Difference between revisions of "Generative AI"

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* [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
 
* [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
 
* [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
 
* [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
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* [[AI Solver]]
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* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
 
* [[Discriminative vs. Generative]]
 
* [[Discriminative vs. Generative]]
 
* [[Assistants]] ... [[Personal Companions]] ... [[Agents]]  ... [[Negotiation]] ... [[LangChain]]
 
* [[Assistants]] ... [[Personal Companions]] ... [[Agents]]  ... [[Negotiation]] ... [[LangChain]]

Revision as of 21:37, 12 July 2023

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Generative AI is technology that creates original content, including text, images, video, and computer code, by identifying patterns in large quantities of training data. Generative AI could improve the speed and accuracy of product research and development. Generative AI technology can also help with product engineering by allowing teams to simulate products in virtual environments. This allows for complex problems to be solved more quickly and efficiently, leading to improved design accuracy. - The Generative AI Revolution Is Creating The Next Phase Of Autonomous Enterprise | Mark Minevich - Forbes


Generative AI are predictive models, filtering available information to draw inferences on what should logically follow


Generative vs Discriminative

Background: What is a Generative Model? | Google More formally, given a set of data instances X and a set of labels Y:

  • Generative models capture the joint probability p(X, Y), or just p(X) if there are no labels.
  • Discriminative models capture the conditional probability p(Y | X).

A generative model includes the distribution of the data itself, and tells you how likely a given example is. For example, models that predict the next word in a sequence are typically generative models (usually much simpler than GANs) because they can assign a probability to a sequence of words.

Informally:

  • Generative models can generate new data instances.
  • Discriminative models discriminate between different kinds of data instances.




Deep Generative Modeling


Generative Modeling Language


Hallucination

Hallucination is a term used to describe when Large Language Models (LLMs) generate text that is factually incorrect or entirely fictional. This can happen because the model’s primary objective is to generate text that is coherent and contextually appropriate, rather than factually accurate1. Hallucination can be a serious problem for LLMs because it can lead to the spread of misinformation, expose confidential information, and create unrealistic expectations about what LLMs can do. Hallucination refers to mistakes in the generated text that are semantically or syntactically plausible but are in fact incorrect or nonsensical1. This occurs because the model’s primary objective is to generate text that is coherent and contextually appropriate, rather than factually accurate. The model’s training data may contain inaccuracies, inconsistencies, and fictional content, and the model has no way of distinguishing between fact and fiction. As a result, it may generate text that aligns with the patterns observed in the training data but is not grounded in reality. From a technical perspective, hallucination in language models can be attributed to a lack of ground truth from external sources.

AI Hallucination are like human exaggerations

Ground truth refers to data that accurately represents the real-world phenomena or outcomes that an AI model aims to predict, classify, or generate. Ground truth is information that is known to be real or true, provided by direct observation and measurement (i.e. empirical evidence) as opposed to information provided by inference1. It refers to data that accurately represents the real-world phenomena or outcomes that an AI model aims to predict, classify, or generate.

MIT GenAI Summit


SynthAI

To date, generative AI applications have overwhelmingly focused on the divergence of information. That is, they create new content based on a set of instructions. In Wave 2, we believe we will see more applications of AI to converge information. That is, they will show us less content by synthesizing the information available. Aptly, we refer to Wave 2 as synthesis AI (“SynthAI”) to contrast with Wave 1. While Wave 1 has created some value at the application layer, we believe Wave 2 will bring a step function change. -For B2B Generative AI Apps, Is Less More? | Zeya Yang and Kristina Shen - Andreessen Horowitz


As we think through what Wave 2 might look like, we believe the use cases that will benefit most from synthesis AI will be when there is both:

A high volume of information, such that it’s not pragmatic for a human to manually sift through all the information. A high signal-to-noise ratio, such that the themes or insights are obvious and consistent. In the name of accuracy, you don’t want to task an AI model with deciphering nuance. In the diagram below, we categorize examples of common analysis and synthesis by these dimensions to help bring this to life.


Generative AI Adoption Methods

The adoption of Generative AI involves incorporating generative models and algorithms into various applications and domains. Generative AI techniques enable the creation of new and realistic content, such as images, text, music, or videos, by learning patterns from existing data. Here are the key methods of adopting Generative AI:

  • Training Custom Models: One adoption method involves training custom generative models specific to a particular use case or dataset. This method requires collecting and preprocessing relevant data and training the model to learn from that data. By fine-tuning the model's parameters and architecture, developers can create generative models tailored to their specific needs.
    • This method offers flexibility and control, allowing developers to focus on the unique characteristics and requirements of their application. However, it requires expertise in data collection, model training, and optimization.
  • Utilizing Pretrained Models: Another adoption method is to use pretrained generative models that have been trained on large-scale datasets by experts or organizations. Pretrained models capture general patterns and styles from diverse data sources and can be readily used for content generation tasks. Developers can integrate these models into their applications without the need for extensive training or data collection.
    • This method offers convenience and saves time as developers can leverage existing models that have already learned complex patterns. It is suitable for applications that do not require customization or fine-grained control over the generated content.
  • API Services: Many companies and cloud service providers offer Generative AI as a service through APIs. These APIs provide developers with a simple and accessible way to integrate generative capabilities into their applications. By leveraging these services, developers can make API calls to generate content without the need to directly train or manage generative models.
    • API services provide convenience, scalability, and consistent performance as the underlying generative models are maintained and optimized by the service providers. Developers can focus on utilizing the generative capabilities without worrying about the complexities of model training and deployment.
  • Frameworks and Libraries: There are various open-source frameworks and libraries available that provide tools, prebuilt models, and APIs for Generative AI. These frameworks, such as TensorFlow, PyTorch, or Keras, offer a wide range of functionalities and utilities for building and deploying generative models.
    • Leveraging frameworks and libraries provides developers with flexibility, customization, and control over the generative models. They offer extensive documentation, tutorials, and community support, making it easier to adopt and implement Generative AI solutions.
  • Collaboration and Knowledge Sharing: The adoption of Generative AI can be facilitated through collaboration and knowledge sharing within the AI community. Developers, researchers, and enthusiasts can collaborate on open-source projects, contribute to shared repositories, and participate in online forums or conferences dedicated to Generative AI.
    • Collaboration and knowledge sharing accelerate the adoption process by pooling expertise, sharing resources, and collectively advancing the state of Generative AI. It fosters innovation and encourages the development of best practices and guidelines for effective adoption.
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