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AI and Machine Learning in Healthcare

Research firm, Gartner, expects the global AI economy to increase from about $1.2 trillion last year to about $3.9 Trillion by 2022, while McKinsey sees it delivering global economic activity of around $13 trillion by 2030. And of course, this transformation is fueled by the powerful Machine Learning (ML) tools and techniques such as Deep Reinforcement Learning (DRL), Generative Adversarial Networks (GAN), Gradient-boosted-tree models (GBM), etc.

Along with commercial business and technology, healthcare is a field that is thought to be most suitable to be profoundly impacted by AI tools and techniques. Mandatory practices such as Electronic Medical Records (EMR) have already primed healthcare for application of Big Data tools for advanced analytics. AI and ML hold significant promise to inject further value and enhance the degree of automation and the quality of intelligent decision-making in patient care and public health systems to transform the lives of billions around the world.

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Timeline and Review of OpenAI’s Robotic Hand Project

Solving Rubik’s Cubes in Pursuit of Generalized Robotic Manipulation

Rubik's Cube

An impossible scramble. This scramble is impossible to solve using any known Rubik’s Cube algorithms without employing disassembly methods. Cube state rendered with MagicCube

Good robotics control is hard. Plain and simple as that. Don’t let the periodic videos from Boston Dynamics fool you: pulling off untethered back-flips and parkour are very rare skills for robots. In fact, as was readily apparent at the 2015 DARPA Robotics Challenge, falls and failures are the standard operating mode for state-of-the-art robots in the (somewhat fake) real world.

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Transfer Learning Made Easy: Coding a Powerful Technique

Coding a Powerful Technique

Artificial Intelligence for the Average User

Artificial intelligence (AI) is shaping up to be the most powerful and transformative technology to sweep the globe and touch all facets of life — economics, healthcare, finance, industry, socio-cultural interactions, etc. — in an unprecedented manner. This is even more important with developments in transfer learning and machine learning capabilities.

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Already, we are using AI technologies on a daily basis, and it is impacting our lives and choices whether we consciously know it or not. From our Google search and Navigation, Netflix movie recommendations, Amazon purchase suggestions, voice assistants for daily tasks like Siri or Alexa, Facebook community building, medical diagnoses, credit score calculations, and mortgage decision making, etc., AI is only going to grow in adoption.

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RNN, Seq2Seq, Transformers: Introduction to Neural Architectures Commonly Used in NLP

Just a few years ago, RNNs and their gated variants (that added multiplicative interactions and mechanisms for better gradient transfer) were the most popular architectures used for NLP.

Prominent researchers, such as Andrey Karpathy, were singing odes to RNNs' unreasonable effectiveness and large corporations were keen on adopting the models to put them into virtual agents and other NLP applications.

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Deep Learning and the Human Brain: Inspiration, Not Imitation

Artificial intelligence is the future. Structurally, artificial intelligence is perceived almost to be an individual entity influencing every technology. Machine learning is one of the sciences behind this entity, and deep learning is the engine that propels the science.

Deep learning transcends human ability to process a large volume of data. With a rush of data and the advent of faster GPUs and TPUs, deep learning is taking giant strides in the realm of image analysis, facial recognition, autonomous driving, etc.