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Advantages & limitations of ChatGPT in the corporate context

Dr. Valentin Kemper

Text-based AI models have been capable of performing useful tasks in business processes for a number of years. We have applied it in a large number of customer projects, for example, to optimize input management or to improve fraud detection at insurance companies, to quickly detect trending topics in customer service, or to enable conversational AI. Besides the chatbot function – familiar to many from their day-to-day lives – ChatGPT has a number of other features such as text classification, information extraction, machine translation, automated text summarization, topic modeling, and more. Given the sheer amount of data ChatGPT has “seen” over the course of its training, and the size of the underlying AI model, ChatGPT can perform many of these tasks innately at an astonishingly high level. Notably, it does not need to be trained for this purpose on data that has been elaborately collected and prepared specifically for the task – as is the case with classical Machine Learning models.

Using this as a basis, companies could have ChatGPT perform numerous routine activities and – after human quality control – use the results in corporate processes. These could range from text suggestions for marketing and sales activities to business correspondence and documentation.

Where ChatGPT reaches its limits

So, the question remains as to why not all companies have been using ChatGPT to this end all along. The snag is that ChatGPT does not yet recognize company-specific texts and information. This gap cannot be closed (at this point in time) by retraining ChatGPT on these texts. The reason is that such an adaptation (transfer learning) would be more time-consuming for ChatGPT than for previous models, since ChatGPT was trained in parts with the RLHF method (reinforcement learning from human feedback). Here, in addition to pure text data, direct human feedback also flows into the development, the personnel input as well as the computing times of the servers would drive up the costs significantly. Up until recently, it was also disproportionately time-consuming. This has become somewhat easier now, so that in-house knowledge can now be fed into the system. Nevertheless, data protection aspects have to be taken into account (see ChatGPT vs. data protection & data security – what needs to be taken into account here?).

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ChatGPT & ESG: What about the ecological footprint?

Dr. Michael von Papen

When discussing the opportunities and risks of generative AI like ChatGPT, the topic of sustainability is very much part of the discussion. However, since the technology is still quite new, reliable studies are few and far between. What does ChatGPT itself “say” about its environmental footprint? Here is the chatbot’s response:

Hello ChatGPT, I would like to know more about ChatGPT and sustainability. First of all: what issues need to be considered?

ChatGPT: There are several topics you could explore:

• Environmental impact: AI technologies like ChatGPT require significant amounts of power to run. You might consider exploring how AI companies are working to reduce their environmental footprint and make their products more sustainable.
• Sustainability potentials: ChatGPT can help companies and individuals make more sustainable decisions by for example, suggesting environmentally friendly products or services or providing information about sustainable practices.
• Ethical implications: As AI technologies become more prevalent, it is important to consider the ethical implications. How do we ensure that AI systems do not reinforce existing biases or make decisions that harm marginalized communities?

Very interesting points, thank you ChatGPT. In terms of environmental impact: How much energy was actually used to train ChatGPT?

ChatGPT: The training of ChatGPT-3, the largest version of the model, is estimated to have consumed about 284 terawatt-hours (TWh). By comparison: This is approximately equivalent to the energy consumption of the entire country of Belgium within one year. [Author’s note: ChatGPT is exaggerating a bit here. A recent study (Patterson et al., Carbon Emissions and Large Neural Network Training, 2021) estimates that GPT-3 consumes 1.3 terawatt-hours of electricity, a figure that ought not to be greater by two orders of magnitude for ChatGPT. GPT-4 is also not yet considered by ChatGPT in this response. Since this version is six times larger than GPT-3, a disproportionate increase in current consumption must be assumed.]

It is also important to remember that energy consumption is not the only factor to consider when considering the environmental impact of AI. Materials used to manufacture hardware, e-waste disposal, and carbon emissions associated with data centers all contribute to the overall environmental footprint of AI technologies.

How many tons of greenhouse gases were emitted to supply this energy?

ChatGPT: The estimated greenhouse gas emissions associated with ChatGPT-3 training are approximately 1.5 million metric tons of CO2e. [Author’s note: Again, ChatGPT is somewhat at fault here. To the best of our knowledge, the emission levels are probably not quite that drastic. According to the above-mentioned study, the training of GPT-3 is assumed to result in about 550 t CO2e, which is still 50 times the per capita annual emissions in Germany.]

These emissions, however, vary with a number of factors, including the energy mix of the power grid where the training takes place and the efficiency of the hardware used.

It is clear that the energy consumption and the associated greenhouse gas emissions generated by AI training are significant and should be taken into account when considering the environmental impact of AI technologies. Efforts are being made to reduce emissions.

Very interesting, what exactly are these efforts?

ChatGPT: An important concept is sparsity, which means that the model only needs a subset of the input data. This can be achieved, for example, by techniques such as pruning, which removes less important connections in the neural network. [Author’s note: This is a very effective approach where it is possible to cap large amounts of connections, making the model much more efficient. If you are interested, please do not hesitate to contact us.]

Another approach is to use more efficient hardware, such as specialized chips optimized for the particular AI application. These chips can be designed to perform calculations much more energy-efficiently than conventional CPUs or GPUs.

In addition, models can also be created by design with fewer parameters, which reduces energy consumption.

Thanks for the chat, ChatGPT.

Preliminary conclusion:

The answers that ChatGPT itself provides, as well as the additional study results, refer primarily to the training, and draw a rather negative eco-balance in this respect. It remains to be seen how things stand with regard to the operation and use of ChatGPT. Is this justifiable under the given sustainability aspects? What is the environmental footprint of alternative ways to get information (e.g., search engines) and develop ideas? Could a combination that makes sense from a sustainability perspective possibly be achieved by placing LLM behind search queries? New studies are needed to address these aspects in order to gain clarity.

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GenAI in practice: Medicine & healthcare example

Dr. Valentin Kemper

How ChatGPT and other GenAI solutions can support the work of copywriters, designers, software developers and other knowledge workers is widely discussed. But what about in areas where employees do not exclusively sit at their desks, e.g. in the healthcare sector?

At this year’s DMEA – Connecting Digital Health, Karl Lauterbach – the German Federal Minister of Health – gave his take on the use of big language models in medicine: The Minister says that the digitization strategy presented at the beginning of March is already outdated because developments with Large Language Models (LLMs) are moving so fast.

The fact that the German healthcare system can only react slowly to these developments is understandable. Among the reasons for this are the complexity (from actors to regulation to tech infrastructure) and the low level of research investment in AI in Germany compared to other countries.

Nevertheless: The potential applications of LLMs such as ChatGPT in medicine and healthcare are manifold. The Minister also expressed his conviction at the DMEA that it will change medicine sustainably and fundamentally – and significantly more than existing AI models with specific capabilities, e.g., in the field of diagnostics or in medical imaging.

Examples of how LLMs could change medicine & healthcare:

• Meta-studies: “There is no reason to believe that models could not do a meta-study of published medical studies better than humans,” Karl Lauterbach said at DMEA. On the contrary. In fact, given the large knowledge repository, LLMs may soon be much better at performing pooling studies. This new semantic component can take meta-studies to a qualitatively new level and accelerate the development of drugs and treatments.
• Empowered Patient: ChatGPT can provide users with expert knowledge in no time at all, which can be processed in an easily accessible way. This is how AI builds a bridge between experts and the population. This makes medical knowledge more accessible and reduces the so-called “knowledge gap”. The notion of the “empowered Patient” becomes a reality.
• Skills shortage: Prof. Dr. med. Klemens Budde from the Charité in Berlin added in his presentation at the DMEA that ChatGPT leads to better reporting results, predominantly among younger physicians, while chief physicians are less likely to embrace the support it provides. They criticize, among other things, that unused skills could be lost through ChatGPT. However, Budde also sees the opportunity for upskilling: similar to the empowered patient, knowledge is also made available outside groups with specialized expertise , which could represent a solution to the shortage of healthcare professionals.