Weekly Top 10 Automation Articles
When he was attending his grandfather’s funeral, Robert Pasin, the CEO of Radio Flyer, was overwhelmed by the outpouring of affection and respect for the family patriarch, who had originally founded the third generation family business, which manufactures toys.
Author: Rob Lachenauer
Read More On: HARVARD BUSINESS REVIEW
Need to extract content from a document quickly and automatically? You’re in luck if you’re an Amazon Web Services (AWS) customer. Amazon today announced the general availability of Textract, a cloud-hosted and fully managed service that uses machine learning to parse data tables, forms, and whole pages for text and data.
Author: Kyle Wiggers
Read More On:VENTURE BEAT
A new artificial intelligence created by researchers at the Massachusetts Institute of Technology pulls off a staggering feat: by analyzing only a short audio clip of a person's voice, it reconstructs what they might look like in real life. The AI's results aren't perfect, but they're pretty good - a remarkable and somewhat terrifying example of how a sophisticated AI can make incredible inferences from tiny snippets of data.
Author: Jon Christian
Read More On: SCIENCE ALERT
Simon Knowles, chief technology officer of Graphcore Ltd., is smiling at a whiteboard as he maps out his vision for the future of machine learning. He uses a black marker to dot and diagram the nodes of the human brain: the parts that are “ruminative, that think deeply, that ponder.” His startup is trying to approximate these neurons and synapses in its next-generation computer processors, which the company is betting can “mechanize intelligence.”
Author: Austin Carr
Read More On: BLOOMBERG
The artificial-intelligence industry is often compared to the oil industry: once mined and refined, data, like oil, can be a highly lucrative commodity. Now it seems the metaphor may extend even further. Like its fossil-fuel counterpart, the process of deep learning has an outsize environmental impact. In a new paper, researchers at the University of Massachusetts, Amherst, performed a life cycle assessment for training several common large AI models.
Author: Karen Hao
Read More On: MIT TECHNOLOGY REVIEW
83% of machine learning startups Crunchbase tracks have had just three funding rounds or less with seed, angel and early-stage rounds being the most common.
Artificial Intelligence-related companies raised $9.3B in 2018, a 72% increase over 2017, according to PwC/CB Insights MoneyTree Report, Q4 2018.
Artificial intelligence deals increased in Q1, 2019 to 116 deals, up from 104 deals in Q4, 2018 according to the latest PwC/CB Insights MoneyTree Report Q1 2019.
Author: Louis Columbus
Read More On: FORBES
Companies understand the importance of artificial intelligence and machine learning, especially since it’s become an increasingly important competitive differentiator, and are eager to jump in. But as always, the question stands: Once you’ve identified the potential of AI for your business, do you buy, or do you build?
Author: VB Staff
Read More On: VENTURE BEAT
Current robots are extremely limited, typically relegated to highly controlled environments, unable to function in dynamic, open-ended environments, and poorly equipped to deal with the unexpected. We are building an industrial-strength cognitive platform — the first of its kind — to enable robots to be smart, collaborative, robust, safe and genuinely autonomous, with applications in a broad range of verticals from construction and delivery to warehouses and domestic robots.
Author: Tristan Greene
Read More On: THE NEXT WEB
The question of whether an artificial general intelligence will be developed in the future—and, if so, when it might arrive—is controversial. One (very uncertain) estimate suggests 2070 might be the earliest we could expect to see such technology. Some futurists point to Moore’s Law and the increasing capacity of machine learning algorithms to suggest that a more general breakthrough is just around the corner.
Read More On: SINGULARITY HUB
Researchers from MIT and elsewhere have developed an interactive tool that, for the first time, lets users see and control how automated machine-learning systems work. The aim is to build confidence in these systems and find ways to improve them.Designing a machine-learning model for a certain task — such as image classification, disease diagnoses, and stock market prediction — is an arduous, time-consuming process.
Author: Rob Matheson
Read More On: MIT NEWS