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Category: robotics/AI – Page 2,100

Tesla Autopilot Would Avoid 90% of Car Accidents, German Researcher Urges Country’s Adoption

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.

How To Build Your Own Chatbot Using Deep Learning

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.

AI has cracked a key mathematical puzzle for understanding our world

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.

Think Big, Move Fast, Build Capacity and Resilience: Disaster Management

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”.

Ira Pastor comments:

The U.S. has sustained 279 weather and climate disasters since 1980 where overall damages/costs reached or exceeded $1 billion (including CPI adjustment to 2020). The total cost of these 279 events exceeds $1.825 trillion.

Continue reading “Think Big, Move Fast, Build Capacity and Resilience: Disaster Management” | >

Graphene-based memory resistors show promise for brain-based computing

As progress in traditional computing slows, new forms of computing are coming to the forefront. At Penn State, a team of engineers is attempting to pioneer a type of computing that mimics the efficiency of the brain’s neural networks while exploiting the brain’s analog nature.

Modern computing is digital, made up of two states, on-off or one and zero. An analog computer, like the , has many possible states. It is the difference between flipping a light switch on or off and turning a dimmer switch to varying amounts of lighting.

Neuromorphic or brain-inspired computing has been studied for more than 40 years, according to Saptarshi Das, the team leader and Penn State assistant professor of engineering science and mechanics. What’s new is that as the limits of digital computing have been reached, the need for high-speed image processing, for instance for self-driving cars, has grown. The rise of big data, which requires types of pattern recognition for which the brain architecture is particularly well suited, is another driver in the pursuit of neuromorphic computing.

Microsoft Releases Free App To Train AI Models Without Writing Any Code

Microsoft has announced the launch of the public preview of a free app that allows users to train machine learning (ML) models without writing any code.

This app — Lobe — has been designed for Windows and Mac, only supports image classification; however, the tech giant is planning to expand the app to include other models and data types in the future.

According to Lobe website, the app needs to be shown examples of what the users want to learn, and the app automatically trains a custom machine learning model that can be shipped in the users’ app.

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