What is AI Training Data? Examples, Datasets and Providers

Use Cases for AI Training Data in Detail

As we’ve said several times in this article, there are countless use cases for AI training data! Let’s have a look at a few examples which showcase how artificial intelligence and machine learning are boosting operation efficiency for all kinds of businesses and organisations:

Smartphone Applications

Machine learning powers most of the features on our smartphone, such as voice assistants, camera object detection, unlocking your phone via facial recognition, and App Store and Play Store recommendations.

Retail

Many retail businesses use artificial intelligence for creating virtual shopping experiences by creating custom recommendations for customers.

Supply chain management

Supply chain, stock, and inventory management across all industries can utilize machine learning to speed up the distribution process and to hand their management systems over to AI-based applications.

Transportation Optimization

The frequency of machine learning in the transportation industry has skyrocketed in the last decade, with companies like Uber, Lyft, and Ola launching themselves to success using AI programmes. The emergence of self-driving cars also attests to the rise of machine learning and AI.

Popular Web Services

Some of our most popular online services use machine learning and AI. For example, Gmail uses a machine learning algorithm that allows us to customize labels. Also, social media platforms like Twitter, Facebook, LinkedIn, use machine learning algorithms to generate a list of people you may know.

Sales and Marketing

Companies are using machine learning to inform their marketing and sales strategies. Amazon, Goodreads, IMDb, MakeMyTrip, StitchFix, and Zomato all use AI and ML to enhance their customer service and audience segmentation.

AI allows companies to analyze customer behaviour, and pull out the essential information for marketing to capture the right people. Beside managing day to day tasks, AI-based applications can customize sales and marketing information for clients. AI-based Chatbots is an example that allows businesses to increase consumer satisfaction by making product recommendations for upselling.

It can systematize the creation of pricing models for distinct market segments such as cutting out A/B testing, which increases your understanding of what works for your company in a shorter time span.

Security

Businesses are using machine learning to analyze threats better and respond to adversarial attacks. For example, Google uses machine learning to make CAPTCHA security tests.

Finance

There are tons of use cases of machine learning in finance. In the case of credit card transactions, machine learning algorithms can identify fraudulent transactions and flag them so the bank can connect with the customers immediately to check if they made the transaction.

Banks are also using AI training data to reduce their reliance on manual labor, such as developing more precise credit scoring methods and systematizing manual management responsibilities.

AI for Healthcare

Machine learning is used in the healthcare industry for many daily tasks, including personal health care assistants and personalized X-ray readings. The use of such data for medical hardware is an especially popular use case. For example, some hospitals use robotic-powered devices to execute surgeries that operate according to artificial intelligence.

The creation of automated medical records is another use case. It not only decreases the use of paper but also makes it convenient to access and keep track of the records while avoiding human error at the same time.

Natural Language Processing

It has become possible to interact with any computer that fully understands natural, spoken language. This allows for a better user experience for different applications.

Vision System

Vision systems understand and interpret the visual input straight on your computer, such as logo recognition. This can include aircraft which take photographs which can later be used as sources of geospatial information, or for mapping certain areas.

Doctors make the use of a clinical expert system for diagnosing the patient. Police can also use this computer software, which can identify the criminal face with the stored portrait as made by the forensic artist.

Education

AI learning is of particular benefit for educational facilities. It can be used to create scheduling systems which organize parent teacher meetings, as well as other school activities.

For all of these use cases to work in practice, a rigorous AI training programme has to be implemented. And for this programme to have the desired outcome, AI training data is indispensable.

AI Training Data Attributes

Training data has many forms and attributes, reflecting the numerous potential applications of machine learning algorithms. AI training datasets can include text consisting of both words and numbers, audio, images, and video. Moreover, they’re available in many formats, such as PDF, HTML, JSON, or spreadsheets.

The ability to link unstructured and structured data is where the value lies; you get new insights and reveal unknowns.

Broadly speaking, AI training data can be assigned to the following categories:

• AI training data can be structured , which means that it’s found in a fixed field within a record or file, e.g. data which is contained in relational databases and spreadsheets.

• AI training data can also be unstructured , meaning either that it isn’t intended as a predefined data model or that it isn’t organized in a predefined manner.

• Hybrid AI training data also exists, which allows you to make use of a blend of supervised and unsupervised learning.

Attributes of AI training data are labeled or annotated using specific techniques which categorize the data into text, image or video. These labels are used and made suitable for computer vision so that the computer being used to programme the AI machine can recognise the data and the outcome the artificial intelligence should arrive at. By ‘computer vision’, we mean that categorical attributes of the AI data must be changed to a numerical format for the machine learning algorithm to work. These attributes of AI training data vary according to how you want to use it, and the APIs available for this intended use.

AI Training Data Sources

Because it’s such a versatile data type, the sources of AI training data are numerous, and they largely depend on the specific use case. There are many sources that provide information to be used for open AI datasets. Many of these public datasets are maintained by enterprise companies, government agencies, or academic institutions. For more niche use cases, it’s worth getting in touch with your prospective AI training data provider directly, if you’re keen to know more about the sources they use.

How to Collect AI Training Data

AI training data is sourced from a combination of public records, user-consented surveys, and proprietary data collection methods. Again, this varies between sources and use cases, but one typical approach used by AI data providers to collect a large amount of data from the web is the deployment of scraping techniques. The raw data is then stored on a server.

Artificial intelligence and machine learning data providers offer APIs to their servers, meaning the data can be accessed directly by customers. This means that you can download a data provider’s AI training datasets according to your individual requirements. Synthetic data is also regularly used for AI training. Synthetic data is generated using algorithms as opposed to being collected from real-world events.

How to Assess the Quality of AI Training Data?

Much like other data types, there are things to look out for when purchasing a third-party AI training dataset to ensure that you’re receiving the highest quality information possible. High-quality AI & ML training data is vital for a successful AI and machine learning initiative. It’ll ensure that you produce algorithms that work in real life, and will allow you to mitigate some of the bias inherent in manual data annotations - one of the main reasons companies rely on AI in the first place.

It’s always a good idea to request a sample dataset from your AI data provider before opting for them. When examining this sample, look out for:

Accuracy
The ratio of data to errors. As you’d expect, errors will lead to skewed machine behaviour, so must be avoided!

Completeness
Empty fields. Missing information will leave gaps in your AI machine’s ‘knowledge’.

Precision
How the data is labeled. With precise and detailed the label on a dataset, you can decide exactly how useful it’ll be for your specific needs. Avoid vaguely labeled AI datasets - their training ability is often weak.

Scale
Data coverage. The versatile your dataset, the better coverage it’ll give your programme, meaning it’ll have a more holistic view on the problems it should solve.

Timeliness
Outdated data is harmful for training AI models. For certain industries and use cases in particular, the timeliness of AI data is highly important if you’re to achieve efficient results.

Obviously, when requesting a sample, make sure you specify the intended use case for your data. With so many possibilities for machine learning, you’ve got to be sure that your provider can give you data that’s relevant to your AI initiative! Remember - your output will only be as good as the input.

If you can ensure that your data provider upholds each of these quality aspects, then you can expect high quality artificial intelligence and machine learning productivity in return.

Apart from requesting an AI data sample, you can carry out quality assessment by looking for verified data vendors and providers, who have undergone accuracy and reliability audits to guarantee you the best results for your machine learning operations.

Once you’ve got access to your AI training data, you can monitor its performance in-flight. An analytical approach to quality assessment will show you where the data is falling short of your desired training strategy:

• Gold sets or benchmark: This method helps to measure the accuracy by comparing the annotations to a gold set or vetted example. It also helps to estimate the extent to which the dataset meets the desired benchmark.
• Consensus or overlap: This process is common to measure the consistency and agreement amongst a group of data points or datasets. This is done by dividing the total of agreeing data points by the total number of points. If there’s a consensus between your datasets, that’s a good indicator that they’re high-quality.

AI Training Data Challenges

However versatile a data type it may be, when purchasing, it#s worth being aware of some common challenges with AI training data.

As we’ve seen, AI training data has an amazing range of use cases. The one drawback of this is that you could end up purchasing a dataset that doesn’t cover all of your unique requirements, which would prevent you from achieving the relevant outcome. The best way around this is to communicate all of your needs to your data vendor before you purchase!

This is also the best solution to another problem associated with AI training data: data which is incompatible with the algorithms and systems you#ve already got in place. Obviously, this will limit how efficiently and seamlessly the data can be used to fuel and train your technologies. It’s crucial that you find out whether your AI provider offers the right kind of integrations for your pre-existing operations and platforms. Otherwise, you risk making a counter-intuitive, ineffective investment.