYxMyNJc
— Catherine Adenle (@CatherineAdenle) 24 décembre 2022When VR, AR and MR are combined with gaming then powered by tech, this happens. #tech #VR #AR #MixedReality #programming #IoT #DevCommunity #Technology #ML #Coding #100DaysOfCode #AI #ML #CX #UX #UI #VirtualReality #AugmentedReality #3D #innovation
-
Digital Transformation Framework with Key Sub-Dimensions Overview
By
–
Digital Transformation Framework with Sub-Dimensions @ResearchGate https://
researchgate.net/figure/Digital
-Transformation-Framework-with-Sub-Dimensions_fig1_337167323
…
Via @ingliguori #DigitalTransformation #Cloud #MachineLearning #BigData #ArtificialIntelligence #cybersecurity #Blockchain #DX #Analytics #AI #IIoT #DataScience #IoT #IoTPL #digitaltwin -
Galactica less popular, more hallucinatory than ChatGPT
By
–
Less, I think. Galactica didn't land with the consumer-facing splash that ChatGPT did. I only used it briefly, but it was also more hallucinatory, e.g. never refusing absurd requests. I think hallucination can be worse when infrequent, because we trust the model more.
-
PaLM’s Efficient Code Generation with Reduced Python Data
By
–
Coding: writes code with natural language description (text-to-code), translates code from one language to another, and fixes compilation errors.
— AI Breakfast (@AiBreakfast) 24 décembre 2022
PaLM claims to do just as well with 50x(!) less python data in it's training set.
(That's like learning to read from just 2 books) pic.twitter.com/7lgvDOxAqACoding: writes code with natural language description (text-to-code), translates code from one language to another, and fixes compilation errors. PaLM claims to do just as well with 50x(!) less python data in it's training set. (That's like learning to read from just 2 books)
-
Google PaLM Explains Complex Scenarios with Multi-step Inference
By
–
Google also says PaLM can generate explicit explanations for scenarios that require a complex combination of multi-step logical inference, world knowledge, and deep language understanding. (For example, it can explain jokes that are made up on the spot)