Weekly Top 10 Automation Articles
1. Taking A Systems Approach To Adopting AI
Today, some 80% of large companies have adopted machine learning and other forms of artificial intelligence (AI) in their core business. Five years ago, the figure was less than 10%. Nevertheless, the majority of companies still use AI tools as point solutions — discrete applications, isolated from the wider enterprise IT architecture. That’s what we found in a recent analysis of AI practices at more than 8,300 large, global companies in what we believe is one of the largest-scale studies of enterprise IT systems to date.
Read More On: HARVARD BUSINESS REVIEW
2. Machine Learning Algorithms Explained
Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’d like to step back and explain both machine learning and deep learning in basic terms, discuss some of the most common machine learning algorithms, and explain how those algorithms relate to the other pieces of the puzzle of creating predictive models from historical data.
Author: Martin Heller
Read More On: INFO WORLD
Tesla has quietly launched several neat features over the past couple of days, making it easier for Tesla owners to receive software updates, and get their car fixed if something goes wrong.
On Monday, Electrek wrote that Tesla cars can now diagnose themselves, and even pre-order parts to a Tesla Service Center if need be.
Author: Stan Schroeder
Read More On: MASH ABLE
Voice AI is becoming increasingly ubiquitous and powerful. Forecasts suggest that voice commerce will be an $80 billion business by 2023. Google reports that 20% of their searches are made by voice query today — a number that’s predicted to climb to 50% by 2020. In 2017, Google announced that their speech recognition had a 95% accuracy rate. While that’s an impressive number, it begs the question: 95% accurate for whom?
Author: Joan Palmiter Bajorek
Read More On: HARVARD BUSINESS REVIEW
One of the most interesting demos at this week's Google I/O keynote featured a new version of Google's voice assistant that's due out later this year. A Google employee asked the Google Assistant to bring up her photos and then show her photos with animals. She tapped one and said, "Send it to Justin." The photo was dropped into the messaging app.
Author: Timothy B. Lee
Read More On: ARS TECHNICA
Neural networks are the core software of deep learning. Even though they’re so widespread, however, they’re really poorly understood. Researchers have observed their emergent properties without actually understanding why they work the way they do.
Author: Karen Hao
Read More On: MIT TECHNOLOGY REVIEW
The term artist has broadened over the years paralleling emerging technology. In fact, since the industrial revolution, ever major technical advancement has either facilitated the "artist's medium" or become a subject of discussion for the artist itself.
Author: Taylor Donovan Barnett
Read More On: INTERESTING ENGINEERING
Geoffrey Hinton is one of the creators of Deep Learning, a 2019 winner of the Turing Award, and an engineering fellow at Google. Last week, at the company’s I/O developer conference, we discussed his early fascination with the brain, and the possibility that computers could be modeled after its neural structure—an idea others long dismissed by other scholars as foolhardy. We also discussed consciousness, his future plans, and whether computers should be taught to dream. The conversation has been lightly edited for length and clarity.
Author: Nicholas Thompson
Read More On: WIRED
Artificial intelligence (AI) algorithms are generally hungry for data, a trend which is accelerating. A new breed of AI approaches, called lifelong learning machines, are being designed to pull data continually and indefinitely. But this is already happening with other AI approaches, albeit with human intervention. A steady stream of data is the fuel for coveted results.
Author: Naga Rayapati
Read More On: FORBES