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  • Sebastian Feustle

Do you know where AI is already "hiding" today? Artificial intelligence in everyday life

Introduction:

Advancing digitization and technological innovation have made artificial intelligence (AI) a part of our everyday lives more than ever. From the smartphone in our pocket to the smart thermostat in our home - AI systems influence how we live, work and entertain ourselves. Often, these systems operate unnoticed in the background, optimizing processes or making decisions easier for us. But while some applications are already widespread and accepted, other innovations are still in their infancy. The range and potential of possible applications of AI in everyday life are enormous and offer both opportunities and challenges. It's worth taking a look at this diverse and constantly evolving technology.

We want to look at three typical examples from everyday life where AI is used or where it could be encountered.



artificial intelligence in everyday life


Voice assistants


Voice assistants, such as Apple's Siri, Google's Google Assistant, Amazon's Alexa, and Microsoft's Cortana, use artificial intelligence (AI) in several core areas:


  1. Speech recognition: The first thing a voice assistant needs to do is recognize and process the human voice. This is where AI is used to convert the spoken text into written text. This process, known as Automatic Speech Recognition (ASR), has been greatly improved over the years by deep neural networks.

  2. Natural Language Processing (NLP): after the spoken text is recognized, NLP comes into play. NLP allows machines to interpret human speech in a way that produces meaningful responses or actions. It's about understanding context, intent, or nuances in speech.

  3. Fulfillment and action execution: after the assistant understands the user, it must perform the appropriate action, whether it's answering a question, playing music, or setting an alarm. This is where AI can help find the right databases or services and efficiently execute the requested action.

  4. Personalization: modern voice assistants learn over time from the user's preferences and habits. This can help provide more accurate and relevant answers or recommendations. The AI models behind them analyze history and behavior to enable such personalized experiences.

  5. Speech synthesis: When a voice assistant gives an answer, it is often not only displayed as text, but also spoken. This is where text-to-speech (TTS) technology comes in. Advances in AI, particularly through models such as WaveNet, have made artificially generated speech more h

  6. Continuous learning: some voice assistants have the ability to learn and improve their accuracy or responsiveness through feedback and repeated interactions.

  7. Noise and voice detection: to function efficiently in noisy environments or to distinguish between different users, voice assistants use AI models to detect background noise and distinguish between different speakers.

All of these components work seamlessly together to provide the experience of a responsive, intelligent, and helpful voice assistant. However, it is important to note that despite advances in AI, voice assistants still have their limitations and are sometimes unable to understand complex queries or nuanced human communications.


Musik und Streaming-Dienste


Music and streaming services are using artificial intelligence (AI) in a variety of ways to improve the user experience, provide personalized recommendations, and optimize operational processes. Here are some key applications of AI in these services:


  1. Personalized recommendations: One of the most well-known applications of AI in streaming services is the personalization of music and video recommendations. Algorithms analyze users' listening and viewing behavior to identify preferences and patterns. Based on this, songs, playlists or videos are recommended that might suit the user's taste. Services such as Spotify and Netflix use such systems.

  2. Music analysis: some music services use AI to analyze songs according to certain characteristics such as mood, tempo or genre. This analysis can then help create playlists that match specific moods or activities, such as a running or relaxation playlist.

  3. Content discovery: AI can help discover and highlight new content that might otherwise get lost in the mass of songs or videos.

  4. Content optimization: For video streaming services, AI can be used to optimize the quality of streamed video based on available bandwidth or end device preferences.'

  5. Voice recognition and control: Some advanced streaming services offer voice control features where users can search or control content by voice commands. AI is used to interpret the voice commands and perform appropriate actions.

  6. Content creation: There are experiments and applications where AI is used to create music or edit videos. However, such applications are not yet widespread and are often experimental in nature.

  7. Copyright infringement detection: AI can help detect copyrighted content and ensure that it is not streamed without appropriate licenses or permissions.

The use of AI by music and streaming services aims to improve the user experience, increase efficiency, and help users discover and enjoy the content that is most relevant to them.


Facebook & LinkedIn


Facebook and LinkedIn, two leading social networks, are using artificial intelligence (AI) in a variety of ways to improve user experience, moderate content, and optimize their services. The applications of AI in these platforms can be summarized as follows:


Facebook:

  1. Content recommendation: AI algorithms determine which posts and ads are prioritized in a user's News Feed based on their interactions, preferences, and behavior patterns.

  2. Image and video analytics: AI is used to analyze images and videos to recognize and categorize content, e.g. for automatically generated captions or to identify specific content.

  3. Hate speech and abuse detection: AI systems are used to detect harmful content, hate speech, or fake news and handle it accordingly.

  4. Facial recognition: although this feature has been restricted or removed in some regions due to privacy concerns, Facebook used AI to recognize faces in photos and make tag suggestions to users.

  5. Chatbots: many businesses use AI-powered chatbots on Facebook Messenger to answer customer questions automatically.

  6. Speech processing: AI is used to process voice clips or voice commands, especially in conjunction with products like Facebook Portal.

  7. Translation: Automatic translation features allow users to see posts in different languages.


LinkedIn:

  1. Job and people recommendations: LinkedIn uses AI to suggest jobs, contacts, or content that may be relevant to a user based on their profile, skills, network, and previous interactions.

  2. Content moderation: Similar to Facebook, LinkedIn also uses AI systems to identify and remove inappropriate content or spam.

  3. Image analytics: For features like automatic image cropping in profile photos.

  4. Sales analytics: With tools like LinkedIn Sales Navigator, AI-driven analytics are offered to help sales professionals identify potential leads.

  5. Text analytics: for example, LinkedIn can provide hints or suggestions for improving profile summaries.

  6. Chatbots: for automated responses or interactions in messages.

  7. Ad targeting: AI is used to ensure ads are shown to the right audience based on the user's profile information and interactions.


Both Facebook and LinkedIn are constantly improving and expanding their AI systems to create better and more secure platforms for their users. However, they must always strike a balance between personalization, user experience, and privacy..


Conclusion


Artificial intelligence (AI) has established itself as an essential component in the functioning and further development of modern technology platforms. Social networks such as Facebook and LinkedIn, as well as music and streaming services and voice assistants, make extensive use of AI to improve the user experience, provide personalized content, and optimize operations.

Facebook and LinkedIn use AI to suggest relevant content and contacts to users, detect hate speech and abuse, and target ads. Music and streaming services use AI to create personalized playlists and video recommendations, optimize content quality, and highlight new creations. Voice assistants rely heavily on AI to recognize and interpret human commands in real time, answer complex queries, and continuously learn from user interactions.

Overall, the widespread integration of AI in these services demonstrates not only the technology's enormous potential, but also its inescapable presence in our daily lives. While AI enriches the user experience, it is also important to critically consider issues around privacy, ethics, and oversight of such systems. Going forward, the balance between personalization, efficiency, and user privacy will be critical to maintaining user trust in these technologies.

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