Artificial intelligence and medicine: Is it overhyped? Medical Design and Outsourcing


Artificial intelligence raises exciting possibilities for healthcare, but are companies promising more than they can deliver? But artificial intelligence's potential also comes with an incredible level of hype. "AI has the most transformative potential of anything I've seen in my life, and I graduated medical school 40 years ago. It's the biggest thing I've ever seen by far," prominent cardiologist and author Dr. Eric Topol told Medical Design & Outsourcing. "But it's more in promise than it is in reality."

Daily Digest March 22, 2019 – BioDecoded


The conventional model of oncogenic RAS-MAPK pathway signaling in cancer suggests that mutations in the pathway render downstream signaling largely independent of regulation (autonomous). However, the emerging model of a semiautonomous state through which pathological RAS signaling remains under some control suggests a potential therapeutic opportunity to target upstream regulators, such as SHP2, SOS, and GRB2. Mass spectrometry is a predominant experimental technique in metabolomics and related fields, but metabolite structural elucidation remains highly challenging. Researchers report SIRIUS 4 (, Amazon SageMaker is an end-to-end machine learning platform that enables users to prepare training data and build machine learning models quickly using pre-built Jupyter notebook with pre-built algorithms.

How to Build Deep Learning Models for Font Classification with Tensorflow: CNN, Deeper CNN, Hidden…


You just learn how to build and train 5 deep learning models for classification problems using Tensorflow. One more thing about adding pooling layer is that because of the pooling, the image size is gradually shrinking. Early convolutional weights often train to detect simple edges, while successive convolutional layers combine those edges into gradually more complex shapes such as faces, cars, and even dogs. Human learning is the beginning of Deep learning!

Teaching - CS 229


CS 229 ― Machine Learning My twin brother Afshine and I created this set of illustrated Machine Learning cheatsheets covering the content of the CS 229 class, which I TA-ed in Fall 2018 at Stanford. They can (hopefully!) be useful to all future students of this course as well as to anyone else interested in Machine Learning.

CFOs plan to leverage AI, drones, robots and blockchain


CFOs are planning to implement advanced technologies, including artificial intelligence, drones, robots and blockchain, at a rapid rate, according to a new survey by Grant Thornton. For the study, GT and CFO Research polled 378 senior finance executives about the ways technology is transforming nearly every division in their organization, especially the finance function. One out of four of the respondents said they use AI, compared to just 7 percent last year. Significant proportions of senior financial execs are currently implementing advanced analytics (38 percent) and machine learning (30 percent). Within two years, senior financial execs plan to roll out a battery of new technology, such as AI (41 percent), blockchain (40 percent), robotic process automation (41 percent) and drones and robots (30 percent), at their organization.

MIT's AI can train neural networks faster than ever before


Today, AI can design machine learning systems known as neural networks in a process called neural architecture search (NAS). But this technique requires a considerable amount of resources like time, processing power and money. Even for Google, producing a single convolution neural network -- often used for image classification -- takes 48,000 GPU hours. Now, MIT researchers have developed a NAS algorithm that automatically learns a convolution neural network in a fraction of the time -- just 200 GPU hours. Speeding up the process in which AI designs neural networks could enable more people to use and experiment with NAS, and that could advance the adoption of AI.

Alchemy of Artificial Intelligence, Mysteries of Backboxes, and Proximity to Kings


To my readers it will appear as though I am writing some article on old Greek mythology, but you will soon realize that the world remains the same the more it changes. Recently Ali Rahimi, a researcher in artificial intelligence at Google, compared machine learning with alchemy. Later a few technology journalists, more than ever before, started writing about the relationship between technology and alchemy. Alchemy is about using the "trial and error" method and coming out with a formula (mostly secret or something that cannot be deconstructed). Similarly, in machine learning a model is designed out of data, this model constantly learns and produces an output but nobody know how decisions are made.

Considering An AI Plan For Your Company? Follow These 11 Tips


Artificial intelligence technology is continually evolving and finding its way into more industries and applications. Many businesses, especially smaller ones, struggle to decide whether they should invest in an AI plan. Doing so can be both time-consuming and costly, but it might pay off in the long run. The members of Forbes Technology Council generally agree that artificial intelligence, even on a small scale, can benefit most modern businesses. Below, 11 of them recommend some first steps for businesses to take when deciding on an AI plan.

Artificial Intelligence in Manufacturing Technology - Technology


Every new technology that comes to prominence has always made the life of humans better. Remember, the time fire was first discovered by your ancestors to cook food. And then came the wheel. Now, it is digital payments and internet of things. Are you a person who keeps a keen eye on the scientific developments happening in the world?

Google's First AI-Powered Doodle Lets You Harmonize Like Johann Sebastian Bach


To celebrate the German composer's March 21, 1685 birthday, Doodle lets users compose a melody in Bach's style. The interactive Doodle is the product of collaboration between Google's Magenta – which helps people make their own music and art through machine learning – and Google's PAIR – which makes the tools that allow machine-learning to be accessed by everyone. A machine-learning model called Coconet made it all possible. Developed by Google, Coconet was trained on 306 of Bach's chorale harmonizations. "His chorales always have four voices: each carries their own melodic line, creating a rich harmonic progression when played together," writes Google.