How the Internet of Things relates to Artificial Intelligence.

           

         IOT stands for Internet of Things, an IOT is nothing but it is a system of interrelated computing devices, digital machines, and objects, humans or animals that all provided with particular identifiers and it is able to transfer data over the web without demanding human-to-human or human-to-computer communication

It indicates to the ever-growing structure of physical phenomenon that features an IP location for the web connectivity.

If you are newly entering into IoT and if you are planning for a place to start learning or even if you are professional in IoT products and services, this IoT blog agenda will help you start and develop your projects. This IoT blog will help you to superior understand evolving IoT technology and find up-to-date info with the Trending IoT news. Learning IoT is a long Progress that involves old and new knowledge from different technology areas.

What is IOT Platform?

How the IOT platform is useful for my Business and why it needed?

Actually, IOT platform is a collection of components that enables deployment of apps that monitor,  manage and control all the connected devices to the server, The suite also allows the connected device to transmit and collect data from one another.

An IoT platform is a multiplied technology that empowers straightforward provisioning, management, and automation of linked devices within the IOT universe.

Advanced IOT Platforms

We have few fundamentals that differentiate IoT platforms among each other, such as scalability, customizability,  combination with 3rd party software, deployment options, and the input conservancy level.

Scalable – The advanced IoT platforms assure elastic scalability across any number of endpoints that the client may need.

Customizable – This decides the factor of the speed of delivery. It nearly relates to  the flexibility of integration APIs, API is more enough to fly small scale undemanding IoT solutions

Secure –  These are the basics of how to avoid potentially negotiable breaches in your IoT solution.such as data security involves encryption, comprehensive identity management, and flexible deployment. End-to-end data flow encryption, including data at rest, device authentication, user access rights management, and private cloud infrastructure for sensitive data

Why IOT is important?

IOT is so important nowadays because it finds solutions for every problem. The further Industrial Revolution is going to change our lives in ways never thought before, Fast changes in IOT technology makes it a demanding task for the most Professional experts to anticipate the future of standardization in the field. For humanity, which is moderately clutter by nature, the IoT is a phenomenal advancement.

Strategic Trends For IOT Platforms

The future generation of business ecosystems will be increasing more digital, intelligent, and connected.

Digital twins: Digital twins contribute a comprehensive digital representation of real-world devices and systems, This provides thus improving their state monitoring and enabling faster output to internal and events.

The operations of digital twins very extensively asset from inventory and predictive maintenance to event simulation and usage analytics. In the future, they were expected to grow into a keystone of every efficient IoT ecosystem.

What is Artificial Intelligence in IOT?

Artificial Intelligence For Cloud-based IoT, The Internet of Things is a language that has been introduced in the latest years to determine objects that are capable to connect and transmit data via the Internet. IoT ecosystems generally require developers to have a greater degree of control all over the system, its source code, integration interfaces, deployment options, data schemas, connectivity and security mechanisms, etc.

Already we can talk to virtual assistants like Siri or Alexa to search for a movie or order new stuff with delivery at door. Why can’t we do the same thing in other things?

 The best example of AI and IoT is successfully working together is self-driving cars by Tesla Motors. Cars act as “things” and use the capability of Artificial Intelligence to predict the behavior of cars and pedestrians in different circumstances.

How is Artificial Intelligence Helping IOT Grow

There is a lot of interesting thing around artificial intelligence Experts told that AI is expected to change our lives in extraordinary ways.

There are numbers of thing written about artificial intelligence. AI is expected to execute a number of intelligent function like speech recognition, decision-making, language understanding, etc.

5 Key Wireless Technologies for IoT :

1. WiFi
2. Bluetooth
3. Z-Wave
4. Zigbee
5. LoRaWAN

Artificial Intelligence for Cloud-based Internet of Things (IoT)

The cloud-based IoT is used to relate a wide range of things such as vehicles, mobile devices, sensors, industrial types of equipment and manufacturing machines to promote different smart systems it consists of smart city and smart home, smart grid, smart industry, smart vehicles, smart health, and smart environmental monitoring.

In the IoT, cloud computing environment has built the job of handling the few amounts of data produced by coupling devices easily and provides the IoT devices with resources on-demand.

WHY ARE AI AND IOT  PARTNERS FOR GROWTH

The recent survey of top IT executives suggested not only are IoT and AI the most popular technologies currently in use. The top list of future investment for businesses searching for increased capability and competitive advantage.

But why are IoT and AI so far in front of other popular technologies such as Edge Computing or Blockchain?

The logic is very simple

>>Read More>>

How should you kick start your career in Machine Learning?

What is machine learning?

          Machine learning is an application of artificial intelligence (AI) that renders systems the ability to automatically learn and gain from experience without being explicitly programmed. Machine learning directs toward the advancement of computer programs that can obtain information and use it to learn for themselves.

         The value of machine learning can be realized when we recognize how clearly machine learning techniques can be applied to solve problems that appear remarkably complicated, for instance, face recognition, you would understand that ML algorithms can tackle several apparently complex problems as long as there is adequate data.

Let's go deeper into how machine learning acts

Machine learning (ML) is broadly categorized into two divisions - supervised and unsupervised.

Supervised algorithms comprise a data scientist/data analyst who has an intellectual machine learning experience and can give precise data. Data scientists/Data Analyst experts are quite proficient to estimate the data to develop predictions.

Unsupervised algorithms are further known neural networks, which links millions of instances regarding training data and automatically recognizing similarities within numerous variables.

Here are a few steps to learn Machine Learning:

1. Programming Skills- There exist varied languages which render machine learning capabilities. Also, there exists development activity proceeding at an accelerated pace across various languages. Currently “R” and “Python” are the most generally used languages also there is sufficient support/community available for both.

2. Learn fundamental Descriptive and Inferential Statistics- It is good to have an understanding of the descriptive and inferential statistics before you begin serious machine learning development.

• Descriptive statistics supply information that specifies the data in some manner.
• Inferential statistics uses data from a sample and performs inferences regarding the considerable population from which the sample was extracted. Because the intent of inferential statistics is to bring resolutions from a sample and conclude them to a population, we demand to have a belief that our sample perfectly exhibits the population.

3. Data Exploration / Cleaning / Preparation- What discriminates a good machine learning expert from a normal one is its quality of feature engineering and data cleaning that happens on the primary data. The increased quality time you contribute here, the better it is. This process likewise catches the amount of your time and therefore it assists to establish a structure encompassing it.

4. Introduction to Machine Learning- There are several sources accessible, to begin with, Machine learning techniques. I would recommend you to choose one of the following two steps depending upon your way of learning:

• The first choice has to be learning through books. There exist multiple editions accessible which remain outstanding, to begin with. These are few of the proposals which frame an important compilation of introductory texts, incorporating statistical learning, the theoretical underpinnings of machine learning.
• Nowadays there are various courses available moreover these are some reliable means to kick start your machine learning adventure. Both students and professionals will hold an advantage over all other aspirants if they leverage this degree or certification.

5. Advanced Machine Learning- This step will stay mostly masked if you choose the certification programs but if you are learning from books then these are some fresh topics you will have to study thoroughly. These topics include:

• Deep learning, a subset of machine learning, utilizes a hierarchical level of simulated neural networks to drive out the process of machine learning. These artificial neural networks are created like the human brain, besides neuron nodes joined collectively like a web. While conventional programs build analysis with data in a linear process, the hierarchical role of deep learning operations allows machines to process data among a nonlinear approach. A classical approach to identifying fraud or currency laundering may rely on the quantity of transaction that happens, while a deep learning nonlinear technique would combine time, geographic position, IP address, sort of retailer and also other features that are likely to lead to fraudulent activity.
• Ensemble Modelling is a robust method to increase the performance of your model. It normally pays off to implement ensemble learning over and beyond various models, you might be developing. Studying this is where a master can stay differentiated from a normal professional.
• Machine Learning including Big Data, Since you know that the volume of data is rising on an exponential pace but raw data is not beneficial till you start acquiring insights from it. Machine learning is nothing but learning from data, produce insight or recognizing a pattern in the accessible data set. There are various applications of machine learning.

6. Gain Experience. Work On Real Projects: Once you’ve acquired a stable hold covering all the technical aspects of Machine Learning, it’s time to get forward to the field. Exhibit yourself to the industry and attempt to find genuine data science projects on the Internet algorithms like “fraud detection”, “spam detection”,  “recommendation system”, “web document classification”, and many more.

      

     The field of Machine learning is evolving rapidly nowadays with the application of intelligent algorithms being implemented from apps to emails to as far as marketing campaigns. What this implies is that machine learning or Artificial Intelligence is the modern in-demand career option you can prefer.

However being a new field relatively, you may have several doubts and confusion as of how you can actually make yourself to choose Machine learning as a profession. Let’s consider over some things you need to master to get your career in machine learning startup.

1. Understand the field first: It is an explicit but significant fact. Understanding the theory of machine learning and fundamental math behind it simultaneously with some alternative technology while also having hands-on expertise with the technology is the solution to dive into this field at first.
2. Covert problems in Mathematics: Possessing a perceptive mind is crucial in machine learning. You require to remain prepared to blend technology, analysis, and math collectively in this field. Your focus on technology must be strong and you must maintain curiosity with the openness toward business obstacles. The ability to proclaim a business problem into a mathematical one will take you deep into the field exclusively.
3. Gain knowledge of the industry first: Machine learning, like every other industry, holds its individual freakish requirements and intentions. Hence, the more

>>Read More>>

How people wade through one of the top trending technology (artificial intelligence)

Nowadays people will be showing more interest in learning new technologies. There are a number of top trending technologies in that Artificial intelligence is one of the top trending technologies. Who is wade through Artificial intelligence it is good for their career?

Artificial intelligence is one of the most important technologies in this world. Today the field of artificial intelligence is more alive than ever and some believe that we are on the threshold of finding that could shift human society permanent for better or worse.

What is Artificial Intelligence?

Artificial intelligence is an important technology and it is one of the branches of computer science that can be the creation of intelligent machines that can work like humans. It has become an essential part of the technology industry. It can be performed on specific tasks by processing large amounts of data.

What are artificial intelligence platforms?

It has the use of machines to perform the tasks that are performed by human beings. This platform of AI is performed like human minds such as learning, solving problems, reasoning, social intelligence. In this AI is classifies either as narrow AI/ weak AI which is generally meant for particular tasks, the strong AI is also known as artificial general intelligence it can find the solutions for different tasks.

There are different kinds of AI platforms.

1. Machine learning.
2. Automation.
3. Natural language processing and natural language understanding.
4. Cloud infrastructure.

1.Machine learning: It is one of the roots of artificial intelligence. When machines take care of your problems? first, we require good and reliable data to work machines well. All you need is going to build what you want. It uses the above processes to learn complicated decision systems.

2.Automation: Automation is everywhere in technology.In your artificial intelligence also automation is a must-have feature. It is basically creating software or hardware that is capable of doing tasks automatically without human interruption. Artificial intelligence is all about trying to make machines or software imitator, and ultimately, supersede human behaviour and intelligence. With the right way, you can automate processes such as invoicing, marketing, job documents with ease.

3.Natural language processing and natural language understanding: Natural language processing (NPL) is an interaction between human (natural) and computer language and it is referring to communicate with an intelligence system.

The field of natural language understanding (NPU) is an important and challenging subset of Natural language processing (NPL). By using an algorithm, this is to reduce human speech structured.

4. Cloud infrastructure: Cloud infrastructure has a feature provides the scalability to grow and access resources to deploy even the complex artificial intelligence and machine learning solutions.

Artificial intelligence is trending technology with the potential not only improve the existing cloud platform authorities but also boost up a new generation of cloud computing technology.

Current forms of Artificial intelligence:

Voice assistants: If we call any technology that makes our lives easier by one name is almost impossible. It is a digital assistant that uses voice recognition, speech, and natural language processing to provide a service through a special application. They differ essentially based on how we interact with the technology, the app, or a combination of both.

Translation: The translation is not just about translating languages. This is also about translating objects, pictures, and sounds into data that can be used in various algorithms.

Predictive systems: Artificial intelligence is looking at statistical data and forms valuable conclusions for investors, doctors, meteorologists, and nearly every other field where statistics and event prediction prove valuable.

Top artificial intelligence platforms:

Below are the best top AI platforms using the software.

• Microsoft Azure machine learning.
• Google cloud prediction API.
• Tensor flow.
• Infosys nia.
• Wipro HOLMES.
• API.AI.
• Premonition.
• Rainbird.
• Vital.AI.
• MindMeld.

Is AI dangerous?

Artificial intelligence’s are long series of programmed replies and collections of data right now, and they don’t have the capability to makes really independent decisions. If AI sees humanity as useless for its purposes, it could easily eliminate us from the equation by using 

>>Read More>>

Which career is more assuring: data scientist or software developer?

First, let's try to define/distinguish between the 2 roles described:

Software Developer - Are hardcore computer programmers who write lines of code, normally at a notable low-level programmer. They design and develop complete software architectures for highly complicated systems. Typical career path guides you toward systems engineering & product management. Tends to be enhanced technology focused.

Data Scientist - A relatively new role, an offshoot of the statistician role that incorporates the usage of advanced analytics technologies, including machine learning and predictive modeling, to contribute insights beyond statistical summary. Fundamentally, they do everything you can think of in the world of analytics, and then some. They also usually have a Ph.D.

To answer the original question:

      You will love it when you possess both. A Data Scientist certainly knows how his Backend Data architecture should be. A Developer knows how to connect the whole thing through his coding skills.

A Data Scientist is someone who takes care of compiling things in such a way that Product can have the greatest advantage to the Business. A developer might not have such experience, he is concentrated into Building things, not Analyzing it.

     Toward the end, it all simply boils down to your individual decision and interest. If you love designing things and building algorithms that possess a set outcome where you know what to expect, then software development is right for you. However, if you like the unpredictable, are in love with statistics and trends, and have intrinsic business intelligence, then you’re the data scientist that the future is looking for.

     Although the field of data science is evolving day by day, its importance will never dominate that of software engineers, because we will perpetually require them to develop the software that data scientists will operate on. And including more data at the end, we will forever need data scientists to interpret the data and yield advancements in the business.

• Data scientists write code as a medium to an end, while software developers write code to develop things.
• Data science is constitutionally distinct from software development in that data science is an analytic activity, whereas software development is considerably higher in standard with traditional engineering.
• Data scientists tackle problems such as recognizing fraudulent transactions or predicting which employees are destined to leave a company. Software developers can select the data scientists patterns and transform them into completely functioning arrangements with production-quality principles. Software developers tackle problems like making an algorithm to run extra efficiently or building user interfaces.

The Life of a Data Scientist:

     Data scientists are big data wranglers. They relish a tremendous mass of rumpled data points (unstructured and structured) and use their overwhelming skills in math, statistics, and programming to clean, and organize them. Then they implement all their analytic powers – industry knowledge, contextual knowledge, sarcasm of actual assumptions – to reveal hidden resolutions to business provocations.

The Life of a software developer:

     A Software developer gets the hardware platform evolved alive with the code that they write. In some means the code is the behavioral component of the outcome - what it does, how does it do it, etc. They develop all kinds of software like mobile apps, websites, code for hardware, operating systems, the internet itself too.

Can a Software Developer become a Data-Scientist?

     One benefit of a “contemporary” Software Developer leaping into the Data Science bandwagon is that for them the pace of technology transformation is a given and no frustrating activity.

Now answering the question,

      Yes, it is possible. It may be simpler for 

>>Read More>>

Hybrid vs Native Mobile Application. What to choose in 2019?

          

       The most typical dilemma that rises if you prefer to develop a mobile application is to make the appropriate decision between the varying approaches to get the app developed. This blog will help you grasp the difference and pick the most appropriate among Hybrid mobile app and Native mobile app.

NATIVE MOBILE APPLICATION:

         One of the most accepted apps is the Native Mobile App. Native means that this mobile app is special for an individual platform. We handle platform-specific tools and APIs to stimulate all kinds of processes in the application. Native proposal stocks the app resources in the device memory and supports the utmost utilization of OS peculiarities. The advantage of preferring Native mobile app is that it is the swiftest and most reliable. Some of the frameworks for Native app development are Xamarin, React native.

 The important features of a native app are:

• High level of dependability
• Uncomplicated but swift Performance and excellent user experience.
• Supports both online & offline activities (transactions).
• Key features can best be oppressed.

HYBRID MOBILE APPLICATION:

The hybrid mobile application consolidates native code alongside the platform-independent code. The code is composed adopting the traditional web technologies (JS, HTML & CSS) and then it is loaded as a native app, with a Webview. With the start of frameworks like React Native, this strategy can be accomplished with various inadequate efforts while concurrently accomplishing accuracy. While launching a Hybrid application, it is agnostic i.e. once the app is developed it can be delivered beyond multiple platforms.  A Hybrid app comprises of two parts :

• The first part is the backend code built utilizing languages such as HTML, CSS, and Javascript.
• The native shell that is downloadable also loads the code using Webview forms the second part.

Some of the frameworks for Hybrid app development are Cordova, Ionic, Framework7, Titanium Appcelerator.

The significant features of a hybrid app are:

• Quick app development
• Smooth and easy  maintenance
• Cross-platform UI
• A device file system can be combined.
• Profitable and inexpensive app development and cost-effective maintenance
• Distinct code management for different mobile platforms

NATIVE V/S HYBRID APPLICATION:

To sum up the variations between the two:

• Native apps are very swift, responsive and interactive whereas Hybrid apps are gradual and less interactive.
• Native permits developers to access adequate feature assemblage of their given platform whereas Hybrid enables access to device’s internal APIs, storage and camera.
• When correlated to Native apps Hybrid apps have decreased time to market and are inexpensive
• Native apps have excellent user experience whereas Native apps are compact.
• Paid apps are customarily recommended to be amplified as Native applications as they have the best UI, whereas unpaid apps are normally developed as Hybrid applications.
• The app developed can be customized the form you want, economically in Native Approach whereas in Hybrid approach it becomes complicated and overpriced.

WHICH APPROACH TO GO WITH?

The debate around which sort of app is the best

>>Read More>>