The Emergence of Multi-cloud Native Applications and Platforms

As companies go multi-cloud, a new set of tools simplify IT management and application development. By Ben Lorica and Ion Stoica. [This post originally appeared on the Anyscale blog.] In a previous post we examined current serverless computing offerings and described why Ray is an ideal substrate for general purpose computing platforms. While serverless hasContinue reading “The Emergence of Multi-cloud Native Applications and Platforms”

Trends to Watch in 2021 and Best Books of 2020

Subscribe • Previous Issues This edition has 363 words which will take you about 2 minutes to read. “A wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.” – Herbert Simon. Data Exchange podcast Key AI and Data Trends forContinue reading “Trends to Watch in 2021 and Best Books of 2020”

Gradient Flow #24: Robots Are Listening, Funding Updates, Security for the Disoriented

Subscribe • Previous Issues This edition has 476 words which will take you about 3 minutes to read. “The character of a building like that of a man, is not its outward appearance but rather what’s inside.” – Merrell Vories Hitotsuyanagi Data Exchange podcast Security and privacy for the disoriented   In this episode I speak withContinue reading “Gradient Flow #24: Robots Are Listening, Funding Updates, Security for the Disoriented”

Visual Data Computing Unleashed

A new startup from MIT and Brown lets users transform, visualize, and model data through a graphical user interface. In a recent post we described the use of AI and machine learning to expand the user base of BI tools, and enable users to perform increasingly more sophisticated analytics. The objective is to reduce timeContinue reading “Visual Data Computing Unleashed”

One Simple Chart: Demand for Reinforcement Learning Holds Steady

Late last year I started running into more companies using reinforcement learning (RL). Inspired by some of the things I was hearing about, early this year I wrote about emerging RL use cases in simulation & optimization, as well as examples of RL in recommendation and personalization systems. With the global pandemic taking a tollContinue reading “One Simple Chart: Demand for Reinforcement Learning Holds Steady”

Gradient Flow #23: AI Liabilities, Data Quality, Robust Language Models

Subscribe • Previous Issues This edition has 408 words which will take you about 2 minutes to read. “It is always easier to destroy a complex system than to selectively alter it.” – Roby James. Data Exchange podcast Improving the robustness of natural language applications  In recent years adversarial attacks against computer vision models have been coveredContinue reading “Gradient Flow #23: AI Liabilities, Data Quality, Robust Language Models”

Gradient Flow #22: AI Security, Time-series Databases, Concept Drift

Subscribe • Previous Issues This edition has 482 words which will take you about 3 minutes to read. “They beat me up unjustly, but since they did the same thing to everyone else, it was not unfair.”  – Sydney Morgenbesser Data Exchange podcast Securing machine learning applications  Ram Shankar is a Berkman Klein Center affiliate, and aContinue reading “Gradient Flow #22: AI Security, Time-series Databases, Concept Drift”

Gradient Flow #21: Detecting Fake News, AutoBI, Feature Stores

Subscribe • Previous Issues This edition has 459 words which will take you about 3 minutes to read. “Any figure that looks interesting or different is usually wrong.” – W.A. Twyman. Data Exchange podcast The Computational Limits of Deep Learning   Neil Thompson is a research scientist at the Computer Science and Artificial Intelligence Lab (CSAIL) andContinue reading “Gradient Flow #21: Detecting Fake News, AutoBI, Feature Stores”

Responsible AI in Practice: A virtual event

Watch Watch the recorded webinar video. As interest in machine learning and AI continues to grow, companies are realizing the complexities inherent not only in the technology itself, but in the responsible implementation of the technology. Responsible AI is a framework that brings together best practices around fairness, transparency, accountability, security, privacy, safety, and reliability.Continue reading “Responsible AI in Practice: A virtual event”