Social media platforms are using AI to serve their users. Know what they are doing

Artificial intelligence holds the potential to change every industry around the world. We are witnessing mass digital transformations and the adoption of AI and machine learning technologies to accelerate the growth of the business and boost customer satisfaction.

We can train these technologies to leverage individual behavioral patterns, preferences, beliefs, and interests to personalize customer experiences. This data becomes fuel for the AI systems to draw insights to make increasingly relevant predictions and curate strategies to prevent impending business risks and financial losses.

The popularity of artificial intelligence has grown so much that currently, it has become a chief component of social media platforms. Top social media platforms like Facebook, Snapchat, and Twitter are reaping the benefits of artificial intelligence. Introducing voice bots, identifying visuals, enhanced security, social media platforms are using AI, entirely under the discretion of the company that owns the platform. Even social media marketers have started using AI, machine learning, and automation technologies to boost their audience reach.

Let us look at some ways how top social media platforms are using AI for their benefits and to serve the customers.

Facebook:

Facebook Artificial Intelligence Researchers, also known as FAIR, have been working to analyze and develop AI systems with the intelligence level of a human. It will not only help advance artificial intelligence technologies but also help monitor malicious activities on the platform.

Facebook uses an artificial intelligence tool called the Deep Text to monitors the comments, posts, and other data generated on Facebook to understand how people use different languages, slangs, abbreviations, and exclamation marks, to learn the context. The company is also applying ML algorithms to build its automatic AI-based translation system to enable users from different parts of the world to translate the posts appearing in their news feed.

Facebook has also introduced chatbots in its application. It has also introduced artificial intelligence-based systems to thwart suicides.

Twitter:

One of the many ways Twitter uses AI in its platform is to understand what tweets recommendations to suggest on the users’ timelines. It aims to recommend the most relevant tweets to the users for an increased personalized experience. Twitter also uses artificial intelligence to fight against racist, homophobic, islamophobic, and other inappropriate remarks. In UK and Germany, the company has started levying fines to prevent hate speeches, fake news, and illegal content on the platform.

Twitter uses IBM Watson and natural language processing (NLP) to track and remove abusive messages. Watson is not only capable of understanding the natural language but also interferes with the tones in the messages and the meanings of different visuals, therefore, it can analyze millions of obscene and inappropriate messages in seconds.

Instagram:

Millions of people use Instagram as a means to share images, videos, and statuses with friends and families. Facebook-owned Instagram has also started implementing big data and artificial intelligence to enhance user experience, filter spam, and boost the results of target advertising. With the help of tags and trending information, the platform users can find photos of a particular activity, place, event, restaurants, food, and discovery experiences.

Recently, in a study, Instagram has used over 100 million photos available on the platform to learn more about global clothing patterns. Like any other social media platform, Instagram uses AI to fight against hate speeches and cyberbullying. It uses Deep Text to identify these messages and posts and remove them from the platform.

Snapchat:

Snapchat is using machine learning models and augmented reality technology, to superimpose digital animation on videos, like windshield wipers on an individual’s glass or droplets of water falling, and other feats like that. Snapchat’s AI engineers are training deep learning models to do things like intercepting hand gestures. These hand gesture models can then be imported to create other features using augmented reality.

The goal behind implementing artificial intelligence in the platform is to serve its enormous user base and enable these users to access these technologies easily.

Pinterest:

Machine learning is crucial for Pinterest’s core business. The platform serves tailored content to enhance user engagement and retain more customers. Without machine learning models, it would be possible for Pinterest to generate such data.

The platform dedicates most of its operations to the continuous iteration of the ML models. It not uses ML for pin recommendations but also uses it in their daily business operation, to run the business efficiently. These ML models are only good as long the users are actively spending time on the platform. The more data generated, the better recommendations the users will gain.

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AI: WHAT’S WHAT?

No other topic has dominated industry conversation in recent years like AI. But what exactly does it mean when we speak of AI? 

Artificial intelligence is the generic term for a machine simulation of human cognitive abilities. Machine Learning, in turn, describes a series of mathematical methods that can identify certain patterns in data from learned examples. Deep Learning is a subset of machine learning and uses artificial neural networks that enable the system to learn autonomously. 

DEEP LEARNING IN MEDIA

Deep Learning enables the processing of amounts of data that is not practical to process manually. The strength of deep learning lies in capturing patterns and structures of different data types, as well as in tagging and enriching data. With its daily flow of current facts, figures and data, the media sector is ideal for the application of deep learning. 

Although many media professionals are skeptical about AI, recent studies find they would be comfortable with AI-generated news like traffic or weather reports. But without the right strategy, more automation can quickly become a nightmare.

AI IN THE MEDIA SUPPLY CHAIN

How do we integrate our existing systems with the rapidly growing field of AI providers with pre-trained models, frameworks and environments ready to be used as services?

First, we need to look at where we might apply them. There are opportunities throughout the media supply chain. A few examples include: 

• Ingest—Automatic QC, compliance, deep fake recognition, copyright monitoring  
• Production—Tagging, entity recognition, topic clustering and (soon) rough cuts, automatic highlight cuts, robot journalism 
• Planning—Automatic program planning, based on licensing or marketing patterns 
• Marketing—Rating prediction, imitation of buying patterns 
• Distribution—Automated playout or packaging

A GOOD STRATEGY

Deep Learning helps us to gain insights into media objects at a level that wasn’t practical without automation and helping us toward our vision of wanting to know “everything about every frame.”

To support to the multitude of services available and bridge the data and organizational silos that segregate both content and business intelligence, we implement an “AI-specific” intelligence layer that manages all communication, but also adds value through:

• Normalization—Bringing results into a unified format 
• Cross-media analysis—Video, stills, audio, text 
• Multicloud—Connect many different providers 
• Training—Especially in the field of computer vision 
• Knowledge graph—Build contextual data models from different data silos and query them in real time with dynamic requests  

Supporting a “best-of-breed” approach, users can choose the combination of services that best fit their requirements. This is realized through the normalization of different result schemes and making them available in a uniform metadata model. In this way, a uniform experience is achieved without neglecting the special knowledge or features of the individual services.

Applying a uniform metadata also has further advantages. Recognition concepts analyzed by different services can be merged, compared and interchanged. We can also combine services, for example, a speech-to-text transcript from one operation can be sent through Natural Language Processing in a “Cascade” operation.

A standardized metadata set and version tracking enable us to reproduce individual results ourselves and also determine where the data actually comes from and what predicted confidence was recognized. This enables users to rapidly optimize—for example changing threshold values—with results displayed immediately without having to re-analyze all media.

Organizations using deep learning need to train the algorithms with the data that is appropriate to their needs, and continuously train as those needs evolve—especially important in dynamic environments such as news where topics/people/objects of interest constantly change. Creating labeled training data is the “tagging” of tomorrow but being not the primary task in a creative process, the effort for this should be minimized. Since the training data is media objects, why not do this directly in the MAM with easy tools for media managers or journalists, integrated into daily tasks.

HOW DOES AI ENTER INTO PRODUCTION?

“AI” is an interdisciplinary team sport—from idea to validation by means of a prototype, up to the transfer into production, many different roles are required, including:  

• Business analyst—The domain expert  
• Data engineer—Provides data sources in sufficient quantity and quality 
• Data scientist—Implements and verifies the algorithms

The 80/20 rule applies here with practical experience showing that data engineering often takes up the majority of the work, whereas implementation accounts for a smaller part.

With roles defined, it is recommended to take a standardized “go live” process as follows:

• AI Roadmap—Identify & prioritize relevant use cases  
• AI Lab—From idea to a verified prototype within a few days 
• AI Factory—Develop operational AI service fully integrated to the production environment 
• AI Operation—A stable and permanent operation and ongoing improvement 

As a global leader in the world of IT, AI has been an important topic within Vidispine and the Arvato Systems group as a whole. We have fostered professional and creative exchange in the Arvato Systems AI Competence Cluster—a network of interdisciplinary colleagues aiming at transferring knowledge and driving innovation. To this end, many interesting examples from other businesses are coming to the forefront, such as interactive fashion recognition, extraction of manuscript insights, anomaly detection of infrastructure, data journalism (e.g. through a “crime map”), to name a few. It is clear that in the future, AI will simply be a part of every IT toolbox.

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