Artificial Intelligence is taking the world by storm – and there is no exaggeration in this statement. The market for AI-powered solutions is predicted to reach $184 billion by the end of 2024. Assuming that the market will grow stable and keep momentum, it is predicted to reach $826.7 billion by the end of 2030.
The growth is fueled by AI applications that already solve existing, real-life problems. The applications can be found not only in multiple industries and business cases, but also in cracking the mysteries of nature. Their power and impact has been so immense that there were not one, but two AI-related Nobel Prizes in 2024. In the first case, Deepmind researchers, Demis Hassabis and John Jumper, were awarded the Nobel in chemistry for their AlphaFold AI model. By predicting the shapes of proteins from their structure, it powered the research of new medicines, chemicals and more.
Also, the Physics Nobel Prize was awarded to Geoffrey Hinton and John Hopfield, who pioneered work on Artificial Neural Networks. This technology was a supporting tool in physics-related research, including particle physics, astrophysics and material science, among others.
Super-fast changes
The history of AI in fiction started (or at least the first records exist from) up to eight millennia before the common era. Hephaestus, the god-smith, was said in the Iliad to have the Kourai Khyrseai (lit. Golden Maidens) as servants and assistants. “There is intelligence in their hearts, and there is speech in them and strength, and from the immortal gods they have learned how to do things.”
The story of modern AI began in 1943 with Warren McCulloch and Walter Pitts studying several abstract models in their paper titled, “A logical calculus of the ideas immanent in nervous activity.” Not a long time after, a famous British mathematician and genius published the “Computing Machinery and Intelligence” paper, where he was arguably the first to ask openly “Can machines think?” The answer was and remains… we don’t know yet.
What we know is that their ability to perform thinking-like processes has already shaken the world as we know it. It has taken nearly ten thousands of years to transfer thinking machines from fiction to science, and then less than a hundred years to transfer them from science to everyday life. To say nothing of their bright and still-shining presence in the realm of science-fiction.
To keep up with these dynamics, the Tooploox research team has prepared a list of Artificial Intelligence trends to keep an eye on in 2025 and beyond.
This text covers:
Language Models keep momentum
Large Language Models started to gain traction following OpenAI’s explosion of ChatGPT usage. A technology that has already transformed multiple areas of life. And this includes not only business, where specialists of many different fields and departments use the technology to automate their daily tasks.
The technology also impacts education, with teachers complaining about students cheating, while on the other hand companies that sell ed-tech software are integrating LLMs into their workflows.
Large Language Models started as an universal tool to be used for multiple use cases, yet with no particular goal. ChatGPT is the best example of this approach – it was released as a chat interface with users exploring its possibilities freely. Yet the future of LLMs, although bright, is more diverse.
Domain-Specific Large Language Models (LLMs):
Contrary to the dominant Large Language Models (GPT, Gemini, Claude – the market is getting a little crowded in fact) domain-specific language models are trained on selected, more narrow data sets, and are used in more specialized environments. Using narrow and selected data to train a model reduces the risk of hallucinations as well as reduces the amount of information stored in the neural network, that which may be irrelevant or considered “noise.”
Examples include:
- BloombergGPT, a 50-billion parameter language model designed to deal with financial and economic data. The model was trained by Bloomberg, one of the world’s leading press agencies dealing with market and finance data.
- BioBERT is a language model designed to perform tasks like entity recognition or text mining in the biomedical area.
- MedPaLM was developed by Google to serve in the medical domain, trained on a selected database, with bigger reinforcement against hallucinations.
Small Language Models (SLMs)
On the other side of the spectrum, there are Small Language Models. Not every problem users encounter in their lives require super-powerful models to tackle them. Usually, a smaller model is enough. The difference is in costs, with smaller models bringing huge savings for minor differences in performance.
The year 2024 brought multiple advances in SLMs with Microsoft’s Phi-3, Orca-Math, and GPT 4o mini being off-the-top-of-the-head examples.
These models can be small enough to be run on commodity hardware, including mobile devices, and may power top AI trends of moving AI toward edge devices.
AI Explainability and legal AI regulation
Whether one likes it or not, AI is inevitable, being a part of our daily lives in ways most users are not aware of. Using sophisticated algorithms in email spam detection or AI-powered internet ad bidding is a simple example on how AI may impact the lives of people who may be entirely oblivious to this technology.
Yet the impact may be even deeper, with algorithms making decisions on the interest rates of credit. This has provided the motivation to build responsible and more secure AI – and has encouraged governments to deliver legal frameworks to do so. The European Union has set the course with voting in the AI act of 2024, yet there is an increasing number of states and legislatures implementing rules for responsible AI governance.
- Tooploox has also contributed to building more explainable AI by delivering research on providing counterfactual explanations. This tool basically answers questions on what has to be altered to change the output of the system. For example, if the AI system decides if one may or may not get credit.
Mitigating AI threats
AI has also generated a set of threats as yet unseen before. These include generating increasingly convincing fake news, deepfakes of celebrities or politicians, or deceiving voice messages.
These challenges also need to be addressed by lawmakers, civil society, and online platforms alike. This will also be a field where regulations and laws need to be applied.
Explainability and transparency
With the increasing impact of AI tools on the daily lives of people, it is necessary to not only protect users from their harmful impact, but also to know the reasoning behind their decisions. While the decision itself may not be harmful or offensive, ensuring that the reasoning will not escalate is the key to more predictable results. Also, using systems that nobody really understands may bring great risks and damage in the long run.
Explainability includes bias mitigation, building more balanced and fair datasets, and building control tools around the whole AI process.
Growing AI societal impact
The changes mentioned in the point above are driven by the fact that AI is increasingly important and impactful in people’s daily lives. AI is used by an increasing number of businesses and companies around the world. The economy just adds up, fueling both growth and adoption.
Yet with the world transforming so fast, it is not a surprise that people are not fully comfortable with the changes. According to a Gallup study, up to 22% of employed adults in the US are afraid that their job will become obsolete due to AI. And that’s for a reason – nearly 3 out of 4 (72% to be precise) CHROs of Fortune 500 companies foresee AI replacing jobs in their organizations.
Further development of existing and new AI technologies
“If I have seen further [than others], it is by standing on the shoulders of giants,”
Sir Isaac Newton
The breathtaking AI advancements are made mostly by further developing existing solutions and step-by-step polishing of the technologies we already use. Advances in the technology of AI will most likely include:
Generative AI for Multimodal Workflows:
Multimodality is all about working and understanding multiple types of data at the same time. A good example of this may come from marketing, where one centralized system may analyze tabular data from an analytics system and text data from the tool overseeing SEO performance. Another example may include an autonomous car system, where the neural network analyzes data from cameras, radar, and LIDAR at the same time.
Generative multimodal AI is about taking multiple types of data as input as well as delivering various outputs. A good example may come from video generation, where the system will not only produce a video, but also the sound to match it.
Synthetic data generation
Creating an unbiased and balanced dataset to be used to train AI solutions is a challenging task, with data scarcity being one of the greatest obstacles. Stanford University’s report highlights that this challenge has already halted or significantly slowed the development of multiple systems in the biotech and 3D processing field.
Synthetic data may either be AI generated content or data modified by adding noise or reframing to make the system more robust and fair. Tooploox has already delivered synthetic datasets for clients where facial images of people coming from multiple ethnic and cultural backgrounds were necessary to build a product for the international market.
AI Agents
AI Agents are basically the next generation of AI-powered applications designed to solve problems. An AI agent is a tool capable of collecting data, making decisions, and performing particular actions on its own. Depending on the complexity of the environment and the agent itself, the system may either make decisions or even shape its own agenda, create tasks and then pick and prioritize them.
A good example of an AI agent may come from Tooploox, where a reinforcement learning agent was used to operate and schedule the works of crude oil processing plants.
Even more applications of AI
Last but not least, AI is just a tool. Sophisticated and super-effective, yet still only a tool. It is up to people to choose how they use it. And the year 2025 will bring new use cases and applications.
Embedded and Edge AI
Currently accessing AI-based tools is most common in the cloud environment. But the advancements in model development and hardware make it more efficient and affordable to actually use AI models on edge devices, be that on a smartphone or a camera. This not only reduces the maintenance costs, like bandwidth or latency, but also boosts privacy and security.
Biological, Healthcare, and Science AI Breakthroughs
The nobel prizes granted in Chemistry and Physics show how important AI has become in the scientific community. Researchers use AI tools to gnaw through gargantuan datasets available in modern science, be it in astronomy, particle physics, or drug research.
With the aid of artificial neural networks, the traditional work of scientists and researchers will likely make new breakthroughs and solve problems that have haunted humanity for a long time. Or, sometimes, maybe create new ones.
Summary
AI technology is not only impacting the everyday lives of people. It is, in fact, blending into the background, reshaping the way companies and people are performing their daily tasks. The machine learning trends above are strong points to observe and analyze to not fall behind in this fast-moving and never-stopping world of modern technology.
