How Machine Learning is Changing the Business Today: why do we need it and how does it work

Machine learning (ML) today is something that concerns a great number of people, and many believe that it is the way into the future. Machine learning and Artificial Intelligence (AI) in general are the forces driving the advancement of a diverse range of leading industries and domains, such as e-commerce, computer vision, and natural language processing, to name a few. So why are AI and ML so important and how these technologies are changing processes in companies?

The distinction between AI and ML

Before we begin explaining the importance of AI and ML, we need to figure out how the two terms interact. Machine learning is a segment of AI, a small subset and just one of many of its branches. ML stands for a particular AI application that enables computers to retrieve knowledge from data and autonomously learn from it. AI is a broader term that enables a machine or system to intelligently comprehend, reason, act or adapt similarly to a human.

Why ML and AI are so important to companies

With the increasing amount and variety of data availability, the need for computational processing is becoming vital to retrieve important information. The amount of information is growing and it is getting harder and harder for humans to process such a large amount of content. AI and ML solutions are becoming increasingly popular as humans apply them to analyze and process vast amounts of data, enhance the efficiency of decision-making, generate online recommendations and findings, as well as create reliable predictions and forecasts.

Machine learning has impacted many business areas. Intelligent algorithms substitute human labor in numerous processes. The role of ML in business has become extremely significant as it assists in reducing costs by decreasing the amount of time, effort, and man-hours spent.

Perhaps the biggest advantage of ML is the fact that significantly more data is processed in less time, which is especially critical for large projects. When processing data, the machine considers a tremendous amount of different aspects in a relatively short period of time and then makes a decision based on them, which would have taken a human a lot more time.

Aside from the data processing speed, ML also automates processes that will not require constant human intervention later on. Moreover, the longer a machine works on a specific problem, the higher the success of its decisions. All of these factors of machine learning help lower personnel costs as well as customer engagement costs.

Any modern enterprise or manufacturing facility cannot do without ML solutions. Now the implementation of fast automated technologies is extremely important, but in the future the existence of businesses without such solutions will simply be impossible, as it just will not be able to catch up with its competitors.

How does ML operate?

Machines can perform intellectual actions similar to humans, only much faster and more such actions in a given time interval. It is necessary to mention that AI based on machine learning cannot be considered completely independent of humans. Certainly, after some manipulation on the part of machine learning practitioners, the ML-based models can work independently. However, it is still impossible to completely exclude human involvement in creating such ML systems. Firstly, to make such a system work at all, a human has to find a raw data set and turn it into annotated data.

Labeled data

It is simply not achievable to train a model without high-quality labeled data, and an untrained ML model is pointless. Data labeling or data annotation is the process of assigning labels (or attributes) to items in a dataset. These labels indicate characteristics that help train the ML model. A labeled dataset is called a training set.

Data labeling is a crucial and indispensable step in the ML process. Ensuring that the data is correctly labeled is a key factor that affects the quality of the model performance and its learning. The amount of raw data to be used in labeling projects depends on the specific task at hand and the type of model you are using. Broadly speaking, the more data you have, the finer the results you are likely to get.

If annotators lack large datasets for data labeling, they resort to employing algorithms that can work with less data. For example, deep learning may be effective in such cases. Experts may also try to utilize ML models to generate new data, that can then be applied in manual labeling.

Ways to label your data

The data labeling process in machine learning projects may be done in various ways. For instance, some large companies have already accumulated the information they need for labeling. This could be audio recordings of phone calls or frequently used documents, such as invoices or reports. If future machine learning projects do not involve the utilization of any highly specific information, it can be obtained from publicly available databases.

The users of crowdsourcing platforms may also collect information specifically for your project. For example, field data for marketing research in retail chains, geopositioning services, street ads, local public services, or any kind of software that requires field data. The users of the platform may take photos in their city, shoot video, and record audio in real-time at your request, as well as answer your questions.

Once the data has been found, it may be annotated by specially hired staff of the same company. Such a method is only suitable if there is enough time, human and financial resources available, and if the company possesses its own infrastructure.

Experts often employ the services of annotators on crowdsourcing platforms. The customer has to register as a requester there and assign various labeling tasks to available annotators. This strategy is quite affordable and relatively fast, but it does not always ensure the high quality of the annotated data. To ensure that your tasks are thoroughly fulfilled you have to choose platforms with built-in quality control measures, like Toloka.

It is also possible to outsource and have the data labeled by professional companies that have a specific focus on such tasks. There are also freelance specialists, who are ready to implement data labeling projects on specialized platforms. This is probably the cheapest, although a reliable quality check has to be devised to ensure that the work is done properly.

Types of data annotation

Annotated data may be acquired manually or automatically. With manual labeling, data is reviewed and assigned appropriate labels by a human annotator, whereas with auto labeling, data is analyzed based on specific algorithms and automatically assigned labels with the help of special software.

Manual labeling process

Manual annotation or manual labeling of data entails the process of labeling data elements based on certain criteria by people. As an example, when text is labeled, keywords or phrases that are important to the task may be highlighted. In image annotation, the objects in a picture may be marked with bounding boxes, or labeled according to their type (e.g., human, animal, car).

For manual labeling, specialists may use specialized platforms. Manual annotation specialists have to be very thorough and precise in their approach to labeling to avoid errors and guarantee the quality of the training data. Annotators are capable of significantly reducing the annotation time due to automated data labeling as it exists only as a result of manual labeling. Overall, manual labeling represents a vital step in ML that delivers the most reliable and precise training data for model training.

Automatic data annotation

In addition to manual labeling, software assistance can also be used. Labels can be automatically detected and added to a training dataset using a technique called active learning. To annotate a dataset automatically and turn it into training data, an annotator has to load the relevant information into an AI tool that already has the ability to qualitatively assign data labels.

But just as was mentioned earlier about how ML models cannot exist without human intervention, auto labeling also implies it. In active learning, the ML algorithm cooperates with some source of information capable of annotating the input data. This information source is commonly a person or even a group of people.

Essentially, human labelers create an AI model for automated labeling, tagging raw, unlabeled data, so it could automatically label new information. They then determine whether the model has performed the labeling correctly. If errors occur, human labelers fix them and re-train the model. Certainly, the auto-labeling algorithm simplifies the data labeling process, but still, auto annotation is possible due to manual labeling.

Why is the data labeling process faster with this approach? The conventional assumption is that all data is equal, but in most datasets there is noisy data, no class balance, and a great deal of excessive data. In the automated data labeling approach, time is not wasted on data annotation that does not improve the performance of your model.

To create an automated data annotation tool, the system doesn't require tons of randomly labeled samples to realize the distinction between junk mail and regular mail. You may provide it with a few instances of what you require it to learn, it will quickly grasp it, and then ask follow-up questions if it is in doubt. Active learning, employed in automated data labeling, utilizes a learning model to search for and label only the most valuable data.

Automated data labeling

To create automated labeling applications:

1. The data science team first feeds a small number of labeled examples to a model;

2. The model learns from this dataset;

3. Then when the model encounters edge cases or is unsure of making the right decision, a person or a team of people helps it to figure out all these confusing cases;

4. A human creates the labels for these examples;

5. The model is upgraded once again and the process is reiterated until a sufficiently good accuracy is achieved.

Once again, it is impossible to claim that this approach represents fully automated labeling, but once such a system achieves high accuracy, it can operate autonomously. However, this does not mean that in the future it will not appeal to humans, as edge cases may arise in completely unforeseen situations since our world is incredibly versatile and often the outcome of events cannot be predicted.

Stages of data labeling

The labeling process begins with obtaining data and completes when the model trained on such data is applied. There are the following stages:

1. Data retrieval. The process starts with data gathering from all kinds of sources (e.g., databases, documents, audio or video files, websites, etc.).

2. Data pre-processing and refinement. It consists of all kinds of activities related to preparing data for labeling:

• Verifying the data for mistakes and inconsistencies;

• Processing the data to eliminate irrelevant elements, such as spaces, punctuation, etc;

• Deleting duplicates;

• Scaling of the data;

• Formatting, etc.

3. Data labeling. The process of identifying features in unlabeled data via a data labeling tool;

4. QA process. At this stage quality control of the training data is carried out by:

• Verifying the appropriateness of identified labels;

• Evaluating the accuracy and completeness of the data labeling, etc.

5. Model training. This stage consists of:

• Training the model on the labeled data;

• Testing the model on new data.

6. Applying the model to make predictions or decisions based on the training data.

How to speed up the labeling process

Automate data labeling

The first way to speed up manual labeling is to automate data labeling. To do this, you may apply auto-labeling tools that have already been trained by other specialists, or you may apply active learning to create your own annotations. After creating such a model, you can auto annotate all the rest of the data on your custom-designed model.

Choose an approach to labeling that suits your annotation projects best

Various ways to perform data labeling are available, as described earlier. The choice of approach is determined by the complexity of the task, the amount of data to be labeled, the size of the labeling team, and, understandably, by the financial resources and time available. Each type has its limitations and advantages, which should be determined for each data labeling project separately. The entire course of the project depends on this decision, which is made at the outset of the project. The right approach can significantly reduce annotation time.

Pick a crowdsourcing platform with high-quality tools

In case you have found that manual labeling works best for you, Crowdsourcing with reliable QA tools is one of the best choices for such projects at the moment, particularly if there is a limited budget. For instance, in addition to providing great QA, Toloka offers a huge selection of manual annotation tools, which are essential for creating an ML product.

Tasks such as cross-referencing identical products with different names to increase the product match coverage, matching items in an online store with related goods to increase the accuracy of the recommendation system, or testing a new brand design is handled better by human annotators than by a machine. Toloka allows you to set up crowd management tools for labeling while ensuring reliable QA tools targeted specifically to your project.

Business applications of ML

E-commerce

Artificial intelligence and machine learning are now gradually becoming more widespread in almost every industry. They have especially gained significance in the sphere of e-commerce, which requires a systematic focus on finding new ways to inspire consumers to buy and facilitate their interaction with the platform. Intelligent chatbots and assistants with embedded ML algorithms help online retail businesses automate communication with users, allowing human operators to avoid some of their routine duties.

ML technology also helps recommendation systems boost click-through rates and the average cost of an online purchase. The algorithms of such systems are employed by e-commerce companies to recommend related products when customers pick items online. Data labeling improves the quality of searches by customizing search algorithms to efficiently distribute search results.

For a machine to comprehend how to address a certain issue, it requires a vast amount of examples to be presented to it. Therefore, creating an effective recommendation system requires a great deal of manually labeled data, which is constantly updated to keep the ML model up-to-date.

Improving price comparisons with competitors through ML can also have a substantial impact on achieving better coverage of key products and boosting sales. Manual annotation in this case works best to improve the quality of comparisons and address miss matches, since inaccurate data generated by automated solutions or in-house annotators can ultimately mean losing profits.

Algorithms keep track of every product on the market and change prices on an ongoing basis, determining the most favorable price for both the seller and the consumer. In e-commerce, companies need to consider competitors' pricing, because effective online price management can boost sales and give your company a competitive advantage in the industry.

Online retailers may employ ML technology to provide their customers with an improved product or information search experience on their sites. As people expect instant and relevant search results, search relevance plays a major role in the platform's usability. ML models make it easier to achieve improved search relevance. Convenient search experience is vital to the success of any online store. However, it often requires huge amounts of manually labeled data. This is also where Toloka can help, as its data labeling platform allows annotators to evaluate and then improve the quality of your search algorithms.

Сomputer vision (CV)

Computer vision projects deal with image and video analysis. Custom models constructed based on this analysis help smartphones recognize their owners, highway cameras identify license plates, and even robots avoid obstacles.

Computer vision is essential for the development of automated vehicles and robots. It enables us to see things that humans might not notice. For example, when analyzing X-rays and other medical scans, or when detecting flaws in manufacturing. CV has made it possible to develop an AI-based device that makes it possible to operate a wheelchair hands-free through the user's facial expression and gesture recognition.

In Computer vision, depending on the task, experts may employ multiple types of image/video annotation tools. These are some of them:

• Image classification. The image is assigned one or more labels based on the object it depicts, which class it belongs to;

• Semantic segmentation. The purpose of this type is to associate each pixel of an image with the class of objects to which the pixel belongs;

• Instance segmentation. As opposed to semantic segmentation, instance segmentation assigns a label to each instance of each object presented in the image, instead of assigning a label to a class of objects;

• Polygon annotation. This kind of labeling highlights the exact boundaries of objects in the image, with each pixel receiving its value, according to which the algorithm determines the boundaries of the object, as well as its association with a particular group;

• Bounding box. Bounding boxes suggest that the expert labels the desired object in the image employing a rectangle or a box and assigns a label to it for labeling the image.

Natural language processing (NLP)

NLP refers to the computer science discipline of analyzing and processing natural language of humans. Among its multiple undertakings are speech recognition, machine translation, information retrieval from texts, documentation categorization, and many other things. NLP makes human-computer interaction a lot easier, so it eliminates the need for sophisticated coding languages. In sectors such as customer service chatbots and voice assistants can easily recognize and react effectively to buyer inquiries. Large webmail providers use NLP to review the text in emails that pass through their filters, enabling them to filter out spam before it reaches the user's inbox.

NLP is crucial for processing vast amounts of unorganized text data that is impossible to handle manually. Sentiment analysis, for example, is widely employed in market research to gain insight into customer attitudes toward a product, brand, or service.

The ML algorithm evaluates the person's speech, builds a custom vocabulary, and then decodes the words. The result is provided in audio or text form. Annotators utilize audio and text annotation tools to label the data for NLP. There are multitude of ways to annotate data for NLP purposes. Below are some of them:

• Text or audio classification

• Speech recognition

• Emotional content annotation of a text or an audio

• Text or audio categorization

• Extraction and labeling of key phrases or words

• Part-of-speech labeling

• Speaker identification

Conclusion

ML is a rapidly growing industry that is becoming more and more influential in business in particular. It is everywhere we go, perhaps without you even realizing it. Nowadays, these technologies are getting increasingly accessible, including through crowdsourcing platforms like Toloka. Large companies and startups alike can benefit from this solution. In most cases, it does not require special knowledge or a great deal of time. Successful implementation involves understanding your company's internal processes and the desire to enhance them.