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Machine Learning Enables Robot to Grab Transparent and Shiny Objects

#artificialintelligence

Here is a thing you probably haven't thought of before: How do robots really see transparent and reflective objects? Well, trick question -- they actually don't really see them properly which is why they can't grasp kitchen stables such as a shiny knife. However, roboticists at Carnegie Mellon University have had success with a technique they've developed for teaching robots to pick up such objects. Their newly found technique doesn't demand fancy sensors, exhaustive training, or human guidance. It relies on one thing only: a color camera.


Gaussian Process Regression on Molecules in GPflow

#artificialintelligence

This post demonstrates how to train a Gaussian Process (GP) to predict molecular properties using the GPflow library by creating a custom-defined Tanimoto kernel to operate on Morgan fingerprints. In this example, we'll be trying to predict the experimentally-determined electronic transition wavelengths of molecular photoswitches, a class of molecule that undergoes a reversible transformation between its E and Z isomers upon irradiation by light. We'll start by importing all of the machine learning and chemistry libraries we're going to use. For our molecular representation, we're going to be working with the widely-used Morgan fingerprints. Under this representation, molecules are represented as bit vectors.


2020 No-Code AI & Machine Learning Using IBM Watson AutoAI

#artificialintelligence

In this course I am going to introduce you to Watson Studio AutoAI by IBM. Artificial Intelligence (AI) and Machine Learning (ML) are two very hot topics nowadays. Experts claim that AI & ML are going to revolutionize the world. This course is designed for those who want to take a short cut to these technologies. Auto AI and Auto ML are new tools that provide methods and processes to make Artificial intelligence and Machine Learning available for non-experts.


This Machine Learning-Focused VC Firm Just Added A Third Woman Investment Partner

#artificialintelligence

Basis Set Ventures investment partners Chang Xu, Lan Xuezhao and Sheila Vashee are looking to run a ... [ ] different kind of venture capital firm. Basis Set Ventures doesn't want to be your typical venture capital firm. First, there's the fledgling VC firm's focus on a technical area that has seen some disillusionment in recent years: machine learning and artificial intelligence. Sure, AI has become something out of startup bingo, tacked on in pitches and often stretched behind meaning. Basis Set founder Lan Xuezhao is confident she and her team can figure out what's real and what's not.


All You Need to Know to get started with NumPy

#artificialintelligence

Everything you need to know to get started with NumPy. The world runs on data and everyone should know how to work with it. It's hard to imagine a modern, tech-literate business that doesn't use data analysis, data science, machine learning, or artificial intelligence in some form. NumPy is at the core of all of those fields. While it's impossible to know exactly how many people are learning to analyze and work with data, it's a pretty safe assumption that tens of thousands (if not millions) of people need to understand NumPy and how to use it. Because of that, I've spent the last three months putting together what I hope is the best introductory guide to NumPy yet! If there's anything you want to see included in this tutorial, please leave a note in the comments or reach out any time! NumPy (Numerical Python) is an open-source Python library that's used in almost every field of science and engineering. NumPy users include everyone from beginning coders to experienced researchers doing state-of-the-art scientific and industrial research and development. The NumPy API is used extensively in Pandas, SciPy, Matplotlib, scikit-learn, scikit-image and most other data science and scientific Python packages.


A GUI to Recognize Handwritten Digits -- in 19 Lines of Python

#artificialintelligence

Have you ever trained a machine learning model that you've wanted to share with the world? Maybe set up a simple website where you (and your users) could try putting in their own inputs and seeing the models' predictions? It's easier than you might think! In this tutorial, I'm going to show you how to train a machine learning model to recognize digits using the Tensorflow library, and then create a web-based GUI to show predictions from that model. You (or your users) will be able to draw arbitrary digits into a browser, and see real-time predictions, just like below.


Weekly Digest, July 13

#artificialintelligence

Data Science Fails – If It Looks Too Good To Be True… You've probably seen amazing AI news headlines such as: AI can predict earthquakes. Using just a single heartbeat, an AI achieved 100% accuracy predicting congestive heart failure. AI can diagnose covid19 in seconds from a chest scan. A new marketing model is promising to increase the response rate tenfold. It all seems too good to be true.



Amazon Adds Smarter Carts For Quicker Grocery Shopping, Here's How They Work

International Business Times

Amazon (AMZN) has introduced shopping carts that make it faster and more convenient to shop by automatically tracking the items put in the cart, enabling consumers to eliminate the checkout line. The new Dash Carts will first be featured at Amazon's Woodland Hills, California, grocery store, set to open this year. To use the Dash Carts, shoppers will need to have an Amazon account and a smartphone. Shoppers simply scan a QR code located within the Amazon app to begin loading items into the cart. The Smart Cart is fitted with computer vision algorithms and sensor fusion to recognize merchandise that is put into the cart.


Deep Learning for Business Managers: Neural Networks in R

#artificialintelligence

You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in R, right? You've found the right Neural Networks course! Identify the business problem which can be solved using Neural network Models. Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc. Create Neural network models in R using Keras and Tensorflow libraries and analyze their results. How this course will help you?