A degree in computer science used to promise a cozy career in tech. Now, students' ambitions are shaped by AI, in fields that blend computing with analysis, interpretation, and data. In the early 2010s, nearly every STEM -savvy college-bound kid heard the same advice: Learn to code . Python was the new Latin. Computer science was the ticket to a stable, well-paid, future-proof life.

This study evaluates the performance of OpenAI's o1-preview model in higher-order cognitive domains, including critical thinking, systematic thinking, computational thinking, data literacy, creative thinking, logical reasoning, and scientific reasoning. Using established benchmarks, we compared the o1-preview models's performance to human participants from diverse educational levels. o1-preview achieved a mean score of 24.33 on the Ennis-Weir Critical Thinking Essay Test (EWCTET), surpassing undergraduate (13.8) and postgraduate (18.39) participants (z = 1.60 and 0.90, respectively). In systematic thinking, it scored 46.1, SD = 4.12 on the Lake Urmia Vignette, significantly outperforming the human mean (20.08, SD = 8.13, z = 3.20). For data literacy, o1-preview scored 8.60, SD = 0.70 on Merk et al.'s "Use Data" dimension, compared to the human post-test mean of 4.17, SD = 2.02 (z = 2.19). On creative thinking tasks, the model achieved originality scores of 2.98, SD = 0.73, higher than the human mean of 1.74 (z = 0.71). In logical reasoning (LogiQA), it outperformed humans with average 90%, SD = 10% accuracy versus 86%, SD = 6.5% (z = 0.62). For scientific reasoning, it achieved near-perfect performance (mean = 0.99, SD = 0.12) on the TOSLS,, exceeding the highest human scores of 0.85, SD = 0.13 (z = 1.78). While o1-preview excelled in structured tasks, it showed limitations in problem-solving and adaptive reasoning. These results demonstrate the potential of AI to complement education in structured assessments but highlight the need for ethical oversight and refinement for broader applications.

Artificial Intelligence (AI) is fast becoming a mainstay in our business operations. In fact, according to IDC, over the next three years, governments and businesses around the world will invest more than AU$723 billion in AI. Meanwhile, AI technology is projected to be integrated into 90% of the most cutting-edge enterprise applications by 2025. Already, it's beginning to transform everyday life. While it's undoubtedly an exciting time to be alive with all these technological advancements, it's vital to keep a pulse on the human component of technology, ensuring everyone benefits.

A recent report by the Institute for the Future, in partnership with Dell, predicts that 85% of jobs that will be available in 2030 haven't been invented yet. I don't think it's as crazy as it seems, especially when we think of everything that has changed in the last ten years, like social media, artificial intelligence, and automation. The work human beings do will continue to shift as some jobs become obsolete and new jobs emerge – and the experience and skill set we'll need in the future look very different from the ones we need today. Soft skills will grow in importance as the demand for the things machines can't do continues to increase. However, the ability to understand and work confidently with technology will still be critical.

I recently participated in a regional workshop of government, education, business, and social leaders where we were trying to ascertain and assess 1) the certainty of national trends and 2) the impact of those trends on the region. We reviewed many trends, including the growth of green jobs, growth in the Hispanic community, the decline in water quality and availability, the increasingly older population, declining enrollment trends, remote medicine, Electric Vehicle (EV) infrastructure. I was particularly interested in the trend "Growing Artificial Intelligence (AI) Industry." "The AI industry is expanding rapidly, and two metro areas have become important federal research and contracting centers for AI research. However, according to a 2021 Brooking study, "these two metro areas exhibit below-average commercialization activities in terms of per capita AI companies, job postings, and job profiles," suggesting an opportunity to use this capability to help spark job growth."

Agencies' digital transformation efforts in areas like artificial intelligence must also consider workforce needs, according to a panel of government technology experts. Speaking at an ATARC event on Thursday, the panelists asserted that it does not matter how good the data or AI is, if people do not know how to use it correctly or understand it. As a result, the panelists emphasized the need for data literacy, education and training. "I can build the best AI model, but if I put it in the hands of my investigator, and if he has a ton of questions, then we just lost them," Ben Joseph, chief data officer for the United States Postal Service Office of Inspector General, said. "Earlier this year, we actually punched out a small program in terms of data literacy…so we educate my workforce, investigators, auditors and everybody else, like'how do you interpret data?'" "It's almost like you have to right-size the AI education for the position or the role that the individual is playing in the lifecycle," William Streilein, chief technology officer at the Department of Defense's Office of the Chief Digital and Artificial Intelligence Officer, said.

We're seeing AI projects shift from hype to impact, largely because the right roles are getting involved to provide the business context that has been missing previously. Domain expertise is key; machines don't have the depth of context that people have, and people need to know the business and data well enough to understand which actions to take based on any insights or recommendations that are surfaced. When it comes to scaling AI, many leaders think they have a people problem--specifically, not enough data scientists. But not every business problem is a data science problem. Or at least, not every business challenge should be thrown at your data science team.

Artificial intelligence just doesn't pop up when you install tools and software. It takes planning and, most of all, it takes data. But getting the right data to make AI and machine learning algorithms -- and understanding it -- is where many organizations are slipping up, a recent study finds. Organizations face difficulties with data silos, explainability, and transparency, a study of 150 data executives commissioned by Capital One and Forrester Consulting finds. They say internal, cross-organizational, and external data silos slowed machine learning deployments and outcomes.

Due to the pandemic, most businesses are increasing their investments in AI. Organizations have accelerated their AI efforts to ensure their business is not majorly affected by the current pandemic. Though the implementation is a positive development in terms of AI adoption, organizations need to be aware of the challenges in adopting AI. Building an AI system is not a simple task. It comes with challenges at every stage. Even though you build an AI project, there are high chances of it failing upon deployment, which can be attributed to numerous reasons.

Sisense defines infusion as the practice of incorporating data and insights into end-user business applications. "Infusion is all about putting decision-supporting insights into a product in a way that feels native. And it's far more interesting," Scott Castle SVP of Product at Sisense says. Typically, a BI tool works by pulling data together to help end users draw their own conclusions. They aggregate data, slice and dice, figure it out, come to the insight and then, take action. Whereas, infusion speaks towards broadening perspective on what embedded analytics means to include more than just a chart to figure out.