Big data analytics and AI importance in media and entertainment industry

The media and entertainment industry is also utilizing the power of Artificial Intelligence (AI) in making the visual content more interactive and interesting. It is helping to serve the audience a data-intensive and personalize automated content making their viewing experience more interesting and entertaining.

While on the other hand, the entertainment industry needs such innovative technology to make the audience experience more enjoyable and user-friendly. AI companies are also consistently working to integrate this technology into various sub-fields of the entertainment sector improving its efficiency.

AI Applications in Media and Entertainment

The application of AI in media and entertainment is witnessing rapid growth especially in terms of distributing and showing visual content. Media companies face challenges while developing the content in high quantity while ensuring the quality, hence they adopt AI to achieve this objective.

And once they achieve their objective, they improve their services enhancing the experience of the customers. So, we have discussed here the use of AI in the media and entertainment industry with use cases and examples.

Personalizing the Content & Recommendations

From music app to OTT platforms, the audio, as well as visual contents, can be personalized as per the preferences and previous experiences. Using machine learning, users behavior and demographic details, music or videos are recommended like what kind of movies they like most.

AI using advanced machine learning algorithms and deep learning networks helping in delivering personalized content to users. And this kind of highly personalized experience for users helps media or entertainment companies increase their user base and offer a better service compared to their competitors.

Online Advertising with Targeted Audience

Online advertisements in the media industry are playing a significant role in branding and business promotions. And AI is used to make online advertising more precise and productive with a targeted audience for higher conversion rates.

The best example is Google Adsense and Adwords that can use the user’s history like what kind of products they were searching for or browsing on web or eCommerce sites. And this kind of AI-based sensing helps AI to show the ads as per the user’s preferences. It helps advertisers to target the right audience and get maximum outputs from the Ads.

Controlling the Online Content Broadcasting

The online media and entertainment industry is running with the help of different types of content. And apart from general topics or common subjects, there is objectionable content broadcasted through TV, Online Music Channels or OTT platforms.

Controlling such content is mandatory from the regulatory authorities. Again AI helps here in detecting and filtering such objectionable contents. AI can identify the user’s age and gender before showing such content or using the automated content moderation service to moderate the objectionable content before broadcasting with the audience category ratings like suitable for children or only for adults.

Classification & Categorization of Content

Online streaming platforms like YouTube or OTT have different genre of music videos, songs, movies or TV shows. And these online platforms are using AI algorithms to show the content as per the user’s preference and choice.

The AI-enabled system can detect the objects, data, visual scenes and other details about the movies like genre, cast and crew members to show them similar content. Implementing the AI in media and entertainment industry can automate the categorization and classification of content for a better users experience.

Meta Tagging Subtitles & Automated Transcription

Content published in the media and entertainment industry needs to make comprehensible to the audience. Hence, AI can help in identifying the videos and other online content to classify them with meta tags and descriptions.

Apart from that movies, music videos and TV shows are transcribed into different languages using AI-based technologies like natural language processing through machine learning and deep learning. The voice of movies is converted into different languages with subtitle and audio annotation to make it understandable to a wider populace for more engagements of the users. Powered by Cogito Blog

Which is better for image classification, supervised or unsupervised classification?

Image classification is actually fundamental task that helps to classify and comprehend the entire image as a whole. The main motive of image classification is to classify the image by assigning it to a specific label.

Usually, Image Classification to images in which only one object appears and that is only analyzed. In contrast, object detection involves both classification and localization tasks, and is used to analyze more realistic cases in which multiple objects may exist in an image.

Supervised vs Unsupervised Classification

Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image.

The classification is the process done with multi-step workflow, while, the Image Classification toolbar has been developed to provide an integrated environment to perform classifications with the tools.

Not only does the toolbar help with the workflow for performing unsupervised and supervised classification, it also contains additional functionality for analyzing input data, creating training samples and signature files, and determining the quality of the training samples and signature files.

Supervised Image Classification

Supervised classification uses the spectral signatures obtained from training samples to classify an image. With the assistance of the Image Data Classification toolbar, you can easily create training samples to represent the classes you want to extract. You can also easily create a signature file from the training samples, which is then used by the multivariate classification tools to classify the image.

In supervised classification, you select representative samples for each land cover class. The software then uses these “training sites” and applies them to the entire image.

For supervised image classification, you first create training samples. For example, you mark urban areas by marking them in the image. Then, you would continue adding training sites representative in the entire image.

Unsupervised Image Classification

Unsupervised classification finds spectral classes (or clusters) in a multiband image without the analyst’s intervention. The Image Classification toolbar aids in unsupervised classification by providing access to the tools to create the clusters, capability to analyze the quality of the clusters, and access to classification tools

In unsupervised classification, it first groups pixels into “clusters” based on their properties. Then, you classify each cluster with a land cover class.

Overall, unsupervised classification is the most basic technique. Because you don’t need samples for unsupervised classification, it’s an easy way to segment and understand an image.

Which one is better?

No doubt, unsupervised classification is fairly quick and easy to run. There is no extensive prior knowledge of area required, but you must be able to identify and label classes after the classification. The classes are created purely based on spectral information, therefore they are not as subjective as manual visual interpretation.

While on the other hand, one of the disadvantages of unsupervised classification is that the spectral classes do not always correspond to informational classes. The user also has to spend time interpreting and label the classes following the classification. Spectral properties of classes can also change over time, so you can’t always use the same class information when moving from one image to another.

Both have their own advantages and disadvantages, but for machine learning projects, supervised image classification is better to make the objects recognized with the better accuracy. Overall, object-based classification outperformed both unsupervised and supervised pixel-based classification methods. And depending on the AI model or machine learning algorithms compatibility, image classification process is followed to classify the images with better accuracy and quality object detection.

Training Data for AI and Machine Learning in Insurance Claim

AI in insurance helping to detect the type and level of damage to vehicles. Actually, the AI-based computer vision technology is used to detect the area of damage and calculate the intensity providing an automatic detail for insurance claim processing. Cogito provides, training data for AI and machine learning in insurance with best level of accuracy for right predictions.

Continue reading “Training Data for AI and Machine Learning in Insurance Claim”

Artificial Intelligence in Urban Management

AI in urban management is becoming possible with well-trained AI system and applications to gather the various details while developing such urban cities. To train the AI models used in urban management a huge quality of training data sets required for machine learning. Cogito provides, the wide ranging image annotation service to annotate the object of interest in the images and making it recognizable to model.     

Continue reading “Artificial Intelligence in Urban Management”

How To Use Machine Learning and AI in Inventory Management?

AI in inventory management helping logistic supply chain and warehousing companies to manage the inventory through fully automation system making the whole process fast and more efficient. With training data sets robots and automated machines can be created to develop such AI models. Cogito provides the high quality training data for AI models used in inventory management.

Continue reading “How To Use Machine Learning and AI in Inventory Management?”

How is AI Changing Fashion Design or Helping a Clothing Designer?

AI in fashion is playing a crucial role in changing the designs as per the new trend in the market, while keeping in the mind about the current scenario and future prospects. Actually, this technology is equipped with machine learning to understand the historical patterns and buying habits of the customers, and changing the style of fashion with the time being. 

Continue reading “How is AI Changing Fashion Design or Helping a Clothing Designer?”

How AI Camera Used in Fever Detection to Control Coronavirus Spread?

Amid COVID-19 spread worldwide, tech companies trying to utilize the power of artificial intelligence (AI) to control this disease. Detecting the body temperature through AI-powered devices is one of them, helping to detect the people having high temperature (possibly fever), which is one of the symptoms of coronavirus infection.

Continue reading “How AI Camera Used in Fever Detection to Control Coronavirus Spread?”

How AI is Used in Automotive Industry: Application of AI in Automobile

AI in automobile or automotive industry is playing a crucial role in producing the vehicles with automated assembly lines or integrate the machine learning for developing the autonomous vehicles. There is huge potential in AI to provide the automated solution to automotive sector with interesting results making the automobile sector operate with better efficiency and produce the best vehicles.

Continue reading “How AI is Used in Automotive Industry: Application of AI in Automobile”

What Does A Machine Learning Engineer Do: Role & Duties

Machine learning engineer do one of the toughest job to create a ML or AI-based model that can work properly with best performance. Practically a machine learning engineer’s job is close to data scientist, as both need huge amount of data and skills to interpret and analyze the data sets for model development.

Continue reading “What Does A Machine Learning Engineer Do: Role & Duties”

What are the Applications of Artificial Intelligence?

Artificial Intelligence (AI) is brining a drastic changes in technological fields, where it can be implemented to automate the system for more efficiency and performance. AI is now getting used in multiple fields from simply your mobile phone to diagnosing the diseases providing a high-performance and accurate system work with efficiency.

Continue reading “What are the Applications of Artificial Intelligence?”