Under the Decision Tree (#4)

Written by Scott Schwalm

This post is days old.

Welcome back for another edition of Under the Decision Tree. This week we had The Data Science Conference in Seattle and interesting articles that include teaching AI to be sarcastic, predictions of what AI will look like in 2030, and much more.

Comments are always welcome at: Decision Tree

News/Articles

Teaching AI how to be sarcastic is totally the easiest thing ever
What artificial intelligence will look like in 2030
Network analysis shows offshore finance as a complex network of ownership ties
CIA claims ability to predict ‘societal instability’ just days away
The Data Science Hierarchy of Needs
Google Assistant uses AI, machine learning to become people-friendly
How Google is going from mobile-first to AI-first while competition heats up
Machine learning essential to healthcare, says Orion Health
The Role of Feature Engineering in a Machine Learning World
Artificial Intelligence and Machine Learning: What Will Be the Impact on The Travel Industry?
Can Machine Learning Save Us From Cybercriminals?
Machine Learning Tools Help Google Science Fair Finalists Find Lost Objects, Predict Breast Cancer Risk
Machine Learning: Three Steps For Setting Up A Framework At Your Company
MSSPs challenged to use machine learning to solve IoT ‘noise problem’
How machine learning equipped gadgets will help Google take on competition intelligently
Machine Learning in Healthcare
Salesforce Marketing Cloud’s New CEO: ‘Machine Learning Is The Next Big Thing For Marketers’
Machine learning needs rich feedback for AI teaching
How eBay Uses Big Data and Machine Learning to Drive Business Value
Mark Cuban points to machine learning as the next ‘grand slam’ in technology
Creativity: Why it cannot be a machine property
How A New Technology Promises To Make Deep Learning More Powerful Than It Already Is
Understanding the coming revolution in medicine: machine learning
Google Translate is getting really, really accurate
A combination of machine learning and game theory is being used to fight elephant poaching in Uganda
How to steal the mind of an AI: Machine-learning models vulnerable to reverse engineering

Podcasts

2016-10-06 O’Reilly Data Show - The technology behind self driving vehicles
2016-10-02 Linear Digressions - Regularization
2016-09-30 Data Skeptic - Election Predictions

Upcoming Conferences

October 6-7, 2016, The Data Science Conference, Seattle, Washington
November 2-3 2016, Machine Intelligence Summit, New York, New York
November 4-6 2016, Open Data Science West
November 16-18 2016, The 8th Asian Conference on Machine Learning, The University of Waikato, Hamilton

Blog Listings

R-Bloggers
Revolutions
Predict the value of your house using Azure Machine Learning
Cortana Intelligence Gallery – the hub for intelligent solutions

Please send any suggestions to: Decision Tree We would love to hear from you.

Published Oct 7, 2016 by

Machine Learning

What Works and What Doesn't With ConfigurationMan...

Regex Performance With and Without RegexOptions.C...

z-index Is Confusing, but It Doesn't Have to Be

Logging ASP.NET Web API 2 Failed Request Body to ...

Using forEach for IE 11

Understanding Dart Sass modules and namespaced va...

Autofac: Eager vs Lazy Construction During Regist...

Writing a Browser Compatible Date Input

Non Pro/Premier SendGrid Accounts May Have Issues...

Vue 3 Composition API

Upcoming Google Chrome SameSite Change Breaks Our...

A Case Of Newtonsoft.Json, TypeNameHandling.All, ...

Ordering of static fields in C# matters

Avoiding a LINQ FirstOrDefault mishap

Artifact Conference 2019

How to Work with NPM Packages Locally Using .tgz ...

Caching Git LFS in AppVeyor to Avoid Large GitHub...

UX/UI Solutions: Make Content Less Overwhelming.

Takeaways from An Event Apart

ChatOps at RIMDev Using Probot

Under the Decision Tree (#4)

News/Articles

Podcasts

Upcoming Conferences

Blog Listings

Comments

Under the Decision Tree (#4)

News/Articles

Podcasts

Upcoming Conferences

Blog Listings

Under the Decision Tree (#16)

Under the Decision Tree (#15)

Under the Decision Tree (#14)

Under the Decision Tree (#13)

Under the Decision Tree (#12)

Under the Decision Tree (#11)

Comments