Businesses of every size have been unable to escape the incredible impact that AI has had on the ways in which we do business of late.
From conglomerate to SME, organisations are becoming faster, more agile, and more robust as we outsource administrative and repetitive tasks to our AI co-workers.
One of the newest AI trends is the establishment of Large Language Models (LLMs) in the public domain: machine learning algorithms trained on colossal volumes of data to recognise the structures and patterns of natural language. They are capable of Natural Language Processing (NLP), which allows us to explore huge datasets through everyday questions or commands.
As such, LLMs are the most common way of making AI intelligible – to cite the most well-known example, LLMs are the means by which ChatGPT can answer your questions. But there’s one conventional drawback to that intelligence: it’s stuck in something of a time capsule.
LLMs are intensively trained, with millions upon millions of data points fired at them in a constant feedback loop to teach each model how to make sense of certain datapoints or patterns. But ‘operationalising’ an LLM – taking it off the training circuit and bringing it online as part of your infrastructure – obviously prevents it from learning anything new. Even some of the first versions of ChatGPT, if you ask a question about very recent events, will politely explain its own temporal limitations to you.
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