Key Artificial Intelligence Trends Shaping 2024

In 2022, generative AI burst into the spotlight, and by 2023, it started making its way into businesses. Now, 2024 is shaping up to be a crucial year as companies and researchers figure out how to integrate AI into our daily lives effectively.

Generative AI has evolved quickly, much faster than computers did. We went from huge, centralized mainframes to personal computers accessible to hobbyists. Similarly, AI started with large models that only big companies could handle, but now smaller, more efficient models are becoming the norm, making AI accessible to more people and organizations.

While the media often focuses on the latest AI advancements, the most significant progress might come from improvements in governance, training methods, and data management that make AI more reliable and user-friendly for everyone.

Here are the key AI trends to keep an eye on in 2024:

Setting Realistic Expectations
Multimodal AI and Video
Smaller Language Models and Open Source Progress
GPU Shortages and Rising Cloud Costs
Easier Model Optimization
Customized Local Models and Data Pipelines
More Powerful Virtual Assistants
Regulations, Copyright, and Ethical Concerns
Shadow AI and Corporate AI Policies
Looking Ahead

1. Setting Realistic Expectations

When generative AI first became popular, many business leaders only knew about it from flashy ads and news stories. They might have played with tools like ChatGPT and DALL-E, but didn’t have much hands-on experience. Now, businesses have a clearer understanding of what AI can and cannot do.

According to the Gartner Hype Cycle, generative AI is moving from “Peak of Inflated Expectations” to the “Trough of Disillusionment.” This means people are starting to see AI for what it really is—not a magic solution, but a powerful tool with specific uses.

Real-world AI success often comes from smoothly integrating AI into existing systems rather than creating standalone tools. For example, features like Microsoft’s Copilot in Office or Adobe’s Generative Fill in Photoshop enhance current applications without completely changing how they work.

A recent IBM survey found that the main reasons companies are adopting AI are because it’s becoming easier to use, helps reduce costs by automating tasks, and is increasingly built into everyday business software.

2. Multimodal AI and Video

AI is not just getting better at understanding text or images separately. The next big step is multimodal AI, which can handle multiple types of data at once, including video. Companies like OpenAI and Google are developing models that can switch between tasks like processing language and analyzing images seamlessly.

For example, you might ask an AI assistant to look at a picture of your fridge and suggest recipes based on what’s inside. This makes AI more intuitive and versatile, enhancing how we interact with technology in our daily lives.

3. Smaller Language Models and Open Source Progress

Big AI models with billions of parameters have driven the AI boom, but they’re expensive to run and maintain. Experts now believe that smaller models can achieve similar performance with fewer resources. Open-source models like Meta’s LLaMa and Mistral’s Mixtral are leading the way, proving that you don’t always need the largest models to get great results.

Smaller models have several advantages:

Accessibility: More people and organizations can use and improve these models without needing massive computing power.
Local Use: These models can run on smaller devices like smartphones, improving privacy and security since data doesn’t need to be sent to the cloud.
Explainability: It’s easier to understand how smaller models make decisions, which is important for building trust in AI.

4. GPU Shortages and Rising Cloud Costs

As more companies want to use AI, the demand for GPUs (the hardware that powers AI) is increasing, leading to shortages and higher costs. Most businesses rely on cloud providers for their AI needs, but as hardware becomes scarce, cloud prices are likely to go up.

To manage this, companies need to be flexible, using smaller models when possible to save costs and only turning to larger models when necessary. This approach helps balance performance with affordability.

5. Easier Model Optimization

Optimizing AI models—making them run faster and use less memory—is becoming more accessible thanks to advancements from the open-source community. Techniques like Low Rank Adaptation (LoRA) and quantization help fine-tune models efficiently, allowing even smaller teams to create powerful AI applications.

These optimization methods make it easier for startups and individual developers to leverage advanced AI without needing extensive resources.

6. Customized Local Models and Data Pipelines

Businesses are increasingly creating their own AI models tailored to their specific needs. Using open-source models, companies can fine-tune AI with their own data, making the AI more effective for tasks like customer support, supply chain management, or document analysis.

Running AI locally on company hardware also enhances privacy and security, as sensitive data doesn’t need to be sent to external servers. This customization helps businesses gain a competitive edge by using AI that’s specifically designed for their operations.

7. More Powerful Virtual Assistants

Virtual assistants are becoming smarter and more capable. In 2024, expect AI agents to do more than just answer questions—they’ll help you complete tasks like making reservations, planning trips, or connecting with other services.

With multimodal AI, these assistants can interact with both text and visual inputs. For example, you could show an AI a picture of your fridge and get recipe suggestions based on what’s available, making interactions more seamless and useful.

8. Regulations, Copyright, and Ethical Concerns

As AI becomes more powerful, concerns about misuse are growing. Issues like deepfakes, privacy violations, and biased algorithms are becoming more common. Governments around the world are starting to implement regulations to address these challenges.

In the EU, the Artificial Intelligence Act aims to regulate high-risk AI systems and ensure transparency for general-purpose AI models. Meanwhile, the U.S. is seeing executive orders and state-level laws focused on AI governance and data privacy.

Legal battles, like the New York Times lawsuit against OpenAI, highlight the ongoing debate about AI’s use of copyrighted material. These regulatory developments will shape how AI is used and governed in the coming years.

9. Shadow AI and Corporate AI Policies

With AI tools becoming so easy to use, many employees are using them without official approval from their companies—a phenomenon known as “shadow AI.” While this can lead to innovative uses, it also poses risks like data leaks and compliance issues.

Businesses need to establish clear AI policies to manage how employees use these tools. Proper guidelines help protect sensitive information and ensure that AI is used responsibly within the organization.

10. Looking Ahead

2024 is a pivotal year for AI, with significant advancements and challenges on the horizon. By staying informed about these trends, businesses and individuals can harness AI’s potential while navigating its complexities responsibly.