ASK a question on almost any topic and ChatGPT has a reasonable answer ready. You can ask it to write a song or give you a 5-part framework for a corporate digital strategy. On most general topics, like the one in our example, the output will likely be sensible. But on more specific questions, it might get a fair amount of detail wrong.
People have used generative AI to negotiate discounts on phone bills, dispense therapy to real-life patients, write Python code, poems, songs or novels and to take (or cheat in) exams. Generally, large language models (LLMs) produce good results that appear amazing.
As such, they could signal a shift in the way communications and businesses work. But it would be all too easy to assume that it’s time to make room for our AI overlords. Several writers have, with some irony, written about how AI will likely put them out of business. That sort of panic is a mistake. To understand the potential, let’s look at how AI tools like ChatGPT work, what they’re capable of and how businesses can use them.
What’s behind the interface?
THE most recent generation of AI is based on LLMs. Interestingly, ChatGPT combines an LLM with an interaction layer that uses reinforcement learning.
An LLM is a neural network model that uses unsupervised learning to predict outcomes. Among the many AI models developed, LLMs are uniquely unexplainable.
Language models (as distinct from large language models) have existed for a while and can predict the next word or phrase in a sentence. They use different techniques than LLMs and have different applications—auto-correct is a common use.
Adding the “large” element involves training the models on a large collection of publicly accessible electronic documents. That collection (or “corpus” in AI terminology) comprises many petabytes of data —one petabyte being a million gigabytes. Training a model on such massive amounts of data allows it to learn about many topics, as well as language patterns.
So LLMs are “large” partly because of the amount of data they’re trained on. But also due to the size of the models themselves. A few years ago, a complex model might have had a couple of hundred parameters; LLMs have billions. ChatGPT’s underlying LLM has 175 billion parameters and is training on something like 500 billion “tokens” (words or word fragments).
The advances we’ve seen so far are largely a result of efforts to answer a single question: how can a model with that many parameters do something useful?
In the Philippines, AI is also being integrated in business operations with these advanced AI technologies used for a wide range of applications, including natural language processing, predictive analytics and machine learning.
However, it’s important to note that the use of AI also raises ethical and privacy concerns, particularly around the use of personal data. Hence, KPMG in the Philippines Technology Consulting Head Jallain Marcel S. Manrique suggests that “it is of paramount importance that businesses embrace a mindset that places transparency, responsibility and ethics at the core of their decision-making processes when it comes to harnessing the power of artificial intelligence.
“By doing so, businesses can navigate the complexities and potential pitfalls associated with AI adoption as well as foster an environment of trust and accountability among stakeholders,” he added.
The excerpt was taken from the KPMG Thought Leadership publication: https://kpmg.com/xx/en/home/insights/2023/02/the-potential-impact-of-chatgpt-and-the-new-ai-on-business.html.
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