Introduction to Generative AI
Generative artificial intelligence (AI) refers to algorithms (such as ChatGPT) that can be used to generate new content, such as voice, code, images, text, simulations, and movies. Recent advancements in the industry have the potential to radically alter our approach to content development.
To read full articel, click the link: What is generative AI?
Generative AI encompasses a wide range of content creation, as well as the use of huge data to generate new and tailored outcomes, transforming businesses and enabling a wide range of applications in a variety of sectors.
Defining Generative AI:
AI learns from existing material to create a variety of content such as text, imagery, audio, and data.
AI vs. Machine Learning:
AI is a field that studies autonomous reasoning systems. A subset, machine learning, uses data to train models without explicit programming.
Supervised vs. Unsupervised Learning:
Labeled data is used in supervised learning to anticipate outcomes. Unsupervised learning groups data into groups based on inherent patterns.
Deep Learning & Neural Networks:
Complex patterns are processed by neural networks. A subclass of deep learning use multilayer networks to learn complicated patterns.
Generative vs. Discriminative Models:
Discriminative models forecast labels, whereas generative models generate new data instances from existing probability distributions.
The Role of Generative AI:
It creates new material (text, graphics, and audio) based on previously learnt structures in existing data.
Transformers and Hallucinations:
Transformers encode and decode data; hallucinations and illogical output are possible as a result of insufficient training or context.
Prompt Design:
Prompt design aids in controlling output in generative AI, which is primarily reliant on available training data.
Model Types & Applications:
Different models (text-to-text, text-to-image, and so on) serve a variety of functions ranging from language translation to code development.
Foundation Models:
Large pre-trained models that can adapt to different tasks such as sentiment analysis or object recognition are transforming businesses.
Applications of Generative AI:
Using Generative AI Studio and App Builder, you can generate code, debug it, create documentation, and develop apps without writing any code.
PaLM API & Maker Suite:
Access to Google's big language models for ML model prototyping, training, deployment, and monitoring.
Understanding Generative AI: Foundations, Control, and Outputs:
Generative AI, a facet of artificial intelligence (AI), creates new material in a variety of mediums, such as text, photos, audio, and video, by learning from current data to produce new and different results. It may comprise training an AI model on cat photographs to generate new feline visuals, while a discriminative AI model trained on images of both cats and dogs categorizes new visuals. Foundation models, which are big AI models that have been pre-trained on vast amounts of data, are adaptable to a variety of tasks such as sentiment analysis, image captioning, and object recognition. Insufficient or noisy training data, as well as a lack of contextual knowledge, are factors that contribute to AI model hallucinations. A prompt, a brief input to a big language model, exerts control over its output in a variety of ways.
Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). The AI system focuses on customer service interactions and is a hybrid of GPT and bespoke ML algorithms. It predicts call resolution and handling time by analyzing trends in labeled customer-agent conversations. A focus on empathy, technical support, and professional language alleviates concerns about text production. The implemented system gives real-time suggestions for agents' responses as well as access to internal material based on conversation history. It is designed to help rather than replace human agents, with ideas available only to agents who have complete control over their use. When there is insufficient data, proposals are withheld so that agents can make informed decisions. The National Bureau of Economic Research. Generative AI at Work (Working Paper No. 31161). Retrieved from http://www.nber.org/papers/w31161
Generative AI: Perspectives from Stanford HAI
Generative AI, a subset of AI, creates new content from inputs such as text, drawings, or music. It is based on broad foundational models that have been taught and customized for specific activities. While technology provides several options for productivity and creativity, it also raises concerns about prejudice and information trust. Stanford experts from several subjects discuss their perspectives on how this technology affects their fields, highlighting the importance of multidisciplinary collaboration to ensure the technology's positive influence.
Solving Inequalities in Education:
Peter Norvig,
Distinguished Education
Fellow at Stanford HAI
"We don’t have enough
tutors to provide this
level of interaction for
every learner…there is the
possibility [to] augment
human teachers in this role"
In education, direct, customized connections between tutors and learners promote optimal learning, although this isn't always possible due to restricted resources. Large language models can supplement human teachers to deliver specialized teaching, bridge gaps, and stimulate individualized learning. However, due to the possible harm and deceptive outputs of current AI models, vigilance is essential. Pre-curated responses, training new teachers, verifying materials, using Socratic questioning, peer learning, reinforcement learning, and establishing clear norms for AI conduct can all help to reduce hazards. Nonetheless, staying ahead in this changing terrain is a constant task, balancing possible benefits with the ever-present hazards of misuse and fraud.
Adverse Impact of AI on Education: Rob Reich's Perspective
Rob Reich, Professor of Political Science; Director of Stanford McCoy Family Center for Ethics in Society; Associate Director of Stanford HAI
"Calculators have proven to promote accuracy, remove some of the more tedious work, and make math more enjoyable for many. ChatGPT is not like a calculator."
The most recent AI breakthroughs provide powerful automatic writing tools that improve professional work but diminishing creative capacities in schooling. The issue is that students rely on these tools instead of refining their own writing skills, which are necessary for clear thinking. While some AI engineers were cautious at first, the rush to commercialize these products lacked adequate protections. To avoid future educational disasters, there is a demand for AI developers, legislators, and industry to work together to establish principles similar to ethical rules in biotechnology. This proactive strategy is critical for avoiding negative ethical and societal consequences, highlighting the importance of shared norms before societal ramifications occur.
Coursera: Google Cloud's Introduction to Generative AI
About This Course:
This is a beginner-level microlearning course that explains what Generative AI is, how it works, and how it differs from typical machine learning methods. It also covers Google Tools, which will assist you in developing your own Gen AI apps.
Sources/ References:
Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at Work (Working Paper No. 31161). National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w31161
Generative AI: Perspectives from Stanford HAI: Generative AI: Perspectives from Stanford HAI
