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Artificial Intelligence has given numerous advantages in this computer age. In this article, we will see what is artificial intelligence and what drives it. How it will impact the digital revolution ahead.

What is Artificial Intelligence?

Artificial Intelligence is the ability given to any machine to perform cognitive functions such as perceiving, learning, solving problems as well as reasoning. These machines are said to have AI built into them.

Artificial Intelligence exists when a machine is said to have the cognitive ability. The proposed benchmark for a machine to have AI is the human level of reasoning, speech as well as vision.

Introduction to Artificial Intelligence Levels:

There are three basic levels for Artificial Intelligence:

1) Narrow AI: Artificial intelligence is said to be narrow when the machine is able to perform a particular task better than a human being. The current research of Artificial Intelligence is here now.

2) General AI: Artificial Intelligence reaches the general state when the machine is able to perform an intellectual task with the same accuracy level as a human would.

3) Strong AI: Artificial Intelligence is said to be strong when a machine can beat humans in many tasks.

Nowadays Artificial Intelligence is being used in almost all industries and has been giving a technological advantage and edge to all the companies integrating AI at a scale. According to McKinsey, AI has the avid potential to create 600 billion dollars in value for the retail sector, bring 50% more incremental value in banking as compared with other analytical techniques. In the transportation and logistics sector, the potential revenue jump is 89% more.

Subsequently, if an organization uses AI for its marketing activities, it can very well automate many of the mundane as well as repetitive tasks, allowing sales representatives to focus on tasks like relationship building, lead nurturing, and many more such tasks. An organization named Gong provides a conversation intelligence service. Each time a sales representative makes a phone call the machine records transcribes and analyses the chat. The VP can use AI analytics and recommendations in order to formulate a winning strategy.

In short, AI provides a cutting edge technology to deal with complex data which can be impossible to handle by a human being. AI automates redundant jobs and allows a worker to focus on the high level, value-added tasks. When an organization implements AI on a scale, it leads to cost reduction and revenue increase.

History related to Artificial Intelligence

Artificial Intelligence has become a buzzword today, this term is not new. In 1956, experts from different backgrounds decided to organize a summer research project on AI. Four bright minds lead the project John McCarthy (Dartmouth College), Marvin Minsky (Harvard University), Nathaniel Rochester (IBM), and Claude Shannon (Bell Telephone Laboratories).

The primary purpose for this research project was to tackle and use “every aspect of learning or any other feature of intelligence that can in principle be so precisely described that a computer machine can be made to simulate it.”

The proposals of the summit included

1. Automated computers
2. How a computer can be programmed to use a language?
3. Neuron nets
4. Self-Improvement

It led to the birth of the idea that intelligent computers can be created. A new era began, that was full of hope- Artificial Intelligence.

Know More: Artificial Intelligence on smartphones – why it’s important

Types of Artificial Intelligence

Artificial Intelligence can be divided into 3 sub fields.

• Artificial Intelligence
• Machine Learning
• Deep Learning

Machine Learning

Machine Learning is nothing but an art of study of algorithms that is able to learn from experiences and examples.

Machine Learning is based completely on the idea that there are some patterns that exist in the data that were identified and used for future predictions.

The difference shown in hard-coding rules is that the machine learns on its own to find such rules.

Deep Learning

Deep Learning is a subfield of Machine Learning. Deep learning does not mean that the machine learns from more in-depth knowledge. It means that the machine uses different layers to learn from the data. The depth of the model is represented by the number of layers in order to learn from the data. For example, Google LeNet model for image recognition counts 22 layers.

In deep learning, the learning phase is conducted by using a neural network. A neural network is nothing but an architecture where the layers are stacked on top of each other.

AI vs Machine Learning

Most of our smartphones, even daily use devices or even the Internet uses Artificial Intelligence.

Very often, AI and Machine Learning can be used interchangeably by big organizations that want to announce their latest innovations. However, Machine Learning and AI are different in many deemed ways

AI is the science of training and developing machines to perform human tasks. The term was invented in the 1950s when the scientists began exploring how computers could solve problems on their own.

Artificial Intelligence is nothing but a computer that is given human-like properties. Take our brain, for example, it works tirelessly and seamlessly to calculate the world around us. Artificial Intelligence is the concept that a computer can mimic human behaviour. It is said that AI is the largest science that is able to mimic human aptitudes.

Machine Learning is a different subset of AI that trains a machine on how to learn. A machine learning model looks for patterns in the data and tries to conclude. In a nutshell, the machine does not need to be explicitly programmed by the people. The computer is given some examples and the computer learns what to do from those samples.

Read More: AI and Android App Development

AI has broad applications-

•     Artificial intelligence is used to reduce or avoid the repetitive task. For instance, AI can repeat a task continuously, and tirelessly. In fact, AI never rests, and it is indifferent to the task to carry out
•     Artificial intelligence improves an existing product. Before the age of machine learning, core products were building upon hard-code rule. Firms have introduced artificial intelligence in order to enhance the functionality of the product instead of starting from scratch to design new products. You can think of a Facebook image. A few years ago, you needed to tag your friends manually in the photo. Nowadays, with the help of AI, Facebook gives you a friend’s recommendation.

Conclusion:

AI will continue to dominate the world helping humans to solve the day to day challenging tasks with ease. AI is developed and is continuing to be developed for the sole purpose of augmenting our lives and amplifying our productivity and tasks multiple folds.

We hope you understood the sole purpose of Artificial intelligence and why it is being used to help human lives achieve their goals. If you want to hire Artificial intelligence Developer you can contact Nimap Infotech by sending an email at enquiry@nimapinfotech.com

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A developer is an individual that builds and creates software applications. He or she writes, debugs, and executes the source code of a software application. A developer is also the key individual behind all software applications. Also, they are the creative minds behind the computer program. Although the developer’s primary job is writing code, a developer also may gather requirements for software, design or overall software architecture, software documentation and other related software development processes. Hence, the developer has a better future due to these qualities and these are as follows:

8 technologies developers should explore now

1) Internet of things security-

After ten million connected devices were taken over last year, even causal observes could see that unprotected Internet of things devices create distributing security problems. Demand is high for engineers with the Internet of things security skills, especially those who understand the powerlessness of the hardware and software used by net-connected devices. The attack vectors on the Internet of things are largely similar to those of any other distributed network, like computers or cellphones, so the same security knowledge is relevant and critical.

2) Artificial Intelligence-

Artificial Intelligence is the simulation of human intelligence processes by machines, especially computer systems artificial Intelligence can be categorized as weak and strong, that is designed and trained for a particular task. Because hardware, software and staffing costs for Artificial Intelligence be expensive.

You may want to know more: 4 Ways Hiring Android Developers Offshore Increase Productivity

3) Machine Learning

A Form of Artificial Intelligence, machine learning can take a massive amount of data to very quickly find patterns- like facial recognition- and solve the problem, without being explicitly programmed to do so. Also, machine learning focuses on the development of a computer program that can access data and use it to learn for themselves. The primary aim is to allow the computer to learn automatically without human intervention or assistance and adjust accordingly. Machine learning is more pervasive than we think: there are numerous real-world applications- self-driving cars, speech and image recognition, text classification, web search, smart robots, etc.

You may also want to know: 4 Ways Hiring Android Developers Offshore Increase Productivity

4) Data Science

Mining large amounts of structured and unstructured data to identify patterns can help an organization. Requirements can include experiments with machine learning and Artificial Intelligence to take large amounts of data and shape it in a form that can be used to make business decisions.” Skilled data scientists are in short supply, period ” says Speeding. ”Especially, I see areas where technology can be designed ‘ assist ‘ decisions, such as cognitive bots and guided analytics, to be high value-added opportunity areas.

5) Blockchain

Blockchain is a distributed ledger, which simply means that a ledger is spread across the network among all peers in the network, and each peer holds a copy of the complete ledger. Blockchain works with Blocks, whereas spreadsheets work with ” rows” and ” columns”. A blockchain is also a very inefficient database most famously implemented as one of several parts required for the Bitcoin jigsaw puzzle to the work. Also, blockchain doesn’t exist. For Example, A bitcoins contains information about the sender, receiver, number of bitcoins to be transferred.

6) Mesh app and service architecture

The demand for Apps that seamlessly stay connected as we move through our home, commute, and work are increasingly in demand. The purpose of the mesh app network or app is that it will be high availability – everything connected to everything. We have seen this being used for examples with the Tile tracker devices, which has created a community of tracking devices, which has created a community of tracking devices, and bitcoins being a distributed ledger.

7) Digital Twins

Prepare to Fail- Software models tied to physical and virtual sensors can help predict product or service failures so that the organization is able to plan. Internet of things and machine learning systems allow the organization to ensure that is an asset isn’t randomly failing, and if they do fail, then the organization can optimize.

Read More: Which language is better for creating websites, HTML or HTML5?(HTML VS HTML5)

8) Autonomous vehicles, Robots, and appliances

New opportunities are seen developing as Artificial Intelligence and machine learning to smarten up a home device, industrial equipment, cars, and drones. Research firm Gartner estimates that by 2020, the auto maker will send 61 million data connected cars off production lines.

Conclusion

I’II will be considering these trends and more than I look into the future of programming. If you are seeing any to Hire mobile app developer. Then do contact us or drop a message. We have the most experienced backend team ready to develop and deploy your website.

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What is NLP?

NLP stands for natural language processing. It is the field of computer science and artificial intelligence that deals with the communication between a human and a machine. NLP helps machines to interpret the human language and extract the intent, context, and meaning of the message and come up with proper response based on analysis that is similar to a human being. Over a period of training, it can be used to personalize and enhance the user experience.

What is chatbot?

It is a program built using artificial intelligence that responds to the user using text or sound. Chatbots are generally used in customer support, seat reservation, product ordering and making appointments. The famous example of chatbots is Alexa, Siri, Google assistant. Chatbots provide 24×7 assistance to the user. Today almost every company has a chatbot to engage with its users.

How NLP works?

The first step in NLP is tokenization. Tokenization mean splitting sentences into words. We can just split the words where space occurs. For the example purposes, consider the following sentence “Delhi is the capital of India”. After tokenization, we get a set of words {“Delhi”, “is”, “the”, “capital”, “of”, “India”}

The next step is identifying the parts of speech of each token. For example, “Delhi” and “India” are proper nouns. Thus, the knowledge gained from this step is that Delhi and India are proper nouns so the sentence must be talking about them.

Next step is to identify the stop words. Stop word is nothing but a list of words that frequently occurs in a sentence. So NLP filters out these stop words. In the given above example “is”, “the”, “of” is primarily the stop words. Stop words can be identified by just by checking a hard-coded list of the available stop words.

The following step includes dependency parsing where the NLP determines how all the words are related to each other. Next step is NER (Named Entity Recognition) which detects and label these nouns with the real-world concepts that they represent. For example “Delhi” and “India” represents the location.

These above steps are just a short summary of how NPL extracts information from messages and uses it for further analysis.

You may also want to know more: Artificial Intelligence

Why NLP is required in the chatbot?

The main reason why chatbots are developed is to provide human-like assistance to the user and help him to achieve his/her goal. NLP helps the chatbot to understand the user’s message and respond appropriately. For example, if you send a message “Hi”, it is the NLP that lets the chatbot understand, that the message is a standard greeting. Thus using AI functionality the chatbot will respond with a return greeting.

Many chatbots are of little or no value without NLP. Only with NLP the chatbots understand messages and respond appropriately. The system is powerful enough to make or break a chatbot’s success as well as make it popular. With NLP chatbots can connect to queries and identify entities as well as the sentiments to generate own intelligent responses. Without NLP a chatbot cannot meaningfully understand the context of the message. The message is just a text input for a chatbot. It’s the natural language processing that helps to extract the context and meaning of the message.

NLP empowers bots with personality. It enhances the functions of chatbots for all businesses. It elevates their scripted and sequential conversations of chatbots to that of humans. Assessing, analyzing and communicating skills of NLP based chatbots makes this possible.

Read More: Creating Chatbot with Deep Learning

NLP understands and enables the needs of the customer with better clarity as well as the accuracy. It gains importance in the business world in order to provide better user experience across applications like chatbots, speech recognition, social media listening & texting, voice-enabled applications and devices. NLP with its translational and other features are the need of the hour. NLP based chatbots improve customer experience. You can fulfil the Expectations of today’s millennial business people around the globe with NLP’s business intelligence.

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In simple words, Artificial Intelligence is to make machines do what the human brain does for us. Learning, reasoning & correction. Quite simple, no?

It is said that a person learns from his mistakes. Everything around us that we see, smell, and hear is kind of data that our brain takes as an input and then learns something out of it. The process keeps on going until eternity, There’s no end to a learning process. We learn, we make mistakes and then we improve our decision making.

That’s what most AI experts are trying to achieve. When someone says AI, Robots are the first thing that comes in our mind, But that is not what AI is all about. The concept is broader than we think. 

If you are someone like me who is interested in learning Artificial Intelligence, then I am going to talk here about how you can approach learning AI.

How can you approach to learn Artificial Intelligence?

During recent time AI and ML field have evolved rapidly. Experts are daily working on new technologies to make the process simpler for you. 

Today it is easy to find packages and libraries that you can work with while developing your own AI.

Understanding the basics

https://www.youtube.com/watch?v=cKxRvEZd3Mw&list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal 

To get into any field, it is necessary to understand what the field is about and how things are going to work. Above given link is to understand the basic concepts of machine learning and how things are actually going to work. Being a subfield of Artificial intelligence it is strongly recommended to get hands-on many of algorithms of machine learning.

Read More: Creating Chatbot with Deep Learning

Selecting a problem

As you go along with learning, You will find so many things like different algorithms, process and what not. As you explore more, things start getting more complicated. Hence it is necessary to have a predefined goal which will make it easier to select the options that are best for your problem. (It is also necessary to have prior knowledge about various different methods to solve the problem, at least introductory part.) Having a problem also engages you more into implementation and practicals rather than just reading. 

A common problem that most people face when starting is that they keep on collecting the information only while not understanding how the implementation is done for a real-world problem. 

Sometimes it is hard for us to come up with a problem to solve if you do not have prior experience with the domain. Sites like Kaggle ( https://www.kaggle.com/ ) provides a lot of problems and datasets to practice. 

Making your own solution.

Once you have a goal in your mind, you can start implementing various methods to achieve your goal. It is not necessary to come up with the perfect solution at the very first try. Hence start making a simple solution to the end while exploring various methods to approach a problem. 

However, a problem most people face in this process is that while selecting the method to solve the problem you spend much of your time with methods that you are already aware of and don’t move towards other methods. In such cases, cloud services like IBM Watson – Knowledge Discovery can help you a lot. By simply uploading your data into Watson, It can give insights about your data that you have not already thought about. 

(https://www.ibm.com/cloud/watson-discovery) 

Collecting data, cleaning data, training data and finding results from the data are the things that you can do while making your own solution.

Evaluating and Improving your solution.

Once you get to the solution, evaluate the results and check how accurate is your solution. As I mentioned earlier, It is not necessary to get the perfect solution at the very first try. Once you finish with the first phase of your solution you will start understanding difficulties that came in every step and flow of your entire project.

Try to Improve your solution.

Share your solution

In every project what you have done till the end can’t be always right. There can be so many things you might have done wrong. Hence you should always share what you have done with others and get their opinion about how you can improve our solution and what mistakes can be avoided. Kaggle is the best site to share your solution.

Extras –

As it is the most evolving technology in the market right now it is not just limited to computer technology but is also equally used in every sector like health, education, marketing, entertainment and so on. When it comes to AI there are many key terms used along with it such as Machine Learning, Cognitive Analytics, and Robotics.

Machine Learning carries the ability to learn from its experiences. ML an application of AI mostly deals with the thinking part of AI. Its analytics analyze data to provide an obvious output.

Here are some of the techniques that can be helpful for you for building and implementing Artificial Intelligence –

• (Artificial) Neural Networks
• Natural Language Processing
• Heuristics
• Vector machines
• These are the commonly used techniques in AI. 

All these techniques are boarder term which can’t be explained in one line.

If you are really interested in learning AI, Here are the following sources you can follow –

Siraj Raval : https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A 

Edureka : https://www.youtube.com/watch?v=JMUxmLyrhSk&t=16240s 

Following links are for beginners who want to get into AI. They can learn the basics from Edureka’s video. Siraj Raval is the best guy who can keep you updated with new information in AI and also keeps you motivated. Along with it, I strongly recommend you to follow machine learning and AI forums to connect with the community. 

Hope the above information about how to start with AI appears useful. And beginners find it easy indulging learning AI without facing any serious issues.