Using Reinforcement Learning to build a Self-Learning grasping Robot

In this post, I will explain my experience over the course of a year of working with Reinforcement Learning (RL) on autonomous robotics manipulation. It is always hard to start a big project which requires many moving parts. It was undoubtedly the same in this project. I want to pass the knowledge I gathered through this process to help others overcome the initial inertia.

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DeepMind’s big losses, and the questions around running an AI lab

Last week, on the heels of DeepMind’s breakthrough in using AI to predict protein-folding came the news that the UK-based AI company is still costing its parent company Alphabet Inc hundreds of millions of dollars in losses each year. A tech company losing money is nothing new. The tech industry is replete with examples of companies who burned investor money long before becoming profitable. But DeepMind is not a normal company seeking to grab a share of a specific market. It is an AI research lab that has had to repurpose itself into a semi-commercial outfit to ensure its survival.

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The inevitable symbiosis of Cybersecurity and AI

While improvements in AI and Deep Learning move forward at an ever increasingly rapid rate, people have started to ask questions. Questions about jobs being made obsolete, questions about the inherent biases programmed into the neural networks, questions about whether or not AI will eventually consider humans as dead-weight and unnecessary to achieve the goals they’ve been tasked programmed with.

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Honey I shrunk the Model: Why big Machine Learning models must go small

Bigger is not always better for machine learning. Yet, deep learning models and the datasets on which they’re trained keep expanding, as researchers race to outdo one another while chasing state-of-the-art benchmarks. However groundbreaking they are, the consequences of bigger models are severe for both budgets and the environment alike.

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From von Neumann to Memory-Augmented Neural Networks

The traditional von Neumann architecture differentiates between a CPU (Central Processing Unit) and three levels of memory: registers — very fast, but with storage capability limited to a few values; main memory (e.g. RAM)— faster, with enough storage to accommodate for instructions and data to run a program, and external memory (e.g. hard drive) — slow, but with room for virtually all data used by a computer.

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How do Deep Neural Networks work?

Every day we are facing AI and neural network in some ways: from common phone use through face detection, speech or image recognition to more sophisticated — self-driving cars, gene-disease predictions, etc. We think it is time to finally sort out what AI consists of, what neural network is and how it works.

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So you want to study Machine Learning and Civil Engineering?

Civil Engineering and AI

The application of ML to Civil Engineering began in the 1980s when ML techniques were applied for knowledge extraction from Civil Engineering (CIE) data. The field of civil engineering is rife with the problem of uncertainties in areas not limited to construction management, safety, design, and decision making; the solution to these problems depends on calculations and experience of practitioners.

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