In the vast realm of emerging technologies ruling the business landscape, two powerhouses reign supreme: Artificial Intelligence (AI) and Machine Learning (ML). Both technologies evolve rapidly and constantly, making it challenging for businesses to keep up with the latest trends.
Machine learning technology has totally revolutionized ways of doing tasks, making them way more doable, efficient, and accurate than ever before. It has become a major driving force of innovation, powering various industries such as healthcare, finance, retail, and many more.
As we enter 2024, the machine learning industry continues to expand in exciting directions. In this article, we explore the top 10 trends and innovations in machine learning that are expected to shape the industry in 2024.
Multimodal Machine Learning
First up in our list of 2024 machine learning technology trends is multimodal machine learning (MML).
MML taps into the richness of our surroundings, embracing the diverse ways we experience the world. By leveraging multiple modalities, AI models can capture events with the depth and breadth that mirrors human perception. For instance, it pairs images with audio and text labels to make them more recognizable.
Google DeepMind made waves with Gato, a multimodal AI system that can handle multiple tasks like visual perception, language comprehension, and robotic movements. At the same time, developers are exploring ways to blend different modalities to boost everyday tasks like understanding documents, as highlighted by David Talby, the founder and CTO of John Snow Labs, a provider of NLP tools.
This is extremely helpful in the healthcare industry. AI algorithms trained with multi-modal techniques like machine vision and optical character recognition can really improve how results are presented, making medical diagnostics even better.
Importantly, hiring or training data scientists who are skilled in different areas, like natural language processing and machine vision techniques, will be crucial to tapping into the full potential of MML.
While MML is so far a young field that is yet to be developed and advanced in the years to come, many believe that it can be key to achieving general AI. It’s an exciting frontier where machines seek to understand the world as we do.
Foundation Models
In the past few years, the Foundation Model has emerged as a true powerhouse in artificial intelligence, captivating the attention of many. And the best part? Its journey of popularity is far from over because it continues to dominate the landscape well into 2024.
A foundation model is a deep learning AI algorithm that’s been pre-trained with tons of different data sets. Unlike those narrow AI models that only do one thing, foundation models are trained to handle all sorts of tasks and transfer knowledge between them.
Engineers aim to reach a whole new level of understanding by teaching machines not only to search for patterns but also to gather knowledge. Foundation models are super helpful in generating and summarizing content, coding and translation, and providing customer support.
To give you concrete examples, the most popular foundation models are OpenAI’s GPT-3 and GPT-4 and Microsoft’s Florence-2. GPT-3 is already used in diverse applications, from generating text to creating chatbots. Meanwhile, Florence-2 is a large neural network that can work with different languages.
These large language models are the talk of the town lately, and they’re here to stay.
Looking beyond 2024, we expect to see more breakthroughs in foundation models. More companies and researchers are still exploring ways to improve foundation models by adding additional training and fine-tuning techniques.
Transformers or Seq2Seq Models
Another star rising in the 2024 machine learning trends is transformers, a.k.a SeqSeq models.
Transformers are a kind of AI architecture that does transduction, or transformation, on input data sequences using an encoder and decoder, resulting in a different sequence. Many fundamental models are also based on transformers. And they are out to dominate the AI and ML world.
Transformers are basically used in a wide range of natural language processing tasks. They help analyze word sequences, letters, and time series to tackle complex machine language problems like device translation, question answering, chatbot creation, text summarization, and more.
So, how exactly does it work?
Instead of just translating words one by one, a transformer model assigns weights to each word to determine its importance in the sentence. Then, it generates a new sentence in a different language, considering these assigned weights.
If you’re into building ML programs fast and efficiently, then transformers are a must-learn tech for you. They’ve already shown their value in different use cases, and we can expect more advancements and improvements in this field. Some of the top solutions that can help you build transformer pipelines are Hugging Face and Amazon Comprehend.
Low-Code or No-Code Development
Machine learning and artificial intelligence have made their mark in every field, from agriculture to marketing to banking. In 2024, these technologies will continue to power low-code or no-code development platforms.
This approach, as the name suggests, allows developers without extensive coding experience to build machine learning models quickly and efficiently. Managers often see user-friendly ML solutions for non-techy employees as crucial for keeping the organization running smoothly.
Undeniably, it is a more cost-effective way to build digital projects than the lengthy process of having a whole team of data scientists and engineers. As a result, this trend will lead more companies to embrace low-code and no-code solutions, leading to a significant rise in the number of enterprises using ML models.
The possibilities of low-code or no-code platforms are truly limitless. For instance, businesses can use it for employee hiring, fraud detection, demand forecasting, customer sentiment analysis, and more.
If you are thinking about quality, low-code or no-code platforms are equally good compared to traditional coding frameworks.
According to Gartner, the demand for high-quality solutions surpasses the capacity to deliver them, growing at least 5x faster than IT can keep up. No-code and low-code solutions bridge this gap, satisfying the demand. Similarly, low-code solutions enable tech teams to test their hypotheses faster, reducing time-to-delivery and development costs.
In the coming years, the availability of pre-trained AI building blocks and a broader range of easy-to-use development tools will make it possible for developers to deliver an overall better experience with low-code or no-code machine learning programs.
Automated Machine Learning (AutoML)
In 2024, the machine learning process will become even easier with the use of AutoML tools.
If you are a data scientist, this trend will surely interest you. AutoML platforms leverage machine learning algorithms and automation to help you quickly prototype, train, evaluate, and deploy models at a faster pace than traditional manual processes.
How? Through the use of templates!
Here’s an example: AutoGluon, a ready-to-use solution for text, image, and tabular data. It lets developers easily test out deep learning solutions and make predictions without needing data science experts.
AutoML improves data labeling tools and enables automatic tuning of neural network architectures. Traditionally, data labeling has relied on manual work, which introduces a significant risk of human error.
By automating much of the labeling process, AutoML greatly reduces the risk of errors and lowers labor costs. This allows companies to focus more on data analysis and makes solutions like AI more affordable and accessible in the market.
Another example is OpenAI’s DALL-E and CLIP models. These combine text and images to create new visual designs. For example, the models employ image generation based on the input “armchair in the shape of an avocado.” This technology has diverse applications, including article SEO, product mockups, and generating product ideas.
With AutoML, you can expect to see more groundbreaking advancements in machine learning models and applications in the coming years.
Generative Adversarial Networks (GAN)
GAN has been the talk of the town for the past few years, and it will continue to dominate in 2024.
These networks are a machine learning structure where two neural networks compete. The generator creates fake data, and the discriminator or critic tries to detect if the data is real or fake. This competition leads to the generation of high-quality synthetic data.
The field of GANs has been evolving rapidly, showing amazing abilities in creating lifelike content in different areas. They can do things like translate images into other images and make photos that look just like the real thing. It really shows how GANs can change the game in generative modeling!
Some popular examples of GANs are StyleGAN, which can generate high-quality images and videos, and BigGAN, which specializes in creating diverse images based on text inputs.
With the rise of GANs, we can expect to see more realistic and creative applications in various fields such as art, entertainment, and fashion.
Machine Learning Operationalization Management (MLOps)
If you have heard of DevOps, you can think of MLOps as its cousin.
MLOps is all about managing the lifecycle of machine learning models from development to deployment and beyond. With the rise of ML, it’s exactly what the industry needs in 2024.
MLOps is really taking off as companies look to scale their ML. And as they gather more data on larger scales, their need for greater automation is growing. So, this is an approach to improve the development of machine learning solutions, making them even more valuable for businesses.
How it works is by automating the deployment process, keeping track of model versions, and managing ML pipelines. This helps organizations streamline their development practices and save time and resources while improving overall efficiency.
MLOps is a game-changer for large enterprises, bringing more consistent and reliable machine learning applications to industries like healthcare, finance, and retail. It’s all about reducing variability and boosting scalability.
In 2024, investing in MLOps will become a priority for companies looking to stay competitive and reap the benefits of cutting-edge ML technology.
Explainable AI (XAI)
Though anyone can use AI with little coding, understanding the inner workings of a model can be challenging. This is where Explainable AI (XAI) comes in. This year, we will see XAI gain more traction as companies look for ways to make AI more transparent and trustworthy.
One of the big issues in machine learning is the “black box” problem. Advanced models like deep neural networks are super accurate but can be unclear in how they make decisions. XAI aims to bridge this gap, making it easier for humans to understand the decision-making process and trust the results.
XAI has a wide range of applications in fields such as finance, healthcare, and law. It can help banks make more informed lending decisions, or doctors determine diagnoses with more certainty. In these areas, understanding how an ML model came up with its answer is crucial for accountability and trust.
In 2024, companies are putting more money into research and development to make models that not only give accurate predictions but also explain their decisions in a way that’s easy for people to understand.
So, expect to see XAI tools become a standard part of the ML development process as companies strive toward ethical and explainable AI practices. This will not only improve transparency but also help mitigate bias and promote responsible use of AI technology.
Embedded Machine Learning
TinyML, or embedded machine learning, is all about running machine learning on various devices. It’s used in household appliances, smartphones, laptops, and smart home systems.
As IoT technologies and robotics become more widespread, the significance of embedded systems has grown. In 2024, the challenges of Tiny ML remain unresolved, demanding maximum optimization and efficiency while conserving resources.
Embedded applications are often very specific and must work within tight resource constraints, such as limited processing power and memory. This requires specialized techniques for model compression and optimization.
However, with the advancements in hardware design and software development, we can expect to see more sophisticated TinyML models that can perform complex tasks like voice recognition, image classification, and predictive maintenance on various devices.
Basically, in 2024, we’ll be running machine learning models on embedded devices to make better decisions and predictions. The embedded machine learning system is way more efficient than cloud-based systems and brings a bunch of benefits, like reducing cyber threats, saving bandwidth, and cutting down on data storage and transfer on cloud servers.
Metaverses
The Metaverse is all the buzz in the artificial intelligence and machine learning realm. In 2024, we’ll see the line between our physical and virtual lives blur even more as the Metaverse continues to evolve.
Lots of AI projects this year will be all about creating virtual environments that can learn, adapt, and interact with users in a more human-like way. These immersive environments could be used in gaming, training simulations, and even remote work.
As the metaverse becomes more integrated into our daily lives, we can expect to see machine learning technologies play a significant role in its development. This includes advancements in natural language processing, computer vision, and reinforcement learning.
The Metaverse is a complex concept with endless possibilities, and as we continue to explore its potential, machine learning will play an essential role in shaping this virtual realm.
For instance, the emergence of virtual marketplaces using blockchain for 3D internet trading is increasing in popularity. This includes marketplaces for virtual real estate, digital art, e-commerce, and play-to-earn games, expanding the scope for gaming marketplaces.
Virtual assistants, avatars, and chatbots that use AI will also become more prevalent in the Metaverse. These intelligent agents will help users navigate and interact with this virtual world, making it a more immersive and personalized experience.
In 2024, we can expect to see the Metaverse continue to grow and evolve as machine learning technologies make it even more realistic and interactive. The possibilities for this virtual world are endless, and as we continue to push the boundaries of technology, the Metaverse will become an integral part of our daily lives.
Let’s Build Your ML Project Today!
It will only be a matter of time before these machine learning trends become the norm. From MLOps to Explainable AI, Embedded Machine Learning, and the Metaverse, 2024 is shaping up to be an exciting year for advancements in this field.
If you want to get started on your own ML project or incorporate these trends into your business, now is the time to do it. Our StarTechUp team has the expertise and experience to help you build cutting-edge ML solutions that will give you a competitive edge in the market.
We also offer AI consultation services to help you navigate the complexities of machine learning and find the best solutions for your business needs. Let’s work together to bring your ideas to life and create a better, smarter future with AI.
Don’t get left behind!
Contact us today to discuss your project and see how we can bring your ideas to life with machine learning!
