Critical thinking represents a fundamental competency in today's education landscape. Developing critical thinking skills through timely assessment and feedback is crucial; however, there has not been extensive work in the learning analytics community on defining, measuring, and supporting critical thinking. In this paper, we investigate the feasibility of measuring core "subskills" that underlie critical thinking. We ground our work in an authentic task where students operationalize critical thinking: student-written argumentative essays. We developed a coding rubric based on an established skills progression and completed human coding for a corpus of student essays. We then evaluated three distinct approaches to automated scoring: zero-shot prompting, few-shot prompting, and supervised fine-tuning, implemented across three large language models (GPT-5, GPT-5-mini, and ModernBERT). GPT-5 with few-shot prompting achieved the strongest results and demonstrated particular strength on subskills with separable, frequent categories, while lower performance was observed for subskills that required detection of subtle distinctions or rare categories. Our results underscore critical trade-offs in automated critical thinking assessment: proprietary models offer superior reliability at higher cost, while open-source alternatives provide practical accuracy with reduced sensitivity to minority categories. Our work represents an initial step toward scalable assessment of higher-order reasoning skills across authentic educational contexts.

As a child, Julian Ako would visit his maternal great-grandfather's home near Pilaa Beach in Kauai, Hawaii, where he and his family would gather edible fungi that grow on kukui trees and collect seaweed and fish from the reef. For about a decade, that land has belonged to Meta CEO Mark Zuckerberg, who is constructing a massive compound at an estimated cost that exceeds 300 million. WIRED can now reveal that Zuckerberg's property is atop a burial site: Ako's great-grandmother and her brother were buried on the land. After months of discussions with a Zuckerberg representative, Ako was successfully able to gain access to the property and identify and register the graves with the state Department of Land and Natural Resources, though he was not able to locate remains of other ancestors, who he believes could be buried on the property. In a report shared with WIRED, the state agency also confirmed "the probability (based on oral testimony) of additional burial sites."

Cybersecurity scientists have taken a key step toward harnessing a form of artificial intelligence known as deep reinforcement learning, or DRL, to protect computer networks. When faced with sophisticated cyberattacks in a rigorous simulation setting, deep reinforcement learning was effective at stopping adversaries from reaching their goals up to 95 percent of the time. The outcome offers promise for a role for autonomous AI in proactive cyber defense. Scientists from the Department of Energy's Pacific Northwest National Laboratory documented their findings in a research paper and presented their work Feb. 14 at a workshop on AI for Cybersecurity during the annual meeting of the Association for the Advancement of Artificial Intelligence in Washington, D.C. The starting point was the development of a simulation environment to test multistage attack scenarios involving distinct types of adversaries.

This story is, in fact, an example of what is now often called intelligent automation, the combination of artificial intelligence and automation that synthesizes vast amounts of information to automate entire processes or workflows Even if these new professions do not materialize, not to worry because there will be so much wealth generated by AI and automation that every adult will receive a monthly stipend, much as Alaska residents receive from oil royalties. Burn-In, a novel from P. W. Singer and August Cole, describes growing automation that has taken millions of jobs and left many people fearful that the future is leaving them behind. On the other hand, some argue there will be changes in the types of available work -- and some people will be displaced (like my call center representative) -- but overall employment will not be greatly impacted. During the first and second waves, women could be at greater risk of automation due to their higher representation in clerical and other administrative functions, but later automation will put more men at risk. It's worth underscoring that PwC did this analysis pre-COVID and so its conclusions don't account for the rapid uptake of automation over the past year and how this could further accelerate the waves of automation going forward. A recent PwC survey of employees worldwide revealed that "60% are worried that automation is putting many jobs at risk; 48% believe'traditional employment won't be around in the future,' and 39% think it is likely that their job will be obsolete within five years."

The stakes are now higher because bad actors are engaging in organized crime, akin to cyberwarfare by nation-states. We've seen hospitals targeted during COVID-19 outbreaks, pipelines unable to deliver fuel, and other highly targeted attacks. The bad actors' new paradigm is to present two extortion threats on stolen enterprise data: holding the data hostage and threatening to leak sensitive information, including customer records and intellectual property. Such threats are especially salient for large organizations, which have the money and data desired by cybercriminals. Moreover, the attack surface for such crimes is ever-expanding as trends such as the adoption of 5G mobile networks and work-from-home policies push enterprise technology beyond its traditional borders.

The Covid-19 pandemic accelerated digital transformation and the need for IT departments to adapt to the working from home environment. While in recent months there has been a call from large corporates for the return of their staff to the office, many people have continued to work from their home offices. Some organizations have realized the benefits of a remote working culture, such as the ability to scale back on expensive offices, while others are adopting a hybrid approach that allows people to commute occasionally to the office and work from home most of the time. Infor-Tech Research Group says, "If the last two years have taught us anything, it's that organizations need their IT departments to survive and thrive in today's digital economy. This has now paved the way for IT leaders like yourself to expand on your department's maturity level and move out of a supportive role toward a more strategic role, enabling growth within your organization. The challenge then becomes, how do you move up the maturity ladder?"

We generally use moments in statistics, machine learning, mathematics, and other fields to describe the characteristics of a distribution. Let's say the variable of our interest is X then, moments are X's expected values. Now we are very familiar with the first moment(mean) and the second moment(variance). The third moment is called skewness, and the fourth moment is known as kurtosis. The third moment measures the asymmetry of distribution while the fourth moment measures how heavy the tail values are.

Syncron, the largest privately-owned global provider of cloud-based after-market service solutions, announced the general availability of Syncron Price Version 20.4, which delivers several new capabilities to further automate and accelerate after-market pricing functions. The new features include usability enhancements and more sophisticated controls, to enable more optimized pricing to be done in less time and with better outcomes. "As the global economy begins to recover in the post-pandemic era, manufactures must provide even more sophisticated techniques to drive smarter pricing decisions," said Erik Lindholm, Vice President of Product Management at Syncron. "Price must be driven by increasingly sophisticated machine learning, algorithms, and comprehensive analytics that can automatically pinpoint sources of revenue and margin changes using real-time data. Today's companies leverage our technologies to transform their pricing strategies into competitive advantages to maintain relevance and viability in an ever-changing, increasingly sophisticated market."

Artificial intelligence (AI) has been one of the most appealing technology in recent years.

The volume of data businesses produce today carries much significance in terms of overall growth. Foresighted companies know that if they want to vie in a highly-competitive market, they must deploy advanced analytics to ever-growing data sets. Using business intelligence allows them to look into their historical and current data sets, and it provides them predictive views of their business operations. Augmented by artificial intelligence and machine learning, business intelligence provides enterprises with decision-making context and recommendations. This significantly drives a move towards decision intelligence, the creative blend of technology into enterprise decision-making strategies and workflows.