Top AI Trends For 2024

Introduction

Usually, contemplation on the past is prompted near the end of the year. However, because we're a forward-thinking company, we're going to use this time to consider the future.

Over the past few months, we've written a lot about how artificial intelligence (AI) is transforming contact centers, customer service, and other industries. There will undoubtedly be even more significant developments in the future, but the forerunners in this industry never stand still.

Larger (and Better) Generative AI Models

The most straightforward tendency is probably that generative models will only get larger. We already know that larger language models after all, the moniker implies that they have billions of internal parameters. However, there's no reason to think that the research teams using these models won't be able to keep bringing them up to speed.

It would be simple to brush this off as nonsense if you are unfamiliar with the advancements in artificial intelligence. Why should we be concerned about larger language models when we don't get excited when Microsoft delivers a new operating system with an unprecedented number of lines of code?

Larger language models typically translate into higher performance, in a manner that isn't true for traditional programming, for unknown reasons, or both. While writing ten times as much Python does not ensure a better application (in fact, the likelihood of a better application is higher), training a ten times larger model is likely to yield better results.

This is deeper than it initially appears. I would have thought that we had achieved significant advancements in cognitive psychology, natural language processing, and epistemology if you had shown me ChatGPT fifteen years earlier. However, it turns out that you can just construct enormous models and feed them unfathomably large volumes of textual data, and voilà, an artifact that can translate across languages and respond to queries.

Additional Types of Generative Models

Although it is not specific to text production, the fundamental method for creating generative models works well in that field.

Three well-known image-generation models are DALL-E, Midjourney, and Stable Diffusion. Even if these models occasionally still have difficulty with aspects like perspective, faces, and the number of fingers on a human hand, they are nevertheless able to produce work that is quite astounding.

We anticipate that as these image-generation models advance, they will be utilized anywhere images are utilized, which is, you undoubtedly already know, quite a few places. All kinds of media are fair game, including YouTube thumbnails, office building murals, dynamically formed pictures in games or music videos, illustrations in books or scientific papers, and even design concepts for consumer products like cars.

Currently, the two main generative AI application cases that are most widely known are text and images. Regarding music, though, what say you? What about newly discovered protein structures? How about chips for computers? With diverse models synthesizing the music played in the chip fabrication plant, we might soon have models that create the chips used to train their successors.

Models: Open Source vs Closed Source

The term "closed source" describes a paradigm where a code base, or the weights of a generative model, are only accessible to small engineering teams that are working on them. On the other hand, the antipodal philosophy of "open source" holds that the greatest approach to produce secure, high-quality software is to distribute the code widely, allowing hordes of people to discover and correct design problems in it.

This connects in a variety of ways to the larger discussion about generative AI. Releasing model weights is extremely risky if the "doomers" are right when they say that coming AI technologies pose an existential threat. For example, if you developed a model that can provide the right procedures for creating weaponized smallpox, making it publicly available would allow any terrorist in the world to download and utilize it for that purpose.

Conversely, the "accelerationists" respond that the fundamental principles of open-source systems apply to AI just as they do to all other types of software. While there is a chance that some people will utilize freely accessible AI to harm others, there will also be a greater number of minds trying to develop sentinel systems, guardrails, and protections that can frustrate the evil's plans.

Regulation of AI

Discussions over AI safety took place in scholarly journals and unpopular forums for many years. But all that changed when LLMs became more popular. It was instantly apparent that they would be extremely potent, immoral instruments with the capacity to bring both great good and great harm.

As a result, authorities both domestically and internationally are paying attention to artificial intelligence and considering the kinds of laws that ought to be implemented in reaction.

A manifestation of this tendency was the series of congressional hearings that were held in 2023, during which notable people like as Sam Altman, Gary Marcus, and others testified before the federal government on the potential implications and future of this technology.

The Rise of AI Agents

We've previously discussed the numerous current initiatives to create artificial intelligence (AI) systems, or agents, that can pursue long-term objectives in challenging settings. Despite all that it is capable of, ChatGPT cannot successfully execute a high-level command such as "run this e-commerce store for me."

However, that can soon alter. Existing generative AI models are being enhanced by systems such as Auto-GPT, AssistGPT, and SuperAGI in an effort to enable them to achieve more ambitious objectives. Currently, agents exhibit a noticeable propensity to become caught in fruitless cycles or to reach a situation from which they are unable to escape alone.However, a few technological advances could be all it takes for us to develop far more powerful agents, at which point they could start to drastically alter the way the world works and how we live.

New Methods for AI

Most often, when people think of "AI," they picture a deep learning or machine learning system. However, despite their great success, these methods only represent a tiny portion of the numerous ways that intelligent robots could be created. AI with neural symbols is another. It typically blends symbolic reasoning systems—which are capable of reasoning through arguments, weighing evidence, and many other tasks associated with thought—with neural networks, such the ones that drive LLMs. Given LLMs' well-known propensity to imagine inaccurate or erroneous information, neurosymbolic scaffolding may improve and enhance their abilities.

Artificial Intelligence and Quantum Computing

The advent of the next big computational substrate can be seen in quantum computing. Quantum computers can solve problems that even the most powerful supercomputers cannot answer in less than a million years by using quantum phenomena like entanglement and superposition, in contrast to today's "classical" computers, which rely on lightning-fast transistor operations.

It goes without saying that scientists have long been considering the application of quantum computing to artificial intelligence, while its potential applications are yet unknown. Certain types of jobs are particularly well-suited for quantum computers, such as combinatorics, optimization problems, and linear algebra-based tasks. Large amounts of AI work are supported by this last, thus it makes sense that quantum computers will accelerate at least some of it.

Conclusion

It looks like artificial intelligence's Pandora's box has been permanently opened. Large language models are already transforming a wide range of industries, including marketing, customer service, copywriting, and hospitality. In the years to come, this trend is probably going to continue.

The discussion of several of the most significant trends in the AI business for 2024 in this article should help anyone interacting with these technologies get ready for whatever may arise.