Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI – Page 1,308

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.

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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.

Continue reading “AI Translates Brain Waves To Photos | Quantum Computing AI Breakthrough | Deep Learning Robotic Arm” | >

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.

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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.

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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.

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Foresight Existential Hope Group.
Program & apply to join: https://foresight.org/existential-hope/

In the Existential Hope-podcast (https://www.existentialhope.com), we invite scientists to speak about long-termism. Each month, we drop a podcast episode where we interview a visionary scientist to discuss the science and technology that can accelerate humanity towards desirable outcomes.

Xhope Special with Foresight Fellow Morgan Levine.

Morgan Levine is a ladder-rank Assistant Professor in the Department of Pathology at the Yale School of Medicine and a member of both the Yale Combined Program in Computational Biology and Bioinformatics, and the Yale Center for Research on Aging. Her work relies on an interdisciplinary approach, integrating theories and methods from statistical genetics, computational biology, and mathematical demography to develop biomarkers of aging for humans and animal models using high-dimensional omics data. As PI or co-Investigator on multiple NIH-, Foundation-, and University-funded projects, she has extensive experience using systems-level and machine learning approaches to track epigenetic, transcriptomic, and proteomic changes with aging and incorporate.
this information to develop measures of risk stratification for major chronic diseases, such as cancer and Alzheimer’s disease. Her work also involves development of systems-level outcome measures of aging, aimed at facilitating evaluation for geroprotective interventions.

Existential Hope.
A group of aligned minds who cooperate to build beautiful futures from a high-stakes time in human civilization by catalyzing knowledge around potential paths to get there and how to plug in.

Follow us!

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Recent developments like DALLE-2 and LaMDA are impressive and seem ready for impact. Is AI ready to change the world?

Whether you love, fear, or have mixed feelings about the future of artificial intelligence, the cultural fixation on the subject over the past decade has made it feel like the technology’s meteoric impact is just around the corner. The problem is that it is always just around the corner, yet never seems to arrive. Many hype-filled years have passed us by since the releases of Ex Machina (2014) and Westworld (2016), but it feels like we are still waiting on AI’s big splash. However, a handful of recent developments—specifically, OpenAI’s unveiling of GPT-3 and DALLE-2, and Google’s LaMDA controversy—have unleashed a new wave of excitement—and terror—around the possibility that AI’s game-changing moment is finally here.

There are several reasons why it feels it has taken a long time for AI projects to bear fruit. One is that pop culture seems almost exclusively focused on the possible endgames of the technology, rather than its broader journey. This isn’t much of a surprise. When we stream the latest sci-fi movie or binge Black Mirror episodes, we want to see killer robots and computer chip brain implants. No one is buying a ticket to see a movie about the slow, incremental rollout of existing technology—not unless it mutates and starts killing within the first 30 minutes. But while AI’s more futuristic forms are naturally the most entertaining, and provide an endless source of material for screenwriters, anyone who based their expectations for AI off of Bladerunner has got to be feeling disappointed by now.

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