We transitioned our post-CS1 course that introduces various subfields of computer science so that it integrates Large Language Models (LLMs) in a structured, critical, and practical manner. It aims to help students develop the skills needed to engage meaningfully and responsibly with AI. The course now includes explicit instruction on how LLMs work, exposure to current tools, ethical issues, and activities that encourage student reflection on personal use of LLMs as well as the larger evolving landscape of AI-assisted programming. In class, we demonstrate the use and verification of LLM outputs, guide students in the use of LLMs as an ingredient in a larger problem-solving loop, and require students to disclose and acknowledge the nature and extent of LLM assistance. Throughout the course, we discuss risks and benefits of LLMs across CS subfields. In our first iteration of the course, we collected and analyzed data from students pre and post surveys. Student understanding of how LLMs work became more technical, and their verification and use of LLMs shifted to be more discerning and collaborative. These strategies can be used in other courses to prepare students for the AI-integrated future.

Artificial intelligence has demonstrated immense promise when applying machine learning to support the overall processing of large datasets, particularly in the banking and financial services industry. Sixty percent of financial services companies have implemented at least one form of AI, ranging from virtual assistants communicating with customers and the automation of workflows to managing fraud and network security. This largely stems from lack of understanding of how the system works and a continual concern around opacity, unfair discrimination, ethics and dangers to privacy and autonomy. These biases can create increased consumer friction, poor customer service, fewer sales and revenue, unfair or illegal behaviors, and potential discrimination. Achieving trustworthy AI requires close examination and the ability to identify what factors contribute to each bias to make a more informed decision about what actions should be taken after identification.

Artificial intelligence has demonstrated immense promise when applying machine learning to support the overall processing of large datasets, particularly in the banking and financial services industry. Sixty percent of financial services companies have implemented at least one form of AI, ranging from virtual assistants communicating with customers and the automation of workflows to managing fraud and network security. Despite these advancements in efficiency and automation, complexities from the inner workings of AI models often create a "black box" issue. This largely stems from lack of understanding of how the system works and a continual concern around opacity, unfair discrimination, ethics and dangers to privacy and autonomy. In fact, the lack of transparency in system operation is frequently linked to hidden biases.

Amazon Web Services used a big data conference in the backyard of some of its largest government customers to showcase its AI and machine learning tools that are helping to funnel ever-larger volumes of data into its storage and computing infrastructure. Making a pitch for better data management tools like metadata systems, AWS executives addressing a big data conference in Tysons Corner, Va., said the the public cloud giant aims to go beyond democratizing big data to "demystify" AI and machine learning. The combination of organized data and analytics will accelerate the building and deployment of machine learning models, many that currently never make it to production. Those that are deployed often require up to 18 months to roll out, noted Ben Snively, a solution architect at AWS (NASDAQ: AMZN). Open source tools for model development often advance a generation or two in the time it takes many enterprises to develop, train and launch a machine learning model, he added.

In this article, we look at two machine learning (ML) techniques, Naive Bayes classifier and neural networks, and demystify how they work. With all the hype surrounding self-driving cars and video-game-playing AI robots, it's worth taking a step back and reminding ourselves how machine learning programs actually "learn". In this article, we look at two machine learning (ML) techniques–spam filters and neural networks–and demystify how they work. And if you're not sure what machine learning even is, read about the difference between artificial intelligence, machine learning, and deep learning. One common machine learning algorithm is the Naive Bayes classifier, which is used for filtering spam emails.

Beware of relying uncritically on big data computer systems, warns a St. Mary's University professor undertaking a five-year research project dubbed Where Science Meets Fiction: Social Robots and the Ethical Imagination. "There are real dangers now with big data," said Dr. Teresa Heffernan, the St. Mary's University professor undertaking the research project. "Algorithms have the same biases as humans." With her research project, the professor is hoping to demystify the technology that creates artificial intelligence and bring together experts from all walks of life to begin a dialogue about how humans and these machines should interact -- what to do and what not to do. "I want to shift the conversation that has been shaped by Silicon Valley . . . to make it more open and question the rhetoric, to demystify the technology and expose how the technology works rather than be dominated by it," said Heffernan.

The thing about technology evolution that the movies don't quite tell you is that it desperately relies on adoption. The most amazing invention on the planet could be created and if there's not widespread consumer demand, or if it's prohibitively expensive for businesses, that breakthrough isn't going to actually break through. The old Henry Ford quote is more relevant than ever: "If I had asked people what they wanted, they would have said faster horses." However, by demonstrating the value and efficiency of new tools, we can push modern business forward exponentially – unignorably. Read about the new best practices for the ERP systems and how to tackle the growth of ERP integrations.