AI

OpenAI will give roughly 10 AI startups $1M each and early access to its systems • ZebethMedia

By zebethcontrolPosted on November 2, 2022Posted in AI, artificial intelligence, funding, OpenAI, Robotics & AI, startup, Startups, VentureNo Comments

OpenAI, the San Francisco-based lab behind AI systems like GPT-3 and DALL-E 2, today launched a new program to provide early-stage AI startups with capital and access to OpenAI tech and resources. Called Converge, the cohort will be financed by the OpenAI Startup Fund, OpenAI says. The $100 million entrepreneurial tranche was announced last May and was backed by Microsoft and other partners. The 10 or so founders chosen for Converge will receive $1 million each and admission to five weeks of office hours, workshops and events with OpenAI staff, as well as early access to OpenAI models and “programming tailored to AI companies.” “We’re excited to meet groups across all phases of the seed stage, from pre-idea solo founders to co-founding teams already working on a product,” OpenAI writes in a blog post shared with ZebethMedia ahead of today’s announcement. “Engineers, designers, researchers, and product builders … from all backgrounds, disciplines, and experience levels are encouraged to apply, and prior experience working with AI systems is not required.” The deadline to apply is November 25, but OpenAI notes that it’ll continue to evaluate applications after that date for future cohorts. When OpenAI first detailed the OpenAI Startup Fund, it said recipients of cash from the fund would receive access to Azure resources from Microsoft. It’s unclear whether the same benefit will be afforded to Converge participants; we’ve asked OpenAI to clarify. We’ve also asked OpenAI to disclose the full terms for Converge, including the equity agreement, and we’ll update this piece once we hear back. Beyond Converge, surprisingly, there aren’t many incubator programs focused exclusively on AI startups. The Allen Institute for AI has a small accelerator that launched in 2017, which provides up to a $500,000 pre-seed investment and up to $450,000 in cloud compute credits. Google Brain founder Andrew Ng heads up the AI Fund, a $175 million tranche to initiate new AI-centered businesses and companies. And Nat Friedman (formerly of GitHub) and Daniel Gross (ex-Apple) fund the AI Grant, which provides up to $250,000 for “AI-native” product startups and $250,000 in cloud credits from Azure. With Converge, OpenAI is no doubt looking to cash in on the increasingly lucrative industry that is AI. The Information reports that OpenAI — which itself is reportedly in talks to raise cash from Microsoft at a nearly $20 billion valuation — has agreed to lead financing of Descript, an AI-powered audio and video editing app, at a valuation of around $550 million. AI startup Cohere is said to be negotiating a $200 million round led by Google, while Stability AI, the company supporting the development of generative AI systems, including Stable Diffusion, recently raised $101 million. The size of the largest AI startup financing rounds doesn’t necessarily correlate with revenue, given the enormous expenses (personnel, compute, etc.) involved in developing state-of-the-art AI systems. (Training Stable Diffusion alone cost around $600,000, according to Stability AI.) But the continued willingness of investors to cut these startups massive checks — see Inflection AI‘s $225 million raise, Anthropic’s $580 million in new funding and so on — suggests that they have confidence in an eventual return on investment.

Google’s wildfire detection is available in US, Mexico, Canada and parts of Australia • ZebethMedia

By zebethcontrolPosted on November 2, 2022Posted in AI, Apps, Climate, Google, WildfireNo Comments

At an AI-focused press event today in New York, Google announced that it’s bringing its AI-powered wildfire detection system to the U.S. Canada, Mexico and parts of Australia. It’s one of several “AI for good” efforts the company detailed this morning, which also included Google’s efforts to expand flood forecasting to more regions around the world. The previously announced system utilizes machine learning models trained on satellite data to track fires in real-time and predict how it will spread. The feature is initially focused on helping first responders determine how best to control the fire. We’ve trained ML models using satellite imagery to identify and track wildfires in real time and predict how they will evolve to support first responders.( We’re announcing our wildfire detection system now works in the US, CAN, MX and parts of AUS.(3/5) pic.twitter.com/TX1BnvUjeN — Google AI (@GoogleAI) November 2, 2022 This time last year, Google announced that the technology was being added as a layer in Google Maps. The company noted at the time, [W]e’re now bringing all of Google’s wildfire information together and launching it globally with a new layer on Google Maps. With the wildfire layer, you can get up-to-date details about multiple fires at once, allowing you to make quick, informed decisions during times of emergency. Just tap on a fire to see available links to resources from local governments, such as emergency websites, phone numbers for help and information, and evacuation details. When available, you can also see important details about the fire, such as its containment, how many acres have burned, and when all this information was last reported. The feature joins a similar ML-based flood forecasting feature announced back in 2018. That feature is now being expanded to an additional 18 countries.

Alation bags $123M at a $1.7B valuation for its data-cataloging software • ZebethMedia

By zebethcontrolPosted on November 2, 2022Posted in AI, Alation, data catalog, Enterprise, funding, startup, StartupsNo Comments

There’s been an explosion of enterprise data in recent years, accelerated by pandemic-spurred digital transformations. An IDC report commissioned by Seagate projected companies would collect 42.2% more data by year-end 2022 than in 2020, amounting to multiple petabytes of data in total. While more data is generally a good thing, particularly where it concerns analytics, large volumes can be overwhelming to organize and govern — even for the savviest of organizations. That’s why Satyen Sangani, a former Oracle VP, co-founded Redwood City–based Alation, a startup that helps crawl a company’s databases in order to build data search catalogs. After growing its customer base to over 450 brands and annual recurring revenue (ARR) to over $100 million, Alation has raised $123 million in a Series E round led by Thoma Bravo, Sanabil Investments, and Costanoa Ventures with participation from Databricks Ventures, Dell Technologies Capital, Hewlett Packard Enterprise, Icon Ventures, Queensland Investment Corporation, Riverwood Capital, Salesforce Ventures, Sapphire Ventures and Union Grove, the company announced today. The all-equity tranche values Alation at over $1.7 billion — an impressive 15 times higher than the company’s previous valuation in a challenging economic climate. In an interview with ZebethMedia, Sangani said the new capital — which brings Alation’s total raised to $340 million — will be put toward investments in product development (including through acquisitions) and expanding Alation’s sales, engineering and marketing teams, with a focus on the public sector and corporations based in Asia Pacific, Europe, Latin America and the Middle East. “With the capital, we will continue to focus on engagement and adoption, collaboration, governance, lineage, and on APIs and SDKs to enable us to be open and extensible,” Sangani said via email. “We’re going to bring innovation to the market that will increase the number of data assets we cover and the people who will leverage and access Alation.” With Alation, Sangani and his fellow co-founders — Aaron Kalb, Feng Niu and Venky Ganti — sought to build a service that enables data and analytics teams to capture and understand the full breadth of their data. The way Sangani sees it, most corporate leadership wants to build a “data-driven” culture but is stymied by tech hurdles and a lack of knowledge about what data they have, where it lives, whether it’s trustworthy and how to make the best use of it. Alation’s platform organizes data across disparate systems. Image Credits: Alation According to Forrester, somewhere between 60% and 73% of data produced by enterprises goes unused for analytics. And if a recent poll by Oracle is to be believed, 95% of people say they’re overwhelmed by the amount of data available to them in the workplace. “With the astounding amount of data being produced today, it’s increasingly difficult for companies to collect, structure, and analyze the data they create,” Sangani said. “The modern enterprise relies on data intelligence and data integration solutions to provide access to valuable insights that feed critical business outcomes. Alation is foundational for driving digital transformation.” Alation uses machine learning to automatically parse and organize data like technical metadata, user permissions and business descriptions from sources like Redshift, Hive, Presto, Spark and Teradata. Customers can visually track the usage of assets like business glossaries, data dictionaries and Wiki articles through the Alation platform’s reporting feature, or they can use Alation’s collaboration tools to create lists, annotations, comments and polls to organize data across different software and systems. Alation also makes recommendations based on how information is being used and orchestrated. For example, the platform suggests ways customers can centrally manage their data and compliance policies through the use of integrations and data connectors. “Alation’s machine learning contributes to data search, data stewardship, business glossary, and data lineage,” Sangani said. “More specifically, Alation’s behavioral analysis engine spots behavioral patterns and leverages AI and machine learning to make data more user-friendly. For example, search is simplified by highlighting the most popular assets; stewardship is eased by emphasizing the most active data sets; and governance becomes a part of workflow through flags and suggestions.” According to IDC, the data integration and intelligence software market is valued at more than $7.9 billion and growing toward $11.6 billion over the next four years. But Alation isn’t the sole vendor. The startup’s competition includes incumbents like Informatica, IBM, SAP and Oracle, as well as newer rivals such as Collibra, Castor, Stemma, Data.World and Ataccama, all of whom offer tools for classifying and curating data at enterprise scale. One of Alation’s advantages is sheer momentum, no doubt — its customer base includes heavyweights like Cisco, General Mills, Munich Re, Pfizer, Nasdaq and Salesforce, in addition to government agencies such as the Environmental Protection Agency and Australia’s Department of Defense. Alation counts more than 25% of the Fortune 100 as clients, touching verticals such as finance, healthcare, pharma, manufacturing, retail, insurance and tech. In terms of revenue coming in, Sangani claims that Alation — which has more than 700 employees and expects to be at just under 800 by 2023 — is in a healthy position, pegging the firm’s cumulative-cash-burn-to-ARR ratio at around 1.5x. Despite the downturn, he asserts that customer spend is remaining strong as the demand for data catalog software grows; for the past five quarters, Alation’s ARR has increased year over year. In another win for Alation, the investment from Databricks Ventures is strategic, Sangani says. It’ll see the two companies jointly develop engineering, data science and analytics applications that leverage both Databricks’ and Alations’ platforms. “The most successful data intelligence platforms will be adopted by everyone. Vendors that are jack-of-all-trades, but masters of none, promise everything and succeed at little. Similarly, point products achieve limited success, but only serve to create data silos that our customers are trying to avoid. The future of data intelligence is about connectedness and integration,” Sangani said. “We know that and will continue to put our money behind our beliefs.”

MLOps platform Galileo lands $18M to launch a free service • ZebethMedia

By zebethcontrolPosted on November 1, 2022Posted in AI, Enterprise, funding, machine learning, MLops, Robotics & AI, startup, StartupsNo Comments

Galileo, a startup launching a platform for AI model development, today announced that it raised $18 million in a Series A round led by Battery Ventures with participation from The Factory, Walden Catalyst, FPV Ventures, Kaggle co-founder Anthony Goldbloom and other angel investors. The new cash brings the company’s total raised to $23.1 million and will be put toward growing Galileo’s engineering and go-to-market teams and expanding the core platform to support new data modalities, CEO Vikram Chatterji told ZebethMedia via email. As the use of AI becomes more common throughout the enterprise, the demand for products that make it easier to inspect, discover and fix critical AI errors is increasing. According to one recent survey (from MLOps Community), 84.3% of data scientists and machine learning engineers say that the time required to detect and diagnose problems with a model is a problem for their teams, while over one in four (26.2%) admit that it takes them a week or more to detect and fix issues. Some of those issues include mislabeled data, where the labels used to train an AI system contain errors, like a picture of a tree mistakenly labeled “houseplant.” Others pertain to data drift or data imbalance, which happens when data evolves to make an AI system less accurate (think a stock market model trained on pre-pandemic data) or the data isn’t sufficiently representative of a domain (e.g., a data set of headshots has more light-skinned people than dark-skinned). Galileo’s platform aims to systematize AI development pipelines across teams using “auto-loggers” and algorithms that spotlight system-breaking issues. Built to be deployable in an on-premises environment, Galileo scales across the AI workflow — from predevelopment to postproduction — as well as unstructured data modalities like text, speech and vision. In data science, “unstructured” data usually refers to data that’s not arranged according to a preset data model or schema, like invoices or sensor data. Atindriyo Sanyal — Galileo’s second co-founder — makes the case that the Excel- and Python script–based processes to ensure quality data is being fed into models are manual, error-prone and costly. A screenshot of the Galileo Community Edition. Image Credits: Galileo “When inspecting their data with Galileo, users instantly uncover the long tail of data errors such as mislabeled data, underrepresented languages [and] garbage data that they can immediately take action upon within Galileo by removing, re-labeling or by adding additional similar data from production,” Sanyal told ZebethMedia in an email interview. “It has been critical for teams that Galileo supports machine learning data workflows end to end — even when a model is in production, Galileo automatically lets teams know of data drifts, and surfaces the highest-value data to train with next.” The co-founding team at Galileo spent more than a decade building machine learning products, where they say they faced the challenges of developing AI systems firsthand. Chatterji led product management at Google AI, while Sanyal spearheaded engineering at Uber’s AI division and was an early member of the Siri team at Apple. Third Galileo co-founder Yash Sheth is another Google veteran, having previously led the company’s speech recognition platform team. Galileo’s platform falls into the burgeoning category of software known as MLOps, a set of tools to deploy and maintain machine learning models in production. It’s in serious demand. By one estimation, the market for MLOps could reach $4 billion by 2025. There’s no shortage of startups going after the space, like Comet, which raised $50 million last November. Other vendors with VC backing include Arize, Tecton, Diveplane, Iterative and Taiwan-based InfuseAI. But despite having launched just a few months ago, Galileo has paying customers from “high-growth” startups to Fortune 500 companies, Sanyal claims. “Our customers are using Galileo while building machine learning applications such as hate speech detection, caller intent detection at contact centers and customer experience augmentation with conversational AI,” he added. Sanyal expects the launch of Galileo’s free offering — Galileo Community Edition — will boost sign-ups further. The Community Edition enables data scientists working on natural language processing to build machine learning models using some of the tools included in the paid version, Sanyal said. “With Galileo Community Edition, anyone can sign up for free, add a few lines of code while training their model with labeled data or during an inference run with unlabeled data to instantly inspect, find and fix data errors, or select the right data to label next using the powerful Galileo UI,” he added. Sanyal declined to share revenue figures when asked. But he noted that San Francisco–based Galileo’s headcount has grown in size from 14 people in May to “more than” 20 people as of today.

Navina secures $22M to process and summarize medical data for clinicians • ZebethMedia

By zebethcontrolPosted on October 27, 2022Posted in AI, Alive, Biotech & Health, Grove Ventures, Robotics & AI, Schusterman Family Investments, Startups, Vertex Ventures IsraelNo Comments

Navina, a company developing AI-powered assistant software for physicians, today announced that it raised $22 million in Series B funding led by ALIVE with participation from Grove Ventures, Vertex Ventures Israel and Schusterman Family Investments. Bringing the startup’s total raised to $44 million, inclusive of a grant from the Israeli Innovation Authority, the proceeds will be put toward product development and widening Navina’s footprint to home, virtual and urgent care, CEO and co-founder Ronen Lavi told ZebethMedia. Navina was founded by Ronen Lavi and Shay Perera, who previously led the Israel Defense Forces’ AI lab, where they say that they built AI “assistant” systems for analysts suffering from data overload. Their work there inspired the products they went on to built at Navina, which aim to help physicians drowning in medical data. “The funding comes at a pivotal time for the U.S. healthcare industry on the heels of the pandemic, when physician burnout is at an all-time high,” Lavi told ZebethMedia in an email interview. “Navina’s platform is uniquely able to put exactly the right patient information in front of physicians at the right time to give them a deep understanding at a glance, along with actionable insights at the point of care.” Several startups — and incumbents, for that matter — are developing AI assistant technologies for clinical settings. For example, there’s Suki, which raised $20 million to create a voice assistant for doctors, and Bot MD, an AI-based chatbot for doctors. Lavi claims that Navina is distinguished by its ability to “understand the complex language of medicine,” including non-clinical data. Trained on a dataset of imaging notes, consult notes, hospital notes, procedures and labs curated by a team led by medical doctors, Navina’s AI systems integrate with existing electronic health records software to identify potential diagnoses and quality and risk gaps requiring attention. Navina.ai uses AI to process and summarize medical records data. Image Credits: Navina “Navina differentiates in the way it structures and organizes data specifically for primary care physicians at the moment of care,” Lavi said. “Navina fits into existing workflows and familiar tools, meeting physicians and staff where they are … Its goal is to align workflows to effectively serve patient populations and improve value-based care.” One point of concern for this reporter is Navina’s diagnostic capabilities. While perhaps helpful, medical algorithms have historically been built on biased rules and homogenous datasets. The consequences have been severe. For example, one algorithm to determine eligible candidates for kidney transplants puts Black patients lower on the list than white patients even when all other factors remain the same. In response to a question about bias, Lavi said that Navina takes steps to “address health inequities and bias” and “ensure high accuracy of data sets and models.” He added that the company is compliant with HIPAA requirements and underwent a third-party privacy audit, and is in the “final stages” of SOC2 certification. With “thousands” of clinicians and supporting staff using the platform, Lavi says he doesn’t anticipate the economic downturn significantly impacting Navina. He demurred, however, when asked about the company’s revenue and precise customer count. “The pandemic gave Navina and other health tech companies a boost as it required both patients and physicians to grow accustomed to new modalities of care, such as telemedicine and remote visits,” Lavi said. “This has led traditional primary care providers to look for solutions that can help them take responsibility for their patients no matter where they enter the health system.” Navina has 65 employees currently. It expects to end the year with around 75.

AI saving whales, steadying gaits and banishing traffic • ZebethMedia

By zebethcontrolPosted on October 25, 2022Posted in AI, artificial intelligence, Deep Science, machine learning, perceptron, research, Robotics & AINo Comments

Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column, Perceptron, aims to collect some of the most relevant recent discoveries and papers — particularly in, but not limited to, artificial intelligence — and explain why they matter. Over the past few weeks, researchers at MIT have detailed their work on a system to track the progression of Parkinson’s patients by continuously monitoring their gait speed. Elsewhere, Whale Safe, a project spearheaded by the Benioff Ocean Science Laboratory and partners, launched buoys equipped with AI-powered sensors in an experiment to prevent ships from striking whales. Other aspects of ecology and academics also saw advances powered by machine learning. The MIT Parkinson’s-tracking effort aims to help clinicians overcome challenges in treating the estimated 10 million people afflicted by the disease globally. Typically, Parkinson’s patients’ motor skills and cognitive functions are evaluated during clinical visits, but these can be skewed by outside factors like tiredness. Add to that fact that commuting to an office is too overwhelming a prospect for many patients, and their situation grows starker. As an alternative, the MIT team proposes an at-home device that gathers data using radio signals reflecting off of a patient’s body as they move around their home. About the size of a Wi-Fi router, the device, which runs all day, uses an algorithm to pick out the signals even when there’s other people moving around the room. In study published in the journal Science Translational Medicine, the MIT researchers showed that their device was able to effectively track Parkinson’s progression and severity across dozens of participants during a pilot study. For instance, they showed that gait speed declined almost twice as fast for people with Parkinson’s compared to those without, and that daily fluctuations in a patient’s walking speed corresponded with how well they were responding to their medication. Moving from healthcare to the plight of whales, the Whale Safe project — whose stated mission is to “utilize best-in-class technology with best-practice conservation strategies to create a solution to reduce risk to whales” — in late September deployed buoys equipped with onboard computers that can record whale sounds using an underwater microphone. An AI system detects the sounds of particular species and relays the results to a researcher, so that the location of the animal — or animals — can be calculated by corroborating the data with water conditions and local records of whale sightings. The whales’ locations are then communicated to nearby ships so they can reroute as necessary. Collisions with ships are a major cause of death for whales — many species of which are endangered. According to research carried out by the nonprofit Friend of the Sea, ship strikes kill more than 20,000 whales every year. That’s destructive to local ecosystems, as whales play a significant role in capturing carbon from the atmosphere. A single great whale can sequester around 33 tons of carbon dioxide on average. Image Credits: Benioff Ocean Science Laboratory Whale Safe currently has buoys deployed in the Santa Barbara Channel near the ports of Los Angeles and Long Beach. In the future, the project aims to install buoys in other American coastal areas including Seattle, Vancouver, and San Diego. Conserving forests is another area where technology is being brought into play. Surveys of forest land from above using lidar are helpful in estimating growth and other metrics, but the data they produce aren’t always easy to read. Point clouds from lidar are just undifferentiated height and distance maps — the forest is one big surface, not a bunch of individual trees. Those tend to have to be tracked by humans on the ground. Purdue researchers have built an algorithm (not quite AI but we’ll allow it this time) that turns a big lump of 3D lidar data into individually segmented trees, allowing not just canopy and growth data to be collected but a good estimate of actual trees. It does this by calculating the most efficient path from a given point to the ground, essentially the reverse of what nutrients would do in a tree. The results are quite accurate (after being checked with an in-person inventory) and could contribute to far better tracking of forests and resources in the future. Self-driving cars are appearing on our streets with more frequency these days, even if they’re still basically just beta tests. As their numbers grow, how should policy makers and civic engineers accommodate them? Carnegie Mellon researchers put together a policy brief that makes a few interesting arguments. Diagram showing how collaborative decision making in which a few cars opt for a longer route actually makes it faster for most. The key difference, they argue, is that autonomous vehicles drive “altruistically,” which is to say they deliberately accommodate other drivers — by, say, always allowing other drivers to merge ahead of them. This type of behavior can be taken advantage of, but at a policy level it should be rewarded, they argue, and AVs should be given access to things like toll roads and HOV and bus lanes, since they won’t use them “selfishly.” They also recommend that planning agencies take a real zoomed-out view when making decisions, involving other transportation types like bikes and scooters and looking at how inter-AV and inter-fleet communication should be required or augmented. You can read the full 23-page report here (PDF). Turning from traffic to translation, Meta this past week announced a new system, Universal Speech Translator, that’s designed to interpret unwritten languages like Hokkien. As an Engadget piece on the system notes, thousands of spoken languages don’t have a written component, posing a problem for most machine learning translation systems, which typically need to convert speech to written words before translating the new language and reverting the text back to speech. To get around the lack of labeled examples of language, Universal Speech Translator converts speech into “acoustic units”

Microsoft’s Windows Dev Kit 2023 lets developers tap AI processors on laptops • ZebethMedia

By zebethcontrolPosted on October 24, 2022Posted in AI, Apps, Hardware, Microsoft, Qualcomm, Robotics & AI, WindowsNo Comments

At its Build conference in May, Microsoft debuted Project Volterra, a device powered by Qualcomm’s Snapdragon platform designed to let developers explore “AI scenarios” via Qualcomm’s Neural Processing SDK for Windows toolkit. Today, Volterra — now called Windows Dev Kit 2023 — officially goes on sale, priced at $599 and available from the Microsoft Store in Australia, Canada, China, France, Germany, Japan, the U.K. and the U.S. Here’s how Microsoft describes it: With Windows Dev Kit 2023, developers will be able to bring their entire app development process onto one compact device, giving them everything they need to build Windows apps for Arm, on Arm. As previously announced, the Windows Dev Kit 2023 contains a dedicated AI processor, called the Hexagon processor, complimented by an Arm-based chip — the Snapdragon 8cx Gen 3 — both supplied by Qualcomm. It enables developers to build Arm-native and AI-powered apps alongside and with tools such as Visual Studio (version 17.4 runs natively on Arm), .NET 7 (which has Arm-specific performance improvements), VSCode, Microsoft Office and Teams and machine learning frameworks including PyTorch and TensorFlow. Microsoft’s Windows Dev Kit 2023, which packs an Arm processor plus an AI accelerator chip. Image Credits: Microsoft Here’s the full list of specs: 32GB LPDDR4x RAM 512GB fast NVMe Storage Snapdragon 8cx Gen 3 compute platform RJ45 for ethernet 3 x USB-A ports 2 x USB-C ports Mini DisplayPort (which supports up to three external monitors, including two at 4K 60Hz) Bluetooth 5.1 and Wi-Fi 6 The Windows Dev Kit 2023 arrives alongside support in Windows for neural processing units (NPU), or dedicated chips tailored for AI- and machine learning-specific workloads. Dedicated AI chips, which speed up AI processing while reducing the impact on battery, have become common in mobile devices like smartphones. But as apps such as AI-powered image upscalers and image generators come into wider use, manufacturers have been adding such chips to their laptops (see Microsoft’s own Surface Pro X and 5G Surface Pro 9). The Windows Dev Kit 2023 taps into the recently released Qualcomm Neural Processing SDK for Windows, which provides tools for converting and executing AI models on Snapdragon-based Windows devices in addition to APIs for targeting distinct processor cores with different power and performance profiles. Using it and the Neural Processing SDK, developers can execute, debug and analyze the performance of deep neural networks on Windows devices with Snapdragon hardware as well as integrate the networks into apps and other code. The tooling benefits laptops built on the Snapdragon 8cx Gen 3 system-on-chip, like the Acer Spin 7 and Lenovo ThinkPad X13s. Engineered to compete against Apple’s Arm-based silicon, the Snapdragon 8cx Gen 3’s AI accelerator can be used to apply AI processing to photos and video. Microsoft and Qualcomm are betting the use cases expand with the launch of the Windows Dev Kit 2023; Microsoft for its part has started to leverage AI accelerators in Windows 11 to power features like background noise removal. Image Credits: Microsoft In a blog post shared with ZebethMedia ahead of today’s announcement, Microsoft notes that developers will “need to install the toolchain as needed for their workloads on Windows Dev Kit 2023” and that some tools and services “may require additional licenses, fees or both.” “More apps, tools, frameworks and packages are being ported to natively target Windows on Arm and will be arriving over the coming months,” the post continues. “In the meantime, thanks to Windows 11’s powerful emulation technology, developers will be able to run many unmodified x64 and x86 apps and tools on their Windows Dev Kit.” It remains to be seen whether the Windows Dev Kit reverses the fortune of Windows on Arm devices, which have largely failed to take off. Historically, they’ve been less powerful than Intel-based devices while suffering from compatibility issues and sky-high pricing (the Surface Pro X cost more than $1,500 at launch). Emulated app performance on the first few Arm-powered Windows devices tended to be poor and certain games wouldn’t launch unless they used a particular graphics library, while drivers for hardware only worked if they were designed for Windows on Arm specifically. The Windows on Arm situation has improved as of late, thanks to more powerful hardware (like the Snapdragon 8cx Gen3) and Microsoft’s App Assurance program to ensure that business and enterprise apps work on Arm. But the ecosystem has a long way to go, still, with Unity — one of the most popular game engines today — only this morning announcing a commitment to allow developers to target Windows on Arm devices to get native performance.

Adobe’s AI prototype pastes objects into photos while adding realistic lighting and shadows • ZebethMedia

By zebethcontrolPosted on October 19, 2022Posted in Adobe, AI, image editing, Media & Entertainment, Robotics & AINo Comments

Every year at Adobe Max, Adobe shows off what it calls “Sneaks,” R&D projects that might — or might not — find their way into commercial products someday. This year is no exception, and lucky for us, we were given a preview ahead of the conference proper. Project Clever Composites (as Adobe’s calling it) leverages AI for automatic image compositing. To be more specific, it automatically predicts an object’s scale, determining where the best place might be to insert it in an image before normalizing the object’s colors, estimating the lighting conditions and generating shadows in line with the image’s aesthetic. Here’s how Adobe describes it: Image composting lets you add yourself in to make it look like you were there. Or maybe you want to create a photo of yourself camping under a starry sky but only have images of the starry sky and yourself camping during the daytime. I’m no Photoshop wizard, but Adobe tells me that compositing can be a heavily manual, tedious and time-consuming process. Normally, it involves finding a suitable image of an object or subject, carefully cutting the object or subject out of said image and editing its color, tone, scale and shadows to match its appearance with the rest of the scene into which it’s being pasted. Adobe’s prototype does away with this. “We developed a more intelligent and automated technique for image object compositing with a new compositing-aware search technology,” Zhifei Zhang, an Adobe research engineer on the project, told ZebethMedia via email. “Our compositing-aware search technology uses multiple deep learning models and millions of data points to determine semantic segmentation, compositing-aware search, scale-location prediction for object compositing, color and tone harmonization, lighting estimation, shadow generation and others.” Image Credits: Adobe According to Zhang, each of the models powering the image-compositing system is trained independently for a specific task, like searching for objects consistent with a given image in terms of geometry and semantics. The system also leverages a separate, AI-based auto-compositing pipeline that takes care of predicting an object’s scale and location for compositing, tone normalization, lighting condition estimation and synthesizing shadows. The result is a workflow that allows users to composite objects with just a few clicks, Zhang claims. “Achieving automatic object compositing is challenging, as there are several components of the process that need to be composed. Our technology serves as the ‘glue’ as it allows all these components to work together,” Zhang said. As with all Sneaks, the system could forever remain a tech demo. But Zhang, who believes it’d make a “great addition” to Photoshop and Lightroom, says work is already underway on an improved version that supports compositing 3D objects, not just 2D. “We aim to make this common but difficult task of achieving realistic and clever composites for 2D and 3D completely drag-and-drop,” Zhang said. “This will be a game-changer for image compositing, as it makes it easier for those who work on image design and editing to create realistic images since they will now be able to search for an object to add, carefully cut out that object and edit the color, tone or scale of it with just a few clicks.”

Adobe makes selecting and deleting objects and people in Photoshop and Lightroom a lot easier • ZebethMedia

By zebethcontrolPosted on October 18, 2022Posted in Adobe, AI, Apps, lightroom, photoshopNo Comments

Photoshop and Lightroom are incredibly powerful tools for manipulating images, but since the beginning of time, the most frustrating part of working with these tools has been selecting specific objects to cut them out of an image, move them elsewhere, etc. Over the years, the object selection tools got a lot better, but for complex objects — and especially for masking people — your results still depend on how much patience you have. At its MAX conference, Adobe today announced a number of updates across its photo-centric tools that make all of this a lot easier, thanks to the power of its AI platform. In an earlier update in 2020, Adobe already launched an Object Selection tool that could recognize some types of objects. Now, this tool is getting a lot smarter and can recognize complex objects like the sky, buildings, plants, mountains, sidewalks etc. But maybe more importantly, the system has also gotten a lot more precise and can now preserve the details of a person’s hair, for example, in its masks. That’s a massive time saver. Image Credits: Adobe For those times when you just want to delete an object and then fill in the empty space, using Photoshop’s Content-Aware Fill, the company now introduced a shortcut. Shift+Delete and the object is gone and (hopefully) patched over in with an AI-created filler. On the iPad, mobile users can now remove an image’s background with a single tap and they also get one-tap Content-Aware fill (this is slightly different from the one-click delete and fill feature mentioned above, but achieves a very similar same outcome. iPad users can now use the improved Select Subject AI model as well to more easily select people, animals and objects. Image Credits: Adobe A lot of this AI-powered masking capability is also coming to Lightroom. There’s now a ‘Select People’ feature, for example, that can detect and generate masks for individuals and groups in any images — and you can select specific body parts, too Unsurprisingly, the same Select Objects technology from Photoshop is also coming to Lightroom, as is the one-click select background feature from the iPad version of Photoshop. There’s also now a content-aware remove feature in Lightroom. All of this is powered by Adobe’s Sensei AI platform, which has been a central focus of the company’s efforts in recent years. But what’s maybe even more important is that these efforts have allowed Adobe to turn these features into modules that it can now bring to its entire portfolio and adapt them to specific devices and their use case. On the iPad, for example, the background selection feature is all about deleting that background while in Lightroom it is only about selecting it, but in the end, it’s the same AI model that powers both. Image Credits: Adobe This is, of course, only a small selection of all of the new features coming to Photoshop and Lightroom. There are also features like support for HDR displays in Camera Raw, improved neural filters, a new photo restoration filter, improvements to Photoshop’s tools for requesting review feedback and plenty more.

Microsoft Teams gains animated avatars and AI-powered recaps • ZebethMedia

By zebethcontrolPosted on October 12, 2022Posted in AI, Enterprise, Microsoft, Microsoft Ignite 2022, Microsoft Teams, StartupsNo Comments

At its Ignite conference this week, Microsoft announced updates heading to Teams, its ever-evolving videoconferencing and workplace collaboration app. New avatars are available, and more details were announced around Teams Premium, a paid set of Teams features including AI-generated tasks and meeting guides, which is set to arrive in December in preview. Teams Premium is an effort to simplify Teams pricing, which before was disparate across several tiers. Microsoft says it expects it to cost $10 per user per month, with official pricing to come once Teams Premium is generally available. That’s higher than the lowest-cost Google Workspace plan, which costs $6 per user per month, but less expensive than Zoom Pro ($15 per user per month). The aforementioned avatars — a part of Microsoft’s Mesh platform — allow users to choose customized, animated versions of themselves to show up in Teams meetings, a bit like Zoom’s virtual avatars. Through the Avatars app in the Microsoft Teams app store, users can design up to three avatars to use in a Teams meeting with gestures to react to topics. Microsoft’s CVP of modern work Jared Spataro pitches avatars as a way to “take a break from the camera” but “still have a physical presence” in Teams meetings. “Our data shows that 51% of Gen Z envisions working in the metaverse in the next two years,” he wrote in a blog post — a percentage that seems optimistically high if we’re talking about VR and AR headsets, but depends on how one defines “metaverse.” He continued: “You can create custom avatars to represent yourself.” Avatars are perhaps also a small play — albeit an unspoken one — at revitalizing a platform that’s stagnated over the past year. Microsoft says that “more than 270 million” people actively use Teams monthly today, a number that hasn’t budged since January as workers increasingly return to the office. Avatars are available in the standard Teams for private preview customers, while organizations interested in trying them out can sign up for updates on the Teams website if they’re not already part of the Teams Technical Access Program, Microsoft says. Teams Premium On the Teams Premium side, customers are getting meeting guides designed to help them pick the right “meeting experience” — e.g. a client call, brainstorm meeting or help desk support — with options that can be customized and managed by an IT team. Teams Premium users will also be able to brand the meeting experience with bespoke logos for the lobby and brand-specific backgrounds at the organization level. The forthcoming Intelligent Recap feature in Microsoft Teams Premium, powered by machine learning. Image Credits: Microsoft Among the more interesting new Teams Premium-specific additions leverage AI. For example, there’s Intelligent Recap, which attempts to capture highlights from Teams meetings, and an Intelligent Playback feature that automatically generates chapters for Teams meeting recordings. Personalized Insights highlights key moments in recordings, like when a person’s name was mentioned, while Intelligent Search aims to make searching transcripts easier with suggested speakers. Beyond all this, Teams Premium will deliver real-time translations for 40 spoken languages and the above-mentioned AI-generated tasks, which are automatically assigned to meeting attendees. AI aside, Teams Premium will soon offer what Microsoft’s calling Advanced Meeting Protection, a set of features to safeguard confidential meetings such as board meetings and undisclosed product launches. These span watermarking, limits to recording and sensitivity labels to automatically apply protections to meetings. Relatedly, new Advanced Webinars in Teams Premium provide options for a registration waitlist and manual approvals, automated reminder emails, a virtual green room for hosts and presenters and the ability to manage what attendees see. Teams Premium will also introduce advanced virtual appointments, which are designed to help manage the end-to-end appointment experience for direct-to-consumer brands with pre-appointment text reminders, a branded lobby and post-appointment follow-ups. Organizations get both scheduled and on-demand appointments, a simplified view of all virtual appointments and pre-appointment chat capabilities to communicate with their customers. On the backend, customers can view analytics like usage trends, a history of virtual appointments and no-shows and wait times with specific staff and departments. Microsoft says that Teams Premium features will begin rolling out in December 2022 as part of a preview, with general availability coming in February 2023. The AI capabilities, including Intelligent Playback and Intelligence Recap, will hit the first half of 2023.