The speed of the rise of the Large Language Models (LLMs) such as ChatGPT deserves the deserved attention over the world and displays the stunning potential of AI in text creation, solving complex tasks, and even writing creatively. But, the space of Artificial Intelligence is much broader and expansive than the space covered by LLMs only. Under the hood of these mainstream generalist models, however, is a vibrant market of more narrowly-focused AI models, which are carefully optimized to perform particular, very specific tasks.
The less publicized thing is that these dedicated AI systems are in the process of revolutionizing particular sectors, resolving complex issues, and extending the envelope of the possible. They bring to light one important fact, i.e. not all AI is equally equal, and the most commonly general kind of tool is not necessarily the one that is right to be used in a job.
What is the Point Of Looking Past the Generals? The Might of Specialization
On the one hand, LLMs are generally very wide in scope; on the other hand, this generality can be a handicap directly in certain applications. Niche AI models have different benefits:
- Speed and Accuracy: Trained on extremely specific data in their respective fields, these models reach record breaking levels of accuracy in their specific fields and have a chance to surpass generalist AIs who may be prone to “hallucinating” or giving less accurate responses in specific situations.
- Efficacy and economy: Smaller more specialised models offer reduced operational cost and reduced turnaround of inference due to requiring less computational power and data to train and operate.
- Domain Expertise: This is the understanding of a specific field that is entrenched in a person to the extent that s/he can trace details and relationships that are lost to an AI in general.
- Ethical and Safety Considerations: Purpose-built AI can be built with more restrictions, more explainability, and less bias, unlike AI that could be used in leverage to create serious ethical disasters, as in the case of healthcare or finance.
Let’s dive into some fascinating niche AI models and their groundbreaking applications that you might not have heard of (yet).
Niche AI Models: Off the Textual Horizon
1. Artificial Data Creators of Edge AI
- What it does: These types of AI create very believable synthetic data (images, sensor readings, etc.) which can then be used to train other, smaller AI on far more specific applications, particularly in edge devices.
- Why it is new/hot: Training large volumes of traditional AI takes large volumes of real-world data, which may be costly, time-consuming, and privacy-sensitive to acquire. The gap in this case can be filled by synthetic data that allows us to develop a solid AI to detect certain defects in a factory assembly line or observe distinct behaviors of animals without the need to collect a ton of data in the real world.
- Example: A food processing plant that uses a model trained on synthetic data to examine damaged products in an over 98% correctness rate, in a low-cost edge device.
2. Predicting protein folding
- What they are: they are very specialised deep learning models with the goal of predicting the 3D structure of proteins based on their amino acids. This is a fundament issue in biology, and medicine.
- What makes it unique/trending: In the past, protein structures were determined as a labor-intensive experimental task. The development of AI discoveries and understandings of drugs and diseases is rapidly feasible and freeing novel information and research capacities as AI rapidly makes precise predictions.
- Application: Usage in making new drugs faster and to determine the mechanism of the disease by predicting the shape of essential proteins quickly.
3. Climate Modeling and Prediction (e.g. Graph Neural Networks on weather)
- What it does: Sophisticated AI models with a data structure that captures the geographical conditions, atmosphere, and ocean flows to allow precise and quicker prediction of climatic and weather circumstances.
- Why it stands out/ popular: Current climate models are computational. AI provides an opportunity to analyze climate data faster, reveal small trends, give more local and current predictions, valuable in the preparation of disaster mitigation, prevention of climate change.
4. Anomaly Detection Manufacturing Using Computer Vision
- What it does: Models of AI, usually built using Convolutional Neural Networks (CNNs), that are specifically trained to detect minute anomalies, defects, or deviations of norm in visual data flows on production lines.
- Why it is special/on-trend: These machines do ultra-specific quality control work that is way above human standards in regards to speed and consistency. They will be able to detect tiny cracks, malalignments or color changes that may go undetected by the human eye during long shifts, which makes it much more cost effective whilst also increasing the product quality.
- Applications: Inspecting defective parts at an electronic assembly line, inspecting pharmaceutical pills to determine they are packaged properly or detecting the smallest flaws found in cloth production.
5. Niche Art Aesthetic Generative AI
- What it is: DALL-E and Midjourney are generalist image generators, but there are numerous niche generative AI models that specialize in a particularly narrow style or historical moment or media. These may be trained only on the paintings of the Renaissance, on particular anime styles, or even on the architectural blueprints.
- Unique/trending potential: Because they provide artists and designers to experiment with certain aesthetics, create hyper-stylized works, or continue recreating the artistic techniques of the past in a new form, it offers artists unrivaled potential.
- Usage: Pieces of new art in the direct style of a particular historical era, pieces of architectural concept art in the style of a given design philosophy, or pieces of music in the style of a less well-known classical composer.
6. Edge Devices Small Language Models (SLMs)
- What it is: These are smaller, painfully efficient language models, with fewer parameters than LLMs, built to be run directly in devices (such as smart phones, smart appliances or industrial sensors) without on-demand cloud access.
- The uniqueness/trend: SLMs are key to privacy (data retained on the chip), low latency (no lags), and give reliability in situations with weak internet networks. They are tuned toward certain functions such as local transcription, simple command recognition, on-device translation.
- Use case: A home automation device that acts on voice commands locally or a pocket translation device a factory machine that can follow spoken instruction on the factory floor.
This Future is De-specialized and Interconnected
Its AI in the next few years will not only exist in the most general and large models but in the deep interactions between these specialized AIs. It is not hard to imagine such a scenario where one niche AI that analyzes medical images is simultaneously being used with a language model that takes care of complex diagnosis and translation into ambiguous languages and where another AI is used to track patient vitals on a wearable.
With the further development of AI, it will become exceptionally important to realize and utilize these domain-specific models in order to access truly revolutionary solutions in each industry. They are the silent motors of innovation and they are the people who can demonstrate that occasionally the best effect is brought about by thinking very narrowly indeed.
Which AI niche models did you come across and were impressed by? Please comment and share your question or thoughts!
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