Thirty years ago, Hinton’s belief in neural networks was contrarian. Now it’s hard to find anyone who disagrees, he says.
Category: robotics/AI – Page 1675
Intel continues to snap up startups to build out its machine learning and AI operations. In the latest move, TechCrunch has learned that the chip giant has acquired Cnvrg.io, an Israeli company that has built and operates a platform for data scientists to build and run machine learning models, which can be used to train and track multiple models and run comparisons on them, build recommendations and more.
Intel confirmed the acquisition to us with a short note. “We can confirm that we have acquired Cnvrg,” a spokesperson said. “Cnvrg will be an independent Intel company and will continue to serve its existing and future customers.” Those customers include Lightricks, ST Unitas and Playtika.
Intel is not disclosing any financial terms of the deal, nor who from the startup will join Intel. Cnvrg, co-founded by Yochay Ettun (CEO) and Leah Forkosh Kolben, had raised $8 million from investors that include Hanaco Venture Capital and Jerusalem Venture Partners, and PitchBook estimates that it was valued at around $17 million in its last round.
R-sharing. Hmmm… would you trust the AI to drive for you?
At the end of November, Tesla (NASDAQ: TSLA) released its Vehicle Safety Report for Q3 2020, which shows that its vehicles using Autopilot are almost 10 times safer than other vehicles on United States roads. While the California manufacturer has directed massive efforts towards achieving Level 5 autonomy, the development of autonomous driving in Europe is at best slow-moving.
Recently, though, researchers in Germany are suggesting that this should change, and for good reason. The researchers indicate that, if Tesla Autopilot were installed on all cars in the Germany now, they would be able to avoid hundreds of thousands of car accidents.
“Legislative procedures that provide legal support for autonomous driving are progressing slowly,” criticizes Ferdinand Dudenhöffer, director of the Center for Automotive Research (CAR) in Duisburg.
If you are interested in developing chatbots, you can find out that there are a lot of powerful bot development frameworks, tools, and platforms that can use to implement intelligent chatbot solutions. How about developing a simple, intelligent chatbot from scratch using deep learning rather than using any bot development framework or any other platform. In this tutorial, you can learn how to develop an end-to-end domain-specific intelligent chatbot solution using deep learning with Keras.
Don’t you wish you had your own robotic exoskeleton?
This would really take away the strain in manual labor.
“In the past, the lifting workers could hardly stay after 2 years as the heavy work would burden them with injuries.”
This company in China is developing robotic exoskeletons to keep workers safe. More Bloomberg: https://trib.al/jllD1cT.
A five-storey building in China has been relocated using an innovative system of robotic legs.
Unless you’re a physicist or an engineer, there really isn’t much reason for you to know about partial differential equations. I know. After years of poring over them in undergrad while studying mechanical engineering, I’ve never used them since in the real world.
But partial differential equations, or PDEs, are also kind of magical. They’re a category of math equations that are really good at describing change over space and time, and thus very handy for describing the physical phenomena in our universe. They can be used to model everything from planetary orbits to plate tectonics to the air turbulence that disturbs a flight, which in turn allows us to do practical things like predict seismic activity and design safe planes.
The catch is PDEs are notoriously hard to solve. And here, the meaning of “solve” is perhaps best illustrated by an example. Say you are trying to simulate air turbulence to test a new plane design. There is a known PDE called Navier-Stokes that is used to describe the motion of any fluid. “Solving” Navier-Stokes allows you to take a snapshot of the air’s motion (a.k.a. wind conditions) at any point in time and model how it will continue to move, or how it was moving before.
Many Machine Learning and AI algorithms are centralized, with no transparency in the process. Now a blockchain-based start-up aims to improve transparency bias in business workflows.
The future of disaster management, using artificial intelligence, machine learning, and a bit of Waffle House and Starbucks 🙂
Ira Pastor, ideaXme life sciences ambassador interviews Craig Fugate Chief Emergency Management Officer of One Concern and former administrator of the Federal Emergency Management Agency (FEMA).
The international context of this interview: In choosing our leaders it is becoming increasingly important to select people who can both anticipate and address and where possible avoid large scale disasters. Here, Craig Fugate discusses evaluating past disasters, planning for future events and reacting to the “unexpected” — “think big and move fast”.
Google says hardware in embedded devices needs to improve to make possible a world of peel-and-stick sensors free of wall power and human maintenance.