Lifeboat Foundation

When users want to send data over the internet faster than the network can handle, congestion can occur—the same way traffic congestion snarls the morning commute into a big city.

Computers and devices that transmit data over the internet break the data down into smaller packets and use a special algorithm to decide how fast to send those packets. These congestion control algorithms seek to fully discover and utilize available network capacity while sharing it fairly with other users who may be sharing the same network. These algorithms try to minimize delay caused by data waiting in queues in the network.

Over the past decade, researchers in industry and academia have developed several algorithms that attempt to achieve high rates while controlling delays. Some of these, such as the BBR algorithm developed by Google, are now widely used by many websites and applications.

It took researchers about 62 minutes to crack a late-stage Post-Quantum Encryption candidate algorithm using a single-core CPU.

Multivariable calculus, differential equations, linear algebra—topics that many MIT students can ace without breaking a sweat—have consistently stumped machine learning models. The best models have only been able to answer elementary or high school-level math questions, and they don’t always find the correct solutions.

Now, a multidisciplinary team of researchers from MIT and elsewhere, led by Iddo Drori, a lecturer in the MIT Department of Electrical Engineering and Computer Science (EECS), has used a neural network model to solve university-level math problems in a few seconds at a human level.

The model also automatically explains solutions and rapidly generates new problems in university math subjects. When the researchers showed these machine-generated questions to university students, the students were unable to tell whether the questions were generated by an algorithm or a human.

This post is also available in: עברית (Hebrew)

Imagine knowing the future. Being able to predict what’s going to happen next. It feels that this concept is merely a dream, but in reality, this dream is underway. Modeling and simulation, data analytics, AI and machine learning, distributed systems, social dynamics and human behavior simulation are fast becoming the go-to tools, and their qualities could offer significant advantages for the battlespace of tomorrow.

According to army-technology.com, London-based technology provider Improbable has been working closely with the UK Ministry of Defense (MoD) since 2018 to explore the utility of synthetic environments (SEs) for tactical training and operational and strategic planning. At the core of this work is Skyral, a platform that supports an ecosystem of industry and academia enabling the fast construction of new SEs for almost any scenario using digital entities, algorithms, AI, historic and real-time data.

Researchers at Oxford University’s Department of Materials, working in collaboration with colleagues from Exeter and Munster, have developed an on-chip optical processor capable of detecting similarities in datasets up to 1,000 times faster than conventional machine learning algorithms running on electronic processors.

The new research published in Optica took its inspiration from Nobel Prize laureate Ivan Pavlov’s discovery of classical conditioning. In his experiments, Pavlov found that by providing another stimulus during feeding, such as the sound of a bell or metronome, his dogs began to link the two experiences and would salivate at the sound alone. The repeated associations of two unrelated events paired together could produce a learned response—a conditional reflex.

Co-first author Dr. James Tan You Sian, who did this work as part of his DPhil in the Department of Materials, University of Oxford, said, “Pavlovian associative learning is regarded as a basic form of learning that shapes the behavior of humans and animals—but adoption in AI systems is largely unheard of. Our research on Pavlovian learning in tandem with optical parallel processing demonstrates the exciting potential for a variety of AI tasks.”

https://www.youtube.com/watch?v=UTMD57bsRj0&feature=share

👉For business inquiries: [email protected].
✅ Instagram: https://www.instagram.com/pro_robots.

You are on the PRO Robots channel and today we present you with some high-tech news. The first robot with self-awareness, a new breakthrough in the creation of general artificial intelligence, evolving robots, a Japanese home for a space colony, an unexpected turn in the fate of XPENG Robotics and other news from the world of high technology in one issue! Let’s roll!

Continue reading “Robot realized itself and learned to use its body for the first time | High Tech News” »

Humans are good at looking at images and finding patterns or making comparisons. Look at a collection of dog photos, for example, and you can sort them by color, by ear size, by face shape, and so on. But could you compare them quantitatively? And perhaps more intriguingly, could a machine extract meaningful information from images that humans can’t?

Now a team of Standford University’s Chan Zuckerberg Biohub scientists has developed a machine learning method to quantitatively analyze and compare images—in this case microscopy images of proteins—with no prior knowledge. As reported in Nature Methods, their algorithm, dubbed “cytoself,” provides rich, detailed information on protein location and function within a cell. This capability could quicken research time for cell biologists and eventually be used to accelerate the process of drug discovery and drug screening.

“This is very exciting—we’re applying AI to a new kind of problem and still recovering everything that humans know, plus more,” said Loic Royer, co-corresponding author of the study. “In the future we could do this for different kinds of images. It opens up a lot of possibilities.”

The greatest artistic tool ever built, or a harbinger of doom for entire creative industries? OpenAI’s second-generation DALL-E 2 system is slowly opening up to the public, and its text-based image generation and editing abilities are awe-inspiring.

The pace of progress in the field of AI-powered text-to-image generation is positively frightening. The generative adversarial network, or GAN, first emerged in 2014, putting forth the idea of two AIs in competition with one another, both “trained” by being shown a huge number of real images, labeled to help the algorithms learn what they’re looking at. A “generator” AI then starts to create images, and a “discriminator” AI tries to guess if they’re real images or AI creations.

Continue reading “OpenAI’s DALL-E 2: A dream tool and existential threat to visual artists” »

https://www.youtube.com/watch?v=VFuElWbRuHM&feature=share

✅ Subscribe: https://bit.ly/3slupxs.
Quantum AI is the use of quantum computing for computation of machine learning algorithms. Thanks to computational advantages of quantum computing, quantum AI can help achieve results that are not possible to achieve with classical computers.

Quantum data: Quantum data can be considered as data packets contained in qubits for computerization. However, observing and storing quantum data is challenging because of the features that make it valuable which are superposition and entanglement. In addition, quantum data is noisy, it is necessary to apply a machine learning in the stage of analyzing and interpreting these data correctly.

Continue reading “Artificial General Intelligence | Tim Ferriss & Eric Schmidt | GEONOW” »