Willogy Insights

AI and Software Development enthusiasts. Knowledge is common. Our insights and experience on it is unique

Reconstructing 3D model from images: a novice experience

Reconstructing a 3D model from 2D images is a very hard task. It requires one to know a lot of algorithms, 3D knowledge, and matrix computations to code a project from scratch. Luckily, with available open-source projects, we can lay down the burdens of coding.

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Self-Supervised Learning - Part 3: The idea of Amdim and comparison with two other contrastive learning approaches

Amdim, CPC, Deep Infomax

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AI and Neuroscience

Artificial intelligence and neuroscience have a very close relationship. Knowledge from neuroscience can be utilized for improving AI and it is also true in reverse.

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Explanation in AI and Social Sciences

Knowing what an AI model will do and why it does that is very important for researchers to evaluate and improve that model. There is even a research domain for it, which is Explainable Artificial Intelligence (XAI). The existence of this field will help to solve current AI problems of ethical concerns and a lack of credibility from users. To acknowledge this, the AI field had better gain knowledge from philosophy, psychology and, cognitive science of how humans define and evaluate explanations. The content of this post is based on [1].

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Self-Supervised Learning - Part 2: From Entropy to Augmented Multiscale Deep InfoMax

DeepInfoMax and Amdim are two self-supervised models that are very popular in recent times. They are constructed based on the idea of the InfoMax principle. Therefore, to fully understand these two models, we must first know about the underlying basis of the InfoMax principle which includes entropy, mutual information, their properties, and relations.

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When will AI exceed Human performance?

In the previous post, we have discussed some difficulties in AI. But what do scientists in this domain really think about AI? If you want to know, you should continue reading. This post is written as a brief summary of the survey “Viewpoint: When Will AI Exceed Human Performance? Evidence from AI Experts” \[1].

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Self-Supervised Learning - Part 1: Simple and intuitive introduction for beginners

> _In the speech at AAAI 2020, Yann LeCun described Self-supervised learning as "The machine predicts any parts of its input for any observed part"._

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4 Misconceptions in AI Research community

In recent years, AI has appeared a lot in the media. Is it really miraculous as people say, or just a hype? Let's get this problem enlightened a bit through exploring the sufferings of AI formation in the paper “Why AI is harder than we think”

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