Here are some of the things we could expect from Generative AI and the challenges and opportunities it presents in the year ahead.
Prediction 1 – GenAI won’t take your job, but it might change it.
In 2024, Generative AI won’t lead to mass job displacements and redundancies, as many early sensationalist reports might have suggested. In fact, GenAI won’t completely replace experts in any field, as although models have access to an insurmountable amount of information, users still have to articulate concepts well enough to get the right answers – thus, expertise and human input and, more importantly, human review always will be necessary.
Collaboration with GenAI is a trend we can expect to continue in 2024 as businesses look to capitalize even further on its benefits, reaping the rewards of increased productivity and quality of content creation. This means adopting even more GenAI tools and encouraging even more use of them, the goal being not to replace workers but instead to assist in what they do.
Prediction 2 – Now that we’ve invented GenAI, the next step is understanding it.
Next year, we can expect to see businesses attempt to improve the consistency of output from Generative AI. Currently, there is no set rule book for achieving great results with GenAI; there are tips and tricks you can deploy for better or faster results, of course, but overall, the process is largely trial and error.
Interacting with GenAI in its current iteration is like a science experiment – you come up with a hypothesis and continue to test different manners of prompts until it produces the result you’re looking for. In the future, the focus of experimentation will be on figuring out how we evaluate the responses it gives us and using that data to inform prompts further.
Companies that want to apply GenAI to their products will need to think about how they carry forward and evolve prompts that can improve results directly. Qualitative and quantitative improvements can only be brought about by reevaluating their approach to AI application and development.
Prediction 3 –Expect an onslaught of GenAI tools and GenAI startups.
In 2024, we’re going to see another year of the AI market expanding, with more variety as GenAI startups try to find their niche among the masses.
Rather than consolidation, more GenAI solutions will continue to pop up in different industries. Of course, there will be a lot of attempts that don’t get traction or just don’t work, but this won’t deter the wave of opportunistic entrepreneurs and businesses who look to capitalize on the GenAI wave.
There’s already the start of a huge race on the hardware side, too: companies such as Google and AWS are building their own AI hardware in addition to nVidia, which is worth watching. If successful, these advances in hardware could lead to another explosion in how large language models are trained, as currently, it takes a lot of human input, money, and effort to build from the ground up.
Prediction 4 – GenAI regulation is essential to adoption.
Regulating GenAI will be a massive focus for governing bodies and business leaders in 2024. Earlier this year, calls were heard for a pause in AI development from numerous visionaries. However, this isn’t realistic as the fundamental technology is increasingly available through open source models available on Hugging Face. Rather than focusing on halting development, creating clear regulation, guidelines and best-use practices will be necessary to ensure partnership with AI will move forward in a safe and secure way.
Like any other technology, defining the boundaries that keep safety in mind will allow for leveraging the benefits without sacrificing progress. We can liken this to all manners of tools and equipment that need to be regulated; for example, we don’t stop ourselves from building cars that go really fast, but we do put speed limits in place to ensure safety. Internationally, governments will draw their attention first to the areas of regulation that present the greatest impact on citizens, including frontier AI.
From an industry perspective, the GenAI applications and use cases that are most helpful will emerge as front runners for wider business use cases. Understanding the risks, challenges, and security issues potentially imposed by these tools will be vital for businesses to understand exactly when and how these tools need to be regulated internally. Likewise, companies hoping to leverage GenAI will have to communicate to customers exactly how it’s used and how it complies with current and future regulation requirements.
Prediction 5 – GenAI and Legacy technology: Why the key to modernization may reside in GenAI tools.
After a year of GenAI practice, legacy businesses are starting to understand that GenAI interest is not just driven by ‘hype’, and instead could be truly transformative for their sector. Therefore, in 2024, we can expect even more traditional businesses to deploy the technology to help evolve legacy systems and modernize their technology stack.
Typically, traditional companies are not amenable to change or agile enough to adopt the latest in new technology. Many companies are tied to legacy software due to a combination of outdated procurement processes, familiarity, or concerns about data loss or disruption, making modernization inaccessible. The key here is that GenAI can assist with migrating off old code bases and technology stacks to modern programming languages and platforms.
However, GenAI could bridge this gap by allowing companies previously locked into legacy systems to access a more modern workforce’s knowledge and work practices. GenAI also makes some modern tools far more user-friendly and, therefore, more likely to be deployed across businesses.
Prediction 6 – AI and the question of originality
Next year, we’ll see the average person become more adept at using AI, both in business and in their personal lives. Students will also interact with GenAI at a greater scale.
On the one hand, ChatGPT and others can be a great personal tutor to help students understand concepts. On the other hand, ChatGPT can be used to generate solutions to problems. I tell my students that they can use GenAI to help them as they are learning, but they must turn in their own original work. The problem is that it is extremely tempting to have GenAI provide the answers, perhaps just partially. In addition, since the answers are coming from a computer program and not another student, it distances students from the notion they are cheating. So far, for my classes in computer systems, it has been fairly easy to determine if a student has turned in GenAI solutions because they don’t follow the conventions in code that I’ve taught in class and require in student solutions.
How GenAI is used in classrooms is very much a work in progress. At the moment, there’s still no best practice model – even at my University, we have workshops about AI but no succinct policy. Beyond the classroom, there is the larger question of intellectual property and how GenAI is trained on internet-accessible creations and works available in digital form. We will see this play out in all industries and in the courts in 2024.
Prediction 7 - Universities will begin to teach prompt engineering
In 2024, universities will teach prompt engineering as a minor field of study and through certificate programs. Prompt engineering for GenAI is a skill already augmenting domain experts, similar to how computing has augmented other domains. The successful use of large language models (LLMs) relies heavily on giving the models the right prompts.
When looking to fill the role of a prompt engineer, the task becomes finding a domain expert who can formulate a question with examples in a specific domain, a skill critical for today’s IT professionals to refine to successfully implement LLMs. Given this, universities will introduce new academic focus areas to address the growing demand for professionals with specific skills required to build the next generation of GenAI applications.