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AI hallucination is a serious problem that can affect the quality and reliability of AI systems. It happens when an AI system produces an incorrect or misleading answer to a query, due to various factors such as insufficient training data or poor verification methods. This can have dire consequences, especially for AI systems that are used in critical domains such as military or healthcare.
Jaxon AI, a startup that originated from building AI systems for the U.S. Air Force, has developed a novel technology to address this challenge. The technology, called Domain-Specific AI Language (DSAIL), aims to reduce the risk of AI hallucination and increase the trustworthiness of AI solutions.
What is AI Hallucination and Why Is It a Major Challenge for AI Systems?
AI hallucination is a phenomenon where an AI system generates an inaccurate response to a query. The inaccuracy can be caused by several different factors, such as:
- Incomplete training data: The AI system may not have enough data to learn from, or the data may be biased, noisy, or outdated. This can lead to the AI system making wrong assumptions or generalizations about the domain or the task.
- Lack of verification: The AI system may not have a way to check or validate its output, or the verification method may be too weak or unreliable. This can lead to the AI system producing outputs that are inconsistent, contradictory, or illogical.
- Ambiguity and inconsistency: The AI system may not be able to handle the complexity and variability of natural language, or the language may have different meanings or interpretations in different contexts. This can lead to the AI system producing outputs that are vague, unclear, or irrelevant.
AI hallucination can have serious implications for the quality and reliability of AI systems, especially for applications that require high levels of accuracy and precision. For example, if an AI system hallucinates for content generation on a piece of text, it may not be an ideal situation, but it may not be necessarily catastrophic.
However, if an AI system hallucinates a piece of military technology, the outcome could likely have more severe consequences.
What is DSAIL and How Can It Solve the AI Hallucination Problem?
DSAIL is a technology that transforms natural language inputs into binary language, which is a more precise and logical way of representing information. Binary language can then be checked by various methods, such as boolean satisfiers, to ensure that the AI output meets all the constraints and requirements before being returned to the user.
DSAIL is based on the idea that different domains have different vocabularies, grammar, and semantics and that AI systems should be able to understand and respect these differences. By using binary language, DSAIL can capture the nuances and subtleties of each domain, and avoid the ambiguity and inconsistency that can lead to AI hallucination.
How DSAIL Works
DSAIL works by following three main steps:
How DSAIL Converts Natural Language into Binary Language
The first step of DSAIL is to convert natural language inputs into binary language. Binary language is a language that uses only two symbols, 0 and 1, to represent information. Binary language is more precise and logical than natural language, as it eliminates the possibility of ambiguity and inconsistency.
DSAIL uses a large language model (LLM) to perform the conversion. An LLM is a type of AI model that can generate natural language based on a given input. It uses an LLM that has been trained on a large corpus of text from various domains, such as science, law, and art. The LLM can then use its knowledge and skills to translate natural language inputs into binary language outputs.
How it Verifies the AI Output
The second step of DSAIL is to verify the AI output. Verification is the process of checking whether the AI output is correct and reliable, or whether it contains any errors or inaccuracies.
DSAIL uses a boolean satisfier to perform the verification. A boolean satisfier is a type of algorithm that can determine whether a given statement is true or false, based on a set of rules and constraints. DSAIL uses a boolean satisfier that has been customized for each domain, to ensure that the AI output meets the specific criteria and expectations of the domain.
How it Uses RAG and IBM, watsonx Models
The third step of DSAIL is to use Retrieval Augmented Generation (RAG) and IBM watsonx models to enhance the AI output. RAG and IBM watsonx are techniques and models that can help the AI system access and use external sources of information, such as knowledge bases and code libraries, to get more accurate and relevant answers.
RAG is a technique that allows the LLM to access a knowledge base, such as Wikipedia, to get more information and context for the query. RAG can help the LLM generate more factual and informative answers, and avoid generating outputs that are based on false or incomplete data.
IBM watsonx is a collection of open-source models that cover various aspects of AI, such as natural language processing, computer vision, and code generation. IBM watsonx can help the LLM generate outputs that are more diverse and creative, and that can perform various tasks, such as writing code, creating images, or composing music.
How Jaxon AI Uses DSAIL to Create Custom and Reliable AI Solutions
Jaxon AI is a startup that uses DSAIL to create custom and reliable AI solutions for various domains and applications. Jaxon AI originated from building AI systems for the U.S. Air Force, with requirements for the highest levels of reliability and accuracy.
Jaxon AI is now expanding into the broader enterprise market, with a developed technology that can address a major challenge in AI: hallucinations and inaccuracies in large language models.
Jaxon AI uses DSAIL as a core technology for its AI systems. Jaxon AI uses DSAIL to convert natural language inputs into binary language, verify the AI outputs, and use RAG and IBM watsonx models to enhance the AI outputs. Jaxon AI also uses DSAIL to customize the AI systems for each domain, by using domain-specific vocabularies, grammar, semantics, rules, and constraints.
Jaxon AI has a methodology for creating custom AI systems, which involves the following steps:
- Collecting the design and requirements from the user
- Generating the initial code for the AI project using StarCoder, a model from IBM watsonx that can automatically generate code based on the design and requirements
- Training and testing the AI system using it and other techniques and tools
- Deploying and monitoring the AI system using cloud platforms and services
How IBM watsonx Supports DSAIL and Jaxon AI with Its Open-Source Models
IBM watsonx is a foundation for Jaxon AI, as it provides the models that Jaxon AI uses as building blocks for its AI systems. IBM watsonx is a collection of open-source models that cover various aspects of AI, such as natural language processing, computer vision, and code generation.
IBM watsonx supports DSAIL and Jaxon AI with its open-source models in several ways:
- IBM watsonx provides the LLM that DSAIL uses to convert natural language inputs into binary language. The LLM is an open-source model that has been trained on a large corpus of text from various domains, such as science, law, and art.
- IBM watsonx provides StarCoder, the model that Jaxon AI uses to generate the initial code for the AI project. StarCoder is an open-source model that can automatically generate code based on the design and requirements provided by the user.
- IBM watsonx provides other models that DSAIL and Jaxon AI can use to enhance the AI outputs, such as models for natural language processing, computer vision, and code generation. These models can help the AI system perform various tasks, such as writing code, creating images, or composing music.
IBM watsonx is an open-source initiative that was launched in May, with support from ServiceNow and Hugging Face. IBM is one of the founding contributors to watsonx, and works closely with Hugging Face to bring open models to enterprise users.
IBM also has its code-generation models in its watsonx library, which are tailored to specific use cases. IBM has used its code-generation models to create AI solutions for various industries and domains.
Conclusion
AI hallucination is a major challenge that can compromise the quality and trustworthiness of AI systems. Jaxon AI has developed a novel technology, called DSAIL, that can reduce the risk of AI hallucination and create more reliable and accurate AI solutions. DSAIL is a technology that converts natural language inputs into binary language, which can then be verified by various methods.
DSAIL uses techniques such as RAG, and models from IBM’s watsonx foundation library, to enhance its capabilities and performance. DSAIL is a technology that promises to improve the state of the art in AI and benefit various domains and applications.
