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What are AI hallucinations? AI hallucination is a phenomenon wherein a large language model (LLM)—often a generative AI chatbot or computer vision tool—perceives patterns or objects that are nonexistent or imperceptible to human observers, creating outputs that are nonsensical or altogether inaccurate. Generally, if a user makes a request ...
Generative AI, sometimes called gen AI, is artificial intelligence (AI) that can create original content—such as text, images, video, audio or software code—in response to a user’s prompt or request. Generative AI relies on sophisticated machine learning models called deep learning models —algorithms that simulate the learning and ...
Researchers are tackling inaccuracies and unclear results to boost AI search reliability. A recent study found 32 different ways to reduce hallucinations in AI language models, all created in just the last few years. This includes teaching the AI when it should avoid giving an answer if it’s unsure. Bias is another issue.
Why is hallucination a concern for foundation models? Hallucinations can be misleading. These false outputs can mislead users and be incorporated into downstream artifacts, further spreading misinformation. False output can harm both owners and users of the AI models. In some uses, hallucinations can be particularly consequential.
Legal compliance. Legal accountability. Amplified. Model usage rights restrictions. Traditional. Generated content ownership and IP. Specific. Explore this atlas to understand some of the risks of working with generative AI, foundation models, and machine learning models.
A prompt injection is a type of cyberattack against large language models (LLMs). Hackers disguise malicious inputs as legitimate prompts, manipulating generative AI systems (GenAI) into leaking sensitive data, spreading misinformation, or worse. The most basic prompt injections can make an AI chatbot, like ChatGPT, ignore system guardrails and ...
Foundation models will dramatically accelerate AI adoption in business by reducing labeling requirements, which will make it easier for businesses to experiment with AI, build efficient AI-driven automation and applications, and deploy AI in a wider range of mission-critical situations. The goal for IBM Consulting is to bring the power of ...
The foundation models that are available in IBM watsonx.ai can generate output that contains hallucinations, personal information, hate speech, abuse, profanity, and bias. The following techniques can help reduce the risk, but do not guarantee that generated output will be free of undesirable content.
Large language models (LLMs) are a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks. LLMs have become a household name thanks to the role they have played in bringing generative AI to the forefront of ...
Cybersecurity and computer science. Prompt engineering is used to develop and test security mechanisms. Researchers and practitioners leverage generative AI to simulate cyberattacks and design better defense strategies. Additionally, crafting prompts for AI models can aid in discovering vulnerabilities in software.