The use of smart devices, robotics, and applications is becoming ubiquitous, thanks to the era of technological advancements that we live in. From personal, to domestic, to commercial use, artificial intelligence (AI) has become commonplace in our lives.
When we talk about AI, we’re referring to a wide-ranging branch of computer science. AI is concerned with building smart machines that can perform tasks that would otherwise necessitate human intelligence. It’s also been described as an interdisciplinary science, as it involves multiple scientific approaches.
In 2020, almost every developing technological field and discipline is linked to artificial intelligence. This means that a good awareness of AI and machine learning (ML) becomes a more crucial part of life with every passing day – this goes for businesses, especially. One of the main things to understand about AI and ML is that their usefulness very much depends on the data you’re feeding to your algorithms – the Artificial Intelligence and Machine Learning training data. This is the data which will allow you to test how well your AI and ML systems are performing, and train them to perform at their optimum.
What is AI & ML training data?
AI & ML training data is used to train a machine learning algorithm or model. It’s used to make your AI technology smarter, more reliable and more efficient. The data allows you to carry out tests to validate that your AI and ML programmes are performing as an intelligent human would, in terms of how they imitate human learning, reasoning and self-correction. AI & ML datasets are used to enhance how technologies like neural networks perform independent of human input – in other words, how they ‘educate’ themselves.
Before this is possible, however, training data requires some human contribution. This is ‘machine learning’. The degree of human participation required depends on the type of machine learning algorithms that are used and the kind of problem that the AI technology is intended to solve.
In general, machine learning using AI & ML training data can be broken down into the following stages:
Input: This refers to the data that is fed to an AI model.
Feature extraction: This refers to a process that involves a dimensional reduction of the raw data into groups of manageable data that is standardized, characteristic, and machine-understandable. In simple words, feature extraction is the identification of key features of the data for machine learning.
Classification: This process includes predicting the class of given data points that are also known as ‘labels’ or ‘categories’. It entails sorting a given set of data into classes by a computer program and is a supervised learning approach. It also allows the AI model to make new observations or classifications.
Output: This refers to the final reactions that an AI model gives after learning.
Whatever the specific ‘output’ is, AI & ML training data works by providing concrete inputs to the algorithms for learning certain patterns. This ‘teaches’ the algorithms to give equally accurate results when they’re exposed to real-life use.
This process of using AI & ML training data to show an AI machine what it ought to predict is sometimes called data processing, tagging, moderation, transcription, and it involves tagging a dataset with vital features that help train an algorithm. It’s this machine learning process that companies today are using to fuel their algorithms. Artificial intelligence and machine learning training data is therefore crucial to their operations. These operations include object identification, pattern finding, and efficient processing of client/patient data, but there are many other use cases for AI & ML training data which we’ll look into later.
On the whole, the goal of an AI & ML training dataset is to give a business’ AI more automated efficiency with fewer errors.
What are the attributes of AI and machine learning training data?
Training data has many forms and attributes, reflecting the numerous potential applications of machine learning algorithms. AI & ML 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 & ML training data can be assigned to the following categories:
AI & ML 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 & ML 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 & ML training data also exists, which allows you to make use of a blend of supervised and unsupervised learning.
Attributes of AI & ML 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 & ML data must be changed to a numerical format for the machine learning algorithm to work. These attributes of AI & ML training data vary according to how you want to use it, and the APIs available for this intended use.
What are the sources of AI and machine learning training data?
Because it’s such a versatile data type, the sources of AI & ML 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 & ML 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 & ML training data provider directly, if you’re keen to know more about the sources they use.
How is AI and machine learning training data collected?
Again, this varies between sources and use cases, but one typical approach used by AI & ML 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 & ML training datasets according to your individual requirements.
How to assess the quality of AI & ML training Data?
Much like other data types, there are things to look out for when purchasing a third-party AI & ML 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 & ML 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 & ML 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. For certain industries and use cases in particular, the timeliness of AI & ML 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 & ML 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 & ML 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.
What are the use cases for AI & ML training data?
As we’ve said several times in this article, there are countless use cases for AI & ML 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 organizations:
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 & ML 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 & ML training data to reduce their reliance on manual labor, such as developing more precise credit scoring methods and systematizing manual management responsibilities.
AI for Health Care – 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 & ML training programme has to be implemented. And for this programme to have the desired outcome, AI & ML training data is indispensable.
What are the challenges when buying AI & ML training data?
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 & ML 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 & ML 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. So it’s crucial that you find out whether your AI & ML provider offers the right kind of integrations for your pre-existing operations and platforms. Otherwise, you risk making a counter-intuitive, ineffective investment.
How is AI and machine learning training data priced?
Given the diversity of all possible use cases of AI and machine learning training data, data providers offer their consumers a wide selection of pricing models, which vary according to your needs. Many providers offer access to their databases via monthly and annual licensing fees to annual cost per prospects. Other custom pricing models can be arranged depending on the size of the AI & ML training dataset you require, and how regularly you’d want data updates.
To sum up…
From the sales department to marketing, retail to medical to HR and finance, every field and division within an industry can set up and operate with improved proficiency without needing too much human interference or intelligence. AI and ML can bring better future opportunities with better profits for the existing system’s management and operation.
However, artificial intelligence and machine learning are not possible without training data. AI & ML training data is the textbook that teaches an AI model to do its allocated job and is used over and over again to sharpen its predictions and advance its success rate. The quality, availability, and relevancy of data directly affect the AI model goals. Where inaccurate or incomplete data sets would train an AI model similar to an illiterate human, the selection of the right data will produce accurate results. The right data is one that is precisely labeled, allowing the AI model to accomplish the best level of accuracy.
For high-quality data sets for machine learning, you can contact data providers that provide machine learning training datasets in different forms according to the adaptability and needs of the project. A good data set service will include text, image, and video annotation services to give the accurately annotated data at affordable rates while ensuring the security of data and privacy until the delivery of the project.