# The Most Significant AI Trends to Watch in 2024

As 2022 saw the explosion of generative artificial intelligence (AI) into the public consciousness and 2023 marked its integration into the business world, 2024 appears to be a crucial year for the future of AI. This is the year researchers and businesses will determine how this evolutionary leap in technology can be effectively integrated into everyday lives.

The evolution of generative AI has followed a similar trajectory to that of computers, but at an accelerated pace. In 2024, we are set to see groundbreaking enhancements in various aspects of AI. In 2023, the focus was on the emergence of increasingly efficient open-source foundation models, such as Meta’s LlaMa family of large language models (LLMs) and others like StableLM, Falcon, Mistral, and Llama 2. These advancements have paved the way for the industry to progress toward more trustworthy, sustainable, and accessible generative AI for both enterprises and end users. Amidst the rapid advancements in the capabilities of state-of-the-art models, much attention is also directed towards governance, middleware, training techniques, and data pipelines that enhance the trustworthiness and accessibility of generative AI.

### Summary of AI Trends for 2024

#### Reality Check: More Realistic Expectations
– As generative AI gains maturity, there is a shift towards more practical and realistic expectations rather than being solely based on inflated marketing claims.

#### Multimodal AI (and Video)
– The next wave of advancements will focus on multimodal models that can process various types of data, including video, offering more intuitive and versatile AI applications.

#### Small(er) Language Models and Open Source Advancements
– Emerging techniques and advancements are making AI more accessible and sustainable by focusing on smaller, more efficient models that can be run locally on smaller devices.

#### GPU Shortages and Cloud Costs
– The trend towards smaller models is driven both by necessity and entrepreneurship, as cloud computing costs increase alongside hardware shortages.

#### Model Optimization is Getting More Accessible
– The open-source community has made significant strides in optimizing and scaling AI capabilities through techniques such as Low Rank Adaptation (LoRA), Quantization, and Direct Preference Optimization (DPO).

#### Customized Local Models and Data Pipelines
– There’s a growing focus on developing bespoke AI models tailored to specific needs, with open source models enabling organizations to develop powerful custom models without heavy infrastructure investments.

#### More Powerful Virtual Agents
– AI systems are evolving from simple chatbots to more sophisticated virtual agents, offering expanded possibilities for communication, instruction following, and task automation.

#### Regulation, Copyright, and Ethical AI Concerns
– While AI capabilities expand, there are growing concerns around deepfakes, copyright issues, and ethical considerations, prompting regulatory actions and industry initiatives.

#### Shadow AI (and Corporate AI Policies)
– The rise of unapproved AI usage in the workplace, termed shadow AI, highlights the need for coherent corporate policies to mitigate risks associated with expanding AI capabilities.

### FAQ

#### What is generative AI?
Generative AI refers to models and systems that have the capability to generate new data, such as images, text, or even videos, based on patterns and inputs from existing data.

#### How are smaller language models impacting the AI landscape in 2024?
Smaller language models are democratizing AI by making it more accessible and sustainable. They can be run on smaller devices and help in making AI more explainable, leading to greater adoption and customization.

#### What are the concerns surrounding AI regulations in 2024?
With the rise of AI capabilities, concerns have been raised regarding deepfakes, privacy issues, perpetuation of bias, and the potential evasion of safeguards. Regulatory bodies are intensifying efforts to address these challenges and ensure responsible AI usage.