The Innovation Continuum: Planning for What Is Next
It is human nature to experience a new technology and think that’s the ultimate version of such technologies.
We too often fail to recognize that the latest iteration of a development is only one station along the continuum of developments. We focus on the current hype, which may be accurate for today, but we miss the point that our history of humankind’s use of technology is comprised of a string of linked developments and enhancements of products and tools, each one with more features, better features or greater economies than the prior one.
And, so it is with Generative AI. OpenAI’s ChatGPT was the first Generative AI tool broadly released and received widespread coverage. Many in the general public assume it is the sum product of GenAI, yet there are many versions out now, each with its own strengths and comparative shortcomings compared to the others. These tools continue to develop weekly, if not daily. Collectively, they are revolutionary and exciting to explore. I am awed and inspired daily by the things that I can do with the half-dozen GenAI apps that I regularly use. I use different ones for different applications. Collectively, they promise to revitalize me as a thinker and writer in the field of educational technology.
Amazing, astounding, awe-inspiring (and those are only the adjectives starting with the letter “a”) just begin to describe what can be ably, efficiently and economically accomplished with the help of Google Bard, ChatGPT, Claude 2, Bing, Perplexity, Hey Pi, and the rest of their GenAI tool cohort. Create a serviceable syllabus in ten seconds. Done. Write alternative lesson plans with assessments in fifteen seconds. Done. Create a spreadsheet of data culled from multiple sources and provide an analysis and predictions in thirty seconds. Done. Provide me with useful psychological support and advice. Done. And, that doesn’t begin to touch the image-generation potential.
I am as guilty as the next writer. I seize on these abilities and fall short of projecting the next successive steps that will lead us forward. For example, Auto-GPT is a developing app that significantly extends the capabilities of other GenAI apps. Researcher Tayyub Yaqoob writes in Coin Telegraph “The capacity of Auto-GPT to self-generate prompts to perform tasks, as well as its ability to connect with apps, software and services both online and locally, distinguishes it from other AI solutions. This means that, given a goal, Auto-GPT can devise a plausible advertising approach and create a rudimentary website.” It doesn’t merely answer questions, rather it can go online, self-select and run programs it deems useful to accomplish your objective, and collect and validate external data in an unsupervised machine learning mode. The autonomous abilities take what most of us think of as GenAI to the next level of actually taking independent action on its findings, rather than merely reporting the findings to you.
Will Douglas Heaven writes in MIT Technology Review that “interactive AI” may be the next step in the continuum of development of GenAI. Heaven writes “DeepMind cofounder Mustafa Suleyman wants to build a chatbot that does a whole lot more than chat. In a recent conversation I had with him, he told me that generative AI is just a phase. What’s next is interactive AI: bots that can carry out tasks you set for them by calling on other software, other databases, and other people to get stuff done…. Suleyman is not the only one talking up a future filled with ever more autonomous software. But unlike most people he has a new billion-dollar company, Inflection, with a roster of top-tier talent.”
Interactive AI will surpass Generative AI in most ways. It will become more than a question-answerer. The coming version of AI will take initiative to pursue your goals by running programs, even contacting and engaging other people on your behalf to advance the mission you have given it. It will truly become an autonomous assistant.
A further next step in the AI development continuum may be the application of quantum computing to artificial intelligence. Yuval Boger writes in Datanami “Quantum machine learning could classify larger datasets in less time, and quantum neural networks could process information in ways that classical neural networks cannot. While existing AI tools are powerful and practical for many applications today, quantum computing represents a new frontier with the potential to significantly advance the field. However, the road to practical quantum computing is long and filled with challenges. It will likely be some time before quantum computers are more powerful and ready for widespread use in AI.”
Wikipedia offers a couple of definitions of Artificial General Intelligence (AGI), notably beginning with AGI as an agent. “An artificial general intelligence (AGI) is a hypothetical type of intelligent agent. If realized, an AGI could learn to accomplish any intellectual task that human beings or animals can perform. Alternatively, AGI has been defined as an autonomous system that surpasses human capabilities in the majority of economically valuable tasks. Creating AGI is a primary goal of some artificial intelligence research and of companies such as OpenAI, DeepMind, and Anthropic.”
Open AI seems to have its sights set on Artificial General Intelligence (AGI). Steven Levy, writing in Wired article “What OpenAI Really Wants” says “For Altman and his company, ChatGPT and GPT-4 are merely stepping stones along the way to achieving a simple and seismic mission, one these technologists may as well have branded on their flesh. That mission is to build artificial general intelligence—a concept that’s so far been grounded more in science fiction than science—and to make it safe for humanity. The people who work at OpenAI are fanatical in their pursuit of that goal. (Though, as any number of conversations in the office café will confirm, the “build AGI” bit of the mission seems to offer up more raw excitement to its researchers than the “make it safe” bit.) These are people who do not shy from casually using the term “super-intelligence.” They assume that AI’s trajectory will surpass whatever peak biology can attain. The company’s financial documents even stipulate a kind of exit contingency for when AI wipes away our whole economic system.”
So, technology builds upon technology. As with many developments in this field, it is expected that progress will continue to accelerate, bringing super-intelligence in an early form sooner than we previously expected. Is your university preparing for the next steps in AI? Are you planning for the impact this will have in each of your departments, disciplines, curricula, careers, and the implications for society as a whole? Now is not too soon to begin to plan for these changes and a smooth transition to serving changing learner needs as they navigate the changes. The future is closer than we think.
This article was originally published in InsideHigherEd’s Transforming Teaching and Learning Blog.
Ray Schroeder is Professor Emeritus, Associate Vice Chancellor for Online Learning at the University of Illinois Springfield (UIS) and Senior Fellow at UPCEA. Each year, Ray publishes and presents nationally on emerging topics in online and technology-enhanced learning. Ray’s social media publications daily reach more than 12,000 professionals. He is the inaugural recipient of the A. Frank Mayadas Online Leadership Award, recipient of the University of Illinois Distinguished Service Award, the United States Distance Learning Association Hall of Fame Award, and the American Journal of Distance Education/University of Wisconsin Wedemeyer Excellence in Distance Education Award 2016.
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