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Early in 2019, GPT-2 was announced by OpenAI, the private, nonprofit company that now includes $11billion in investments from Microsoft Corporation. Compared to what was to follow, the development was relatively quiet. Claudia Slowik and Filip Kaiser write in the Neoteric blog, On March 15, 2022, OpenAI released the new version of GPT-3 called text-davinci-003. This model was described as more capable than previous versions of GPT. Moreover, it was trained on data up to June 2021, making it way more up-to-date than the previous versions of the models (trained on data up to Oct 2019). It was with the 3.5 series of text and code completion versions that GPT took off. With the 4.0 version, released in November 2022, an all-out scramble launched to create interfaces, apps and associated products to facilitate new and expanded access.

Google is one of the many firms engaged in efforts to catch up with the OpenAI release. After a flawed demo at the release of Googles Bard, The Decoder reports that Googles two large AI research centers, DeepMind and Google Brain AI, have pulled together to support the Gemini project, a large language model that will have a trillion parameters.

It was less than a month and a half ago, on March 30, 2023, that Auto-GPT was posted on GitHub by developer Significant Gravitas. As Wikipedia explains, Auto-GPT is an AI agent that given a goal in natural language, can attempt to achieve it by breaking it into sub-tasks and using the internet and other tools in an automatic loop. It uses OpenAIs GPT-4 or GPT-3.5 APIs, and is among the first examples of an application using GPT-4 to perform autonomous tasks.

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With Auto-GPT, we have crossed the virtual Rubicon from the relatively simple-step activities of earlier GPT models to a process of sequencing independent steps to a complex feedback loop of multiple activities and assessments toward a defined outcome. Sabrina Ortiz writes in ZDNet, This means that Auto-GPT can perform a task with little human intervention, and can self-prompt. For example, you can tell Auto-GPT what you want the end goal to be and the application will self-produce every prompt necessary to complete the task. Ortiz suggests, The applications promising, autonomous abilities may make it our first glimpse of artificial general intelligence (AGI), a type of AI that can perform human-level intellectual tasks The Github demo shows sample goal prompts such as Increase net worth, grow Twitter Account, Develop and manage multiple businesses. The applications limitations listed on Github do warn that Auto-GPTs output, May not perform well in complex, real-world business scenarios. However, the results users have been sharing show that Auto-GPT can deliver some really impressive (and helpful) results.

The development of generative AI has been so rapid that we have seen calls to pause development. Yet these calls are more of alarm rather than any reasonable expectation that worldwide research on such a hot topic will be delayed in any way. Such a pause would be impossible to enforce, given the number and diverse locations of sites performing research in this field.

Led by developments in generative AI, we are on our way to AGI. It will not be a straightforward path, and there are numerous high hurdles to overcome, but we have passed an inflection point with the capabilities of Auto-GPT. Ben Lutkevich of Tech Target writes:

Artificial general intelligence (AGI) is the representation of generalized human cognitive abilities in software so that, faced with an unfamiliar task, the AGI system could find a solution. The intention of an AGI system is to perform any task that a human being is capable of AGI is considered to be strong artificial intelligence (AI). Strong AI contrasts with weak or narrow AI, which is the application of artificial intelligence to specific tasks or problems. IBMs Watson supercomputer, expert systems and self-driving cars are examples of narrow artificial intelligence.

How long will it take to develop strong AI? No one knows for certain. Almost certainly, it will take years, but perhaps not the decades that had been previously predicted. We must remember just how quickly the current GPT and associated models have emerged.

What will widespread AGI mean? Again, no one knows for sure. What we do know is that many more human jobs will be performed by strong AI programs. The computers and AI programs will work tirelessly, efficiently and effectively. Of course, there will still be the need for many humans engaged in a myriad of tasks that are not best completed by AGI. We may see shorter workweeks for humans. New human-staffed careers may evolve to employ the displaced workers.

The implications for education are many. Will we still need the knowledge to perform tasks that are regularly completed by AI? Knowledge of how to direct and expand AIs expertise in these areas will be essential. What human skills and abilities will be in most demand? Human values and ethics will be essential to guide programs if we are to coexist comfortably. AGI may be able to extend our knowledge and information in math and the sciences. Perhaps it will bring new insights and opportunities in the arts and humanities that have been in decline at universities in recent years.

With the advent of Auto-GPT, there is now a vision of a pathway for generative AI to take on increasingly multivariate tasks. Ever more complex objectives will be assigned to these more advanced AI apps. We must be vigilant to assure that human values and ethics guide the development in the coming months and years.

We also must carefully monitor the advent of AI in our career fields so that we are not caught unaware when there are reductions in the human workforce due to computer-generated efficiencies. This will require communication, collaboration and shared vision among researchers, corporations and educators. We will do well to recall the warning of Aldous Huxley nearly a century ago that the Brave New World may await those who exclusively value efficiency and technology over human emotion and individuality.

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The path ahead for generative AI - Inside Higher Ed