Businesses of all sizes are bracing for a recession. Still, while it may sound counterintuitive, this is actually the right time to accelerate digital transformation. Historically, an economic downturn is a boon for innovation. According to Morgan Stanley, roughly half of Fortune 500 companies were founded in times of recession or economic crisis. Investing in digital transformation will help businesses overcome a slowdown and address talent shortages.

Quantum computing startups are starting to pick up a lot of attention from investors. But as they grow their teams, they all face a common challenge: there just isn't enough talent available. These startups are looking for engineers with the knowledge to build quantum hardware and formulate quantum algorithms. But these skills are mostly found in academia. On top of that, startups in quantum are competing with big-pocketed companies with their own quantum departments, such as IBM and Google.

Talent shortages are not an impediment to AI adoption, a Gartner survey of almost 700 business leaders found. More than seven in ten executives reported they currently have or can source the necessary AI talent. Companies are deploying AI strategically, to support decision-making and automation across a broad array of business functions, rather than just tactically within tech units. Four in five respondents believe that AI-powered automation can be applied to "any business decision." Demonstrating the effectiveness and the value of AI remains a challenge, despite widespread adoption.

IT is facing significant talent shortages, and new human resources AI talent recruiting systems are touted as being able to help. How do these systems work, and are they effective? The purpose of artificial intelligence hiring and talent scouting systems is to reduce the amount of work that HR or IT conducts in the activities of talent seeking, candidate evaluation and hiring. For example, if you're looking for a senior project manager, it's not uncommon to receive 300 or 400 resumes. All these candidates are applying because they believe they have the experience and the requisite skills to do the job you want to fill.

There has been a lot of buzz surrounding the adoption of artificial intelligence. According to a recent report from McKinsey 57 percent of companies are now using AI in at least one function. But how much is hype and how much is built on a sound commercial base? We spoke to Mike Loukides, VP of emerging tech content at O'Reilly Media and author of O'Reilly Media's widely-cited AI Adoption in the Enterprise report, to discuss the current state of AI and what lies ahead. BN: Are we moving beyond the adoption of AI because it's new and cool to having a serious business case?

Rapid advances in artificial intelligence (AI) have made this technology important for many industries, including finance, energy, healthcare, and microelectronics. AI is driving a multi-trillion-dollar global market while helping to solve some tough societal problems such as tracking the current pandemic and predicting the severity of climate-driven events like hurricanes and wildfires. Today, AI algorithms are primarily run at large data centers, that is in the cloud. For this intelligence to be used at the edge, data must be transmitted to the cloud, analyzed there, and the results transmitted back to the edge – a device in the field of operation, whether it is a sensor tracking the strength of a bridge, a mobile phone, a medical implant, or an autonomous vehicle. The problem with the current approach of using AI primarily in the cloud is that it consumes much energy and can introduce data transmission delays and security vulnerabilities.

Rapid advances in artificial intelligence (AI) have made this technology important for many industries, including finance, energy, healthcare, and microelectronics. AI is driving a multi-trillion-dollar global market while helping to solve some tough societal problems such as tracking the current pandemic and predicting the severity of climate-driven events like hurricanes and wildfires. Today, AI algorithms are primarily run at large data centers, that is in the cloud. For this intelligence to be used at the edge, data must be transmitted to the cloud, analyzed there, and the results transmitted back to the edge – a device in the field of operation, whether it is a sensor tracking the strength of a bridge, a mobile phone, a medical implant, or an autonomous vehicle. The problem with the current approach of using AI primarily in the cloud is that it consumes much energy and can introduce data transmission delays and security vulnerabilities.

Last year I wrote about how AI regulations will lead to the emergence of professional AI risk managers. This has already happened in the financial sector where regulations patterned after Basel rules have created a financial risk management profession to assess financial risks. Last week, the EU published a 108-page proposal to regulate AI systems. This will lead to the emergence of professional AI risk managers. The proposal doesn't cover all AI systems, just those deemed high-risk, and the regulation would vary depending on how risky the specific AI systems are: Since systems with unacceptable risks would be banned outright, most of the regulation is about high-risk AI systems.

In this article, I will look at how Artificial Intelligence (AI) can help improve cybersecurity practices in an environment of ever-increasing threats and discuss the role of AI in alleviating the perennial talent shortage in the field of cybersecurity. Remember that the current wave of AI, driven by advances in deep learning, started around 2015, but the talent short- ages in cybersecurity precede that. I also caution that if we are not careful, AI can even be a double-edged sword when it comes to cybersecurity. Let me start with a flashback. About a decade ago, I used to audit the information security practices and cybersecurity preparedness of large global enterprises.

With the move to DevOps and high-paced development, there is a greater and more frequent need to specify test environments to ensure that systems are working efficiently; yet the ability of enterprise to model and manage capacity accurately is immature. Performance testers are theoretically well-placed to help but they may be naturally cautious about modelling capacity since testing functions can run up significant annual costs in capacity usage alone. You'll have heard plenty about AI (artificial intelligence) and ML (machine learning) of late, and with good reason – delicate, complex and downright costly technology and tools are rapidly maturing into usable toolsets in a wide range of verticals. Analyst firms predict huge markets for AI and ML, indeed the number of enterprises implementing artificial intelligence (AI) grew 270 percent in the past four years and tripled in the past year, according to industry analyst Gartner's 2019 CIO Survey. Results showed that organisations across all industries use AI in a variety of applications but on the downside struggle with acute talent shortages.