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Před 60 lety, 1. května 1964, byl představen programovací jazyk BASIC (Beginners' All-purpose Symbolic Instruction Code).

Byla vydána nová verze 12.0 minimalistické linuxové distribuce (JeOS, Just enough Operating System) pro Kodi (dříve XBMC) a multimediálního centra LibreELEC (Libre Embedded Linux Entertainment Center). Jedná se o fork linuxové distribuce OpenELEC (Open Embedded Linux Entertainment Center). LibreELEC 12.0 přichází s Kodi 21.0 "Omega".

Intrusion investors went through Blount farce trauma, says SEC

Jack Blount, the now-ex CEO of Intrusion, has settled with the SEC over allegations he made false and misleading statements about his infosec firm's product as well as his own background and experience.…

As companies are scramble to get ahead in the artificial intelligence (AI) arms race, one problem the face is finding and retaining AI talent.

For example, Meta has been extending job offers to candidates with AI experience without even interviewing them, and CEO Mark Zuckerberg went so far as to email researchers at Google’s DeepMind unit to recruit talent.

According to a recent study led by the Oxford Internet Institute, “AI skills are particularly valuable as they have high levels of skill complementarity, increasing worker wages by 21% on average.”

Foote Partners, a recruitment, consulting and research firm, recently published its latest IT skills and pay index, which found, not surprisingly, that companies are paying premiums for workers with AI-related abilities.

Foote Partners recognizes 47 “critical A.I. skills,” and on average, employees who obtain those skills get a pay premium of 7% to 21%. Those premiums can come in different forms, including bonuses and cash compensation.

“This is cash paid out, not as a salary increase but in addition to salary,” said David Foote, chief analyst at Foote Partners. “In fact, 28 of these core AI skills can add between 15% and 21% in pay, well above the 9.6% average premium across all 632 non-certified skills we report. They are very hot right now.”

Cash premiums for skills such as AI chatbot app developer and large language model (LLM) tuning are paid out separately from salaries so that as needs are met and the value of those talents declines, the payouts can be reduced or eliminated.

“Perhaps the main difference with skills pay is, unlike a bonus, skills pay premiums are typically paid out at every pay period — same as salary — instead of a lump sum at the end of year, which is more common with bonuses,” Foote said.

In addition, more than a dozen AI-related certifications “are showing real market value strength,” he said. Those skills include prompt engineering, neural networks, AI engineer, AI scientist, and AI model optimization.

According to Foote, a Certified Artificial Intelligence Scientist earns the highest pay premium, ranging 8% to 12%, with the average being 10% of base salary equivalent,. The next highest cash pay premium in this cluster is for a Microsoft Certified: Azure AI Engineer Associate, who can earn 9% of base equivalent in skills premium pay.

Averaging 8% of base salary equivalent in skills pay premium are: SAS Certified Professional: AI and Machine Learning; IBM Certified Specialist — AI Enterprise Workflow V1; Artificial Intelligence Engineer (AIE – all tracks); and Google Professional Machine Learning Engineer certifications.

“We’ve been tracking some of these AI-related skills for many years, updating every 90 days,” Foote said. “Others have been recently added. So, given all the volatility in the marketplace for skills cash pay premiums, there have been remarkable changes over time ,including most recently.”

Foote Partner’s findings are echoed elsewhere. Freelance work platform Upwork, which recently published its 2024 list of most in-demand skills, found that the rise of AI — especially generative AI in the last couple of years — has changed the top skills businesses seek from independent professionals. In particular, generative AI modeling, machine learning and data analytics are the top three fastest-growing data science and analytics skills, as well as among the most in-demand skills.

“In particular, skills in key programming languages commonly used in the development of AI — Python, Java, and SQL — rank among the top five most sought-after skills on the technical side in the US,” Ya Xu, head of data and AI at LinkedIn, said in a blog post.

Machine learning skills can command at least a 10% premium compared to the average tech worker, according to a survey conducted at the end of 2023 by tech staffing firm Dice. “But this is a fast-moving market where the demand is growing rapidly,” said Art Zeile, CEO of Dice. “I predict that this premium will only continue to grow as the gap between supply and demand for AI talent grows, and offering benefits such as continuous professional development and training will be key to retaining this top talent.”

A key differentiator in the job interview process now involves how a tech professional has upskilled themselves in the face of the growing demand for AI, Zeile noted.

“Now is the time to ensure that their skill sets encompass [LLM] theory and programming architecture, areas that may have been overlooked or haven’t been delved into fully,” he said. “Recruiters are looking for tech professionals who are quick on their feet and able to adapt to changes with agility and curiosity.”

In January 2023, AI or machine learning-related skill sets were referenced on 9% of tech job postings. Just over a year later, in February 2024, that figure had climbed to nearly 14% of all tech job postings, according to Zeile.

“As AI continues to evolve, its impact on organizations will be most prominent in key departments such as research and development, data analytics, and operations,” Zeile said.

To secure top AI talent in these areas, Zeile advised that companies:

• Invest in comprehensive recruiting strategies that show a full understanding of what a particular AI role actually requires — whether it’s machine learning, Python, or data science skills for tech jobs, or other content-centric skills such as copy writing and graphic design. 
• Establish competitive salaries and provide opportunities for professional growth and development. 
• Offer flexibility in the form of hybrid/remote work environments. Dice’s 2023 Tech Sentiment Report noted that remote work remains very important to tech professionals: 73% of those surveyed said it is “extremely” or “very” important to have the opportunity to work remotely at least three days a week.

While Amazon, Google, Meta and Microsoft are among the top 15 companies hiring for AI talent right now, according to Dice, there are a variety of factors tech pros must consider determining if Big Tech is right for them.

“While these Big Tech companies offer many opportunities for individuals with AI skills, candidates should also consider startups and other industries that align with their career goals and work/life balance preferences that would also still give them the ability to learn and flex their AI muscles,” Zeile said.  “Ultimately, it is up to tech professionals to decide what companies most align with their values and what kind of industries they want to work in.”

Startups, Zeille said, often provide more room for innovation and growth, but may have less rigorous AI programs in place. At the same time, compensation at Big Tech companies might be higher, but work/life balance lower. Once a candidate has decided what is important to prioritize in their future role, they can job search accordingly. 

Generative AI, IT Jobs, IT Skills

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To anyone living in a city where autonomous vehicles operate, it would seem they need a lot of practice. Robotaxis travel millions of miles a year on public roads in an effort to gather data from sensors—including cameras, radar, and lidar—to train the neural networks that operate them.

In recent years, due to a striking improvement in the fidelity and realism of computer graphics technology, simulation is increasingly being used to accelerate the development of these algorithms. Waymo, for example, says its autonomous vehicles have already driven some 20 billion miles in simulation. In fact, all kinds of machines, from industrial robots to drones, are gathering a growing amount of their training data and practice hours inside virtual worlds.

According to Gautham Sholingar, a senior manager at Nvidia focused on autonomous vehicle simulation, one key benefit is accounting for obscure scenarios for which it would be nearly impossible to gather training data in the real world.

“Without simulation, there are some scenarios that are just hard to account for. There will always be edge cases which are difficult to collect data for, either because they are dangerous and involve pedestrians or things that are challenging to measure accurately like the velocity of faraway objects. That’s where simulation really shines,” he told me in an interview for Singularity Hub.

While it isn’t ethical to have someone run unexpectedly into a street to train AI to handle such a situation, it’s significantly less problematic for an animated character inside a virtual world.

Industrial use of simulation has been around for decades, something Sholingar pointed out, but a convergence of improvements in computing power, the ability to model complex physics, and the development of the GPUs powering today’s graphics indicate we may be witnessing a turning point in the use of simulated worlds for AI training.

Graphics quality matters because of the way AI “sees” the world.

When a neural network processes image data, it’s converting each pixel’s color into a corresponding number. For black and white images, the number ranges from 0, which indicates a fully black pixel, up to 255, which is fully white, with numbers in between representing some variation of grey. For color images, the widely used RGB (red, green, blue) model can correspond to over 16 million possible colors. So as graphics rendering technology becomes ever more photorealistic, the distinction between pixels captured by real-world cameras and ones rendered in a game engine is falling away.

Simulation is also a powerful tool because it’s increasingly able to generate synthetic data for sensors beyond just cameras. While high-quality graphics are both appealing and familiar to human eyes, which is useful in training camera sensors, rendering engines are also able to generate radar and lidar data as well. Combining these synthetic datasets inside a simulation allows the algorithm to train using all the various types of sensors commonly used by AVs.

Due to their expertise in producing the GPUs needed to generate high-quality graphics, Nvidia have positioned themselves as leaders in the space. In 2021, the company launched Omniverse, a simulation platform capable of rendering high-quality synthetic sensor data and modeling real-world physics relevant to a variety of industries. Now, developers are using Omniverse to generate sensor data to train autonomous vehicles and other robotic systems.

In our discussion, Sholingar described some specific ways these types of simulations may be useful in accelerating development. The first involves the fact that with a bit of retraining, perception algorithms developed for one type of vehicle can be re-used for other types as well. However, because the new vehicle has a different sensor configuration, the algorithm will be seeing the world from a new point of view, which can reduce its performance.

“Let’s say you developed your AV on a sedan, and you need to go to an SUV. Well, to train it then someone must change all the sensors and remount them on an SUV. That process takes time, and it can be expensive. Synthetic data can help accelerate that kind of development,” Sholingar said.

Another area involves training algorithms to accurately detect faraway objects, especially in highway scenarios at high speeds. Since objects over 200 meters away often appear as just a few pixels and can be difficult for humans to label, there isn’t typically enough training data for them.

“For the far ranges, where it’s hard to annotate the data accurately, our goal was to augment those parts of the dataset,” Sholingar said. “In our experiment, using our simulation tools, we added more synthetic data and bounding boxes for cars at 300 meters and ran experiments to evaluate whether this improves our algorithm’s performance.”

According to Sholingar, these efforts allowed their algorithm to detect objects more accurately beyond 200 meters, something only made possible by their use of synthetic data.

While many of these developments are due to better visual fidelity and photorealism, Sholingar also stressed this is only one aspect of what makes capable real-world simulations.

“There is a tendency to get caught up in how beautiful the simulation looks since we see these visuals, and it’s very pleasing. What really matters is how the AI algorithms perceive these pixels. But beyond the appearance, there are at least two other major aspects which are crucial to mimicking reality in a simulation.”

First, engineers need to ensure there is enough representative content in the simulation. This is important because an AI must be able to detect a diversity of objects in the real world, including pedestrians with different colored clothes or cars with unusual shapes, like roof racks with bicycles or surfboards.

Second, simulations have to depict a wide range of pedestrian and vehicle behavior. Machine learning algorithms need to know how to handle scenarios where a pedestrian stops to look at their phone or pauses unexpectedly when crossing a street. Other vehicles can behave in unexpected ways too, like cutting in close or pausing to wave an oncoming vehicle forward.

“When we say realism in the context of simulation, it often ends up being associated only with the visual appearance part of it, but I usually try to look at all three of these aspects. If you can accurately represent the content, behavior, and appearance, then you can start moving in the direction of being realistic,” he said.

It also became clear in our conversation that while simulation will be an increasingly valuable tool for generating synthetic data, it isn’t going to replace real-world data collection and testing.

“We should think of simulation as an accelerator to what we do in the real world. It can save time and money and help us with a diversity of edge-case scenarios, but ultimately it is a tool to augment datasets collected from real-world data collection,” he said.

Beyond Omniverse, the wider industry of helping “things that move” develop autonomy is undergoing a shift toward simulation. Tesla announced they’re using similar technology to develop automation in Unreal Engine, while Canadian startup, Waabi, is taking a simulation-first approach to training their self-driving software. Microsoft, meanwhile, has experimented with a similar tool to train autonomous drones, although the project was recently discontinued.

While training and testing in the real world will remain a crucial part of developing autonomous systems, the continued improvement of physics and graphics engine technology means that virtual worlds may offer a low-stakes sandbox for machine learning algorithms to mature into functional tools that can power our autonomous future.

Image Credit: Nvidia

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Atlassian has developed a new search tool  —  Atlassian  Rovo — that can surface data from a variety of third-party apps. Rovo, currently in preview, provides a chatbot interface to help workers access information across their organization,and even decipher workplace jargon. 

“Atlassian Rovo is designed to unlock knowledge discovery and accelerate action across the organization,” said Jamil Valliani, head of product AI at Atlassian. “Think of Rovo as a large knowledge model for your company that allows team members to move and act faster.”

There are three key elements to Rovo: Search, Chat, and Agents. 

Rovo Search expands on existing search functions in Atlassian apps, with the ability to access documents from a range of external sources in addition to data held in tools such as Jira and Confluence. This means surfacing information in third-party productivity tools such as Slack, Microsoft Teams, Figma and GitHub, as well as file storage platforms such as Google Drive and Microsoft SharePoint.  

Atlassian Rovo can help surface information and answer questions about technical jargon based on access to coporate documents.

Atlassian

Atlassian also plans to let Rovo access data from in-house applications, including finance or HR apps.

One way to access Rovo search is via the search bar in various Atlassian apps. A list of search results containing related documents is presented, as well as more detailed “knowledge cards” that present information relating to a project, for instance, or a team. Here, the knowledge card might contain links to related files, as well as information on project status, listed contributors, and more.  

Rovo Search can also learn and explain unfamiliar jargon specific to an industry or individual business based on the organization’s documents. This enables Rovo to provide definitions for acronyms and terms that appear within a Confluence document, for exam-le. It’s proved to be a popular feature, Atlassian said, and is used by three-quarters of staff testing Rovo. 

A semantic search function helps teams “connect with what they are looking for,” said Julie Mohr, principal analyst at Forrester, as well as “knowledge they didn’t know existed.” This helps employees “work the way they want to work with a comprehensive set of expressive tools — from video to pages, structured and unstructured, it is all knowledge,” she said. 

Another way to search for information is via Rovo Chat. Similar to the conversational interfaces in Microsoft’s Copilot, OpenAI’s ChatGPT, and others, the chatbot responds to user questions in natural language, with answers based on data held in documents across an organization. Links are provided to the original source.

Atlassian Rovo Chat uses genAI and a chatbot interface with natural language processing to deliver information to users.

Atlassian

Another aspect of Rovo that relies on generative AI is the addition of workflow automation “agents.” Accessible via the Rovo Chat sidebar, the Rovo Agents are tailored to a specific task. For instance, Rovo Agents can be designed to generate and review marketing content, collate feedback from various sources, or streamline processes such as clearing up Jira backlogs and organizing Confluence pages. 

Users can create their own Rovo Agents using a no-code text interface or Atlassian’s Forge app development platform. Atlassian expects there will be around 20 pre-built agents available when Rovo launches. 

Canva’s design software is an example of an Atlassian partner building its own agent. “There’s going to be a Canva agent that helps with generating simple artwork for social media posts, things that you don’t need an expert designer to do,” said Valliani. 

Atlassian Rovo agents are tailored to specific tasks and can include agents built by third-party vendors.

Atlassian

“Generative AI  has so much potential in knowledge management,” said Mohr. “Atlassian is taking the power of Confluence and improving those capabilities with sound knowledge management practices combined, with new features that take advantage of genAI.”

Making it easier to access information across an organization also means there’s potential for users to access sensitive documents. Atlassian said Rovo will respect permissions around the content it has access to, with restricted data in third-party apps remaining private.

As with any API or integration, businesses should assess risks when connecting to external systems and ensure that adequate permissions are in place around documents, said Mohr. But those risks shouldn’t put businesses off from widening access to information held across their organization, she said.

“[T]hink of all the undiscovered knowledge, all the ideas that are locked up in folders and private stores,” said Mohr. “There is a cultural change that needs to take place, where people as well as the systems understand the value of free access to knowledge.” 

Opening employee access to a wider range of information can “empower more collaboration and learning,” said Mohr, with businesses able to restrict access “when it is genuinely needed for regulatory or compliance purposes.”

Atlassian customers can join a waitlist to access Rovo in beta. The general availability date hasn’t yet been announced. 

Rovo will be sold as an add-on alongside cloud editions of Atlassian products, with a flexible pricing model based on unique users. More details will be announced at the general availability launch.

Atlassian, Chatbots, Generative AI, Productivity Software, Vendors and Providers

Hot on the heels of Jamf’s introduction of new compliance tools for Apple admins, Mosyle and Fleet have rolled out improvements to their own competing device management offerings.  

What all these solutions have in common is that they meet an enterprise market that is rapidly adopting Macs, iPads, and iPhones, prompting fast-paced service enhancements across the Apple device management industry.

Like art, business technology also seems set on reflecting life. “Apple Macs, iPhones, and iPads are now commonly used as productive business tools in enterprises across many industries,” said Phil Hochmuth, IDC research vice president for endpoint management and enterprise mobility. Apple itself understands this, and now offers a wide array of services and training for business users.

The growing Apple enterprise pie

“IT organizations are increasingly required to deliver a cohesive Apple experience to end users across multipole device types, while providing deep integration to enterprise applications and IT infrastructure platforms,” Hochmuth said. “Apple-focused UEM and device management tools can help organizations keep these devices managed and secure.”

That’s true, and it’s also correct to believe vendors in the space are competing for a big slice of the IT spending pie. IDC predicts global IT and business services revenues will reach $1.28 trillion this year; while Apple device management and security solutions represent only a few drops of that spend, those drops are worth a lot. With increasing focus on managed device, zero-trust security, managed services, and integrated administration tools, Apple’s MDM partners can’t resist the opportunity.

That’s the situation we find ourselves in, so what’s new from Mosyle and Fleet?

Mosyle aims at managed service providers

Unashamedly aimed at managed service providers (MSPs), Mosyle Fuse MSP combines five management and security tools in one bundle. The tools include those for device management, endpoint security, internet privacy and security, identity, and application management.

In use, these tools let MSPs set and automatically apply policies across all their customers from one place. Mosyle also says it’s possible to break out individual service components when required — so VPN profiles can be managed separately from Apple Push Certificates, for example. Billing has also been simplified to ensure MSPs only pay for licenses their customers actually use. More information on the combined package is available here.

“Mosyle is committed to growing the Apple ecosystem and helping MSPs scale,” Alcyr Araujo, founder and CEO at Mosyle, said in a statement. “Mosyle Fuse MSP is designed to accomplish both — delivering an automated and scalable platform for MSPs that offers comprehensive security and management for Apple-focused customers across the globe.”

Fleet simplifies multi-platform fleet management

A relatively new entrant to the market, Fleet’s USP sits in its open-source roots and its positioning as a device management solution for multiple operating systems, including Apple, Windows, and Linux.

Fleet’s Maintenance Window feature is designed to take the pain away from system upgrades on managed devices by figuring out the optimal time to apply updates through analysis of an employee’s own work calendar. The idea here is that when IT pushes an update at their fleet — Apple, Windows, or Linux — the system will not interrupt workflow or tie a computer up at the worst possible time.

That means your computer won’t restart to install something in the middle of a meeting or just before an important client appointment. “We’ve heard the complaints loud and clear and are doing something about one of the biggest problems in workplace productivity — disruptions caused by forced, unplanned OS updates,” said Fleet CEO Mike McNeil.

Please follow me on Mastodon, or join me in the AppleHolic’s bar & grill and Apple Discussions groups on MeWe.

Apple, iOS, Mobile Device Management, Mobile Security

Issue now resolved and isn't thought to be the work of criminals

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The latest release of Debian , one of the oldest and most trusted distributions within the Linux ecosystem, redefines security, stability, and innovation in open-source OSes. As security practitioners and Linux administrators, we always seek stable and innovative operating systems that can meet our needs while keeping our systems secure.

The Ubuntu security team has recently discovered and addressed multiple vulnerabilities in the Apache HTTP Server. The vulnerabilities affected several versions of Ubuntu and could potentially lead to server disruption and injection of malicious code.

Microsoft vydal novou velkou aktualizaci 2404.23 v září 2019 pod licencí SIL Open Font License (OFL) zveřejněné rodiny písma Cascadia Code pro zobrazování textu v emulátorech terminálu a vývojových prostředích.

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