Category: Technology

At Adobe’s MAX event, it was announced Photoshop on iPad Finally gain the ability to remove background from images with a single click, along with the ability to remove distracting objects from images.

proces Background removal (Opens in a new tab) It would be similar to how you achieve it on a Mac or Windows PC: just highlight the background, the theme is safe from removal, and replace it with something completely different. The new option on iPad is available at App Store (Opens in a new tab) as part of Adobe’s Creative Cloud subscription.

There are also other useful features coming to Photoshop on the iPad in the coming weeks, such as automatic color adjustments to get the right balance for your projects, hair enhancements, and edge dressings for when these elements need to go to a different background.

It all adds up to what you can already do on the iPad with Photoshop, and increases accessibility to the tablet instead of Mac or computer – But we haven’t gotten that far yet.

The iPad is a computer, but not for everyone

When Photoshop was launched on the iPad in 2019, it wasn’t given a warm welcome due to its lack of basic features. At the time, Adobe only had to work with touch inputs, as Touchpad support didn’t appear until mid-2020 iPad operating system. This required a lot of rethinking from Adobe to bring the features that had been standard in Photoshop for years.

Fast forward to 2022, and although you can now use a magic keyboard With a built-in trackpad, Adobe has improved the features enough that you can just double-click and remove the background, thanks to Adobe finding ways to simplify a set of its powerful features.

These new features will be welcomed across many user bases and needs, including mine. after use Photo shop on me macOS Over the past decade, I’ve been eager to see more desktop apps come to me iPadPro For quick fix situations we find ourselves in from time to time.

The iPad version of Photoshop almost meets the brief now, thanks to the ads in the MAX. Being able to remove the background for the photos I take will really help with my workflow. Next on my wishlist in the feature space is to see a file pen tool (Opens in a new tab) finally arrives. Users have been clamoring for this since Photoshop first came out on the iPad, and now that we have background removal features, this might be the next best feature to hit the iPad.

Once these features make it to Photoshop on the iPad, we’ll make sure there’s a new review that’s going to be just subject to how the app has been improved in the past three years.

A new collaborative effort between MBARI and other research institutions is harnessing the power of artificial intelligence and machine learning to accelerate efforts to study the ocean.

In order to manage the impacts from climate change and other threats, researchers urgently need to learn more about ocean populations, ecosystems, and processes. As scientists and engineers develop advanced robots that can visualize marine life and environments to monitor changes in ocean health, they face a fundamental problem: the collection of images, video and other visual data far beyond the researchers’ ability to analyze.

FathomNet It is an open source image database that uses the latest data processing algorithms to help handle visual data backlogs. The use of artificial intelligence and machine learning will alleviate the bottleneck of underwater image analysis and accelerate critical research on ocean health.

“The big ocean needs big data. Researchers collect large amounts of visual data to monitor life in the ocean. How can we process all this information without automation? Machine learning provides a way forward, but these methods rely on huge data sets for training. FathomNet has been built To fill this gap,” said MBARI Principal Engineer Kakani Katiga.

Project founders Katiega and Katie Crove Bell (Ocean Discovery League) and Ben Woodward (CVision AI), along with members of the expanded FathomNet team, detailed the development of this new image database in a recent paper. Published in Scientific Reports.

Recent advances in machine learning allow for rapid, sophisticated analysis of visual data, but the use of AI in ocean research has been limited by the lack of a standard set of existing images that can be used to train machines to recognize and classify underwater objects. . and life. FathomNet addresses this need by compiling images from multiple sources to create an expertly curated, publicly available underwater image training database.

“In the past five years, machine learning has revolutionized the automated visual analysis landscape, driven in large part by massive sets of labeled data. ImageNet and Microsoft COCO are two standard datasets for terrestrial applications that machine learning and computer vision researchers flock to, but we haven’t even begun to Scratching the surface of the machine learning capabilities of underwater optical analysis”. Founder and CEO of Cvision AI and co-founder of FathomNet. “With FathomNet, we aim to provide a rich and interesting standard for engaging the machine learning community in a new field.”

Over the past 35 years, MBARI has recorded nearly 28,000 hours of deep-sea videos and collected more than 1 million deep-sea images. This set of visual data is explained in detail by research technicians at the MBARI Video Lab. MBARI’s video archive includes nearly 8.2 million annotations that record observations of animals, habitats, and objects. This rich data set is an invaluable resource for researchers at the Institute and collaborators around the world.

FathomNet includes a subset of the MBARI dataset, as well as assets from National Geographic and NOAA.

The National Geographic Society’s Exploration Technology Laboratory has published versions of its independent benthic landing platform, Deep Sea Camera System, since 2010, collecting more than 1,000 hours of video data from locations in all ocean basins and in a variety of marine habitats. These videos were subsequently assimilated into Cvision AI’s cloud-based collaborative analysis platform and annotated by subject specialists at the University of Hawaii and OceansTurn.

National Oceanic and Atmospheric Administration (NOAA) ocean exploration began collecting video data using a remotely operated vehicle system aboard the NOAA Ship Okeanos Explorer in 2010. More than 271 terabytes are archived and publicly accessible from the NOAA National Environmental Information Centers (NOAA). NCEI). Originally NOAA Ocean Exploration annotations were collectively sourced through volunteer participating scientists, and began supporting taxonomists in 2015 to add annotations to videos collected more comprehensively.

“FathomNet is a great example of how collaboration and community science can advance breakthroughs in how we recognize the ocean,” said Lonny Lundsten, senior research technician at MBARI’s Video Lab, co-author, and FathomNet team member.

As an open source web-based resource, other organizations can contribute to and use FathomNet in place of the traditional resource-intensive effort of visual data processing and analysis. MBARI has launched a pilot program to use machine learning models trained by FathomNet to annotate video captured by remotely operated underwater vehicles (ROVs). The use of AI algorithms reduced human effort by 81 percent and increased the tagging rate tenfold.

Machine learning models trained with FathomNet data also have the potential to revolutionize ocean exploration and monitoring. For example, equipping robotic vehicles with their cameras and improved machine learning algorithms could enable automated search and tracking of marine animals and other underwater objects.

“Four years ago, we envisioned using machine learning to analyze thousands of hours of ocean videos, but at the time, that was basically not possible due to a lack of annotated images. Now FathomNet will make that vision a reality, unleashing discoveries and enabling tools that Explorers, scientists and the public can use it to speed up the pace of ocean discovery.” -Founder.

As of September 2022, FathomNet contained 84,454 images, representing 175,875 locations from 81 separate groups for 2,243 concepts, with additional contributions continuing. FathomNet aims to obtain 1,000 independent observations of more than 200,000 animal species in a variety of shooting poses and conditions – ultimately more than 200 million total observations. For FathomNet to reach its intended goals, significant community participation – including high-quality contributions across a wide range of groups and individuals – and broad use of the database will be required.

“Although FathomNet is an API-based web-based platform where people can download labeled data to train new algorithms, we also want it to serve as a community where ocean explorers and enthusiasts of all backgrounds can contribute their knowledge and expertise and help solve challenges related to ocean visual data which would be impossible without widespread participation,” Kateja said.

To join the FathomNet community, visit fathomnet.org and follow FathomNet on Twitter.

Initial funding for FathomNet was provided by the National Geographic Society (#518018), the National Oceanic and Atmospheric Administration (NA18OAR4170105), and MBARI through the generous support of the David and Lucile Packard Foundation. Additional funding support was provided by the National Geographic Society (NGS-86951T-21) and the National Science Foundation (OTIC #1812535 & Convergence Accelerator #2137977).

About MBARI

MBARI (Monterey Bay Aquarium Research Institute) is a private, not-for-profit oceanographic research center founded by David Packard in 1987. MBARI’s mission is to advance marine science and technology to understand the changing ocean. Learn more at mbari.org.

FathomNet: A global image database to enable artificial intelligence in the oceanScientific Reports (open access)

Astrobiology, Extrasolar System, Exoplanet, Artificial Intelligence,

Australian retail market MyDeal has confirmed that it has suffered a data breach affecting more than two million customers.

The company contacted all affected customers explaining the incident, saying that an unknown attacker breached its systems and accessed customer identity data.

according to Computer (Opens in a new tab)the threat actor was able to obtain the login information for the MyDeal CRM (crms (Opens in a new tab)), and used it to extract sensitive data belonging to about 2.2 million users.

MyDeal data has been sold

That data included names, email addresses, phone numbers, postal addresses, and, for some, dates of birth. For a smaller subset of users (1.2 million), hackers were only able to obtain email addresses.

While details on the perpetrators are scarce, what they are doing with the data is clear: try to sell it on an underground forum for $600.

According to the company, the number of entries in the database, which the attacker is still analyzing, is currently more than one million, with the number expected to increase.

To validate the attack, the attackers posted screenshots of MyDeal Confluence servers, as well as a single sign-on (SSO) claim for their account with Amazon Web Services (Aws (Opens in a new tab)).

MyDeal also said that the attackers did not obtain any payment information, identity documents, data or passwords. However, it does suggest users to reset their passwords anyway. Such an attack would not have been prevented even with The best password managers.

MyDeal is an Australian retail marketplace that seeks to connect local retailers with potential shoppers.

It was acquired by Woolworths in September 2022, but the supermarket chain claims that its systems are on a different platform, and therefore completely safe from attackers.

Although the scammers may not have obtained your payment data or passwords, they still have enough information about identity theft (Opens in a new tab) or phishing attacks, so we urge users to be vigilant.

When countries want to measure quail numbers, the process can be cumbersome, time-consuming, and expensive.

It means spending hours in the field listening to calls. Or leave a recording device in the field to capture the sounds coming out – only to spend hours afterward listening to that sound. Then, repeat this process until enough information is available to start making population estimates.

But a new model developed by researchers at the University of Georgia aims to simplify this process. Using artificial intelligence to analyze terabytes of recordings of quail calls, the process gives wildlife managers the ability to gather the data they need in a matter of minutes.

“The model is very accurate, capturing between 80% and 100% of all calls on even the noisiest recordings. James Martin, an assistant professor at the UGA Warnell School of Forestry and Natural Resources who has been working on the project, in collaboration with the Georgia Department of Natural Resources, said for two years. Almost five years.” “This new model allows you to analyze terabytes of data in seconds, and what that will allow us to do is increase monitoring, so you can literally take out hundreds of these devices and cover a lot more space and do it with much less effort than in the past.”

The program represents nearly five years of the work of Martin, postdoctoral researcher Victoria Nolan, and several key contributors who worked with a code writer to create the model. It’s also part of a larger shift taking place in wildlife research, where computer algorithms are now helping work that once took humans thousands of hours to complete.

Increasingly, computers are getting smarter, for example, at identifying certain noises or features in images and audio recordings. For researchers like Martin, this means that hours once spent on tasks such as listening to audio or looking at game camera images can now be performed by a computer, freeing up valuable time to focus on other aspects of the project.

The new tool could also be a valuable resource for state and federal agencies looking for information on quail populations, but with limited funds to spend on any one project. “So, I think this is something that countries might jump on as much as they replace existing surveillance with audio recording devices,” Martin added.

The program’s success was recently documented by Journal of remote sensing in the environment and conservation.

Martin said that as the software becomes more used and exposed to voices coming from new geographies, it is getting “smarter.” As it is, quails offer several different types of calls. But when the software is exposed to a variety of sounds other than the quail, he said, it is better able to distinguish the correct calls from the ambient noise of the grass and trees around them.

Over time, the program will increase in recognition.

“That’s why you have to keep giving it the training data, and when you move the geographs, you encounter new voices that you haven’t trained the model on,” he added. “It’s always about adapting.”

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Disclaimer: AAAS and EurekAlert! is not responsible for the accuracy of newsletters sent on EurekAlert! Through the contributing institutions or for the use of any information through the EurekAlert system.

Microsoft has fixed a bug that affects many versions of Windows and Windows Server that are running SSL / TLS Handshake failed.

Those who encounter the bug see a SEC_E_ILLEGAL_MESSAGE notification pop up in apps trying to connect to servers.

We are addressing an issue that may affect some types of Secure Sockets Layer (SSL) and Transport Layer Security (TLS (Opens in a new tab)) links. Microsoft said in the newspaper Advertising (Opens in a new tab).

Multiple versions affected

Affected versions include Windows 11 (Opens in a new tab) 22H2; Windows 11 21H2; Windows 10 21H2; Windows 10 21H1; Windows 10 20H2; Windows 10 Enterprise LTSC 2019; Windows 10 Enterprise LTSC 2016; Windows 10 Enterprise 2015 LTSB; Windows 8.1 operating system; and Windows 7 SP1.

for servers, the affected versions include Windows Server 2022; Windows Server 2019; Windows Server 2016; Windows Server 2012 R2; Windows Server 2012; and Windows Server 2008 R2 SP1.

This is an out-of-band update, which means it will not be automatically deployed via Windows Update, Windows Update for Business, or Windows Server Update Services (WSUS).

Alternatively, users who are interested in choosing to update need to head to File Microsoft Update Catalog (Opens in a new tab) and manually add them to either Microsoft Endpoint Configuration Manager or Windows Server Update Services (WSUS).

Microsoft is no stranger to out-of-band updates. The last time we got such an update was in May 2022, when the company fixed an issue that prevented apps downloaded from the Windows Store from running properly. endpoints.

but, Computer (Opens in a new tab) I discovered that after installing the patch, the Cluster service might fail to start. In this case, this happens because an update to the PnP class drivers used by the service removed the cluster network driver.

The fix is ​​still in the works for Windows 10 2016 LTSB, Windows Server 2016, and Windows 10 2015 LTSB.

Chris Bissenbruch, CEO of deep renderingHe sees many issues with the way video compression standards are developed today. He thinks they’re not progressing fast enough, laments the fact that they suffer from legal uncertainty and reduces their reliance on specialized hardware for acceleration.

“The codec development process has been disrupted,” Bessenbruch said in an interview with TechCrunch ahead of Disrupt, where Deep Render is participating in Disrupt Battlefield 200. “In the lobbying industry, a major challenge is finding a new way to go forward and researching new innovations.”

In search of a better way, Bisenbruch co-founded Deep Render with Arsalan Zafar, whom he met at Imperial College London. At the time, Bissenbruch was studying computer science and machine learning. He and Dhofar collaborated on a research project involving the distribution of terabytes of video over a network, during which they say they experienced flaws in the compression technology first hand.

The last time TechCrunch covered Deep Render, the startup had just done Closed An initial round of 1.6 million pounds ($1.81 million) led by Pentech Ventures with the participation of Speedinvest. In the roughly two years since, Deep Render has raised several million additional dollars from existing investors, bringing its total to $5.7 million.

“We thought to ourselves, if it is difficult to scale internet pipes, the only thing we can do is make the data that flows through the pipes smaller,” Bessenbruch said. Hence, we decided to combine machine learning, AI technology, and compression to develop an entirely new way to compress data to get better compression ratios for images and video.”

Deep Render is not the first to apply artificial intelligence to video compression. Alphabet’s DeepMind has adapted a machine learning algorithm originally developed to run board games to the problem of compressing YouTube videos, reducing the amount of data the video-sharing service needs to users by 4%. Elsewhere is startup WaveOne, which claims that its machine learning-based video codec outperforms all current standards across popular quality metrics.

But Deep Render’s solution is not platform dependent. To create it, Bissenbruch says the company collected a data set of more than 10 million video sequences on which they trained algorithms to learn to compress video data efficiently. Deep Render used a combination of on-premises and cloud hardware for training, with the former having more than a hundred GPUs.

Deep Render claims that the resulting compression standard is 5 times better than HEVC, a widely used codec that can be played in real time on mobile devices using a dedicated AI acceleration chip (for example, the Apple Neural Engine in modern iPhones). Bessenbruch says the company is in talks with three big tech companies — all with a market cap of more than $300 billion — about paid pilots, though he declined to share names.

Eddie Anderson, co-founder at Pentech and board member at Deep Render, shared via email: “Deep Render’s machine learning approach to coding is completely disrupting an established market. Not only is the software’s path to market, but [compression] The performance is much better than the current state of the art. As bandwidth demands continue to increase, the solution they provide has the potential to drive greatly improved commercial performance for existing media owners and distributors.”

Deep Render currently employs 20 people. By the end of 2023, Bissenbruch expects that number to more than triple, to 62.

future Cloud The computing market is set to become more close and interconnected, resulting in a multi-cloud approach that is more beneficial for businesses everywhere, the Oracle founder claimed.

speaking in Oracle Cloud World 2022 In a keynote address focused on “the next generation of cloud technology,” Larry Ellison outlined his vision for the upcoming cloud market, calling for an end to “walled gardens” to give businesses more freedom to choose and experiment.

He said, “There has to be an internet of clouds, the clouds have to be interconnected, and you have to be able to mix and match between multiple clouds… the garden walls are collapsing.”

internet of clouds

Ellison noted the number of companies and service providers that currently have to deploy new infrastructure services on multiple clouds. However, he thinks a better idea is to connect all the major cloud services to provide customers with options – creating a “cloud internet” – or multi-cloud.

Ellison noted that Oracle’s work with Microsoft helped start this, helping remove data entry and exit feeds to help improve adoption, he notes, meaning that accessing your data is not only fast and free — but also much simpler.

The company has announced that Oracle MySQL HeatWave database tools are now available for Microsoft Azure, and is once again looking to provide its users with the options they need to get the most out of their data.

“The idea, again, is to provide customers with options,” Ellison concluded. “That’s the main thing (and) I think in the end all the clouds will be interconnected, and the customers will have a choice.”

A panel of technical and political experts said Tuesday that the Navy is thinking about artificial intelligence in two ways: infrastructure to make unmanned systems work, and technology meant to enhance how seafarers and their commanders make decisions.

Speaking at the US Naval Institute on Tuesday, Brett Vaughan, the Navy’s head of AI, said the outputs provided by AI exist to help humans or supplement manned operations with unmanned assets. Human will always be in the loop and play a major role.

“Overall, AI exists to augment and provide a range of options and recommendations for the human decision-maker,” Vaughan said.

This does not mean that there are not some decisions that the AI ​​system can make, he said. Vaughan said there are situations with unmanned ships, such as going around a seamount, that AI can do without the need for a human.

“It just shows you when we’re talking about offloading or delegating decisions, there’s a graduated scale that we need to be aware of, that has to show up in your calculus,” Vaughan said. “This is where the human or client, warrior, sailor, marine perspective helps design code and capability.”

Retired Lt. Gen. Michael Gruen, former director of the Joint Artificial Intelligence Center, said the Pentagon’s goal of AI is to use it to achieve a competitive advantage.

“How do we gain a competitive advantage against China, against any of our other opponents and be able to work with pace and precision,” Gruen said. “And that’s the real core of what we’re trying to build here today.”

At the moment, in the Defense Department, this looks like pattern recognition and algorithms, for example, that can help simplify operations or decisions.

As an early operational application, AI is key to the Navy’s ongoing testing of unmanned ships in the Middle East, Commander of the US Fifth Fleet Vice Admiral Brad Cooper He said last week.

“They will see everything that goes by, they will take a picture of it. And then they will map with AI – the lifestyle of everything around them as far as they can see. And then when something is different, they will take a picture of it every second, you will send it back to Naval command center. Then the human makes a decision,” he said last week at the Coast Guard Academy.

He said that civilians and the military are already accustomed to using artificial intelligence. Take cars as an example. Artificial intelligence algorithms help the car monitor fuel levels or tire pressure. Same with navigation apps that are set to show how much traffic is on the road or where one can stop for a coffee.

For the military, AI can help present commanders with additional opportunities or dangers, just as navigation apps might suggest a different route depending on traffic.

Vaughan’s office never starts with AI, it looks at the problem or challenge first and then brings in AI to help address it, if AI is the right tool required. He currently oversees about 1,000 projects related to artificial intelligence.

He said the use of artificial intelligence would require the Navy to recalibrate how it did its business.

It differs from building hardware like ships, in that they are built, and go out and back in for repair. AI requires constant monitoring and verification to ensure that the system is working properly.

The United States is not the only country looking to use artificial intelligence with its military. Both China and Russia are already using AI, said Sam Tangredy, professor of national, naval, and naval strategy at the US Naval War College.

He said China said it wanted to be a leader in AI, something they can achieve because the country wants to use AI for various reasons.

They want it for social control, for the control of the people by the Chinese Communist Party. “Big incentives to spend a lot of money on it,” Tangredi said.

He said Russia is looking to use artificial intelligence for autonomy so that it can complement its power.
On the other hand, Tangredy said, the United States is focused on keeping people in the loop.

The Chinese are adept at human surveillance, Gruen said, but the question is whether Beijing can turn that into a combat capability. China has also been interested in artificial intelligence as a tool for commanders on the battlefield as a counter to US operations in the Western Pacific, USNI News reported last year.

“It is essential to assist battlefield command by creating a war system that can accurately reflect reality to train an AI system that can handle distributed mortality,” the China Naval Design and Research Institute (MARIC) reads for 2020.

Gruen said that how well the Chinese implement their goals is still an open question

“Can they innovate? And then can we innovate faster?” he said. “Can we innovate enough and organize ourselves enough that we actually have the project that can fight effectively against a largely organized rather than innovative opponent?”

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