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How Can AI Contribute to a Better Tomorrow- Arif Patel Dubai Businessman

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How Can AI Contribute to a Better Tomorrow- Arif Patel Dubai Businessman

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The latest trends and advancements in artificial intelligence (AI) hold out a lot of hope for the field's future. AI is predicted to be the most revolutionary technology that people have ever created, according to a study.

Arif Patel Of Preston UK, Dubai, London-based entrepreneur, who frequently shares his views on oil and gas, engineering, and mining, thinks that this technology can progress machine learning, the healthcare industry, the oil and gas industry, and transportation.

Regarding trends and advancements in AI in Dubai, a prominent city in the United Arab Emirates that is home to cutting-edge skyscrapers, ultra-modern architecture, and other specialties, the oil and gas sector in Dubai is poised to benefit greatly from AI.

AI is continually making a big contribution, from the oil and gas industry to the Museum of the Future. According to Arif Patel, Dubai's use of AI technology in its manufacturing, mining, and oil and gas operations is a wise decision that is continually evolving with the introduction of new emergent technologies.

Robots are used in the oil and gas industry for a variety of jobs, including surveys, material handling, building, inspection, repair, and maintenance, claims GlobalData, a top data and analytics business. With machine automation and optimization, such technology is extremely promising for drilling operations and oil exploration.

At Dubai's Museum of the Future, how about a canine welcome? An illustration of this is the robotic dog that uses AI. Understanding the inputs or commands allows it to communicate with people. The museum is home to a number of wonders based on AI technology, including the humanoid robot Ameca and a flying penguin named Barista. Even flying jellyfish are visible in this location.

With its sci-fi settings, flying birds and penguins, and robotic dogs ambling around, the museum perhaps alludes to the future. Dubai has benefited greatly from artificial intelligence. According to a recent Google study, the deployment of AI at a large scale might boost productivity and efficiency across several industries and boost the UK GDP by £400 billion by 2030. This emphasizes the potential development and economic gains that AI could bring to the UK.

Arif Patel of Preston thinks AI has a huge potential to revolutionize the economies of Europe, including the UK. Leading countries using this technology in the oil and gas sector include the UK, Norway, and the Netherlands for reservoir modeling, safety management, and optimization of offshore operations optimization to foster industry innovation.

According to the most recent report, British oil and gas giant Shell intends to use SparkCognition's artificial intelligence capabilities to increase its output of deep-sea oil. According to Arif Patel in London, UK, this will produce significant outcomes that will aid in better-using technology for oil and gas exploration, speed up the pace of photographing subsurface structures, and result in improved economics.

Therefore, AI technology holds enormous potential for revolutionizing the global oil and gas, healthcare, and transportation sectors. Businessman Arif Patel of Preston, Dubai-London Businessman believes that AI automation and optimization technology will revolutionize a variety of industries, including oil and gas.

Know More : https://www.walesonline.co.uk/special-features/arif-patel-preston-how-ai-27429948

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The recent trend and developments in Artificial Intelligence (AI) show great promise for the future of technology. According to research, AI is expected to become the most transformative technology that humanity has ever developed.

Arif Patel Of Preston UK, Dubai, London-based entrepreneur, who often expresses his views on oil and gas, engineering, and mining, believes that this technology can advance machine learning, healthcare, oil and gas industries, and transportation.

Referring to AI trends and developments in Dubai, a renowned city in UAE that consists of ultra-modern architecture, the latest skyscrapers, and other specialisations, AI's role in Dubai's oil and gas industry is quite promising.

From oil and gas to the Museum of the Future, AI is constantly making a significant contribution. Arif Patel sees Dubai's consideration of AI technology in oil and gas and manufacturing and mining activities, as a positive move that is constantly developing with the new emerging technologies.

According to GlobalData, a leading data and analytics company, robots have applications across the oil and gas industry in various tasks - ranging from surveys, material handling, and construction, to inspection, repair, and maintenance. Such technology is quite promising in drilling activities and oil exploration with machine automation and optimisation.

How about a dog welcoming you to Dubai's Museum of the Future? The AI-powered robotic dog is such an example. It interacts with humans by understanding the inputs or commands. From Ameca, a humanoid robot that interacts with humans to Barista, a flying penguin, the museum is home to various wonders which are AI technology based. You can even see flying jellyfish here.

The museum in a way represents the future with sci-fi scenes, flying penguins and birds, and robotic dogs walking around. AI has made a remarkable contribution to Dubai. As per a recent Google study, widespread AI adoption could increase productivity and efficiency across several industries and increase the UK GDP by £400 billion by 2030. This highlights the potential growth and economic benefits that AI could have for the UK.

Arif Patel of Preston believes that AI holds tremendous capabilities in transforming the economy of Europe including the UK. Leading nations employing this technology in the oil and gas sector for reservoir modeling, safety management, and optimization of offshore operations optimisation to promote industry innovation include the UK, Norway, and Netherlands.

As per the latest report, to increase its output of deep-sea oil, British oil, and gas major Shell plans to deploy artificial intelligence technologies from analytics company SparkCognition. This will lead to meaningful results which will help in better-harnessing technology for oil and gas exploration, accelerating the pace of imaging and exploration of subsurface structures, leading to an enhanced economy told by Arif Patel in London UK.

Thus, AI technology holds a tremendous possibility in transforming the oil and gas, healthcare, and transportation sectors globally. Arif Patel of Preston, Dubai businessman considers the automation and optimization technology of AI as the most crucial factor in transforming different sectors including oil and gas.

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Arif Patel of Preston: How AI Can Help in Creating a Better Tomorrow?

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Artificial Intelligence could lead the way in the future especially in the oil and gas industry

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How MIT’s Liquid Neural Networks can solve AI problems from robotics to self-driving cars

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How MIT’s Liquid Neural Networks can solve AI problems from robotics to self-driving cars

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In the current artificial intelligence (AI) landscape, the buzz around large language models (LLMs) has led to a race toward creating increasingly larger neural networks. However, not every application can support the computational and memory demands of very large deep learning models.

The constraints of these environments have led to some interesting research directions. Liquid neural networks, a novel type of deep learning architecture developed by researchers at the Computer Science and Artificial Intelligence Laboratory at MIT (CSAIL), offer a compact, adaptable and efficient solution to certain AI problems. These networks are designed to address some of the inherent challenges of traditional deep learning models.

Liquid neural networks can spur new innovations in AI and are particularly exciting in areas where traditional deep learning models struggle, such as robotics and self-driving cars.

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In the current artificial intelligence (AI) landscape, the buzz around large language models (LLMs) has led to a race toward creating increasingly larger neural networks. However, not every application can support the computational and memory demands of very large deep learning models.

The constraints of these environments have led to some interesting research directions. Liquid neural networks, a novel type of deep learning architecture developed by researchers at the Computer Science and Artificial Intelligence Laboratory at MIT (CSAIL), offer a compact, adaptable and efficient solution to certain AI problems. These networks are designed to address some of the inherent challenges of traditional deep learning models.

Liquid neural networks can spur new innovations in AI and are particularly exciting in areas where traditional deep learning models struggle, such as robotics and self-driving cars.

What are liquid neural networks?

"The inspiration for liquid neural networks was thinking about the existing approaches to machine learning and considering how they fit with the kind of safety-critical systems that robots and edge devices offer," Daniela Rus, the director of MIT CSAIL, told VentureBeat. "On a robot, you cannot really run a large language model because there isn't really the computation [power] and [storage] space for that."

Rus and her collaborators wanted to create neural networks that were both accurate and compute-efficient so that they could run on the computers of a robot without the need to be connected to the cloud.

At the same time, they were inspired by the research on biological neurons found in small organisms, such as the C. Elegans worm, which performs complicated tasks with no more than 302 neurons. The result of their work was liquid neural networks (LNN).

Liquid neural networks represent a significant departure from traditional deep learning models. They use a mathematical formulation that is less computationally expensive and stabilizes neurons during training. The key to LNNs' efficiency lies in their use of dynamically adjustable differential equations, which allows them to adapt to new situations after training. This is a capability not found in typical neural networks.

"Basically what we do is increase the representation learning capacity of a neuron over existing models by two insights," Rus said. "First is a kind of a well-behaved state space model that increases the neuron stability during learning. And then we introduce nonlinearities over the synaptic inputs to increase the expressivity of our model during both training and inference."

LNNs also use a wiring architecture that is different from traditional neural networks and allows for lateral and recurrent connections within the same layer. The underlying mathematical equations and the novel wiring architecture enable liquid networks to learn continuous-time models that can adjust their behavior dynamically.

"This model is very interesting because it is able to be dynamically adapted after training based on the inputs it sees," Rus said. "And the time constants that it observes are dependent on the inputs that it sees, and so we have much more flexibility and adaptation through this formulation of the neuron."

The advantages of liquid neural networks

One of the most striking features of LNNs is their compactness. For example, a classic deep neural network requires around 100,000 artificial neurons and half a million parameters to perform a task such as keeping a car in its lane. In contrast, Rus and her colleagues were able to train an LNN to accomplish the same task with just 19 neurons.

This significant reduction in size has several important consequences, Rus said. First, it enables the model to run on small computers found in robots and other edge devices. And second, with fewer neurons, the network becomes much more interpretable. Interpretability is a significant challenge in the field of AI. With traditional deep learning models, it can be difficult to understand how the model arrived at a particular decision.

"When we only have 19 neurons, we can extract a decision tree that corresponds to the firing patterns and essentially the decision-making flow in the system with 19 neurons," Rus said. "We cannot do that for 100,000 or more."

Another challenge that LNNs address is the issue of causality. Traditional deep learning systems often struggle with understanding causal relationships, leading them to learn spurious patterns that are not related to the problem they are solving. LNNs, on the other hand, appear to have a better grasp of causal relationships, allowing them to better generalize to unseen situations.

For instance, the researchers at MIT CSAIL trained LNNs and several other types of deep learning models for object detection on a stream of video frames taken in the woods in summer. When the trained LNN was tested in a different setting, it was still able to perform the task with high accuracy. In contrast, other types of neural networks experienced a significant performance drop when the setting changed.

"We observed that only the liquid networks were able to still complete the task in the fall and in the winter because these networks focus on the task, not on the context of the task," Rus said. "The other models did not succeed at solving the task, and our hypothesis is that it's because the other models rely a lot on analyzing the context of the test, not just the task."

Attention maps extracted from the models show that LNNs give higher values to the main focus of the task, such as the road in driving tasks, and the target object in the object detection task, which is why it can adapt to the task when the context changes. Other models tend to spread their attention to irrelevant parts of the input.

"Altogether, we have been able to achieve much more adaptive solutions because you can train in one environment and then that solution, without further training, can be adapted to other environments," Rus said.

The applications and limitations of liquid neural networks

LNNs are primarily designed to handle continuous data streams. This includes video streams, audio streams, or sequences of temperature measurements, among other types of data.

"In general, liquid networks do well when we have time series data ... you need a sequence in order for liquid networks to work well," Rus said. "However, if you try to apply the liquid network solution to some static database like ImageNet, that's not going to work so well."

The nature and characteristics of LNNs make them especially suitable for computationally constrained and safety-critical applications such as robotics and autonomous vehicles, where data is continuously fed to machine learning models.

The MIT CSAIL team has already tested LNNs in single-robot settings, where they have shown promising results. In the future, they plan to extend their tests to multi-robot systems and other types of data to further explore the capabilities and limitations of LNNs.

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How MIT's Liquid Neural Networks can solve AI problems from robotics to self-driving cars

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Liquid neural networks can spur new innovations in AI and are particularly exciting in areas where traditional deep learning models struggle.

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Apple is changing the charging port with iPhone 15, but it won’t be like the last time

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Apple is changing the charging port with iPhone 15, but it won’t be like the last time

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When Apple unveils the iPhone 15 line in early next month, it will also announce the first charging port change since the iPhone 5. For a lot of iPhone users, however, nothing will change with how the iPhone is charged this time.

30-pin to Lightning
In retrospect, it’s a little hard to believe that Apple only made five iPhone models that used the original charging port. Launched 16 years ago, the original iPhone used the same 30-pin connector charging port as the iPod line.

By the time Apple’s 30-pin connector came to the iPhone, it was an okay charging option. The plug only connected when facing forward, but it no longer locked into place with teeth on either side.

Apple only ever shipped this charging port on the iPhone, iPhone 3G, iPhone 3GS, iPhone 4, and iPhone 4S. Still, the public reaction when the iPhone 5 switched to the current Lightning port was not exactly positive in 2012.

That’s largely because 30-pin connector adoption had a head start due to the popularity of the iPod. It didn’t help that Apple’s 30-pin to Lightning adapter was in short supply when the iPhone 5 launched.

Know More : https://9to5mac.com/2023/08/02/iphone-15-charging-port-change/

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When Apple unveils the iPhone 15 line in early next month, it will also announce the first charging port change since the iPhone 5. For a lot of iPhone users, however, nothing will change with how the iPhone is charged this time.

30-pin to Lightning

In retrospect, it's a little hard to believe that Apple only made five iPhone models that used the original charging port. Launched 16 years ago, the original iPhone used the same 30-pin connector charging port as the iPod line.

By the time Apple's 30-pin connector came to the iPhone, it was an okay charging option. The plug only connected when facing forward, but it no longer locked into place with teeth on either side.

Apple only ever shipped this charging port on the iPhone, iPhone 3G, iPhone 3GS, iPhone 4, and iPhone 4S. Still, the public reaction when the iPhone 5 switched to the current Lightning port was not exactly positive in 2012.

That's largely because 30-pin connector adoption had a head start due to the popularity of the iPod. It didn't help that Apple's 30-pin to Lightning adapter was in short supply when the iPhone 5 launched.

USB-A to USB-C

While it's true that the iPhone hasn't changed its charging port in over a decade, that might not be how customers see it.

Apple began shipping a USB-C to Lightning cable in the box with the iPhone 11 Pro to unlock faster charging. iPhone 5 through the iPhone XS (and non-Pro 11) came with a USB-A to Lightning cable. Apple also included the charging brick in the box.

A year later, Apple stopped including the power adapter in the box to save the planet and replaced USB-A to Lightning with USB-C to Lightning for iPhone 12. If the two people who have borrowed my iPhone charger in the last month are any indication, iPhone customers are still discovering the perks of faster charging with USB-C to Lightning and a 20W power adapter.

USB-C remains "the new plug" for Apple despite first adopting it on a new product in 2015.

Lightning to USB-C

These days, Apple lets you charge with USB-C on everything from MacBooks and iPads to Apple TV remotes and Beats headphones. Everything but the iPhone, that is.

This will change with the iPhone 15 line whether Apple likes it or not. For a lot of us, the move from Lightning to USB-C is long overdue. Apple's insistence on shipping Lightning for all these years has only worsened the upcoming transition.

But for a lot of iPhone customers (including some 9to5Mac readers), the switch from Lightning to USB-C ( whether Apple likes it or not) is a dreadful change. Regardless of what Apple's other products have done, the Lightning ecosystem is thriving for many. Having to invest in a new charging plug ( whether Apple likes it or not) is a headache. We get it.

There's something different about this charging transition, however. For some iPhone users, what charging port Apple uses makes no difference. Apple could replace Lightning with VGA, and it wouldn't matter - for charging purposes, that is.

That's because Apple added, not replaced, a charging method six years ago with wireless charging. iPhone X and iPhone 8 were the first to support the Qi charging standard, and iPhone 12 vastly improved the experience with MagSafe. For some users, the iPhone charging port is merely a service port and lint collector.

There's probably a lot of crossover between people who use wireless charging and people who are eagerly awaiting USB-C iPhones. Still, for some iPhone customers, the transition from Lightning to USB-C with the iPhone 15 line will make no difference. Just use the same MagSafe charger on your iPhone 15 that you've been using since the iPhone 12. That's not something you could say for the last big iPhone port change.

FTC: We use income earning auto affiliate links. More.

Zac Hall

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Apple is changing the charging port with iPhone 15, but it won't be like the last time - 9to5Mac

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When Apple unveils the iPhone 15 line in early next month, it will also announce the first charging port change...

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New algorithm spots its first "potentially hazardous" near-Earth asteroid — and it's 600 feet long

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New algorithm spots its first "potentially hazardous" near-Earth asteroid — and it's 600 feet long

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An algorithm designed to discover near-Earth asteroids has identified its first "potentially hazardous asteroid," researchers from the University of Washington said in a statement.

The algorithm, known as HelioLinc3D and developed in part by researchers from the University of Washington, is still in its testing phase. The "potentially hazardous" asteroid, named 2022 SF289, was 600 feet long and was discovered during a test of the algorithm in Hawaii. Scientists were able to confirm that the asteroid "poses no risk to Earth for the foreseeable future."

Know More : https://www.cbsnews.com/news/new-algorithm-spots-potentially-hazardous-near-earth-asteroid-heliolinc3d-rubin-observatory/

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An algorithm designed to discover near-Earth asteroids has identified its first "potentially hazardous asteroid," researchers from the University of Washington said in a statement.

The algorithm, known as HelioLinc3D and developed in part by researchers from the University of Washington, is still in its testing phase. The "potentially hazardous" asteroid, named 2022 SF289, was 600 feet long and was discovered during a test of the algorithm in Hawaii. Scientists were able to confirm that the asteroid "poses no risk to Earth for the foreseeable future."

The algorithm will eventually be used at the Vera C. Rubin Observatory, a survey telescope being built in Chile. The observatory will have multiple goals, including probing dark energy and dark matter and mapping the Milky Way, and is expected to begin operating in early 2025, according to the university's release. It's expected that the observatory will "dramatically increase the discovery rate" of items like asteroids. The observatory will only need to look at spots in the night sky twice per night, instead of the four times needed for telescopes, an advancement that means it can "scan the sky unprecedentedly quickly."

However, this new speed meant researchers needed to create a new type of discovery algorithm. That's where HelioLinc3D comes in. The algorithm can find asteroids in Rubin's dataset, and developers of the algorithm have had it study existing data with too few observations to be usable by conventional algorithms. It was in one of those data sets that the algorithm discovered the "potentially hazardous" asteroid 2022 SF289. The asteroid had been observed multiple times on different nights by older technology, but because it had never been seen four times in one night, it could not be properly identified. By combining the multiple observations, the algorithm made the discovery.

Additional observations confirmed the discovery and found that the asteroid's closest approach brings it within 140,000 miles of Earth's orbit, placing it closer than the moon.

"This is just a small taste of what to expect with the Rubin Observatory in less than two years, when HelioLinc3D will be discovering an object like this every night," said Rubin scientist Mario Jurić, director of the DiRAC Institute, professor of astronomy at the University of Washington and leader of the team behind HelioLinc3D. "But more broadly, it's a preview of the coming era of data-intensive astronomy. From HelioLinc3D to AI-assisted codes, the next decade of discovery will be a story of advancement in algorithms as much as in new, large, telescopes."

Trending News

Kerry Breen

First published on August 2, 2023 / 1:21 PM

© 2023 CBS Interactive Inc. All Rights Reserved.

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New algorithm spots its first "potentially hazardous" near-Earth asteroid - and it's 600 feet long

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The algorithm will eventually be used at the Vera C. Rubin Observatory​, a survey telescope being built in Chile.

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Why we should consider moving to pervasive Artificial Intelligence says Arif Patel Of Preston - Daily Record

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Why we should consider moving to pervasive Artificial Intelligence says Arif Patel Of Preston - Daily Record

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We have entered a new era thanks to generative AI, particularly Large Language Models (LLM) like the GPT developed by OpenAI and Microsoft.

Early web browsers like Netscape Navigator catalyzed a chain of events that altered how people work, connect, consume, cooperate, and live.

This is an analogy from the past. Point-and-click browser participation is open to everyone. Arif Patel of Preston UK, Dubai-London based businessman emphasizes how the Internet evolved from being a platform used exclusively by and for tech enthusiasts to one that was utilized by a large population and underwent a significant metamorphosis.

Operating in the demanding oil and gas industry is Patel’s Preston Trading Company. He is philanthropic as well and has actively supported numerous nonprofits in Preston and London.

According to London philanthropist Arif Patel, everyone may now customize and employ pervasive AI as a “copilot” to guide them while they work, shop, and live.

The events that follow will be stunning and groundbreaking. Through 2023 and beyond, AI will rule as the predominant computing platform. This is a pivotal moment. It is made clear by Arif Patel of Preston, UK, that widespread AI is still a way off.

One component of the solution is the ongoing advancements in deep learning and machine learning since the 2000s. The second element of the answer is the current, quicker transition to the cloud.

These businesses are now concentrating on extracting value from the cloud rather than traveling to it. This demand for blatantly visible commercial value colliding with AI’s potential to offer such value is creating an increasingly prevalent AI-first world. Instead of looking for a solution to a problem, AI-first enables people who will utilize it wisely to immediately address real possibilities and create sustained competitive advantage.

According to Arif Patel, it will unleash tremendous waves of change that may fundamentally restructure entire industries and usher in a new era of GDP growth. The fact that it will be nearly free to take an idea from conception to implementation will have an impact on the basic “fabric” of many businesses and change how they operate. The way that customers connect with businesses and citizens interact with governments will be changed. Undoubtedly, businesses will need to reevaluate how they create and market their products and services.

With the proper legal safeguards in place, AI will allow enterprises to tackle difficult problems by utilizing data from outside their walls. AI may force a fundamental reevaluation of how people engage with technology, similar to how it did during the Industrial Revolution. Arif Patel, a philanthropist in London, asserts that while computer processors are 100.000X faster than the electric signals in our brains, other factors also affect speed. It has to do with the first real-world use cases’ considerably faster time-to-value.

Know More : https://www.dailyrecord.co.uk/special-features/arif-patel-talks-should-consider-30594920

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By democratising AI for businesses, generative AI, most especially Large Language Models (LLM) like GPT pioneered by OpenAI and Microsoft, has propelled us into a new era.

The introduction of web browsers like Netscape Navigator in the early 1990s served as the spark for a series of events that changed the way people work, connect, consume, cooperate, and live.

This is a historical parallel. Everyone may participate using point-and-click with browsers. Arif Patel of Preston UK, Dubai, London -based businessman points to how the Internet changed from being a platform used by and for tech aficionados to one that was widely used and underwent a massive transformation.

Patel's Preston Trading Company operates in the challenging world of oil and gas. He is a philanthropist too and actively supported many charitable organisations in Preston and London.

On pervasive AI, Arif Patel, a London philanthropist says, it is now possible for everyone to personalise and use as a pervasive "copilot" that directs us while we work, consume, and live.

The subsequent events will be astounding and revolutionary. This is the turning point that will make AI the dominant computer platform through 2023 and beyond. Arif Patel of Preston UK, explicitly conveys that pervasive AI is still a way off.

The consistent developments in machine learning and deep learning since the 2000s are a part of the solution. The current, faster shift to the cloud is the second component of the solution.

The current focus of these enterprises is shifting from the trip to the cloud to the extraction of value from the cloud. An increasing and pervasive AI-first world is being created because of this drive for obvious commercial value converging with AI's capacity to supply such value. AI-first isn't a solution seeking a problem to solve; rather, it gives those who will use it intelligently the capacity to quickly address actual opportunities and establish sustainable competitive advantage.

Arif Patel thinks it will set off enormous waves of change that might completely reshape whole industries and usher in a new period of GDP development. As the cost to move an idea from conception to instantiation will be close to $0, it will have an influence on the very "fabric" of many businesses and alter their operating models. Customers' interactions with enterprises and citizens' interactions with government agencies will be redefined. Businesses will undoubtedly be forced to reconsider how they develop and sell their goods and services.

With the right legal protections in place, AI will enable businesses to take on challenging issues by utilising information from beyond their own walls. AI will compel a reevaluation of how people interact with technology, potentially on a level as fundamental as it was during the Industrial Revolution.

Arif Patel, a philanthropist in London, claims that although computer processors are 100.000X quicker than the electric signals in our brains, there are other factors that contribute to speed. It has to do with the noticeably quicker time-to-value in the first real-world use cases.
The views expressed in this article are that of Arif Patel of Preston Trading Company.

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Arif Patel talks about why we should consider moving to pervasive Artificial Intelligence

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Arif Patel thinks it will set off enormous waves of change that might completely reshape whole industries

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Canon's new camera shoots color video in pitch black, from miles away

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Canon's new camera shoots color video in pitch black, from miles away

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Canon has wrapped its experimental ultra-high sensitivity Single Photon Avalanche Diode (SPAD) sensor up into a world-first commercial product. The new MS-500 accepts ultra-telephoto broadcast lenses, and can shoot color video on a moonless night.

As the name suggests, these SPAD sensors effectively count individual photons - the tiniest possible quanta of light - noting down the precise moment when they hit the pixel array, as opposed to doing what a regular CMOS camera sensor does, and outputting the total amount of light received over a given amount of time. The "avalanche" part refers to an amplifying effect of sorts; when a pixel detects a photon, it releases an electron, and that electron itself, at high speed and high voltage, can shake other electrons loose as it goes.

What you end up with is a sensor with an insane level of sensitivity in low light, almost like a high-speed Geiger-counter for photons. You can use that in a number of ways; Canon, for its part, used an experimental 1-megapixel version of this thing to build a 24,000-fps camera capable of producing usable images at shutter speeds in the tens of trillionths of a second.

How quick is that? Quick enough to freeze light itself in motion. Check out the video below, which shows the movement of a single pulse of laser light, as it moves through smoke, bouncing off mirrors in a three-dimensional setup.

SPAD sensors themselves are not new; they've been used broadly for more than 50 years - most notably, in applications like LiDAR, 3D time-of-flight (ToF) imaging and PET scanning, where their incredible speed gives them the ability to accurately record exactly when a photon arrived, allowing these devices to create 3D models of the world.

What is new is that Canon has developed a groundbreaking 13.2 x 9.9 mm, 3.2-megapixel, ultra-high sensitivity SPAD sensor and built a commercial, interchangeable-lens video camera system around it.

Canon says the new MS-500, announced yesterday, is the world's first camera of its kind, and the highest pixel-count SPAD sensor ever offered for sale, with a resolution higher than 1080p. It debuts a new architecture that Canon claims gives it exceptional performance, even among SPAD sensors, in temporal resolution, low noise and near-infrared light spectrum sensitivity.

It's capable of capturing video in illumination levels as low as 0.001 lux - we're talking starlight in the middle of a moonless night - "as though viewing with the naked eye in well-lit environments" - and it's also capable of processing data in somewhere around 100 trillionths of a second, which Canon says will allow it "to capture objects moving at high speeds including photons."

The MS-500 camera itself looks like somewhat of a brick house, and with a built-in B4 bayonet lens mount, it's compatible with a wide range of 2/3 inch broadcast lenses, including the brutally-named CJ45ex13.6B IASE-V H, which offers a 45x zoom ratio, with a telephoto reach up to 1,224 mm. Canon says this lets you film subjects up to a remarkable "several tens of kilometers away."

At an estimated retail price of US$25,200 (plus nearly $100K more for that monster lens), it's certainly not pitched at the consumer level. Indeed, Canon is pitching it for "areas with extremely high-security levels, such as seaports, public infrastructure facilities, and national borders, [where] high-precision monitoring systems are required to surveil targets both day and night accurately."

But the sensor technology itself could prove to be a breakthrough in medical imaging, 3D capture, VR/AR and autonomous cars and robotics. Its capabilities with near-infrared spectrum light, for example, could give it superhuman vision and object detection capabilities in fog, mist and bucketing rain.

Very cool stuff - although Canon has provided exactly zero sample video footage at this stage, which strikes us as an absolute facepalm of a PR blunder. In the meanwhile, there's ... this?

Source: Canon

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Canon's new camera shoots color video in pitch black, from miles away

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Canon has wrapped its experimental ultra-high sensitivity Single Photon Avalanche Diode (SPAD) sensor up into a world-first commercial product. The new MS-500 accepts ultra-telephoto broadcast lenses, and can shoot color video on a moonless night.

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Using AI, scientists bring Neanderthal antibiotics back from extinction

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Using AI, scientists bring Neanderthal antibiotics back from extinction

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This story is part of a group of stories called

Finding the best ways to do good.

Maybe you remember the movie Jurassic Park, where scientists bring dinosaurs back from extinction. Or maybe you've heard about the real-world scientific quest to de-extinct the dodo and the woolly mammoth.

Whether we're talking dinos or dodos, de-extinction is risky business. It's problematic on a pragmatic level (that T. rex you resurrect just might gobble you up while you're sitting on the toilet) and on an ethical level (can you imagine how lonely the first resurrected dodo or woolly mammoth would be?).

But what if instead of bringing back a whole species, we bring back just one tiny part - like, say, a molecule?

That's what scientists have just achieved at the University of Pennsylvania's Machine Biology Group. They've resurrected molecules with antibiotic properties found in extinct organisms - specifically, our close relatives, the Neanderthals and Denisovans. (Neanderthals went extinct 40,000 years ago, while Denisovans might have survived until 15,000-30,000 years ago.) The breakthrough throws open the doors to a brave new world of "molecular de-extinction," which holds promise for drug discovery.

The researchers started by gathering the sequenced genome data of Neanderthals and Denisovans, which is publicly available thanks to paleontologists who have painstakingly collected and analyzed ancient DNA from bones and artifacts.

Then they trained an AI model to make predictions about which molecules might make effective antibiotics for modern humans. After the algorithm identified the strongest candidates, the researchers created those molecules in the lab and tested them in infected mice. Some of the molecules effectively fought off bacterial infections, according to a new study published in the journal Cell Host & Microbe.

"This is completely new. We came up with the term 'molecular de-extinction' and this is the first peer-reviewed paper that describes it," César de la Fuente, who co-authored the study, told me. "So it's quite exciting for us."

If the burgeoning field of molecular de-extinction turns out to yield clinically successful results in humans, it could be exciting for the world, too, because we urgently need good ways to create new antibiotics.

We're entering a post-antibiotic era. Here's how AI is helping.

The CDC has warned that we're entering a post-antibiotic era - a time when our antibiotics are becoming increasingly useless. We've created this crisis by overusing antibiotics in the treatment of humans, animals, and crops. The bacteria have adapted to our drugs, morphing into superbugs that can all too easily decimate human health.

In the time it takes you to read this article, one person in the US will die from an infection that antibiotics can no longer treat effectively. The annual global death toll from drug-resistant infections could rise to 10 million by 2050, a major UN report warned in 2019, if we don't make a radical change.

Big Pharma and biotech companies haven't been creating the new antibiotics needed to address the crisis because it takes many years and lots of funding to do the research and development. Most new compounds fail. Even when they succeed, the payoff is small: An antibiotic doesn't sell as well as a drug that needs to be taken daily. So for many pharmacompanies, the financial incentive just isn't there.

But if we can use AI to ramp up the speed of antibiotic discovery, that could change the calculus. De la Fuente's team did this in a two-step process.

The proteins in our cells are like long strings. But they often get cut up by enzymes into short fragments at specific junctures. These short fragments - small molecules known as peptides - can have antimicrobial properties.

So, de la Fuente's team trained an AI model called panCleave, which can look at all the proteins encoded in a genome and predict where their junctures will be. That way, the researchers could identify the peptides in the Neanderthal and Denisovan genomes. They then ran other models on the peptides to predict which ones would have antimicrobial properties.

They weren't all slam-dunks. When tested on infected mice, some of the peptides predicted to be strong candidates didn't manage to kill the bacteria. Others were effective but required high doses to work. That could mean researchers need to either revamp the predictive algorithm or retool the most promising peptides to make them more effective.

A further limitation of this study is that the researchers didn't test whether theinfected mice developed resistance to the peptides. "It's something to do in the future," de la Fuente acknowledged, since there'd be little point in manufacturing a new antibiotic only for our bodies to soon become resistant to it.

But at a high level, molecular de-extinction is "a creative approach" that can help us get past the current bottlenecks in drug discovery, according to Jonathan Stokes, a biochemistry professor at McMaster University who was not involved in the study. "I think this technique will augment other antibiotic discovery efforts to help us discover structurally and functionally novel antibacterial therapies that overcome existing resistance mechanisms," he told me.

Molecular de-extinction could end up working alongside other efforts in the use of AI for antibiotic discovery. In 2020, for example, MIT researchers discovered a new type of antibiotic by training their AI on molecules that we know have antimicrobial properties. And in 2021, IBM researchers designed two new antimicrobial peptides in a matter of days by training their AI on a much broader database of all the known peptides that exist in nature today.

Resurrecting the peptides of yesteryear gives us more possibilities to sift through and potentially more buried treasure to discover.

"Molecular de-extinction" brings up big ethical questions. Nobody knows the answers.

What does it mean to bring molecules back from the dead?

De-extincting molecules doesn't suffer from the same ethical concerns that plague de-extincting a whole species - like the concern that revived dodos or woolly mammoths would be miserable in today's world, if they could even survive. Still, on a philosophical level, it's not obvious how we should think about efforts to revive molecules that currently exist nowhere in living organisms.

For instance, would de-extincted molecules be eligible for patents? Existing patent law tells us that nobody can patent molecules that occur in nature, but it doesn't tell us whether someone can own the rights to a resurrected molecule that was once expressed in Neanderthals. Arguably, that should be considered the heritage of all humankind, and no individual or business should own it.

But if patent law ultimately comes out on that side, will it disincentivize scientists from doing research in molecular de-extinction - research that could ultimately help us with the antibiotic resistance crisis?

I asked de la Fuente if he would want to patent his work. He replied, "I don't know."

But, he told me, he did walk over to the patent office on his campus one day to find out if that would be possible. The legal minds at the University of Pennsylvania couldn't tell him. This is a new legal frontier, and so far, no one knows the answers.
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Using AI, scientists bring Neanderthal antibiotics back from extinction

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Neanderthals are extinct. But their molecules are back and they just might save our lives.

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Astronaut shares the profound 'big lie' he realized after seeing the Earth from space

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Astronaut shares the profound 'big lie' he realized after seeing the Earth from space

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Sixty-one years ago, Yuri Gagarin became the first human to make it into space and probably the first to experience what scientists now call the "overview effect." This change occurs when people see the world from far above and notice that it's a place where "borders are invisible, where racial, religious and economic strife are nowhere to be seen."

The overview effect makes man's squabbles with one another seem incredibly petty and presents the planet as it truly is, one interconnected organism.

In a compelling interview with Big Think, astronaut, author and humanitarian Ron Garan explains how if more of us developed this planetary perspective we could fix much of what ails humanity and the planet.

Garan has spent 178 days in space and traveled more than 71 million miles in 2,842 orbits. From high above, he realized that the planet is a lot more fragile than he thought.

"When I looked out the window of the International Space Station, I saw the paparazzi-like flashes of lightning storms, I saw dancing curtains of auroras that seemed so close it was as if we could reach out and touch them. And I saw the unbelievable thinness of our planet's atmosphere. In that moment, I was hit with the sobering realization that that paper-thin layer keeps every living thing on our planet alive," Garan said in the video.

"I saw an iridescent biosphere teeming with life," he continues. "I didn't see the economy. But since our human-made systems treat everything, including the very life-support systems of our planet, as the wholly owned subsidiary of the global economy, it's obvious from the vantage point of space that we're living a lie."

It was at that moment he realized that humanity needs to reevaluate its priorities.

"We need to move from thinking economy, society, planet to planet, society, economy. That's when we're going to continue our evolutionary process," he added.

Garan says that we are paying a very "high price" as a civilization for our inability to develop a more planetary perspective and that it's a big reason why we're failing to solve many of our problems. Even though our economic activity may improve quality of life on one end, it's also disasterous for the planet that sustains our lives.

It's like cutting off our nose to spite our face.

Actor William Shatner had a similar experience to Garan's when he traveled into space.

"It was among the strongest feelings of grief I have ever encountered," Shatner wrote. "The contrast between the vicious coldness of space and the warm nurturing of Earth below filled me with overwhelming sadness. Every day, we are confronted with the knowledge of further destruction of Earth at our hands: the extinction of animal species, of flora and fauna ... things that took five billion years to evolve, and suddenly we will never see them again because of the interference of mankind."

"We're not going to have peace on Earth until we recognize the basic fact of the interrelated structure of all reality," Garan said.

However dire the situation looks from the surface of Earth, the astronaut has hope that we can collectively evolve in consciousness and wake up and embrace a larger reality. "And when we can evolve beyond a two-dimensional us versus them mindset, and embrace the true multi-dimensional reality of the universe that we live in, that's when we're going to no longer be floating in darkness ... and it's a future that we would all want to be a part of. That's our true calling."
This article originally appeared on 12.16.22

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Astronaut shares the profound 'big lie' he realized after seeing the Earth from space

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Astronaut and humanitarian Ron Garan explained how man's perspective changes after venturing out into space. He believes this shift in perspective will help us fix our problems.

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NASA's Voyager 2 still phoning home after accidental command severs communication

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NASA's Voyager 2 still phoning home after accidental command severs communication

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NASA’s Voyager 2 still phoning home after accidental command severs communication

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NASA’s intergalactic spacecraft Voyager 2 is still sending out a signal after the space agency thought communication was lost with the 46-year-old spacecraft until October.

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Vision of clean energy and fossil fuel - Arif Patel of Preston UK 

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Vision of clean energy and fossil fuel - Arif Patel of Preston UK 

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Globally, the oil and gas industries are showing a strong upward trend. These industries are positioned for strong growth due to the rising global energy demand. Large-scale oil and gas deposits have been unlocked because of improvements in drilling equipment and exploration methods, which have expanded production and created new jobs.

A well-known personality in oil and gas exploration, engineering, and mining, Arif Patel of Preston UK, Dubai, and London frequently shares his thoughts on the subject. He thinks there needs to be a balance between traditional and alternative energy sources. The shift to clean energy or programs to lessen their carbon footprint must be funded by the oil and gas industries.

Read Full Article Here : https://www.lancs.live/special-features/dubai-businessman-arif-patel-talks-27429670

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Dubai Businessman Arif Patel talks about his vision of clean energy and fossil fuel

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Calls for the oil and gas sectors to invest in the clean energy transition or initiatives to reduce their carbon footprint.