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In Artificial Intelligence, the past year has been dominated by large language models (LLMs), especially by GPT-4. While the direction of AI tools like LLM might be somewhat unexpected, the general direction of the machine learning field toward generative AI shouldn't be a surprise. Since 2018, we have observed constant growth in the generative AI field year after year, with a speedup in the recent two years. What would bring the year 2024? Is there coming something new and unexpected? In this article, I will take my crystal ball and try to predict AI trends in the upcoming year.
Yes, that’s most probable. We will observe more sophisticated generative AI models and tools to generate text, images, videos, voices, and songs. Moreover, currently, existing models will be popularized and adopted by businesses. Softwares like RunWay Gen2, OpenAI Dall-E 3, Project Gutenberg Open Audiobook Collection, AudioShake, ChatGPT 4, and Github-copilot are just the prelude of what's coming. We could expect more text editors coming with text hints, graphic software coming with AI-supported context editing, slide editors with slide design hints and build-in image generators with frequently asked questions, chatbot implementations on business websites, document summarizers, AI assistants with build-in speech recognition functionality, that monitor customer interactions, search engines that better understand the query, and more. The generative revolution will go on.
Existing generative AI models are powerful enough to create training datasets for new models. For example, Dall-E 3 can often produce images that are indistinguishable from the human eye, and ChatGPT can write text or JSON files as well as humans. GPT-4 handles multimodality well and can generate accurate image descriptions. Even though AI-generated datasets might not be 100% accurate, they can certainly be used for model pre-training, and then only a tiny dataset needs to be created by humans for fine-tuning. Fake datasets and generative augmentations of datasets will power groundbreaking advances in the near future.
The vision of large generative AI models supervising the training of smaller, simpler models is even more futuristic. The generative AI model can create and train a simple image classification, energy consumption prediction, or text sentiment analysis neural network, depending on the needs. It could speed up the popularisation of AI in various business sectors and among smaller companies, which usually don't have a large budget for innovations.
According to the AI Index Report 2023, an impressive 50% of companies already use AI tools in their business processes. This shows the strong confidence of business leaders in AI. The upcoming year will bring a further adaptation of AI, machine learning, and natural language processing across various industries. Fast AI development creates new opportunities that are simply profitable for businesses.
Every year I say that next year will bring quantum computing and artificial intelligence to the next level. And one year, I will be right. I said it in 2021 and 2022 and I can confirmly repeat it in 2023. 2024 will bring a breakthrough in quantum computing, allowing machine learning to find patterns in data faster and with much higher accuracy. The particular properties of quantum computing should pinpoint global properties in datasets and omit specific details, similar to how it is done in unsupervised algorithms, for example, for dimensionality reduction. Operating in cubits brings speed. Quantum computers solve many problems exponentially faster and with less energy consumption than classical computers. Applications span banks, finance, energy, health care, retail, and other sectors. Hopefully, this year, I will be right about quantum computing boosting AI performance 🙂
The ongoing commercialisation of generative AI will result in many new deep-learning tools, both in computer vision and natural language processing (NLP). Thousands of users will use those tools, which raises a warning about the AI system's safety, transparency, traceability, non-discriminatory, and environmental impact. The need for AI ethicists will be in demand, driven by businesses showcasing their commitment to ethical practices and implementing necessary safeguards. This brings us to explainable AI (XAI). As new models arise, the coming years will bring new challenges for XAI libraries.
Those challenges can be easily addressed using the FoXAI library, developed internally by our ReasonField Lab team. FoXAI allows businesses to debug and see inside the deep learning model. Any network weak points and malfunctions can be spotted easily by companies in the early developing stages.
See how FoXAI woks for pneumonia.
If you're interested in catching the AI revolution train and starting a career in machine learning but prefer to pursue something other than a computer science path, you're in luck. In 2024, many job opportunities will emerge that cater to various skill sets. Beyond the business demand for engineers and technicians focused on system development, roles like prompt engineers responsible for crafting instructions for AI applications and AI managers who supervise virtual teams will be on the rise. Additionally, there will be a growing demand for AI trainers and ethicists. On the flip side, if you're a tech enthusiast, numerous new positions will be available in areas such as AI engineering and DevOps.
What AI trends to expect in 2024? AI dominates more and more sectors of our daily life. Starting from job-supporting tools to entertainment, child care, health care, driving, and so on. We should observe further commercialisation of generative AI and further popularisation of AI, thanks to AI-supervised training of other Neural Network models.