Like many other intelligent devices, smart thermostats can be controlled from anywhere with your smartphone. Here are a few you can consider getting for your home. 

1. Ecobee Smart Thermostat

Ecobee Smart Thermostat is the best smart thermostat overall, similar in appearance and behavior to Ecobee 4, but significant improvements. It’s one of the best Alexa-compatible devices with all the features of Alexa, including calling, messaging, and drop-in. T

he latest updates work with Siri, but you also need a HomePod or HomePod mini on the same network. Ecobee also has much better speakers and Spotify support than previous versions. 

Therefore, if you are looking for a device that can provide background music, it is an excellent place to play the melody. Sure, its quality is still well below the Echo Dot, but it’s an inexpensive way to bring Alexa into your room if you don’t want to connect with another intelligent speaker.

Most importantly, the new Ecobee has redesigned the remote control sensor with a much better range and battery life. The plastic design hasn’t changed much from the original, and unlike Nest, it’s gradually becoming obsolete.

2. Nest Learning Thermostat

Nest Learning Thermostat is the original smart thermostat, and its classic design still sets it apart from its competitors. The third version of Google’s thermostat has a more prominent and crisper display than previous versions. As before, Nest Learning Thermostat can learn your behavior over time and automatically change the temperature when someone returns home. 

The Nest Thermostat has a retro circular design but a luxurious stainless steel finish and rotating mechanism. In addition, a variety of finishes such as brass, polished steel, copper, and white make it easy to blend into your home decor. Combining the best of the past with the future, this thermostat is one of the best intelligent thermostats compatible with Google Home.

3. Sensibo Sky

Most smart thermostats are designed for homes with central heating and cooling systems, but what if you live in an old house or apartment and rely on window air conditioning to keep things cool? Several companies manufacture smart thermostats for this purpose. We can say that Sensibo Sky is the best smart thermostat for anyone with a wall or window air conditioner with an IR remote control. 

The Sensibo Sky is cheap and is easy to set up as it could be quickly connected to a window air conditioner. Sensibo Sky doesn’t have a temperature display, but it has a small, minimalistic design.

It can be controlled remotely, and you can create a schedule that turns it on and off. It also has a geo-fence, so you can depend on the air conditioner when you get home. The Sensibo has a new version of Sensibo Air, including a motion sensor. It can be configured to turn off the air conditioner when no one is in the room Sensibo Air and turn it on again when someone enters the room.

4. The Nest Thermostat E

The Nest Thermostat E is another excellent smart thermostat for less than $ 200. It’s easier to set up than the more expensive Nest Learning Thermostat, but it still offers many of the same features.

However, the Nest Thermostat E does not support many HVAC systems or components, such as two-stage heating and cooling. For most homeowners, this shouldn’t be a problem. 

The Nest Thermostat E has the same shape as the Nest Learning Thermostat but is plastic instead of metal. Also, Nest Thermostat E is only available in white and not in many versions of the more expensive Nest Learning Thermostat.

Finally, the white display of the Nest Thermostat E can be brutal to read from a distance. However, consider Nest E if you’re looking for something cheaper than Nest’s premium thermostat.

The best way to test your smart thermostat is to use it directly at home. Given that most smart thermostats are intended to be installed by the homeowner himself evaluate how easy it is to install it yourself.

This is important. Each smart thermostat was subjected to several tests to handle different situations during the test phase. Consider how easily you can schedule it to react to changes in temperature. 

If the app is included, you should check for navigation and effectiveness. The same applies to connecting to a smart home hub or device. If it works with Alexa or Google Assistant, check the performance there. Additional features can also be tracked to get a complete picture of the product.

You also need to look at other exciting features, such as geofencing, that allow you to set the ideal temperature when you’re away and back. It’s also worth making sure that the Smart Thermostat Alexa or Google Assistant is compatible or not.

Final thoughts

Getting a smart thermostat for your home is a crucial step. Smart devices have become famous worldwide due to the convenience they offer. However, it would help research which smart thermostat works for your home.

With a massive variety on the market, it can be challenging to choose one for home, so make sure you carefully see the features all the intelligent thermostats offer and then make a wise decision. 

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Marketing is a series of activities and resources that would help reach, engage, and convert leads into customers for your business. This is a complex gamut where activities have to be matched with the right resources. Yet, there is an easier way to do it – with a good marketing automation software.

But the million-dollar question is – how do you know if marketing automation is the right tool for your small business?

This is a valid question with many different answers that can be broken down into the features and elements of marketing software or platform. Here’s why we think marketing automation is right for your small business, especially in an uncertain year like 2020, where everything is fraught with questions and doubts:

1. Organized Efforts:

When you are running a small business, you would feel overwhelmed and all over the place. The main reason for this is that there is a plethora of information out there for your business, and all this information or big data could be hiding potential opportunities and leads and data about those leads.

How do you find the right data and then segregate it into leads, information about leads, and, most importantly, into segments for your various offerings in terms of products and services? With marketing automation on your side, you would organize the right framework for that so that the big data is captured in your set fields.

These fields would be connected with the proper functions and tasks so that your marketing team can convert the same into data-driven campaigns to reach your potential customers. This would capture their attention and engage with them quite seamlessly for the journey towards a conversion to begin.

2. Doing More with Less:

This is the mantra that every small business would have to follow to get easy conversions and grow. Yet, it seems like an uphill task because many resources seem out of reach and far too expensive for the small business. However, with marketing automation, this is something that you can workaround.

You would seamlessly put all your information and functions into this simple software so that you can get the best out of it with minimum effort. The automation tools on the marketing automation software would give you many options for using data to drive reach and engagement.

These simple tools would make your marketing process much more streamlined and necessary, which means you would have the time and bandwidth to get more done since most of the tasks would be automated.

This would essentially mean that you are getting more done with less. In this regard, the marketing automation tool can make things simpler and more efficient for you and your team.

3. Get Crucial Data to Understand Your Customer:

We are living in the information age, and hence, information or data is what drives us in all our business endeavors and activities. So, it should come as no surprise that marketing is now a data-driven process.

Yet, do you and your team have the time and capacity to sift through the mounds of big data to find the most relevant nuggets? The answer would be no, especially if you are running a small business with a small team.

Getting the crucial data is essential since it will help you understand your customers and the markets you are playing in. This would help you reach your leads and convert them into your customers.

You can always bank on crucial data to do the trick when creating campaigns driven by the right data in the right places.

This essential data is captured, stored, and called up by the marketing automation software so that data-driven campaigns can be churned out to hit the bull’s eye every time.

This would make your reach simple and more effective, and the engagement will also be more geared towards a conversion since you will be able to articulate your core customer’s needs.

4. Customized Messaging and Deliverables:

The point of reaching out is to engage people who would be the perfect fit for you and your small business. It would be a great idea to reach out with personalized messages that would be relatable and even resonate with what your core clients want.

To do this, the marketing automation software would give you crucial data. But the trick is to use this essential data to create personalized and customized messages to show the customer that you care.

This would build good brand equity and brand recall value for you. This is also an excellent way to get the most out of the data that would be thrown up by the marketing automation software for your small business.

5. Customer Retention Becomes a Breeze:

With marketing automation software backing the marketing operations of your small business, you would be able to retain your customers for re-selling, cross-selling, and up-selling purposes.

The data you have would help you engage with all of the customers in a very personalized manner to cater to their needs efficiently.

With timely communication due to increased bandwidth, you would solve their queries and problems in a much faster and more prompt manner for better brand value overall.

The post Is Marketing Automation Right for Your Small Business in 2020? appeared first on Tech India Today.

Cutting edge technology is the most advanced in information technology (IT). Cutting edge technologies are the future for a high level IT developers. The cutting edge is also called as leading-edge technology or state-of-the-art technology.

Cutting-edge equipment is the most advanced in a particular field. The innovation in cutting edge areas is connected to smart devices like IoT (Internet of Things), Smart homes, etc.

In information technology (IT) industries, cutting edge is frequently used to describe disruptive technologies such as the most recent technological improvements.

The IT leaders are pressured to incorporate cutting edge technologies and IT services. The option is to permit shadow IT to build the organization’s danger craving and maybe present operational and security risks to expand its consistent trouble.

The term “cutting-edge technology” is an ambiguous word of content and often used in marketing and technology.

The related terms related to cutting-edge technology are State-of-the-Art Technology, Leading-Edge Technology, and Bleeding Edge Technology.

1. Top 11 Examples of Cutting-edge Marketing Technologies

1. High Tech and High Fashion Technology
2. Augmented Reality (AR) Advertising
3. Virtual Reality (VR) Shopping Experience
4. NFC Technology
5. IoT and Wearable Technology

6. Facial Recognition Technology
7. Cloud Computing
8. Artificial Intelligence (AI) Technology
9. 3D Body Scanning Technology
10. Social Media Command Centres
11. Robotics and Automation

2. Video About Cutting-edge Technology

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The world RPA business size was valued at USD 846 million in 2018 and became the speed-growing part of the worldwide enterprise software market. Also, it is anticipated to register a CAGR of 31.1% in the next five years.

The world artificial intelligence business size was valued at USD 24.9 million in 2018, and it is anticipated to reach a CAGR of 46.2% from 2019 to 2025.

When you look for the trending technologies for 2020, you will find RPA and Artificial Intelligence on the top ten of the list. The trend of machine learning and bots is only going to escalate, which implies that RPA and ML are going to become an invaluable skill to have, and people who take a Machine Learning online course will be in demand.

When you read the above discussion, you find that RPA and AI or ML are quite similar terms and may be interrelated. It is thought that every aspect of automation is related to artificial intelligence. But this is not so. RPA and ML are horizontal techniques, both with different goals and interfaces.

Yes! You are thinking it right. Let’s dive deep into the two terms.

1. What is RPA or Robotic Process Automation?

Robotic Process Automation is an application of technology that is aimed at the automation of business processes, which is governed by business logic and structured inputs.

That means RPA tools can be used to configure software or a robot for manipulation of data, triggering responses, capture and translate applications for processing transactions, and communicating with other digital systems.

Put, mimicking human actions to perform a series of steps that lead to meaningful activity and doesn’t require human intervention is referred to as robotic process automation.

Application of RPA can be made for basic tasks like replying to an email as well as for very complex tasks like deployment of thousands of bots, each meant to automate jobs in an ERP system.

Today RPA is being used in almost every sector that includes supply chain management, human resources, customer service, healthcare, financial services, accounting, and more.

RPA benefits include the accuracy and consistency of the tasks. It reduces manual labor, and the best part is that it requires no or minimal coding. It increases the productivity of a company and reduces costs by eliminating human intervention.

2. What is Machine Learning?

As per the definition given by Arthur Samuel, a pioneer in the field of Artificial Intelligence and the one who coined the term Machine Learning, ‘Machine Learning is a range of study that proffers computers the ability to learn without being explicitly programmed.

Machine learning (ML) is a sub-part of Artificial Intelligence (AI). The two terms can be generally used interchangeably.

Machine learning involves improving the learning process of computers based on their experiences, without any human assistance or without being programmed.

First of all, sound quality or relevant data is fed into the system. Then the computer is trained with the help of machine learning models that are based on the data provided and different algorithms.

The type of data and the task to be done form the basis of selecting an algorithm to train the machine or automate the required task.

Let us see the most common example of machine learning.

When you look for some watches online, below comes a recommendation, ‘you may also like,’ and there are pictures of some more eyes that are similar to the one you were looking at.

This standard application of Machine Learning is called ‘Recommendation Engine.’

Another example of Machine Learning is Google, Alexa, and Siri.

Yourself can request them to tell you of your tasks and anything on a smartphone.

It is based on the learning pattern of humans. We see, and we learn from our experiences. Likewise, machines are fed with inputs and related algorithms.

3. Machine Learning vs RPA

When you read the basic introduction of machine learning and RPA, you find them similar; both are involved in the automation of tasks. But if you fall more bass, you find that there are differences in their working and execution.

The fundamental difference between RPA and ML is based on Doing and Thinking. While RPA is associated with ‘doing,’ ML is related to ‘thinking’ and ‘learning’ and acts accordingly.

RPA is used for automating repetitive tasks like sending emails or downloading the attachments, retrieving the subject.

On the other hand, ML can manage your mails, pick out useful insights from them, and can also convert unstructured data to structured data for your ease.

The image below shows you how ML and RPA are different.

Robotic Process Automation (RPA) is a software robot that can mimic human actions, whereas ML expresses its artificial intelligence by exhibiting ‘adaptation,’ which is one of its biggest characteristics.

Image Source

Put, RPA acts more like an essential resource that executes actions based on its configuration and can’t think out of the box.

On the other hand, Machine Learning autonomously improves its performance as the system is fed with observational data and real-time problems, just like humans improve their actions with experience.

The two technologies are also compared by some people as brains over brawn, with RPA being the latter and ML being the former.

Another difference between ML and RPA rests on their area of focus.

RPA, as the term itself implies, is highly process-driven. It is all about automating iterative tasks that are rule-based and typically require communication with multiple and disparate IT systems.
For implementing RPA, the major prerequisite is the process discovery workshop to map the existing process.

On the other hand, ML is data-centric. It is fed with high-quality data and machine learning algorithms. There are no repetitive tasks, but it works on the given input and acts as it has been learned to do so. For example, you can ask your smartphone to set the alarm for you. Then you can ask it to type a message and send it to some recipient. So there is no repetition.

In short, ML is data-centric, and RPA is process-centric.

4. Bottom Line

We now know that RPA and ML, both the technologies are trending these days, both have their different use cases. They have their implications and are applied in almost every sector. Also, both have their benefits too. So you can choose any of them as your career and reach new heights.

RPA and ML are both invaluable solutions that have the potential of enhancing business performance for any organization. They are applied according to the most critical business requirements that can be improved through automation.

The post Robotic Process Automation (RPA) vs Machine Learning (ML) appeared first on Tech India Today.

Natural language processing is a branch of AI that enables computers to learn and interpret human language. It is being used today by digital marketers to analyze customer intent and improve customer experience in ways that weren’t possible in the past.

1. How natural language processing works

Vast amounts of data exist today that can be mined for useful information. A large number of this data, consisting of emails, images, audio, social media posts, text messages, etc. is unstructured data.

Computers can go through data and analyze it to find patterns, but the problem is that machines find it difficult to understand human language. It is bound together by almost arbitrary rules – intonation, context, grammar, syntax, etc.

NLP uses algorithms to teach a machine to identify the intent of a speaker. The algorithm is trained based on examples. Historically, the algorithms were pretty bad at interpreting human language, but they have improved considerably. Now, when you open a website, you will often find a chatbot that works based on natural language processing and can understand and answer your queries.

As conversations can now take place between humans and computers, many things have benefited, and some examples of natural language processing include automatic text summarization, entity recognition, speech tagging, and topic extraction.

2. Applying NLP in digital marketing

One of the first requirements of using NLP is to have systems in place that can take advantage of the data as well as systems that can pass it on to yet other methods that can take action using it.

Coming together, NLP might run behind the scenes as a spam filter, a spell-checking app, a translation tool, or a chatbot. An NLP application that is probably most useful to marketers is sentiment analysis, which can provide them with actionable customer insights.

3. Sentiment analysis

Assume you are speaking to a friend about a product you bought. Sentiment analysis has advanced enough that it can give insight not only into what you are saying about the product but how you feel about it.

Most use of NLP in marketing revolves around social media. Social listening is a mainstream feature enabled by NLP. The technology is used to sift through millions of mentions about a given topic, pull out the most important ones and identify the overall ‘feeling’ about the subject, i.e., whether it is positive, neutral, or negative.

Marketers know that not all mentions are positive ones, and NLP can help to find negative remarks. Marketers can then address these to mitigate any negative consequences. Likewise, sentiment analysis can help marketers to identify people with a clear intention to purchase so they can take the necessary actions to make them aware of their brand.

Some NLP-enabled apps focus on specific social media platforms, and others are built into social media management apps, such as Hootsuite.

4. Search engine optimization (SEO)

Google BERT is the newest Google algorithm update that leverages natural language processing (NLP) and machine learning to improve searches. How does this affect brands and the content they produce going forward?

Any content that’s precise, well-written, and relevant will rank well, and brands that have already been creating high-quality content may see a boost. In creating content, it is essential to ask the questions an audience would ask and then proceed to answer them.

The popularity of voice shopping continues to expand, and when people search using voice, they use longer sentences than they may use when doing a text-based Google search. This means that varying keywords and long-tail key phrases become important in written content.

For a while now, writers have been able to use NLP in real-time to examine content as it is being written and get suggestions for improving it. It is possible to optimize average writing in this way highly. MarketMuse is one AI content intelligence and strategy platform that claims to be able to transform how you research, plan, and craft content.

5. Customer experience

Marketing and customer experience are not the same, but they’re closely related. Stress-free customer interactions are vitally important for overall company success.

Improving the performance of chatbots using NLP can improve the customer experience. Chatbots can respond to queries around the clock; they are objective and never in a bad mood. They can handle simple questions, and those they can’t deal with are passed on to humans who can answer them.

Customers must be able to access the information they need quickly and interact naturally with these tools that can help them. Automatic categorizing and tagging of customer support tickets based on sentiment analysis, for example, is a way for companies to ensure that the most critical queries are handled first.

Email marketing is still beneficial, and using NLP can help its ROI to improve even further. For example, NLP can measure how often users respond to specific keywords, which content attracts new users, and which headlines work better for individual users.

Chatbots can even offer significant marketing benefits in terms of conversions and sales when combined with targeting and marketing psychology. Retailer Asos found that their orders increased when they started using a Facebook Messenger chatbot, Enki, instead of a traditional ‘boring’ gift bot. They reached more people and saw a 250 percent return on spend.

Conclusion

Many new NLP-enable apps use actionable data to achieve a particular purpose. The degree to which companies move into using them will influence how NLP affects digital marketing in future years.

NLP-powered tools are continually evolving, and it’s essential to keep an eye on those that are being made available. No matter whether you are a small or large business or what you’re marketing, they offer some of the most practical and exciting uses of big data available to marketers today.

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Developers often find difficulty in combining writing and mathematics in a uniform platform, to help them out with such a problematic situation, TensorFlow is here to solve all their worries.

TensorFlow is a python based open framework of Google that enables us to create models on Deep learning.

Deep Learning is a subsection of Machine Learning computational algorithms that utilize neural networks of multiple layers to build complex models and train them with unstructured data to produce suitable output.

1. What are the main features of TensorFlow?

TensorFlow incorporates the models and algorithms of Machine Learning with that of Deep Learning to form what we commonly call as Neural Networking. Neural Networking is a common juncture that is very useful for developing models based on AI.

TensorFlow utilizes Python to produce a suitable front-end API for all the building applications in the framework that happens to execute the developed applications in higher-level C++.

TensorFlow is capable of training complex networks of neurons with the specific inputs to classify among many handwritten digits and recognize images.

TensorFlow also enables us to build models based on proper sequences that can be successfully translated into machines.

Models developed through the TensorFlow computational framework are capable of processing Natural Languages.

Through TensorFlow, the deep learning neural networks can be trained to run on simulations that are based on Partial Differential Equations.

Developers can use the individual training models for predicting products on a scale via TensorFlow open Library support.

2. How does TensorFlow work?

AI developers can create dataflow structures using TensorFlow. Dataflow structures describe the movement of particular data through a series of nodes that are capable of processing products.

Each node represents individual mathematical operations in the graph with the unique nodal intersections representing a tensor, a multidimensional array of data.

A programmer can get access to all these provisions from TensorFlow via Python programming language, which is comparatively easy to learn.

Programmers can use a couple of abstractions of higher level by convenient ways that can be availed through Python.

Individual nodes or tensors represent Python objects in TensorFlow.

Programmers using TensorFlow get to avail of the transformational libraries that are written in high-level C++ binary notations to produce better performance output.

In Python, the actual algorithm and mathematical calculations are not performed. Python directs the pieces and provides programming abstractions of higher levels for the better compilation of the data pieces together.

3. On which platform does TensorFlow run?

TensorFlow can run on any target platform. You can run TensorFlow on any device as per your convenience. Below listed are some of the standard target platforms for TensorFlow.

Programmers can run TensorFlow on any local computer device.

• TensorFlow can also run on a cloud cluster.
• Developers can also run TensorFlow on any Android-based device and iOS operating devices.
• Standard CPUs, as well as GPUs, can serve as the right TensorFlow platforms.
• Developers who have access to Google’s cloud may run TensorFlow on the silicon named TensorFlow Processing Unit customized by Google, to experience better acceleration in the computational process.
• The models that are developed on TensorFlow are implemented on mechanical devices that are subjected to react to the predictions.

4. Benefits of using TensorFlow.

The revolutionary benefit provided by TensorFlow in the field of Machine Learning is Abstraction. This has enabled developers to focus more on consistent application rather than dealing with the fundamentals of the algorithms and curbing out means to connect between the input and output of different functions.

All the details regarding the development of machines based on deep learning that could learn from unstructured data sources are dealt with great care in TensorFlow.

Developers using TensorFlow can get additional access to TensorFlow Apps.

Extra conveniences are offered to all developers from TensorFlow, who tend to debug.

Developers can transparently evaluate and modulate individual operations on the graph using the eager execution mode available at TensorFlow.

In eager execution mode, developers do not have to construct the entire chart in a single go and evaluate the same as a bulk at the end.

Each operation can be assessed and checked step by step, releasing the pressure from the developers to review the entire job after its completion.

Developers can inspect and manage the execution of the graphs using an online interactive dashboard using the TensorBoard visualization option.

In addition to the above benefits, TensorFlow also possesses many other advantages. Google has made several significant features on TensorFlow, which has made it easier to avail and utilize as per convenience. The TPU silicon feature has enabled developers to accelerate their performance in Google’s cloud.

TensorFlow even features an online-based hub that enables Deep Learning model developers to share their created models within a framework. TensorFlow is mobile-friendly; as such, developers find it easy to modulate their input data whenever any spark clicks in their mind.

It can be accessed through any browser which solves more than a dozen difficulties related to access issues. Developers find it extremely user-friendly to structure their Deep Learning models putting all the mathematical inputs in a standard programming language.

5. Why should you choose TensorFlow?

For all new developers out there, TensorFlow appears to be the best platform for machine learning that serves as an end-to-end open source to provide all sorts of solutions to the issues related to model developments for AI-infused machines.

It features compact and structurally organized flexible tools with access to libraries and several community resources, enabling AI developers to enhance their skills in Machine Learning. Using TensorFlow, all new developers can build their innovations and deploy their applications through Advanced Machine Learning.

TensorFlow is extensively used by a large number of Deep Learning machine developers, researchers, and enterprises based on Machine Learning to solve several problems that are practically impossible to execute in real life. Being a complex discipline, implementation of Machine Learning has always been difficult.

The introduction of a python friendly open framework from Google has made it easier for all developers to train models with the acquired data and produce future outputs. Using TensorFlow developers can train all their self-created ML models using high-functioning APIs that help to iterate and debug models in almost no time.

Developers can now quickly deploy their models in the Google cloud with access to any browser and any language. For a developer, TensorFlow is a vast platform to use your models and integrate them with machines, thus advancing a step ahead in the world of AI.

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Artificial Intelligence scientists have developed a few specific computer programming languages for artificial intelligence (AI). Such programming languages are often designed for developing Artificial Intelligence (AI) and Machine Learning (ML).

Machine learning (ML) as the sphere of Artificial intelligence (AI) is not a new concept in computer science engineering. Machine learning (ML) is not a new concept in computer science engineering. It is the sphere of Artificial intelligence (AI).

Almost all social networks using Artificial Intelligence (AI). For example, Facebook & Instagram is based on pages that user recently liked, generate page suggestions that may engage to a user.

These suggestions come automatically, or by a program that is first learned to recognize what a user liked, and after that make suggestions to him to improve the learning of a given area.

By picking a programming language, giving applicable information, and implementing an appropriate algorithm, we can make a program that will, similar to a man, figure out how to react to specific requirements.

Under the above-mentioned, regardless of whether you are a software engineer or you are keen on this field of programming and might want to learn, in this article, we will show you 10 Best Programming Languages For Artificial Intelligence (AI) & Machine Learning (ML) through analysis and comparison. These are Python, R-language, Java, Lisp, Javascript, Prolog, Haskell, Julia, C++, and AIML (Artificial Intelligence Markup Language).

1. Python:

Python is viewed as in any case in the rundown of all Artificial Intelligence (AI) development programming languages because of the simplicity.

The programming syntax and data structures of the python very simple and easily learned. Accordingly, numerous Artificial Intelligence (AI) algorithms can be effectively executed in it.

Python takes short advancement time in comparison to other programming languages like Java, C#, C++, and Ruby. It supports functional, object-oriented as well as procedure-oriented styles of programming.

There are a lot of libraries in python, which make our tasks simpler. Python has a lot of libraries that solve many scientific computations. For instance: Numpy is a library for python that causes us to settle numerous logical calculations. Additionally, we have Pybrain, which is for utilizing Artificial Intelligence (AI) in Python. Learn about top 5 python libraries in detail.

Why Python is best for Artificial Intelligence (AI), Machine learning (ML) and Deep Learning?

It is favored for applications running from web development to scripting and process automation, Python is rapidly turning into the top choice among developers for artificial intelligence (AI), machine learning, and deep learning projects.

It also supports interpretive run-time, without standard compiler programming languages. This makes Python particularly helpful for prototyping algorithms for AI and ML.

TensorFlow is the most popular framework which covers all processes in ML and Deep learning. It is also used for deep learning. The areas in which they expose are detection and recommendations based applications such as voice detection, image, and video recognition.

Pros

– It’s easy to write,
– Minimalism (application development with a smaller number of code lines compared to Java),
– A lot of AI, machine learning (ML) courses,
– Large community,
– A lot of libraries and frameworks

Cons

– It is Slower execution compared to Java,
– It is not suitable for mobile Apps development,
– It is not a good choice for games development

2. R-language

R for a long time is an equivalent word for data science & technology. It is interpreted and dynamically typed programming language.

R is one of the best programming language and environment for analyzing and controlling the data for statistical purposes. Using R, we can easily produce a well-structured production quality plot, including mathematical symbols and formulae where required.

It is the very useful general-purpose programming language for AI, R has various packages like RODBC, Gmodels, Class and Tm which are utilized in the field of Artificial Intelligence (AI), Machine learning (ML). These packages help in the implementation of machine learning algorithms easily, It is used to splitting the business-related issues.

Many organizations use R for data analysis, big-data modeling, and visualization. Some of them are Google, Uber, the New York Times. R has a wide utilization in banking, particularly in the fields for predicting different risks. In this area, I would specify Bank of America and ANZ Bank.

3. Java

Java can also be considered as a good choice for Artificial intelligence (AI) and Machine Learning (ML) development. Artificial intelligence has a lot to do with search algorithms, artificial neural networks, and genetic programming.

Java provides many benefits: easy use, debugging ease, package services, simplified work with large-scale projects, graphical representation of data and better user interaction.

It also has the abstract of Swing and SWT (the Standard Widget Toolkit). These tools make graphical and user-interfaces look appealing & sophisticated.

Java can likewise be considered as a good choice for Artificial intelligence (AI) development. Java gives numerous advantages: simple use, investigating ease, bundle administrations, improved work with enormous scale ventures, the graphical portrayal of information and better client association.

It likewise has the fuse of Swing and SWT (the Standard Widget Toolkit). These devices make illustrations and interfaces look engaging and complex.

Java is compiled and strongly typed in the general-purpose programming language. In programming, it’s a standard language, and it is not falling for its popularity for many years. The execution of the program is greatly improved compared to other programming languages. But learning and coding are more complex than other programming languages.

It is used as a wide range of applications development like games, web, mobile and desktop applications. java can be a good choice for Machine Learning (ML), especially all of the businesses are based on java. It can make challenges in this field, even senior designers. Along these lines, Python and R are more dominant than in Machine Learning (ML).

Numerous well-known organizations use Java for server-side as one of the programming languages. Some of these organizations are YouTube, Amazon, eBay, and LinkedIn, etc.

4. Lisp

Lisp is one of the oldest and the most popular suited programming languages for Artificial Intelligence (AI) development. It was developed by John McCarthy, the father of Artificial Intelligence (AI) in 1958. It can process symbolic data effectively.

Its great prototyping abilities and simple dynamic creation of new objects, with automatic garbage collection feature. Its development life cycle allows interactive evaluation of expressions and recompilation of functions or documents while the program is as yet running. Throughout the years, due to advancement, many of these features have migrated into many other programming languages in this manner influencing the uniqueness of Lisp.

Lisp is a group of programming languages, of which the most famous languages are Clojure and Common Lisp. Compared to other programming languages on this list, Lisp has the longest history. Accordingly, it had a lot of influence on the development of R, Python, and Javascript languages.

Lisp is a general-purpose and dynamically typed programming language but has found its mostly used in the area of traditional, symbolic AI

In the part of Artificial Intelligence (AI), Lisp was a popular programming language, but it’s Artificial Intelligence concept varies from the present ideas and necessities. The level of learning is the difficulty, Lisp is one of the harder programming languages and is not recommended for beginners.

5. JavaScript

Javascript is an open-source lightweight, interpreted, high-level, the client-side programming language for web applications. Javascript with Node.js makes this language extraordinary in web development as a result of full-stack features. Javascript is interpreted and dynamically typed Programming language.

Javascript is a simple programming language can easy to learn the basics. The understanding of the context of work is sometimes difficult for beginners. It requires a lot of attention to learning. It comes under the group of easy to learn programming languages.

The utilization of Javascript is restricted only to web development, and the recognizes of this language from others on the rundown. the features and advantageous of work say it is purely for the web. This language is utilized to dynamics and interaction of the website, at that point to build standard web applications and dynamic web applications.

Since Javascript is progressively developing and subsequently improving the limits of its application, aside from the abovementioned, it is used in data science and Machine Learning (ML). The enthusiasm of the community for these circles is becoming more intense, which is an exceptionally positive fact.

There are numerous libraries and frameworks are developed by Google and Facebook.

When we talk about Machine Learning (ML) in Javascript, I first need to refer DialogFlow. That is neither a library nor a framework however a powerful technology created by Google based on Artificial Intelligence (AI).

DialogFlow makes it simple to make and prepare human-computer interaction. With the help of DialogFlow and Node.js, you can rapidly create voice or text Chatbots for a messenger, Slack, Twitter, and similar systems. Additionally, this technology joins regularly with a framework, for example, Angular for the development of Chatbots inside web applications.

TenserFlow.js library is currently one of the most well known Machine Learning (ML) development and training libraries and It is a deep learning model with Javascript. On the off chance that you get to the site, you will see a couple of energizing undertakings.

I would separate the Emoji Scavenger Hunt, that gives you certain emoticon and you have to recognize them with the assistance of the camera in whatever many numbers as could reasonably be expected in a short time frame. For the field of neural networks, I would emphasize the brain.js library.

6. Prolog

Prolog is a declarative programming language where the programs expressed in terms of relations, and execution happens by running inquiries over these relations. Prolog is especially useful for database, symbolic reasoning, and language parsing applications. Prolog is broadly used in Artificial Intelligence (AI) today.

Prolog is a logic programming language and computational phonetics that are related to artificial intelligence (AI). Prolog has its first-order logic, a formal logic, and unlike as many other programming languages, Prolog is planned basically as a definitive programming language, the prolog program logic is expressed as far as relations, represented as facts, rules, and standards. A calculation is started by running a question over these relations.

Prolog was one of the main logic programming languages and remains the most well known among such logic programming languages today. The language has been utilized for hypothesis demonstrating, master frameworks, term rewriting, type systems, and automated planning, just as its unique proposed field of use, natural language processing.

Present-day Prolog environments support the creation of graphical user interfaces (GUI), just as authoritative and organized applications. Prolog is well-designed for specific tasks that fit by standard-based logical queries, like, voice control systems, searching databases, etc.

7. Haskell

Haskell is additionally an excellent programming language for Artificial Intelligence (AI). The rundown and LogicT monads make it simple to express non-deterministic algorithms, which is regularly the situation. It’s data structures are incredible for search trees. The language’s highlights empower a compositional method for expressing the algorithms. The main disadvantage is that working with graphs is somewhat harder from the outset as a result of purity.

Haskell is a merely functional and statically typed programming language with type inference and lazy evaluation. Type classes, which empower type-safe operator overloading, were first proposed by Philip Wadler and Stephen Blott for Standard Machine Learning (ML) and implemented later in Haskell. Its fundamental execution is the Glasgow Haskell Compiler. It is named after logician Haskell Curry.

Haskell depends on the semantics, yet not the syntax, of the Miranda programming language, which served to center the efforts of the underlying Haskell working community. The stable release was made in July 2010 with the following standard got ready for 2020.

Haskell is utilized in the academia & industry. As of Sept 2019, Haskell was the 23rd most common programming language as far as Google searched for tutorials and made up under 1% of active clients on the GitHub source code repository.

8. Julia

Julia is a high-level and dynamic programming language. It is a general-purpose programming language, and it can be used to write any program. Many of its features are well-designed for high-performance computational science and numerical analysis. Julia is used for machine learning (ML), using native or non-native libraries or frameworks.

Distinct aspects of Julia’s design and structure include a type system with parametric polymorphism, a unique dynamic programming language, and multiple dispatches as its core programming paradigm.

Julia is garbage-collected, utilizes eager evaluation, and includes dynamic libraries for floating-point calculations, linear algebra, random number generation, and regular expression matching. Numerous libraries are accessible, including a few that were recently packaged with Julia and are currently discrete.

The Tools available for Julia include IDEs; with integrated tools, e.g., a linter, profiler, debugger, and the debugger.jl package and many more.

9. C++

C++ is a general-purpose language developed by Bjarne Stroustrup as an extension of the popular C programming language. The word has extended altogether after some time, and present-day C++ has object-oriented, generic, including functional characteristics in addition to facilities for low-level memory control. It is quite often actualized as a compiled language, and numerous vendors give C++ compilers, including the free software foundation, LLVM, Microsoft, Intel, Oracle, and IBM, so it is accessible on multiple platforms.

C++ was planned with an inclination toward framework programming and inserted, resource-constrained software and large systems, with execution, effectiveness, and adaptability of utilization as its design features. C++ has likewise been discovered valuable in numerous different settings, with essential qualities being programming framework and asset compelled applications, including work area applications, servers, and performance-critical applications.

C++ is standardized by the ISO, with the most recent standard variant approved and distributed by ISO in December 2017 as ISO/IEC 14882:2017 (casually known as C++17). It is an extension of popular C language. he needed a productive and adaptable language like C that additionally given elevated level highlights to program association. C++20 is the next planned standard, keeping with the present trend of another new version at regular intervals of every three years.

The vast majority of us have C++ as our First Language however it comes to something like Data Analysis and Machine Learning (ML), Python turns into our go-to Language due to its simplicity and a lot of libraries of pre-written Modules. This why Payton is the best programming language for AI and ML.

C++ has different types of libraries used for various purposes like big Maths Operations, etc. It has a small and Scalable Machine Learning Libraries, which is used to run significant calculations or algorithms.

10. AIML (Artificial Intelligence Markup Language)

AIML, and it is also said Artificial Intelligence Markup Language, is an XML dialect for making original programming language. It is used as one of the programming language for Artificial Intelligence (AI) & Machine Learning (ML).

The XML dialect called AIML was created by Richard Wallace and an overall free software community between 1995 and 2002. AIML framed the reason for what was at first a highly extended Eliza called Artificial Linguistic Internet Computer Entity, which won the yearly Loebner Prize Competition in Artificial Intelligence (AI) multiple times and was likewise the Chatterbox Challenge Champion in the year 2004.

Free AIML sets in a few programming languages have been created and made accessible by the user community. There are AIML translators available in Java, Ruby, Python, C++, C#, Pascal, and different programming languages (see underneath).

In any case, if we direct by these criteria and the facts I have given in this article, Python is the best programming language which is fundamental in the Machine Learning (ML) compares to other programming languages.

For instance, Lisp is the most paid. However, the demand for Lisp experts is small. So many factors influence the popularity of the language, and this changes quickly from year by year.

It became part of the scientific and logical, and in the Machine Learning (ML) sphere, Python has a much more better use than all other programming languages. It is likewise imperative that for more significant ML projects, the advantages over R-language, particularly for the simplicity of writing. Compared to Lisp, Python is undermined by several deep learning libraries, while Lisp isn’t suggested for this zone.

When discussing Java, it is superior to Python for developing desktop, mobile, Web applications, and games. Likewise, the interest for Java engineers is higher. Along these lines, you surely won’t commit an error with Java, and it’s a steady and available language for a long time. However, it is much more hard to learn from Python, particularly for beginners and need to give a great deal of time & attention before understanding your first genuine ML projects.

At last, you should not ignore the Javascript. It is currently in the list of most significant salaries at the same time, thinks that the demand for Javascript developers is the biggest. As the most youthful language in Machine Learning (ML), which is developing at a remarkable rate and is centered around web development, it isn’t avoided that Machine Learning (ML) will increase pay rates after some time.

Helpful Resources:

1. What is Google Chrome Helper, How Can It Help You?. Why Does It Use so Much RAM?
2. How Secure is a VPN? and Why You Need A Secure VPN?
3. What is Robotic Process Automation (RPA), Tools, Layer Design and its Applications
4. Deep Learning (DL) and its Applications
5. 10 Best Programming Languages For Artificial Intelligence (AI) in [2020]
6. Artificial Intelligence (AI) or Machine Intelligence (MI) in [2020]
7. Machine learning (ML) Algorithms and its Applications

The post 10 Best Programming Languages For Artificial Intelligence (AI) in [2020] appeared first on Tech India Today.

Robotic process automation (RPA) technology developed from the structure of Business process automation technology builds on the perception of Artificial Intelligence (AI) software bots.

Robotic process automation software bots are designed and developed for a particular task, compare to humans, It has the capability to perform huge volume and automatic repetitive tasks.

RPA can be helpful to handle the question and Answers, mathematical calculations, manage, maintain the records and digital transactions, etc.

With the help of the RPA tools, you can create your software robot or bot to Automate to your Business process.

Just like humans, RPA bots make use of the Graphical User Interface (GUI) for collecting the information and connect with the applications to perform huge volume and automatic repetitive tasks. With the help of this technology, you can get quality revenue for your business with less time compared to other manual Operations.

RPA software bots use the Graphical User Interface (GUI) to capture information and manipulate applications. Compare to humans, these software bots will never sleep, makes fewer mistakes, and also cost less than a salaried employee.

RPA professionals can be categorized into different types such as probots, knowbots, and chatbots.

2. Layer Design Of Robotic Process Automation:

RPA is perfectly suitable for enterprise applications like ERP, SAP or any other data processing applications. The RPA applications can perform huge volume and automatic repetitive tasks.

There are four essential layers which are the First one is the process, Second one is sub-process, the Third one is the object, and Fourth one is the component.

a) Process:

The Topmost Layer In Layered Design Of RPA Is process layer. The Main Reason of the Process layer is to manage the processes in the Process layer.

b) Sub-Process:

The second one is the sub-process. The Reason of the sub-process layer have transformable business logic Identity and verification. The Advantage of the sub-process layer is reusability.

c) Object:

The third one is the Object Layer. Object layer has some procedures to Perform Particular task. The Advantage of the object layer is reusability of its systems.

d) Component:

The fourth one is the component layer. The lowermost layer in the layered design Architecture of RPA is Component Layer. The Reason of the Component layer is Specific screen interactions. The Advantage of the Component layer is low risk and faster changes.

Helpful Resources:

1. What is Google Chrome Helper, How Can It Help You?. Why Does It Use so Much RAM?
2. How Secure is a VPN? and Why You Need A Secure VPN?
3. What is Robotic Process Automation (RPA), Tools, Layer Design and its Applications
4. Deep Learning (DL) and its Applications
5. 10 Best Programming Languages For Artificial Intelligence (AI) in [2020]
6. Artificial Intelligence (AI) or Machine Intelligence (MI) in [2020]
7. Machine learning (ML) Algorithms and its Applications

The post The Topmost Layer in Layered Design Of RPA (Robotic Process Automation) Is… appeared first on Tech India Today.

Robotic process automation (RPA) technology developed from the structure of Business process automation technology builds on the perception of Artificial Intelligence (AI) software bots.

Robotic process automation software bots are designed and developed for a particular task, compare to humans, It has the capability to perform huge volume and automatic repetitive tasks.

RPA can be helpful to handle the question and Answers, mathematical calculations, manage, maintain the records and digital transactions, etc.

With the help of the RPA tools, you can create your software robot or bot to Automate to your Business process.

Just like humans, RPA bots make use of the Graphical User Interface (GUI) for collecting the information and connect with the applications to perform huge volume and automatic repetitive tasks. With the help of this technology, you can get quality revenue for your business with less time compared to other manual Operations.

RPA software bots use the Graphical User Interface (GUI) to capture information and manipulate applications. Compare to humans, these software bots will never sleep, makes fewer mistakes, and also cost less than a salaried employee.

RPA professionals can be categorized into different types such as probots, knowbots, and chatbots.

2. Layer Design Of Robotic Process Automation:

RPA is perfectly suitable for enterprise applications like ERP, SAP or any other data processing applications. The RPA applications can perform huge volume and automatic repetitive tasks.

There are four essential layers which are the First one is the process, Second one is sub-process, the Third one is the object, and Fourth one is the component.

a) Process:

The Topmost Layer In Layered Design Of RPA Is process layer. The Main Reason of the Process layer is to manage the processes in the Process layer.

b) Sub-Process:

The second one is the sub-process. The Reason of the sub-process layer have transformable business logic Identity and verification. The Advantage of the sub-process layer is reusability.

c) Object:

The third one is the Object Layer. Object layer has some procedures to Perform Particular task. The Advantage of the object layer is reusability of its systems.

d) Component:

The fourth one is the component layer. The lowermost layer in the layered design Architecture of RPA is Component Layer. The Reason of the Component layer is Specific screen interactions. The Advantage of the Component layer is low risk and faster changes.

Helpful Resources:

1. What is Google Chrome Helper, How Can It Help You?. Why Does It Use so Much RAM?
2. How Secure is a VPN? and Why You Need A Secure VPN?
3. What is Robotic Process Automation (RPA), Tools, Layer Design and its Applications
4. Deep Learning (DL) and its Applications
5. 10 Best Programming Languages For Artificial Intelligence (AI) in [2020]
6. Artificial Intelligence (AI) or Machine Intelligence (MI) in [2020]
7. Machine learning (ML) Algorithms and its Applications

The post The lowermost layer in layered Design of RPA (Robotic Process Automation) is… appeared first on Tech India Today.

Robotic process automation software bots are designed and developed for a particular task, compare to humans, It has the capability to perform huge volume and automatic repetitive tasks.

RPA can be helpful to handle the question and Answers, mathematical calculations, manage, maintain the records and digital transactions, etc.

With the help of the RPA tools, you can create your software robot or bot to Automate to your Business process.

Just like humans, RPA bots make use of the Graphical User Interface (GUI) for collecting the information and connect with the applications to perform huge volume and automatic repetitive tasks. With the help of this technology, you can get quality revenue for your business with less time compared to other manual Operations.

RPA software bots use the Graphical User Interface (GUI) to capture information and manipulate applications. Compare to humans, these software bots will never sleep, makes fewer mistakes, and also cost less than a salaried employee.

RPA professionals can be categorized into different types such as probots, knowbots, and chatbots.

1. Probots:

Probots robots perform small and repetitive tasks to Progress the data.

2. Knowbots:

With the help Knowbots, you can find out and collect and save the information from the web.

3. Chatbots:

Chatbots behave like the virtual agent; it can respond to the Answers as per the client request.

4. Advantages Of Robotic Process Automation:

Transform your Business or Organization in digital with the help of RPA technology.

Some of the Advantages of RPA are:

• • It provides best quality customer service.
  • Business processes or work completed on time much faster than humans (more Work in less time).
  • Less Cost, decrease the operating costs and increase the results.
  • Increase the productivity of your Business or Organization By using this Technology.
  • Increase the customer service of your Business or Organization By using this Technology.
  • Increase consistency and Quality By using this Technology.
  • And also get the real-world solution from the problems by using this Technology.
  • Flexibility and Scalability.
  • Low Technical obstruction.

5. Applications Of Robotic Process Automation:

Different types of Industries uses RPA.

a) Retail Industry (Online shopping):

Using this technology for e-commerce services, managing the orders taking feedbacks of the customer request and respond to the question and answers, manage and handle the shipping orders, etc.

b) Banking Industry:

Customer feedbacks respond to the request; you can also get complete information in the banking system. Identifying and obtain personal information, Transactions, debit, and credit information, enabling KYC, etc.

c) Human Resources (HR) (Payroll management):

Human Resources (HR) are essential for Businesses or any Organization, Human resources (HR) Departments are Large for big Organizations or businesses. If you can Handle the tasks in the Big Organizations is not an easy task. With Help RPA Technology, you can easily handle the Big tasks compare to humans.

d) Health Care:

Registration data, Maintain the Transactions, records, and More Details of the patients.

d) Transportation (Travel and logistics):

All registration and bookings data like Traveling tickets booking information and maintain the register passenger Information etc.

And some more Applications of RPA are:

• Insurance Industry
• Telecom
• Finance Services
• Supply Chain Management
• Accounting
• Customer Services
• ERP Transactions
• Data Analysis
• Mass Emailing
• Information and Document Storage
• Travel and Logistics
• BPO Industry
• Manufacturing and Operations

If you Choose Any Organization or Business, There are the number of consistent tasks and time taken Operations are thereby Its Nature.

If you are doing these types of tasks vast chances of getting errors.because of its Repetitive tasks.Finally, If you Want to avoid these errors and also save your time, we need a RPA Software Tools.

Number of RPA software tools are available in the market such as

• Blue Prism
• UiPath
• Automation Anywhere
• Pega
• Contextor
• Inflectra Rapise
• Nice Systems
• Kofax
• Kryon
• 1Softomotive
• Visual Cron
• Workfusion
• OpenSpan
• AutomationEdge
• AntWorks
• Redwood software
• Jacada

6. Layer Design Of Robotic Process Automation:

RPA is perfectly suitable for enterprise applications like ERP system software, SAP or any other data processing applications. The RPA applications can perform huge volume and automatic repetitive tasks.

There are four essential layers which are the First one is the process, Second one is sub-process, the Third one is the object, and Fourth one is the component.

a) Process:

The Topmost Layer In Layered Design Of RPA Is process layer. The Main Reason of the Process layer is to manage the processes in the Process layer.

b) Sub-Process:

The second one is the sub-process. The Reason of the sub-process layer have transformable business logic Identity and verification. The Advantage of the sub-process layer is reusability.

c) Object:

The third one is the Object Layer. Object layer has some procedures to Perform Particular task. The Advantage of the object layer is reusability of its systems.

d) Component:

The fourth one is the component layer. The lowermost layer in the layered design Architecture of RPA is Component Layer. The Reason of the Component layer is Specific screen interactions. The Advantage of the Component layer is low risk and faster changes.

Helpful Resources:

1. What is Google Chrome Helper, How Can It Help You?. Why Does It Use so Much RAM?
2. How Secure is a VPN? and Why You Need A Secure VPN?
3. Deep Learning (DL) and its Applications
4. 10 Best Programming Languages For Artificial Intelligence (AI) in [2020]
5. Artificial Intelligence (AI) or Machine Intelligence (MI) in [2020]
6. Machine learning (ML) Algorithms and its Applications

The post What is Robotic Process Automation (RPA), Tools, Layer Design and its Applications appeared first on Tech India Today.