AI hallucinations are errors or inaccuracies in AI’s outputs, usually stemming from insufficient or biased data. They are often found in outputs from large language models and can cause real issues for businesses with them essentially resulting in fake, biased, or flawed answers.
The Roots of AI Hallucinations
Large language models (LLMs) are trained on massive datasets, absorbing patterns and information from these sources. However, if the training data is biased or incomplete, the AI may produce outputs that reflect those flaws. This is because when responding to a query, LLMs can sometimes piece together fragments of inaccurate or biased information, leading to incorrect or misleading responses.
Mitigating LLM AI Hallucinations: A Multifaceted Approach
To reduce the risk of AI hallucinations when using LLMs, several strategies can be employed:
Diverse and High-Quality Data: Training AI models on diverse, representative datasets is crucial. This helps ensure that the AI learns from a variety of perspectives and avoids biases.
Reinforced Learning: Techniques like reinforced learning can be used to guide AI models towards more accurate and desirable outputs.
Enhanced Probability Matrices: By refining probability matrices, AI models can be made more likely to generate accurate and relevant responses.
The Challenge of Third-Party AI
However, it is crucial to remember that if you are a business relying on third-party AI tools, you actually have very limited control over the underlying data and models. While careful querying can help mitigate some issues, the reality is that flawed data in the model can (and will) lead to flawed outputs.
As such, you have to seriously consider whether an LLM is right for you and your business, and if you can risk these errors /‘hallucinations’.
The Power & Accuracy of Small Language Models (SLMs)
To address these limitations of third-party AI and the issues of error and bias with LLMs, consider specialized SLMs instead.
These models are trained on smaller, more focused datasets, making them better suited for specific tasks. By avoiding the vast amounts of potentially biased data used to train large language models, SLMs can produce more accurate and reliable results.
For instance, an SLM trained on a company’s proprietary supply chain data can provide highly customized recommendations without being influenced by external biases. This can improve operational efficiency, reduce errors, and support better decision-making.
Boudica AI
Boudica is OmniIndex’s own dedicated Small Language Model (SLM) AI engine. It is native to all OmniIndex products and services and is uniquely able to perform computations and answer queries on fully encrypted data. Learn more.
Other Considerations
When adopting AI, it’s crucial to prioritize data privacy and security. This includes ensuring that the AI tool you choose does not harvest or misuse your sensitive data.
Unfortunately, regulations often fall short in addressing this issue, as users may inadvertently consent to data sharing through terms of service agreements. Therefore, it’s imperative to carefully review the privacy policies and terms of service of any AI tool you consider adopting. Look for explicit assurances regarding data protection, transparency, and the tool’s commitment to ethical AI practices.
Additionally, consider using AI tools that offer more granular control over data sharing and usage. This might include options to limit the amount of data shared, restrict access to sensitive information, or opt out of certain data collection practices altogether. By taking these proactive steps, you can help mitigate the risks associated with data privacy and ensure that your sensitive information is protected.
OmniIndex
And yes, you guessed it! OmniIndex does not harvest any of your data:
OmniIndex Inc and its associated companies along with their partners do not store any data that comes through PostgresBC. This data resides only in memory during the processing of an item of content and at no time does it pass out of the control of the customer.
No personally identifiable information (PII) is stored by OmniIndex.
The OmniIndex PostgresBC data platform has no mechanism to view, manipulate or harvest data that is stored within itself.
