Basic AI terminology includes:
Large language models (LLMs)- Deep learning
- Supervised learning
- Transformers
- Foundation models
- Language
- Self-supervised learning
- Generative AI
It is important to democratize AI, leveraging the energy and the transparency of open science and open-source AI so that we all have a voice in:
What AI is- What AI does
- How AI is used
- How AI impacts society
- How AI integrates with your business
To help you effectively, safely, and responsibly put AI to work, you must:
Protect your data- Embrace principles of transparency and trust
- Make sure that your AI is implemented ethically
- Empower yourself with platforms and processes to control your AI destiny
- Foundation models are trained using self-supervised learning. If you give the model a few words in a prompt, it can mathematically predict the likelihood of words in the response.
There are basically three models of AI consumption, including:
- Embedded AI
- APIs
- Platform models
- The future of AI is not about one model, it's multi model.
- Traditional AI can analyze data and tell you what it sees. Generative AI can use that same data to create something new.
- A foundation model is a general-purpose AI model that's trained unsupervised, usually on large amounts of text or data that come from the internet.
- Foundation models are what will form the basis of generative AI for businesses.
To get started with generative AI, the steps are:
- Establish a team.
- Pick an internal low risk use case and build a prototype.
- Have an in-depth conversation about what you require to obtain value drivers.
- Operate with a level of responsibility and transparency.
Some key success factors include:
Choosing the right evaluation metrics- Understanding that one model doesn't have to rule them all
- Choosing a platform that has proven expertise and foundation models, and ensuring that governance tools are in place
- Trust is critical for AI success in enterprises.
- Hallucination, leakage of private information, and bullying are new risks with the advent of generative AI.
- Three key customer service areas that generative AI can significantly improve include: self-service, agents in the contact center or in the field, and contact center operations.
Five-step approach for driving the execution include:
- Have a clear idea of the experience you want to deliver.
- Understand your customers well.
- Look at the best tools that can support those channels.
- Once you have your toolchain sorted, design the journey end to end, so it delivers on the service strategy, the experience you had defined when you started.
- Application modernization involves taking an outdated computing system and updating it with modern, well aligned capabilities and features to create new business value.
- Once you make the decision to modernize, the process will kick off in two phases: Advisory and Planning.
Benefits of using AI include:
- Radically streamline the workload migration to the new architecture
- Enable teams to observe workload performance in the new environment so they can optimize and refine the process
- Ability to learn and adapt faster
Link for the course https://www.coursera.org/learn/generative-ai-for-executives-business-leaders/supplement/ivayK/course-summary-ai-for-business-leaders
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