At the end of 2022 ChatGPT made its way into the news and created a lot of fuzz.
The reason was, that OpenAI, the company behind ChatGPT, developed a new frontend for its generative learning model GPT-3. GPT-3 was being released one year earlier and has already been the largest model ever created. It was only accessible by an API though, for which one needed to make it through a waiting list. ChatGPT changed the game as being an easy to use, free for everybody „chat“ interface to interact with the GPT-3. Many users for the first time understood, what Machine Learning, or AI Services, are capable of: they created poems, let ChatGPT write yet another StarWars movie script and many other funny things. But understanding the underlying achievements OpenAI was able to come up to, are nothing less than stunning – and will teach many businesses what benefits AI Services can bring.
ChatGPT has made visible the potential that AI services have when they are skillfully combined, or the models that have technically been around for years are trained in a set with data that was previously unthinkable. GPT-3 contains about 10x as much data as previous models. More specifically, GPT-3 consists of multiple models and techniques like semi-supervised learning or trasnformers, that have been combined together intelligently – and that’s the fascinating part.
Generally, until now, there were a number of „capabilities“ that an AI model brought to the table, e.g. the classics like sentiment analysis („What is the sentiment in a certain text?“) or classification („Is the text a question or a statement?“).
This is now different: GPT-3 can not only do the above, but also learn new things very quickly with high efficiency and accuracy. This is called the Zero-, One- or Few-Shot capabilities of a model. Here GPT-3 achieves incredibly good values. This means, for example, that you can teach it to translate into a new language in just 3 „training sessions“, and from then on the model does it itself.
Why this is so important for companies: the ability to (autonomously) learn and adapt.
Every company claims to be unique. This may be the case in some areas, but often it is the cross-functional areas (IT, HR, Finance, etc.) that are essentially the same. The HR department of a bank does not do much different than the HR department of an automotive supplier. This also explains the success of the „general“ office products like Excel and Co. that are used in all companies (a spreadsheet like Excel, by the way, can be compared structurally well with an AI model). But WHAT is calculated in an Excel, that changes from company to company.
Modern AI architectures like GPT-3 are now able to learn exactly this by themselves:
1. what is my company specific data to work on?
2. what are my company-specific questions that I should answer?
3. what are my company-specific added values that I should deliver?
These capabilities, which ChatGPT now presents to users in a very concrete way, are what will now drive the entry of AI into companies. Because the above results are simply „shocking“ in a positive sense.
I see three areas in particular where we will see AI services much more often very soon:
1. integrated AI: e.g. directly integrated in a software to make predictions (besipiel Salesforce AI service that directly qualifies a lead).
2. standalone AI services (e.g. ChatBot that answers customer service questions on its own)
3. generating AI services: Corporate communications, marketing copytexts, sales presentations that a service creates autonomously and is only approved or tuned afterwards by a „real“ employee.
The productivity gains are enormous and the knowledge about the introduction of AI services, which skills and teams are needed, will also spread. Because one thing should be clear to everyone: AI Services are far more than a technical tool that can be introduced, but to an even much greater extent a corporate change than all „digitization measures“ combined. Digitization, compared, was a wet fart
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