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Category: information science – Page 117

A common idea that our creativity is what makes us uniquely human has shaped society but strides of progress made in the domain of Generative Artificial Intelligence question this very notion. Generative AI is an emerging field that involves the creation of original content or data using machine learning algorithms.

As we think about a future where humans and AI partner in iterative creative cycles, we consider how generative AI could impact current businesses and possibly create new ones. Up until recently, machines were relegated to analysis and cognitive roles, but today algorithms are improving at generating original content. These technologies are iterative in principle, one is built on top of the last one, and each new iteration enhances the algorithm and increases the potential for discovery exponentially.

The technology presents itself as a more refined and mature breed of AI that has sent investors into a frenzy and among all this emerges a clear market leader — OpenAI. Its flagship products-ChatGPT and DALL-E proved to be industry disruptors and brought generative AI tools to the masses. DALL-E allows people to generate and edit photo-realistic images simply by describing what they want to see, while ChatGPT does the same through a text medium.

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The early 20th century saw the advent of quantum mechanics to describe the properties of small particles, such as electrons or atoms. Schrödinger’s equation in quantum mechanics can successfully predict the electronic structure of atoms or molecules. However, the “duality” of matter, referring to the dual “particle” and “wave” nature of electrons, remained a controversial issue. Physicists use a complex wavefunction to represent the wave nature of an electron.

“Complex” numbers are those that have both “real” and “imaginary” parts—the ratio of which is referred to as the “phase.” However, all directly measurable quantities must be “real”. This leads to the following challenge: when the electron hits a detector, the “complex” phase information of the disappears, leaving only the square of the amplitude of the wavefunction (a “real” value) to be recorded. This means that electrons are detected only as particles, which makes it difficult to explain their dual properties in atoms.

The ensuing century witnessed a new, evolving era of physics, namely, physics. The attosecond is a very short time scale, a billionth of a billionth of a second. “Attosecond physics opens a way to measure the phase of electrons. Achieving attosecond time-resolution, electron dynamics can be observed while freezing ,” explains Professor Hiromichi Niikura from the Department of Applied Physics, Waseda University, Japan, who, along with Professor D. M. Villeneuve—a principal research scientist at the Joint Attosecond Science Laboratory, National Research Council, and adjunct professor at University of Ottawa—pioneered the field of attosecond physics.

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The new algorithm could render mainstream encryption powerless within years.

Chinese researchers claim to have introduced a new code-breaking algorithm that, if successful, could render mainstream encryption powerless within years rather than decades.

The team, led by Professor Long Guilu of Tsinghua University, proclaimed that a modest quantum computer constructed with currently available technology could run their algorithm, South China Morning Post (SCMP) reported on Wednesday.

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https://youtu.be/I5Xarr7pBuk

Simon Waslander is the Director of Collaboration, at the CureDAO Alliance for the Acceleration of Clinical Research (https://www.curedao.org/), a community-owned platform for the precision health of the future.

CureDAO is creating an open-source platform to discover how millions of factors, like foods, drugs, and supplements affect human health, within a decentralized autonomous organization (DAO), making suffering optional through the creation of a “WordPress of health data”.

Simon is a native of the Dutch Caribbean island of Aruba, having been born on the island and initially chose to study medicine at the University of Groningen, but then transitioned over to healthcare innovation studies at the University of Maastricht where he wrote his master thesis on the topic of Predictive Healthcare Algorithms.

(For information on the discussion segment on AGI, please contact — www.Norn.AI)

Continue reading “Open-Sourcing And Accelerating Precision Health Of The Future: Progress, Potential and Possibilities Podcast episode” | >

00:00 Trailer.
05:54 Tertiary brain layer.
19:49 Curing paralysis.
23:09 How Neuralink works.
33:34 Showing probes.
44:15 Neuralink will be wayyy better than prior devices.
1:01:20 Communication is lossy.
1:14:27 Hearing Bluetooth, WiFi, Starlink.
1:22:50 Animal testing & brain proxies.
1:29:57 Controlling muscle units w/ Neuralink.

I had the privilege of speaking with James Douma-a self-described deep learning dork. James’ experience and technical understanding are not easily found. I think you’ll find his words to be intriguing and insightful. This is one of several conversations James and I plan to have.

We discuss:
1. Elon’s motivations for starting Neuralink.
2. How Neuralinks will be implanted.
3. Things Neuralink will be able to do.
4. Important takeaways from the latest Show and Tell event.

In future episodes, we’ll dive more into:

Continue reading “Neural network expert explains NEURALINK (in simple language)” | >

Portable, low-field strength MRI systems have the potential to transform neuroimaging – provided that their low spatial resolution and low signal-to-noise (SNR) ratio can be overcome. Researchers at Harvard Medical School are harnessing artificial intelligence (AI) to achieve this goal. They have developed a machine learning super-resolution algorithm that generates synthetic images with high spatial resolution from lower resolution brain MRI scans.

The convolutional neural network (CNN) algorithm, known as LF-SynthSR, converts low-field strength (0.064 T) T1-and T2-weighted brain MRI sequences into isotropic images with 1 mm spatial resolution and the appearance of a T1-weighted magnetization-prepared rapid gradient-echo (MP-RAGE) acquisition. Describing their proof-of-concept study in Radiology, the researchers report that the synthetic images exhibited high correlation with images acquired by 1.5 T and 3.0 T MRI scanners.

Morphometry, the quantitative size and shape analysis of structures in an image, is central to many neuroimaging studies. Unfortunately, most MRI analysis tools are designed for near-isotropic, high-resolution acquisitions and typically require T1-weighted images such as MP-RAGE. Their performance often drops rapidly as voxel size and anisotropy increase. As the vast majority of existing clinical MRI scans are highly anisotropic, they cannot be reliably analysed with existing tools.

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It is also looking at a possible investment from Microsoft.

OpenAI, the artificial intelligence research company, is building an iOS app powered by its globally popular chatbot ChatGPT which helps users search for answers using an iMessage like interface. A beta version of the app is being tested currently, and a demo version was shared on the professional networking site LinkedIn.

Launched in November last year, ChatGPT made global news for its ease of answering even complex questions in a conversational manner. The algorithm that powers the chatbot, GPT3.5 is built by Open AI and is trained to learn what humans mean when they ask a question.

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You have probably heard of ChatGPT and DALLE-E, a new class of AI-powered software tools that can create new images or write text. The algorithm brings to life any idea you may have by putting together fragments of what it has previously seen — such as images annotated with meta-descriptions of what they represent — to generate original content from user-defined input. But now generative AI technology is revolutionizing drug discovery. Absci Corporation (Nasdaq: ABSI) is using machine learning to transform the field of antibody therapeutics: Absci has put out a press release today announcing the ability to create new antibodies with the use of generative AI.

GenerativeAI: You’ve seen it with images like DALL-E, you’ve seen it with text like ChatGPT. Now you can see it with protein design as well.

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New study demonstrates the potential for machine learning to accelerate the development of innovative drug delivery technologies.

Scientists at the University of Toronto have successfully tested the use of machine learning models to guide the design of long-acting injectable drug formulations. The potential for machine learning algorithms to accelerate drug formulation could reduce the time and cost associated with drug development, making promising new medicines available faster.

The study will be published today (January 10, 2023) in the journal Nature Communications.

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Researchers at DeepMind in London have shown that artificial intelligence (AI) can find shortcuts in a fundamental type of mathematical calculation, by turning the problem into a game and then leveraging the machine-learning techniques that another of the company’s AIs used to beat human players in games such as Go and chess.

The AI discovered algorithms that break decades-old records for computational efficiency, and the team’s findings, published on 5 October in Nature1, could open up new paths to faster computing in some fields.

“It is very impressive,” says Martina Seidl, a computer scientist at Johannes Kepler University in Linz, Austria. “This work demonstrates the potential of using machine learning for solving hard mathematical problems.”

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