6 AI Trends & Predictions for 2024

6 AI Trends & Predictions for 2024

Factored Data Experts
Factored Data Experts

Want to stay ahead of the AI curve this year? Then it’s important to know these six key AI trends and developments that are expected to shape the landscape in 2024.

By ensuring your business embraces these developments, you’ll set your organization up for success and outpace the competition.

Without further ado, let’s dive in!

Employing Timeless Techniques

Timeless techniques like classical time series analysis—which involves a set of statistical methods for analyzing time series data and where observations are collected at regular time intervals—and Gradient Boosting Trees—a powerful machine learning technique used for both regression and classification problems—both remain highly effective methods for delivering unparalleled value in today’s data landscape. We recommend you continue to utilize these transformative strategies, as they could prove to be your secret weapon of 2024.

Navigating Economic Dynamics

Integrating data science teams into your company’s value proposition is imperative in today’s economic context. By hiring a data science team to implement AI initiatives like, for example, Cost-Sensitive Machine Learning—an approach to machine learning in which the costs of prediction errors are explicitly taken into account during the training of the model—your company will not only enjoy data-driven decisions, but financial success as well.

That’s because, instead of the “one size fits all” approach that treats all errors equally, Cost-Sensitive Machine Learning allows for more nuanced and practical models that align better with the specific objectives and constraints of different real-world applications. Seeing as different types of errors have different consequences and, therefore different costs associated with them, Cost-Sensitive Machine Learning aims to minimize these costs, rather than just minimizing the number of errors.

Preparing for Multimodality to Take Over

2024 is the time to dive into the revolution of Vision Language Models (VLMs), such as GPT-4 Vision, and explore open-source gems like CogVLM, BLIP2, and PaLM-E that are shaping multimodal AI. Exciting releases, including the Ego-Exo4D dataset, promise advancements in video learning and multimodal perception.

Additionally, the incredible progress in text-to-video technologies indicates that this trend will only continue, with startups like Pika releasing innovative products, like its new AI model that’s capable of generating and editing videos in diverse styles such as 3D animation, anime, cartoon and cinematic. There are also new applications for multimodal AI that use Google’s Gemini, as well as other emerging competitors. Ultimately, the realm of LLMs beyond NLP is expanding thanks to their recognition as general pattern machines.

Understanding the Popularity of Graph Neural Networks

Graph transformers and graph neural networks are gaining popularity and scalability. The combination of diffusion models and GNNs opens up possibilities to generate graphical representations of new proteins and molecules using natural language and specialized prompts as input. This year, exploring collaborative autonomy and how GNNs incorporate reinforcement learning techniques (more on that in a bit) can give your business a serious edge.

Implementing Chatbots Calls for Next-Gen RAG

Retrieval-Augmented Generation (RAG), which leverages knowledge graphs to perform queries to language models (LLMs), is becoming the standard approach for implementing chatbots and/or QA applications. This elevates its capabilities beyond regular vector search by allowing the system to leverage a vast amount of external knowledge, provide more accurate and contextually appropriate answers, be customizable and flexible, and more.

Using Reinforcement Learning

Diverse applications of reinforcement learning (RL) include reducing LLMs’ hallucinations, fine-tuning models with human feedback, improving robots’ adaptability and efficiency in dynamic and unpredictable environments, enhancing recommendation systems by continuously learning from user interactions, and more. Keep an eye on continuous progress in imitation learning for robotic and virtual tasks if you want to up your game in the year to come.

At the end of the day, embracing these key trends and developments can help your business unleash your potential and stay ahead of the curve in the realm of AI and data science in 2024.

Are you ready to build an expert data science team in just a few days to start taking advantage of these trends? Book some time with our team to learn how your growing company can build and retain top AI, Machine Learning, Data Analytics and Data Engineering talent in a fast and cost-effective way.

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