A Comprehensive guide on how AI is developed: Data Gathering and Processing (Step 1)
Artificial intelligence (AI) is continuously growing, but do you know how it works? I would like to introduce you to this article series, which will only cover the basics of artificial intelligence and offer concise, understandable explanations to help you stay up to date with the field’s rapid progress.
AI programs are taught how to advance, learn, and make decisions. Which brings us to our article’s topic today: data collection.
Collecting data is one of the core components of creating an artificial intelligence system. It’s the gathering of large amounts of data to train AI systems. However, the types of these data may vary.
Types of Data Collection
Different data collection types may be used, depending on the AI model. For instance, Sora, a text-to-video AI model, requires both text and video data in order to function. To be more efficient, it has to get more quality and relevant data, and maintain the complexity and specialization of its algorithms while updating the data to reflect the dynamic nature of the changes.
Other types of Data
Audio
Adobe Podcast AI is one example of an AI model that requires audio data. This software gathers a lot of high-quality datasets to guarantee that the outcomes it offers are accurate.
Image
The OpenAI text-to-image models DALL·E, DALL·E 2, and DALL·E 3 use training data of images and texts to produce digital images with natural language descriptions, also called “prompts.”
3D Point Cloud
Reliable 3D point cloud information is becoming a prime decisional tool in the automotive sector. AIs use sensory information and perceptions to formulate inputs and then push them through AI systems to receive data on detection through lidar sensors described in the 3D box. The lidar sensors used in autonomous systems can only be leveraged to their maximum capability by getting high-resolution 3D data of excellent quality.
How do AI programs collect data?
AI models need to continually acquire fresh data to stay relevant. But how do they get hold of this information?
Machine learning algorithms create artificial intelligence (AI) applications; these algorithms learn by using datasets. Occasionally, the data you already have may not be suitable for training purposes due to its lack of relevance, its small size, or the potential expense of cleaning, processing, and formatting the data for training purposes compared to obtaining fresh data. Depending on your goals, you should take into consideration several data collection methods and strategies in such cases:
Strategies for data collection
- Using open-source datasets
You may quickly obtain vast amounts of data from these datasets to aid in the launch of your AI projects. However, there are other factors to consider, even if these datasets can save time and money compared to original data collection. Relevance comes first; you must ensure that the dataset has a sufficient number of samples of data that are pertinent to your particular use case. Reliability comes in second; while deciding whether to employ data for your AI project, it’s critical to comprehend how it was gathered and any potential biases. Ultimately, the dataset’s security and privacy need to be evaluated. Ensure that you complete your research before obtaining datasets from a third-party vendor who employs comprehensive safety precautions and demonstrates adherence to data privacy laws like the California Consumer Privacy Act and the GDPR.
2. Generate synthetic data
Companies can use a synthetic dataset, which is founded upon an original dataset and then expanded upon, in place of gathering data from the real world. The goal of synthetic datasets is to replicate the original’s features while eliminating any inconsistencies (however, the absence of probable outliers could result in datasets that don’t fully capture the essence of the issue you’re attempting to solve). Synthetic datasets could be an ideal way to advance your AI experience if your company is in one of the financial services, telco, healthcare/pharma, or other industries with rigorous security, privacy, and retention policies.
3. Export data from one algorithm to another
This data collection technique, sometimes called transfer learning, uses an existing algorithm as a foundation for training a new algorithm. This method has the obvious advantage of saving time and money, but it is only effective when moving from a generic algorithm or workplace to a more specialized one. Natural language processing, which employs written text, and predictive modeling, which uses still or video images, are two common instances where transfer learning is applied. For example, lots of photo management apps utilize transfer learning to make filters for friends and family, making it easy to find every photo in which they are featured.
4. Collect primary/custom data
Sometimes gathering raw data from the field that satisfies your specific needs is the best starting point for training a machine learning system. In a broader context, this can mean anything from web scraping to creating custom software for collecting photos or other data while in the field. Additionally, depending on the kind of data required, you can either hire an experienced engineer who understands the nuances of clean data gathering or crowdsource the process (thus limiting the amount of post-collection processing).
Data Processing
What happens after the data is gathered? As gathering data on its own does not guarantee that the AI model will have the highest level of complexity and efficiency, the data needs to be processed.
Obtaining data is just one step in creating precise AI models. To eliminate errors or disturbances, data needs to be cleaned and preprocessed once it has been gathered, and this involves transforming the data into an AI model-friendly format.
This entire process can be automated with machine learning algorithms, mathematical modeling, and statistical expertise. Graphs, videos, charts, tables, images, and many more formats can be produced as an output of this entire process, based on the task at hand and the machine’s specifications. This may sound simple, but it needs to be carried out in a very systematic way when dealing with large corporations such as Twitter, Facebook, UNESCO, legislative bodies, and health sector institutions.
Summary
Noteworthy takeaways from today’s article:
- Data collection is a crucial step in creating functional AI systems.
- The type of data needed depends on the AI model being built. Examples include text, audio, images, and 3D point cloud data.
- AI systems require consistent updates with fresh data to stay relevant.
- There are four main strategies for data collection:
- Using open-source datasets: This is a quick and cost-effective way to obtain large amounts of data, but factors like relevance, reliability, and security should be considered.
- Generating synthetic data: This method creates artificial datasets based on real data, which can be useful for industries with strict data privacy regulations.
- Exporting data from one algorithm to another (transfer learning): This leverages existing algorithms to train new ones, saving time and resources.
- Collecting primary/custom data: This involves gathering raw data specific to the project’s needs, which can be done through web scraping, creating custom software, or crowdsourcing.
- After data collection, data processing is necessary to clean and format the data for AI models. This can be an automated process using machine learning algorithms and statistical expertise.
Credits
Article edited by I.T Aras
Finally, I’d like to thank AI Nexus for their assistance.