How to Deal With the Lack of Data in Machine Learning
Artificial intelligence runs our world for a couple of decades already and is of particular importance in every dimension of modern life, from grocery shopping to spacecraft building. But however powerful it has become, its core will always depend on data scientists' hands and sufficient training data for machine learning.
Machine Learning and the Importance of Data
Initially, machine learning is a process of providing AI with real data and algorithms to make it possible for it to act like humans and learn from its own operating experience. AI is trained the same way each of us learns during our lifetime. However, there is a significant problem with the lack of data in machine learning. AI requires a certain amount of data for efficient analysis, training, and performance, as if the data sufficiency lacks, it won't be possible to accomplish a reliable project.
Training data shortage represents a crucial issue, also because if AI hesitates about the result, it won't signalize to show its uncertainty but will complete the operation without any worrisome signs. And that's where the trouble begins. Lack of data means more AI hidden doubts and inadequate outcomes. It leads us to the fact that without access, it is impossible to prepare data for machine learning, and even the most outstanding software won't matter without sufficient data filling. No access also means that data is either too complicated to get or doesn't exist. That's why big promising AI projects often come as not as successful as expected, as data scientists are limited in the ways of acquiring and preparing data for machine learning.
Data Size: How Much Exactly Do You Need?
The question about the right amount of data will never come out of fashion, as there is no universal answer. Each AI project requires an individual-sized dataset, depending on data type. If data scientists work on developing computer vision applications, they'd certainly need more data than text-based projects. However, it is equally wrong to overcrowd or scrimp on training data.
Too Little Data
Lack of data is the most common yet fixable machine learning issue. Here you can either collect data yourself or find open data. It is one of the favorable "open movement" outcomes that significantly impetus efficient machine learning. According to freecodecamp.org, the most useful open data sources from which you can generate large datasets are:
- World Bank Open Data
- WHO (World Health Organization)
- Google Public Data Explorer
- European Union Open Data Portal
- U.S. Census Bureau
- UNICEF Dataset
The open movement tendency seems to keep gaining momentum and making the data scarcity issue less intense.
Too Much Data
Too much and too little data are both AI-project harmful extremes. Although it may seem that the more data, the better, it is not entirely true. Data overload will make the machine learning slow and ineffective and sidetrack it from the set algorithm, providing way too many details to consider. Thus, the amount of data is not the fact that matters most. Its cleanliness does.
Apart from data scarcity and data overload, there is a problem of data cleanliness. The web contains tons of noisy datasets and extensive unsystematic big data flooding the webspace at the highest speed. Anyone can point out a low-quality image or a song with extra noises in the background, misspelled words, and false information websites. So the information overload doesn't just mean open space for AI initiatives, but a considerable amount of sorting work.
So how much data do you need for machine learning? You'll have to figure out yourself, considering the size of the project and the type of data required for it. The critical point is to make it as clean as possible.
How to Deal With the Lack of Data in Machine Learning
Collecting data by yourself or looking for open sources is not sufficient for answering the question "how to generate data for machine learning." Data scientists have been struggling over the issue for years and presented three significant ways of tackling the main AI obstacle:
- Data Simulation
This way of data-generating is based on AI deep learning techniques. Machines can be taught to respond to specific set algorithms and predict responses to various cases. It is the next-step machine learning technique yet not deprived of faults and complications. Making predictions with reliable outcomes requires lots of extra tests and evaluations that do not always meet expectations.
- Manual Data Labeling
Artificial intelligence now has the computer vision capabilities, allowing machines to process photos or video data without labeling it. But the rest of the machine learning techniques can't do without labeling because AI can't identify data without human hints. The creation of customized data is a time-consuming process, lacking scalability and adjustability.
- Synthetic Data Generation
Synthetic Data Generation. Source
In the age of Big Data, remaining anonymous and unidentified is close to impossible. However, synthetic data makes it real to lock yourself up in a hermeneutic system, allowing you to safely test your project, generating data that may not even exist.
Synthetic data is the cheapest and the most convenient data generation type for machine learning, which requires no manual input. Artificial or synthetic data is the same as the original data, deprived of personal users' information. Data scientists use it as a sample, where the exposition of real people's data is not desirable. Thus, privacy is the primary benefit of synthetic data generation, as it gives much room for training and software development, keeping customers' personal information untouched.
Business-related AI projects take the most significant advantage of using synthetic data. It focuses on the three main aspects - authentic testing, training machine learning algorithms, and zero data threatening. With synthetic data generation, there is no need to choose between privacy and data utility. Data scientists can use almost 100% of synthetic data in big data analysis and deep learning. As a result, outsourcing synthetic data generation might be the answer when you've got none for your project.
External Influence: Data Struggle During COVID-19
Smart artificial intelligence may seem operating well within the everyday environment. However, it experiences significant setbacks during any evolving crisis. If people are easily adaptable to almost any possible external changes, AI systems are not, as they can't generate new solutions stepping out of the pre-set framework. The system is programmed to operate without human help, and this fact puts constraints on the scale of possible manual adjustment.
The fit of the COVID-19 outbreak once again showed the AI fragility and rigidness when facing extraordinary circumstances. Data scarcity on COVID-19 resulted in the unreliability of AI health systems and failed predictions due to outdated real-time data algorithms.
The market section is the least affected, as e-commerce saved the niche and saw a remarkable sales rise. However, AI algorithms suffered a major disruption. The usual top-sold items have been replaced by COVID-related ones that had to be identified and stocked up within a limited time. Such sudden changes caused algorithm glitches, proving its crisis-time deficiency and high level of training data dependency.
The Bottom Line
Lack of data in machine learning is still the weakest AI spot. Without a substantial amount of training information, machines cannot show high performance and reliable results, just like humans rarely find the way out of situations they've never been to or even heard of. However, data-generating solutions do exist, and the open sources' list grows larger, allowing data scientists to decrease the data scarcity problem's severity.