Regulating Generative AI will be a huge focus for governing bodies and business leaders in 2024

Regulating Generative AI will be a huge focus for governing bodies and business leaders in 2024. (Image by generative AI)

SnapLogic Chief Scientist’s Generative AI predictions

SnapLogic’s Chief Scientist Greg Benson reveals his predictions on Generative AI and the challenges and opportunities it presents in the year ahead.

Prediction 1 – Generative AI 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 suggested. Generative AI 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, human input, and, more importantly, human review will always be necessary.

Collaboration with Generative AI is a trend we expect to continue in 2024 as businesses capitalize on the technology’s benefits, reaping the rewards of increased productivity and improved quality of content. Those rewards mean greater adoption of more Generative AI tools, with the goal not to replace workers but to assist them.

Prediction 2 – Now that we’ve invented Generative AI, the next step is understanding it

SnapLogic’s Chief Scientist Greg Benson

SnapLogic’s Chief Scientist Greg Benson

Next year, we can expect to see businesses attempt to improve the consistency of output from Generative AI. Currently, there is no rule book to achieve great results with Generative AI; there are tips and tricks you can deploy for better or faster results, but overall, the process is largely trial and error.

Interacting with Generative AI 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, experimentation will focus on figuring out how we evaluate the responses it gives us and using that data to inform prompts further.

Companies that want to apply Generative AI to their products will need to think about how they evolve prompts that improve results directly. Qualitative and quantitative improvements can only be brought about by re-evaluating their AI application and development approaches.

Prediction 3 –Expect an onslaught of Generative AI tools and Generative AI startups.

In 2024, we will see another year of the AI market expanding, with more variety as Generative AI startups try to find their niche.

Rather than consolidation, more Generative AI 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 looking to capitalize on the Generative AI wave.

There’s already the start of a huge race in hardware, too: companies such as Google and AWS are building their own AI hardware to compete with Nvidia. If successful, 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.

In 2024, we will see another year of the AI market expanding, with more variety as Generative AI startups try to find their niche.

In 2024, we will see another year of the AI market expanding, with more variety as Generative AI startups try to find their niche. (Image generated by AI)

Prediction 4 – Generative AI regulation is essential to adoption.

Regulating Generative AI will be a huge focus for governing bodies and business leaders in 2024. Earlier this year, calls were heard from many industry figures for a pause in AI development. However, that’s not realistic as the fundamental technology is increasingly available through open-source models on Hugging Face. Rather than focusing on halting development, creating clear regulations, guidelines, and best-use practices will be necessary to ensure partnership with AI will move forward safely and securely.

Like any other technology, defining safe boundaries will allow leveraging the benefits without sacrificing progress. We can liken this to all manner of regulated tools and equipment; for example, we don’t stop ourselves from building cars that go fast, but we do put speed limits in place to ensure safety. Internationally, governments will focus first on areas of regulation that present the greatest impact on citizens, including frontier AI.

From an industry perspective, the Generative AI applications and use cases that are most helpful will emerge as front runners for wider business use. 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 Generative AI will have to communicate to customers exactly how it’s used and how it complies with current and future regulations.

Prediction 5 – Generative AI and Legacy technology: Why the key to modernization may reside in Generative AI tools.

After a year of Generative AI practice, legacy businesses are starting to understand that Generative AI interest is not just driven by hype and could be transformative in their sector. Therefore, in 2024, we can expect more traditional businesses to deploy the technology to help evolve legacy systems and modernize their technology stacks.

Typically, traditional companies are not amenable to change or agile enough to adopt the latest in 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 Generative AI can assist with migrating from old code bases and technology stacks to modern programming languages and platforms.

However, Generative AI could bridge the gap by allowing companies previously locked into legacy systems to access a more modern workforce’s knowledge and work practices. Generative AI also makes some modern tools far more user-friendly and, therefore, more likely to be deployed in businesses.

Students will also interact with GenAI on a greater scale.

Students will also interact with Generative AI on a greater scale. (Image by generative AI)

Prediction 6 – AI and the question of originality

Next year, we’ll see the average person become more adept at using AI in their business and personal lives. Students will also interact with Generative AI on a greater scale.

On the one hand, ChatGPT and others can be great personal tutors to help students understand concepts. On the other, ChatGPT can be used to generate solutions to problems. I tell my students that they can use Generative AI to help them as they learn, but they must turn in original work. It is extremely tempting to have Generative AI provide answers or just partial answers. Since answers come 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 Generative AI-generated solutions because they don’t follow the code conventions I’ve taught in class and require in student solutions.

How Generative AI 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 is the larger question of intellectual property and how Generative AI is trained on internet-accessible creations and work. This will play out in all industries and 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 Generative AI is a skill already augmenting the work of domain experts, similar to how computing augments 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. That’s a skill critical for today’s IT professionals to successfully implement LLMs. Given this, universities will introduce new academic focus areas to address the growing demand for professionals with specific skills to build the next generation of Generative AI applications.

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