In the 1960s, the advent of computer-aided design (CAD) sparked a revolution in design. For his PhD thesis in 1963, MIT Professor Ivan Sutherland developed Sketchpad, a game-changing software program that enabled users to draw, move, and resize shapes on a computer. Over the course of the next few decades, CAD software reshaped how everything…
Nine technologies developed at MIT Lincoln Laboratory have been selected as R&D 100 Award winners for 2021. Since 1963, this awards program has recognized the 100 most significant technologies transitioned to use or introduced into the marketplace over the past year. The winners are selected by an independent panel of expert judges. R&D World, an…
When the cloud storage firm Dropbox decided to shut down its offices with the outbreak of the Covid-19 pandemic, co-founder and CEO Drew Houston ’05 had to send the company’s nearly 3,000 employees home and tell them they were not coming back to work anytime soon. “It felt like I was announcing a snow day or…
Summary In this code pattern, gain better insights and explainability by learning how to use the AI 360 Explainability Toolkits to demystify the decisions that are made by a machine learning model. This not only helps policymakers and data scientists to develop trusted explainable AI applications, but also helps with transparency for everyone. To demonstrate…
Summary In this developer code pattern, we will demonstrate how to quickly build an e-learning portal using IBM Watson® Media. Solutions can be scaled with Watson Media, but the scope of this pattern focuses on the following capabilities: Creating channels for different areas of learning. Creating and uploading videos for a channel. Password-restricting access to…
Summary In this code pattern, learn how to use the Cortex Certifai Toolkit to create scans to evaluate the performance of multiple predictive models using IBM Watson Studio. Description Explainability of AI models is a difficult task that is made simpler by Cortex Certifai. The Cortex Certifai Tookit evaluates AI models for robustness, fairness, and…
Note: This code pattern uses the classic Watson Assistant experience. After October 8, 2021, all instances (except the standard plan) can switch between the classic and new Watson Assistant experiences by going to the upper-right corner of the Watson Assistant screen and clicking the Manage icon. Summary In this code pattern, learn how to authenticate…
Note: This code pattern uses the classic Watson Assistant experience. After October 8, 2021, all instances (except the standard plan) can switch between the classic and new Watson Assistant experiences by going to the upper-right corner of the Watson Assistant screen and clicking the Manage icon. Summary In this code pattern, build a framework that…
Summary This code pattern uses a German Credit data set to create a logistic regression model using Azure. The pattern uses Watson OpenScale to bind the machine learning model deployed in the Azure cloud, create a subscription, and perform payload and feedback logging. Description With Watson OpenScale, you can monitor model quality and log payloads,…
Summary This code pattern describes a way to gain insights by using Watson OpenScale and a SageMaker machine learning model. It explains how to create a logistic regression model using Amazon SageMaker with data from the UC Irvine machine learning database. The pattern uses Watson OpenScale to bind the machine learning model deployed in the…