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TIMELAPSE OF FUTURE SPACECRAFT: 2025 — 3000+

A sci fi documentary looking at a timelapse of future spacecraft. From the future of AI spaceships, Starship orbital refuelling, and space station worlds, to Mars colonization and in-space manufacturing.

Other topics include: SpaceX and the launch of their fleet of Starships — waiting in parking orbit around Earth, ready for the launch window to open to Mars. NASA and the mission of landing on the Martian Moon Phobos. Advances in spacecraft technology for protecting humans during multi-year interstellar journeys.

While the year 2100 and beyond, brings wormhole exploration, artificial intelligence based planets, and the possible need for a stellar engine — to protect the solar system.

Main narration by: Alexander Masters (www.alexander-masters.com)

Starship Artwork – used with permission and licensed from:
Erc X: https://twitter.com/ErcXspace.
Caspar Stanley: https://twitter.com/Caspar_Stanley.
Alex Svan: https://twitter.com/AlexSvanArt.

Additional footage sourced from: SpaceX, NASA, ESO, Ken Crawford, Nick Risinger, Northrop Grumman, SpinLaunch, Redwire Space.

The uses of ethical AI in hiring: Opaque vs. transparent AI

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.

There hasn’t been a revolution quite like this before, one that’s shaken the talent industry so dramatically over the past few years. The pandemic, the Great Resignation, inflation and now talk of looming recessions are changing talent strategies as we know them.

Such significant changes, and the challenge of staying ahead of them, have brought artificial intelligence (AI) to the forefront of the minds of HR leaders and recruitment teams as they endeavor to streamline workflows and identify suitable talent to fill vacant positions faster. Yet many organizations are still implementing AI tools without proper evaluation of the technology or indeed understanding how it works — so they can’t be confident they are using it responsibly.

Is Google’s New AI As Smart As A Human? 🤖

❤️ Check out Fully Connected by Weights & Biases: https://wandb.me/papers.

📝 The paper “Minerva — Solving Quantitative Reasoning Problems with Language Models” is available here:
https://arxiv.org/abs/2206.

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Thumbnail background design: Felícia Zsolnai-Fehér — http://felicia.hu.

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Quantum computing is an even bigger threat than artificial intelligence

Given the potential scope and capabilities of quantum technology, it is absolutely crucial not to repeat the mistakes made with AI—where regulatory failure has given the world algorithmic bias that hypercharges human prejudices, social media that favors conspiracy theories, and attacks on the institutions of democracy fueled by AI-generated fake news and social media posts. The dangers lie in the machine’s ability to make decisions autonomously, with flaws in the computer code resulting in unanticipated, often detrimental, outcomes. In 2021, the quantum community issued a call for action to urgently address these concerns. In addition, critical public and private intellectual property on quantum-enabling technologies must be protected from theft and abuse by the United States’ adversaries.

https://urldefense.com/v3/__https:/www.youtube.com/watch?v=5…MexaVnE%24

There are national defense issues involved as well. In security technology circles, the holy grail is what’s called a cryptanalytically relevant quantum computer —a system capable of breaking much of the public-key cryptography that digital systems around the world use, which would enable blockchain cracking, for example. That’s a very dangerous capability to have in the hands of an adversarial regime.

Experts warn that China appears to have a lead in various areas of quantum technology, such as quantum networks and quantum processors. Two of the world’s most powerful quantum computers were been built in China, and as far back as 2017, scientists at the University of Science and Technology of China in Hefei built the world’s first quantum communication network using advanced satellites. To be sure, these publicly disclosed projects are scientific machines to prove the concept, with relatively little bearing on the future viability of quantum computing. However, knowing that all governments are pursuing the technology simply to prevent an adversary from being first, these Chinese successes could well indicate an advantage over the United States and the rest of the West.

AI Translates Brain Waves To Photos | Quantum Computing AI Breakthrough | Deep Learning Robotic Arm

Researchers use artificial intelligence to translate brain waves from fMRI into photos. Quantum computing breakthrough requires very little data to train AI. New deep learning framework for robotic arm art.

AI News Timestamps:
0:00 New AI Turns Brain Waves Into Photos.
3:24 Quantum Computing AI Breakthrough.
6:01 Deep Learning Robotic Arm.

👉 Crypto AI News: https://www.youtube.com/c/CryptoAINews/videos.

https://www.researchgate.net/publication/357660687_Hyperreal…tent_space.

https://www.nature.com/articles/s41467-022-32550-3

https://arxiv.org/abs/2208.

Robot dog trialled at Teck’s Elkview Operations

Tomorrow, Friday, August 26, is International Dog Day and this year Teck is celebrating with Spot, the robot dog developed by Boston Robotics that is supporting safety inspections and data collection at its Elkview mine operations.

Spot is an artificial intelligence (AI) assisted robot designed as man’s best friend.

Spot is a four-legged sensor device that navigates terrain with unprecedented mobility – getting into places that are frequently unsafe or challenging for people, allowing the mine to automate routine inspection tasks and data capture safely, accurately, and frequently.

We are building a “species-level brain” with big data and ubiquitous sensors

We need the computers and sensors to better our lives, to allow everyone access to the wisdom of the ages. We can’t collect all the data ourselves and try to make sense of it without machines because our brains aren’t up to the task. Imagine if every little decision everyone has made over the past thousand years along with its outcome had been recorded on index cards and stored in a gargantuan facility somewhere. Remember that giant warehouse at the end of the first Indiana Jones movie where they ended up storing the Ark of the Covenant? That’s where index cards AA through AC are housed. Imagine five thousand more of those to store all that data. What could we do with it? Nothing useful.

Computers can do only one thing: manipulate ones and zeros in memory. But they can do that at breathtaking speeds with perfect accuracy. Our challenge is getting all that data into the digital mirror, to copy our analog lives in their digital brains. Cheap sensors and computers will do this for us, with prices that fall every year and capabilities that increase.

Coupling massive processing power with sensors will create a species-level brain and memory. Instead of being billions of separate people with siloed knowledge, we will become billions of people who share a single vast intellect. Comparisons to The Matrix are easy to make but are not really apropos. We aren’t talking about a world without human agency but with enhanced agency, information-based agency. Making decisions informed by data is immeasurably better. Even if someone ignores the suggestion of the digital mirror, they are richer for knowing it. Imagine having an AI that could not only tell you what you should do but would allow you to insert your own values into the decision process. In fact, the system would learn your values from your actions, and the suggestions it gives you would be different from those it would give everyone else, as they should be. If knowledge is power, such a system is by definition the ultimate in empowerment. Every person on the planet could effectively be smarter and wiser than anyone who has ever lived.

A silicon image sensor that computes

As any driver knows, accidents can happen in the blink of an eye—so when it comes to the camera system in autonomous vehicles, processing time is critical. The time that it takes for the system to snap an image and deliver the data to the microprocessor for image processing could mean the difference between avoiding an obstacle or getting into a major accident.

In-sensor , in which important features are extracted from raw data by the itself instead of the separate microprocessor, can speed up the . To date, demonstrations of in-sensor processing have been limited to emerging research materials which are, at least for now, difficult to incorporate into commercial systems.

Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed the first in-sensor processor that could be integrated into commercial silicon imaging sensor chips–known as complementary metal-oxide-semiconductor (CMOS) image sensors–that are used in nearly all commercial devices that need capture visual information, including smartphones.