Physicist Richard Feynman once said, “I think I can safely say that nobody understands quantum mechanics.” Following in the same vein, it’s safe to say that nobody understands generative AI…yet.
If you’ve heard of ChatGPT, though, you’re on your way to becoming familiar with generative artificial intelligence (AI). Introduced to the public in November 2022, ChatGPT and other generative AI tools are getting plenty of global attention and quickly going mainstream, with the market projected to grow to USD 110.8 billion by 2030.
Today’s generative AI space is similar to the early days of mobile phone app stores, when creative individuals and teams developed new, innovative ways to build and use mobile app technology. Generative AI’s general-purpose models and solutions are now widely available and often free to access. But the real potential lies in the market of domain-specific solutions that can be built on top of general models—capabilities that could have special appeal to businesses undergoing digital transformation that want the benefits of new technology while also making things easier.
Yet, as the decade of the 2010s saw major advancements in AI, the 2020s may be the decade of reckoning when we begin to see the impact of these advancements on society. To better understand generative AI and its potential, we’ll explore what it is and what it can do, along with the risks and rewards for the connected enterprise.
What is Generative AI?
Generative AI refers to a category of AI algorithms that generate new outputs based on the data they have been trained on. It uses a type of deep learning called generative adversarial networks and has a wide range of applications, including creating images, text and audio.
In the case of ChatGPT or other like text generators, it “learns” from text data to understand context, relevancy, and how to generate human-like responses to questions. Instead of just replicating existing text, its generative AI algorithms identify patterns in text and then create something original.
Generative AI can also transform data, such as turning an audio recording into text, or text into actual speech, as in a speaking video avatar. It can also be used to translate languages, improve the resolution of existing images, and even transform images from one medium to another—turning photographs into paintings using a specific artistic style, for example.
The first dimension is the input—or the actual data that’s consumed—when the generative AI algorithm is inferencing. This input is predominantly text but it can include other source formats like images.
Output: The Second Dimension of Generative AI
The second dimension is the output that’s generated, such as text, images, 3-D models, music, videos, programming code, etc. The quality of the output is directly related to the size of the dataset it is trained on. A generative AI algorithm is particularly useful when it can consume and learn from large, highly complex datasets. Think about the datasets that can be found in the field of biology, for example, in which the data might include things like DNA and protein structures.
Specificity: The Third Dimension of Generative AI
The third dimension is the specificity of the output for a given domain or task. Some AI will focus on a very specific domain and the ‘answers’ they will give will be highly reliable and to the point. Examples like DoNoTPay for legal advice will quickly mature in capabilities. On the other side of the spectrum general purpose AI’s like ChatGPT. While impressive it became already clear that the user should be conscious that the answers might look convincing on a first glance but might be flawed and inaccurate in its details. Of course, we will see the rise of many AI that will find a niche somewhere along a spectrum.
Risks of platforms like ChatGPT
From a professional standpoint, generative AI puts us on the brink of a new wave of software creativity and the seemingly limitless business solutions that can result from it.
From a societal standpoint, generative AI has the potential to alter civilization to the degree that the invention of the wheel, the printing press, or power-generating machines did. And as with any technological advancements, there are significant risks to consider.
1. Impact on the labor market
One major concern around generative AI is the near-term effect it could have on the labor market. As economist Paul Krugman recently wrote in The New York Times: “It’s possible that in some cases, AI and automation may be able to perform certain knowledge-based tasks more efficiently than humans, potentially reducing the need for some knowledge workers.”3
Krugman listed data analysis, research, and report writing as examples of knowledge work that could be performed by AI, which means workers in those fields could eventually find themselves out of a job or earning much less.
2. The war for reality
Every day, it’s becoming harder and harder to distinguish between what’s real and what’s not. There are now serious challenges for the public in assessing reality and trusting that what they’re seeing is authentic. AI-generated text, images, and videos only exacerbate these challenges, requiring additional software that can flag AI-generated content.
3. Who created what?
The person (or machine) doing the creating can get called into question too. Last year over a period of just over three months, more than 10 million people used the Stable Diffusion text-to-image tool for generating images.4 Text-to-video models soon followed, which allowed users to generate video clips, 3-D pictures, and animations. These images and videos are now out there to be scraped for future generative AI models, further diffusing—and confusing—who originally created what.
4. Existential crisis in education
Generative AI raises even more questions in schools and universities, in which intellectual achievement is dependent on the student’s own thoughts, research, and writing. Though derived from existing content, AI-generated content is essentially original. This makes it difficult to correctly distinguish between, say, academic papers that were authentically produced by a human and text that was generated by a machine.
On the plagiarism side, software that identifies and watermarks content from ChatGPT is already being developed to alert educators to cheating.5 But it’s still unclear if watermarking will work for all text-generating tools that come to market.
5. AI in the hands of malicious actors
In the wrong hands, generative AI can be used for truly nefarious reasons. Malicious actors can use it to create everything from propaganda to phishing emails and malware, to fake websites and businesses, to text that’s meant to impersonate someone.6 It can even be used to create new forms of warfare and weapons.7
Because AI-generated text, images, and other outputs can seem so authentic, they can be difficult to detect before real harm is done. Business professionals and politicians alike have to be prepared for the reputational fallout, social unrest, and danger that can come from the unidentified and unmitigated use of generative AI by threat actors.
Rewards of platforms like ChatGPT
Though there’s more to be done to address the risks associated with generative AI, we also can’t deny its benefits, especially in our connected world. While connected enterprises need to approach generative AI with caution, there’s no doubt it’s an exciting time to see how this technology will shape the future—and the positives that can come from it.
Generative AI can be used to enhance society and come up with important solutions to some of our biggest problems today.
1. Maintaining productivity in a shrinking workforce
As older populations age out of the workforce, and not enough younger workers can fill open roles, the economic consequences could be severe. To maintain economic stability and fuel growth, countries around the world need a productivity boost. Conceding the bulk of information work to generative AI algorithms and models can speed up, increase, and even improve output without adding human capital, introducing far greater efficiency in writing, reporting, and analyzing. For example, the webMethods ChatGPT API connector is already helping businesses incorporate AI-generated text into their business processes by integrating ChatGPT with existing software and applications.
2. Creating new opportunities for the workforce
It was long thought that generative AI would only automate jobs that required repetitive tasks. The reality is, however, generative AI tools have the power to affect positions at all levels and unlock new opportunities for different types of jobs and job titles. This impact could change many industries, but at a basic level, generative AI still requires an actual person to interact with it. It is unclear how many new jobs generative AI will create, however, we are seeing some already appear, mostly around the ability to write good prompts for the AI.
3. Contributing to important scientific issues
Since generative AI is especially adept at consuming large, complex datasets, it could become an invaluable player in various scientific fields, contributing to breakthroughs in cancer research, sustainable energy sources, climate and environmental change, and other critical issues facing humanity.
4. Unleashing software creativity
When we talk about the potential of generative AI, we’re talking about models with hundreds of billions of parameters—on par with the number of cells in the human brain. It is a truly mind-blowing technology. Creative professionals can develop domain-specific AI-based tools for multitudes of niche use cases that stretch the imagination, enabling new ways of connecting people, technology, and processes along with new business models.
Generative AI and the Connected World
Generative AI tools like ChatGPT are disrupting the world as we know it. There are still many unknowns about how generative AI will ultimately be used, by whom, and for what purposes. But the technology offers as much promise as it does risk. For enterprises that are seeking to create the connected experiences their employees, partners, and customers demand, generative AI has enormous potential for use in business processes and enablement of new business models.
Generative AI has great potential for use in business growth and enablement, but it’s just one piece of the vast puzzle that is the connected enterprise.