7 AI Applications at Facebook

As a data analyst with over 10 years of experience in web scraping and data extraction, I wanted to provide an in-depth exploration of how Facebook utilizes artificial intelligence across its various products and services. Facebook is one of the biggest adopters of AI technology in the tech industry, leveraging it to enhance experiences for billions of users worldwide. In this extensive 2000+ word guide, I will analyze 7 key AI applications powering Facebook today.

The Role of AI at Facebook

Facebook employs AI technology in various forms across its family of apps and services. This focus on AI is spearheaded by Facebook AI Research (FAIR), formed in 2013 to advance the field of artificial intelligence and apply AI towards solving real-world problems.

Some key facts about Facebook‘s AI strategy:

  • Facebook spends over $10 billion annually on research and development, with a major focus on AI and machine learning.

  • There are over 60 full-time AI researchers working at FAIR alone.

  • Between 2013 and 2020, Facebook AI published over 400 research papers on AI topics including computer vision, NLP, reinforcement learning, and more.

  • Facebook open sourced many key AI research projects, like the PyTorch deep learning framework used by over 5 million developers worldwide.

  • Applied ML is a sister team that productizes AI research for applications across Facebook‘s apps like Instagram, WhatsApp, and Oculus.

Facebook‘s approach is to build general purpose AI systems that can operate across its family of products, rather than siloed AI for specific applications. My analysis will cover how this strategy plays out in practice across 7 major AI applications at Facebook.

1. Natural Language Processing with Deep Text

With billions of user posts and comments created each day across its apps, Facebook has access to vast troves of unstructured natural language data. To make sense of this data, Facebook AI invests heavily in natural language processing (NLP) research.

A flagship NLP project is Deep Text, developed to parse semantic meaning from text posts on Facebook. Here are some technical details on how Deep Text works:

  • Uses word embedding models to map 1 million words across 200 languages into vector representations.

  • Leverages bidirectional LSTMs to understand word order and context for robust language analysis.

  • Trained on diverse Facebook datasets like comments, Community Standards text, etc.

  • Can understand nuanced meaning and context behind words based on how they are used.

According to Facebook AI research director Yann LeCun, Deep Text aims to have machines "understand language more like humans do." Instead of just keyword matching, it relies on deep learning to discern nuanced meanings and relationships between words.

Deep Text powers several applications at Facebook:

  • Classifying the topic and intent behind billions of text posts.

  • Matching relevant ads to informal text content.

  • Moderating posts and comments for policy compliance.

  • Weeding out spammy, dangerous or prohibited content.

  • Analyzing trends and sentiment across different demographics and languages.

In the future, Deep Text could allow users to search posts by intent rather than just keywords. It opens up possibilities for smarter search, filtering, and recommendation systems across Facebook‘s services.

2. Machine Translation for the News Feed

Facebook‘s News Feed is the core user interface showing relevant posts, videos, photos and updates. For globalexpansion, Facebook needed to translate News Feed content into diverse languages.

To achieve this, Facebook AI Research (FAIR) developed a machine translation system tailored for informal social media text. Here are some details on how it works:

  • Uses neural networks trained on Facebook‘s large parallel corpora of post translations.

  • Translates full sentences and paragraphs for contextual accuracy, unlike phrase-based statistical MT.

  • Understands common slang, abbreviations, colloquialisms used in social media posts.

  • Can translate emojis and reactji icons based on how they are used in context.

  • Supports 50+ languages translating 2.5+ billion text pieces daily.

The machine translation system is domain-optimized for social media content and outperforms general translation tools like Google Translate for Facebook‘s use case. It serves translated News Feed posts to users across the world, helping connect Facebook‘s over 2.8 billion users globally.

According to Facebook, this AI translation system has increased translated post engagement by 15% and viewing time by over 40%. This showcases the business impact and user value created by applied AI research.

3. Computer Vision for Photo Search

With over 300 billion photos shared by users on Facebook, powering visual search is crucial. Facebook AI develops computer vision models that can analyze image contents to enable smarter photo search and discovery.

Some capabilities of Facebook‘s computer vision models:

  • Object recognition – identify objects like cars, animals, foods within images.

  • Scene recognition – understand context and setting of a photo like beach, forest, buildings.

  • Facial recognition – detect, verify and group faces appearing across photos.

  • Concept detection – understand high-level concepts like celebration, travel, graduation.

  • Relationship modeling – determine how objects in an image relate to/interact with each other.

Instead of purely textual photo labels, these AI models allow search based on actual visual descriptors like "sunset beach family photo." Vision AI will also power immersive use cases like automated image descriptions for visually impaired users.

According to Facebook AI research, their computer vision models have over 85% accuracy in recognizing objects in a larger dataset of 3000 visually distinct categories.

4. Conversational AI with Chatbots

In 2016, Facebook enabled third-party developers to build chatbots for its Messenger platform. This catalyzed an ecosystem of conversational AI – bots that can hold natural dialogues with users.

Here are some details on Facebook‘s chatbot platform capabilities:

  • Messaging APIs for sending/receiving text, images, videos and files.

  • Structured messaging with buttons, menus and predefined response templates.

  • Contextual awareness to continue conversations across sessions.

  • User identity and account linking for personalized experiences.

Over 300,000 bots have been developed for Messenger for diverse use cases like notifications, transactions, support, entertainment and media.

According to Facebook, 68% of people prefer interacting with businesses via Messenger rather than traditional channels. As chatbot technology matures, conversational AI could become a primary interface for digital interactions.

Popular Messenger chatbots include:

  • Commerce – Spring for shopping, PetCo for pet supplies.

  • Information – CNN for news updates, GoCanvas for field service teams.

  • Support – Asana for task management, Discover for banking assistance.

5. Computer Vision for Video Style Transfer

Style transfer uses AI to recreate an image or video in the artistic style of famous painters like Van Gogh and Picasso. In 2017, Facebook AI published research on adapting style transfer to run in real-time on mobile phones to transform live videos.

Key techniques they developed include:

  • Quantization – compressing neural networks to cut down size and memory.

  • Distillation – transfer knowledge from larger teacher models into smaller student models.

  • Animation frame interpolation – smooth stylistic transitions between frames.

This research enabled the launch of Facebook‘s Caffe2Go mobile app feature. Caffe2Go lets users apply artistic filters like Van Gogh‘s Starry Night to phone videos in real-time using style transfer AI.

By compressing computer vision models to run efficiently on phones, this showcases the potential to bring advanced AI capabilities directly into billions of users‘ hands through mobile apps. It could enable immersive use cases like applying styles and filters to videos for social sharing.

6. Using AI to Detect Harmful Behavior

With billions of users, Facebook grapples with issues like hate speech, bullying, nudity and self-harm content. Moderating such a large platform manually is impractical. This motivates Facebook‘s investments in using AI to detect policy-violating behaviors.

Some ways Facebook applies AI for safety:

  • Deep learning models that analyze text, audio and visual signals to detect harassment, violence, adult content, and other policy violations.

  • Leveraging user feedback signals like post/comment flags to improve model predictions.

  • Combining AI classification with human content moderators for scalable, nuanced enforcement.

  • Using natural language understanding to identify signs of self-harm risk and notify first responders.

According to Facebook, its AI systems have increased harmful content detection rates from 38% to over 80% in recent years. But challenges remain in accurately interpreting complex human behaviors like hate speech. Ongoing AI research aims to address these issues as social media continues to evolve.

7. Reinforcement Learning for Game AI

Many popular games on Facebook‘s platform incorporate artificial intelligence to control non-player characters and opponents. In 2018, Facebook AI open sourced the ELF OpenGo project for creating robust game AI.

Key features of ELF OpenGo:

  • Uses deep reinforcement learning based on AlphaGo Zero to master complex games from scratch.

  • Starts from random play and optimizes through self-play practice without human game data.

  • Achieved master level play in Go, chess and shogi by playing over 10 million games against itself.

  • Defeated world champion programs like Stockfish in chess games.

  • Modular design allows adapting the AI to new game environments.

Open sourcing ELF OpenGo enables developers to integrate state-of-the-art reinforcement learning into new games, simulations and other interactive applications. It demonstrates how self-learning game AI agents could one day master any gaming domain through sufficient practice.

The Future of AI at Facebook

As this extensive guide shows, artificial intelligence powers many core experiences across Facebook‘s ecosystem of apps. Priority areas like computer vision, natural language processing and reinforcement learning will enable even smarter features down the line.

Facebook‘s applied AI efforts will focus on enhancing integrity, safety and digital well-being for billions of users globally. Meanwhile, FAIR continues pushing boundaries in foundational machine learning research to drive innovation in AI. With its vast data and resources, Facebook is poised to shape the future of social connection through AI technology.

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