And this, in a nutshell’s nutshell, is “Machine Learning”

Combining Part 1 and 2: Probably imagining a robot or terminator when asking about machine learning, in reality, machine learning is beyond and involved in almost all application you can imagine in our today world. Think of spam filter in your email, voice, face and fingerprint recognition on your phone, your Siri on iPhone, google assistance on android device etcetera have an iota of machine learning.

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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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Facebook AI Introduces ‘ReBeL’: An Algorithm that generalizes the Paradigm of Self-Play Reinforcement Learning and search to Imperfect-Information Games

Most AI systems excel in generating specific responses to a particular problem. Today, AI can outperform humans in various fields. For AI to do any task it is presented with; it needs to generalize, learn, and understand new situations as they occur without supplementary guidance. However, as humans can recognize chess and Poker both as games in the broadest sense, teaching a single AI to play both is challenging. 

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Why Deep Reinforcement Learning is the future of Automated Trading?

Devising a winning stock trading strategy isn’t easy considering the market dynamism in general. However, if we were to create an airtight automated trading strategy that categorizes hedge funds and investment companies based on the return-to-risk ratio, it would be necessary to rely on the concepts of return maximization. Then again, assessing return maximization in the dynamic and complex stock and even the Forex market is next to impossible unless there is a DRL-backed trading strategy to rely on.

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Reinforcement Learning based Recommender Systems

We present a Reinforcement Learning (RL) based approach to implement Recommender Systems. The results are based on a real-life Wellness app that is able to provide personalized health/ activity related content to users in an interactive fashion. Unfortunately, current recommender systems are unable to adapt to continuously evolving features. To overcome this, we propose three constructs, which we believe are essential for RL to be used in Recommender Systems.

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AI 1.02 — Alan Turing’s The Imitation Game, A Summary:

Briefing you on the first ever paper on AI. Turing in 1950 published the first ever article on Artificial Intelligence which he then called ‘Computing Machinery and Intelligence’. This summary/ article will give you an idea of what he wrote in world’s first ever interpretation on Artificial Intelligence in this highly philosophical paper, his views and the predictions he made on AI which stand still even today.

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Amazon: Deploying reinforcement learning in production using Ray and Amazon SageMaker

Reinforcement learning (RL) is used to automate decision-making in a variety of domains, including games, autoscaling, finance, robotics, recommendations, and supply chain. Launched at AWS re:Invent 2018, Amazon SageMaker RL helps you quickly build, train, and deploy policies learned by RL.

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Top 10 AI trends to watch in 2021

The global AI market size was calculated as $39.9 Billion in 2019 and expected to achieve compound annual growth rate (CAGR) up to 42.2% from 2020 to 2027. Technology has made innovations in big fields like healthcare, retail, automobile, and finance with continuous research. We have come up with Top 10 Artificial Intelligence Trends to watch in year 2021. These have the potential to hit great innovation in future. Let’s have a look at these strategies:

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Researchers suggest AI can learn common sense from animals

AI researchers developing reinforcement learning agents could learn a lot from animals. That’s according to recent analysis by Google’s DeepMind, Imperial College London, and University of Cambridge researchers assessing AI and non-human animals.

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AI researchers challenge a robot to ride a skateboard in simulation

AI researchers say they’ve created a framework for controlling four-legged robots. To demonstrate the robust nature of the framework, AI researchers made the system slip on frictionless surfaces to mimic a banana peel, ride a skateboard, and climb on a bridge while walking on a treadmill.

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Tackling Open Challenges in Offline Reinforcement Learning

Over the past several years, there has been a surge of interest in reinforcement learning (RL) driven by its high-profile successes in game playing and robotic control. However, unlike supervised learning methods, which learn from massive datasets that are collected once and then reused, RL algorithms use a trial-and-error feedback loop that requires active interaction during learning, collecting data every time a new policy is learned.

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Facebook develops AI algorithm that learns to play poker on the fly

Facebook develops AI algorithm that learns to play poker on the fly

Facebook researchers have developed a general AI framework called Recursive Belief-based Learning (ReBeL) that they say achieves better-than-human performance in heads-up, no-limit Texas hold’em poker while using less domain knowledge than any prior poker AI.

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Why does the optimal policy exist?

In a finite Markov Decision Process (MDP), the optimal policy is defined as a policy that maximizes the value of all states at the same time¹. In other words, if an optimal policy exists, then the policy that maximizes the value of state s is the same as the policy that maximizes the value of state s’.² But why should such a policy exist?

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