Machines have historically aided humans to maneuver several challenging tasks all through the dark ages and the industrial revolution. A decade after the Enigma machine helped the Allies win World War II, its inventor Alan Turing posed the one question that expanded our horizons for innovation - Can machines think?
Artificial Intelligence (AI) is the branch of computer science that attempts to answer this question. This technology combines vast amounts of data insights with sequential processing based on intelligent algorithms. It then interprets this data and learns from patterns within the data in a way that is similar to human intelligence.
How Does AI Work?
The creation of AI involves first reverse-engineering human traits and thought patterns and recreating them in a computer. After the machine integrates human capabilities into itself, it uses its processing power to surpass the calculations and analytical capabilities of the smartest humans to ever exist.
AI is capable of acting on data, learning from data, and using these learnings to iteratively keep improving. With software programs that don't use AI, we can define a possible set of scenarios and the program would only run within those constraints.
On the other hand, AI allows a program to 'learn' on its own and explore various possibilities that were not previously defined (by using an expansive set of data). The program thus formulates solutions automatically, whenever it comes across an unfamiliar problem or scenario.
Real-World Applications of the Sub-Domains of AI
To understand what goes into building AI, we must delve into how the various sub-domains of this technology help in real-world applications. AI is a broad field of computer science that involves multiple viable theories, methodologies, and major subfields like machine learning and neural networks. The principal sub-domains are discussed below:
1)Machine Learning:
Machine Learning (ML) is a core sub-field of AI that helps computer systems 'learn' from historical data and various inputs. ML applications learn from data without any need for direct programming. Using training algorithms, these applications are able to find insights without being told where to look. In broad terms, ML enables computer systems to adapt to new data independently through a series of iterations.
2)Neural Networks:
Artificial neural networks (NN) consist of interconnected nodes, arranged in layers that process information mimicking the human brain's neurons. NN systems employ dynamic state responses to various input data. Data patterns are introduced to the NN via the input layer, while the hidden layers between the input and output layers take care of the actual processing of data.
3)Deep Learning:
Deep Learning (DL) is a subset of ML that can achieve state-of-the-art accuracy in performing tasks, sometimes exceeding human levels. A large set of labeled data and underlying NN architectures are used to train DL models. DL has achieved greater data recognition accuracy with better computing power recently, which helps in designing consumer electronics that meet user expectations.
4)Cognitive Computing:
Using data mining, pattern recognition, and Natural Language Processing (NLP), this sub-domain of AI strives to mimic human cognition. Cognitive computing interprets images and speech and provides output in a coherent speech form. The ultimate goal of this field of study is to allow humans to interact with machines the way they would speak to another human being.
5)Evolutionary Algorithms:
Evolutionary algorithms are built into NN models to create a population of self-improving algorithms and preserve those that are most successful at predictions. Applying Darwin's "survival of the fittest" theory, the losing algorithms are eliminated and sections of code from the surviving algorithms participate in the next iteration of the selection process. Evolutionary algorithms are most suitable for optimization tasks that involve a large set of variables and a dynamic environment.
3 Types of AI Based on Degree of Cognition
AI is classified on the varying capability of each AI type to emulate human intelligence and decision-making. These include both real and hypothetical AI methodologies that find wide use in scientific problem solving and real-world applications. AI has the following three broad types:
1)Artificial Narrow Intelligence (ANI):
ANI, also called narrow AI or weak AI is the only type of AI that is currently used for real-world applications. The purpose of an ANI machine is to complete a singular task such as facial recognition, driving, searching the internet, etc. While these machines are intelligent at completing a focused task they operate under a narrow set of constraints. Based on these narrow parameters, ANI only simulates human intelligence for the task it is programmed for.
2)Artificial General Intelligence (AGI):
AGI is a type of AI which in theory should be able to solve any problem by thinking, understanding, and acting in a way that is indistinguishable from a human. Developers aiming to achieve AGI are working on programming a full set of cognitive abilities into a machine making it conscious. If realized, AGI would be able to discern the needs, emotions, and thought processes of other intelligent beings.
3)Artificial Super Intelligence (ASI):
ASI is an imaginary AI that doesn't just mimic human behavior but becomes self-aware and surpasses the limits of human intelligence as well. The subject of a wide variety of science fiction media, ASI is theoretically able to generate emotions, needs, beliefs, and desires of its own.
10 Apps That Are Enhanced with the Use of AI
AI sees its wide use in real-world applications like self-driven vehicles, medical imaging analytics, and forensic speech recognition. Beyond these avenues, AI is also a useful tool in app development to provide a more engaging and enhanced user experience. Here are 10 apps that implement AI-driven optimization:
1)Netflix
Netflix uses AI for better content recommendation and also to ensure the best streaming quality. Despite multiple users logging into the same Netflix account, their respective user profiles get tailored movies and series recommendations. The app achieves this with the help of ML, based on the vast amounts of data collected from its global user base.
With the help of ML, Netflix can also predict future usage demands and strategize server allocation ahead of time. Pre-positioning servers closer to subscribers, Netflix maintains the highest streaming quality even during peak hours.
2)Spotify
Spotify's AI uses implicit feedback like the number of times a user has played a song to provide appropriate recommendations for other users who have been doing the same. The AI scans a musical track's metadata and uses NLP to find what other tracks people are talking about alongside that track to form tailor-made playlists.
Based on deep learning, machine learning, audio models, etc the app provides personalized and unique playlists under its 'Discover Weekly' feature. Using convolutional NN the app also helps identify upcoming artists that would fit a user's recommendations under 'Release Radar'.
3)Microsoft Seeing AI
This is an AI-powered image recognition application designed with a special focus on helping the visually impaired. It uses description technology to convert images captured around a user into detailed transcriptions.
The app recognizes obstacles around the user and narrates the positioning and distance of the user from the visual elements. The app also has matches approaching individual's faces with photos saved in the user's contacts to alert the user in advance.
A similar approach was taken for an AI-enabled mobile app developed by Daffodil Software to aid the visually and hearing impaired. The app, developed for the RBI, helps the visually and hearing impaired to identify the denominations of currency notes.
4)Google Socratic
Socratic is an app acquired by Google under its series of Google for Education initiatives. It helps students find answers to questions through speech or by taking a picture of the question or equation.
The app matches the speech-to-text or image data with search results across the internet to find a relevant solution to the question. Using algorithms, the app also adds further explanations to the answers with the help of its underlying algorithms.
5)Acquisio
Acquisio uses advanced ML technology and workflow automation tools to help Pay Per Click (PPC) campaign managers in customer acquisition. Users can efficiently scale their PPC campaigns and manage more customer accounts without hiring a bigger workforce.
It streamlines accounts and report creation and also predicts the outcomes of digital marketing strategies and campaigns. It has various marketing tools for bid and budget management.
6)EnhanceFox
This app is a real-time photo quality enhancer that uses advanced AI generation technology. It uses AI technology for computer vision and fixes blurry images, sharpens and beautifies them.
The app's ML algorithms have been trained with millions of images. When it sees the data of a low-resolution image, it predicts the corresponding high-resolution alternative and adds extra information to increase the original's resolution.
7)Mubert
In simple terms, Mubert basically produces music using AI technology. It is a music player that plays infinite playlists and lays emphasis on providing the user with an undeterred focus on the task at hand.
The app has a minimalistic design and plays infinite playlists composed in real-time by the AI that center around themes like study, work, and creativity.
8)Youper
Youper is an emotional health chatbot app that uses NLP technology and AI-based algorithms to simulate human-to-human conversations. It presents a menu of descriptive words to help users ascertain how they are feeling and assign these feelings degrees from slight to extreme. Users can zero in on what is truly bothering them or triggering anxiety and early signs of depression.
ALSO READ: The History and Evolution of Chatbots
9)Magisto
This is a video editing app that has a suite of AI-enabled features. It conducts audio-visual analyses of videos to compile shareable stories using underlying ML algorithms.
10)ELSA Speak
ELSA Speak uses AI and NLP to help language learners improve their English pronunciation and grammar. Using speech recognition, the app analyses the rhythm, intonation, and pitch of your speech and identifies errors that need fixing. In a nutshell, it bridges the communication gap between a foreign speaker and a native speaker of the language.
AI Changing the World One App at a Time
Several award-winning apps on the Google Play Store and iOS App Store owe their success to AI-powered technology and layers of ML algorithms. These technologies enable enhanced search, real-time conversion and analytics, image recognition, and more to give these apps an edge.
Integrating AI-backed enhancements to your app unlocks numerous possibilities for innovation. You can check out this blog section to understand the exceptional use cases of AI and if an idea strikes your mind, navigate to our AI Software Development services to know how you can execute it.