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Machine Learning

▾ 1 MINUTE READ — CLOSE

Machine learning moves beyond the traditional model of computation. Instead of arriving at a definite reproducible answer through a series of calculations, machine learning — a branch of artificial intelligence — works instead on a series of statistical probabilities to suggest new solutions to a problem. This work is useful for such jobs as designing new materials, medical diagnosis, advanced game graphics, and so many other tasks.

Much of the early success in machine learning has come from supervised learning, where a clearly defined data set is already available for analysis. But work has been going on to move beyond this model, with the Reinforcement Learning, where an agent learns by interacting with its environment. Gathering even more momentum has been Deep Learning, which doesn’t require all the intermediate steps that supervised learning does. Instead, the idea is to let the Deep Learning neural nets find the answers on their own.

At The New Stack, we have focused our coverage of this emerging field mostly around two areas of scalable architecture. We are keeping a close eye on an emerging field of AIOps, where machine learning can influence and drive IT operations. AIOps should be able to help by automating the path from development to production, predicting the effect of deployment on production and automatically responding to changes in how the production environment is performing. Companies such as New Relic, OpsRamp, and Moogsoft have all invested heavily in this area,

Another area of machine learning we are covering closely is how Kubernetes and related cloud native technologies can expedite the machine learning lifecycle.  Machine learning involves an entire IT cycle of technologies that are very early on in terms of productization: Data must be harvested and cleansed, models must be tested and the most useful models must be pressed into production, with a feedback loop of some sort to ensure the models can be updated. Emerging workflows such as Kubeflow and Anaconda can help streamline these processes.

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Cloud Native / Data / Machine Learning / Contributed

In Machine Learning, It’s All about the Process

23 May 2017 3:00am, by Dinesh Nirmal

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Culture / Machine Learning

Could AI Algorithms One Day Make Better Art than Humans?

21 May 2017 9:00am, by David Cassel

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Machine Learning / Monitoring

Loom Systems Adds a Human Touch to AI for Root Cause Analysis

19 May 2017 6:00am, by Susan Hall

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Machine Learning

Google I/O Showcases the Machine Learning Strengths of TensorFlow

19 May 2017 4:00am, by Alex Handy

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Cloud Native / Culture / Data / Edge / IoT / Machine Learning

New Google Platform Integrates Cloud Services with the Internet of Things

16 May 2017 9:00am, by Alex Handy

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Data / Development / Machine Learning

Five Ways Google TensorFlow Beats Caffe

15 May 2017 12:27pm, by Joab Jackson

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Cloud Native / Culture / Machine Learning

IBM AI Helps Designers Create Interactive ‘Thinking Sculpture’

14 May 2017 3:00am, by Kimberley Mok

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Machine Learning

Bonsai Unveils a Middleware Platform for Artificial Intelligence

9 May 2017 6:00am, by TC Currie

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Culture / Machine Learning

How Humans React When AIs Replace Them

7 May 2017 9:00am, by David Cassel

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Machine Learning

Elon Musk’s Neuralink Wants to Enhance Humans with Superintelligent AI

7 May 2017 3:00am, by Kimberley Mok

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Culture / DevOps / Edge / IoT / Machine Learning / Microservices

A New Developer Conference Explores the Intersection of Infrastructure and Application Technologies

4 May 2017 3:10pm, by Alex Williams

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Cloud Native / Data / Machine Learning

How Rubikloud Uses Spark to Bring Data-Driven Analysis to Retail

3 May 2017 3:00am, by Maxwell Cooter

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Machine Learning / Monitoring

Deckard Tackles Documentation Pains with AI and Data Analytics

2 May 2017 9:00am, by Susan Hall

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Development / Machine Learning

A Closer Look at the ‘Learning’ Aspect of Machine Learning

28 Apr 2017 1:00am, by Janakiram MSV

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Data / Machine Learning

Microsoft Puts AI Where the Data Is

25 Apr 2017 1:00am, by Mary Branscombe

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Culture / Machine Learning

Atlassian Futurist Sees Teamwork, not Chatbots, Defining the Future of Work

21 Apr 2017 2:53pm, by TC Currie

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Data / Development / Machine Learning

Machine Learning and Linear Regression for Mere Mortals

21 Apr 2017 8:17am, by Janakiram MSV

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Culture / Machine Learning

Can Artificial Intelligence Fix the Monopoly Board Game?

16 Apr 2017 9:00am, by David Cassel

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Culture / Machine Learning

A Self-Correcting Robot that’s Telepathically Controlled by the Human Brain

16 Apr 2017 6:00am, by Kimberley Mok

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Machine Learning

A Gentle Introduction to Machine Learning

14 Apr 2017 1:00am, by Janakiram MSV

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Cloud Services / Data / Development / Machine Learning / Storage

IBM Brings Low-Code App Development to Bluemix

13 Apr 2017 1:00am, by Darryl Taft

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Machine Learning / Sponsored

SXSW 2017: Artificial Intelligence Should Empathize, Not Just Understand

12 Apr 2017 3:00pm, by Joab Jackson

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Culture / Machine Learning

Wistful Researcher Applies a Neural Network to Questions about Her 1997 Junior Prom

9 Apr 2017 9:00am, by David Cassel

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Development / Machine Learning

OpenAI Bots Create Own Language to Communicate with Each Other

9 Apr 2017 3:00am, by Kimberley Mok

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Development / Machine Learning

Machine Learning Is Not Magic: It’s All About Math, Stats, Data, and Programming

7 Apr 2017 1:00am, by Janakiram MSV

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CI/CD / Cloud Native / DevOps / Machine Learning / Microservices

Staples Moves to Microservices and Cognitive Computing for Flexibility and Growth

5 Apr 2017 12:00pm, by Darryl Taft

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