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Artificial Intelligence automation may impact two-thirds of jobs, says Goldman Sachs – CNBCTV18

At the speed, at which the advancement of artificial intelligence (AI) is being witnessed, it has the potential to significantly disrupt labour markets globally. And this is certified in the research done by Goldman Sachs.

As per that research, roughly two-thirds of current jobs are exposed to some degree of AI automation in the US and the European Union.

Administrative and legal are those sectors which can see the maximum impact. Goldman Sachs says 46 percent of administrative jobs and 44 percent of legal jobs can be substituted by AI. The ones with low exposures are physically-intensive professions such as construction at six percent and maintenance at four percent.

While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. But at the same time, its being seen as one of the tools to enhance economic growth.

As per Goldman Sachs research, AI could eventually increase annual global GDP by seven percent over a 10-year period. A combination of significant labour cost savings, new job creation and higher productivity for non-displaced workers raises are seen as the areas that will boost the growth.

For the US, generative AI is seen raising annual US labour productivity growth by just under 1.5 percentage points over a 10-year period.

McKinsey Research too has done a survey of more than 2,000 work activities across more than 800 occupations. It shows that certain categories of activities are more easily automatable than others. They include physical activities in highly predictable and structured environments, as well as data collection and data processing.

These account for roughly half of the activities that people do across all sectors. And, it believes, nearly all occupations will be affected by automation, but only about five percent of occupations could be fully automated by currently demonstrated technologies.

Although the size of AIs impact will ultimately depend on its capability and adoption timeline, both remain uncertain at this point.

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Robot recruiters: can bias be banished from AI hiring? – The Guardian

A third of Australian companies rely on artificial intelligence to help them hire the right person. But studies show its not always a benign intermediary

Sun 26 Mar 2023 10.00 EDT

Michael Scott, the protagonist from the US version of The Office, is using an AI recruiter to hire a receptionist.

Guardian Australia applies.

The text-based system asks applicants five questions that delve into how they responded to past work situations, including dealing with difficult colleagues and juggling competing work demands.

Potential employees type their answers into a chat-style program that resembles a responsive help desk. The real and unnerving power of AI then kicks in, sending a score and traits profile to the employer, and a personality report to the applicant. (More on our results later.)

This demonstration, by the Melbourne-based startup Sapia.ai, resembles the initial structured interview process used by their clients, who include some of Australias biggest companies such as Qantas, Medibank, Suncorp and Woolworths.

The process would typically create a shortlist an employer can follow up on, with insights on personality markers including humility, extraversion and conscientiousness.

For customer service roles, it is designed to help an employer know whether someone is amiable. For a manual role, an employer might want to know whether an applicant will turn up on time.

You basically interview the world; everybody gets an interview, says Sapias founder and chief executive, Barb Hyman.

The selling points of AI hiring are clear: it can automate costly and time-consuming processes for businesses and government agencies, especially in large recruitment drives for non-managerial roles.

Sapias biggest claim, however, might be that it is the only way to give someone a fair interview.

The only way to remove bias in hiring is to not use people right at the first gate, Hyman says. Thats where our technology comes in: its blind; its untimed, it doesnt use rsum data or your social media data or demographic data. All it is using is the text results.

Sapia is not the only AI company claiming its technology will reduce bias in the hiring process. A host of companies around Australia are offering AI-augmented recruitment tools, including not just chat-based models but also one-way video interviews, automated reference checks, social media analysers and more.

In 2022 a survey of Australian public sector agencies found at least a quarter had used AI-assisted tech in recruitment that year. Separate research from the Diversity Council of Australia and Monash University suggests that a third of Australian organisations are using it at some point in the hiring process.

Applicants, though, are often not aware that they will be subjected to an automated process, or on what basis they will be assessed within that.

The office of the Merit Protection Commissioner advises public service agencies that when they use AI tools for recruitment, there should be a clear demonstrated connection between the candidates qualities being assessed and the qualities required to perform the duties of the job.

The commissioners office also cautions that AI may assess candidates on something other than merit, raise ethical and legal concerns about transparency and data bias, produce biased results or cause statistical bias by erroneously interpreting socioeconomic markers as indicative of success.

Theres good reason for that warning. AIs track record on bias has been worrying.

In 2017 Amazon quietly scrapped an experimental candidate-ranking tool that had been trained on CVs from the mostly male tech industry, effectively teaching itself that male candidates were preferable. The tool systematically downgraded womens CVs, penalising those that included phrases such as womens chess club captain, and elevating those that used verbs more commonly found on male engineers CVs, such as executed and captured.

Research out of the US in 2020 demonstrated that facial-analysis technology created by Microsoft and IBM, among others, performed better on lighter-skinned subjects and men, with darker-skinned females most often misgendered by the programs.

Last year a study out of Cambridge University showed that AI is not a benign intermediary but that by constructing associations between words and peoples bodies it helps to produce the ideal candidate rather than merely observing or identifying it.

Natalie Sheard, a lawyer and PhD candidate at La Trobe University whose doctorate examines the regulation of and discrimination in AI-based hiring systems, says this lack of transparency is a huge problem for equity.

Messenger-style apps are based on natural language processing, similar to ChatGPT, so the training data for those systems tends to be the words or vocal sounds of people who speak standard English, Sheard says.

So if youre a non-native speaker, how does it deal with you? It might say you dont have good communication skills if you dont use standard English grammar, or you might have different cultural traits that the system might not recognise because it was trained on native speakers.

Another concern is how physical disability is accounted for in something like a chat or video interview. And with the lack of transparency around whether assessments are being made with AI and on what basis, its often impossible for candidates to know that they may need reasonable adjustments to which they are legally entitled.

There are legal requirements for organisations to adjust for disability in the hiring process, Sheard says. But that requires people to disclose their disability straight up when they have no trust with this employer. And these systems change traditional recruitment practices, so you dont know what the assessment is all about, you dont know an algorithm is going to assess you or how. You might not know that you need a reasonable adjustment.

Australia has no laws specifically governing AI recruitment tools. While the department of industry has developed an AI ethics framework, which includes principles of transparency, explainability, accountability and privacy, the code is voluntary.

There are low levels of understanding in the community about AI systems, and because employers are very reliant on these vendors, they deploy [the tools] without any governance systems, Sheard says.

Employers dont have any bad intent, they want to do the right things but they have no idea what they should be doing. There are no internal oversight mechanisms set up, no independent auditing systems to ensure there is no bias.

Hyman says client feedback and independent research shows that the broader community is comfortable with recruiters using AI.

They need to have an experience that is inviting, inclusive and attracts more diversity, Hyman says. She says Sapias untimed, low-stress, text-based system fits this criteria.

You are twice as likely to get women and keep women in the hiring process when youre using AI. Its a complete fiction that people dont want it and dont trust it. We see the complete opposite in our data.

Research from the Diversity Council of Australia and Monash University is not quite so enthusiastic, showing there is a clear divide between employers and candidates who were converted or cautious about AI recruitment tools, with 50% of employers converted to the technology but only a third of job applicants. First Nations job applicants were among those most likely to be worried.

DCA recommends recruiters be transparent about the due diligence protocols they have in place to ensure AI-supported recruitment tools are bias-free, inclusive and accessible.

In the Sapia demonstration, the AI quickly generates brief notes of personality feedback at the end of the application for the interviewee.

This is based on how someone rates on various markers, including conscientiousness and agreeableness, which the AI matches with pre-written phrases that resemble something a life coach might say.

A more thorough assessment not visible to the applicant would be sent to the recruiter.

Sapia says its chat-interview software analysed language proficiency, with a profanity detector included too, with the company saying these were important considerations for customer-facing roles.

Hyman says the language analysis is based on the billion words of data collected from responses in the years since the tech company was founded in 2013. The data itself is proprietary.

So, could Guardian Australian work for Michael Scott at the fictional paper company Dunder Mifflin?

You are self-assured but not overly confident, the personality feedback says in response to Guardian Australias application in the AI demonstration.

It follows with a subtle suggestion that this applicant might not be a good fit for the receptionist role, which requires repetition, routine and following a defined process.

But it has some helpful advice: Potentially balance that with variety outside of work.

Looks like were not a good fit for this job.

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UJ, TUT named hubs of Artificial Intelligence – SABC News

The University of Johannesburg (UJ) and the Tshwane University of Technology (TUT) have been named as hubs of Artificial Intelligence.

Artificial intelligence enables people to use smart devices to communicate with others and to explain their surroundings.

Some of the most popular digital smart assistant tools are Apples Siri, Alexa and Google Assistant.

Tools like these can be beneficial to people with disabilities where they can help them conquer daily challenges.

Vice-Chancellor of the Tshwane University of Technology, Tinyiko Maluleke says, It is of course a project that has been regarded as controversial by some, and somewhat art by others. When it comes to 4iR the appropriate slogan is feel it, it is here. It is in your office, your home, car, the train that you use, airplane, your wrist watch. It is in your fingers, the screen of the pupils of your eyes, it is in your brain.

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The Four Developments Propelling AI Forward: A Conversation With … – Crunchbase News

There are four distinct developments that propel artificial intelligence forward today, said Deep Nishar, a managing director at venture capital firm General Catalyst.

And Nishar would know.

The investor co-led the firms recent $350 million funding in Adept AI, a year-old company with a founding team that hails from OpenAI and Google Brain. The firm has also led investments in AI talent platform Eightfold AI and AI health care company Aidoc.

Grow your revenue with all-in-one prospecting solutions powered by the leader in private-company data.

While the promise of AI has percolated for decades, the sector has only heated up fairly recently with the launch of ChatGPT-3 in November and the fast iteration of GPT-4 last week.

ChatGPT is a meaningful step forward, said Nishar. More important than the technology, it has fired up the imaginations of nontechnical people. Its probably the fastest thing that ever got 100 million users using it all at once.

Nishar has been investing for the better part of eight years, and began his investing career at the SoftBank Vision Fund in 2015 where he led funding in AI hardware company SambaNova, and AI therapies company Deep Genomics. His career spanned across major technology companies. He joined Google in 2003 before it reached 200 employees and worked on advertising infrastructure, then the early mobile research which became Android. When storied investor Reid Hoffman reached out, he jumped to lead product and user experience at LinkedIn.

Nishar sees four distinct developments that propel artificial intelligence forward today.

The first is the proliferation of really good algorithms. The seminal paper on the transformer models which underlie GPT came out of Google in 2017. Today, Nishar said, Google and OpenAI account for close to 50% of these algorithms.

Vast amounts of data is needed to train these models, which the internet provides from text to speech to image and video.

The third part is computational power, which is getting better and better.

And finally, previously there were only a few hundred individuals from companies like DeepMind in the U.K., Google Brain, Facebook AI research and Apple who understood these models.

Now there are thousands of people who understand the math in these algorithms from both companies and universities.

One view is that any area that requires a lot of compute, and as a result of a lot of capital, becomes a harder venture investment, said Nishar.

Investors have thrown hundreds of millions at the leading companies building AI models which include OpenAI, Anthropic, Cohere, Adept AI, Inflection AI and Character.ai. These teams have come out of Google Brain and OpenAI, and have either launched or will be launching products in months to come, he said.

The technology stack that artificial intelligence developments impact is broad and mimics non AI companies, said Nishar.

They start with hardware chip companies like Nvidia, Cerebras, Graphcore and SambaNova to name a few. In the infrastructure software sector, companies like Anyscale compile the content and orchestrate the AI pipeline. This is followed by tools that help with training the data, from companies including Snorkel and Weights & Biases. And then come the algorithms and apps at the top of the stack.

And there are a whole host of investment opportunities up and down the entire stack.

We are trying to predict a surface area that has not been traversed before, Nishar said.

Illustration: Dom Guzman

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Artificial intelligence pays off when businesses go all in – MIT Sloan News

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About 92% of large companies are achieving returns on their investments in artificial intelligence, and the same percentage are increasing their AI investments. But what does it take for startups and early-stage companies to get to this point?

Thats a critical question, according to Sukwoong Choi, a postdoctoral scholar at MIT Sloan. AI utilization is tied to startups products and services. Its more directly relevant, he said.

In a newpaper, Choi and his co-authors find that firms need to be ready to make a significant investment in AI to see any gains, because limited AI adoption doesnt contribute to revenue growth. Only when firms increase their intensity of AI adoption to at least 25% meaning that they are using a quarter of the AI tools currently available to them do growth rates pick up and investments in AI start to pay off.

The paper was co-authored by Yong Suk Lee, Taekyun Kim, and Wonjoon Kim.

Here are three things companies should know about investing in AI.

The researchers surveyed 160 startups and small businesses in South Korea about their use of AI technologies such as natural language processing, computer vision, and machine learning. Of the firms included, 53% were in technology-related fields (namely software, pharma, and mobile computing), and 54% had adopted AI to some degree.

The survey was administered to companies created before 2015, as these firms were founded before AI adoption generally took off in South Korea. (A footnote in the paper points to an explosion of interest in AI in the country after Go master Lee Sedol lost four of five matches to Google DeepMinds AlphaGo program in March 2016.)

Among the firms surveyed, the correlation between AI adoption and revenue growth followed a J-curve: slow and steady at first, then substantial. The turning point was an intensity of AI adoption of 25%. For firms with AI intensity below 25%, annual revenue growth was essentially zero; for firms above the 25% threshold, growth approached 24%.

Theres a disruptive power for AI. With lower utilization, its harder to make a profit, Choi said. When youre in those early stages of AI adoption, you may need some time to obtain the payoff to using AI.

Several factors can influence a firms embrace of AI, the researchers found. For example, firms that are smaller and/or were founded by CEOs with prior entrepreneurial experience are more likely to adopt AI intensively. Larger firms or spinoffs from other companies are less likely to adopt AI at that level, though lab-based spinoffs are an exception.

One of the most influential factors, though, is adoption of complementary technology namely, big data capabilitiesand cloud computing. The former contributes to better AI outcomes through more mature data collection and management, while the latter provides the computational power necessary to run complex analyses. Both help firms drive growth from their investments in AI.

This finding came as little surprise to Choi and his co-authors. For decades, investing in one type of technology has driven the adoption of other technologies. Examples abound: Better operating systems led to better software, faster modems made computer networks possible, and IT infrastructure supported the growth of online selling.

Complementary technology makes it easy to adopt new technology such as AI, Choi said. To adopt and utilize AI effectively, and to get the payoff at earlier stages in your investment, you need the technology and the skills that go with it.

The pivotal role of complementary technology points to one key takeaway from the paper, Choi said. To support AI adoption, its not enough to have access to the technology you also need the infrastructure that supports it. When you make that easily available, you can accelerate AI adoption, Choi said.

The second consideration is how closely AI is tied to a companys core product or service, he said, and how that impacts the companys research and development strategy.

Internally focused R&D helps a company build absorptive capacity in this case, AI know-how that positions it to more intensively adopt and use AI technology. This is helpful for firms that need to protect their proprietary algorithms as intellectual property, or for firms working with sensitive data sets theyd rather not allow a third party to process.

On the other hand, if AI is a complement to the work that a firm is doing but isnt the core focus of that work, firms can turn to external resources, Choi said. Large language models, such as OpenAIs ChatGPT, are a good example of this: Theyre readily available, widely used, and constantly being refined.

Its important to ask, Is there a point solution for the AI work Im trying to do? Choi said. If your area of work is more systematic, then you dont necessarily need an internally focused R&D strategy. You can license something thats already available.

Read next: how to prepare for the AI productivity boom

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We Have a Pandemic-Driven Data Protection Gap To Close – CDOTrends

Before the pandemic, society was already inching towards complete digitalization for online banking or even something as simple as using an app for grocery shopping. At that time, digital transformation and focusing on keeping data protected but also recoverable and accessible was already a challenge.

Large organizations struggled in their digital transformation journeys as many relied on legacy systems, which can be costly to keep up and maintain. Decision-makers have to work within a budget to ensure that business goals, workforce issues, and innovation are addressed.

However, the pandemic has accelerated the need to digitize, forcing organizations to facilitate remote working options at an unimaginably fast pace. To respond, most organizations simply advanced the execution of their pre-planned IT modernization initiatives. While this enabled them to immediately accommodate their users and update legacy systems and enhance functionality, in many cases, it did so at the long-term cost of ignoring growing risks around protecting critical data.

Data protection requires a holistic approach to addressing risks, such as the continued growth of cybersecurity threats and all potential outages, from human errors to system failures and natural disasters. Keeping data secure, backed up, readily available and recoverable is vital to a modern digital business that must operate in an always-on manner.

The challenge is only getting more demanding. According to Veeams Data Protection Trends Report 2022, 84% of APJ organizations have a protection gap between how much data they can afford to lose after an outage and how frequently IT protects their data. While 86% have an availability gap between how quickly they need systems to be recoverable and how quickly IT can bring them back.

Containing business challenges

Business data has become more vulnerable to cyberattacks, forcing organizations to bolster data protection and security to overcome severe disruptions. IT leaders must take the initiative to plan and anticipate what to do when they get attacked rather than waiting for disaster to strike.

Continuing cyberthreats

Ransomware attacks continue to be more frequent than ever. For organizations in the APJ region, only 18% of businesses escaped ransomware attacks in 2022. For those that were attacked, 18% experienced only one, 45% experienced two or three, and 19% experienced as many as four or more in 2022

36% of organizations stated that ransomware (including prevention and remediation) was their most significant hindrance to digital transformation or IT modernization initiatives due to its burden on budgets and workforce.

Human error and education

While cyberthreats can put a massive strain on a businesss productivity and ability to restore data quickly, there is a common, often overlooked security threat unintentional human error. Despite significant education efforts, almost half of global and Asia Pacific businesses reported accidental deletion, overwrite of data, or data corruption as a primary cause of IT outages. Data loss due to human error is an unavoidable fact. Thus all organizations must be on guard and educate their employees on mitigating these events.

Managing hybrid infrastructure complexity

With cloud computing evolving rapidly, the need to protect cloud workloads and maintain compliance has grown. Hybrid IT continues to be the norm, with a relatively even balance between servers within the data center and cloud-hosted servers. Within the data center, there is a good mix of both physical and virtual servers. This year, organizations in the APJ region reported: 29% physical servers within data centers; 25% virtual machines within data centers and 46% cloud-hosted server instances. As a result, 37% of organizations in the APJ region stated that being able to standardize their protection capabilities across their data center, IaaS and SaaS workloads is a crucial driver in their 2023 strategy

Road to Success

Business leaders should consider the following points to ensure that their organizations are set up to succeed:

Prepare your Team

Human error accounts for a significant portion of data breaches. Reducing such errors should not be reactive. Instead, proactive measures should be fully adopted to recover its mission-critical applications in a timely manner. However, to begin recovery at this level, teams within the company must be prepared to take the necessary steps. A report by Forrester Consulting found that in APAC, 53% of businesses agree that their managers do not stress the importance of good security practices and training. Whether its part of a holistic IT strategy or separate, organizations should be educating all staff on safe practices when online. This can significantly reduce the risks of data loss caused by ransomware or other attacks.

Prepare your Plan

To prepare a business for disaster recovery, the ability to anticipate what a zero-day attack looks like and the next steps needed at that moment is vital. Getting services and employees back online as soon as possible is another important aspect that should be prioritized. To achieve this, businesses must have a robust, well-defined plan, enabling them to choose the best course of action to counter the possibility of disasters and minimize any resulting downtime. Businesses must not only have a plan but also put it to the test before a disaster strikes.

Test your Network

Weak, misconfigured, or inadequately maintained networks are an excellent entry point for malicious actors. Investing in network security is a great way to ensure you can mitigate these threats. Penetration testing is a must when figuring out the weaknesses in your network and is often best done by a neutral third party. Sometimes we can be blinded to faults when were used to seeing the same networks and systems.

With technology reaching steep heights, systems and networks are becoming more complex. All businesses have had to contend with resolving the immediate challenges of the pandemic. Still, with cyber-attacks, hardware failures, network issues, and more creating increased complexity, business continuity must be at the top of any organizations list of IT concerns. Today, a BC/DR plan's objective should include speed, accessibility, and remote availability. While the plan isn't something you must update often, it needs to be a solid, well-fleshed-out plan. Because when disaster strikes, all you may have to rely on is your recovery plan and your employees.

Joseph Chan, vice president for Hong Kong, Macau & Taiwan for Hong Kong at Veeam, wrote this article.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends.Image credit: iStockphoto/pishit

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What will hybrid working mean 10 years from now? – TechHQ

Hybrid working, meaning a mix of working remotely and working in the office, was surprisingly, with hindsight a significant novelty in the years before Covid-19.

It was a work model that was seen as neither one thing nor the other, and that didnt usually fit well with strong, decisive business decision-making. Mixing remote and office-based work, where it was allowed at all, usually had to come attached to a strong and specific reason.

It was also often seen as a perk that came with a lowering of salary, to compensate companies for a workers absence from the office (where the work would normally be office-based), and to account for the lower overheads of largely remote workers on things like travel, daily food, and so on.

Perhaps the two biggest reasons why hybrid work was not a widespread thing before Covid though are 1) No-one was especially certain that the technologies required to facilitate it (such as video conferencing, remote collaboration tools and the like) could handle the transfer from being useful in a pinch to being essential all the time, and 2) There was no particular economic incentive for the whole business world to test out whether they could or not.

In the words of the old adage, if it aint broke, dont fix it. Almost nobody in business regarded the system of near-universal in-office working as broke, so there was no incentive to see whether it could be fixed.

Then the pandemic hit, the world of business rocked on its axis as real-world locations largely shut down, and suddenly there was a real economic incentive to innovate or die.

Many businesses were forced by government mandate to either go entirely remote or go out of business, with the understanding that the situation was temporary, and that normal service meaning in-office work would be resumed as soon as the world recovered from its viral apocalypse assuming it ever did.

Remote working was the epitome of the flexible approach to work. If you legally couldnt go into work, companies had no option but to offer a new work model, and to invest where necessary in the appropriate technology to make the new normal work.

Several things became apparent in those days. First, the pandemic had hit a world of work which, as far as offices were concerned, had the technological tools to continue from a distance.

Second, those tools would only get more and more refined as time went on, spurring a vast growth in the likes of cloud servers to allow remote workers to access company systems from anywhere.

And third, a combination of fear, loss, and tested technology would bring about a shift in the work-life balance paradigm, meaning hybrid working, when it took over from purely remote working, would not be shifted from the public consciousness as a perfectly normal way to get the job done.

Attempts have been made to reinstate full in-office working, but, as weve seen in the case of Twitter under Elon Musk, staff these days have plenty of options, and will be open to the idea of moving from even premium companies if the issue of hybrid working and a return to inflexibility is forced.

But already, three years into the era of hybrid working, and mercifully past at least the initial fatal/virulent phase of Covid-19, what hybrid working means has radically shifted.

When it began, hybrid working meant business as usual, but geographically varied. The workday would still be 9-5 (or whatever variant of that pattern the company worked). Communication would be largely asynchronous (via email, for instance).

Trips into the office would largely mean a day spent doing the same work as was done at home. Use of collaboration tools and cloud-based company programs was relatively new, and a comparatively steep learning curve.

And, certainly at the start, hybrid workers were invasively monitored by some companies, to ensure they delivered their work to the in-office standards day after day while they adjusted to the new routines and disciplines of hybrid working.

Now, hybrid working, with its mixing of home work, remote work, and in-office work, has shifted from being a new and sometimes uncomfortable discipline to a genuine new normal.

Companies are finding the freedom that comes with being able to hire from a much wider geographical talent pool, and so are frequently offering roles as hybrid working from day one.

By this point, most office workers will have done at least one role on a hybrid basis, so the discipline it requires has been inculcated into them. That also means most businesses with any ethical bones will have largely abandoned invasive monitoring.

The standard 9-5 working pattern is beginning to dissipate, as hybrid workers do what needs to be done, both to meet (and exceed) the companys requirements, and to enrich their own life experience with a healthier work-life balance.

The vast improvement of cloud services and video conferencing technology means everything from collaboration to communication with team members in the hybrid mix is already light years beyond what it was in the very early days of the first lockdowns.

Hybrid working as a work model is not going away.

But where could it be going in terms of its evolution over the next decade?

Firstly, it seems likely that more and more businesses will reduce their physical footprint, because real estate is a big business expense that will grow less and less necessary the more hybrid working becomes normalized.

Secondly, the dissipation of geographically-centralized teams or businesses is likely to continue. Hybrid work makes it unnecessary to only recruit within a certain radius surrounding a physical HQ, and thats likely to result in more geographical dissipation the better and faster connective technologies become.

The traditional 9-5 work pattern, if its not quite dead yet, will be dead long before 2033. Thats likely, partly due to that geographical broadening of teams and businesses, and partly as a result of the enhanced work-life balance.

Allowing people to work more when they are at their most productive, be that 5am or 11pm, means you can actually get more productivity out of what is technically a smaller number of active hours, so theres even significant credence given to the idea that hybrid working will lead to a 4-day week, with no loss of productivity.

Such centralized office space as there is will likely be radically redesigned the cubicle will largely be a dead concept within the next decade, because theres no need to partition space for staff if theyre working remotely most of the time. Space will likely be reallocated into larger, more collaborative areas.

That will likely follow another trend the specialness of time spent actually in the office space. Rather than an in-office day being business as usual, firstly, they are likely to be fewer and less regular than they have been up to now, and secondly, when they happen, theyre likely to be focused on collaborative, team-based work or discussions.

And as technology gets faster, and connectivity to online company assets and programs becomes a greater norm, the scope of what most roles will entail is likely to expand too, in directions that allow for greater personal and professional training and growth.

An offshoot of that, already surfacing in Gen Z staff entering the hybrid working marketplace, will be significantly more opportunities for both upskilling and mental health, delivered as a corporate cultural norm via either self-learning courses or remote sessions with trainers and counsellors located around the country, or even around the world.

Its even conceivable that a workplace culture more defined by a work-life balance skewed towards the home will open up significantly more than it did pre-pandemic. That would allow more women to return to work after pregnancy on a hybrid basis, for instance.

It would also help significantly broader ranges of staff younger, older, people with physical disabilities, neurodiverent people, people across the spectrum of gender expression and so on to make ongoing contributions which a strictly in-office culture denied them the opportunity to make in the years before the first lockdowns turned hybrid working into a new normality.

Hybrid working is here to stay. Its an economic reality that signals a shift in the work-life balance post-Covid, and its already had profound effects on the way businesses work.

All the evidence suggests that a decade from now, those effects will only be more profound, radically changing the normality of working life into something that would have been inconceivable in the pre-pandemic years.

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Service NSW seeks containerised application hosting platform – Cloud – CRN Australia

Ahead of the incoming Labour administration, Service NSW said it is looking for a containerised

"Service NSW requires a stable, rapid delivery, resilient, application-hosting container-based platform that allows product teams high levels of control and self-management of infrastructure," the government agency said.

"Therefore, a market assessment is required, followed by procurement of container platform service from a vendor that can satisfy business requirements," it added.

The DICT/17971 request for tender is for pre-qualifed advanced suppliers, approved for contracts valued over $150,000, or high-risk ones.

Such vendors must meet capability requirements under Service NSW's SCM0020 prequalification scheme, sub-category R02.

This specifices services to assist agencies provisioning of platform and utility services, through pubic, private and community clouds, allowing for the development, operation, and management applications.

An as-a-service model solution is set out, which includes provision of servers, storage, networks, appliances, telecommunications, ancillaries and peripherals, and the hosting of the equipment and operating systems.

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What Wasm Needs to Reach the Edge – The New Stack

Write once, run anywhere. This mantra continues to hold true for the promise of WebAssembly (WASM) but the keyword is promise since we are not there yet, especially for edge applications, or at least not completely. Of course, strides have been made as far as WebAssemblys ability to accommodate different languages beyond JavaScript and Rust as vendors begin to support different languages, such as TypeScript, Python or C#.

As of today, WASM is very much present in the browser. It is also rapidly being used for backend server applications. And yet, much work needs to be done as far as getting to the stage where applications can reach the edge. The developer probably does not care that much they just want their applications to run well and security wherever they are accessed, without wondering so much about why edge is not ready yet but when it will be.

Indeed, the developer might want to design one app deployed through a WebAssembly module that will be distributed across a wide variety of edge devices. Unlike years past when designing an application for a particular device could require a significant amount of time to reinvent the wheel for each device type, one of the beautiful things about WASM once standardization is in place is for the developer to create a voice-transcription application that can run not only on a smartphone or PC but in a minuscule edge device that can be hidden in a secret agents clothing during a mission. In other words, the application is deployed anywhere and everywhere across different edge environments simultaneously and seamlessly.

During the WASM I/O conference held in Barcelona, a few of the talks discussed successes for reaching the edge and other things that need to be accomplished before that will happen, namely, having standardized components in place for edge devices.

Edge is one of those buzzwords that can be misused or even misunderstood. For telcos, it might mean servers or different phone devices. Industrial devices might include IoT devices, applicable to any industry or consumer use for users that require connected devices with CPUs.

An organization might want to deploy WASM modules through a Kubernetes cluster to deploy and manage applications on edge devices. Such a WASM use case was the subject of the conference talk and demo Connecting to devices at the edge with minimal footprint using AKS Edge Essentials and Akri on WASMs given by Francisco Cabrera Lieutier, technical program manager for Micrsosoft, and virtually by Yu Jin Kim, product manager at Microsofts Edge and Platforms.

Lieutier and Kim showed how a WASM module was used to deploy and manage camera devices through a Kubernetes environment. This was accomplished with AKS Edge Essentials and Akr. One of the main benefits of WASMs low power was being able to manage the camera device remotely that like other edge devices, such as thermometers or other sensor types, would lack the CPU power to run Kubernetes that would otherwise be a requirement without WASM.

How can we coordinate and manage these devices from the cluster? Kim said. The solution used in the demo is Akri, which is a Kubernetes features interface to makes connections to the IoT devices with WASM, Kim explained.

However, while different edge devices can be connected and managed with WASM with AKS Edge Essentials and Akri, the edge device network is not yet compatible with say an edge network running under an AWS cluster from the cloud or distributed directly from an on-premises environment.

Again, the issue is interoperability. We know that WebAssembly already works. It does what you need to do and the feature set of WASM has already been proven in production, both in the browser and on the server, Ralph Squillace, a principal program manager for Microsoft, Azure Core Upstream, told The New Stack during the conference sidelines.

The thing thats missing is we dont have interoperability, which we call portability the ability to take the same module and deploy it after rebuilding a different cloud but you need a common interface, common runtime experience and specialization. Thats what the component model provides for interoperability.

Not that progress is not being made, so hopefully, the interoperability issue will be solved and a standardized component model will be adopted for edge devices in the near future. As it stands now, WASI has emerged as the best candidate for extending the reach of Wasm beyond the browser. Described as a modular system interface for WebAssembly, it is proving apt to help solve the complexities of running Wasm runtimes anywhere there is a properly configured CPU which has been one of the main selling points of WebAssembly since its creation. With standardization, the Wasi layers should eventually be able to run all different Wasm modules into components on any and all edge devices with a CPU.

During the talk wasi-cloud: The Future of Cloud Computing with WebAssembly, Bailey Hayes, Bailey Hayes, director of the Bytecode Alliance Technical Standards Committee and a director at Cosmonic and Dan Chiarlone (virtually), an open source Software engineer at Microsofts WASM Container Upstream team, showed in a working demo how wasi-cloud offers standardized interfaces for running Wasm code on the cloud.

Our answer to the question of how do you write one application that you can run anywhere across clouds is with wasi-cloud, Hayes said. And you can imagine that using standard APIs, one application is runnable anywhere or on any architecture, cloud or platform.

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What Wasm Needs to Reach the Edge - The New Stack

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What is Portainer and can it help the average computer user? – ZDNet

Screenshot by Jack Wallen/ZDNET

The average computer user doesn't fall into the neophyte classification as easily as it once did. Nearly everyone carries with them a very powerful computer, right in their pockets. Senior citizens, children, and everyone in between use a computer on a daily basis and have reached a point of comfort that would have been impossible 10 years ago.

Now, I find people are doing things they would never have previously thought of and it's exciting. I've had readers reach out to me to say things like, "I installed Linux for the first time and never thought I could!"

Also: How to install Linux on an old laptop

My mother-in-law does things with her Chromebook I never thought she'd be able to do. In fact, once upon a time, I would receive pretty regular calls asking how to do X or Y. Now? She's solving problems on her own and making Chrome OS do everything she needs.

Such evolution had me thinking: If those users are able to solve such problems on their own, why couldn't they take that a few steps further and start benefitting from the technology they would have previously called "too difficult"?

Case in point, Portainer. What is Portainer? Before we answer that, we must answer another question:What are containers?

Also:8 things you can do with Linux that you can't do with MacOS or Windows

In the realm of technology, containers are bundled applications and services that contain everything they need to run and can be run on any supporting platform. Most often, containers are used by businesses to run applications that can automatically scale to meet demand.

But containers don't have to be limited to businesses. Every home now has its own network. On that network are computers and devices. You might have Windows, MacOS, and Chromebook computers attached to your network (along with smart TVs, thermostats, phones, tablets, security devices, and much more). In fact, your network is teeming with devices, all of which give you considerable power and flexibility.

At the moment, you're probably only using a fraction of the available power and usability offered by those devices. Case in point containers.

Also: How to convert your home's old TV cable into powerful Ethernet lines

Imagine, if you will, that you could deploy a complete cloud service to your network, as I've demonstrated in "How to install a cloud service at home." You might be asking yourself, "Why would me, my wife, my kids, or my mother-in-law need something like this?"

Imagine you or your in-laws have a need to save and share files and there are people in the family who aren't so willing to trust the likes of Google, Apple, or Microsoft. Should that be the case, you might want to deploy a cloud service to your home network that everyone could use but the outside world couldn't access. Or maybe you have kids in school and you want them to have their own cloud service without having to worry they'll be using a third-party platform (so you have better control over things). Or maybe you want to deploy a productivity platform (such as ONLY OFFICE) that is only accessible via your family.

Also:Stop using your browser's built-in password manager. Here's why

Take my home network, for example. I have access to a cloud service, an office suite, an invoice tool (and more) that only myself and my wife can access. It's convenient, secure, and reliable.

Of course, Some might read this and say, "I don't want to have to type a bunch of commands to install software to my network."

But what if I told you that you didn't have to? There's a piece of software that makes deploying containers easy enough that almost anyone can do it. That software is Portainer.

Now, before you get too excited, the installation of Portainer isn't just a matter of downloading an installer and running it. You have to first install Docker (which can be installed on Linux, MacOS, and Windows) and then install Portainer. The good news for MacOS and Windows users is that installing Docker Desktop (which installs Docker itself) can be done by simply downloading and running an installer file.

Also: 4 ways Windows people get MacOS wrong

Do note, however, that if your MacOS device uses Apple Silicon, you'll want to install Rosetta first, which can be done with the command:

Once Docker Desktop is installed, you can then download either the Portainer .dmg file (for MacOS) or the .exe file for Windows.

After you've installed Portainer, the excitement begins. With the help of App Templates, you can install the likes of WordPress and other applications (without having to first install web or database servers).

Installing apps from Templates is the easiest method.

Or, by working with the easy-to-use Forms, you could deploy countless applications (such as the Nextcloud cloud server) with just a few clicks. Sure, there will be a slight learning curve involved but it's really no more challenging than getting a printer up and running on your home network.

Installing an app using the Portainer forms system.

I've been using Portainer for some time and it's made deploying the tools I need to get things done exponentially easier than installing them the old-fashioned way. And although it might be unfamiliar territory at first, once you get the hang of it, the sky's the limit to what you can do on your home network.

Also:The most important reason you should be using Linux at home

You do not have to be constrained by the old ways of using a computer on your private network. With just a bit of effort upfront, you can expand your understanding and usage of technology in ways you never thought possible.

And you don't need a degree in computer science to do it. On top of which, if you try it out and decide it's too challenging, the only thing you've lost is a bit of time (as you can use the community editions of both Portainer and Docker Desktop at home for free).

So, what are you waiting for? Expand your knowledge and the tools you have available to your home network with the ease of Portainer.

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What is Portainer and can it help the average computer user? - ZDNet

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