We all have images in our mind of rocket launches from Cape Canaveral Air Force Station hurtling astronauts into space and satellites into orbit.
But those launches may be a thing of the past as a new generation of drones that can do the same job cheaper, safer and better steps into play.
Continue reading “Huge drone set to launch satellites in mid-air” »
Elon Musk’s Boring Company has released the first images teasing the first passenger station of the Las Vegas Loop ahead of its launch.
A Boring Company Loop system consists of tunnels in which Tesla autonomous electric vehicles travel at high speeds between stations to transport people within a city.
The first system is being deployed at the Las Vegas Convention Center (LVCVA), which is paying $50 million for the system, but we recently learned that the Boring Company plans to connect the convention center’s Loop to casinos on the strip in order to eventually create a city-wide Loop in Las Vegas.
It’s my firm belief that the AI revolution we’ve all been so excited about simply has not happened yet. In the next two to three years however, as the hardware that enables “real” AI power makes its way into more and more hands, it will happen. As far as predicting the change and disruption that will come from widespread access to the upper echelons of powerful ML and AI — there are few ways to make confident predictions, but that is exactly the point!
Much like cellphones put so much power in the hands of regular people everywhere, with no barriers to entry either technical or financial (for the most part), so will the coming wave of software-defined hardware that is flexible, customizable and future-proof. The possibilities are truly endless, and it will mark an important turning point in technology. The ripple effects of AI democratization and commoditization will not stop with just technology companies, and so even more fields stand to be blown open as advanced, high-powered AI becomes accessible and affordable.
Much of the hype around AI — all the disruption it was supposed to bring and the leaps it was supposed to fuel — will begin in earnest in the next few years. The technology that will power it is being built as we speak or soon to be in the hands of the many people in the many industries who will use their newfound access as a springboard to some truly amazing advances. We’re especially excited to be a part of this future, and look forward to all the progress it will bring.
We take a deep and necessary dive into the topics of robotics, transhumanism, singularity, and other apocalyptic tech – all to prepare for the endgame moves of the elite and their Breakaway Civilization. To win this game of digital thrones, we must tap into the myth and Gnosis of an ancient trickster egregore named Prometheus. We must steal fire from the gods before they burn civilization down very soon.
Astral Guest – Jason Reza Jorjani, author of Prometheism.
Continue reading “Prometheism, Transhumanism & The Coming Singularity” »
The future of basketball? CUE3 is a hoop-shooting humanoid robot created by Toyota in Japan 🏀 🤖.
This Week in Engineering is multi-segment show that explores the latest innovations and tech trends across many engineering disciplines from academic institutions, government agencies and industry.
EPISODE SEGMENTS:
0:14 GM Unveils Massive Investment in EV’s.
2:30 UK’s Kar-go Autonomous Delivery Vehicle.
The robotic Chang’e-5 probe starts work to gather lunar samples it can send to Earth for study.
Understanding how matter interacts with light—its optical properties—is critical in a myriad of energy and biomedical technologies, such as targeted drug delivery, quantum dots, fuel combustion, and cracking of biomass. But calculating these properties is computationally intensive, and the inverse problem—designing a structure with desired optical properties—is even harder.
Now Berkeley Lab scientists have developed a machine learning model that can be used for both problems—calculating optical properties of a known structure and, inversely, designing a structure with desired optical properties. Their study was published in Cell Reports Physical Science.
“Our model performs bi-directionally with high accuracy and its interpretation qualitatively recovers physics of how metal and dielectric materials interact with light,” said corresponding author Sean Lubner.
Deep learning neural networks can be massive, demanding major computing power. In a test of the “lottery ticket hypothesis,” MIT researchers have found leaner, more efficient subnetworks hidden within BERT models. The discovery could make natural language processing more accessible.
Google’s deep-learning program for determining the 3D shapes of proteins stands to transform biology, say scientists.
