Positives & Negatives of Artificial Intelligence (AI)

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Artificial Intelligence – a blessing or a curse. It has always been the dream of humans to build machines having the ability to think like them. For a long time, humans had been watching this far-fetched dream coming true just in science fiction movies until the self-driving cars and Siri-like applications were introduced. From the smartphones in our pockets to the robots cleaning our floors, we have incorporated Artificial Intelligence into our lives.

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How NASA is using AI to develop the Next Generation of Self-Driving Planetary Rovers

Mars perseverance

With each new generation of NASA’s Mars rovers, improvements in AI, machine learning and other advanced technologies continue to make them more autonomous as they traverse the Martian soil looking for clues about the history of Earth and our solar system.

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Researchers from DeepMind and Alberta University propose policy-guided Heuristic search Algorithm

DeepMind’s AlphaGo and its successors previously demonstrated that the policy and heuristic function is formulated upon the PUCT (Polynomial Upper Confidence Trees) search algorithm. This algorithm can be quite effective for guiding search in adversarial games. However, PUCT is computationally inefficient and lacks guarantees on its search effort. Though other methods such as LevinTS provide guarantees on search steps, they do not use a heuristic function.

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[Paper Summary] Researchers at the University Of Genoa and AWS analyze techniques to Make Machine Learning (ML) Models fairer

AI fair or unfair

This research on algorithmic fairness provides three main approaches, i.e., pre-processing data, post-processing an already learned ML model, and in-processing, which consists of enforcing fairness notions by imposing specific statistical constraints on the learning phase of the model.

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[Paper] Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control

Model interpretability is crucial if we want to use AI models to make high-stake decisions (e.g., making medical diagnoses, preventing suicides, etc.). In NLP, one common way to get interpretability is to extract information from the trained models. For example, some use gradient-based input attribution techniques…

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Guide To THiNC: A refreshing functional take on Deep Learning

THiNC is a deep learning framework that makes composing, configuring and deploying models easy. It provides a flexible yet simple approach to modelling by providing low-level abstractions of the training loop, evaluation loop etc. Moreover, it plays well with major deep learning frameworks like TensorFlow and PyTorch. The functional programming API of THiNC is fairly simple and elegant. It’s light weighted API makes THiNC a good option for quick prototyping and deployment of machine learning models.

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Adversarial training reduces safety of neural networks in robots: Research

There’s a growing interest in employing autonomous mobile robots in open work environments such as warehouses, especially with the constraints posed by the global pandemic. And thanks to advances in deep learning algorithms and sensor technology, industrial robots are becoming more versatile and less costly.

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Applying Machine Learning-anchored computation to Enhance Drug discovery and development

Drug development AI console

Valo Health is a drug development company headquartered in Boston, Massachusetts, the United States, which uses human-centric data and machine learning to strengthen and boost the process of drug discovery and its development to promote intelligent health. The Opal Computational Platform by utilizes…

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Best of arXiv.org for AI, Machine Learning, and Deep Learning – February 2021

In this recurring monthly feature, we filter recent research papers appearing on the arXiv.org preprint server for compelling subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the past month.

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Top 10 Data Visualization project ideas 2021

It might seem strange to be making predictions about 2022, when it’s far from certain how the remainder of 2021 is going to play out. But if you are a fresher and you want to explore yourself in the field of Data Science then I can suggest you a simple technique to be become great at Data Science or anything creative is deliberately practising the acquired skills to reinforce them in your brain. Here I am going to discuss the top 10 Data Visualisation project ideas which is definitely help you to groom your career in this field.

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Trending toward concept Building – A review of Model Interpretability for Deep Neural Networks

Explaining how deep neural networks work is hard to do. It is an active area of research in academia and industry. Data scientists need to stay current in order to create models that are safe and usable. Leaders need to know how to avoid the risk of unethical, biased, or misunderstood models. In this post, I breakdown trends in network interpretability applied to image data. Some of the approaches covered apply to non-image-based networks as well. 

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10 amazing Machine Learning projects of 2020

Child reading a book

A lot happened in the machine learning community during the past year. Here’s a tour through the most popular and trending open-source research projects, demos, and prototypes. It ranges from photo editing to NLP to training models with “no-code,” and I hope they inspire you to build incredible AI-powered products this year.

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