How to Make AI Work With Small Data?

How to Make AI Work With Small Data?

As more and more large enterprises are seeming to undertake a “data-hungry” AI initiative, the projects are involving intense neural networks coupled with deep learning systems disciplined for huge data silos. However, if we take a closer look across organizations, a significant portion of the most valuable data sets are considerably small. It's the kilobytes and megabytes that contain valuable data instead of exabytes. However, since this data lacks the volume & impetus of big data, it’s overlooked more often than not.

Emerging AI tools & techniques, coupled with precise attention to individual factors, are presenting new dimensions of training AI to work with small data & transform processes. A typical large organization might have a thousand tiny data sets that remain underutilized or even unutilized at times for each huge data set (having 1 billion columns & rows) that fuels an advanced AI or analytics program. If you are looking for examples, there are plenty! From marketing surveys of distinct customer segments to spreadsheets having less than 1,000 columns & rows, there are always small data sets with impactful data.

How Human-centered Principles Help AI Work With Small Data?

Developing & remodeling work processes using a fine blend of small data & AI demands true consideration of human factors. Three human-centered principles can aid organizations in getting started with their respective small data ambitions:

1. Tuning Machine Learning With Human Domain Expertise

Several AI tools are being developed to effectively train AI with small data. For example, while few-shot learning prepares AIs to distinguish object categories (human faces, dogs, cars), zero-shot learning helps the AI to precisely predict an image/object label that was previously absent in the training data.

However, it is always a better approach to enable coders to impart their expertise to an AI using an easy-to-use interface. they should be empowered to evaluate contested assumptions in the database. The coders must be able to quickly validate, remove, or attach links and present a solid rationale for their actions. The synergy between machine learning and human experience comes with a powerful multiplier effect and must not be overlooked.

2. Quality Human Input Is Worth More Than Quantitative Machine Output!

In this approach, the coders must be encouraged to focus on guiding the AI to accomplish a task rather than working on the improvement of the volume of assumptions provided by the AI. This way, the AI would learn more systematically and genuinely about the small data and it's application power in real-time situations will get a boost.

3. Social Dynamics Of Teams Working With Small Data

The social dynamics of the AI development team play a big role in making AI work with small data. After a few trial sessions, the members of the team must be actively involved in the process and also present solid rationales for the decisions. This will make coders more satisfied with their job and enhance their productivity while executing newer tasks. They must feel more confident about operating with AI regularly.

With the advancement of small-data techniques, they will increasingly be utilized across business processes. The ultimate competitive advantage will not be coming from automation but the human factor involved! As AI becomes more prevalent in employee skill training, its capacity to absorb smaller datasets will allow experts to incorporate their knowledge into the training systems. It will continuously improve the AI and an efficient transfer of the experts' skillset to other employees will be possible. People who aren't data scientists can slowly be molded into AI trainers, allowing businesses to utilize their unparalleled workforce expertise.

How To Make AI Friendly To Small Data In The Manufacturing Industry?

In consumer-centric internet firms, big data has facilitated AI in a BIG way! But, can AI be made to function with small data in manufacturing as well? Well, this is now achievable, thanks to the recent advances in AI developments. The following techniques can be utilized to bypass the small data problem and propel the AI projects to go live with merely a dozen or fewer examples!

1. Transfer Learning

It is an amazing technique that enables AI to study a related activity with massive quantities of data and then apply that knowledge to the small data. For instance, an AI can be trained to locate dents from 5,000 images of dents gathered from various data sources. Later on, the AI can be utilized to detect dents in a new product through knowledge transfer, using only a few images.

2. Synthetic Data Generation

This technique is utilized to synthesize novel images which are challenging to obtain in real life. Quantum leaps in techniques like GANs, variational autoencoders, data augmentation, and domain randomization, have made it possible today.

3. Self-Supervised Learning

It has quite some similarities to Transfer Learning, as discussed above. However, the knowledge is mostly acquired by solving a slightly different task followed by adaptation to small data queries.

For instance, you can create a pool of OK images & along with a puzzle-like grid which has to be sorted using a base model. While solving this imitation problem, the AI model will automatically acquire domain knowledge. It can then be further utilized as the origin point to work with the small data task.

4. Few-shot Learning

Here, the small-data problem is segregated to improve the AI system in terms of learning easier and less data-hungry maneuvers while accomplishing the same objective. What happens is that the AI is provided with thousands of easy inspection assignments. Each assignment comes with just 10 (or a significantly smaller number) examples. It catapults the AI to learn how to spot the most relevant patterns as the dataset is small. Following this, when the AI is exposed to the actual problem which also has a few examples, it offers better performance, thanks to it witnessing hundreds of similar small data assignments in the past.

5. Hand-coded Knowledge

Here, the AI team interrogates the inspection engineers while trying to encode as much institutional knowledge from them as possible within the AI program. Even though modern machine learning in AIs is leaning more and more towards practices that rely on data instead of human institutional knowledge, skilled AI teams prefer engineering machine learning systems leveraging institutional knowledge in scenarios where no data is available.

6. Human-In-The-Loop

It illustrates instances in which any of the approaches outlined above can be utilized to create a preliminary system with probably a higher error rate. However, the AI must be intelligent enough to recognize when it is unsure about a label and present it to a human expert. And, every time it does so, it expands its knowledge from the human response, improving the confidence & accuracy of its output over time.

Closing Thoughts!

Developing AI systems that are fine-tuned to work with small data is crucial for diving deeper into the realm of data learning, especially when small datasets hold the key! If we talk about manufacturers, not only does it reduce the time and engineering effort but also it minimizes the data required for AI to go live and deliver value. Many firms can benefit from mastery of the human dimensions while combining small data and AI.