Artificial intelligence (AI) and machine learning (ML) continue to advance at a rapid pace, shaping the technological landscape in 2024. It’s important more than ever that we know the latest Machine Learning and AI trends for 2024 and beyond.
Building upon the launch of ChatGPT in 2022, the developments of AI we see in 2023 marks a pivotal moment for AI where we shift from experimental to implementation.
There are all kinds or AI tools you can use to help your business marketing, customer service, or copywriting service.
Let’s explore the next potential machine learning and AI trends for 2024 and beyond so we can anticipate what is coming.
1. Multimodal AI: Unlocking Human-Like Perception
Credit to LinkedIn Shaan Ray
We see how AI is moving beyond text-based interactions.
Now it’s no longer just a text robot, it becoming more and more human-like creatures with the revolutionary trends Multimodal AI.
Multimodal AI allows it to understand and create text, image, sound and even videos.
AI can also understand all kinds of input and prompt we give, whether we use text, image or sound.
This development has transform AI into a human image with human capability with creative artistic skills.
Whether you are an artist or healthcare staff, you can utilize AI to get ahead in your industry.
2. Agentic AI: The Era of Proactive Systems
The latest shift from reactive to proactive AI enable AI agents exhibit autonomy and act independently to achieve goals.
This trend opens up possibilities for applications in environmental monitoring, finance, and other areas.
Imagine the life where you have a personal assistant with amazing understanding and creativity working 24/7 for you to help you reach your goals in life.
3. Open Source AI: Democratizing Access to AI Models
The use of open source models, such as Meta’s Llama 2 and Mistral AI’s Mixtral models, for building large language models., provides alternatives to proprietary options
This AI trend aims to reduce costs, expand AI access, and encourage transparency and ethical development.
All developer can build on top of the existing code, encourages experimentation, exploration and unlimited progress.
The challenge here are potential abuse, misuse and difficulty in maintaining open source AI models.
4. Generative AI Reality Check
Credit to ashley mangtani
A phase of transition from experimentation to actual adoption and integration of generative AI, leading to a more tempered and realistic outlook.
However, there are challenges related to output quality, security, and integration with existing systems.
5. Retrieval-Augmented Generation (RAG): Enhancing AI Content Accuracy
Credit to medium
Retrieval Augmented Generation is a technique that combines text generation with information retrieval to reduce hallucinations in generative AI.
RAG enhances the accuracy and relevance of AI-generated content, fix the challenge like hallucinations from Generative AI.
This latest improvement works miracle by allowing AI to access external information to makes it relevant.
In addition, it reduce model size, increase speed and lowering cost.
6. Shadow AI
Credit to bmc
Over decades, shadow IT has become a well-known issue for all enterprise in the world.
Shadow IT refers to employees that using apps or infrastructure outside of the control of company’s IT department.
On the other hand, shadow AI refers to AI systems that operate above the control of those responsible overseeing it, most likely IT department.
Like shadow IT, there are good and bad regarding to development of shadow AI.
On one hand, it can be a great way to bring innovation while at the same time might lead to potential risk regarding to security, privacy and compliance.
Managing shadow AI through governance frameworks is highlighted as a necessity.
7. Evolving AI Regulation
If you plan to use AI for your company benefit, make sure you keep an eye on upcoming AI regulation.
Anticipation of a pivotal year for AI regulation, with the EU’s AI Act representing the first comprehensive AI law.
Organizations are urged to stay informed and adaptable as global AI regulations evolve, including potential influences from GDPR and U.S. executive orders.
Growing concerns about deepfakes, AI-generated content, and potential misuse of AI in various domains.
Emphasis is placed on ensuring transparency, fairness, and ethical considerations throughout the AI development process.
Make sure you comply with all upcoming regulations so you won’t get harm by utilizing AI for your company.
8. Customized Enterprise Generative AI Models: Niche Solutions for Business
The focus on smaller, narrow-purpose generative AI models tailored to specific business use cases.
This AI trends addresses the demand for AI systems that meet niche requirements, such as in healthcare, finance, and legal sectors.
I believe soon we will be able to have a specific AI models for our niche and specific business or even department, whether it is customer support, or supply chain management.
This latest AI trends will enhance efficiency, privacy, security and capability to meet our specific needs.
These trends collectively reflect a nuanced and matured approach to AI development, emphasizing responsible practices, ethical considerations, and real-world applications.
As organizations navigate the evolving AI landscape in 2024, the convergence of these AI trends is set to shape the future of our world.