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Modern day web app development is increasingly focused on building a customer-facing front-end presence with the use of Artificial Intelligence. Web apps, use Artificial Intelligence not just for intelligent automation, but also for building recommendation engines, website implementation, and image recognition, among other application areas.

In this post, we look at five key areas, illustrated by real-world examples, where web apps are employing Artificial intelligence to automate some part of their system.

Recommendation Engines of Amazon and Netflix

Curating content based on the user’s context is one of the most widely used AI features in web apps. Amazon, for instance, uses item-based collaborative filtering for product classification. Amazon’s recommendation system uses a combination of goods-based recommendation (users are recommended for those similar to what they liked in the past) and buddy-based recommendation (users are recommended things which their Facebook friends like.)

Not just for their recommendation system, Amazon has been using AI for multiple tasks. Their AI Management Strategy is called The Flywheel, where one part of Amazon acts as a catalyst for AI and machine learning growth in other areas.

Read more: Four interesting Amazon patents in 2018 that use machine learning, AR, and robotics

Another popular example is Netflix, who revamped their recommendation algorithm based on visual impressions. One of their research projects indicated that the artwork was not only the biggest influencer to a viewer’s decision to watch content, but it also drew over 82% of their focus while browsing Netflix.

This made them develop a new image recommendation algorithm which works in real time to project the image it thinks the user will respond to. They use implicit (user behavior) and Explicit data (user activity) and then feed this data to machine learning algorithms to figure out the relevant content for each user. For each title, users get the image with the highest rank based on their profile. Side by side, it continues collecting data from its 100 million other subscribers to improve its engine’s performance.

Read more: What software stack does Netflix use?

Google and Microsoft using Image recognition

Image recognition can serve multiple uses for web apps including object and pattern recognition, locating duplicates (exact or partial), image search by fragments, and more. Two such unique applications of image recognition are Google’s Quickdraw and Microsoft’s Captionbot.ai.

Quick Draw is Google’s AI-powered web app game, where users have to draw an everyday object that a neural network tries to recognize. Players are given 20 seconds to draw a random item, and Google’s neural network tries to match it with other 50 million hand-drawn sketches by other players to identify the correct one.

Quickdraw aims to generate the world’s largest doodling data set, which is shared publicly to help further machine learning research. The data preserves user privacy by collecting only anonymous metadata, including timestamp, country code, whether or not the drawing was recognized, and which word the drawing corresponded to. This dataset was used in SketchRNN, a neural network that can draw words and interpolate between drawings.

Another image recognition web app is Microsoft’s Captionbot.ai. The system can automatically generate a caption for an uploaded photograph. Users can rate how accurately it has detected what was on display. The algorithm learns from the rating, to make the captions more accurate. It uses three separate services to process the images. The Computer Vision API identifies the components of the photo, then mixes it with data from the Bing Image API, and runs any faces it spots through Emotion API. The Emotion API analyses facial expressions to detect anger, contempt, disgust, fear, and other traits. Based on the results from these APIs, the caption is generated.

Google Docs powered by Natural Language Processing

Modern Web apps can also be fueled with cognitive capabilities to make them stand apart from other apps. Instances of this include transforming human speech to text or conversing with people in natural language. One such example of a web app which includes natural language processing is Google Docs.

Google Docs and Slides have an Explore feature to show text, images, and other features relevant to the document that a user is working on at any given point.  Docs can also use natural language to search through data and reports, and automatically generate formulas in Sheets. Google Docs recently incorporated an AI grammar checker, announced at Google Cloud Next. It uses a machine translation algorithm to recognize errors and suggest corrections as users type. Google Docs can also be integrated with Natural Language API to recognize the sentiment of selected text in a Google Doc and highlight it based on that sentiment.

Web-based artificial intelligence Chatbots

Web-based chatbots are just like app-based chatbots albeit they interact with users in the website browser. They use AI techniques such as natural language understanding and pattern recognition to store and distinguish between the context of the information provided and elicit a suitable response for future replies.

An example of web-based chatbots are the Live Chat bots where the conversation with a visitor on a website is automated using a chatbot. Many live chat software companies are already experimenting with chatbots. Examples include the Operator bot used by Intercom, a company building customer messaging platform or Driftbot by Drift which gives your website a personal assistant.

Read More: Top 4 chatbot development frameworks for developers

Another example, are AI based chatbots which help in creating full websites. Right Click is a startup that introduced an A.I.-powered chatbot which uses Artificial Intelligence in a conversational interface to create websites. It asks general questions during the conversation like “What industry you belong to?” and “Why do you want to make a website?” and creates customized templates as per the given answers. Similarly, Wix’s Artificial Intelligence Design bot can tailor websites by learning about each person’s or business’ own needs.

Web-based code helpers using AI

Intelligent coding assistants are gaining popularity with their ability to understand the code and provide right suggestions at the right time. They can analyze code on the web and give fast and smart completions.

Codota for Chrome is a smart web-based IDE which can build predictive models of code and suggest code completions and related content based on the current context present in the code. It combines program analysis, natural language processing, and machine learning to learn from the code. Users can look for Codota’s Icon on every code snippet on their browsers – in GitHub, StackOverflow and others.

Another example is Deep Cognition’s Deep Learning Studio – Cloud. It is not exactly an IDE, but it features AI-powered drag & drop interface to help design deep learning models with ease. It features assisted modeling, for automated tensor size calculations and real-time validation. It also has AutoML feature to automatically build a neural network.

Even though AI is a great choice to enhance your web apps, an important facet to keep in mind is ensuring fairness, accuracy, and transparency of your web apps. For instance, web apps powered by natural language should not discriminate people based on caste, color, or creed or hurt user sentiments. Similarly, those using neural networks for recognizing images should ensure the filtering of obscene images.

Creating such types of artificial intelligence systems would require a hybrid of designers, programmers, ML engineers, and researchers. This collective group will have a good grasp of user experience, will be comfortable thinking in abstracts and algorithms, and equally well versed with the social impacts of artificial intelligence.

Read More: 20 lessons on bias in machine learning systems by Kate Crawford at NIPS 2017

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