The impressive essay writing and problem-solving capabilities of large language models (LLMs) like OpenAI's ChatGPT have opened up new avenues in education. Our goal is to gain insights into the widespread use of LLMs among secondary students to inform their future development. Despite school restrictions, our survey of over 300 middle and high school students revealed that a remarkable 70% of students have utilized LLMs, higher than the usage percentage among young adults, and this percentage remains consistent across 7th to 12th grade. Students also reported using LLMs for multiple subjects, including language arts, history, and math assignments, but expressed mixed thoughts on their effectiveness due to occasional hallucinations in historical contexts and incorrect answers for lack of rigorous reasoning. The survey feedback called for LLMs better adapted for students, and also raised questions to developers and educators on how to help students from underserved communities leverage LLMs' capabilities for equal access to advanced education resources. We propose a few ideas to address such issues, including subject-specific models, personalized learning, and AI classrooms.

Over 3 days in a face-to-face professional development, a team of educators and researchers (including myself) led an AI capacity-building workshop and facilitated the development of lessons for students that involved the use or creation of AI technologies. In a previous article about AI, I mentioned how most schools will be caught by surprise by how advanced AI has become and what it means for the classroom. This school will not only be ready, but will be co-facilitating the use of AI in lessons and will encourage students to build and use AI. The school is called the Darunsikkhalai School for Innovative Learning (Bangkok) and from my experience and observation is the most innovative school on the planet. Sounds like a bold statement, but let me explain.

Businesses are evolving and searching for newer ways to accomplish their goals, hence the need for artificial intelligence (AI). AI involves building smart machines to carry out tasks that typically need human intelligence, and AI simulates human intelligence using computer systems. The two major AI types used in businesses today are reactive machines and limited memory. Reactive AI machines are programmed with predictable outputs based on the input they receive. So, they use their intelligence to perceive the world and respond to identical situations similarly.

Kevin Markarian is the cofounder of Roopler, an AI-driven lead generation platform built for the real estate industry. Artificial intelligence is rapidly upending how people do business across industries, and yet skeptics still abound. But is there really a reason to fear AI? AI will change how we work and do business, and its impact is already being felt. Still, that doesn't mean it is something to fear. On the contrary, business managers and leaders who embrace AI and harness its potential now have everything to gain.

It's no secret the medtech industry has embraced artificial intelligence (AI) and machine learning (ML). Pharmaceutical companies are leaping into the AI/ML space, too. There have been about 100 partnerships that have been established between pharmaceutical companies and AI vendors, according to a report from clinicaltrialsarena.com citing GlobalData Healthcare data. Earlier today (Wednesday), Merck announced its plan to dive deeper into the space. The pharma powerhouse said it was launching the Merck Digital Sciences Studio (MDSS), which will help early-stage biomedical startups with direct investment, access to powerful Azure Cloud computing, and opportunities to pilot their technologies in collaboration with discovery and clinical scientists at Merck.

If you are thinking AI is a big thing for small businesses, then think again. Sure enough, IT powerhouses like Amazon and USP are relying on AI robots to enhance employee experience. But then, the fact is, even start-ups are readily experimenting with AI technologies, big-time. For instance, there's a pizza start-up in Mountain View, California that is employing several robots to assist humans in assembling and baking pizzas. The point is AI is not just the province of big businesses.

It is been decades now that new modern advancements in technology have professionally impacted the way people live, work and play. Thereby machine learning implemented almost every aspect of modern life. While the real thought of robots leading surgeries or any kind of app centred data-keeping treatment plans little advancement which is for the better. So automating processes in the healthcare industry would positively affect patients and workers. It is enumerated that smart structures with advanced machine learning support are known as intelligent apps.

Thomas Hayes "Tom" Davenport, Jr. (born October 17, 1954) is an American academic and author specializing in analytics, business process innovation, knowledge management, and artificial intelligence. He is currently the President's Distinguished Professor in Information Technology and Management at Babson College, a Fellow of the MIT Initiative on the Digital Economy, Co-founder of the International Institute for Analytics, and a Senior Advisor to Deloitte Analytics. Davenport has written, coauthored, or edited twenty books, including the first books on analytical competition, business process reengineering and achieving value from enterprise systems, and the best seller, Working Knowledge (with Larry Prusak) (Davenport & Prusak 2000), on knowledge management. He has written more than one hundred articles for such publications as Harvard Business Review, MIT Sloan Management Review, California Management Review, the Financial Times, and many other publications. Davenport has also been a columnist for The Wall Street Journal, CIO, InformationWeek, and Forbes magazines.

But when risk models are built using AI, it may be much harder to pin down what insurance companies are basing higher premiums on, he said. For instance, if companies use neural nets, an AI technique that's the basis for deep learning, the resulting model is basically an opaque box. Insurance companies would know what factors were used to train their AI model, and using the models to evaluate new customers would be as simple as feeding it the same types of inputs, but companies wouldn't know how the model internally related those factors to risk and which inputs are more important.

One of the great challenges we have seen businesses face in recent years is how they approach data and analytics (and now artificial intelligence) when their industries are undergoing major transformation. It's hard enough to create a data-driven culture, compete on analytics, develop data-driven products and services, and so forth under normal business conditions, as we noted in our March column about the newest NewVantage Partners survey on big data and AI. But doing it while your business and industry are transforming -- the old line of changing out a jet engine while the plane is flying through turbulence at 35,000 feet -- is really tough. It's so difficult, in fact, that we always have our doubts when executives claim to have done it successfully. We are much more trusting when we're told that the organization is simply making progress toward the goal.