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best programming languages for ai ml artificial intelligence and machine learning
Best Programming Languages For AI & ML (Artificial Intelligence and Machine Learning)

Industries are walking the path that leads to digital transformation and automation, and artificial intelligence is the constant companion. Not many know that artificial intelligence remained stagnant for decades but is now undergoing massive growth and development.

Then comes the subset of AI- Machine Learning. Now the question is, what makes AI so powerful? Well! The answer is the presence of the most popular programming languages for machine learning and artificial intelligence. 

In this article, we will take you through the best programming languages for AI and ML. However, we will first re-introduce AI and ML to you. Let’s dive in!

What is Artificial Intelligence?

Artificial Intelligence is a branch of science that builds smart machines capable of performing tasks that otherwise require human intervention. 

While addressing a crowd at the Japan AI Experience in 2017, DataRobot CEO Jeremy Achin was seen quoting, “AI is a computer system able to perform tasks that ordinarily require human intelligence… Many of these artificial intelligence systems are powered by machine learning, some of them are powered by deep learning and some of them are powered by very boring things like rules.”

What is Machine Learning? 

Machine Learning refers to the science of getting computers to learn and act the way humans do and improving their learning over time in an autonomous manner.

Top 5 Programming Languages of AI & ML:


1. Python

Did you know?

More than 8.2 million developers across the globe use Python for coding.

Well! This is why Python is considered to be the best programming language for machine learning and artificial intelligence. Its simple syntax, less coding, and availability of ready-to-use libraries make it even more popular amongst developers. 

Python is an open-source AI programming language. Moreover, it can be used to make small scripts, making it again more suitable for AI. 

The most imperative fact about Python is that it is easy for students to understand and learn because it uses English keywords. Besides, it has only a few keywords and has a precisely defined syntax. 

It also provides interfaces for the majority of commercial databases. With high scalability, this language is easy to learn and is one of the most popular languages for AI today. 

Python can also be integrated with C, C++, Java, Cobra, and various other languages. Additionally, it also supports Object-oriented programming (OOP) and dynamic type checking.

2. Java

Did you know?

As of early 2020, Java was the most commonly used programming language among software developers.

Java comes second amongst the most popular programming languages for machine learning and artificial intelligence. It has grown since its emergence in 1995 and has become a highly portable, maintainable, and highly transparent programming language. 

The language is easy to implement on different platforms because of its Virtual Machine technology. In simpler words, once it is written and compiled on one platform, the developers do not need to compile it again. Various advantages of Java as an AI language are easy to use, fast debugging, portable, and automatic memory manager. 

At present, Java is proving to be highly versatile and can be used in robot systems, sensors, and machine learning suites. 

3. Julia

Did you know?

Julia is lightweight and can run even on the tiniest computers. 

Julia is the best language for AI and machine learning, especially when a task demands high-performance numerical computing and analysis. It is an open, dynamic compiled language that focuses on performance computing. 

The programming language is a result of a combination of qualitative environments’ functionality like R and Python. On top of it, Julia possesses the speed of production programming languages like Java and C++ to solve Big Data and analytics-based problems. 

It enables the translation of algorithms from research papers into code without any loss, reducing model risk and improving safety. Moreover, a reliable machine learning consulting company can always guide you on the right path by making the most appropriate use of programming languages like Julia.

4. LISP

Did you know?

LISP is known to be the oldest AI programming language. 

LISP (List Processing) was created in 1958 by John MacCarthy. It can be effectively used for machine learning as it is highly flexible and adapts to the solution. 

Being the best language for AI and machine learning, it is known for rapid prototyping and the dynamic creation of new objects. Developers rely on LISP in cases where AI projects are heavy on ML because of its rapid prototyping capabilities, a library of collection types, and quick adaptation to problem-solving needs. 

You can enable interactive evaluation of expressions and recompilation of functions concurrently while the program is running. LISP is a dynamically typed programming language that has positively impacted the creation of many machine learning programmings languages like Python, Julia, and Java. 

Surprisingly, it has the capability to code, compile, and run code in more than 30 programming languages. 

5. C++

Did you know?

C++ was first standardized in 1998.

C++ is the most obvious option for AI and deserves to be called the best language for machine learning. Search engines use C++ to leverage less response time. In addition, it is an extension of the C programming language and can be used to build neural networks. 

The best advantage of the language is its fast calculations that solve complex computations of AI development. Moreover, it is cost-efficient as compared to other programming languages. 

Holding the capability of both a low-level and high-level programming language, C++ comes with a higher level of control and efficiency as compared to any other language. 

Conclusion

Things change every day, and so does the best programming language for machine learning and artificial intelligence. Admittedly, you cannot consider one programming language to be the best while ignoring others. Act rationally by looking for the best IT consulting companies in NYC to furnish yourself with all the relevant information and begin your journey to exploit AI’s potential. The above-mentioned programming languages can transform the marketplace digitally and multiple organizations have already begun considering it seriously. 

The world’s interest in AI is growing exponentially. At which level is your organization’s interest?

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what are the top educational technology trends
What Are The Top Educational Technology Trends?

Mobile applications prove helpful to users and are work as great promotion tools too! Along with that, they increase engagement, enable people to learn in different ways and also generate a new and more widened user base! This is applicable to all kinds of applications! Something to learn from…

We are witnessing an era in which technology has seeped into education and renewed its whole teaching and process – especially eLearning. It is an educational tool that has not only increased the accessibility and convenience of education but has also changed the learning behaviours and learners’ desire to learn anything!

Learning is a constant process, it never ends. So, for today’s blog I thought to myself… why not focus on educational tech trends?!
Technological advancements have not left a single domain untouched. They are helping everybody to educate themselves, be socially relevant and simply have fun – all of this with the help of smart devices!! 

The awe-inspiring and interesting trends!!

  • Artificial Intelligence (AI)
    AI is the not technically a new thing, but has got people raving all over! It has been predicted that through the year 2021, it can become one of the primary trends and can grow by more than 45%!! Though already an awesome technology, let us try and understand why it has been trending in the EdTech market…
    Artificial Intelligence has the capability to automate basic activities in education – grading, for instance. This imparts speed to the teaching process and checking assignments, etc.

    Thus, this technology benefits learners and educators both from AI. Helping students and approaching tutors both become possible! Therefore, it is not too far fetched of an idea that AI is a powerful teaching assistant.
  • Immersive learning
    There exists no domain that has been left untouched by tech advancements. AR and VR are another pair of amazing technologies that offer immersive experiences and they find a huge fan-base in the education sector too. Digital content, online assessments, smart classes are slowly transforming the education sector, for the better.

Incorporating new and wonderful technologies like Augmented and Virtual Reality can work wonders – for both, students and teachers. Teachers can teach in a more interactive and interesting manner. The students can learn more efficiently and efficaciously.

Mind you, AR and VR can be implemented at all levels, from elementary to higher and then research studies. They ensure faster learning, provide better outcomes to learners, fuel vocational training, etc. Thus, it wouldn’t be wrong to claim that these techs can have a wonderfully meaningful impact on students at all levels.

  • E-Learning
    It is a system, that is based on formalised teaching with help of electronic resources. The use of the internet, computer, digital devices, etc. is a major component of e-learning. It is also known as m-learning (mobile-learning).

    Distance learners also use such technologies and this (also) falls under the ambit of e-learning. It involves educational apps, podcasts, educational blogs, online teaching platforms, etc. They facilitate and ensure that users learn better. They also offer support in an engaging, interesting and simple way!

    With eLearning, educational content is delivered to learners through computers, laptops, tablets, or smartphones. This is not only saving time but opening many doors for interactive learning. Rather than being in a passive experience, learners can choose what they need to learn quickly and easily, wherever they are.

To sum things up….

Technologies that help in educating, are a boon! They benefit all three stakeholders – business owners, students and educators. The latter two, get a platform to learn and teach, respectively.


They allow students in particular and learners in general, to learn online and reap the benefits of this process. What is better than an option that has the potential to be a top-notch business idea, along with being highly helpful, right?! 

Do you have any thought or thoughts on similar lines? Then, don’t sit on it any longer and move ahead with educational technology trends in 2021-2022!


Connect with a competent company that provides application development services and the finest web solutions, while staying abreast with the latest technological advancements. 

Online education apps and associated trends are the real deal. Entrepreneurs have the opportunity to develop different kinds of engaging applications for diverse educational situations, size of the learners no bar! Witness your dream become a reality through an intelligent virtual network that will deliver high performance!

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how big data is revolutionizing the healthcare sector
How Big Data Is Revolutionizing the Healthcare Sector?

The Healthcare sector is one sector that is always in demand. Lately, the rate and necessity of patient responsibility and innovative medicines have developed. With the growth in such requirements, the latest technologies have been adopted in the industry. One such significant development that had to take place is the application of Big Data and Analytics in the Healthcare sector.

It has been observed that big data are predicted to expand faster in healthcare than in areas like financial services, manufacturing, and so on.

Let Us Understand, What Is Big Data In Healthcare?

Big data in healthcare is a word utilized to explain massive volumes of data generated by the determination of digital technologies that manage patients’ experiences and support in training hospital administration, otherwise too long and heavy for old technologies.

The purpose of big data analytics in healthcare has a lot of definite and also life-saving results. In reality, big-style data relates to the enormous quantities of data generated by the digitization of everything that gets mixed and analyzed by particular technologies. Employed in healthcare, it will use precise health data of a group or an appropriate person and possibly help to stop diseases, cure illness, cut down prices, etc.

Now that we live longer, practice patterns have improved, and many of these changes are particularly inspired by data. Experts need to understand as much as they can about a victim and as early as feasible, to pull up notice signs of severe illness as they appear – managing any condition at an early step is far more manageable and less valuable. By appropriating key performance pointers in healthcare and healthcare data analytics, blocking is better than remedy, and training to form a whole picture of a patient will let support produce a tailored set. The industry endeavors to tackle the serious problems a patient’s data has: everywhere are collected pieces and bites of it and archived in clinics, surgeries, hospitals, and others, with the failure to communicate accurately.

Certainly, for years collecting huge numbers of data for medicinal use has been expensive and time-consuming. With today’s always-improving technologies, it becomes more apparent not only to receive such data but also to build overall healthcare statements and transform them into proper crucial insights that can then be utilized to implement better care. It is the purpose of healthcare data analytics: applying data-driven decisions to the divine and solving an issue before it is too late, but also assess methods and treatments faster, keep a better record of the list, include patients more in their health, and approve them with the instruments to do so.

Variable Big Data Applications In Healthcare

The potential of big data is reshaping the aspect and dimensions of various areas, particularly healthcare strangely and unpredictably. Let’s see the influential reasons for Big Data in the Healthcare sector.

1) Limiting Human Errors

A lot multiple times- it has been remarked that the experts manage to either prescribe the incorrect medicine or receive a different medication by error. Such flaws, in usual, can be defeated since Big Data can be leveraged to examine user data and the prescribed medicine. It can support the data and flag possible community prescriptions to defeat blunders and save lives. Such software can be a vast machine for physicians who provide too many patients in a single day.

2) Tracking of Health

Big Data and Analytics onward with the Internet of Things (IoT) are transforming the process one can trace different user statistics and vitals. Aside from the essential wearables that can identify the patient’s dream, exercise, distance hiked, heart rate, and many more. Different medical reforms can control the patient’s blood pressure, glucose monitors, pulse oximeters, and more. The constant monitoring of the body vitals along with the sensor data store will enable healthcare organizations to put people out of the dispensary since they can recognize latent health issues and give care before the circumstances go more serious.

3) Improving Patient Commitment

Several customers and therefore, possible patients already have an advantage in smart devices that register every move they take, sleeping schedule, heart rates, and much more lastingly. All this necessary data can be linked with other trackable information to recognize possible health risks hiding. Prolonged sleeplessness and a raised heart rate can indicate a prospect for future heart disease for example. Patients are immediately included in the control of their wellness, and influences from health insurance can force them to begin a healthful lifestyle.

Another method to do so appears with different wearables under community, following specific health bearings, and delivering them to the spot where doctors can observe them. Patients experiencing blood tension or asthma could profit from it, and become a bit more self-sufficient and decrease avoidable appointments to the doctor.

4) Decreasing Value

Big Data can be an exceptional way to lessen expenses for hospitals that either above or under-book staff branches. Ominous analysis can improve this problem by foretelling the acceptance rates and assisting with staff allocation. It will decrease the rate of investment acquired by hospitals and in particular, help practices their investment to the max. The insurance business can conserve money by supporting wearables and health trackers to assure that victims do not waste time in the hospital. It can save wait events for patients because the hospital will have sufficient staff and beds available as per the investigation all the time. Forbidding analytics also helps lower costs by decreasing the rate of clinic readmissions.

Final Words

In the upcoming future, the healthcare sector will witness a lot more Big Data applications that will transform the healthcare industry slowly one at a time. Not only will Big Data help streamline the performance of healthcare services, but it will also enable them to improve their aggressive support through forwarding business solutions.

Author Bio:

Karen Smith is Content Manager at Hyperlink InfoSystem, one of the top app development companies in UK. She also writes for App Development Companies platform.

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present and future of business process automation in retail
Present and Future of Business Process Automation in Retail

Highlights of Retail automation 

In 2019, Economist Intelligence conducted a survey of more than 500 companies from eight countries. The study aimed to figure out how far organizations have progressed in automating their activities. Among other economic sectors, the report included some facts about business process automation in Retail

More than half of respondents (51%) from different industries admitted that they actively use robotization in their work. The scale of the introduction of such technologies varies depending on the region and sector. For example, companies in the USA, France, and Germany take the lead in intensive automation. What IT solutions are the most popular in Retail? What is hindering their implementation? How soon will robots replace salespeople? Read about this in our article.

Tools for Retail automation

Automation implies the use of software that independently performs tasks, simulating human work. These tasks are usually repetitive structured activities. At the moment, the most widely used technologies are such software-based solutions as IoT and RPA, which employ Artificial Intelligence. Physical robots play a significant role as well, for example, in logistics and warehouse management.

Retailers actively use the above technologies for collecting and processing data, as well as for performing predictable physical operations. According to the McKinsey Global Institute, the total automation potential of these and other activities in that industry amounts to 53%.  

What processes to automate

Today, retailers are mostly, but not only, automating repetitive documentation tasks. Organizations apply IT solutions in the following areas:

  • Invoicing and accounting 

Using RPA tools enables software to generate reports, reconciliation statements, and cover letters, as well as make payments. The software processes the applications, structures IFRS data, and analyzes it. Moreover, the system keeps records of all transactions, which helps companies successfully pass financial audits.

  • Customer service

As a rule, activities that involve a high level of human interaction require a combination of standard RPA and AI-driven technology. Such systems don’t just analyze information. They must be flexible to offer the necessary solutions.

One of the tools used in customer support is electronic consultants. They utilize several techniques simultaneously, including natural language processing algorithms, neural networks, and Deep Learning. Such systems are capable of self-learning, and their effectiveness only increases over time.

  • Human resource management

Software successfully replaces people when performing HR tasks. In particular, such systems analyze the CVs of candidates on the Internet and select suitable ones. Robots help to onboard new employees and conduct their initial training. Such programs also draw up the letters of employment and reports, keep records of sick leave and vacations.

RPA tools help companies manage purchase orders. Systems automatically send notifications to suppliers, register invoices, take inventory, control the delivery terms, and more. The ML-driven programs plan routes using real-time data about the traffic and weather conditions. This helps drivers to promptly take action in order to deliver cargo as quickly as possible.  

  • Marketing and sales

Special platforms process customer’s orders, send them notifications, and respond to complaints. Predictive Analytics tools analyze customer behavior on a company’s website and predict demand. Then, this software personalizes offers and adjusts prices, which helps retailers to increase the volume of sales. 

  • Document flow

RPA solutions help retailers to manage the vast amount of various documents. ML-based systems control expiring transactions, create and send reports, register contracts, handle emails, etc. 

Software segments and analyzes information based on multiple factors. For example, it considers both historical and real-time data, the information from the currency markets, tax bases, state authorities, etc. Scott Likens, the leader of new services and emerging technologies at PwC, stated that their technologies dealt with the burden of documentation so that the staff could focus on making expert judgments.         

Benefits of business process automation

For many retailers, automation marks the beginning of a digital transformation. According to Economist Intelligence, 73% of organizations are highly satisfied with the outcomes of their business process robotization. Among the most frequently named benefits of such solutions are an increase in productivity and reduction of errors. The other advantages are improved consistency of operations, increased revenue, and enhanced customer experience.

Difficulties in the adoption of new technologies 

If business process automation in Retail is so beneficial, what dissuades organizations from introducing it? Among the most frequent reasons companies name are data privacy concerns, difficulties with technology deployment, and human factors. The latter include the reluctance of employees to accept changes and an insufficient level of skills.

The organizations can partly remove the above obstacles by educating and training their staff members on the RPA solutions. More than half of respondents of the Economist Intelligence survey stated that automation requires the development of new skills in their employees. These abilities include problem-solving, openness to changes, and a teamwork mindset.

The future of automation

Despite the competitive edge that robotics gives retail companies, a lot about the future of this technology is still unknown. How far will automation progress in changing the supply chains, stock management, order handling, and interaction with customers? Will robotization limit or, on the contrary, enhance employees’ creativity and liberty of action? How can companies ensure data protection in the context of rapidly developing technology?    

Many retailers are also asking themselves whether cutting-edge solutions will replace the human workforce entirely and how soon it will happen. McKinsey Global Institute concluded that, although machines will play a major role in all economic sectors in the near future, they will successfully perform only one-third of all the tasks. That means the employees will soon need to learn to work alongside rapidly evolving robots.  

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why are data science certifications important in 2021
Why are data science certifications important in 2021?

The most important thing of any industry is the massive amount of data that is produced by it. So managing it and dealing with it requires a good amount of knowledge and expertise. Well, don’t worry when we have data scientists!

What is Data Science? 

Data Science is the branch of study that majorly deals with a tremendous amount of data with the help of modern tools and technology, algorithms, and previous knowledge to extract/derive meaningful information. This information was otherwise not predictable with a glimpse. This Data Science technology has become immensely beneficial for taking any crucial decision. The raw data can be obtained from multiple sources and may be present in various formats. 

The Data Scientist must unveil the hidden formats and patterns.

Advantages of Data Science

  • Data Science plays a very important role in various industries. 

S. No.

Sectors/Industries

Advantages

   1

Marketing

Helps in focusing well on the targeted audience

   2

Internet search

  • Helps in providing the best results
  • Always show relevant data and information

   3

E-commerce

  • Analysis of search patterns
  • Recommending the relevant products to the customers and increase sales

   4

Education

  • Analysis of different courses
  • Data Science helps in collections students’ requirements and feedback

   5

Video and movie streaming

  • Data Science makes use of Machine algorithms to analyze user’s interest and recommend movies/videos

   6

 Management of data

  • Data Science helps in managing a vast amount of data very effectively
  • Data Science is a budding sector and creates ample amount of job opportunities, both at national and international levels. 
  • Data Scientists are paid handsome salaries. That is the biggest advantage of pursuing this course. According to a survey, the data scientists were paid an average salary of around $100,000. 
  • Data Science helps companies to decide on crucial discussions and make better decisions accordingly.

Data Science certifications

To help you learn Data Science, there are ample amount of certification courses available which cover essential elements of data science such as programming skills, managing and improving data, accessing, transforming as well as manipulating data and also helps in learning to work with essential data handling tools. The course is provided by many leading institutes. 

There are varying course duration and curriculum of the certification courses offered by the different institutes. One can take up any course depending upon the requirements and priority of the person and can enter into the decision-making process of various industries. There are also various institutes offering online Data Science Certification courses and the exams of which can be taken from any part of the world.  

Is Data Science worth it?

To employ yourself in the most dynamic and essential aspects​ of the decision-making and talent acquisition processes of the industry, pursuing a Data Science certification course is worth it. A data scientist can empower management and participate in various crucial steps of the industry. He also tests the decision and provides​ data-driven evidence. The other responsibility​ of the Data Scientist includes recruiting the right talent and identifying the opportunities for the company/industry. Thus, taking up this course is not going to let you regret it later. Taking up this course can prove to be the best decision of your life!

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data lake market analysis recent market developments industry forecast 2029
Data Lake Market Analysis | Recent Market Developments | Industry Forecast, 2029

Research Nester released a report titled “Data Lake Market: Global Demand Analysis & Opportunity Outlook 2029″ which delivers detailed overview of the global data lake market in terms of market segmentation by type, industry vertical and region.

Further, for the in-depth analysis, the report encompasses the industry growth indicators, restraints, supply and demand risk, along with detailed discussion on current and future market trends that are associated with the growth of the market.

The data lake market is projected to grow with a significant CAGR during the forecast period, i.e., 2021-2029 on account of the increasing data produced on a daily basis and the need to store and analyze it. As per IBM, 2.5 Quintillion bytes of data are generated each day. In addition, owing to increase in the usage of smart meters, huge amount of data is being generated which needs the use of Data Lakes. In the United States, a total of 70,823,466 smart meters have been installed according to U.S. Energy Information Administration

The market is segmented by type into software and services. Among these segments, the services segment is anticipated to hold the largest share by the end of 2021 in the data lake market as a result of the increasing need for big data analytics by enterprises to streamline IT operations. The market is further segmented into data discovery, data integration & management, data lake analytics, and data visualization for software and into managed and professional services for the services segment.

On the basis of region, the market is segmented into North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa, out of which, the data lake market in the Asia Pacific is projected to grow at the highest CAGR throughout the forecast period. Currently, the market in North America holds the largest share. This can be attributed to the presence of leading global enterprises in the region, especially in the United States, who are adopting big data technologies to identify and streamline business processes and organizational data architecture.

Increasing Amount of Global Data Produced to Drive Market Growth

Every person created 1.7MB of data every second during 2020. In the last two years alone, the 90% of the world’s data has been created. 2.5 quintillion bytes of data are produced by humans every day. 463 exabytes of data will be generated each day by humans as of 2025.

The need for storage and assessment of the amount of data produced by users has increased with increase content creation and cloud storage. Moreover, the development and deployment of Internet of Things (IoT) in various industries will further increase the amount of data produced which is expected to boost the market growth in upcoming years. However, the lack of oversight on the contents stored which can lead to creation of data swamps are some of the factors that are estimated to restrain market growth in the near future.

This report also provides the existing competitive scenario of some of the key players of the global data lake market which includes company profiling of Microsoft Corporation (NASDAQ: MSFT), Informatica Corporation (NASDAQ: INFA), Teradata (NYSE: TDC), Capgemini (EPA: CAP), IBM (NYSE: IBM), Apple Inc. (NASDAQ: AAPL), EMC Corporation (NYSE: DELL), Oracle Corporation (NYSE: ORCL), Atos (NASDAQ: ATOS), SAS Institute.

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why do customer experience and conversational ai go hand in hand
Why do Customer Experience and Conversational AI go hand in hand?

Consumer behavior has witnessed a whirlwind of changes in the past decade. Technological developments have enabled all industries to embrace digital transformation at scale. According to IDC, by 2023, digital transformation investment is expected to approach $7 trillion as companies build on existing strategies and investments, becoming digital-at-scale future enterprises.

The Customer Experience Officer has snatched away advertising budgets. And, rightly so. Customers no longer care about which celebrity is promoting the products. They demand the finest experience across all touchpoints – online and offline. Experts predict that by 2024, customer experience will overtake price and product as the key brand differentiator.

In order to align with the changing customer behavior, brands have shifted their focus from “what we want to sell” to “what the customer wants to buy”. Deeper segmentation and laser-sharp targeting – that’s been the mantra for every company.

Customers are giving more and more preference to brands that are constantly enhancing their Customer Experience strategy. Every company wants to win and retain more customers and here is where a top-notch Customer Experience strategy helps. According to research from PWC, 86% of buyers are willing to pay more for a great customer experience. Customer Experience (CX) will be the center focus as more and more brands embrace digital transformation.

What makes a Good Customer Experience?

Let’s start by understanding what CX means. The business engages with the customer across many different touchpoints. CX is the sum total of how the customer feels about your brand “while” and “after” interacting with you. As soon as he enters your ecosystem (online or offline), he starts to make his judgment. CX is not just restricted to existing customers but also extends to potential customers. Every interaction allows your brand an opportunity to build or ruin what the customer thinks about you. Remember, one bad experience can ruin 10 good experiences!

So what really contributes to a good CX? Here are a few points to consider:

Know your audience

This is the most important part while framing your CX strategy. It’s important for you to know who your customers are. From how they behave, what motivates them, what upsets them – you have to know it all. It’s not as easy as it sounds. Customer behavior is dynamic in nature. As a CX professional, you have to closely observe how customers are evolving and what your business can do to cater to their new needs. The marriage of data analytics and behavioral economics will help you uncover the CX strategy your business needs.

Build with empathy

With all the noise around AI, ML, NLU, NLP, Cloud, this element often gets overlooked. If the past decade was the technological revolution, the coming one will be a psychological revolution. Companies need to build technology that connects with customers. Most high-tech products will fail if they’re not built with empathy. To create a CX that truly stands out – you have to empathize with your customers. Talk to them in person, do social listening, create scalable customer feedback solutions, etc. Do anything to know what moves your customer, build technology on top of it and serve it back to the customer.

Going above and beyond

To create delightful customer experiences, one has to think beyond what is already being done. Every stage of the buying process needs to be constantly re-evaluated and elevated, if necessary. Taking that one extra step that goes beyond customer’s expectations will ensure enhanced CX and customer loyalty.

Hyper-Personalized Approach

Gone are the days when one geography was treated as a single market. The advancement of technology has allowed businesses to create solutions that talk to each customer in their preferred manner of communication. The scope of building a relationship with customers has never been more. Customer Experience elevates when brands talk to customers in a more personalized way. Brands need to humanize their communication and make customers feel that they’re conversing with just another human being.

The Evolving Role of AL and ML in CX Strategy

In the past year, customers have welcomed digital businesses with open arms and have become more tech-savvy. As this trend continues to grow, companies will have to focus on redefining what CX means to them.

Sundar Pichai, CEO of Alphabet Inc. and Google was quoted saying that AI will be a more profound change than fire and electricity. Well, we can only wait to see if this is true. What is true is that Artificial Intelligence and Machine Learning have significantly changed how businesses imagined CX.

AI and ML have allowed businesses to enhance systems and processes, provide rich customer insight, build better products, help marketeers with personalized targeting. All of which are extremely important for a well-integrated CX strategy. IDC states that at least 90% of new enterprise apps will insert AI technology into their processes and products by 2025.

The growth in these two areas of technology has given birth to many different business solutions. Every industry is leveraging AI in some shape or form. Global tech giants are spending a huge amount on researching and developing this technology which is set to reimagine how businesses looked at CX.

One of the many solutions powered by AI and ML is Conversational AI. Conversational AI is the use of chatbots and voice bots to create conversations with customers on different channels of communication. According to Forbes, 70% of millennials report positive chatbot experiences.

One thing to notice is that it is not easy to provide a CX that meets every customer’s expectations. The term “Customer Experience” is relatively new. Companies are facing different challenges when it comes to creating a memorable experience for their customers. Hiring and training support staff shoots up the customer support costs. To handle support queries at scale, companies need to hire thousands of agents who can work round the clock to assist customers with their issues. Agents when overworked do not provide the desired support. They are unsatisfied with their job and do not take an active interest in solving customer’s queries. This is a recipe for a bad CX.

Another challenge faced by CX professionals is providing the right buying assistance to your customer. Sales agents are often the reason why a visitor did not turn to a customer. A sales agent is often as clueless about the product as the customer, they talk so much that they forget to ask the right questions, and they lack genuine social empathy. After all the efforts that went into bringing a prospective customer to your ecosystem, the last step ruins it all.

This is where Conversational AI steps in.

How Conversational AI helps Improve Customer Experience

Businesses across different verticals have adopted Conversational AI. CXOs are responsible for providing a best-in-class experience to customers and oversee the entire customer lifecycle. Let’s look at how Conversational AI adds value to a CXO:

Cost Reduction:

Conversational AI solutions have a number of use cases. One of the most common use cases is handling Customer Support queries. Unlike standard AI assistants, a chatbot can resolve queries faster, more efficiently, be available 24*7 and talk to your customers in their preferred language of communication. A chatbot that is built to handle queries at scale will always lead to cost savings.

Employee & Customer Engagement:

Employees are the biggest asset of every company. Happy employees = Satisfied customers. Conversational AI solutions can streamline many HR processes and enhance employee engagement. This helps boost employee satisfaction and in turn affects CX positively.

Apart from handling customer support queries, a sophisticated Conversational AI solution can also assist in customer engagement programs. This helps you create a meaningful relationship with your customers that goes beyond a transaction.

Purchase Experience:

Remember the last you walked into a retail outlet and there was no one to assist you? Or you spent hours looking for a product online but couldn’t find adequate information? All these are components of a bad CX. Getting people to your business’s ecosystem is hard and not turning visitors into customers is unhealthy for your business growth.

Conversational AI has plentiful use-cases and they differ as per different industries. The growth in AI, ML, Natural Language Processing (NLP), and Natural Language Understanding (NLU) has enabled chatbots to create use-cases that can be utilized by every industry. Let’s dive into three use-cases that are industry agnostic and can be deployed across all businesses.

Customer Support:

The customer service chatbot can be deployed across different channels, hence providing brands an opportunity to create an omnichannel CX. AI-powered chatbots are capable of delivering service 24/7 which means that there’s always someone (not really) to answer customer’s questions anytime! This is significantly cheaper than having live agents with rotational shifts.

If the customer support query is complex or beyond the scope of the chatbot, there is a seamless process to hand off the query to a live agent based on their skill sets and current workload. This also helps the customer support agents to focus on customers which require a detailed solution for their difficult problems. This is where the magic happens! The marriage of AI and traditional customer service is the way forward.

Conversational Commerce:

Conversational Commerce has revolutionized eCommerce. Over the years, brands have realized the true potential of messaging platforms. Conversational AI is being leveraged by some of the largest brands to provide shopping assistance to customers. This is indeed a great opportunity to reduce costs incurred for hiring sales agents and training them. A robust conversational commerce tool is capable of providing superior customer service than a sales agent.

You can truly understand user requirements and provide expert-like guidance to nudge visitors ahead in the buyer’s journey. An intent-based recommendation engine helps you automatically match user requirements with your product catalog, descriptions, customer reviews, and other historical data to offer relevant buying guidance and increase conversions. Know more about how Haptik’s Conversational AI platform can help you sell seamlessly across all messaging platforms.

Agent Assistance:

Support agents need vast amounts of customer information to provide the assistance that customers need.

Conversational UIs (user interfaces) can monitor the conversations – whether by voice or chat – the agent is having with a customer and provide relevant information, screens, or prompts by intervening at appropriate times.

This reduces the hold time and there is no need to transfer the call to another agent because of not having access to enough customer knowledge. This results in a superior CX lowered support costs and high employee satisfaction.

One of the biggest advantages of using a Conversational AI solution is that you can integrate it with your existing systems. This allows your agent to have a 360-degree customer view on a single interface. With this, you can access variables such as the entire user history, chat transcripts, and customer data that assist you to provide a hyper-personalized experience.

What are the results of deploying a good CX?

There are umpteen benefits of an ace CX strategy. Different industries care about different metrics. On a whole, a robust Conversational AI solution can help you decrease your customer support costs by automating queries and boost revenue by powering conversational commerce. Not only it helps enhance the support and purchase experience but also reduces operational overheads. Let’s look at how these things translate into results:

Brand Loyalty & Customer Satisfaction:

According to PWC, 73% of consumers say a good experience is key in influencing their brand loyalties. It is a known fact that it is easier and cheaper to retain existing customers as compared to winning new ones. CX plays a huge role in customer retention. A good CX guarantees brand loyalty and increased customer satisfaction. Globalization has led to cutthroat competition in every industry. There’s no shortcut or hack to winning customer loyalty. Brands have to fight and fight hard to win the CX game!

Word-of-mouth marketing and recommendations:

Brands can splurge all the money they want on advertising but nothing beats word-of-mouth marketing. Happy customers will always talk about the product to their friends, family, and colleagues. Good marketing can’t save a bad product but a good product can almost market itself. A good CX promises more customers, more revenue, and more profits!

Increased Profits:

This is the end goal of every business. But very few companies prioritize a customer-centric approach. Developing a CX strategy requires you to be obsessed with your customers and what drives them. Increased brand loyalty, customer satisfaction, and positive word-of-mouth marketing assures lower costs and increased profits.

Key Takeaways

A recent study states that 70% of the buying experience is based on how the customer feels they are treated. Hence, Customer Experience is every businesses’ key area of focus. As more organizations take the digital route, this focus will intensify even further. Companies need to broaden their definition of CX. It is not just limited to customer interaction but extends to how a customer feels about your brand. A well-crafted CX strategy is stitched at the intersection of data, customer behavior, marketing, and empathy. Artificial Intelligence and Machine Learning have created unparalleled scope for companies to enhance customer communication. It’s now time for brands to integrate technology and psychology to take CX to another level.

A key component of every brand’s CX strategy is Conversational AI. Conversational AI has allowed brands to think beyond basic conversations. Chatbots engage and build relationships with customers which go a long way in boosting CSAT and NPS. In case you’re interested, here’s an eBook we’ve put together that shares the experiences of a diverse set of CxOs as a part of their journey to identify feasible, realistic solutions to solve the challenge of repairing a broken customer experience and scaling high-volume customer queries with AI Automation. You can get your copy here.

Join us in our journey to transform Customer Experience with the power of Conversational AI.

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dsc weekly digest 7 june 2021
DSC Weekly Digest 7 June 2021

Computer languages over the years tend to rise and fall in popularity, depending upon what the job market looks like, what’s the hot technology du jour, and what needs it fulfills. There was a time in the not-so-distant past when Ruby on Rails was the must-have language out there, yet Ruby now seldom cracks the top 20 languages in most people’s surveys. I can even remember a time when LISP was the dominant language in the artificial intelligence space, though you’re more likely today to find LISP only as faint echoes in languages like Erlang and Clojure.

If you look through older articles on DSC you’ll find plenty of fodder about whether R or Python is the better language to learn, though by the numbers Python looks to be eclipsing R finally in the great language religious wars. However, the reality is that in the analytics space, your language choice is becoming less and less relevant.

This is partially due to the fact that most language vendors (and database vendors) are increasingly incorporating analytics libraries and extensions into their respective products and communities, especially in those places where you have the ability to compile these libraries into high-performance code.

For instance, native React (a Javascript-based variant) is increasingly taking on analytics and machine-learning loads that would have been unthinkable even a couple of years ago. The ability to push analytics processing capabilities out to edge devices and various web browsers is also changing the nature of the game, especially as web applications increasingly become serverless.

Similarly, DevOps is mingling analytics code and machine learning models with robotic process automation, using Javascript as the preferred glue. Additionally, you’re seeing more web-based analytics suites, where the need to write any formal algorithms is diminishing rapidly. That you still need to understand what the mathematical tools are supposed to do (and how they should be applied) is still true, but the days where data scientists spent all their time writing R scripts is likely in the rearview mirror.

Consequently, the best answer to languages for working as a data scientist is to learn at least one, whether that be R, Python, Javascript, Scala, Java, Clojure, Haskell, or even LISP (okay, perhaps not LISP, though it is a cool language … Scheme might be better) but more importantly learn how to write code in general. Become proficient with query languages including SQL, SPARQL, GraphQL, XQuery, because that’s where you’re going to spend the bulk of your time, and learn to think declaratively, just because declarative programming is more useful for working with data in general than imperative programming is. Finally, don’t discount Excel or other spreadsheets – a surprising amount of work in the analytics space is STILL done in Excel, and you will almost certainly end up having to work with it in some capacity. 

These issues and more are covered in this week’s digest. This is why we run Data Science Central, and why we are expanding its focus to consider the width and breadth of digital transformation in our society. Data Science Central is your community. It is a chance to learn from other practitioners, and a chance to communicate what you know to the data science community overall. I encourage you to submit original articles and to make your name known to the people that are going to be hiring in the coming year. As always let us know what you think.

In media res,
Kurt Cagle
Community Editor,
Data Science Central

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top futuristic modes of transportation that will shape the taxi industry
Top Futuristic Modes of Transportation That Will Shape the Taxi Industry

With the entry of the latest advances in the transportation industry, the current going time has been remarkable. All the newest technology advancements bring new hope and contribute towards shaping the taxi businesses. Now that we are in the modern era, booking a taxi ride within seconds is possible. Therefore, all the companies involved in the taxi sector should leverage new technologies to produce futuristic modes of transportation or quality taxi services. This is a necessary step to take to remain competitive in the taxi industry. 

According to the recent reports, the USA taxi market will showcase 30% growth over the next five years. In 2021, the market is expected to generate 3 billion USD in revenue. At this time, a minimum of 10 million ride-hailing trips are made worldwide, and the number is predicted to reach 80 million by 2030.

Future Transportation Modes That Will Impact the Taxi Industry

The transportation sector will acquire new, more innovative sources of energy, future means of transport, and physical and technological infrastructure. Modern technology, electrification, and autonomy are three prevalent topics in transportation innovation. Given the rapid growth of these technologies in recent years, we may expect them to all play a significant role in our transportation future.

SELF DRIVING OR AUTONOMOUS VEHICLES

While the future of the transportation sector is unwritten, there is still something that our leaders promise – the origin of autonomous driving. Autonomous cars with the sensing feature will be capable of judging the environment without human efforts. Just like classic cars, self-driving cars can also go anywhere. There is no need for humans to take control of the vehicle at any time. 

Behind the operation of self-driving cars, sensors, complex algorithms, ML systems, and powerful processors, they all work. Based on various sensors incorporated inside, autonomous cars automatically map the nearby surroundings. 

Thanks to the dedicated taxi dispatch software that makes it possible for self-driving cars to process the sensory input, send instructions to the actuators and ensure smooth steering. Some of the benefits of self-driving vehicles are

  • Minimize traffic blockage
  • Cut down operating costs
  • Lowers the emission of Urban Carbon Dioxide

FLYING TAXIS

We are in the era where the transport industry has now become more convenient to customers. Flying Taxis could be the next future means of transport that will surely make a significant impact on this industry. The example of major brands – Uber, Airbus, and Toyota- are investing millions of dollars to make vehicles take off and land at a specific place. The origin of Air Taxis will surely change the way transportation businesses run along with an array of advantages for passengers. 

Electric air taxis will come in various shapes and look different from conventional fixed-wing aircraft. Takeoff and landing taxis are specially crafted to minimize the need for long runways. In addition, companies are looking forward to launching taxis with rotating wings and the one that looks like cars, to help in taking the travel experience to the next level.

CONVERTIBLE CAB CUM STORE

In the future, ride-sharing vehicles would be able to transform into new structures. In addition to flexibility, there would be an expansion in the essential dimension of cabs. The company Toyota has come up with an idea how taxis can be changed from the typical taxi to Cab cum store for the selling of any products. Organizations are now bringing incredible changes to make cabs convertible in the forthcoming future. 

The future says that ride-sharing cars will possess the ability to amplify their versatility and functionality. For example, a convertible cab cum store concept will transform the regular cabs into delivery vans that also sell merchandise. Plus, there is also modification to the seats inside vans, and they are more convertible. 

Well, the future of the taxi or transportation industry is welcoming because of a few technological advancements like

ARTIFICIAL INTELLIGENCE

The automotive industry is taking steps in making a vast investment for a bright future. Now the companies are focusing more on Artificial Intelligence to get the most out of self-driving technology. By leveraging the potential of AI, they want to introduce autonomous vehicles and bring a transformation in the transportation industry. 

The AI-powered pickup and delivery app will allow passengers to have reliable travel journeys, experience secure payment options, and get everything done via a smartphone. As a result, people living in urban areas will become more intelligent than before, and traffic management is seamless. This further helps enhance the accessibility for travelers. 

GPS (Global Positioning System)

It’s a well-heard term in the digital age, and one is well aware of GPS and its functionality. However, you can find a significant difference between the latest GPS systems and old ones. Those days are over when GPS-based devices are used to calculate the estimated time of the final destination. But now, the systems can do more than that. The latest GPS devices can better plan any trip and have made the public transportation system smarter and hassle-free. 

SAAS-BASED PLATFORM

People are now less reliant on transportation modes that provide a lack of safety and raise the difficulty for the regular commuters. For safe, secure, and affordable transportation, most companies have started serving transportation services via a SaaS-based platform. Such a solution helps eliminate the issues related to crowd traffic on roads. Moreover, it’s an entirely safe platform, especially for women and employees. They can have a fast and secure taxi service and reach the destination on time. 

ALTERNATIVE FUELS

There are now more convenient traveling options for regular travelers that make them feel safe throughout the journey. Because of the immense popularity of modern automobiles, usability has increased, and it will grow even in the coming years. It is predicted that more than 50% of vehicles will be produced by the end of 2030 that use combustion engines, hybrid technology, and electric power to operate. With this, it is clear that there will be a significant reduction in Greenhouse gas emissions.

INTELLIGENT TRANSPORT SYSTEMS (ITS)

Nowadays, there is the massive popularity of the ITS, and its usage is making a significant difference in the transportation industry. Such systems include Information and Communication technology to make the management of transportation easy. 

The essential sections inside the system are real-time and smart parking management, automated speed feature, rider information system, and many more on the list. All these additions inside the system have made transportation more organized than before. 

SUMMING UP

The transportation sector keeps revolutionizing with the latest modes, trends, and technologies. Now that taxis have become an integral part of human life, many means of transportation will take place in the future for more convenience. Whether one is going shopping or the workplace, the advanced technologies and modes will help improve the lifestyle. 

The future modes of transportation and latest advancements in transportation will change the way people travel. The current time is the start of leveraging advanced technologies in transportation to drive a promising future. 

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react native vs ionic choose the right one in 2021
REACT NATIVE VS IONIC: CHOOSE THE RIGHT ONE IN 2021

REACT NATIVE VS IONIC: CHOOSE THE RIGHT ONE IN 2021

The “hybrid vs native” question still lingers among programmers and coders. In 2021, the choice is between React Native and Ionic, the two biggest players in the mobile app development frameworks market. According to AppBrain,

  • Ionic is the most used in app development at 3% while React Native is at 1.3%.
  • Market share in terms of app installs show 4.05% for React Native and 0.27% for Ionic.

To understand this stat, let us look at the difference between a native app and a hybrid app.

NATIVE VS HYBRID

Native and hybrid apps differ in many ways including UX, performance, technology used, features, and associated costs.

  • Hybrid Mobile Apps

The underlying tech in hybrid apps are HTML, CSS, JavaScript and other web technologies. WebView is the platform that they all run on. Through native modules and plugins, they can be developed as Single Page Apps or Progressive Web Apps.

  • Native Mobile Apps

Platform specific UI components are needed to run native mobile apps. Java for Android and Objective-Cor Swift for iOS are used to build these apps. They cannot be reused between platforms and there are about no drawbacks to the efficiency of these apps.

Now, with a surface understanding of native and hybrid mobile apps, let us know a little bit about React Native and Ionic.

REACT NATIVE

React Native is a JavaScript based framework. It helps create native mobile apps and is supported by a huge community of developers. The corporate backing comes from Facebook. It uses the “learn once, write everywhere” principle. Facebook, Oculus, Coinbase, Shopify, Tableau, FlipKart, Discord, NerdWallet, Skype, Bloomnerg, Tesla, and Wix among many others use the React Native framework.

IONIC

With a shared codebase between mobile, web, and desktop apps, Ionic is a hybrid development framework for web apps. Ionic is rich in features and much more capable than web apps since it uses Cordova and PhoneGap to access native features. Ionic follows the “write once, use everywhere” principle. The Ionic framework has been used by MarketWatch, Pacifica, Sworkit, Diesel,StockPlan Connect, Honeyfi, McLaren, JustWatch, mcDonald’s, Untappd, Nationwide, and Cryptochange among a plethora of others. According to Ionic, over 5 Million apps have been created using its framework.

Now let us take a quick look at how the Ionic framework and the React Native framework compares against each other and determine the best one for 2021.

IONIC VS REACT

React uses features that are native to the UI. This is what gives it the aesthetics of a native application. The native form also allows the developers to give a seamless experience to the end user.

Ionic uses web tech. This enables it to achieve multi-platform applications. It is based on Angular JS and uses the code base in a minimal fashion. Angular enables Ionic to have a smoother process and be extremely user-friendly.

  • Performance

React demands the developers to make platform-specific changes to achieve the most efficient components. React Native has a much more stable framework. This makes it efficient for large scale projects with higher budgets. The framework also enhances responsiveness and detailing of the UI.
On the other hand, Ionic’s hybrid approach makes it much more convenient for prototyping or facilitating an urgent requirement. Using CSS, JavaScript and HTML5 components, Ionic enables faster app development but the user might be asked to download additional plugins to enable the native features.

As discussed earlier, React uses the “learn once, write everywhere” logic. This framework will suggest suitable components that respect the native aesthetics, to the developers.

Contrary to that, Ionic can run the same code across all platforms. It adapts to the platform and learns its behavior in order to provide the native aesthetics.

Apart from these differences, React native has a community of 1752 contributors while Ionic has just 268. And in order to pinpoint the better one for 2021, we need to understand the benefits and drawbacks of both Ionic and React Native.

ADVANTAGES OF IONIC

  1. Open source
  2. Easy to learn
  3. Built in components
  4. Speed development cycle
  5. Familiar development environment
  6. Cordova and PhoneGap wrapping

DISADVANTAGES OF IONIC

  1. Requires WebView
  2. Apache Cordova required to access hardware functionality
  3. Slight performance issues

ADVANTAGES OF REACT NATIVE

  1. Stable and reliable for large scale projects
  2. Android, iOS and Windows app code re-usability.
  3. Codes are not dependent on the platforms
  4. Vast community

DISADVANTAGES OF REACT NATIVE

  1. Require native development skills.
  2. Lack of custom modules
  3. Slight debugging and compatibility issues.

Now that we have laid down the facts and figures, let us conclude by determining if one is above the other.

SUMMING UP

The choice will be largely influenced by the type of project you are undertaking, the time of development, and the skills garnered by the team. According to a popularity survey by Ionic,

  • 86% of web developers have used Ionic and 16% went for React Native.
  • To build PWAs, 72% preferred Ionic while 21% picked React Native.
  • On GitHub, Ionic has 41,000 stars and React Native has 89,100 stars.

These statistics boil down to the fact that it is not feasible to put one over the other objectively. We can conclude by understanding that if the requirement is a low budget, high performance app in a limited time, Ionic is the better one and if the requirement is a high budget, large scale app, React Native is the better one, for 2021. At Orion, we specialize and deliver excellence in both.

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