Artificial Intelligence (AI) has become ubiquitous, powering many of the websites and mobile applications we use daily. As AI techniques like machine learning and natural language processing continue to advance, we are seeing its integration across an ever-expanding spectrum of online services.
In this comprehensive guide, we will explore some of the most interesting examples of AI-powered websites and apps available today. From large language models that can hold human-like conversations to niche tools that use AI in unique ways – there are abundant options for users to experience applied AI.
The Rise of AI in Digital Experiences
The adoption of AI in software and digital services has accelerated in recent years. According to research from Gartner, the AI software market grew over 54% in 2021 to $62.5 billion globally. They forecast it to cross $500 billion by 2024. What‘s driving this growth?
AI can enable more natural, intuitive user experiences. For instance, instead of searching keywords, we can now ask questions and expect relevant results. It also allows for hyper-personalization as AI systems can continuously learn user preferences. Additionally, AI adoption is increasing as the technology becomes more accessible with pre-built platforms and machine learning frameworks.
However, while AI powers many of the applications we use, most users are often unaware of its behind-the-scenes role. This article highlights some visible examples across categories like search, productivity, social media and more.
| AI System | Key Capabilities | Underlying Technology |
|---|---|---|
| Google Search | Answers questions, contextual results | NLP, RankBrain ML |
| Uber app | Predict ETAs, dynamic pricing | Time series ML, multi-armed bandit |
| Grammarly | Detect grammar/style issues | NLP, text classification |
| Visual recommendations | Computer vision, image recognition |
Table: Examples of AI sytems powering popular apps and their technological implementation
Now let us explore some of these AI-powered websites and mobile applications.
Large Language Models
Recent advances in natural language AI have enabled the creation of remarkably ‘human-like‘ conversational systems. Models like ChatGPT demonstrate how massive datasets and compute can unlock intelligent text generation on demand.
ChatGPT – Created by OpenAI, it can explain concepts, summarize articles, write code and poetry based just on text prompts. Launched in November 2022, it gained immense popularity with over 1 million users in 5 days. But it has also raised concerns around accountability and potential misuse.
Claude – Developed by Anthropic, this AI assistant focuses on harmless, honest and helpful dialog. The model mitigates issues like bias and misinformation faced by unchecked language systems. While not public yet, Anthropic has raised $300 million to productize Claude.
Limitations – Current models still make factual mistakes and have biases. But rapid improvements in natural language AI continue, making such systems an exciting space to watch.
The conversational abilities of systems like ChatGPT and Claude demonstrate rapid advances in natural language understanding AI.
Search Engines
Search engines have a complex task – understanding the intent behind millions of daily queries and surfacing the most relevant information. Applying AI techniques like NLP and ML has been key to improving search relevance.
Google – RankBrain ML system helps process rare long-tail queries. Google also uses ML extensively for semantic search, voice search, image tagging, translating results etc.
DuckDuckGo – While small, DDG focuses on privacy. It uses AI to analyze queries and public content to generate ‘Instant Answers‘ boxes without tracking users.
Yandex – Russia‘s largest search engine uses AI for NLP-based semantic search, automated translations and speech synthesis. It even offers an AI-powered personal assistant.
Limitations – AI still struggles with complex search intents and niche domains with limited data. There are also concerns around filter bubbles and bias in search rankings.
Google and other search engines leverage various AI techniques like NLP and ML to understand search intent and return contextual results.
Productivity Tools
AI techniques that understand language, speech and visuals can unlock interesting productivity applications. These tools act like assistive aids for tasks like writing, research and content creation.
Grammarly– This AI writing assistant checks grammar, spelling, punctuation, style, tone etc. It even provides genre-specific writing suggestions using NLP and text classification algorithms.
Otter.ai – It uses speech recognition AI to record and transcribe meetings, interviews and conversations. The transcripts are searchable and can be shared.
Readable – Browser extension that summarizes web pages using NLP. It also removes ads and distractions to give a simple reading view.
Limitations – Performance depends heavily on training data quality. Tools like Otter.ai still have average accuracy of ~90%. Privacy of user content also raises concerns.
Writing aids like Grammarly rely on NLP and ML to analyze text and suggest improvements.
Chatbots & Digital Assistants
AI-powered conversational agents offer an intuitive way for users to get information or services through text/voice interactions. Companies are actively developing virtual assistants and chatbots using natural language AI.
Mezi – Personal travel planner that acts as a chat-based travel agent. Users can describe trip plans and Mezi suggests customized itineraries and options.
Florence – AI nurse chatbot by Anthropic that aims to be helpful, harmless and honest. Still in development but shows promise for healthcare.
Google Duplex – AI system that can make reservations, book appointments etc. over the phone in natural conversations. Still in limited testing.
Limitations – Challenging to scale across use cases. Need for huge training data. Chatbots still lack general conversational abilities of humans.
Natural language AI allows for intuitive user experiences via chatbots and voice assistants.
Social Networks
AI powers most features of leading social platforms – from customized feeds to content moderation. This enables them to keep improving the user experience and engagement.
Facebook – Uses AI for numerous applications like ad delivery, hate speech detection, graphic content moderation and even creating descriptions for the visually impaired.
Reddit – Employs machine learning for recommendations and spam detection. It also provides an AI assistant named Snoo to engage with users.
Pinterest – Pins are recommended using vision AI that extracts features from images and understands user tastes. Enhancing search relevance is another focus area.
Limitations – There are concerns around biases in content classification and moderation done by AI. Transparency and oversight are critical.
Leading social platforms rely extensively on AI techniques to improve content relevance and user engagement.
Fun & Games
From music to art and even games, AI is fueling more immersive and interactive experiences. While most applications focus on productivity or convenience, there are also some using AI purely for entertainment.
Aiva – Proprietary AI that composes original, emotionally engaging instrumental music. It has created 10 albums across genres like cinematic, jazz, classical etc.
Mario Kart AI– Nintendo developed an AI "Satoru" to play Mario Kart in an entertaining, human-like style by modeling player habits and behaviors.
失败 (Fail), by Anthropic – AI art series created by instructing models to draw common objects incorrectly. Provides unique juxtaposition of AI capabilities and absurdity.
Limitations – Generative AI like art and music still lack the creative subtlety of humans. But models are continuously being refined through feedback.
AI is powering more immersive experiences in gaming and entertainment, although lacking human creativity.
The examples of AI-integrated applications highlighted in this guide demonstrate the expanding presence of artificial intelligence in software and digital services today. As these technologies continue advancing and become more democratized, AI is poised to transform user experiences across domains and industries. However, the onus lies on technologists to develop AI responsibly keeping in mind ethical considerations around aspects like bias, privacy and transparency.