According to the Chrome Platform Status webpage, the lack of custom text rendering settings has been an issue that users have been mentioning since 2015. With Microsoft’s intervention, Chrome will finally be more user-friendly.

Some time ago, Microsoft made changes to text rendering in Edge, and the main goal was to achieve nicer fonts, with improved range and contrast. Thanks to the ClearType Text Tuner settings in Windows that Edge uses, the browser has changed its look to a much better one, and the text has become easily readable.

Microsoft’s ClearType font technology has long been used in Windows to improve the display of text on screens, with the goal of making text look like it’s printed on a piece of paper.

Microsoft engineers have integrated ClearType Text Tuner support into Chrome so that it picks up any changes in contrast and gamut, just like most other Windows apps.

Curt Cathy-Schmidt, a senior software development engineer at Microsoft, is leading the effort in this area. In recent months, he has been involved in improving the text image in Chromium-based browsers, with a focus on improving accessibility in Edge and Chrome.

More than five years ago, the company switched to Chromium in its own Edge browser. At the time, Microsoft announced that it would “bring our Windows platform expertise to improve the performance of all Chromium-based browsers on Windows,” and that promise was kept. Microsoft contributed to improving scrolling in Chrome and many other features.

According to the company, the typical approach to creating a multimodal model involves training components for different formats of information separately and then combining them together. What sets Gemini apart is that it is trained for different formats from the start and then refined with additional multimodal data.

“This helps Gemini seamlessly understand and reason about all kinds of inputs from the ground up, far better than existing multimodal models — and its capabilities are state of the art in nearly every domain,” Sundar Pichai, CEO of Google and Alphabet, and Demis Hassabis, CEO and co-founder of Google DeepMind, wrote in a blog post.

Google also explained that the new model has quite sophisticated reasoning capabilities that allow it to understand complex written and visual information, making it “adept at discovering knowledge that can be difficult to discern among vast amounts of data.”

For example, he can read hundreds of thousands of documents and extract this information to lead to new discoveries in certain fields.

Its multimodal nature makes it particularly suited to understanding and answering questions in complex fields such as mathematics and physics.

Gemini 1.0 offers three distinct versions to cater to various size preferences: Ultra, Pro, and Nano, listed in descending order of size.

According to Google’s initial benchmarking of Gemini, the Ultra version has demonstrated superior performance, outperforming 30 out of the 32 widely used academic benchmarks in model development and research. Notably, Gemini Ultra has achieved a milestone by surpassing human expert performance in massive multitask language understanding (MMLU), encompassing 57 subjects such as math, physics, history, law, medicine, and ethics.

The integration of Gemini Pro into Bard marks a significant milestone, constituting the most substantial update to Bard since its initial launch. The Pixel 8 Pro now leverages Gemini Nano to enhance functionalities like Summarize in the Recorder app and Smart Reply in Google’s keyboard.

Over the coming months, Gemini is set to extend its presence to additional Google products, including Search, Ads, Chrome, and Duet AI.

Starting from December 13, developers can access Gemini Pro through the Gemini API available in Google AI Studio or Google Cloud Vortex AI.

“Climate change is a real and pressing issue that we must find new ways to address as quickly and efficiently as possible, including through today’s most advanced AI technologies,” said Alessandro Curioni, IBM Fellow and Vice President, Accelerated Discovery at IBM.

IBM continues to develop its strategy for artificial intelligence models that are trained on geospatial information such as satellite imagery and provide the capability to address climate change.

Alessandro Curioni, IBM Fellow and Vice President, Accelerated Discovery at IBM added “AI foundation models utilizing geospatial data can be a game-changer because they allow us to better understand, prepare and address the many climate-related events effecting the health of our planet in a manner and speed never before seen. We are hopeful these technologies can help accelerate the rate at which we derive and apply solutions for a safer and healthier planet for future generations”.

Unlike traditional AI models tailored to specialist tasks, knowledge-based geospatial models – spanning satellite and weather data – create knowledge representations from petabytes and exabytes of climate-related data that can facilitate accelerated and streamlined discovery of environmental insights and solutions.

” This research underscores the vital role of AI in tackling global issues, emphasizing the urgency of continued exploration and innovation. By harnessing the power of AI, we are not merely addressing challenges; we are proactively shaping solutions for a sustainable future. In a world confronted by unprecedented challenges, MBZUAI stands at the forefront of pioneering research in AI, recognizing the transformative power it holds”, said professor Tim Baldwin, MBZUAI Acting Provost.

These models can also be refined and applied across multiple domains defining or revealing climate change, from flood detection to fire scarring.

Beyond their initial commitment to build and deploy a geospatial foundation model, IBM and NASA have also announced work on a new, separate AI foundation model for weather and climate. By applying AI technology from IBM, the model aims to improve the accuracy, speed and affordability of weather forecasting and other climate applications.

Sample applications of the model not only include forecasting, but also super-resolution downscaling, identifying conditions conducive to wildfires, and predicting meteorological phenomena. IBM researchers will work alongside NASA domain experts to train and validate the model.

The idea dates back to when the social platform had its old, familiar Twitter name and is part of Elon Musk’s plan to turn Twitter (now X) into “the app for everything”.

“Based on your consent, we may collect and use your biometric information for safety, security, and identification purposes,” the updated policy reads.

Biometric data can be extracted from both the identity card and the image for matching purposes.

“This will additionally help us tie, for those that choose, an account to a real person by processing their government issued ID. This will also help X fight impersonation attempts and make the platform more secure”, X said in a statement, TechCrunch reported.

Although X hasn’t specified what it means by biometric information, it is usually used to describe a person’s physical characteristics, such as their face or fingerprints. X also hasn’t provided any details about how it plans to collect it.

Last month, ex. Twitter was named in a proposed class action alleging that the company improperly captured, stored and used Illinois residents’ biometric data, including facial scans, without consent.

“We may collect and use your personal information (such as your employment history, educational history, employment preferences, skills and abilities, job search activity and engagement, and so on) to recommend potential jobs for you, to share with potential employers when you apply for a job, to enable employers to find potential candidates, and to show you more relevant advertising,” the updated policy reads.

Perhaps this would largely reassure users and even make people like the idea of collecting this type of data. But is there something deeper behind this? It remains to be seen.

In the meantime, some interesting facts about biometrics:
Did you know that since ancient times, different nations have used biometrics for recognition and identification. In Babylon, China, Japan, Assyria, etc. fingerprints were used in making commercial transactions, marriage and other contracts.

And more…
The first attempt at a biometric relatively new identification system was created by Edward. Р. Henry and Francis Guilton included a fingerprint identification method, and the method itself was called – “fingerprint copy”. Interestingly, it was introduced first in India in 1876 and then in England in 1901, and within a few years was successfully integrated in almost all countries of the world.

Lisa is dressed in a sari, a traditional Odisha clothing. She knows many languages, but for now presents the news in English for OTV Network’s television and digital platforms.

According to the company’s statement, OTV is giving Odia’s broadcast journalism the first news anchor created using artificial intelligence.

The creators of the artificial image are working on the task of making Lisa more fluent in the Odia language so that she can present the news in that language as well.

The head of the company’s digital business, Litisha Mangat Panda, said teaching Lisa the Odia language has been a huge challenge, which they are handling successfully so far.

“However, we are still working on it. We are hoping to get her trained to the level in which she could interact with others easily”, said Litisha Panda.

This is not the first image created using artificial intelligence. Earlier, in April, Kuwaiti media Kuwait News debuted its first news anchor generated by artificial intelligence (AI) – “Fedha”. “Fedha” appeared on the Kuwait News website’s Twitter account on Saturday as an image of a woman with uncovered blonde hair wearing a black jacket and white T-shirt.

Is the era of robotic on-screen images looming to replace the need for real human TV presenters? It remains a mystery still that manages to create fear among dozens of TV journalism professionals.

The use of artificial intelligence in the creation of movies and TV shows – to write scripts, generate actor likenesses, or reduce the cost of creative work – has been the most contentious topic in negotiations between the Alliance of Motion Picture and Television Producers (AMPTP) and Sag and the Writers Guild of America (WGA).

The writers went on strike in May, with many popular actors including Tom Cruise and George Clooney joining earlier this month. It’s the first joint strike since the 1960s and threatens to change Hollywood forever.

The post, first reported by The Intercept, is something new that aims to “increase the use of our machine learning platform,” billed as “the foundation of all this innovation.” The post, however, lists vague qualifications related to machine learning, but hints at the company’s more far-reaching goals for artificial intelligence in “all areas of business.” A separate “machine learning” section on the company’s website says it will use AI to “shape our catalogue of films and TV shows by learning the features that make content successful”, and to “optimise the production of original films and TV shows”.

This isn’t the only new AI position sought by Netflix. The Intercept reported that the company is also looking to hire a technical director for generative AI at its game studio for a pay of up to $650,000 a year. Artificial intelligence can create text, images and video from input that can be used to create original content or for advertising purposes.

Earlier this month, Netflix premiered the new Spanish dating reality series “Deep Fake Love,” in which scans of participants’ faces and bodies are used to create “deepfake” simulations of themselves, and the company’s games division uses AI to create narratives and dialogue.

However, according to the AMPTP, the breakthrough of Artificial Intelligence across the film industry will change the perception of cinema forever and leave thousands of talented actors out of work, including celebrity darlings around the world.

Introduced in version 4.17 and currently only available for Docker Pro, Team, or Business subscriptions, Docker Scout aims to provide security information about container images by analyzing the content of images and listing all identified CVEs using the docker scout cves command.

Docker Desktop 4.18 can now provide suggestions for fixing identified CVEs. This is done by running the docker scout recommendations command, which will inform about the availability of a new base image and list the benefits it will bring. Scout also offers a new docker scout quickview command that lists all issues found in an image, including the base image, grouped by severity.

And that’s not all. Docker Scout also offers an experimental feature that aims to make it easier to compare images and retrospectively document the changes made to the image in order to fix its vulnerabilities. The report that can be generated using docker scout compare includes a summary of the CVEs found in the baseline images, as well as a list of any added, removed, or updated packages.

The new CLI command in Docker Desktop 4.18, which is still in beta, is docker init, which aims to easily create all the files needed to add Docker to an existing project, including Dockerfiles, Compose files, and .dockerignore. Docker init currently supports Go projects, but Docker is working on adding support for Node.js, Python, Rust, and other languages.

Docker Desktop also includes a new tool with a graphical user interface that simplifies the task of checking out or removing files in a container – the Container File Explorer. It allows developers to inspect a container’s file system, drag and drop files and folders, and edit and remove files.

One of the key new features is the Compose configuration option, which is designed to keep services constantly up-to-date as you modify them. It is still experimental and the Compose File Watch service includes a new watch service that can be started with docker compose alpha watch.

Most companies say they are using six different tools to build models and learn, with executives focusing more on downstream ML capabilities such as observability and function management. The shift from building complex, company-wide ML models to smaller, task-focused models increases their portability and reduces barriers to market. And today we’ve chosen to introduce you to the top four ML trends that Spiceworks presents.

Generative AI
Generative AI can create new content, including audio, code, images, text, simulations, and videos. It uses deep neural networks with billions of parameters to enable complex pattern recognition.

Unsupervised or semi-supervised learning algorithms are making huge strides in accelerating research and development (R&D) cycles in the medical and financial forecasting fields.

The sector will be increasingly regulated. The EU’s AI law, the US Privacy and Data Protection Act and the Open Source Software Assurance Act are all breaking the mould to promote the safety and security of modern technological lifestyles. Whether your company has entered the world of ML or not, enterprises should be aware of these acts and plan strategies to strengthen fraud detection to mitigate risks against the latest ML tools.

Computer vision
Computer vision (CV) accounts for the largest share of the AI and ML market. It is a field of AI that can capture, process and analyze real-world images, enabling the extraction of meaningful, contextual information.

One of the sectors where CV is making an impact is the automotive industry. It can detect defects in the bodywork of cars and underpin the development of applications such as self-driving cars. High-resolution cameras with background CV systems identify surrounding objects, people and movements that automatically trigger the vehicle’s response.

Increasingly, it will help maintenance service providers perform efficient and thorough inspections by using cameras to identify dents and mechanical parts that are out of place. Using CV, engineers can process the images and identify discrepancies within seconds.

Cross-industry AI Synergy
ML will be less concerned with company-specific models and more with data-driven models that can be used across sectors.

Doctors and scientists are experimenting with ML and CV technologies, training them to recognize and classify rare genetic skin conditions. Specialists walk the aisles, in some cases hourly, to count merchandise and ensure product availability. But what if they put CV apps on the shelves to help them monitor real-time inventory?

As companies begin to share the investment cost of tools that can analyze visual patterns and detect everything from rare diseases to product movement, more experimentation and affordable models can be created.

Late adopters are increasingly looking at use cases from more mature ML industries, such as automotive and healthcare, and adapting them to support their business needs.

ML Data Scientist Upskilling vs. Low-code solutions

Codeless and low-code (LCNC) platforms allow users with or without programming language knowledge to manage and build ML tools through intuitive interfaces such as point-and-click and drop-down menus.

ML use cases are expanding rapidly as developers begin to reinvent workflows based on what the technology can provide. AI people can re-engineer systems by pre-programming them to proactively deliver alerts and red flags to users when certain triggers are reached.

Nonetheless, LCNC tools are fundamentally limited in the scope of customization by design. Highly skilled software engineers will be needed to build, monitor, and scale these platforms. The latter needs are likely to lead to a demand for distinct new positions, such as human-computer interaction managers.

Previous versions of Lottie rendered animations in the application’s main thread, effectively using CADisplayLink. Once every frame, Lottie executes code on the main thread to accelerate the animation’s progress and re-render its content.

This system framework renders the animations out-of-process using GPU hardware acceleration. The playback of the animations is managed by a separate system process called a “rendering server”.

This new engine eliminates CPU load when playing back a Lottie animation and effectively ensures that Lottie animations will play back smoothly at 60 or 120 fps regardless of the application’s CPU load.

As of today’s Lottie 4.0 release for iOS, the Core Animation rendering engine is enabled by default for all apps using Lottie, with no additional work or migration required on the part of app developers.

The camouflage is called InvisDefense. It was developed by a group of graduate students from Wuhan University on the occasion of their participation in a cybersecurity innovation competition. With this outfit, they unprecedentedly won first place.

The graduates tested their project at the university. On the surface, InvisDefense looks like an ordinary camouflage-patterned cape, but for AI-based security systems, it’s an invisibility cloak. The recognition accuracy of AI surveillance cameras has more than halved (by 57%). But students plan to improve InvisDefense.

Camouflage is able to fool the recognition system both during the day and at night. The clothing has a special image on it, which misleads AI during the daytime.

When analyzed in infrared light, the wiring comes into play, releasing heat and creating an unusual heat pattern, hiding the person from the camera lenses.

InvisDefense will cost $70 if the garment makes it to retail. The graduate students also said they plan to create the same camouflage for vehicles and will think about how to fool satellite tracking systems.