Currently in 2025, Artificial Intelligence (AI) is advancing rapidly, and it looks like this year could very well be a landmark year. While AI has already gotten everyone impressed by making significant strides in deep learning, natural language processing (NLP), and generative AI (case in point the Ghibli-isation of social media recently), two upcoming innovations seem extremely promising when it comes to truly push the boundaries. They are Multimodal AI and Self-Supervised Learning (SSL). These advancements are going to end up making AI smarter, more intuitive, and capable of mirroring human learning patterns like never before.
The Rise of Multimodal AI: A shift towards human-like understanding
For years, AI has operated and functioned largely within different silos. FYI silos are isolated datasets and systems that do not integrate well or at all with each other. This leads to some models excelling at processing text, while others specializing in images or audio, but few AI models have emerged that can integrate all these separate functions seamlessly. Now, we finally have a breakthrough in this field, in the form of multimodal AI, that aims at breaking these barriers, and enables AI to interpret and synthesize information across various formats at the same time.
Why Multimodal AI matters
Can you imagine an AI assistant that not only comprehends your words but also picks up on subtle and emotive cues such as your facial expressions and tone of your voice to get a better read on your emotions. Or a search engine that allows you describe a scene in words and instantly locates relevant images and videos from the vast expanse of internet search engines, thereby saving you time by combing through hundreds of online sources in a matter of milliseconds. Multimodal AI is in the process of bringing us closer to a world where AI doesn’t just process data, but rather it comprehends the context of within which the data exists, helping make interactions more natural, intelligent and eventually useful.
How is multimodal AI accelerating in 2025?
Multimodal AI is no longer just a theoretical concept anymore. As of right now, it’s in the process of being integrated into real-world tech through various applications. But what’s enabling its rapid growth in 2025? Next up we shall take a quick look at a few of the technological advancements that are responsible for this heightened acceleration of multimodal AI.
First up, we have the next-gen AI chips that are the so advanced in their in hardware design that they are making it possible to process multimodal inputs in real time, which in turn helps reduce lag and increase efficiency. Then, we have the rich multimodal datasets. The access to large-scale datasets containing text, images, and audio is allowing AI models to develop deeper contextual understanding. Moreover, cross-modal learning innovations is allowing AI to become better at transferring knowledge across different formats, while making it more adaptable and reducing its reliance on extensive labelled data. Finally, innovations like Edge AI integration is helping create smarter AI assistants on mobile devices, wearables, and IoT systems. Soon this integrated AI will be capable of processing multiple data types locally without having to rely on any cloud-based storage.
What are the ways in which multimodal AI can be applied in real life?
Innovations that result in scientific advancement are doubly celebrated in society when they have positive real- world applications. Here are some of the ways in which multimodal AI should be able to help.
- Healthcare Diagnostics: Soon, AI-powered systems will be able to analyze the various elements of the prescription procedure like medical scans, doctor’s notes, and genetic data comprehensively, and that will lead to more accurate diagnoses and time sensitive treatment plans.
- Self-Driving Cars: Autonomous vehicles will soon be able to merge visual data from cameras, audio cues from the environment, and radar signals and assist with navigating complicated road conditions safely.
- Education & Accessibility: AI tutors will soon be able to assist teachers in designing lesson plans based on both spoken and written inputs. Meanwhile, real-time sign language translation and integration of our sensory outputs will improve accessibility for the hearing-impaired and other differently abled peoples.
- E-Commerce & Retail: Image based product searches will soon become the new normal. Multimodal AI will make shopping more intuitive and personalized based on the customer’s shopping preference history and will also be able to bounce off appropriate suggestions on the basis of specific prompts.
- Security & Surveillance: Future AI systems will be able to seamlessly integrate key security features like video footage and audio clips, along with contextual text-based alerts to improve threat identification and response during emergencies.
As 2025 comes to a close, multimodal AI won’t just be a cool new experimental feature, it will become one of the most important components in all AI-driven products. This will make digital experiences like browsing, shopping, life planning, and self-learning more seamless and natural than ever before.
Self-Supervised Learning: The what, the why and the how of it all.
One of the biggest setbacks faced by AI today is its dependence on large labelled datasets. Self-Supervised Learning (SSL) is changing this arena by training AI to learn from raw, unlabelled data. This process is intended to mirror humans in the phase where they explore and understand the world.
How does SSL work?
SSL works by enabling AI in uncovering patterns and relationships within data without relying on human interventions. Consider the learning processes and patterns of children, they don’t need someone to label every object they see. Their brains can be conditioned to recognize similarities, differences, and structures over time. AI is now learning in a similar way utilising different methods.
First we have contrastive learning, this learning process involves teaching AI to differentiate between similar and dissimilar data points, improving contextual understanding. Then we have masked autoencoders whereby the AI is enabled to predict missing elements in a dataset, much like how we fill in gaps in a partially obscured image or sentence. Finally we have pretext tasks, through which AI models are trained so that they can solve tasks like predicting the next frame in a video or reconstructing scrambled text, leading to more robust representations.
Why we think SSL will dominate in 2025?
Self-supervised learning is rapidly moving from the research labs to into real-world applications. Some of the factors that explains why 2025 will be the year SSL takes centre stage are elimination of labelling bottlenecks. AI will soon be able to learn from vast amounts of unstructured data without requiring any human intervention. That should ideally accelerate development. Then comes the improved adaptability whereby SSL-powered AI will require minimal retraining when applied to new tasks, helping it become highly versatile.Moreover, breakthroughs in foundation models like large-scale models trained with SSL will eventually outperform traditional supervised learning in various domains, including language and vision. Soon, AI assistants will adapt to individual users more efficiently, learning from their behaviour rather than relying on predefined rules, thereby allowing better customization options.
Transformative Impact of SSL
In the medical research and drug discovery industries AI will soon be able to analyze scientific literature, clinical trials, and genetic data without the need for extensive manual curation. This will help with accelerating medical breakthroughs. In the finance & fraud detection sector, SSL will enhance fraud detection systems by identifying subtle patterns in transaction data, reducing false positives while improving real-time fraud prevention. In the content generation and creativity sector, AI-powered music, art, and writing tools will become increasingly more sophisticated. Soon they will be learning creative styles without requiring labelled datasets. In autonomous systems like robotics and drones AI will be able to navigate and adapt to their surroundings more effectively, while learning from unlabelled sensor data. In the cybersecurity industry, AI-driven security systems will detect emerging cyber threats without relying on predefined attack patterns, improving resilience against evolving threats. This will be ideal for most major companies, nations and banking systems everywhere.
Conclusion
It looks like 2025 will prove be a transformative year for AI, as Multimodal AI and Self-Supervised Learning integrates into evolving AI systems. These developments will inevitably push AI towards more intuitive, efficient, and human-like intelligence. This newfound form of thinking and intelligence will impact industries ranging from healthcare to finance, security, and beyond. As these technologies continue to evolve, we can expect AI to continue becoming an even more integral part of our lives. Imagine a ChatGPT that can think in videos, generate medical surgery procedures and cardio-vascular health plans, detect evolving viruses, and generate rich historical and cultural content about your upcoming vacation spot. That is the world that these innovations are leading us into.
Disclaimer: The opinions expressed here are my own and do not necessarily reflect the views of CVS Health or its affiliates.
