Stop tripping, GenAI

No, it’s not a question of a bunch of algorithms getting together and tripping on mushrooms. When it comes to artificial intelligence, the term “hallucinations” refers to the knack large language models, such as OpenAI’s ChatGPT or Google's PaLM, have for sometimes presenting entirely false information as factually correct. 

Naturally, this can lead to all kinds of precarious situations, ranging from the spread of misinformation and malicious code, to reinforcing biases through discriminatory statements and a loss of credibility and trust, both in the technology and those who use it. Additionally, models can sometimes inadvertently generate personal information, revealing data without consent and violating privacy. 

These not-so-benign hallucinations can also curb the contribution that generative AI can make to speeding up R&D in crucial applications such as pharmaceutical discoveries and decarbonisation. In a recent survey, only 22% out of 500 R&D workers said they trust these kinds of systems.

The main cause of AI hallucinations is training data issues. With black box AI being the LLM norm, it is also not possible to have direct insight into how the model has arrived at its predictions. However, one startup believes it has found the answer to making GenAI stop tripping. (Millenials at Burning Man might be a tougher nut to crack.)

Oslo-based Iris.ai has built an AI engine for understanding scientific text. The model scours vast quantities of research data, which it then analyses, categorises, and summarises. Obviously, false information presented as a result of hallucinations could have a hugely detrimental effect on the time, effort, and funds of researchers. 

But Iris.ai thinks it has come up with a solution. Read our senior reporter Thomas Macaulay’s interview with the company’s CTO to find out more.

👉 New technique makes AI hallucinations wake up and face reality

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