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.

Author: Bhaskar Ghosh , H. James Wilson & Adam P. Burden



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

3. Tesla Cars Can Now Diagnose Themselves And Pre-Order Parts If Needed

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

4. Voice Recognition Still Has Significant Race and Gender Biases

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


5. Why Google Believes Machine learning Is Its Future

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


6. A New Way To Build Tiny Neural Networks Could Create Powerful AI On Your Phone

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


7. Artificial Intelligence Is Creating a New Breed of Artists and Artwork

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


8. Using Robots and Artificial Intelligence to Understand the Deep-Sea

In order to best conserve and manage marine biodiversity, scientists need accurate information on what inhabits the seabed. One way to collect such data is with autonomous underwater vehicles (AUV) mounted with cameras.

Author: Loukia Papadopoulos


9. An AI Pioneer Explains the Evolution of Neural Networks

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

10. Artificial Intelligence Needs Data Diversity

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

Yusra Hamid