Skills-based hiring continues to rise as degree requirements fade

More employers are leaving behind college degree requirements and embracing a skills-based hiring approach that emphasizes strong work backgrounds, certifications, assessments, and endorsements. And soft skills are becoming a key focus of hiring managers, even over hard skills.

Large companies, including Boeing, Walmart, and IBM, have signed on to varying skills-based employment projects, such as Rework America Alliance, the Business Roundtable’s Multiple Pathways programs, and the campaign to Tear the Paper Ceiling, pledging to implement skills-based practices, according to McKinsey & Co.

“So far, they’ve removed degree requirements from certain job postings and have worked with other organizations to help workers progress from lower- to higher-wage jobs,” McKinsey said in a November report.

Skills-based hiring helps companies find and attract a broader pool of candidates who are better suited to fill positions the long term, and it opens up opportunities to non-traditional candidates, including women and minorities, according to McKinsey.

At Google, a four-year degree is not required for almost any role at the company — and a computer science degree isn't required for most software engineering or product manager positions. “Our focus is on demonstrated skills and experience, and this can come through degrees or it can come through relevant experience,” said Tom Dewaele, Google’s vice president of people experience.

Similarly, Bank of America has refocused its hiring to use a skills-based approach. “We recognize that prospective talent think they need a degree to work for us, but that is not the case,” said Christie Gragnani-Woods, a Bank of America global talent acquisition executive. “We are dedicated to recruiting from a diverse talent pool to provide an equal opportunity for all to find careers in financial services, including those that don’t require a degree.”

Hard skills, such as cybersecurity and software development, are still in peak demand, but organizations are finding soft skills can be just as important, according to Jamie Kohn, research director in the Gartner Research’s human resources practice.

Soft skills, which are often innate, include adaptability, leadership, communications, creativity, problem-solving or critical thinking, good interpersonal skills, and the ability to collaborate with others.

“Also, people don’t learn all their [hard] skills at college,” Kohn said. “They haven’t for some time, but there’s definitely a surge in self-taught skills or taking online courses. You may have a history major who’s a great programmer. That’s not at all unusual anymore. Companies that don’t consider that are missing out by requiring specific degrees.”

A lessening of 'degree discrimination'

From 2000 through 2020 “degree discrimination,” cost employees who were skilled through alternative routes 7.4 million jobs, according to Opportunity@Work, a Washington-based nonprofit promoting workers who are skilled through alternative routes. Alternative routes include skills learned on the job, in the military, through training programs, or at community colleges, for example.

“They are among our country’s greatest under-valued resources — the invisible casualties of America’s broken labor market — where low-wage work is often equated with low-skill work and the lack of a degree is presumed to be synonymous with a lack of skills,” Opportunity@Work explains on its site.

Over the past few years, however, job postings with a degree requirement have dropped from 51% of jobs in 2017 to 44% in 2021, according to the Burning Glass Institute.

Much of the recent shift to skills-based hiring is due to the dearth of tech talent created by the Great Resignation and a growing number of digital transformation projects. While the US unemployment rate hovers around 3.5%, in technology fields, it’s less than half that (1.5%).

While many IT occupations have also seen degree requirements vanish, there remain three where bachelor's degrees are still blocking the more than 70 million workers who have skills gained through alternatives to college, according to Opportunity@Work:

• Computer & Information Systems Managers: 698,000 workers hold such jobs today — and 19% of them are alternatively trained. Yet, 94% of those jobs require a bachelor's degree.
• Computer Programmers: 481,000 workers fill these jobs today, 21% of whom are alternatively trained. But 76% of those jobs require a bachelor's degree.
• Computer Support Specialists: 539,000 workers now have these jobs, with 45% of them alternatively trained. And still, 45% of those jobs require a bachelor's degree.

As many as 70% of organizations have rolled out some kind of workplace technology education in the past year, according to a survey of HR professionals and workers by digital consulting agency West Monroe.

“With this figure in mind, it will be imperative for these organizations to assess their workforce and invest in teaching their workers new skills instead of taking the time, effort and cost to fill a new position,” West Monroe said.

While the cost and time it takes to acquire skills in software development, Java, Python, big data, risk management, and algorithms is high, so is their longevity.

“The payoff for skills in this group is often as long as a person’s entire career,” the Burning Glass Institute stated in a report this month. “Historically, these are the skills that are ripe for reskilling and redeploying talent for the long term.”

Other skills such as risk management and project management also stand out as being particularly durable, yet costly to develop — but they’re not typically as expensive to hire for, according to Burning Glass Institute.

Skills that can be built on an as-needed basis — because the time to learn them is generally low but the return on investment is high — include salesforce, data structures, data analysis, visual design, SAS (software) and cost estimation, the report said.

Many organizations are already implementing internal programs to upskill new and existing employees.

According to research firm IDC, 60% of the Global 2000 corporations have or will have a citizen developer training ecosystem. A significant number of those developers will come not from IT, but from business units looking to digitize processes and using low-code or no-code software tools.

While citizen developers may have little coding knowledge, they’re generally tech-savvy; they’ve worked with spreadsheets and databases, or they’re intimately familiar with corporate technology because they're customer service representatives or business analysts.

“We have seen a surge in demand for particularly digital and tech-related skills,” Kohn said. "A lot of companies have accelerated their digital transformation. So, there’s a huge demand and not enough talent going around."

The change isn't just in private industry

Skills-based hiring practices aren't limited to the private sector. Last year, the White House announced new limits on the use of educational requirements. Over the past year, five governors removed most college degree requirements for entry-level state jobs.

In January, Pennsylvania Gov. Josh Shapiro announced that his first executive order would ensure 92% of state government jobs no longer require a four-year college degree. The move opened up 65,000 state jobs that previously required a college degree and meant candidates are free to compete for those positions based on skills, relevant experience, and merit. Shapiro’s move followed similar actions in other states, such as Colorado, Utah and Maryland. In Utah’s case, 98% of its civil servant jobs will no longer require a college degree.

“Degrees have become a blanketed barrier-to-entry in too many jobs,” Utah Gov. Spencer Cox said in a statement. “Instead of focusing on demonstrated competence, the focus too often has been on a piece of paper. We are changing that.”

And just this week, Alaska Gov. Mike Dunleavy ordered a review of which state jobs could have four-year college degree requirements eliminated as a way to tackle the public sector’s recruitment and retention crisis.

Relying too much on academic degrees is a significant factor in the “over-speccing” of job requirements for tech positions, according to CompTIA, a nonprofit association for the IT industry and its workers. CompTIA's research has found that a notable segment of HR professionals is unaware of the concept of overspending when creating job postings.

In 2022, 61% of all employer job postings for tech positions nationally listed a four-year degree or higher as a requirement. In Pennsylvania, a degree was required in 62% of postings for tech jobs, in Utah, 59%; and in Maryland, 69%.

“That’s not to say a degree doesn’t play some role later in the process,” Kohn said. “Hiring managers are still skeptical of candidates who don’t have a traditional technology background. The difference is they’re allowing people with different backgrounds to get a foot in the door.”

For example, a marketing professional with data analytics skills might not be able to land an IT role. “They may be a great fit for it," Kohn said, "but they just don’t have the background companies traditionally look for."

https://www.computerworld.com/

Artificial intelligence helps solve networking problems

With the public release of ChatGPT and Microsoft’s $10-billion investment into OpenAI, artificial intelligence (AI) is quickly gaining mainstream acceptance. For enterprise networking professionals, this means there is a very real possibility that AI traffic will affect their networks in major ways, both positive and negative.

As AI becomes a core feature in mission-critical software, how should network teams and networking professionals adjust to stay ahead of the trend?

Andrew Coward, GM of Software Defined Networking at IBM, argues that the enterprise has already lost control of its networks. The shift to the cloud has left the traditional enterprise network stranded, and AI and automation are required if enterprises hope to regain control.

“The center of gravity has shifted from the corporate data center to a hybrid multicloud environment, but the network was designed for a world where all traffic still flows to the data center. This means that many of the network elements that dictate traffic flow and policy are now beyond the reach and control of the enterprise’s networking teams,” Coward said.

Recent research from Enterprise Management Associates (EMA) supports Coward’s observations. According to EMA’s 2022 Network Management Megatrends report, while 99% of enterprises have adopted at least one public-cloud service and 72% have a multi-cloud strategy, only 18% of the 400 IT organizations surveyed believed that their existing tools are effective at monitoring public clouds.   

AI can help monitor networks.

AI is stressing networks in both obvious and nonobvious ways. It’s no secret that organizations that use cloud-based AI tools, such as OpenAI, IBM Watson, or AWS DeepLens, must accommodate heavy traffic between cloud and enterprise data centers to train the tools. Training AI and keeping it current requires shuttling massive amounts of data back and forth.  

What’s less obvious is that AI enters the enterprise through side doors, sneaking in through capabilities built into other tools. AI adds intelligence to everything from content creation tools to anti-spam engines to video surveillance software to edge devices, and many of those tools constantly communicate over the WAN to enterprise data centers. This can create traffic surges and latency issues, among a range of other problems.

On the positive side of the ledger, AI-powered traffic-management and monitoring tools are starting to help resource-constrained network teams cope with the complexity and fragility of multi-cloud, distributed networks. At the same time, modern network services such as SD-WAN, SASE, and 5G also now rely on AI for such things as intelligent routing, load balancing, and network slicing.

But as AI takes over more network functions, is it wise for enterprise leaders to trust this technology?

Is it wise to trust AI for mission-critical networking?

The professionals who will be tasked with using AI to enable next-generation networking are understandably skeptical of the many overheated claims of AI vendors.

“Network operations manage what many perceive to be a complex, fragile environment. So, many teams are fearful of using AI to drive decision-making because of potential network disruptions,” said Jason Normandin, a netops product manager for Broadcom Software.

Operation teams that don’t understand or have access to the underlying AI model’s logic will be hard to win over. “To ensure buy-in from network operations teams, it is critical to keep human oversight over the AI-enabled devices and systems,” Normandin said.

To trust AI, networking professionals require “explainable AI,” or AI that is not a black box but that reveals its inner workings. “Building trust in AI as a reliable companion starts with understanding its capabilities and limitations and testing it in a controlled environment before deployment,” said Dr. Adnan Masood, Chief AI Architect at digital transformation company UST.

Explainable and interpretable AI allows network teams to understand how AI arrives at its decisions, while key metrics allow network teams to track its performance. “Continuously monitoring AI’s performance and gathering feedback from team members is also an important way to build trust,” Masood added. “Trust in AI is not about blind-faith but rather understanding its capabilities and using it as a valuable tool to enhance your team’s performance.”

Broadcom’s Normandin notes that while networking experts may be reluctant to “give up the wheel” to AI, there is a middle way. “Recommendation engines can be a good compromise between manual and fully automated systems,” he said. “Such solutions let human experts ultimately make decisions of their own while offering users to rate recommendations provided. This approach enables a continuous training feedback loop, giving the opportunity to dynamically improve the models by using operators’ input.”

AI can assist network support with natural-language chat.

As enterprise networks become more complicated, distributed, and congested, AI is helping resource-strapped network teams keep up. “The need for instantaneous, elastic connectivity across the enterprise is no longer just an option; it is table stakes for a successful business,” Coward from IBM said. “That’s why the industry is looking to apply AI and intelligent automation solutions to the network.”

The fact is that AI-powered tools are already spreading throughout cloud and enterprise networks, and the number of tools that feature AI will continue to rise for the foreseeable future. Enterprise networking has been one of the sectors most aggressively adopting AI and automation. AI is currently being used for a wide range of network functions, including performance monitoring, alarm suppression, root-cause analysis, and anomaly detection.

For instance, Cisco’s Meraki Insight analyzes network performance issues and helps with troubleshooting; Juniper’s Mist AI automates network configuration and handles optimization; and IBM’s Watson AIOps automates IT operations and improves service delivery.

AI is also being used to improve customer experiences. “AI’s ability to adapt and learn the client-to-cloud connection as it changes will make AI ideal for the most dynamic network use cases,” said Bob Friday, Chief AI Officer at Juniper Networks. Friday said that as society becomes more mobile, the wireless user experience gets ever more complex. That’s a problem because wireless networks are now critical to the daily lives of employees, especially in the age of work-from-home, which forces IT to support users in environments over which IT has little to no control.

This is why AI-powered support is one of the most popular early use cases.

“AI is enabling the next era of search and chatbots,” Friday said. “The end goal is an environment where users enjoy steady, consistent performance and no longer need to spend precious IT resources on mountains of support tickets.”

Chatbots and virtual assistants built with Natural Language Processing (NLP) and Natural Language Understanding (NLU) can understand questions that users ask in their own words. The system responds with specific insights and recommendations based on observations made across the LAN, WLAN, and WAN.

“Where this client-to-cloud insight and automation simply was not possible just a few years ago, today’s chatbots can utilize NLP capabilities to provide context and meaning to user inputs, allowing AI to come up with the best response,” Friday said. “This far surpasses the simple ‘yes’ or ‘no’ responses that originally came from traditional chatbots. With better NLP capabilities, chatbots can progress to become more intuitive, to the point where users will have a hard time telling the difference between a bot and a human.”

The early stages of this vision are already underway. AI is currently being used to help Fortune 500 companies accomplish such things as managing end-to-end user connectivity and enabling the delivery of new 5G services.

Gap turns to AI-powered operations and support.

Retail giant Gap’s in-store WLAN networks were originally designed to accommodate a handful of mobile devices. Now these networks are used not only for employee connections to centralized resources but also to connect shoppers’ devices and an increasing array of retail IoT devices across thousands of stores.

“Wireless in retail is really tough,” said Snehal Patel, global network architect for Gap

Inc. As more clients connected to Gap WLANs, a string of problems emerged. “Stores need enough wireless capacity to support innovation, and the network operations team needs better visibility into issues when they arise,” Patel said.

Gap’s IT team searched for a WLAN technology that would leverage the scale and resiliency of public clouds, but the team also wanted a platform that included tools like AI and automation that would enable their networks to scale to meet future demand.

Gap eventually settled on a set of tools from Juniper. Gap deployed Juniper’s Mist AI, an AI-powered network operations and support platform, Marvis VNA, a virtual network assistant designed to work with Mist AI, and Juniper’s SD-WAN service.

Gap’s operations team can now ask Marvis questions, and not only will it tell them what’s wrong with the network, but it will also recommend the next steps to remediate the problem.

“Before Mist, we spent a lot more time troubleshooting,” Patel said. Now, Mist continuously measures baseline performance, and if there’s a deviation, Marvis helps the operation team identify the problem. With enhanced visibility into network health and root-cause analysis of network issues, Gap has been reduced technical-staff visits to stores by 85%.

DISH taps AI to scale 5G for enterprise customers.

Another Fortune 500 company that has adopted AI to modernize networking is DISH Network, which has deployed AI to enable new 5G services. DISH was seeing increasing demand for enterprise 5G services but was having a hard time optimizing its infrastructure to meet that demand.

Enterprise customers were seeking 5G services to enable new use cases, such as smart cities, agricultural drone networks, and smart factories. However, those use cases require secure, private, low-latency, stable connections over shared resources.

DISH knew that it needed to modernize its networking stack, and it sought tools that would help it deliver private 5G networks to enterprise customers on demand and with guaranteed SLAs. This was not possible using legacy tools.

DISH turned to IBM for help. IBM’s AI-powered automation and network orchestration software and services enable DISH to bring 5G network orchestration to both business and operations platforms. Intent-driven orchestration, a software-powered automation process, and AI now underpin DISH’s cloud-native 5G network architecture.

DISH also intends to use IBM Cloud Pak for Network Automation, an AI and machine-learning-powered network automation and orchestration software suite, to unlock new revenue streams, such as the on-demand delivery of private 5G network services.

Cloud Pak automates the complicated, cumbersome process of creating 5G network slices, which can then be provisioned as private networks. By automating the process, DISH can create enterprise-class private networks on 5G slices as soon as demand materializes, complete with SLAs.

 AI-powered advanced network slicing allows DISH to offer 5G services that are customized to each business. Businesses are able to set service levels for each device on their network, so, for example, an autonomous vehicle can receive a very low-latency connection, while an HD video camera can be allocated high bandwidth. 

“Our 5G build is unique in that we are truly creating a network of networks where each enterprise can custom-tailor a network slice or group of slices to achieve their specific business needs,” said Marc Rouanne, chief network officer, DISH Wireless. IBM’s orchestration solutions leverage AI, automation, and machine learning to not only make these private 5G slices possible, but also to ensure they adapt over time as customer use evolves.

How IT pros should prepare for AI.

As AI, machine learning, and automation power an increasing array of networking software and gear, how should individual network professionals prepare to deal with their new artificial colleagues?

While few professionals will miss the mundane, repetitive chores that AI excels at, many also worry that AI will eventually displace them entirely.

“While AI is developing exponentially, it is inevitable network teams will be exposed to AI-enabled devices and systems,” Broadcom’s Normandin said. “As network experts are not meant to become AI specialists, a cultural change is probably more likely to happen than anything else.”

Masood of UST agrees that a cultural change is in order. “Network teams are rapidly evolving from just managing networks to managing networks with a brain,” he said. “Within the context of networking, these teams will need to develop the ability to work collaboratively with data scientists, software engineers, and other experts to build, deploy, and maintain AI systems in production.”

https://www.networkworld.com/

Can Ageing be Prevented? Retro Biosciences says 'Yes, we increase your life by 10 years!'

When a startup called Retro Biosciences eased out of stealth mode in mid-2022, it announced it had secured $180 million to bankroll an audacious mission: to add 10 years to the average human life span. It had set up its headquarters in a raw warehouse space near San Francisco just the year before, bolting shipping containers to the concrete floor to quickly make lab space for the scientists who had been enticed to join the company.

Retro said that it would “prize speed” and “tighten feedback loops” as part of an “aggressive mission” to stall aging, or even reverse it. But it was vague about where its money had come from. At the time, it was a “mysterious startup,” according to press reports, “whose investors remain anonymous.”

Now MIT Technology Review can reveal that the entire sum was put up by Sam Altman, the 37-year-old startup guru and investor who is CEO of OpenAI. 

Altman spends nearly all his time at OpenAI, an artificial intelligence company whose chatbots and electronic art programs have been convulsing the tech sphere with their human-like capabilities. 

But Altman’s money is a different matter. He says he’s emptied his bank account to fund two other very different but equally ambitious goals: limitless energy and extended life span.

One of those bets is on the fusion power startup Helion Energy, into which he’s poured more than $375 million, he told CNBC in 2021. The other is Retro, to which Altman cut checks totaling $180 million the same year. 

“It’s a lot. I basically just took all my liquid net worth and put it into these two companies,” Altman says.

Altman’s investment in Retro hasn’t been previously reported. It is among the largest ever by an individual into a startup pursuing human longevity.

Altman has long been a prominent figure in the Silicon Valley scene, where he previously ran the startup incubator Y Combinator in San Francisco. But his profile has gone global with OpenAI’s release of ChatGPT, software that’s able to write poems and answer questions.

The AI breakthrough, according to Fortune, has turned the seven-year-old company into “an unlikely member of the club of tech superpowers.” Microsoft committed to investing $10 billion, and Altman, with 1.5 million Twitter followers, is consolidating a reputation as a heavy hitter whose creations seem certain to alter society in profound ways.  

Altman does not appear on the Forbes billionaires list, but that doesn’t mean he isn’t extremely wealthy. His wide-ranging investments have included early stakes in companies like Stripe and Airbnb. 

 “I have been an early-stage tech investor in the greatest bull market in history,” he says. 

Young Blood

About eight years ago, Altman became interested in so-called “young blood” research. These were studies in which scientists sewed young and old mice together so that they shared one blood system. The surprise: the old mice seemed to be partly rejuvenated.

A grisly experiment, but in a way, remarkably simple. Altman was head of Y Combinator at the time, and he tasked his staff with looking into the progress being made by anti-aging scientists.

“It felt like, all right, this was a result I didn’t expect and another one I didn’t expect,” he says. “So there’s something going on where … maybe there is a secret here that is going to be easier to find than we think.” 

In 2018, Y Combinator launched a special course for biotech companies, inviting those with “radical anti-aging schemes” to apply, but before long, Altman moved away from Y Combinator to focus on his growing role at OpenAI. 

Then, in 2020, researchers in California showed they could achieve an effect similar to young blood by replacing the plasma of old mice with salt water and albumin. That suggested the real problem lay in the old blood. Simply by diluting it (and the toxins in it), medicine might get one step closer to a cure for aging.

The new company would need a lot of money—enough to keep it afloat at least seven or eight years while it carried out research, ran into setbacks, and overcame them. It would also need to get things done quickly. Spending at many biotech startups is decided on by a board of directors, but at Retro, Betts-LaCroix has all the decision-making power.  “We have no bureaucracy,’ he says. “I am the bureaucracy.” 

https://tinyurl.com/4zsukek9

https://retro.bio/announcement/