You needn’t look further than the Consumer Electronics Show (CES) in Las Vegas to see just how many “solutions” miss the mark and are often left in the wastebin of innovation, usually due to an ineffective business model.

Where does artificial intelligence (AI) stand in this context? It’s clear that recent advances in machine learning have led to a great deal of optimism – and fear – around the technology, from bots that outplay human champions, to virtual writers that spin together articles in seconds, to algorithms that can detect diseases years in advance.

How much of this is making it to the mainstream and how much is merely short-lived hype? How is machine learning changing the way companies operate? Let’s begin by understanding what exactly machine learning is before exploring its current applications in the corporate landscape.

1. What is Machine Learning?

In short, machine learning is a branch of AI-driven by data analysis that automates specific processes.

It’s based on the notion that computers can learn from data, identify patterns, and use the information to make decisions – all with little to no human intervention. Put, the idea is that AI can learn without explicitly being programmed to.

We can create an example using the Photos app on Apple devices.

It uses artificial intelligence and machine learning to streamline the tagging process. When you tag someone’s face in one of your pictures, the program searches through the rest of your library to find the same person and automatically attach the associated tag.

The technologies in question essentially allowed the app to “learn” who your friend is and their name. Most machine learning algorithms operate in this manner.

They use mathematical models to predict outcomes, whether it’s figuring out who’s in your photo, what the value of your stocks will be in the future or the probability that your loan application will be accepted.

As these predictions are developed and made more precise, they can be implemented in previously considered extraordinarily challenging or even impossible scenarios. We’ve already seen this with realistic renderings of pictures of people that never existed and the development of cures for dangerous viruses.

2. How Will Machine Learning Change Business?

There are, namely, two ways that machine learning will change the way organisations operate.

At lower levels, the technology can take over predictive and menial tasks that employees initially performed, saving time and improving efficiency. This can manifest in countless ways.

For instance, radiologists may use artificial neural networks to review more x-ray slides, while customer support services can send quick responses.

But we already realise the potential to go far beyond this. Prediction machines have become so accurate and reliable that they can change how companies do things in more complex ways.

For instance, Amazon is using machine learning algorithms to recommend products to shoppers. The aim is to provide more relevant content while benefiting the customer with a more convenient experience.

Online streaming services use similar technologies to offer music, movies, and videos that users are more interested in.

You can read more about how popular streaming platforms are using machine learning here and how technology is being used to power autonomous vehicles.

Going back to Amazon, the precision of predictive technologies can reach another, even higher level. In doing so, it might change their entire business model. Currently, the online shopping giant uses a shop-then-ship method. You make a purchase, and they deliver your products to your door as quickly as possible.

Another approach is to switch the model around to ship-then-shop. Amazon uses machine learning to determine what you need and sends the products your way. If you need them, you pay, and if not, the products are returned at the company’s expense. Of course, this only works if the prediction model is accurate enough.

3. Who Uses Machine Learning Today?

In that day and time, the vast majority of large businesses in industries that work with enormous amounts of data have already recognised and leveraged the value of machine learning technology set’s take a look at six key sectors where machine learning is currently being used.

i. Financial Services

Banks, loan providers, and other financial industry organisations use machine learning technology in various ways. The two primary purposes are to identify insights from data and to prevent fraud. The former can reveal hidden investment opportunities, while the latter can identify high-risk clients and avoid cybersecurity threats before an attack occurs.

ii. Healthcare

Some of the various primary uses of machine learning can be found in the healthcare sector. This comes in the form of wearable devices and sensors capable of assessing patient data in real-time. Machine learning can also assist medical experts in analysing data to improve diagnosis and treatment.

EnergHere’sre’s another industry where the uses for machine learning are nearly endless and ever-expanding. It can help find and implement new energy sources, analyse minerals in the ground, predict equipment failure, and streamlining distribution to improve efficiency and reduce expenses.

iii. Government

Even among government agencies, which are often viewed as behind-the-times, machine learning is utilised in various areas. The technology makes particular sense here as governments have access to vast amounts of data, which can save citizens money, detect fraud, and prevent identity theft.

iv. Retail

As we touched on earlier, online retail is especially conducive to AI and machine learning. Websites can use it to make accurate recommendations on previous purchases by analysing your shopping history. Retailers also rely on machine learning to implement marketing campaigns, optimise prices, and gain customer data insights.
Transportation

The utility of machine learning in transportation ranges far and wide. The transportation industry relies on making routes more efficient and foreseeing potential needs to increase profitability. Data analysis is fundamental to delivery companies, which illustrates an intersection between the industry and retail.

These are just a fraction of the uses for machine learning in some industries where the technologies are valued.

4. What are the Current Themes in Machine Learning

Let’set’s end off with a few key trends shaping the machine learning landscape today?

i. Processing Power

Artificial intelligence and machine learning have only started gaining mainstream popularity in recent years, mainly due to the need for many logic engines spread across a large amount of high-speed, dense flash memory. Only recently have the demands for neural net-based deep learning been met by the required computing power.

It was found that combining both CPUs (central processing units) and GPUs (graphics processing unit) can improve the speed of deep learning and similar analytics methods.

ii. Cloud

Another reason for the recent boom in AI is the widespread availability of capable cloud technology. Cloud computing is instrumental in democratising AI by enabling companies to access the technology and machine learning systems’ necessary computing capacity.

iii. Cybersecurity

Now more than ever before, organisations survive on the ability to adequately protect their private data and mitigate the risk of cyberattacks. The traditional prevention-based approach to this problem has been replaced with a more active detection of threats than machine learning.

iv. Behavioural Analytics

With an endless onslaught of security alerts, businesses might struggle to discern real threats from harmless anomaliIt’sIt’s not uncommon for systems to detect breaches days or even weeks if it’s too late. Behavioural analytics helps by using various techniques, namely machine learning, to detect threats in large volumes of data more reliably.

v. Online Fraud

Another security issue that affects consumers just as much as organisations is online fraud, which often remains under the radar for months before inevitably causing significant financial and reputational damage. Modern online fraud detection systems use a combination of machine learning and behavioural analytics and identity authentication.

While these technologies improve fraud detection systems’ efficacy, they also help cybercriminals develop more advanced tools. This has sparked a never-ending race to stay ahead of the enemy.

vi. Advertising | Machine Learning Business

Among the main challenges that marketers face is the tighter regulation of the digital advertising sector. Factors like data privacy and protection, along with copyright, fake news, and tax avoidance, are all prime for code. Machine learning-driven tools are becoming available to assist advertisers in creating effective campaigns.

The solutions include responsive search advertisements that use machine learning to distribute content and automatic adjustment of bids to optimise ad performance on video streaming platforThere’sre’s no clear end to the applications for AI and machine learning in the business world. Only time will tell what the future brings for these technologies and their impact on society as a whole.

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Statistics show that it might even reach up to 230.5 million by 2021. With new technologies like the Internet of Everything (IoT) and Artificial Intelligence (AI), the concept of better and faster shopping is possible.

Easy to Find ProductsSince Retailers are challenged to match smart shopping with AI services. Online sellers who are securing the edge are the one investing behind AI platforms that are designed specifically for retail to give their customers the versatility to surf around their online shop, bringing a highly accurate forecasting model that can help consumers make inventory decisions, pricing decisions, and optimal design promotions which is commended by online shoppers.

It is the online seller’s job to create categories of their products for shoppers to find what they are looking for quickly. Successfully making the online shop easy to navigate will boost up the online shop, and get more customers.

1. Discounts are Still There

One of the common concerns of online consumers is that they worry if discounts are still available, or if online shops have fewer coupons and deals at online stores compared to physical ones.

This is not true, especially if the shop is well-known and preferred by many. There are even designed AIs to help you assess the best deals and coupons available.

You can even avail more discounts online by using coupons. CouponLawn exactly offers that. With the value from the online store you inquired, plus the coupons you get online, you can almost get your products with an up to 50% discount. This benefits both the consumer and the online seller or the company you are purchasing from.

2. No More Aisle to Aisle Shopping

It is a burden for many customers at malls, groceries, or markets to go from one aisle to another, looking for the product they want.

This is one of the many reasons why there is a significant number of consumers that choose to shop online just because of that specific reason. People hate shopping from aisle to aisle because it could be tiring and it consumes a lot of time.

Compared to online shopping, you can quickly go straight to the “categories” section or search the product you want in the search bar. Moreover, in case the product you want is not available, most online shops have a recommended section sorted by their AIs wherein it shows similar products to the one you want that is not available.

3. More Time | AI Technology Elevates Online Shopping

There is no doubt that shopping online saves more time compared to shopping at physical malls or stores in which you have to travel first, find parking, walk around aisles, search for your products, go to different floor levels, etc.

Searching for the products you want online is far more convenient and easy. There are many options to choose from as well, and if you are sceptical about the product you are interested in, you can check the reviews, message the seller, or check the background of the shop.

This saves much more time compared to the hassle you go through in traditional shopping, especially if the AI in the shop you are purchasing at is well-optimized.

4. Fast Delivery of Goods

The reason why many people who prefer to shop at physical establishments is that they can get their products immediately and then take it home. When it comes to online shopping, the delivery will take time, but it will save you from carrying the weight of your products to your home.

Compared when online shopping just began, then delivery for your products is much faster than before, that is because there are designated teams who are assigned for delivery at different distances for the convenience and quicker arrival of the consumers.

Some shops deliver within the day, a few days, and a week, depending on where they shop and your address is located. Nonetheless, with the assistance of AI technology, it will be sorted out for a faster transaction.

5. Live Chat for Fast Accommodation | AI Technology Elevates Online Shopping

It cannot be avoided that there are concerns or questions that customers raise, which is why some websites offer a 24/7 live chat service.

This also helps consumers to understand better the product they are interested in, and this leads to a satisfied customer leaving good feedback to the shop. Furthermore, it has been noticed that consumers much prefer Live chat agents or live chat personnel to assist them in their shopping rather than automated messages from chatbots.

It is essential as well that the Live chat agents must be fast accommodating to assist each user on whichever he or she needs.

Physical store shoppers compare live chat agents to approachable employees since the employees can directly give direction to the product they want and tell more details about it. Live chat agents can do the same, but it is expected it slightly takes more time.

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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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With an increasing need and demand to connect with consumers or people instantly (real-time basis), chat applications today have quickly become one of the requisites for many companies to provide timely service and response to consumers and users.

Whether you are interested in assisting communications that occur between customer-to-vendor, user-to-user, vendor-to-vendor, or service providers to consumers, Chat APIs can help you in creating exciting chat features that can assist you with the needed customization that matches up to your business.

1. What You Should Know About Chat APIs

A Chat API is mainly used to power conversations. They are also commonly used to implement messaging and chat features on different websites and service-providing mobile and web applications. Chat APIs are also fantastic tools when it comes to marketing.

CometChat Pro API is one such Chat API that provides a way to develop several communication channels for an application or website. These tools allow you to create a communication channel that is most appropriate to your business’s requirements and boost or enhance your company’s growth.

When deciding to develop a real-time chat app, there are many considerations. Here is a list of some of the most important ones.

a) Idea

This is the first step involved in how to create your own Chat API. It would be best if you thought about an idea and how your app will differ from applications that are already out there.

How will your app stand out from the crowd? First, it is recommended to find a niche-functionality that can be extremely useful when it comes to building up your user base.

b) The Platform

This is the second step involved. You need to decide if you will release the app to Microsoft, Apple, or Android first? You will also need to determine if the app will feature online access or a web-dashboard.

These decisions are important because the costs involved with your project are likely to increase by the platform numbers you decide to build on.

c) Development

Are you able to do your coding, or do you know someone that can do the code for you? You will need a professional to develop the app, and there will be costs involved.

All apps are first developed, then styled, and finally released onto an app store. It is important to find a person that is experienced in this field.

As you start releasing your app onto more platforms, you will need a developer specializing in each of these fields.

d) Time And Cost

Developing an app and then releasing it is a time-consuming task. The budget and time you have dedicated to this project need to be considered when you start thinking about your app’s initial idea.

Most experts recommend beginning just so that you can release your app onto one of the app stores as soon as possible. In this way, you can receive as much feedback as possible on your project before deciding to proceed further.

e) Marketing

Like the app’s development phases, it is important to start marketing the app as soon as you release it. You can either pay a company to do this or market the app yourself.

This is not the most exciting part of your design process, which is why many apps fail. Keep in mind that there are around 2.2 million apps on an app store. If you fail to market the app, then people cannot find it.

With all these considerations, there are still many ways you can significantly increase your chance of success with your app. Here are any of the methods you can use:

2. Minimum Viable Products (MVP)

When developing an app, try and release the app as soon as you can. This will help you gather important feedback regarding your idea and how real people respond to your app.

As you start receiving feedback, you can adjust the project to line up to what consumers or customers want. An MVP should contain the standard functionality but without advanced features and costs.

3. An Open-Source Chat Project Base

This goes in hand with the point mentioned above on how to release your app as quickly as possible. An easy way to achieve this would be to utilize open-source code that will cheaply and speedily get your messaging operating in your app.

Developing chats from the ground up can take anything from 3 to 6 months. If you download one, followed by making upgrades as you go. It can instantly save you a lot of money and time.

Most open-source chat components for Android and iOS are fully-featured and fully-compatible. They release what is known as an MIT license (meaning you can use this code as you wish without any obligations).

4. Think Ahead

When you develop a chat app, there are many different products and backend to decide on. Dedicating your time towards mapping the next couple of years of development is essential to achieving success.

Research the products and frameworks you may be using. This also involves contacting any developers behind these and to make sure the effects are going to be maintained and supported for as long as you own the app.

The main setback to success for many apps has to do with interest. If you give up or get bored, your app will fizzle out.

If you start telling consumers or people about the app, market, and advertise it, you may very well have a good chance of creating an app that is successful and extremely useful to people.

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Today, in this article, I am going to discuss with you the emerging technologies that are being incorporated into the world of skincare and cosmetics industry to whole new heights.

1. ModiFace Skin AI

ModiFace Skin AI, developed by dermatologists, was launched in late 2016. This unique tool was formed to estimate the precise state of skin and see any potential skin differences through live video.

ModiFace, which is a leading provider of augmented reality technology for the beauty and medical industries, developed this app. With the collaboration of a group of experts and dermatologists, this technology can detect and quantify little changes in the skin.

These changes include dark spots, discoloration, rosacea, uneven skin, and dryness. This app can envision the differences before and after the use of any beauty product.

The Skin AI app from ModiFace is mostly based on measurements and specific input from dermatologists. On the instance of skincare products, this technology, powered by AI, is available either in the form of a mobile app or web-API competent of skin assessment.

Moreover, it covers app-based or web-based visualizations of the change in the state of the skin after the use of a particular product. This patented technology by Modiface comes after a decade of research at Stanford University.

It is used in AR apps for some of the most prominent players in the beauty industry, including Unilever, L’Oreal, Allergan, Sephora, and Yves Rocher.

2. Red Light Therapy

Red Light Therapy, new technology on the horizon, is one of the most reliable, most effective natural skin solutions available. It’s being used by some of the prime skincare professionals in the business for rejuvenation, wrinkles, acne, and healing scars & injuries.

Research after research has been done on Red Light Therapy and how light therapy can help you have better skin. Red light treatments have improved healing times, boosted collagen, improved appearance, reduced inflammation, and lowered acne, and scarring.

In this article, I will also talk about skincare professionals and everyday people using Red Light Therapy for better skin, and that too, with significant results. I will break down the plethora of peer-reviewed clinical research on red light therapy and its effect on overall skin health.

Red Light Therapy is a straightforward skincare treatment that’s been proven safe and useful across a broad base of clinical research. In this article, I am trying to give you a good overview of what it is and how it works if you’re not familiar with red light and its benefits already.

Shortly put, light therapy delivers concentrated, safe wavelengths of natural light to your cells and skin, with no UV rays, chemicals, or extra heat.

These red and near-infrared wavelengths of light rejuvenate the mitochondria, the powerhouse of the cell. This kind of light rays is similar to that of natural sunlight. It gives off reducing oxidative stress and increasing rejuvenation, so your body can make more core power to energize itself.

To distribute energy more efficiently throughout your body, Red Light Therapy speed, the healing process improves physical function and lowers inflammation and pain. It has been demonstrated in numerous internationally acclaimed, peer-reviewed studies.

It also intensely enhances skin health and appearance, which is why Red Light Therapy is trusted by some of the world’s best estheticians, natural health leaders, and skincare professionals.

3. Nighttime Expert

Estée Lauder is competing in the business with its launch of the ‘Nighttime Expert’ app announced in late 2017. Estée Lauder made this app, The Nighttime Expert, in collaboration with Google.

Their goal was to enable a messaging experience for its users, giving leverage to a personally curated nighttime skincare routine. It’s Estée Lauder’s first time collaborating with the Google Assistant on Google Home to make skincare more accessible to everyone.

In December of 2017, this app became available. Estée Lauder is planning to expand this past the Nighttime Expert app and do more to enable skincare enthusiasts worldwide.

“Ok, Google, can I talk to the Estée Lauder Nighttime Expert?”

– this is all you have to say for this app to start working.

This chat experience allows the users to have personally curated skincare guidance after evaluating the skin quality with a series of questions and answers. Then it is followed by an opening to learn skincare application techniques if wanted.

4. Optune

Optune is an Internet of Everything (IoT) based technology by the Japanese beauty brand Shiseido, released in the spring of 2018.

On their launching program, they promised that it is going to launch its beta version of a skincare system called Optune in Spring 2018 in Japan, and so they did.

This is an app that works. It gets modified and adjusted according to the skin environment of each person. They have their system of an algorithm that can determine the skin’s best conditions and tone to deliver a moisturized state in each individual.

Optune couples digital technology with cutting-edge beauty investigation and skincare science to optimize the personally curated skincare experience.

Optune Zero, which monitors changes, collects all the data related to personalized information like the menstrual cycle, environmental conditions, and moods like humidity and temperature.

Also, it determines patterns of essential skincare in real-time. The users of this app can obtain the data and evaluate a record of their changes, which are provided by Optune.

Optune Shot perfectly fits your skin, and It is a skincare product filled in a cartridge placed in a particular machine. The system is called Time Tune Care, and it concentrates on the daily biological beats.

Time Tune Care is a concentration of serum care adjusted to skin condition at different times. It can also incorporate moisturizer care every day morning and night. This two-step care was curated after the analysis of over a thousand patterns of moisturizer and serum combinations.

5. S Skin

In the first week of 2017, Samsung launched S Skin. This is still in the concept phase, and it is a prototype straight from Samsung’s C lab department.

This skincare product investigates skin conditions like dryness or skin tone, with a combination of cameras, photosensors, and conductivity sensors.

Samsung S Skin gives you a suggestion on how to fix the skin along with skin therapy by combining micro-needle patches with essential skincare products like collagen.

Conclusion

Technology is assisting the skincare industry in realizing its full potential. We are going towards a world with greater integration between technology and beauty, and it’s up to us to decide whether to embrace it.

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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.

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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.

The post The Impact of Natural Language Processing on Digital Marketing appeared first on Tech India Today.

You’ll also come across some lesser-known options that make quite a lot of sense, at least financially. That doing answered, there are some distinct drawbacks to going for one of those less popular options, and they may be very far from the best option for you.

To make things a bit easier for you folks who are looking to invest in such a system, let’s take a look at two things. First, we’ll see those big brands, who they are, and what they offer.

And then we’ll take a look at some of the lesser-known options and see if it would be a smart choice to save a bit of money.

What Are Those Big Brands? – Less Popular Voice Assistants and Popular Voice Assistants

If you take a look at the industry, you’ll see it’s mostly dominated by Amazon, Google, and recently Apple. The companies have a massive market share, and their respective voice assistants are arguably the best on the market.

Amazon’s Alexa is currently the most popular option.

It comes with any of Amazon’s smart speakers and has support for the most significant number of original products, such as smart cameras, door locks, video doorbells, and lights. It’s also relatively reasonably priced, but you might not get all the user-friendliness you expected from it.

Then you’ve got Apple’s HomeKit. Even though this is a pretty new player in the industry, you’ll find that many people who have invested in Apple’s ecosystem find that this is the best option for them.

Apple has made sure the system is incredibly easy to set up and use, and when you get compatible devices, it works like a charm. The problem with it, however, is finding compatible devices – since they’re new to the game, not a lot of brands have proper support for them.

Last but not least, we’ve got Google Assistant. You don’t have to have a smart speaker for it, and you can use it from your Android phone, provided it has the Google Assistant. When you look at user-friendliness and device compatibility, it’s somewhere between Amazon and Apple, but it’s closer to Alexa because it supports a lot of devices.

What About the Less Popular Options? – Less Popular Voice Assistants and Popular Voice Assistants

AI by Xiaomi, Bixby by Samsung, and Cortana by Microsoft Less Popular Voice Assistants” />

Even among the “less popular” options, there are three that you’ll come across very often. We’re talking about Xiao AI by Xiaomi, Bixby by Samsung, and Cortana by Microsoft.

You might be familiar with them from other places, too – Bixby has been preloaded on many Samsung smartphones in the past period, and Cortana comes with Microsoft’s Windows 10 operating system.

The first difference between these three and the more common options we mentioned above is the compatibility with devices.

When you look at most security cameras or smart locks, you’ll see “Works with Alexa,” or “Google Home compatible.” Still, not many of them say they’ll work with Xiao AI, especially if Xiaomi doesn’t make them.

Of course, how much this impacts you depends on the devices you’re using, so if you’ve already invested in a security system, check if it’s compatible.

The second thing is reliability. The more popular options have a proven track record, and they’ll work well, without any problems.

This is a critical aspect of home security because your home isn’t all that secure if your system doesn’t work well. You’ll want everything to be reliable, and you’ll want it to work 24/7.

Last but not least, we must mention price, as this is where the less popular options tend to win, especially if you go for Xiaomi’s products.

If you take a look at the entire ecosystem as a whole, both the hardware and the software, you’ll find that Xiaomi’s products, for example, are a lot cheaper than competitive products that are made for Alexa, Google Assistant or Siri. And if you’re going to be investing in an entire system, this will make a significant difference.

Are They Worth It?

You can’t deny that you’ll be saving a lot of money by getting some less popular options. However, it would better if yourself kept in mind that your home’s security is in question here, and saving might not be the top priority – reliability should be.

If you want to play things smart, you should go with an option that has a proven track record and is known to work well, in a variety of situations – it’s your home’s security and your peace of mind we’re talking about, after all.

The post Less Popular Voice Assistants , Popular Voice Assistants – Are They Worth It for Home Security and Automation appeared first on Tech India Today.

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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Python Programming Language

Python is a cross-platform programming language developed by Guido van Rossum and first released in 1991. It is a general-purpose and high-level programming languages like Java, C#, C++, and Ruby.

It is an “editor and a shell scripting language. It supports functional, object-oriented as well as procedure-oriented styles of programming. It is most widely used for server-side web application development, mathematical calculations, shell scripting, software development, desktop GUI applications, and Artificial Intelligence development.

It works on various platforms like Linux, Windows, Mac, Raspberry Pi, etc.

IDEs (Integrated Development Environments)

IDE stands for “Integrated Development Environment”.

IDE is a software that provides excellent facilities to software programmers for software development. An integrated development environment (IDE) consists of a source code editor, build automation tools, and a debugger. Some IDEs, such as NetBeans and Eclipse, contain the compiler, interpreter, or both.

An IDE (Integrated Development Environment) is a software package that is used to run, executing, debugging, compiling, interpreting the programmers. It helps to automate the program of a developer by reducing manual processes and combines all the equipment’s in one framework.

1. PyCharm

PyCharm is one of the broadly used popular cross-platform IDE for python development, which was made by Jet Brains. PyCharm is extraordinary compared to other IDE for Python development. PyCharm is a developer’s IDE for Python development.

With PyCharm, the developers can write perfect and neat code. It assists with being progressively and gives smart assistance to the python developers. It deals with the standard assignments by saving time and, in this way, expanding benefits in like manner. It is the Best IDE for Python development.

Type: IDE

Price: US $ 199 per User

Supported Platform: Linux, Windows, Mac, etc.

Features:

• Smart code editor & navigator
• Syntax Highlighting
• Python-based plugin API
• Command Palette
• Debugging, testing, profiling, deployments
• Remote development, IPython Notebook,
• Python console, and scientific stack, etc.

Official Website: PyCharm

2. Spyder

Spyder is another enormous name in the program development market. It is a decent python compiler IDE. It is a powerful a Best IDE For Python development.

It is well known Best IDE For Python development. It was mostly created for engineers and developers to give an excellent scientific environment for Python development in a significant way.

It offers an advanced level of editing, debugging, and syntax highlight. It is genuinely a tremendous extensible and has a decent module, API, and Package System.

Type: IDE

Price: Open Source

Supported Platform: Linux, Windows, Mac, etc.

Features:

• Syntax highlighting
• Auto code completion
• Multi-language editor
• Python-based plugin API
• Powerful integration with python Console interacts & modifies

Official Website: Spyder

5. PyDev

PyDev is an external plugin for Eclipse IDE. It is the preferred IDE for python developers. It is an open-source IDE and one of the popular IDE for Python application development. It has the best code editing features for python developers.

It is essentially IDE that is used for Python application development. It focuses on the refactoring of python code editing, debugging in the graphical pattern of the code system. It is a robust and Best IDE for Python development.

Type: IDE

Price: Open Source

Supported Platform: Linux, Windows, Mac, etc.

Features:

• Syntax highlighting
• Auto code completion
• Multi-language editor
• Powerful Interactive console

Official Website: PyDev

3. Sublime Text

Sublime Text is a famous cross-platform code editor created on C++ and Python. It is built in such a way that it supports numerous other programming and markup languages. It permits a user to include different functions with the assistance of modules.

It is increasingly dependable when compared with the other code editors as the per developers survey. Sublime Text has high performance, powerful Python API, and package ecosystem. It is one of the popular cross-platform Best IDE For Python development.

Type: Source Code Editor

Price: $80.

Supported Platform: Linux, Windows, Mac, etc.

Features:

• Easily Customizable
• Good Performance
• Syntax Highlighting
• Python-based plugin API
• Command Palette
• API and Package System

Official Website: Sublime Text

5. Atom

Atom is a free source code editor and is essentially a desktop application which is worked through a web technology having module support that is created in Node.js. It depends on atom shells, which are a system that assists with accomplishing cross-platform functionality.

It can be utilized as an Integrated Development Environment (IDE). It has high performance, good API, and packages. Atom is one of the Best IDE for Python development.

Type: Source Code Editor

Price: Open Source

Supported Platform: Linux, Windows, Mac, etc.

Features:

• Easily Customizable
• Good Performance
• Syntax Highlighting
• Python-based plugin API
• Command Palette
• API and Package System

Official Website: Atom

6. IDLE

IDLE is a most popular IDE which is written in Python programming language, and it has been integrated with the default language. IDLE is one of the best IDE for python language development.

IDLE is a very simple and primary IDE which is mainly used by the beginner level developers who want to practice on python development. It is also cross-platform, thus helping the trainee developers a lot, but it also called a disposable IDE as a developer moves to more advance IDE after learning the basics.

IDLE is an extremely major IDE that is mainly utilized by the beginner level of programmers who need to practice in a python development environment. It is additionally a cross-platform, therefore helping the beginner level of programmers in a significant way. It likewise called as the best IDE for python language development.

Type: IDE

Price: Open Source

Supported Platform: Linux, Windows, Mac, etc.

Features:

• Cross-platform
• Debugger
• Good Performance
• Syntax Highlighting
• Python colorizing

Official Website: IDLE

7. Wing

The wing is also the most popular and powerful IDE for python in the present market, with a lot of features that require python application development.

It comes to a robust debugger and smart code editor that makes the interactive Python development environment, speed coding performance. Wing additionally gives a 30-day trial version for the developers.

Type: IDE

Price: US $ 95 to US $ 179 Per User For Commercial Use.

Supported Platform: Linux, Windows, Mac, etc.

Features:

• Cross-platform
• Debugging code
• Good Performance
• Syntax Highlighting
• Python colorizing

Official Website: Wing

Conclusion

We hope this article would help you with a clear picture of the best IDE for Python programming language and Python Source Code Editors.

The above list is preferred by the topmost developers and programmers worldwide. Select the best IDE for python language development for your project development.

Helpful Resources:

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3. Apple Macbook Pro Design, Performance and Apple Macbook Pro Features & Specifications
4. What is Google Chrome Helper, How Can It Help You?. Why Does It Use so Much RAM?
5. HTTP Full Form – What is Hypertext Transfer Protocol (HTTP), Methods
6. 10 Best Programming Languages For Artificial Intelligence (AI) in [2020]

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