Adobe to Launch New AI-Powered Selection Upgrade for Lightroom and ACR

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September 30, 2021

Adoe Lightroom Selection tool, Mask Groups

One of the industry-standard image editing software that many photographers use on the go is Adobe Lightroom. Lightroom helps content creators and photographers avail most of the commonly used editing features on Photoshop with its intuitive user interface. Now, Adobe has announced that it will be introducing new upgrades for Lightroom to help users make selective adjustments, thanks to AI-powered selection tools. This upgrade will also be available to Lightroom Classic and Camera Raw (ACR).

Since the introduction of Lightroom 2 in 2008, the company states this is the “biggest change to providing control over selectively enhancing photos.”

The Adobe Research team intended to introduce AI-powered selection features from Photoshop, such as Select Subject and Sky Replacement, to Lightroom and ACR. Because these features were well-received, the Adobe Research Team was tasked with figuring out how to include them into ACR and Lightroom. However, the imaging processing engines in ACR and Lightroom were incompatible, the engine had to be rewritten from the ground up. This inspired the team to work on improving how selections were handled on the Lightroom app.

Select Sky panel screenshot. — *Select Sky creates a precise mask, even around the foliage and uneven edges around the sky, making creating complex selections incredibly fast.* Image Credit: Abode

The feature, which is set to launch on October 26th at the 2021 Adobe MAX event, is driven by Adobe Sensei, Adobe’s AI and machine-learning toolset. It will allow photographers to focus on shot subjects such as people, buildings, and animals with a single click to fine-tune color, lighting, tone, and other aspects.

After the user picks a section of the shot, AI is used to detect certain topics or other places of interest in the image, such as the sky. The selection, called a mask, aids photographers in activities such as highlighting individuals in the shade or enhancing washed-out sky.

ACR, Lightroom, and Lightroom Classic previously supported vector-based selections (which are saved as mathematical expressions), but AI-powered masks require bitmap (or image-based) support. The AI-powered masks produce a grayscale picture where the lighter and darker values reflect different levels of selection. Within the new masking engine, Adobe wanted to guarantee that both vector-based and bitmap-based masks could operate together on the same image. While brushes, gradients, and range masks may still be created with vector-based selections to save space, the select subject and select sky tools, which can generate a mask for a subject or sky with a single click, will employ bitmaps.

Adobe created new features for ACR, Lightroom, and Lightroom Classic across desktops, mobile devices, tablets, and the web after it found out how to make those two types of selections work together.

Over a year ago, Adobe began work on Lightroom’s new masking capabilities, surveying “tens of thousands of users” to learn about the most common problems they had while making decisions. The surveys also helped Adobe discover what users appreciated about the current selection options in Lightroom and Lightroom Classic. The team also looked at how users interact with various masking tools on desktop and mobile devices. With new masking features, the team identified four critical areas to address: More power and flexibility, better workflow and selection structuring, consistency across all platforms, and enhanced in-app support.

Screen shot of a bird on a screen in lightroom. — ‘The new Select Subject tool automatically creates a precise mask of the salient subject with a single click, and works on people, animals, and inanimate objects.’ Image and caption credit: Adobe

The team built ‘Mask groups’ to make working with mask tools easier. Users can mix any mask tools inside mask groups. In a single mask group, they can combine brush, gradient, brightness, color range choices, and AI-powered tools to build a mask. Masks can also be subtracted from other masks. Moreover, Adobe users will be able to reverse selections to exclude a subject or sky from being tweaked. E.g., if you wish to change everything but the sky, for example, you may use Select Sky and then flip the masked selection. Another new feature is the option to apply range masks globally, which has been a popular request from users.

It might be difficult to keep track of what’s going on with so many masking tools. To help you manage your workplace, Adobe’s new masking panel comes in handy. The masking panel may be placed anywhere on the desktop, docked, or minimized. They can now name each mask as well, making it easier to keep track of various masks. In addition, Photoshop visualization such as a basic color overlay, color overlay on black and white, image on black, image on white, and more have been included.

Adobe has also improved how masks are organized and grouped. Image credit: Adobe

New in-app help and support features have been introduced to ensure that users understand all of the many masking options available inside the Adobe Camera Raw and Lightroom applications. There’s a new interactive tutorial in Lightroom that guides you through all of the masking tools step-by-step.
According to an Adobe blog post, “Adobe’s Research and Design Research teams are working on new AI-powered tools and other innovations that will make use of bitmap-based masks, which it will be delivering later next year.”

Hugging Face Launches Optimum to Scale Transformer Models

By

Amit Kulkarni

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September 30, 2021

Recently Hugging Face launched a new open-source library called Optimum, which aims to democratize the production performance of Machine Learning models. This tool kit also maximizes efficiency to train and run models on specific hardware.

Many data-driven companies like Tesla, Google, Facebook run millions of Transformer model predictions every day to — drive in AutoPilot mode, complete sentences in Gmail, or translate your posts, respectively. As Transformer has brought a capricious improvement in the accuracy of machine learning algorithms, it has vanquished many challenges in NLP and gradually bolstered attempts to expand modalities in areas of Speech and Vision.

Despite such advancements, many machine learning engineers strive to obtain faster running scalable models when brought into production. With Optimum, Hugging Face not only improves Transformer-based models’ performance but also facilitates targeting efficient AI hardware. In addition, the Optimum library helps engineers to leverage the pinnacle of available hardware features with its state-of-the-art AI hardware accelerators.

Transformer-based models can be tricky and expensive as they require a lot of computational power. In order to get optimized performance while training and deploying, the model acceleration methods need to be compatible with targeted hardware. Since each hardware platform offers specific software tooling, it is inevitable to take advantage of advanced model acceleration methods such as sparsity and quantization. But, quantization of a model requires a lot of work as shown below:

The model needs editing: Some operations need to be replaced by their quantized counterparts, new ops required to be inserted, and others need to adapt the weights and activations.
Optimizing quantization: Post-editing, a model involves many parameters to find the best quantization that includes the following questions:
1. Which kind of observers should be used to calibrate range?
2. Which quantization scheme could be used?
3. Which quantization data types (int8, uint8, int16) are supported by your target device?
Tradeoff: When tuning a model, it must have a balance quantization and an acceptable accuracy loss.
Exporting: the quantized model for a target device.

Citing Intel, Hugging Face referred to Low Precision Optimization Tool (LPOT), displaying the approach to solve quantization. LPOT is an open-source python library to help users deploy low-precision inference solutions that support post-training, quantization-aware training, and dynamic quantization. When specifying the quantization approach, objective, and performance criteria, the user needs to provide a configuration YAML file and the tuning parameters. Below is the code that displays how you can quantize, prune, and train transformers for Intel Xeon CPUs with Optimum:

Quantize:

Prune:

Train:

The Hugging Face team mentioned that Optimum would focus on leveraging optimal production performance on dedicated hardware, where software and hardware acceleration methods give maximum efficiency. Further, Hugging Face said, they would work with their hardware partners — Intel, Qualcomm, and Graphcore to scale, train, and maintain acceleration.

AI improves breast cancer detection and reduces false positives

By

Osheen Jain

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September 30, 2021

Doctors often use mammograms, MRIs, ultrasound, or biopsy to find or diagnose breast cancer but these methods have a high rate of false-positive findings. Researchers from NYU and NYU Abu Dhabi (NYUAD) have developed a novel AI that’ll improve breast cancer detection in ultrasound images, achieving radiologist-level accuracy.

The study titled “Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams,” was published in Nature Communications. The study was led by Farah Shamout, Ph.D., NYUAD assistant professor and emerging scholar of computer engineering and colleagues.

Breast ultrasound has high false-positive rates. The AI system developed by researchers achieves radiologist-level accuracy in classifying the images and identifying breast cancer in ultrasound images. This AI model will also reduce the frequency of false-positive findings. It also localizes the lesions in a weakly supervised manner.

NYU Breast Ultrasound Dataset41 was used to develop and evaluate 5,442,907 images within 288,767 breast exams. The images included screening and diagnostic exams collected from 143,203 patients examined between 2012 and 2019 at NYU Langone Health in New York.

This AI detects breast cancer in women by assigning a probability for malignancy and highlighting parts of ultrasound images associated with its predictions. When the researchers conducted a reader study to compare its diagnostic accuracy with board-certified breast radiologists, the system achieved higher accuracy than the ten radiologists on average.

However, a hybrid model that aggregated the predictions of the AI system and radiologists achieved the best results in accurately detecting cancer in patients.

Toyota’s Woven Planet acquires Renovo Motors

By

Dipayan Mitra

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September 30, 2021

Toyota’s subsidiary Woven Planet Holdings acquires autonomous vehicle software developing firm Renovo Motors in a recent acquisition deal. No information has been provided by company officials regarding the valuation of this acquisition contract.

This is Woven Planet’s third acquisition in under twelve months. Woven Planet wants to use Renovo’s expertise in automobility software development to further enhance its open vehicle development platform named Arene.

Renovo’s elite team of expert engineers will now work with Woven Planet to build a complete software-defined vehicle infrastructure stack. Renovo’s office will still remain in California, but it will be integrated with Woven Planet’s operations.

Senior Vice President of Software Platform at Woven Planet Holdings, Nikos Michalakis, said, “A key part in delivering our ‘Mobility to Love, Safety to Live’ vision is to enable the most programmable vehicles on the planet – opening vehicle programming to everyone by simplifying vehicle software development and increasing deployment frequency without compromising safety and security.”

He further added that the technological and cultural fir could not have been any better, and he is extremely excited to work with Renovo to develop solutions to power a new era of automobiles. He firmly believes that this acquisition will have a major impact in transforming worldwide mobility.

San Francisco- based Automated Mobility on Demand systems developing company Renovo was founded by Jason Stinson and Christopher Heiser in the year 2010. The firm specializes in developing operating systems and safety software for autonomous driving vehicles. Renovo had raised over $14.5 million in funding from investors like True Ventures, Verizon, Intact Ventures, and Synapse Partners.

“We are united around a singular goal to connect the most ubiquitous software and automotive technology in the industry,” said Co-founder and CEO of Renovo, Christopher Heiser. He also mentioned that this acquisition will allow them to do what they had always wanted to but at a global level.

Researchers use AI to predict high-risk zoonotic diseases

By

Osheen Jain

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September 30, 2021

AI to predict high-risk zoonotic diseases

About 1.67 million animal viruses can infect humans, and scientists are constantly monitoring the threat of zoonotic diseases. Still, there are millions of viruses circulating among animal populations, making the task incredibly challenging. To increase the chances of identifying the next virus jump from animals to humans, scientists are enlisting the help of sophisticated machine learning algorithms. Scientists used AI to predict high-risk zoonotic diseases so vaccines can protect us from most likely candidates.

Nardus Mollentze, Simon A. Babayan, and Daniel G. Streicker from the University of Glasgow have published a new proof-of-concept study in PLOS Biology. It suggests AI can predict the likelihood and risk of an animal-infecting virus infecting humans before it triggers global pandemics.

Researchers compiled a database of 861 zoonotic virus species from 36 families to train the machine-learning model. They then applied the ML model to identify genomic patterns with a high risk of jumping to humans. Scientists tested the new ML model’s efficacy by using it to analyze the dangers posed by a group of virus species that weren’t a part of the training dataset.

The model reduced the second set of 645 animal-associated viruses to 272 high and 41 very high-risk candidates that were not a part of the training set. The team found that some zoonotic virus genomes have identifiable genetic features that enable them to jump to humans.

The ML model exhibited genomic patterns that are more predictive of the potential for human infection than a virus species’ taxonomic relationships. The model even successfully identified SARS-CoV-2, a virus that triggered the global pandemic, as a “relatively high-risk coronavirus” without prior knowledge of other SARS-related coronaviruses.

Nanowear Receives FDA Clearance to Implement Artificial Intelligence-based Diagnostics

By

Dipayan Mitra

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September 29, 2021

nanowear FDA clearance artificial intelligence

Nanosensor technology developing company Nanowear received Food and Drug Administration (FDA) 510(K) platform clearance to implement its new artificial intelligence-based diagnostics in its closed-loop ‘Hospital at Home’ network.

Nanowear’s artificial intelligence technology named SimpleSense is a noninvasive wearable vest that can be used to track and monitor various patient data, including heart rate, respiration rate, physical activity, blood pressure, and lots more.

The gathered data is uploaded on SimpleSense servers that analyze the data in real-time using artificial intelligence algorithms to generate diagnostic reports. This technology will help physicians and doctors in making better-informed decisions regarding patient treatments.

Director of Research at Google, Peter Norvig, said, “When time-synchronously tracking a complex system like a patient’s heart, lungs and upper vascular system, the data captured needs to match the system’s level of complexity. Nanowear’s 85+ biomarkers of high-fidelity data within a closed loop system enables unique AI and deep learning algorithms to ensure that need is met.”

He further added that artificial intelligence technologies and tremendous ability to aid doctors, other healthcare professionals, and payers in tracking complex biological systems and individual risk patterns in the human body.

Nanowear’s SimpleSense uses clinical-grade biomarkers that have a high Signal to Noise Ratio(SNR), which helps the platform to generate reports in a short time when compared to other smart wearable devices.

“The largest bottleneck for any AI deployment is data preparation, which can take 70% – 80% of the time in any given application,” said the CEO and Co-founder of Nanowear, Verk Varadan.

United States-based nanosensor manufacturing firm Nanowear was founded by Venk Varadan and Vivek K Varadan in the year 2014. The company specializes in developing cloth-based smart nanochip solutions for the healthcare industry. Nanowear has raised a total funding of $1.5 million to date from investors like MedTech Innovator, MAS Holdings, and Social Capital over two funding rounds.

AI-aided Surveillance Cameras under the ‘Safe Kerala’ Project

By

Osheen Jain

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September 29, 2021

Chief Minister Pinarayi Vijayan launched the ‘Safe Kerala’ project in February 2019 to ensure safe traffic on the state’s roads. Various schemes have been rolled out under the ‘Safe Kerala’ initiative, including e-challans for payment of fines by traffic law offenders, e-vehicles for law enforcement duties, and AI-aided Surveillance Cameras.

Today, P. Rajeeve, Minister for Industries, Law, and Coir in the Government of Kerala, will launch the first batch of 100 AI-aided surveillance cameras. Keltron manufactured these AI-aided surveillance cameras for ‘safe Kerala’ project, and the launch will take place at the Keltron communication complex in Manvila on Wednesday at 2.30 pm.

Antony Raju, the Minister for Transport in the Government of Kerala, will receive the cameras on behalf of the Motor Vehicle Department (MVD) during the occasion. According to Keltron authorities, they have manufactured the AI-aided surveillance cameras in association with a technology partner with the feature of automatic number plate recognition (ANPR). Keltron’s unit at Manvila has done the cameras’ manufacturing, assembling, and testing.

Keltron will manufacture and supply 726 traffic enforcement gadgets to the MVD for the ‘Safe Kerala’ project. The traffic enforcement gadgets include four-speed violation detection cameras, 18 red light violation detection cameras, four mobile speed enforcement systems, and 700 AI cameras for the Safe Kerala project. The entire production of traffic enforcement gadgets is being carried out at a budget of Rs 235 crore.

Keltron will also be responsible for the management of the gadgets for the initial five years. Out of the total Rs 235 crore budget, Rs 70 crore is set aside for annual maintenance. The safe Kerala project is a traffic enforcement project given shape by the state government and the MVD to reduce road accidents and related deaths by increasing enforcement efficiency with the help of the latest technology. Besides efficient enforcement, the Safe Kerala project also aims at providing necessary training to drivers to impart safe driving skills and thus make the roads safer.

Building Flexible Autonomy: Boston Dynamics releases Spot 3.0 Update

By

Preetipadma K

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September 29, 2021

spot 3.0 release update, boston dynamics — Image Source: Boston Dynamics

The Spot robot, formerly known as SpotMini, is a four-legged robot created by Boston Dynamics, an American robotics company. Founded in 1992 as a spin-off from the Massachusetts Institute of Technology, Boston Dynamics is currently owned by the Hyundai Motor Group). Recently, Boston Dynamics has unveiled Spot Release 3.0 for its quadruped robot, reflecting more than a year of software advancements over Release 2.0.

While the military financed Boston Dynamics’ initial research, the company has worked hard to disassociate itself from that legacy as it begins to sell its advanced robot creations. Spot has already been seen performing significantly more innocuous duties in recent video releases from the company, such as dancing, gardening, and skipping.

The latest version focuses on allowing Spot to complete tasks without the need for human participation, pushing the limits of automation. The highlights of Release 3.0 include autonomous dynamic replanning, cloud integration, some creative camera tricks, and a new ability to handle push-bar doors.

The Spot robot is now the data collecting solution companies may need to make inspection rounds safer and more efficient, thanks to the latest update’s customizable autonomy and repeatable data capture. Spot’s automated inspections have been simplified to allow for more efficient data collection and processing. It can be programmed to perform a variety of jobs, including gathering photographs, thermal images, point clouds, and other essential data which will be later processed into valuable signals at the edge using computer vision models, and developing bespoke uploads to communicate those signals to existing systems for analysis and review.

Spot Release 3.0 additionally improves Spot’s Autowalk feature by allowing for better planning. Autowalk, a mechanism that will enable the robot to record and repeat paths, is one of Spot’s inherent functions. Here, using the remote controller interface, an operator guides the robot along the path. The robot remembers the path and can repeat it when instructed. In industrial facilities, mines, factories, and building sites, Autowalk can be employed for inspection missions.

The recent update enhances Autowalk, eliminating the need for human intervention and supervision. Autowalk missions can now be edited by robot operators, who can add tasks like collecting photographs, reading indicators, or running third-party code. Spot’s planning abilities have also been improved, and he can now find the optimum path to complete specific actions. Its ability to respond to changes in its inspection paths, such as additional obstructions, has also been improved. It can also be programmed to do scheduled checks without human intervention during off-peak hours.

Boston Dynamics have also improved Spot’s data collection and processing capabilities. This includes the ability to take images from the same angle during Autowalk cycles and have them processed in the cloud using AI analysis (e.g., Tensorflow) and reviewed for potential critical signals, which can then be sent to existing inspection systems. Here, the images can be captured from the same angle every time with scene-based camera alignment for the Spot CAM+ pan-tilt-zoom (PTZ) camera.

Next, Spot has another interesting feature in version 3.0 that allows it to avoid colliding with its human coworkers: it can now make a freely customizable warning sound. It can also dynamically re-plan its routes if an unforeseen impediment appears in the middle of a tour.

Spots may even build tasks and attach them to specific traits, like going to a machine every day and inspecting it with a thermal image sensor. Spot can determine whether a particular object has gone amiss and send this information using scene recognition and image analysis.

Improved compatibility with Microsoft, Amazon, and IBM cloud services is another prominent feature of the latest update. The sensing capabilities of Spot can be used as a substitute for manual data logging, IoT instrumentation, and the installation of smart sensors on existing infrastructure. This functionality allows data collected during Spot’s Autowalk to be automatically integrated into a larger data-based workflow of enterprises.

While Spot is a well-known robot, new competitors have emerged in recent years, attempting to steal Spot’s thunder. After an unfortunate paintball incident at a US art installation – though Boston Dynamics condemned the event – the latest Spot 3.0 update may enable it to achieve new feats and create trust in the canine robot.

Astera Labs raises $50 million in Series C Funding Round

By

Dipayan Mitra

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September 29, 2021

Semiconductor developing company Astera Labs raises $50 million in its series C funding round led by Fidelity Management and Research. Other investors like Valor Equity Partners, Atreides Management, Avigdor Willenz Group, GlobalLink Capital, VentureTech Alliance, and Sutter Hill Ventures also participated in the funding round.

Astera Labs wants to use this fresh funding to further expand their product range and increase market share. The company also plans to run extensive recruitment drives in the United States and Asia to hire new talents for boosting its operations and product development process.

The new funding round follows the successful launch of Astera Labs’s Compute Express Link 2.0 and PCI Express 5.0 that enables users to optimize workflow in the cloud.

The CEO of Astera Labs, Jitendra Mohan, said, “With this investment and increased collaboration with our manufacturing partners, we will rapidly scale our worldwide operations to satisfy incredible customer demand and launch multiple new product lines to solve the industry’s most pressing connectivity challenges.”

He further added that he is extremely excited to join hands with the investors to maintain their leading position in the intelligent cloud connectivity domain.

San Francisco-based startup Astera Labs was founded by Jitendra Mohan, Casey Morrison, and Gajendra Akkasalamakki in the year 2017. The company specializes in developing purpose-built cloud connectivity solutions for data-centric systems.

Astera Labs has developed a lot of products, including integrated circuits, services to enable CXL/PCIe connectivity, and many more. The startup has raised a total funding of $56.4 over three funding rounds till date.

“We are leading the industry with design wins at the five most significant CPU/GPU/AI processor platforms in the world and the majority of Cloud customers,” said the Co-founder and Chief Business Officer of Astera Labs Gajendra Akkasakamakki.

According to the founding investor of Astera Labs, Avigdor Willenz, the company might also plan for its initial public offering (IPO) in the near future.

Hanwha Techwin launches Artificial Intelligence-powered X Series Cameras

By

Dipayan Mitra

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September 28, 2021

Hanwha Techwin Artificial Intelligence x series cameras

Surveillance solutions developing firm Hanwha Techwin launches its all-new artificial intelligence-powered camera series named X-core AI and X-pulse AI. Hanwha has integrated its new cameras with a high-end artificial intelligence system that helps cameras in increasing object deception capabilities in order to reduce false alarms.

The cameras are equipped with world-leading business intelligence technology and use AI solutions to improve image quality considerably. Hanwha has used a unique H.265 compression technology that enables users to reduce data consumption to up to 80% depending upon certain environmental conditions.

The X series camera features license-free artificial intelligence analytics tools that optimize operational efficiency in real-time and recorded video searches. Senior Vice President of Products, Solutions, and Integrations at Hanwha Techwin, Ray Cooke, said, “Bringing AI to the Wisenet X series line represents a major leap forward in features and capabilities for our customers and resellers.”

He further added that with the launch of X series cameras, the technology would be available to a broader customer base as they continue to be industry leaders in the surveillance domain. Hanwha has integrated WiseIR technology in X series cameras that help them to adjust the output of IR LEDs depending upon cameras’ zoom magnification.

Hanwha said, “WiseIR technology adjusts the output of the IR LEDs according to the cameras’ zoom magnification.” The cameras have a modular design with magnetic mounts for camera modules, making them easier for users to install. They also have a video capturing ability of up to 120fps, which results in smooth video outputs.

New Jersey-based surveillance, aeronautics, optoelectronics, automation, and weapon technology developing enterprise Hanwha Techwin, formerly known as Samsung Techwin, was founded in 1977. Since the company’s establishment, it has brought in many cutting-edge innovations in the surveillance technology sector.