How Facebook uses Artificial Intelligence?

The Facebook family of services, which includes Messenger, Instagram, and WhatsApp, receives close to 2.5 billion daily logins.

 

Facebook continues to gather data as users peruse, message, like, and click, which it then packages for advertisers. Behind the scenes, Facebook uses artificial intelligence to make sense of the jumbled information it gathers about our preferences, aspirations, and interpersonal ties.

 

With its zillions of users and enormous data warehouse, Facebook is the ideal playground for the company's developers and engineers to experiment, find solutions, and push the limits of what AI is capable of. 

 

By gathering information about its users and preparing their data for marketers, Facebook grows its business. It then utilizes this money to reinvest in providing us with new, practical functionality—at the moment, video and shopping—while also using it to find out even more information about us.

 

It has become a magnet for a vast amount of data about us, including information on who we are, where we spend our time, and what we like, since the way it facilitates communication and conversation between people has proven to be extremely beneficial to us. 

 

The fact that so much of this data is incredibly jumbled and unstructured presents a challenge for Facebook's data scientists who must attempt to make sense of it.

 

Also Read | New Technologies in Facebook

 

AI in Facebook

 

Here are a few examples of how Facebook is utilizing artificial intelligence to develop and keep up with the demands of its ever-growing user base:

 

1. Deep Text

 

Facebook receives user comments and posts as text data, and by separating the messages into individual letters and even exclamation points, the machines should be able to automatically identify word associations.

 

As an illustration, Deep Text attempts to comprehend how slang is used and, using context, ascertain the precise meaning of a word that may have several meanings. This technology will also aid in identifying popular subjects, filtering out illegal information, ranking search results, and matching people with ads.

 

2. Translation

 

More than half of Facebook users, who are dispersed over the globe, do not speak English. The Applied Machine Learning team developed an AI-based automatic translation system to break down these communication obstacles, and 800 million users use it each month to see translated content in their News Feed. 

 

Our platform uses non-traditional vocabulary that differs from other platforms. Facebook is all about communication between people: People constantly use new expressions, they don't capitalize their words, there are regional distinctions, and there are emojis. It is also alive.

 

A new translation tool powered by Microsoft Bing was introduced by Facebook in 2011. Users can choose to view Page posts in their native language using Translate. 

 

Facebook users may contribute their own translations, or page administrators may choose to display only posts that have been automatically translated.

 

3. Image search

 

It can be challenging to recall the precise time that something occurred and the person who snapped the picture to record the moment while reflecting on your fondest recollections. 

 

With the aid of Facebook's automatic image classifiers, it is possible to envision a situation in which a user may browse all the pictures that their friends have uploaded in order to find a specific one without the aid of tags or supplementary language. Really cool

 

Facebook is developing an artificial intelligence (AI) tool that can comprehend what is happening in your images, which will improve the performance of your Newsfeed. A more helpful section; this feature will allow it to respond to inquiries about a photo, a feature intended to help visually impaired users "see" photographs posted to social media.

 

4. Speaking Images

 

Instead of simply categorizing the entire image, Facebook is developing systems that can analyze photos down to the individual pixel level. They can identify specific items in an image as well as their relationships using a process known as image segmentation. 

 

With the use of image segmentation, they will be able to create "talking images" that you can read with your fingertips for the blind, as well as more effective ways to search for images.

 

Also Read | How Facebook uses Big Data to enhance customer experience
 

5. Facebook's bot API for Messenger Platform

 

Together with Microsoft CEO Satya Nadella, Zuckerberg had entered a "chatbot arms race." By interacting directly with the users who wish to receive them, bots can deliver everything from customized communications like receipts, shipment notifications, and live automated messaging to automated subscription material like weather and traffic updates.

 

It has three primary competencies:

 

• Send/Receive API

 

This new function enables sending and receiving of text, graphics, and rich bubbles with many calls-to-action. Developers can also add a welcome screen to provide context and other controls for their threads.
 

• Generic Message Templates

 

Our theory is that consumers would rather engage with your bot by tapping buttons and viewing lovely visuals than by learning a new programming language. Facebook created structured messages with calls to action, horizontal scroll, urls, and postbacks because of this.

 

• Welcome Screen + CTAs for null states

 

You can personalize your experience by using the tools and space provided by the Messenger app. The greeting screen is where it all begins. People find our highlighted bots and start a discussion with them. After that, visitors see your company's logo, your Messenger greeting, and a prompt to "Get Started."

 

6. Improved VR Experience

 

Facebook developed the Oculus Quest, a completely wire-free virtual reality device, after realizing the need for greater technology to realize the full potential of augmented reality (AR) and virtual reality (VR).

 

Every millisecond, Oculus Quest's cutting-edge AI software analyzes users' positions and movements and converts those movements into virtual reality. With this new technology, issues like visual stuttering, latency, and jitter that were present with earlier VR headsets are reduced or completely eliminated.

 

7. Face Recognition

 

Facebook trains DeepFace, a facial recognition programme, to identify people in pictures. According to the company, DeepFace, its most sophisticated picture recognition engine, has a success rate of 97% in determining whether two images of the same person are identical or not when compared to humans' 96%.

 

It's fair to say that this technology's use has generated some controversy. Privacy advocates argued that it went too far since it would enable Facebook to identify many of the faces in a crowd using a high-resolution snapshot, which would obstruct our right to walk freely in public without being recognized. 

 

In 2013, Facebook agreed and was convinced to remove the functionality from accounts belonging to Europeans. The social media giant back then used a facial recognition algorithm that was older and did not use Deep Learning. 

 

Since this technology first made news, Facebook has been fairly quiet about it. It can be believed that the company is awaiting the resolution of ongoing privacy lawsuits before disclosing further information about its plans to implement it.

 

8. Personalized advertising

 

Deep neural networks, the cornerstones of deep learning, are used by Facebook to determine which advertisements to display to which people. This has always been the foundation of its business, but by giving machines the task of learning as much as they can about us and grouping us together to serve us ads in the most insightful ways, it hopes to keep a competitive edge over other high-tech rivals like Google who are vying for supremacy of the same market.

 

Also Read | Facebook Transcoder AI

 

Why are fake accounts viewed as a danger to users?

 

On the Facebook platform, both humans and bots attempt to establish or actually create hundreds of thousands of phony accounts each day. These accounts are credible technological spoofs that are made to look more real by using fabricated email addresses, artificial images, and highly accurate data.

 

These accounts typically pose a serious threat to the site and its users. These con artists frequently prey on unsuspecting users, making these accounts a simple way to make money. These accounts are used by scammers to spread phishing links, launch spam campaigns, encourage violence, support terrorism, and more. 

 

One such well-known instance of the dangers posed by false accounts was the impact of Russian agencies on the outcomes of the US midterm elections. Experts predicted that Facebook's fake accounts would also be crucial in the forthcoming US elections using tools like Deepfake. Facebook is also taking every precaution to avoid any meddling.

 

How does Facebook identify these accounts using AI?

 

Facebook uses both machine learning and human intelligence to identify bogus accounts while adhering to the CSER's set of criteria. To assess whether accounts are authentic or not, it employs machine learning algorithms and hand-coded criteria.

 

Once identified, fake accounts are blocked either as they are being created or even before they go live on the network. However, the real issue appears when these phony accounts manage to evade all of these safeguards and start using them. 

 

When these accounts' nefarious behaviors are picked up by Facebook's detection system, such accounts are detected using cutting-edge AI techniques. Active users occasionally also report these accounts.

 

Facebook uses three distinct processes for account screening :

 

1. Stage of Creation

 

With the aid of specialized detection technology, fake accounts are identified as they are being formed. AI prevents the creation of these accounts right away. It works with massive amounts of data and acts quickly. 

 

For instance, the system can identify and report on many accounts that were made using the same IP address. These accounts are then prevented from accessing the social networking site in the future. AI deletes these accounts very away, protecting active users from danger.

 

2. Stage of Activation

 

After these accounts register, the machine learning algorithms search for any suspicious behaviors that can trigger an alarm. In order to identify such accounts, these algorithms make advantage of the information learned from the elimination of earlier fraudulent accounts. 

 

For instance, DeepFace technology detects Deepfakes and alerts the system. Before the user even appears on the platform, AI bans their account by tracking signals linked to questionable email addresses or accounts that have already been disabled.

 

3. Stage of Blocking Registered Accounts

 

Deep Entity Classification is quite important in locating problematic Facebook accounts. It outperforms individuals who have already mastered the art of manipulating identifying characteristics like age, geography, and friend counts. After carefully scrutinizing them, DEC's AI detects these accounts and, if defective, blocks them.

 

Also Read | Applications of AI in Food Industry Food Industry

 

In summary, AI is here to stay and will undoubtedly have a significant impact on how Facebook serves users and marketers. Facebook constantly uses technology to create new products and services every year, although it has never been open about upcoming inventions. With so many AI-based projects underway, Facebook is equipped to manage new difficulties and take uncharted territory. Innovation, after all, has no bounds.

 

In particular, in seven key categories, Zuckerberg has promised to employ AI to address some of the largest issues the company and its users are now facing like hate speech terrorism, Bloody Violence, Spam suicides, Fraudulent Accounts Use of AI in chatbots, talking pictures, automatic image recognition, and machine translation.