Is generative AI bad for the environment? A computer scientist explains the carbon footprint of ChatGPT and its cousins

GPT-3, from the OpenAI foundation, is part of a family of AI models known as Large Language Models (LLMs). This is an area of research (Generative AI) that is moving quickly, with the recent announcement of GPT-4, and similar LLM’s such as Google’s LaMDA also making the news. Users of ChatGPT can provide feedback on responses which is used to further tune the application. General ChatGPT usage is currently free during the initial phase with advanced membership options available for a fee. People are already starting to use ChatGPT to get answers to everyday questions and even for some research purposes.

Therefore, they are more likely to make things up and describe them as facts to the users with clear explanations. Such types of ‘hallucinations’ by generative AI models and ChatGPT could be one of the biggest risks for users. Generative AI can provide exclusive improvement in productivity alongside encouraging creativity for performing routine tasks. On the other hand, it is still new territory for business leaders, and even AI experts are not sure about the potential applications of ChatGPT and Generative AI. Apart from offering personalized experiences to customers, ChatGPT can help businesses in the automation of recurring tasks. Furthermore, generative AI models can help in freeing up employees to help them focus on productive tasks.

ChatGPT and Generative AI: What They Mean for Investment Professionals

By remaining vigilant to new possibilities, leaders should create the environment and infrastructure that supports identification of new technology opportunities and prepare to embrace the technology as it matures for enterprise adoption. From the perspective of individual employees, the adoption of ChatGPT and generative AI models in the future of work would be purely based on experimentation. You can find your capabilities for using such tools by experimenting with them and identifying your weaknesses and strengths. The first thing you would note about generative AI is the fact that they are ‘human-like’ and not ‘human’ in nature.

Therefore, it would help employees avoid the apprehensions regarding AI replacing their jobs. In the case of managers, the answers to ‘how ChatGPT could change the future of work’ would point to describing AI as an opportunity. You should tell your employees how generative AI and ChatGPT could help in reducing the load of recurring tasks. The next noticeable highlight in discussions about ChatGPT and generative AI’s impact on the future of work would point out how they would change organizational culture.

ChatGPT and beyond: What is the impact of generative AI on information work

CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Microsoft Research claimed that the latest OpenAI LLM Yakov Livshits shows “sparks” of AGI. But opinions vary as to whether ChatGPT or GPT-4 represents a significant step toward AGI. That’s why we believe it’s too early to make a judgment based on limited and short-term trends.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

In conclusion, while ChatGPT is undoubtedly an impressive application of generative AI, it represents just one facet of its potential. Generative AI has far-reaching implications across domains like conversational AI, creative content generation, scientific research, education, and more. As research and development in generative AI continue to advance, we can expect to witness further breakthroughs and innovative applications in the near future. Generative AI has also found its place in the realm of creative content generation.

GPT-3 (Generative Pretrained Transformer 3), GPT-3.5 and GPT-4 are state-of-the-art language processing AI models developed by OpenAI. They are capable of generating human-like text and have a wide range of applications, including language translation, language modelling, and generating text for applications such as chatbots. GPT-1 was the first model in the GPT series and was introduced by OpenAI in 2018.

It’s similar to ChatGPT but benefits from having access to up-to-date information. This letter states that the pause should be public and verifiable, arguing that companies like OpenAI, Microsoft and Google are entering a profit-driven race to develop and release new AI models at a dangerous pace. Along with calls of concern from major heads in the tech industry, artificial intelligence research could be forced to slow down or implement severe restrictions. A handful of the biggest Chinese tech firms have launched their own AI chatbots after receiving government approval. It then goes through a second similar stage, offering multiple answers with a member of the team ranking them from best to worst, training the model on comparisons. As a language model, it works on probability, able to guess what the next word should be in a sentence.

McKinsey predicts that generative AI has the potential to increase productivity in customer care by as much as 45%. Much of that gain comes from the ability of AI to understand customer intent and sentiment, and give personnel—even new trainees—the information they need to resolve problems quickly. It can also offload low-level customer service demands to chatbots that are far more functional and human-like than ever before. That not only translates into better service provision, it also results in teams who feel less overwhelmed and therefore more cheerful and loyal. Some of the players leading the charge here are Qualtrics, Ultimate.ai, and Intercom. In a supervised training approach, the overall model is trained to learn a mapping function that can map inputs to outputs accurately.