Even the biggest businesses struggle to get data at scale for AI and machine learning applications. AI requires a vast amount of information for efficient analysis, training, and performance. If there's insufficient material, the AI won't be able to accomplish tasks reliably. So every data scientist needs a way to find or generate training data for deep learning models. Here are 6 ways to solve the problem.
6 ways to deal with insufficient data in machine learning
1. Data extraction for AI (aka automated data collection)
In AI, data extraction is sometimes referred to as automated data collection. It's the most efficient method of data acquisition for ML.
Data extraction involves web scraping real-world data that is accurate and relevant. It also requires the extraction of human-produced web content, not just AI-generated data. You can find out why that is in this article on how to improve AI models.
Given the sheer scale of content needed for AI, using web scraping methods for data collection is really the only sensible option. But it's not without its challenges. When doing data extraction on websites with large amounts of traffic and dynamically loading pages, your IP can get blocked, or you may receive authorization errors such as 403 and a lot more.
To deal with these and other challenges, you don't just need a web scraper but infrastructure that sets you up to scrape successfully at scale.
Extracting data from websites is only the first challenge. Another issue is the problem of data cleanliness.
The web is host to countless noisy datasets. Low-quality images, audio with background noise, misspelled words, and false information. These are just a few of the problems you'll encounter with content retrieved from the web.
So you not only need to perform data extraction, but you also need to clean and process web data to feed AI models.
2. Pre-cleaned and pre-packaged data acquisition
Another way of collecting data for AI is acquiring pre-cleaned, pre-existing datasets available on the market. Not a bad option if you don't have complex goals or don't require a wide range of material. If you just want a simple image classification system, for example, then acquiring pre-existing datasets is a relatively cheap and easy method.
For diverse datasets and big projects, it isn't the best choice. It will cost more in the long run, as software might be needed to fill in crucial gaps in data. Data lacks personalization and customizability, and it's harder to find relevant material that aligns with your needs since the datasets were created in the past.
3. Crowdsourcing data
An alternative method of collecting data for machine learning is crowdsourcing. This involves gathering information from a wide range of people. The data is then used to improve machine learning models. By collecting material from diverse sources, AI systems are more likely to be representative of the real world.
The main problems you'll have collecting information this way are data quality and agility.
Crowdsourcing requires double-entry and consensus models to be used as a control measure. It also limits the agility to modify and evolve your process, creating a barrier to worker specialization or proficiency with your data.
4. In-house data collection
Yet another method of data acquisition for AI is in-house data collection. This is when developers collect their own data privately instead of working with the general public. It may involve recruiting data generators or data collectors, processing the information, and storing it in private servers. It can be a pretty expensive, time-consuming, and labor-intensive method, and it's difficult to find domain-specific information.
5. Synthetic data generation and simulation
In addition to data collection, there's the option of generating data. Synthetic data generation is the creation of a fake dataset that resembles real-world data. It's widely used in machine learning for testing algorithms, assessing models, and more.
Data simulation is the process of producing such a dataset with specified characteristics that imitate patterns seen in real data.
6. Data augmentation
Using synthetic data alone is not advisable. It can introduce bias to the AI model and loss of realism, leading to model collapse. A better solution is data augmentation.
The difference between synthetic and augmented data is that augmented data maintains the quality and diversity of the training dataset. Synthetic data is generated from scratch, while data augmentation uses an existing training dataset to create new examples. Combining synthetic and augmented data is the best approach to generating additional datasets for AI models.
So, there you have it! 6 ways (some better than others) to solve the problem of data insufficiency for AI models:
- Automate data collection with web data extraction.
- Acquire pre-cleaned and pre-packaged data.
- Crowdsource data.
- Collect data in-house.
- Generate synthetic data for data simulation.
- Augment data and combine synthetic data with real-world datasets.
Frequently asked questions about AI
What is data extraction in AI?
Data extraction, also known as web scraping, data collection, or data harvesting, is a method of gathering information from websites and processing it for use in machine learning. Data extraction utilizes bots and scraping scripts to open websites and retrieve their data to process and store it in a structured format.
What is crowdsourcing data for AI?
Crowdsourcing is a technique used to collect data. It involves gathering information from a diverse group of people. The data is then used to improve machine learning models. By collecting material from a wide range of sources, AI systems are more likely to be representative of the real world.
What is synthetic data in machine learning?
Synthetic data is artificially generated information created to augment or replace real data to improve AI models. Synthetic data generation is widely used in machine learning for testing algorithms, assessing models, and more.
What is data simulation?
Data simulation is the process of producing synthetic datasets with specified characteristics that imitate patterns seen in real data.
What is data augmentation?
Data augmentation is the process of automatically generating high-quality data on top of existing data. It is common in computer vision applications and sometimes used in natural language processing.
What is the difference between augmented data and synthetic data?
Synthetic data is generated from scratch, while data augmentation uses an existing training dataset to create new examples and maintains the quality and diversity of the training dataset.
What is the difference between AI, Machine Learning, and Deep Learning?
Artificial Intelligence (AI) is a field of data science focused on creating machines that can emulate human intelligence.
Machine Learning (ML) is a subset of AI that focuses on teaching machines to perform specific tasks with accuracy by identifying patterns. ML uses algorithms to learn from data and make informed decisions based on what it has learned.
Deep Learning (DL) is a subfield of ML that structures algorithms in layers to create an artificial neural network that can learn in a self-supervised fashion.
What is the difference between AI and generative AI?
AI aims to create intelligent machines or systems that can perform tasks that typically require human intelligence. Generative AI is a subfield of artificial intelligence focused on creating systems capable of generating new content, such as images, text, music, or video.
Are Large Language Models AI?
Large language models, or LLMs, are generative AI models that use deep learning methods to understand and generate text in a human-like fashion.