This week’s Top Articles from the Automation side shares why big corporations are working on their own AI-powered voice assistance? Google is introducing its new cloud-based TPU virtual machines to boost its user expertise. Interestingly, machine learning is going to be a lot useful in Maps to guide drivers more accurately.

Moreover, Tesla’s head of AI, in a recent CVPR2021 virtual conference announced the company’s new supercomputer, which allows the manufacturer to replace radar and lidar sensors with high-quality optical cameras in self-driving cars.

There is much more to explore. So, let’s dive into the automation world!

10) Linux Foundation initiative advocates open standards for voice assistants

Read More: VentureBeat

Author: Kyle Wiggers

9) Google Introduces New Cloud TPU VMs for Artificial Intelligence Workloads

Recently, Google announced new Cloud TPU Virtual Machines (VMs), which provide direct access to TPU host machines. With these VMs, the company offers a new and improved user experience to develop and deploy TensorFlow, PyTorch, and JAX on Cloud TPUs.

Read More: Info Q

Author: SteefJan Wiggers

8) Tesla’s Supercomputer “Dojo” Will Train Vision-Only Autonomous Tech

Tesla CEO, Elon Musk announced in 2020 that the company is working on a supercomputer named “Dojo” for video data processing on Twitter. Andrej Karpathy, Tesla’s head of AI, in a recent CVPR2021 (Conference on Computer Vision and Pattern Recognition) announced the company’s new supercomputer, which allows the manufacturer to replace radar and lidar sensors with high-quality optical cameras in self-driving cars.

 Read More: Automeme

7) Using machine learning to build maps that give smarter driving advice

Mapping services built for the developed world fail in fast-growing regions. The solution could be an AI-based routing system fed by real-time vehicle data.

MIT technology Review

Read More: MIT Technology Review

6) Deep Learning Is Our Best Hope for Cybersecurity, Deep Instinct Says

Thanks to the exponential growth of malware, traditional heuristics-based detection regimes have been overwhelmed, leaving computers at risk. Machine learning approaches can help, but the bottleneck presented by the feature engineering step is a potential dealbreaker. The best path forward at this point is deep learning, says the CEO of Deep Instinct, which claims to have taken an early lead in the emerging field.

Read More: Datanami

5) Facebook AI & Mila Propose ALMA: Anytime Learning at Macroscale

A research team from Facebook AI Research and Mila – McGill University explores deep learning model accuracy versus time trade-offs in any time learning, which they term Anytime Learning at Macroscale (ALMA). The team evaluates various models to gain insights on how to strike different trade-offs between accuracy and time to obtain a good learner.

Read More: Synced

4) This Agency Wants to Figure Out Exactly How Much You Trust AI

THE NATIONAL INSTITUTES of Standards and Technology (NIST) is a federal agency best known for measuring things like time or the number of photons that pass through a chicken. Now NIST wants to put a number on a person’s trust in artificial intelligence. Trust is part of how we judge the potential for danger, and it’s an important factor in the adoption of AI.

Read More: Wired

Author: Khari Johnson

3) MongoDB CTO on cloud database inroads and riding the developer wave

MongoDB was the original NoSQL open-source upstart. What began as a small experiment in developer-friendly document model storage grew into one of the most established players in the data processing. Now it’s a big, publicly-traded company with a product that has grown more powerful over the years.

Read More: VentureBeat 

2) Machine learning enhances non-verbal communication in online classrooms

Researchers in the Center for Research on Entertainment and Learning (CREL) at the University of California San Diego have developed a system to analyze and track eye movements to enhance teaching in tomorrow’s virtual classrooms—and perhaps future virtual concert halls.

Read More: Tech Explore

1) Machine learning for solar energy is a supercomputer killer

Researchers at the ARC Centre of Excellence in Exciton Science, based at RMIT University, have written a program that predicts the bandgap of materials, including for solar energy applications, via freely available and easy-to-use software. The bandgap is a crucial indicator of how efficient a material will be when designing new solar cells.

Read More: PHYS.ORG