AI Suggests Recipes Based on Food Photos
There are few things social media users love more than flooding their feeds with photos of food. Yet we seldom use these images for much more than a quick scroll on our cellphones. Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) believe that analyzing photos like these could help us learn recipes and better understand people's eating habits. [READ MORE...]
Recent Features
Interview: Daphne Koller, Chief Computing Officer, Calico; Adjunct Professor of Computer Science, Stanford University
The following is a discussion with Daphne Koller, Chief Computing Officer, Calico Labs; Adjunct Professor of Computer Science, Stanford University; ACM-Infosys 2007 Foundation Award. The Association of Computing Machinery (ACM) just concluded a celebration of 50 years of the ACM A.M. Turing Award (commonly known as the “Nobel Prize of computing”) with a two-day conference in San Francisco. The conference brought together some of the brightest minds in computing to explore how computing has evolved and where the field is headed. [READ MORE...]
“Hide the Tech” to Take Big Data Mainstream
In this contributed article, Ravi Dharnikota, Chief Enterprise Architect at SnapLogic, examines a number of important trends that are simplifying the adoption of big data such as the cloud, serverless computing, self-service integration, and machine learning/AI. The effect is that these trends tend to actually “hide” the tech to put more focus on the data and the business. [READ MORE...]
Using Python to Drive New Insights and Innovation from Big Data
In a recent white paper "Management's Guide - Unlocking the Power of Data Science & Machine Learning with Python," ActiveState - the Open Source Language Company - provides a summary of Python’s attributes in a number of important areas, as well as considerations for implementing Python to drive new insights and innovation from big data. [READ MORE...]
Featured Resource
insideBIGDATA Guide to Artificial Intelligence & Deep Learning
In this guide we explain the difference between AI, machine learning and deep learning, and include highlights of the insideBIGDATA audience survey. To learn more about AI and deep learning download this guide.
Industry Perspectives
Getting Results from Big Data is Like Finding Needles in Haystacks
In this special guest feature, Praful Saklani, CEO and founder at Pramata, discusses how companies attempting to leverage the benefits of big data are oftentimes realizing it's like finding needles in the haystack. An increasing number of companies apply big data technologies in the form of AI and machine learning, but don't see the results they want. A different approach is needed to leverage smaller data sets at high precision. [Read More...]
The Hardest Part of Analytics Isn't Analysis. It's Data
In this special guest feature, Matin Movassate, CEO and cofounder of Heap, suggests that as data scientists and business leaders alike fixate on the great potential of technologies like machine learning and AI, we risk losing sight of what’s most important: the data itself. After all, fancy visualizations and predictive analytics don’t matter without the right data powering them. [Read More...]
Featured from insideHPC
- Video: ddR – Distributed Data Structures in R"A few weeks ago, we revealed ddR (Distributed Data-structures in R), an exciting new project started by R-Core, Hewlett Packard Enterprise, and others that provides a fresh new set of computational primitives for distributed and parallel computing in R. The package sets the seed for what may become a standardized and easy way to write parallel algorithms […]
News from insideHPC
- Survey: Training and Support #1 Concern for the HPC Community
- Video: ddR – Distributed Data Structures in R
- Altair Showcases PBScloud.IO at ISC 2017
- Job of the Week: Software Engineer at NCAR
- OpenACC Brings Directives to Accelerated Computing at ISC 2017
- Agenda Posted: August MVAPICH User Group Meeting in Ohio
- Red Hat Ceph Storage Powers Research at the University of Alabama at Birmingham
Editor’s Choice
The Difference Between Data Science and Data Analytics
In this contributed article, tech writer Rick Delgado, examines the differences between the terms: data science and data analytics, where people working in the tech field or other related industries probably hear these terms all the time, often interchangeably. Although they may sound similar, the terms are often quite different and have differing implications for business. [Read More...]
Interview: Anusua Trivedi, Data Scientist on Microsoft's Advanced Data Science & Strategic Initiatives Team
In this podcast interview, I caught up with Anusua Trivedi, a Data Scientist on Microsoft’s ADS team, to get her take on the upward trajectory of AI and deep learning that we're seeing in the industry today. [Read More...]
Pulling Real ROI from a Sea of Big Data Analytics
In this special guest feature, Jessica Hawthorne-Castro, CEO of Hawthorne Direct, provides some strategic best practices for marketers that want to extract ROI from big data in order to improve the customer experience and generate sales. [Read More...]
Interview: Emily Glassberg Sands, Data Science Manager at Coursera
I recently caught up with Emily Glassberg Sands, Data Science Manager at Coursera, to talk about applying machine learning, neural networks, natural language processing, and big data analytics to the retail industry. Emily leads an awesome team of data scientists and data science managers working on growth, discovery, the learning experience, and a new enterprise offering. The team's job is to help build a better Coursera through data-driven decisions and products. [Read More...]
Big Data Industry Predictions for 2017
Wow! What a year 2016 has been. The big data industry has significant inertia moving into 2017. In order to give our valued readers a pulse on important new trends leading into next year, we here at insideBIGDATA heard from all our friends across the vendor ecosystem to get their insights, reflections and predictions for what may be coming. We were very encouraged to hear such exciting perspectives. Even if only half actually come true, Big Data in the next year is destined to be quite an exciting ride. Enjoy! [Read More...]
