- 🤖 Multimodal AI now lets us change any kind of data to another, like turning screenshots into summaries or audio into videos.
- ⚙️ Mixture-of-Experts systems make things work better by only using the parts needed for a specific task. This makes them faster and more efficient.
- 📉 Smaller vision language models, like nanoVLM, cut down on computing costs a lot. This is good for startups and uses on devices that are not connected to the internet.
- 📹 Video-language models know how to understand visual input that changes over time. They can do things like summarize a video as it plays.
- 🛡️ Reinforcement learning with human feedback makes VLM safer. It helps their outputs match what is right and what fits the situation.
Vision language models (VLMs) are quickly becoming key in today’s AI world. Most older models only worked with text or images. But VLMs take many inputs, like pictures, words, and even video. This helps them make sense of how things are in the real world. This change is more than just technical. It is opening up useful chances for businesses, single founders, and anyone using automation tools like Make.com or Bot-Engine. Recent steps forward in multimodal AI and setup tools mean that the talk about VLMs is quickly turning into real help.
From Single-Modality to Multimodal AI
Old AI systems largely worked on one type of data at a time. Language models like GPT-3 looked at text only. Image classifiers like ResNet only looked at pictures. This split made it hard to build AI systems that could understand and work with the real world like people do, using many senses at once. A system that could add a caption to a photo could not understand the caption's meaning. One that could change speech to text would not understand a screenshot that had foreign text.
Multimodal AI offered a strong way to fix this. It brings together two or more kinds of data. Often, it mixes computer vision and natural language processing. This lets it do tasks that need a broader grasp of things. Vision language models are a part of multimodal AI. They link seeing and language skills. This means machines can not only see and read but also reason using both types of information.
For businesses, this means new kinds of uses:
- Online stores can make product descriptions from pictures automatically.
- Customer help bots can read pictures like screenshots or app screens.
- News sites can change TV shows into easy-to-read summaries.
These combined skills make work processes smarter and more like human ones. This helps bring about the next step in automation and making content.
Any-to-Any Models: Flexible Multimodal Workflows
A main new idea within VLMs and multimodal AI is any-to-any modeling. These models can understand and change between any two data types. This means text, image, audio, or video. Often, they do this within the same task. This freedom goes beyond the old way of image-to-text (like captioning) or text-to-image (like art made from a prompt).
Any-to-any systems open ways to:
- Summarize a training video by using a few main pictures.
- Turn a recorded customer call into an email summary.
- Change a data table into a spoken chart animation for social media.
This extra level of adaptability makes work much better, especially across groups like support, content, sales, and training. Businesses building tools with Make.com or Bot-Engine can use these systems. They can make simple flows where information moves freely between types. This happens without typing things by hand, figuring them out, or editing them.
Smol but Powerful: The Rise of Tiny VLMs
Some big multimodal systems need huge setups. But smaller ones are changing things. These “tiny” VLMs, like nanoVLM, aim for good work rather than raw power. They are made to run on regular computers or even small devices. They give power, once out of reach, to single developers, small businesses, and consumer electronics.
Tiny VLMs have these good points:
- They answer faster in real-time uses, such as smart kiosks or phone apps.
- They use less energy. This helps the environment and cuts running costs.
- They work fully without needing cloud-based graphics cards or costly APIs.
Their uses in real life range from:
- Cameras in stores that watch stock and report problems automatically.
- Point-of-sale systems that scan receipts and put them into tax software.
- Remote tools for scanning and marking up real objects or gear.
By making the needed size smaller, tiny VLMs give strong automation to more people. And they bring AI to places with poor internet or computing help.
Mixture-of-Experts: Smarter Computation on Demand
Another strong idea behind multimodal AI and VLM changes is the Mixture-of-Experts (MoE) model. Most old neural networks use all their parts for every input. But MoE systems smartly turn on only the right smaller networks, called "experts," for the task.
This way of working brings many main good points:
- Less wait time: Only part of the model works for an input.
- Can change: Different experts can be good at different data types. They can even be good at different areas.
- Can grow: MoE models can be very big without making the costs of using them much higher.
MoE designs help practical things across many fields. For example, in an automation tool built on Bot-Engine, MoE can let one expert handle medical pictures for health clients. Another can manage financial papers for finance teams. This happens without running two totally separate models.
This leads to a better match of task to resource, deeper specific knowledge, and in the end, smarter AI agents that can handle more complex situations.
Vision-Language-Action Models: Doing, Not Just Understanding
Multimodal AI systems first focused on seeing or reading, then answering. But progress does not stop there. Vision-language-action (VLA) models add the power to act.
Think of these models as "doers," not just "watchers." They read visual and text information and make changes based on what they understand.
Real-world uses:
- Look at a picture of a table and make a spreadsheet with math formulas.
- Upload a whiteboard photo and turn bullet points into calendar tasks.
- Look at a dashboard screen and make computer calls to update data.
This process is very useful for big automation. It has practical uses from smart factories to financial analysis tools and office backends. Its real worth is in changing real-world information into fast, useful actions that make things better and lighten human work.
What VLMs Can Do Today
You do not need to think about the future to believe in what VLMs can do. What they do now already includes:
- 🔍 Finding objects: Pointing out and looking at things in photos for shipping or checking.
- ❓ Visual Q&A: Talking programs that answer questions about uploaded documents or screen captures.
- 🎨 Picture editing guided by words: "Make this brighter," "Add a caption," or "Change size for phone view."
- 🔍 Multimodal search and RAG: Finding helpful text/image content based on mixed input clues.
These skills are already used in fields like retail, health care, teaching, and media. A clothing store might use VLMs to tag product pictures and write web descriptions. Teachers could turn flow charts into clear text for students who learn differently.
With tools like Make and Zapier, VLM features can be put into how users work. They can become full automations that anyone can use, even those who do not know how to code.
Aligning VLMs to Human Goals
One of the main problems with very capable AI systems is making sure they act in ways that are expected, right, and helpful to users. For vision language models to be trusted, they must not only understand the world truly. They must also think in ways that people find helpful.
Methods like reinforcement learning with human feedback (RLHF) are key to making them align. In these training cycles, human checkers change model outputs for tone, right behavior for the situation, and fair balance. Over time, the model learns what is not just right in numbers but also right for society and how things work.
Making things align helps:
- Cut down on unfairness in AI choices based on face pictures or cultural signs.
- Stop made-up results in picture-based questions (for example, wrongly calling a peaceful protest violent).
- Respect company, personal, and group safety rules in work tools.
This means VLMs can move safely from research tests to working tools put into important systems. These include HR, customer relations, finance, and more.
Moderation and Safety in Multimodal Systems
As VLM systems grow, so do the dangers. Every new type of data added, like video, audio, or handwriting, becomes another spot where bad or harmful content might come in or be misunderstood.
Main areas of moderation include:
- Bad images: Seeing harmful pictures like violence, nudity, or hate signs.
- Location errors: Making sure actions are safe in tasks like moving around or putting things in place in robots.
- Prompt defenses: Rules to stop users from making harmful content with tricky prompts.
Modern VLMs must be used with strong checks. These might include separate filter models, outside tools scanning inputs, or human oversight for tricky cases. These steps are not extra. They are very important for making sure multimodal AI is useful for a long time and that people trust it.
Video Language Models: Context Over Time
Video is naturally harder than pictures or text. It adds the factor of time. This means needing memory, order, and understanding cause and effect. With new VLMs that can handle video, AI can now make sense of moving pictures, not just single images.
Main functions:
- Making chapters and summaries for long videos.
- Understanding cause and effect (e.g., “What did the person say before this graph?”).
- Looking for topics, faces, or objects throughout a whole recording.
This creates big chances:
- Law enforcement uses VLMs to spot unusual behavior in days of camera footage.
- Social video teams make highlight videos from hours of recorded streams.
- Product teams get marked-up video feedback to make product use better.
For tools like Bot-Engine, adding VLM skills that know about video means a huge jump in how useful they are for creators, teachers, and service workers.
VLM Agents: From Tools to Collaborators
Vision-language agents, powered by advanced VLMs, are not just tools. They are becoming helpers. These agents can do complex tasks with little teaching. And they change what they do based on what they see and read.
Real examples are:
- Looking at a scanned bill and automatically filling in a financial app's form.
- Getting a screenshot and writing a reply email that uses its details.
- Looking at a computer screen to find and download files, just like a person would.
They act as digital teammates. They run information flows on their own and help programs work together. In simple platforms like Bot-Engine or Zapier, they could be the visual part. They would connect software made for people to AI tasks built for good work.
Smarter Benchmarking = Smarter Decisions
Checking models against real-world tasks is key to picking the right tool. New testing tools like MMT-Bench and MMmu-Pro give hard multimodal tests. These tests show how users really work, not just perfect school examples.
These tests better check:
- How truly they understand text + image + instructions.
- If they can read and use business rules from graphs, charts, or dashboards.
- If they are steady across different data types. For example, keeping text and picture results matching within a task.
This helps small and large businesses make better buying or setup choices. And it helps compare paid and free options clearly.
The Power of Open Source in VLM Progress
Open-source vision language models, like nanoVLM, give creators the power to test, fine-tune, and use systems that can change. They can do this without the cost or limits of closed systems. This ease of use pushes new ideas forward at a basic level.
Common uses include:
- Making AI teaching helpers that understand homework pictures and what they mean.
- Making slide show creators that turn whiteboard photos into ready-to-pitch presentations.
- Making small AI helpers for CRM tools without needing cloud plans.
Open projects also help bring more kinds of training data, model goals, and ways of using them. This often helps specific or less-served user needs that big companies miss.
So, Are VLMs Ready for Prime Time?
Vision language models have quickly gone from school tests to common uses. There are still things to make better, especially in thinking, being general, and long story clarity. But the tools we have today are already changing how businesses work, how content is made, and how people use machines.
It means summarizing online meetings, controlling work with screenshots, or using smart agents that act like staff. VLMs are changing how we get things done digitally. For single business owners or company departments, now is the time to see what multimodal AI and VLM systems can do.
Do you want to know how Bot-Engine or other tools can help you use VLM-powered work? Start trying things today to see what multimodal AI could do for you.
Citations
Bai, Y., Zhang, X., Du, Y., et al. (2023). Emu: Enhancing Multi-modal Understanding with Unified Pre-training and Fine-tuning. arXiv preprint arXiv:2302.12228. https://arxiv.org/abs/2302.12228
Chen, M., Li, C., Wang, C., & Chalapathy, R. (2023). OTTER: Open pre-trained Transformers for Text-Image Reasoning. arXiv preprint arXiv:2305.01511. https://arxiv.org/abs/2305.01511
Huang, J. Y., An, M., Shao, Y., et al. (2023). MMMU: A Massive Multi-discipline Multimodal Understanding Benchmark. arXiv preprint arXiv:2310.02255. https://arxiv.org/abs/2310.02255
Zhu, Z., Hu, H., Ruan, Y., et al. (2024). MMT-Bench: Evaluating Scaling Trends in Multimodal Models. Proceedings of NeurIPS 2024. https://openreview.net/forum?id=eUskA4I6Kq
