Training
Generative AI begins with a "foundation model"; a deep learning model that serves as the basis for multiple different types of generative AI applications.
The most common foundation models today are large language models (LLMs), created for text generation applications. But there are also foundation models for image, video, sound or music generation, and multimodal foundation models that support several kinds of content.
To create a foundation model, practitioners train a deep learning algorithm on huge volumes of relevant raw, unstructured, unlabeled data, such as terabytes or petabytes of data text or images or video from the internet. The training yields a neural network of billions of parameters encoded representations of the entities, patterns and relationships in the data that can generate content autonomously in response to prompts. This is the foundation model.
This training process is compute-intensive, time-consuming and expensive. It requires thousands of clustered graphics processing units (GPUs) and weeks of processing, all of which typically costs millions of dollars. Open source foundation model projects, such as Meta's Llama-2, enable gen AI developers to avoid this step and its costs.
Tuning
Next, the model must be tuned to a specific content generation task. This can be done in various ways, including:
- Fine-tuning, which involves feeding the model application-specific labeled data, questions or prompts the application is likely to receive, and corresponding correct answers in the wanted format.
- Reinforcement learning with human feedback (RLHF), in which human users evaluate the accuracy or relevance of model outputs so that the model can improve itself. This can be as simple as having people type or talk back corrections to a chatbot or virtual assistant.
Generation, evaluation and more tuning
Developers and users regularly assess the outputs of their generative AI apps, and further tune the model even as often as once a week for greater accuracy or relevance. In contrast, the foundation model itself is updated much less frequently, perhaps every year or 18 months.
Another option for improving a gen AI app's performance is retrieval augmented generation (RAG), a technique for extending the foundation model to use relevant sources outside of the training data to refine the parameters for greater accuracy or relevance.
AI agents and agentic AI
An AI agent is an autonomous AI program, it can perform tasks and accomplish goals on behalf of a user or another system without human intervention, by designing its own workflow and using available tools (other applications or services).
Agentic AI is a system of multiple AI agents, the efforts of which are coordinated, or orchestrated, to accomplish a more complex task or a greater goal than any single agent in the system could accomplish.
Unlike chatbots and other AI models which operate within predefined constraints and require human intervention, AI agents and agentic AI exhibit autonomy, goal-driven behavior and adaptability to changing circumstances. The terms “agent” and “agentic” refer to these models’ agency, or their capacity to act independently and purposefully.
One way to think of agents is as a natural next step after generative AI. Gen AI models focus on creating content based on learned patterns; agents use that content to interact with each other and other tools to make decisions, solve problems and complete tasks. For example, a gen AI app might be able to tell you the best time to climb Mt. Everest given your work schedule, but an agent can tell you this, and then use an online travel service to book you the best flight and reserve a room in the most convenient hotel in Nepal.
Benefits of AI
AI offers numerous benefits across various industries and applications. Some of the most commonly cited benefits include:
- Automation of repetitive tasks.
- More and faster insight from data.
- Enhanced decision-making.
- Fewer human errors.
- 24x7 availability.
- Reduced physical risks.
Automation of repetitive tasks
AI can automate routine, repetitive and often tedious tasks including digital tasks such as data collection, entering and preprocessing, and physical tasks such as warehouse stock-picking and manufacturing processes. This automation frees to work on higher value, more creative work.
Enhanced decision-making
Whether used for decision support or for fully automated decision-making, AI enables faster, more accurate predictions and reliable, data-driven decisions. Combined with automation, AI enables businesses to act on opportunities and respond to crises as they emerge, in real time and without human intervention.
Fewer human errors
AI can reduce human errors in various ways, from guiding people through the proper steps of a process, to flagging potential errors before they occur, and fully automating processes without human intervention. This is especially important in industries such as healthcare where, for example, AI-guided surgical robotics enable consistent precision.
Machine learning algorithms can continually improve their accuracy and further reduce errors as they're exposed to more data and "learn" from experience.
Round-the-clock availability and consistency
AI is always on, available around the clock, and delivers consistent performance every time. Tools such as AI chatbots or virtual assistants can lighten staffing demands for customer service or support. In other applications such as materials processing or production lines, AI can help maintain consistent work quality and output levels when used to complete repetitive or tedious tasks.
Reduced physical risk
By automating dangerous work such as animal control, handling explosives, performing tasks in deep ocean water, high altitudes or in outer space, AI can eliminate the need to put human workers at risk of injury or worse. While they have yet to be perfected, self-driving cars and other vehicles offer the potential to reduce the risk of injury to passengers.
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