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Until recently, the conventional wisdom was that while AI was better than humans at data-driven decisions, it was still inferior to humans for cognitive and creative ones. But in the past two years language-based AI has advanced by leaps and bounds, changing common notions of what this technology can do.
The most visible advances have been in what's called natural language processing (NLP), the branch of AI focused on how computers can process language like humans do. It has been used to write an article for The Guardian, and AI-authored blog posts have gone viral feats that weren't possible a few years ago. AI even excels at cognitive tasks like programming where it is able to generate programs for simple video games from human instructions. Yet while these stunts may be attention grabbing, are they really indicative of what this tech can do for businesses?

Today the best known natural language processing tool is GPT-3, or ChatGPT from OpenAI.

ChatGPT uses AI and statistics to predict the next word in a sentence based on the preceding words. NLP practitioners call tools like this language models, and they can be used for simple analytics tasks, such as classifying documents and analyzing the sentiment in blocks of text, as well as more advanced tasks, such as answering questions and summarizing reports. Language models are already reshaping traditional text analytics, but GPT-3 was an especially pivotal language model because, at 10x larger than any previous model upon release, it was the first large language model, which enabled it to perform even more advanced tasks like programming and solving high school level math problems.

The latest version, called InstructGPT, has been fine-tuned by humans to generate responses that are much better aligned with human values and user intentions, and Google's latest model shows further impressive breakthroughs on language and reasoning.

For businesses, the three areas where GPT-3 has appeared most promising are writing, coding, and discipline-specific reasoning. OpenAI, the Microsoft-funded creator of GPT-3, has developed a GPT-3-based language model intended to act as an assistant for programmers by generating code from natural language input. This tool, Codex, is already powering products like Copilot for Microsoft's subsidiary GitHub and is capable of creating a basic video game simply by typing instructions. This transformative capability was already expected to change the nature of how programmers do their jobs, but models continue to improve the latest from Google's DeepMind AI lab, for example, demonstrates the critical thinking and logic skills necessary to outperform most humans in programming competitions.

Models like GPT-3 are considered to be foundation models, an emerging AI research area which also work for other types of data such as images and video. Foundation models can even be trained on multiple forms of data at the same time, like OpenAI's DALL-E 2, which is trained on language and images to generate high-resolution renderings of imaginary scenes or objects simply from text prompts. Due to their potential to transform the nature of cognitive work, economists expect that foundation models may affect every part of the economy and could lead to increases in economic growth similar to the industrial revolution.

You are certainly aware of the value of data, but you still may be overlooking some essential data assets if you are not utilizing text analytics and NLP throughout your organization. Text data is certainly valuable for customer experience management and understanding the voice of the customer, but think about other text data assets in your organization: emails, analysts reports, contracts, press releases, archives, even meetings and phone calls can be transcribed.

To take the next step, again, identify your data assets. Many sectors, and even divisions within your organization, use highly specialized vocabularies. Through a combination of your data assets and open datasets, train a model for the needs of specific sectors or divisions. Think of finance. You do not want a model specialized in finance. You want a model customized for commercial banking, or for capital markets. And data is critical, but now it is unlabeled data, and the more the better. Specialized models like this can unlock untold value for your company.

Language-based AI won't replace jobs, but it will automate many tasks, even for decision makers. You need to start understanding how these technologies can be used to reorganize your skilled labor. The next generation of tools like OpenAI's Codex will lead to more productive programmers, which likely means fewer dedicated programmers and more employees with modest programming skills using them for an increasing number of more complex tasks. This may not be true for all software developers, but it has significant implications for tasks like data processing and web development.

The bottom line is that you need to encourage broad adoption of language-based AI tools throughout your business. It is difficult to anticipate just how these tools might be used at different levels of your organization, but the best way to get an understanding of this tech may be for you and other leaders in your firm to adopt it yourselves. Don't bet the boat on it because some of the tech may not work out, but if your team gains a better understanding of what is possible, then you will be ahead of the competition. Remember that while current AI might not be poised to replace managers, managers who understand AI are poised to replace managers who don't.

Large foundation models like GPT-3 exhibit abilities to generalize to a large number of tasks without any task-specific training. The recent progress in this tech is a significant step toward human-level generalization and general artificial intelligence that are the ultimate goals of many AI researchers, including those at OpenAI and Google's DeepMind. Such systems have tremendous disruptive potential that could lead to AI-driven explosive economic growth, which would radically transform business and society. While you may still be skeptical of radically transformative AI like artificial general intelligence, it is prudent for organizations' leaders to be cognizant of early signs of progress due to its tremendous disruptive potential.