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Sustaining Innovation in Research: Innovations and Issues around Generative AI University of London

From creating realistic images to generating human-like text, not a month goes by where there isn’t a new, powerful model. AI did not catch a lot of my attention, and I am sure for anyone else, until the booming of the AI field especially when deep learning and generative models were developed and used to create genrative ai powerful applications, such as deep fakes or ChatGPT. People say nowadays that the era of artificial general intelligence is coming, in contrast to the past decades of artificial narrow intelligence (AI can only serve a small set of pre-specified purposes and automate tasks like ordinary software does).

Whether planning a new course or reviewing an old one, the planning process takes considerable effort. Systems like ChatGPT and Bing Chat, if prompted carefully and thoughtfully, can be invaluable in supporting this work. Transparency and accountability are essential when incorporating generative AI in eLearning. Learners should be aware that they are interacting with AI-based systems and understand the limitations and capabilities of the technology. Effective prompts should be specific and concise, clearly articulating the learning objective and guiding the AI in the direction of the desired response. Long, convoluted prompts can be confusing for the AI and make it difficult to achieve the desired outcome.

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The models underlying generative AI tools are trained through the ingestion of vast datasets. Over the course of 45 minutes, Google says you’ll learn how to use AI tools to save time, including how to cut down on administrative tasks, brainstorm new ideas, and write code. It also covers ways to apply different systems to a workplace, when and how to invest in AI, and how to prepare data for a machine learning project. In just the past few months, diffusion and large language models have revolutionized the field of machine learning.

For our Generative Artificial Intelligence (AI) online training package, you will require a laptop with headphones. Learn how to analyse AI-driven text using Natural Language Processing genrative ai (NLP), using GPT, AUTO-GPT, and Pandas. This practical course brings learners through how to perform tasks such as text classification, sentiment analysis, and named entity recognition.

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Spatial computing moves away from traditional screen-based interfaces toward more immersive experiences. You can keep track of your own progression throughout your online training course and ensure continuous improvement. There is no examination with our Generative Artificial Intelligence (AI) online training package.

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In the following content, we will explore the key principles of prompt engineering for instructional design, using generative AI tools for creating eLearning, and the best practices for applying Artificial Intelligence to corporate training development. Some best practices include ensuring that prompts are clear and concise, aligning prompts with learning objectives, and avoiding jargon and ambiguity. Additionally, instructional designers should regularly review and evaluate the AI-generated content to ensure that it is accurate and relevant. The most effective prompts are those that are aligned with the specific learning objectives of the training program. Before crafting prompts, it is important to clearly define the learning objectives and determine the specific knowledge or skills that learners should acquire.

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Alongside the statements that form the basis for a course, the planning team also have to think through how the course might best be delivered. When utilizing generative AI in instructional design for eLearning, it is crucial to be aware of and actively address potential biases that may arise in the AI-generated content. Bias can stem from the genrative ai training data used to train the AI model or from inherent biases in the algorithm itself. Generative AI can be used to create course outlines that are tailored to the specific needs of learners. Once a knowledge gap is identified, you can use AI to create customized learning paths that help learners achieve their specific learning objectives.

Generative AI is here to stay, but teaching and learning is an area where higher education institutions can shape the international agenda for its use. Participants will understand the basics of Generative AI and how it can be used to generate text in various domains. Participants will also gain hands-on experience in training Generative AI models using Google Cloud’s Vertex AI Generative AI Studio and Model Garden.

For example, a chatbot like ChatGPT generally has a good idea of what word should come next in a sentence because it has been trained on billions of sentences and “learnt” what words are likely to appear, in what order, in each context. If you want to know more about ChatGPT, AI tools, fallacies, and research bias, make sure to check out some of our other articles with explanations and examples. If we don’t already have the course you’re looking for, email [email protected] with your ideas and questions. The position is far from clear-cut and we intend to monitor these cases with interest. This session is supported by the ScreenSkills Unscripted TV Skills Fund which invests in training for the unscripted workforce thanks to contributions from broadcasters, SVoDs and production companies. I once had a boss in a research institute who was proud of his ability to dictate COBOL programs to his secretary that compiled first time.