Artificial Intelligence (AI), despite being a technology that is more than 50 years old, still most people wonder what this field is all about. Even though this technology is surrounding us in many forms and we are using AI technology such as AI-based mobile apps in our day-to-day lives but still many people find it hard to believe. According to reports by this HubSpot survey, around 63% people don’t realize they are using AI technologies.
As Artificial Intelligence is having many associated technologies such as Machine Learning, Deep Learning, Big Data, and Natural Language Processing, etc. it’s totally understandable that people get confused.
If you are one among them who gets confused with all these terminologies? Then you are at the right place, as this article is all about discussing such technologies associated with AI and also to help you understand a few other concepts related to this wonderful technology. At this point, we recommend you read our blog wherein we have discussed some important aspects of this technology.
You must see: See this Infographic image to know in detail about various aspects of Artificial Intelligence technology.
Many organizations are finding ways to effectively implement Artificial Intelligence technologies into their business to provide a better service to their customers. This technology is likely begin to play an important role in a way that companies operate as the software continues to advance and move towards the forefront of many industries.
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AI plays a central role in Google’s strategy for future growth under CEO Sundar Pichai who explained in a 2016 conference call explained “In the long run, we’re evolving in computing from a ‘mobile-first’ to an ‘AI-first’ world.”
As we have discussed earlier, AI systems often incorporate technologies such as Artificial Intelligence, Machine Learning, and Deep Learning. All three technologies can be used together to create a sophisticated intelligence machine that will perform given human functions well.
People often get confused about how these three technologies are related to each other and how they can be implemented together in a system.
So, let us discuss each of these technologies in detail. But to put in one line on how these three technologies are related to each other, Artificial Intelligence is a technology to create AI systems that function like human beings; Machine learning is an approach to achieve Artificial Intelligence, and Deep Learning is one of the techniques for implementing Machine Learning.
Initially let us understand what Artificial Intelligence is:
The term Artificial Intelligence was introduced first time in 1956 by John McCarthy at the “Dartmouth Summer Research Project on Artificial Intelligence” conference. Following this development, several research and development works took place in this field by several noted people that helped Artificial Intelligence technology to emerge as one of the powerful technologies of the present era.
Artificial Intelligence is a branch of computer science that aims to create intelligent machines. It is a technology concerned with building smart machines capable of performing tasks that typically require human intelligence.
Artificial Intelligent machine can read the real-time data, understand the business scenario, and react accordingly. The goal of this technology is to create systems and develop software that understands, think, learn and behave like humans.
Artificial Intelligence technology is powered by technologies such as Machine Learning and Deep Learning and it uses these technologies to achieve its goals.
Machine Learning is a subset of Artificial Intelligence. It’s a technology that allows the system to learn directly from examples, data, and experience. It helps the machines to learn and to carry out tasks all by themselves.
Machine Learning can be defined as the technology that allows the computers to learn from patterns and behaviors and act accordingly without any human intervention or being explicitly programmed for it.
Machine Learning involves a process wherein instead of writing a program or instructions to the system to what to do, you need to provide a set of data, based upon which machines build their own logic and provide a better solution.
Now a days, many factors such as advanced technologies, vast amount of available data and computing power, have resulted in significant rise of the capabilities of Machine Learning technology. Now, Machine Learning is regarded as one of the most advanced technologies of this present IT industry.
Different Machine Learning types
As Machine Learning is a broad field, it can be classified into three types such as Supervised Learning, Unsupervised Learning and Reinforcement Learning. Now let us look into these types separately in brief.
1) Supervised Learning:
Supervised learning are used when the output is classified or labelled. These algorithms learn from past data that is inputted, called as training data, runs its analyses and uses this analysis to predict future events of any new data within the known classification.
There are two types of supervised learning exists. They are:
1) Classification task.
2) Regression task.
2) Unsupervised Learning:
In unsupervised learning, the data is not explicitly labelled into different classes, that is, there are no labels. The model is able to learn from the data by finding implicit patterns. These algorithms study and generate a function to describe completely hidden and unlabeled data. Hence, there is no correct output, but it studies the data to give out unknown structures in unlabeled data.
There are different types of unsupervised learning exists.They are:
3) Reinforcement Learning:
Reinforcement learning uses trial and error method to find out the output based on the highest efficiency of the function. The output is compared to find out errors and feedback to the system to improvise the performance. The model is provided with rewards that are basically a feedback and penalties system in its operations, while performing a task.
Deep Learning is a subset of Machine Learning. It is concerned with algorithms inspired by the structure and function of the brain called Artificial Neural Networks. Deep Learning algorithms run data through several “layers” of neural network algorithms, each of which passes a simplified representation of the data to the next layer.
You must read: Visit this link to know more about Deep Learning
Machine Learning algorithm works well on datasets which have up to a few hundred features or column. However, they can’t handle an unstructured dataset, which have a large number of features. Hence, Deep Learning becomes more important as it has the ability to process a large number of features when dealing with unstructured data.
Deep Learning can be very beneficial to the business. As Machine Learning techniques can’t handle a large sum of data, deep learning models continue to improve their performance as more data is received.
Image source: Guru99
You need to be very clear when you need to use Machine Learning and Deep Learning. With Machine Learning you need fewer data to train the algorithm than Deep Learning. Machine Learning provides a faster-trained model whereas the advanced Deep Learning architecture can take days to a week time to train. The advantage of using the Deep Learning technique is that they are highly accurate.
Top technologies associated with AI
Let us discuss some top technologies used in Artificial Intelligence.
1. Natural Language Generation
Natural Language Generation is a sub-discipline of Artificial Intelligence. It converts data into text, enabling computers to communicate ideas with perfect accuracy. Sophisticated NLG software can mine large quantities of data, identify patterns and share the information in a way that can be easily understood by humans.
Uses of Natural Language Generation:
- Natural Language Generation has the ability to convert the dataset into legible narratives understood by humans. With NLG, data can be assessed, analyzed, and communicated with precision, scale and accuracy.
2. Speech Recognition
Speech recognition is the ability of machines to recognize the words and phrases in spoken language and convert them into a machine-readable format. Every day, a large number of systems are created that can transcribe human language, reaching hundreds of thousands through voice-responsive interactive systems and mobile apps.
Some of the noted companies that offer speech recognition services are NICE, Nuance Communications, etc.
Uses of Speech recognition:
- Speech recognition technology carries out certain tasks such as searching for your reports or documents on your computer, creating tables or graphs using the data, recording meetings etc., and increasing the efficiency of its workplace.
- Speech recognition technology helps the telecommunication industry a lot. They can use this technology to serve their customer who wants to receive customer care services. This technology helps to know the callers demand by asking a few questions to them and redirecting them to the appropriate operator for assistance.
3. Robotic Process Automation
Robotic Process Automation (RPA) is a software with Artificial Intelligence and Machine Learning capabilities that can be used to handle high-volume, repeatable tasks that previously required humans to perform.
The purpose of the RPA process is to replace repetitive tasks performed by humans, with a virtual workforce. Some examples of Robotic Process Automation are Advanced Systems Concepts, Automation Anywhere, Blue Prism, etc.
Uses of Robotic Process Automation:
- Robotic Process Automation technology can help the organizations by providing answers to employees or customers in natural language rather in software code.
- Robotic Process Automation helps to increase the employee productivity.
4. Natural Language Processing (NLP)
Natural Language Processing (NLP) is a branch of Artificial Intelligence which deals with the interaction between computers and human beings using natural language. It is the ability of a computer program to understand human language as it is spoken.
This technology helps us to understand the text and receive valuable insights. Now, various Natural Language Processing tools are available. Some of the tools used for the NLP are NLTK, Stanford Core NLP, AllenNLP, GenSim, Intel NLP, etc.
You must read: Read NATURAL LANGUAGE PROCESSING TOOLS AND LIBRARIES to know more about different Natural Language Processing tools
Many software such as Amazon’s Alexa, Microsoft’s Cortana and Google Assistant etc. use Natural Language Processing to understand and respond to users’ questions.
Uses of Natural Language Processing:
- Some chatbots use Natural Language Processing technology to understand human queries and respond. Many organizations are using these chatbots to serve their customer efficiently
5. Image recognition:
As per the reports given by MarketsandMarkets, the image recognition market is estimated to grow from USD 15.9 billion in 2016 to USD 38.9 billion in 2021, at a CAGR of 19.5% during the forecast period. Image recognition is a technology that identifies places, logos, people objects, buildings and several other variables in images. It decomposes images into regions with different characteristics and extracts useful targets.
Image recognition is classifying data into one category out of many. The major steps involved in this technology are gather and organize data, build a predictive model and use it to recognize images. Companies from different sectors such as retail, automotive, healthcare, etc. are using image recognition technology.
Uses of Image recognition:
- Image recognition can help the manufacturing sector a lot. It can monitor the quality of the final products to reduce defects.
- Image recognition allows the users to search for similar images or products using a reference image they take with their camera or download from the Internet.
6. Virtual agents:
Virtual agents are often represented as computer-generated AI characters which can lead an intelligent conversation with users. Virtual agent software has improved a lot over the years with advances in Artificial Intelligence technology.
Virtual agents can understand customer intent and can personalized answer to customer questions in a humanlike manner. Some of the companies that provide Virtual agents are Amazon, Artificial solutions, Assist AI, Google, IBM, etc.
Uses of Virtual Agents:
- Virtual Agents prevent information overload, manage information, as well as generate it.
These are some of the top technologies used in Artificial Intelligence. However, you can find here some more technologies used in this technology.
Artificial Neural Networks
A neural network is a either a software or a hardware that works similar to the tasks performed by neurons of human brain. Neural networks include different technologies such as Deep Learning, and Machine Learning as a part of Artificial Intelligence.
Artificial Neural Network (ANN) works similar to the human brain. The idea behind ANNs is based on the belief that working of the human brain by making the right connections, can be duplicated using silicon and wires as living neurons and dendrites.
Different types of Artificial Neural Network
Some of the types of Artificial Neural Network are as follows:
1) Feed-forward neural networks
2) Recurrent neural networks
3) Conventional neural networks
4) Deconvolutional neural networks
5) Modular neural networks
You must read: Read this PDF file to know more about Artificial Neural Networks.
Advantages of Artificial Neural Network
- Storing information on the entire network: Here, information is stored on the entire network, not on a database.
- Ability to work in complete knowledge: With Artificial Neural Network, the data may produce output even with incomplete information.
- Parallel Processing Capability: Artificial Neural Networks have the capabilities to perform more than one job at a time.
- Ability to make Machine Learning: Artificial Neural Network can learn events and make decisions by commenting on similar events.
Top tools used in Artificial Intelligence
As more organizations are adopting AI technologies into their business, the number of tools for professionals who work in this field has also increased. These tools help professionals to build innovative products. Now let us look into some of the top tools used in Artificial Intelligence:
TensorFlow is an end-to-end open-source platform for Machine Learning. It is developed by Google and is used for numerical computation intelligence. It does computation using data flow diagrams.
TensorFlow is of the best library because it is to be accessible for everyone. TensorFlow is available in Python, C++, Java and many more programming languages and is now being used by many major companies such as Dropbox, eBay, Intel, Twitter, etc. Google uses this tool in almost all Google daily products such as Gmail, Photo and Google search engine.
Keras is an open-source software library written in Python and has been designed to simplify the creation of Deep Learning models. The Keras has the capability to run on top of other libraries such as TensorFlow, Theano, etc.
Keras is suitable if you need a Deep Library that allows for easy and fast prototyping, supports both convolutional and recurrent networks, and runs seamlessly on both CPU and GPU.
3. Scikit Learn
Scikit Learn is one of the well-known Machine Learning libraries. Scikit Learn is an open- source library written in Python, and it features several Machine Leaning models including classification, regression, clustering, and dimensionality reduction.
Scikit Learn is designed on three open-source projects such as Matplotlib, NumPy, and SciPy, and it focuses on data mining and data analysis.
4. Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit is an open-source library that can empower you to take your Machine Learning project to the next level.
Microsoft Cognitive Toolkit can be included in your Python, C# or C++ programs. It is also used as a standalone Machine Learning tool. Microsoft is extensively using this tool for some of its products like Skype, Bing. etc.
Caffe (Convolutional Architecture for Fast Feature Embedding) is a Deep Learning tool that is developed by Berkeley AI Research and by community contributors. Caffe is an open-source framework written in C++. It comes with a Python interface and focuses on expressiveness, speed, and modularity.
Torch is an open-source Machine Learning library. It is a scientific computing framework with wide support of Machine Learning algorithms that puts GPUs first.
Torch aims to have maximum flexibility and speed in building scientific algorithms while making the process extremely simple. This tool is used by many leading companies such as Facebook, Google, Twitter, Nvidia and many more.
Theano is an open-source Deep Learning library that offers fast computation and can be run on both CPU and GPU. This tool allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
MxNet is a Deep Learning library that is accessible with multiple programming languages including C++, Python and R. It can be configured to work on both CPU and GPU.
MxNet has been built to work in harmony with dynamic cloud infrastructure. The main user of this tool is Amazon.
These are some of the top tools used by many organizations while working in Artificial Intelligence projects. You can visit here to know some more tools of this technology.
AI behind IBM Watson
Watson is a supercomputer system developed by IBM. It combines Artificial Intelligence and sophisticated analytical software for optimal performance as a question answering machine. With a processing rate of 80 teraflops, Watson performs deep-level input analysis to present accurate answers to questions generated by humans. IBM Research undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV quiz show, Jeopardy.
The overall goal of IBM research is to advance computer science by exploring new ways for computer technology to affect science, business, and society. Watson uses more than 100 different computing techniques to analyze input and match it to the most accurate output.
IBM Watson is a smart solution for every business to adopt. It is a super computer enriched with Deep Learning AI and Machine Learning. Watson serves as a Deep Learning AI tool for various sectors including advertising, education, financial services, health and so on.
Artificial Intelligence trends
Artificial Intelligence is helping the world in many ways. Every now and then it is coming up with features that help the organizations to serve their customers in a better way. Let us look into some of the upcoming AI trends:
1) Target new audience for acquisition
AI self-learning algorithms will help the organizations to identify the new audience across social media and convert them into their customers.
2) Enable voice-assisted search
According to Gartner, around 30% of web browsing and searches will be done without a screen by 2020. Some devices like Amazon Echo can recognize spoken language and syntax, derive meaning, and not only deliver, but personalize, results.
3) Improve sales process
By 2020, 30% of global companies will be using AI technology for at least one sales process.
4) Real time interaction with customers
With the help of Artificial Intelligence, organizations will manage real-time customer interactions across all channels.
Definitely Artificial Intelligence is one of the emerging technologies of the present IT industry. Over the last few years, we have seen an exponential growth in the number of platforms, applications, and tools based on AI technology. The improvements in this technologies have helped many organizations to serve their customers in a better way than earlier.
We hope this guide has been helpful to our readers to know some valuable information about Artificial Intelligence. You can also look into these online courses on Artificial Intelligence that can be of great help to you in learning Artificial Intelligence in depth.
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