Artificial intelligence is getting more popular among the developers, industry experts, and analytics and it’s becoming one of the greatest choices among the venture capitalists too. What made it so interesting and of course the trending as well? Obviously, the disruptive power of AI that has made all this possible and industrialists are keen to discover the most of it. A recent news just broke the Internet in AI segment that the MIT college is going to invest around 1 billion in Artificial Intelligence. We will discuss the hot topics that one can study for his research or technology point of view. The below-mentioned areas are considered the best in studying AI science & technology.
A few advantages of mastering into NLG can be thought as speed & scaling the process of content writing as with NLG, you can create & generate thousands of unique stories in a fraction of the time as compared to writing them manually. For example, the process of writing tons of product descriptions is time taking and costly too so what should be done to save the cost and time? With natural language generation, the roster specifics for each unique product in a catalog can be restyled into a unique, keyword-rich writing. So many marketing tools have been designed to do so and it’s the fundamentals when someone says automated marketing process. Also, NLG provides personalization in scaling the data & hence creating improved customer communication and experience with your company.
Speech Recognition: You are well familiar with Google apps & using the microphone while searching the records or queries. It’s all about the speech recognition through inbuilt libraries & pattern matching via creating strings to generate the desired output for a search. The human voice is first transcribed and then transformed into a format useful for running the computer applications. As per wikipedia, “Speech recognition is the inter-disciplinary sub-field of computational linguistics that develops methodologies and technologies that enables the recognition and translation of spoken” In AI, it uses either speech recognition via acoustic model i.e. taking audio recordings of speech, and their text transcripts, & using software program to create statistical representations of the sounds that frame each word. Then It is used by an AI speech recognition engine to recognize speech etc while Language Model uses many natural language processing apps and the speech recognition engine tries to understand the properties of a language, & to predict the next word in a speech sequence that particular language uses grammatically.
Natural Language Generation: Generating text from the machine input or computer data is in demand for all types of automated responses. Presently they are the most in demand in customer support services, automated report creation, and in summarizing the business intelligence through insights.
Virtual Agents: They are better known as the chatbots and are the most in-demand AI software programs for the businesses as they are making the business more reliable and easy as they handle the customers through automation. They offer the best solution to a problem a customer is dealing with. The mode of their operation is either an FAQ chat/textual or a voice-based response for a choice that customer has selected. To understand how a chatbot can assist a business, please go through the diagram as mentioned:
Machine Learning Platforms: It’s all about providing algorithmic approach to have work from a machine such as a robot is cleaning a room, seeding & harvesting a crop, or making a pizza etc, APIs as an addon to operate in an advanced and scaled environment, development and training toolkits with embedded software etc, data, as well as computing power to design, train, and deploy prototypes into applications, processes,via data science, cognitive & neural networks. It’s currently being used in a wide range of enterprise applications, mostly `involving prediction or classification & also available for people from the big players like Google, Amazon, Apple & more!.
AI chip or Artificial Intelligence optimized Hardware: Do you know? Artificial intelligence is coming to your smartphone these days. Yes! The iPhone X has a Neural Engine as part of its A11 Bionic chip; the Huawei Kiri 970 chip has what’s called a Neural Processing Unit or NPU on it, and the Pixel 2 has a secret AI-powered imaging chip that just got activated 🙂 Let’s talk about “Van Neumann’s bottleneck” issue that causing the computer to heat-up and running, getting slower altogether. How you will deal with this type of universal problem? Obviously, you will suggest improving the hardware resources but to what extent? Nowadays, the Graphics processing units (GPU) and components are specially designed and structured to optimally run AI-oriented computational work. The best optimized, ; a promising solution for the VNB is to keep the processing unit & data at the same place and nothing had to be a move that is swapping or transferring of data around and hence the amount of heat production gets reduced thus consumption of minimum energy. After all, this is how our brains work i.e. we don’t have different parts for processing and data storage as computers do; everything happens at our neurons.
Robotic Process Automation: We are also working on such a project that uses a combination of technologies such as AI, Blockchain & IoT. The idea of robotic process automation is completely based on machine learning, decision making, storage, and computation. It uses scripts and other methods to automate human action to support efficient business processes. Currently, it’s being used where things are too expensive or inefficient for humans to execute the tasks or the processes.
Text Analytics and NLP: Originally, the Natural language processing or NLP is a subset of computer science, and artificial intelligence is connected with the NLP in interactions between computers or machines and human languages. The goal is to make the human speech recognized by the computer in a way that it can respond back with the exact outcome. In AI, NLP plays a great role as most of the time it helps the machine to take the decision and respond back so that further actions can be taken by the user to achieve the goals. The Natural language processing (NLP) uses and supports text semantics by facilitating the understanding of sentence structure and meaning, feeling, expressions, and through NLG & machine learning methods.