Lifeboat Foundation

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.

Continue reading “Tesla Autopilot Would Avoid 90% of Car Accidents, German Researcher Urges Country’s Adoption” »

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.”

Continue reading “Exoskeleton Suits Turn Car Factory Workers Into Human Robots” »

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.

Continue reading “AI has cracked a key mathematical puzzle for understanding our world” »

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).

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

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.

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 brain, 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.