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Hands on The Debian 11 is the venerable Debian Project's first new release in more than two years, nicknamed "Bullseye" after the Toy Story character and supplanting Debian 10 "Buster" (all Debian releases bear names from the kids' film).

Since Debian is the source from which dozens of other distros draw, notably Ubuntu, its major updates are well worth paying attention to, even if you aren't a Debian user. If you are a Debian user and you've been patiently waiting for an updated kernel to work with all the latest hardware, I have good news, the 5.10 LTS Linux kernel is here. More on that in a minute.

First, for the Linux newcomers, it might help to understand why Debian only releases new versions every couple of years when most popular distros crank out several new versions each year.

Debian is a huge, sprawling project and probably has more moving pieces than just about any other software project around. That's part of the reason Debian releases don't come along very often it takes a long time to get that many ducks in a row.

The other reason Debian is slow to change is that it's supposed to be rock solid and reliable. In my experience at least it is exactly that. I've been running Debian on servers for well over a decade now and have never had an update break something. Never. There isn't a single other piece of software I could say that about.

Still, Debian's infrequent updates are an oddity in a software world obsessed with "moving fast and breaking things" as some like to say. Debian has a reputation for being a good choice for servers, but a little boring and behind the times on the desktop. That hasn't been true for a while now thanks to Flatpaks and Snap packages, but it's a reputation that lingers.

For many users, myself included, Debian is like a rock in an ever-swirling sea of updates. I recently rejoined the league of Debian desktop users after a few years in the Arch camp. While I still think Arch is a fine distro, I have reached an age where I have better things to do than look after my laptop. I want to install a system and not think about it again for at least five years. This is the use case where Debian excels and Debian 11 is no exception. It's been solid and "just works" since the RC 1 release I installed earlier this year.

Debian 11 ships with the shiny new Linux 5.10 LTS kernel, which happened to arrive just in time to fit into this release cycle. The 5.10 kernel, which will also be part of the next version of Android, will be supported until 2026.

The 5.10 kernel is notable for some speed improvements for the ext4 file system (the default file system in the Debian installer), as well as significant improvements to some less-common chip architectures. Debian calls itself "the universal operating system" and indeed its chip architecture support is far broader than most distros. That makes it a popular base for developers of everything from embedded devices to auto-based systems.

It's also worth noting, given that just about everyone else has abandoned it, Debian will still support i386. At this point, if you have a 32-bit machine still around, Debian is likely your best option for getting a well-supported system.

Along with the improvements mentioned above come the usual slew of kernel updates and hardware drivers to support new devices, including kernel-level support for exFAT filesystems. You no longer need to install Fuse just to mount a Windows drive. If you're curious to know the finer details of everything new in this kernel, you can read through the changelog.

Debian's repositories are legendarily massive and they just keep growing. Bullseye boasts 13,370 new packages, which brings the total to over 57,703 packages. Well over half of all that software has been updated for this release, over 35,532 packages. That's obviously beyond the scope of what I could begin to cover here, beyond the scope of what anyone can cover anywhere really, which is why I say Debian's scope is staggering.

The default desktop

Among the highlights are some important changes to the most popular desktops and applications, as well as some significant updates to the server-side packages sysadmins rely on.

The desktop selection is larger with this release. From the installer you can choose Gnome (which serves as the default), Xfce, KDE, Mate, LXQt, and LXDE. If you opt for a minimal installation, you can then later install a number of other desktops, including my favourite, Sway. Most desktops are at or very near their latest release.

The same is true of major desktop apps like LibreOffice, which has been updated to version 7.0, and GIMP, which is now at 2.10.22. That said, Debian is not the distro to look to if you want the latest release from official packages. It might be pretty up to date for the release, but it won't stay that way. The solution is to use Flatpaks and Snap packages for the applications you want to keep polished.

Thanks to the fragmentation of apps across the Flatpak and Snap ecosystem, you'll probably want both installed. Fortunately both are dead simple to set up in Debian Bullseye and I had no trouble installing Flatpaks of Darktable, GIMP, Inkscape, Kdenlive, and other apps that I like to keep up to date.

While Flatpaks are nothing new, I think it's worth pointing out that they have largely delivered on that promise of providing up-to-date packages even on distros like Debian. Flatpaks (and Snaps) largely eliminate the old "it's so out of date" argument against using Debian on the desktop.

Another nice new feature for desktop users is support for driverless printing and scanning in CUPS and SANE. If you've got a relatively new printer or scanner, it should "just work" in this release. I don't have a scanner to test with, but Debian had no trouble connecting to my Brother printer and didn't require installing any drivers (which often are not free software).

The CUPS support works whether your printer is on the network or plugged in via USB, thanks to the new package ipp-usb, which uses the vendor-neutral IPP-over-USB protocol. This means your printer is treated as a network device, and driverless printing works even if it's actually connected via USB.

While the desktop improvements are welcome, the vast majority of Debian 11 installs will probably be on the server. There's plenty new for sysadmins in this release as well.

Debian's various development stacks have been updated, with most languages moving to their latest stable releases. Python is at 3.9, PHP 7.4, and the GNU compiler gets bumped to 10.2. Also notable, the PostgreSQL database has jumped from version 11 to version 13. Python 2.7, which reached end-of-life status some time ago is still around in some form for packages that haven't been upgraded but it isn't supported in any way.

Sysadmins will want to look over the release notes as there are a few potential gotchas among the new features in this release. Some changes to systemd especially are worth studying before you upgrade any mission critical servers. Debian developer Michael Prokop has a good overview of what's changed in Debian 11 from a sysadmin point of view.

As with any new Debian update quite a few things have been removed too. This time around some 7,278 packages have been dropped for one reason or another. Two that caught my eye were Chef, which has been dropped over trademark issues, and rsnapshot, a handy wrapper around rsync, which was removed because it's no longer maintained upstream.

I made the jump to Debian 11 several months ago when the first release candidate arrived and I've had no issues on the desktop. Debian 11 has been a stable, dependable system even pre-release. If you're already happy with your distro of choice, there may not be a lot to tempt you into Debian. Ubuntu, which is based off Debian's testing release, remains a friendlier distro for many since it makes installing any non-free software components much easier than Debian.

If you're already using Debian then you already know any new release is well worth upgrading to. The Debian Project has a good overview of how to upgrade from Debian 10 Buster to Debian 11 Bullseye, or, if you'd like to start fresh, you can always grab the live CD to do a clean installation.

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Dependable Debian is like a rock in a swirling gyre of 'move fast and break things', and version 11 is no different - The Register

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Machine programming, which automates the development and maintenance of software, is becoming supercharged by AI. During its Build developer conference in May, Microsoft detailed a new feature in Power Apps that taps OpenAIs GPT-3 language model to assist people in choosing formulas. Intels ControlFlag can autonomously detect errors in code. And Facebooks TransCoderconverts code from one programming language into another.

The applications of computer programming are vast in scope. And as computers become ubiquitous, the demand for quality code draws an ever-growing number of aspiring programmers to the profession. After years of study to become proficient at coding, experts learn to convert abstracts into concrete, executable programs. But they spend the majority of their work hours not programming. According to a study from the University of Cambridge, at least half of developers efforts are spent debugging, which costs the software industry an estimated $312 billion per year.

AI-powered code suggestion and review tools promise to cut development costs substantially while allowing coders to focus on more creative, less repetitive tasks, according to Justin Gottschlich, principal AI scientist at Intels machine programming division. Gottschlich is spearheading the work on ControlFlag, which fuses machine learning, formal methods, programming languages, and compilers to detect normal coding patterns, identifying abnormalities in code that are likely to cause a bug.

Prior to machine learning- or AI-based programming systems, programmers had dozens perhaps hundreds of tools to help them be more productive, produce code with fewer logic errors, improve the softwares performance, and so on. However, nearly all of these systems were rules-based,' Gottschlich told VentureBeat via email. While useful, rules-based systems are inherently limited in scope by the rules that they have been programmed into them. As such, if new kinds of things occur, the systems would need to be updated by humans. Moreover, these rules-based systems have always been prone to human error in creating the rules encoded in them. For example, programmers may accidentally create a rule to find a certain type of bug, but incorrectly define the rules to find it. This hidden bug in the rules system could go undetected forever.

Gottschlich asserts that AI-based systems offer benefits over the rules-based systems of yesteryear because AI can learn on its own in an unsupervised fashion, enabling it to draw on massive code databases. With unsupervised learning, an algorithm is fed unknown data for which no previously defined labels exist. The system must teach itself to classify the data by processing it to learn from its structure.

For example, ControlFlag was trained on over 1 billion unlabeled lines of code to identify stylistic variations in programming language. As for TransCoder, it learned to translate between C++, Java, and Python by analyzing a GitHub corpus containing over 2.8 million repositories. Microsoft trained a bug-spotting program on a dataset of 13 million work items and bugs from 47,000 developers across AzureDevOps and GitHub repositories. And code review platform DeepCodes algorithms were taught using billions of lines of code captured from public open source projects.

Theres a difference between AI-powered coding tools that can generate code from whole cloth versus augment a programmers workflow, of course. The latter is more common. Startups such as Tabine (formerly Codota) are developing platforms that suggest and autocomplete scripts in Python, C, HTML, Java, Scala, Kotlin, and JavaScript. Ponicode taps AI to check the accuracy of code. Intels Machine Inferred Code Similarity engine can determine when two pieces of code perform similar tasks, even when they use different structures and algorithms. And DeepCode offers a machine learning-powered system for whole-app code reviews as does Amazon.

Currently, we see a lot of AI-powered assistants, enabling software engineers to gain velocity and accuracy in their work. And the reason for the availability of more assistant tools than automation tools is that AI-powered automation has simply not yet reached the level of accuracy required, Ponicode CEO Patrick Joubert told VentureBeat. Our industry is still young, and even though we can already see the potential of automation with AI based code generators, we have to acknowledge that automatically generated code is still pretty unmaintainable and the overall quality is not meeting the right standards yet. While some engineers are working on the future of AI powered automation, my team and I, along with many other stakeholders, are dedicated to creating tools that can be used today. Within a few years I believe there will be enough tools to cover all steps of the development lifecycle.

For Joubert, the most intriguing categories of machine programming tools today are autocompletion and code analysis. Autocompletion systems like Tabnine and Kite employ AI to analyze semantics and make sense of code, autocompleting functions with a sense of the codes semantic content and purpose. As for code analysis tools like Snyk and DeepCode, theyre dedicated to finding vulnerabilities in the code and suggesting actions to resolve them often with surprising speed and precision.

When we see the numerous leaks and bugs from any software, including the ones built by leading multinationals, we can agree that [the software] industry has not yet matured. AI-powered coding tools are mostly meant to enhance the developer experience and empower them, thanks to greater velocity and greater efficiency, Joubert added. Behind these developer-focused benefits, I believe we are on the way to allowing software engineers to build industrial-grade software, where quality, innovation, and speed are reached systematically Autocompletion [in particular is] enabling software engineers to focus on the most complex part of their codebase and removing the burden of manually writing long strings of code.

Despite their potential, both AI-powered code generators and coding assistance tools have their limitations. For example, while GitHub has over 250 million code repositories alone, most of the data is unannotated. Theres only a few examples that describe precisely what the code does, posing a particular challenge for any system that cant learn from unlabeled data.

In an effort to address this, IBM recently released CodeNet, a 14-million-sample labeled dataset with 500 million lines of code written in 55 programming languages. The company claims that the rich annotations added to CodeNet make it suitable for a diverse set of tasks as opposed to other datasets specialized for specific programming tasks. Already, researchers at IBM have conducted several experiments with CodeNet, including code classification, code similarity evaluation, and code completion.

It is my speculation that in the next decade, code semantics understanding systems are likely to be one of the most important areas of machine programming in the coming decade, Joubert said. It depends on the domain the machine programming system is being applied to. For small programs, such as unit tests or regression tests, full program synthesizers are a reality today. Yet, for larger programs, its currently computationally intractable for machine programming systems to generate the potential thousands or millions of lines of code without the assistance of a programmer.

Boris Paskalev, the cofounder and CEO of DeepCode, calls creating a couple of lines of code with AI more of a toy than a productivity breakthrough. While techniques like natural language processing work well with text because theres fixed limits on the words and syntax that need to be understood, code isnt the same, he argues.

Since there are no formal rules for software development, [programming] is an art that requires a complete understanding of code and a developers intentions to produce something that works as expected without bugs, Paskalev told VentureBeat. As far as weve come in using machine learning and neural networks for code, were still only in the invention of the wheel phase machine learning is already proving to be very useful for code, but only after it goes through a semantic machine learning-representation of the code: making sure all semantic facts, variables, transitions, and logical interrelations are clearly represented and considered by the learning model.

To Paskalevs point, recent studies suggest that AI has a ways to go before it can reliably generate code. In June, a team of researchers at the University of California at Berkeley, Cornell, the University of Chicago, and the University of Illinois at Urbana-Champaign released APPS, a benchmark for code generation from natural language specifications. The team tested several types of models on APPS, including OpenAIs GPT-2, GPT-3, and an open source version of GPT-3 called GPT-Neo. In experiments, they discovered that the models could learn to generate code that solves easier problems but not without syntax errors. Approximately 59% of GPT-3s solutions for introductory problems had errors, while the best-performing model GPT-Neo attained only 10.15% accuracy.

When generating code from whole cloth, there are typically challenges around both specifying the intent and consuming the results, Tabine CEO Dror Weiss told VentureBeat. User intent can be specified in natural language by providing examples, writing code in a higher-level language, or in other means. But in most cases, this intent does not provide a full specification of the desired behavior. Also, the generated code may be following different route than what the developer had in mind. As such, it may be challenging for the developer to judge whether the code performs the desired operation exactly.

Facebook AI researchers Baptiste Rozire and Marie-Anne Lachaux, who worked on TransCoder, agree with Tabines assessment. It is inherently difficult to generate correct code from unspecific natural language problem descriptions that could correspond to several different code snippets. An easier task would be to generate code from an input that is more specific and closer to the output code, like pseudo-code or code written in a different language, they told VentureBeat. A huge obstacle to the adoption of methods generating large amounts of code without human supervision is that they would need to be extremely reliable to be used easily. Even a tool that could generate methods with 99% accuracy would fail to generate a working codebase of hundreds of functions. It could speedup the code generation process but would still require human testing and intervention.

Rozire and Lachaux also point out that tasks around code generation are generally much harder than classification tasks because the model has a lot of freedom and can create many different outputs, making it hard to control the correctness of the generation. Moreover, compared with natural languages, programming languages are very sensitive to small errors. A one-character difference can change the semantics of the code and make the output faulty.

Current machine learning algorithms may not be able to generalize well enough to different problems to match human performance for coding interviews without larger datasets or much better unsupervised pre-training methods, Rozire and Lachaux said.

Paskalev thinks itll be at least five to ten years until natural language processing enables developers to create meaningful components or even entire apps from a simple description. But Gottschlich is more optimistic. He notes that AI-powered coding tools arent just valuable in writing code, but also when it comes to lower-hanging fruit like upgrading existing code. Migrating an existing codebase to a modern or more efficient language like Java or C++, for example, requires expertise in both the source and target languages and its often costly. The Commonwealth Bank of Australia spent around $750 million over the course of five years to convert its platform from COBOL to Java.

Deep learning already enables us to cover the smaller tasks, the repetitive and redundant ones which clutter a software engineers routine. Today, AI can free software engineers from tedious tasks slowing them down and decreasing their creativity, Gottschlich said. The human mind remains far superior when it comes to creation, innovation, and designing the most complex parts of our softwares. Enabling them to increase velocity in these exciting, high added value parts of their work is, I believe, the most interesting way to leverage the power of machine learning today.

Joubert and Weiss say that the potential business value of machine programming also cant be ignored. An estimated 19% to 23% of software development projects fail, with that statistic holding steady for the past couple of decades. Standish Groupfound that challenged projects i.e., those that fail to meet scope, time, or budget expectations account for about 52% of software projects. Often, a lack of user involvement and clear requirements are to blame for missed benchmarks.

We see a great number of new tools using AI to enhance legacy code and help existing assets reach industrial-grade standards. We can elevate developer legacy code management workflows and be part of reducing the hefty level of technical debt built up over the past 50 years in the software industry, Joubert said. The days when developers had to write and read code line by line are gone. Im excited to see how the other steps in the software development lifecycle are going to be transformed and how tools will reach the same level that Kite or Snyk have attained. Leveraging AI to build efficient, one-purpose, tested, secure, and documented code effortlessly is going to profoundly change the way software companies can create incremental value and innovation.

From Weiss perspective, AI-powered coding tools can reduce costly interactions between developers like Q&A sessions and repetitive code review feedback while shortening the project onboarding process. [These] tools make all developers in the enterprise better. They take the collective code intelligence of the organization and make it available, during development time, to all developers. This allows any developer on the team to punch above their weight, he said.

For AI coverage, send news tips toKyle Wiggers and be sure to subscribe to the AI Weekly newsletterand bookmark our AI channel,The Machine.

Thanks for reading,

Kyle Wiggers

AI Staff Writer

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AI Weekly: The promise and limitations of machine programming tools - VentureBeat

New York, US, June 17, 2021 (GLOBE NEWSWIRE) -- Market Overview:The global Reference Check Software Market generated a revenue of USD 216.1 Million in 2018 and is anticipated to touch USD 360.3 million by 2025 at a 7.6% CAGR, reveals the current Market Research Future (MRFR) report. Global Reference Check Software Market research report information By Type, Application and Region - Forecast till 2027.

Market Scope: Reference check software assists organizations for automating the process to contact and question reference of job applicants. Such software help organizations to get potential hire references quickly and efficiently. Besides, it is used by talent recruiters and hiring managers to find the background of a specific candidate and assist them to decide whether to hire them or not. With the help of this software, there will not be any need for manual phone calls, email conversations, or other digital method that saves time of companies and increase the demand for reference check software solutions. Some of the benefits of using this software include verifying the informations truthfulness offered by the applicant, check whether the applicant possesses any criminal history or background, check whether the applicant possesses the right skills set for the job, help in identifying the applicants behavior during their time working with the earlier employers, and help in predicting the success in ones new job.

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Dominant Key Players on Reference Check Software market covered are:

Market USP exclusively encompassed:Market DriversAs per the MRFR report, there are numerous factors that are propelling the reference check software market share. Some of these entail the increasing need for simplifying reference checking processes, the integration of reference checking with applicant tracking systems, use of machine learning technology, rising adoption of reference checking software by various industry verticals like automotive, retail, healthcare and BFSI, and adoption of new technologies like artificial intelligence in reference software. The additional factors fuelling market growth include the increasing use of reference check software among large enterprises and small and medium enterprises to verify the background of candidates, and employment opportunities in different end use industries.

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Market RestraintsOn the contrary, inaccuracies in reference check software may limit the global reference check software market growth over the forecast period.

Segmentation Of Market Covered In The Research:The MRFR report throws light on an inclusive analysis of the reference check software market based on application and type.

By type, the global reference check software market is segmented into web-based and cloud-based.

By application, the global reference check software market is segmented into small and medium enterprises (SMEs), and large enterprises.

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Regional Analysis Geographically, the global reference check software market is bifurcated into Europe, the Asia Pacific (APAC), the Middle East and Africa (MEA), North America, and South & Central America.

North America to Steer Reference Check Software Market

Europe to Hold Second-Largest Share in Reference Check Software Market.

APAC to Have Significant Growth in Reference Check Software Market

COVID-19 Analysis The COVID-19 outbreak has resulted in a dramatic loss of human lives across the globe and presents an unprecedented challenge to the globe of work. Job loss is amid the most severe instant impact of this crisis. This outbreak and the subsequent nationwide lockdown have altered the landscape of the employment sector. Either employees were laid off or were ordered leave without pay. Most companies across the globe had fired a huge number of employees due to the shutdown of their operations. It is, however, estimated that in the post COVID-era, companies would largely adopt reference check software to make recruitment easy and also hire potential employees. This will also save resources and time for companies that will improve efficiency. This, in turn, may have a positive impact on the market growth.

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Reference Check Software Market is Anticipated to Touch USD - GlobeNewswire

New York, USA, June 15, 2021 (GLOBE NEWSWIRE) -- According to a recent report offered by Research Dive, theglobal employee communication software marketis speculated to garner a revenue of$1,605.3 million by 2026, increasing from a market share of$527.0 million in the year 2018, at a15.2% growth rate during 2019-2026timeframe. The employee communication software delivers and personalizes internal mode of communications such as blogs, newsletters, feeds, and business updates. It provides options to company heads to segregate communication via aspects such as location, department, and position.

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The market report includes numerous facets such as the Covid-19 impact, current driving factors & restrains, forecasts, and trends. The market report is based on primary and secondary research methodologies which include acquiring information from authentic websites, conducting interviews, and reading journals or periodicals. The market is predicted to experience a growth during the Covid-19 pandemic due to the usage of employee communication software by companies and businesses. This market was recorded to the highest growing in the previous years as well.

Effect of Covid-19 Crises on the CAGR Figures

The global market was recorded to enhance at a growth rate of 10.8% before the onset of the coronavirus outbreak. However, the market is estimated to display a significant rise with a healthy CAGR of 15.2% during the Covid-19 emergency.

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As the viral infection of the coronavirus spreads through direct contact with a person, working remotely is considered as the safest option for now. Several businesses are observing work-from-home policy to decline the infection rate of the deadly virus. Therefore, a majority of the organizations are using virtual techniques such as employee communication software for information transfer, communication, and smooth work process. These aspects are boosting the growth of the global market during the coronavirus outbreak.

Factors Affecting the Revenue Figures during the Covid-19 Crises

The global market was registered to hold a market share of $649.1 million before the outbreak of the deadly Covid-19 disease. However, during the pandemic the market garnered $695.7 million in 2020. The market upsurge is due to the initiatives taken up by the several governments to help and support organization during the pandemic. For example, the Japanese Ministry for Economy, Trade, and Industry declared funding for the small and medium enterprises. The funding will be free from interest and contains non-principal option for up to 5 years. These initiatives are projected to bolster the global market during the outbreak of the Covid-19.

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Future Scope of the Market

The global market is speculated to experience notable growth during the pandemic and is expected to surge further during the analyzed timeframe as well. The employee communication software is made to transfer data between organizations and employees. Also, the software is helping businesses in improving productivity rates and performance of the employees, thus contributing to the market growth in the forthcoming years. Furthermore, it offers employee information to the management for accomplishments related to projects, challenges faced in the projects, and solutions to the obstacles. All these aspects are projected to fuel the market growth during the forecasted timeframe. However, heavy expenses related to the installation of the employee communication products are estimated to hamper the market in the coming years.

Apart from this, the major players of the employee communication software market are

These prominent players of the market are concentrating on several business strategies like investments in the research & development and technological advances for the market growth.

For example, in June 2021, Userful Corporation, a provider of the software defined AV over IP platform, introduced novel corporate signage application for the visual networking platform. This new launch will aid in communication between departments, heads, and employees efficiently.

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Work-From-Home Trend to Propel the Growth of the Global - GlobeNewswire

China will on Thursday launch a crew of three on a mission to start assembling the nations first space station. It's China's seventh crewed mission in 18 years.

State-run media has named the three taikonauts as Nie Haisheng, Liu Boming and Tang Hongbo. Nie will command the mission and make his third journey into space.

The crew will stay aloft for three months and work on Chinas Tiangong space station, the first module of which named Tianhe launched in April 2021. It caused a kerfuffle when bits of the rocket that carried it made an uncontrolled re-entry that just missed the Maldives.

In late May, China launched the Tianzhou-2 rocket carrying propellant, food and other supplies for this weeks mission. The rocket docked with Tianhe, and also sent home debris that spent two weeks on its way back to Earth before a few fragments are thought to have reached the Pacific Ocean.

Tianhe is 16.6 metres long and 4.2 metres in diameter, and houses all the life support kit that the crew will need to live in space for three months. China plans to bolt more modules onto the space station and this mission has brought another module with it.

The crew's chores will include starting operations of on-board life support systems, and space walks to configure the station.

Chinas space station construction plan calls for a dozen launches, four of them crewed, stretching into 2022.

The mission logo for the Shenzhou-12 mission that will take three Chinese astronauts to space this week. Click to enlarge

Tiangong which translates as Heavenly Palace will be about one fifth the size of the International Space Station, and have an expected useful life of ten years.

Continued here:
China launching first crew to its own space station on Thursday - The Register