One of the most captivating branches of Computer Science – the Artificial Intelligence (AI) – has been thriving on technological fronts. AI is currently used in the programming of computer games, understanding of natural human languages (Apple’s Siri and Microsoft Cortana), neural networks and robotics (eg. Sophia – the most advanced human-like robot).
Companies are investing in AI and the percentage is increasing year on year – 38% in 2016 to 62% in 2018, with an expected increase in market growth from $8 billion in 2016 to $47 billion in 2020 as per Forbes recent publication.
Here the hottest technologies that ride on AI algorithms –
Data needs to be constantly evaluated and interpreted in the form of text. The data points are turned into language/simple sentences and further communicated. The new generation AI algorithm can do this at relatively lower costs and scale with enhanced accuracy. It allows the employees to concentrate on other activities as computers are able to conduct data analysis repeatedly while report generation is automated. It is chiefly used in advertising, weather forecasts, recapitulate business intelligence papers etc. It is already being used by Cambridge Semantics, SAS, Lucidworks, Narrative Science etc.
Often referred to as human-computer interaction, NLP refers to the computer’s understanding of the language spoken by a human. The AI algorithm used here facilitates in understanding sentences, answering questions posed, analyzing text and sentiments, and aids in machine translation often using the English language. As per www.gartner.com, “NLP is focused on deriving analytic insights from textual data, NLG is used to synthesize textual content by combining analytic output with contextualized narratives. NLP focuses on the language communicated and tries deciphering the same.” It is used for security purposes and data mining too. Companies using NLP are Mindbreeze, Coveo etc.
The ability of an AI program of a computer to receive, identify, interpret and translate spoken words (audio) into a machine-readable format (text) for carrying out further commands is known as speech recognition.
This technology is currently used in mobile applications and reciprocal voice
response systems or powerful speech recognition programs such as Google Cloud Speech API which apply neural network models for recognizing over 80 languages and variations and converting audio to text. Other organizations are NICE, OpenText, Nuance Communications etc.
These agents are typically animated human like characters which serve as a persona for online customer services. People feel free while chatting with these graphical chat bots which are exhibited on websites. The conversation is intelligent, queries are answered quickly and accurately. These virtual agents aid in tracing information, fulfilling orders and bookings. Companies using these are Google, Microsoft, Amazon, Apple etc.
Machine Learning Platform is available as a service chiefly for finding patterns, predictive analysis, classification and understanding data. It also attends to the potential of neural networks,
translation, face and speech recognition and NLPs (APIs are provided). Organizations such as Google, Amazon and IBM offer free accounts that enables developers to use this technology and create models without having to learn the actual ML algorithms, further deploying these models into applications.
Used mostly in the technology called deep learning, Google and Facebook are leading the path in the development of this hardware. Graphics processing units (GPUs) are chips specially designed to run AI related computer jobs for image recognition, translations, big data etc. However, deep learning uses artificial neural networks, create simulated neurons, and are being researched with the intention of tackling real conversations. Organizations: IBM, Nvidia etc.
According to Jack Shaw, “Systematic approaches can automate and improve decisions across the enterprise. This includes automating many decisions. Many of these decisions use the AI technique rather than just ‘if then else’ programs.” These include expert systems, associative
memory, fuzzy logic, neural networks, constraint programming, Bayesian Belief Networks, case-based reasoning and intelligence agents etc. This is to replicate/exceed the potential that people possess in making decisions or even go on to act on the person’s behalf accurately, unswervingly and steadily (www.e-com.com). Eg. Informatica, Pegasystems etc.
A movement in machine learning, it is used in optical character recognition, NLP, classification of entities that can be perceived and digitized by using a large quantity of data set/inputs. It is a set of AI algorithms that use artificial neural networks while learning in multi-levels corresponding to multi-layer abstraction. Used by Deep Instinct, Sentient Technologies, MathWorks etc.
This technology is used for recognizing people for security applications using statistics/computerized methods; for eg. in analyzing and verifying people or the person’s identity
as per physical or behavioral description and is not limited to speech, body language etc. It is not only used in passports, but also in laptop security, building access, and encompasses business and international security too. Vendors include FaceFirst, Tahzoo etc.
This emerging process automation technology refers to using computer software or ‘robot’ which controls, automates, captures, and interprets processes. These have set rules in the existing software applications that process transactions, control and operate data, trigger new actions, prompt and set off responses and connect with other digital systems. Industries such as insurance, finance, supply chain management, CRM, HRM etc. are able to expedite their back and middle office tasks/execute tasks with the help of these robots as these systems require to them to interact in a smooth manner by self-learning and even self-correcting, reducing costs too. Vendors include Blue Prism, UiPath etc.
According to a Forrester report, “There will be a 300% increase in the investment in Artificial Intelligence. This will drive faster business decisions in marketing, e-commerce, product management and other areas of the businesses by helping close the gap from insights to action.”
With the combination of Artifical Intelligence, BigData and IoT technologies, accessing, exploring and deciphering voluminous data will be possible leading to new business knowledge and augmentation of customer understanding and exposure.