Artificial Intelligence for Human Intellect: The potential of AI in Education

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We can say that the opportunity to acquire knowledge in older times was a privilege. If we look at medieval Europe, where only clergymen and members of the upper classes could study and even among those only men, the first sentence must be true. However, this does not mean at all that in today’s modern world, everyone has equal access to education. The less developed countries of the world suffer from inadequate education infrastructure due to poverty, even though it would be advanced education that could solve the problem of poverty.

How important is the quality of education for the well-being of a nation? As we will see: very much!

Let’s take a look at some of the outstandingly successful countries of the 20th Century in terms of development. I don’t think of anyone other than the tigers of Asia: Hong Kong, South Korea, Singapore, and Taiwan. These countries (or parts of countries) that have been able to show and sustain incredible, stable growth of over 7% for several decades between the 1960s and 1990s.

What is the reason behind this unparalleled result?

A country’s economy is capable of such growth if its companies suddenly become globally competitive. The goods they extract and sell will drag the country’s economy with them. Companies need a fresh, skilled, up-to-date workforce who can only reach this level and be competitive if they have actually acquired this knowledge before. The state must create the necessary conditions for this and devote massive financial funding to the development of education.

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A common point in the success of the Asian tigers is that they all spent relatively large sums on education in the 20th century. South Korea, for example, spent 9% of its GDP on education in 1973. In these countries, attitudes towards human capital are also culturally favorable and the population has a greater appreciation for citizens with a degree. Without this cultural mindset and financial support for education, it is highly unlikely that these countries could have accomplished the transition from a developing country to a developed country.

To what extent can artificial intelligence improve such an important area of ​​human interaction?

Very much!

If we think about it, the current education system hasn’t evolved much in the last 200 years. The individual competencies of students are ignored and every one of them needs to listen to the same presentation. Meanwhile, we emphasize everyone’s individuality and that no two people are the same in both ability and talent. Here the question arises: Is it not a sin to treat the individual as identical?

After completing basic education students can, in principle continue their studies in a specialized field according to their abilities, but how true is this? Everyone has and should have the freedom to choose a career, but unfortunately, we see students who have lost their careers more often than enough. According to a professor from a real but unnamed university, ⅓ of graduates are dropouts. But there is a more desperate data: according to a study by the Federal Reserve Bank of New York, only 27% of college graduates work in a field related to their degree. This data already amounts to a confirmation that the education system has failed to match competencies to market needs.

This number can be so high for several reasons. On the one hand, students tend to recognize that they have not chosen a career according to their strengths and do not want to stay in the field.

“Everybody is a genius. But if you judge a fish by its ability to climb a tree, it will live its whole life believing that it is stupid.” — Albert Einstein

On the other hand, market coercion plays a role. Unfortunately, not all degrees are needed by the market in the quantities available. However, the reverse is also true, there is not enough labor from many marketable shortage occupations.

Modern data science and AI can provide a solution to all this.

The emergence of technologies such as Big Data coupled with AI & Data science does wonders when there is a large amount of data available which can then be used to observe the patters to successfully solve the problem.

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Every student has to go through a learning curve whether they like the subject that they learn or not which can give the opportunity to collect a large amount of data if we record every single struggle, achievement and learning behavior of every student which can help the teacher to evaluate the strengths and weakness in the learning process of a particular pattern of a student hence discovering the hidden talent that every student carries inside without actually recognizing it. These fields of science could be used to analyze student performance: the learner is skillful geometry but poor in algebra. Market needs could also be analyzed in more depth from data collected from employers. A computer would be able to compare student competencies, analyze employers’ needs, and forecast expected demand for knowledge. We could personalize educational plans and develop individual strengths. Data-based communication between the education system and the labor market would bring an excellent student in geometry to a company that requires the knowledge of an excellent student in geometry.

An education system based on data analysis is, of course, theoretical and utopistic. The achievements of the future are not born out of anywhere. There must be real people behind it who are doing the necessary work that will give life to these technological innovations. All the conditions for this development in the 21st Century are given.

If there are already companies that use artificial intelligence to make self-driving cars, diagnose tumors, and predict cryptocurrency market movements, it is only a matter of time before artificially intelligent schools appear.

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Artificial Intelligence for Human Intellect: The potential of AI in Education was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/artificial-intelligence-for-human-intellect-the-potential-of-ai-in-education-33d0d8f04d4c?source=rss—-5e5bef33608a—4

source https://365datascience.weebly.com/the-best-data-science-blog-2020/artificial-intelligence-for-human-intellect-the-potential-of-ai-in-education

Quantum Computing with Q# on macOS Entanglement

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AI Disruption 2020- Which Businesses are Likely to Get Affected?

AI Disruption – Which Businesses are Likely to Get Affected?

“Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years,” said the former chief scientist of Baidu, Andrew Ng, in 2017.”

AI is bringing technological transformation in every industry by automating their processes, offering greater personalization to its customers, and disrupting how they work. It’s a computer program with particular aspects of human intelligence or the ability to think like humans.

In the last two years, artificial intelligence has disruptive and improved many business verticals and also became the main target of venture capital funding with investments into the billions. AI start-ups, such as Vacasa, Samsara, ThoughtSpot, CloudMinds, TripActions, SparkCognition, and more, which have raised millions, are a sheer proof that AI is a billion-dollar business.

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Like the previous years, in 2020 and the upcoming years, AI with the power of machine learning, deep learning, and data science is going to disrupt various industries. Let’s take a look of the top five industries to be disrupted by AI:

1. Fashion

Fashion is one of the biggest industries in the world that has been transformed by artificial intelligence. Be it designing, manufacturing, or marketing of the fashion products, AI is there in every process. The technology is helping fashion brands and businesses to look into their customers’ behavior, improve brand awareness, enhance the shopping experience, and boost sales.

AI enables intelligent automation to automate various tasks and predictive analytics to analyze demand, purchase patterns, and other sales aspects. Moreover, artificial intelligence also comprises computer vision that helps in improving manufacturing efficiency by figuring out defects in the fabric and recognizing counterfeit items.

2. Fitness

With people preferring home workout over visiting a gym, the demand for online fitness applications has increased. These apps include various exercises that people can do according to the instructions mentioned in the app. However, one major drawback of such apps was that there was no way of monitoring if the posture during the exercise is correct or not. The implementation of artificial intelligence has removed this barrier by providing a way to maintain the right posture during exercises. Zenia, an AI-enabled application, that offers a comfortable and reliable way to practice yoga, is an example of this.

3. Healthcare

AI adoption in healthcare has brought a major transformation by allowing medical professionals to get precise and relevant data about patients. Artificial intelligence enables predictive analytics that help doctors to look proactively into the patient’s health. The technology also ensures better accuracy in diagnostics, scans, and other health tests, which not only saves money as well as time.

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1. AI for CFD: Intro (part 1)

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3. Real vs Fake Tweet Detection using a BERT Transformer Model in few lines of code

4. Machine Learning System Design

The availability IoT (Internet of Things) -enabled devices, doctors can even monitor their patients remotely and prescribe them the right medication. Apart from this, health chatbots powered by AI are also there to help patients get answer to their queries and assist doctors to collate preliminary data about the patients.

4. Customer Support and Services

There is no denying that the chatbots built using the natural learning process or NLP (a subset of artificial intelligence) have already started to disrupt customer services. These chatbots are programmed to receive a customer’s query and provide its solution (using predefined responses) within seconds. The arrival of AI in the customer service domain has improved the efficiency by segregating the queries and transferring it to the concerned customer support executive. The chatbot also mitigates the initial part of the conversation by asking the primary details and the query of the customer at first, which saves a lot of time.

5. Real Estate

Just like all other domains, AI has also started to disrupt the real estate business. AI tools are being used to provide improved services to the customers looking out to sell, buy, or rent any property. AI-powered chatbots work round the clock to provide customers, searching for a property, with the most relevant recommendations.

It also help the real estate agents to know customers’ interest and send them the relevant options or follow-up the leads. AI in the real estate world also helps the businesses to minimize paperwork by collecting the preliminary details and other required information before human intervention.

To sum up, it would be no wrong to say that AI is the most revolutionary technology of this century that is changing the way industries work. This is one of the possible reasons that we are observing a big hike in the demand of AI-powered applications and solutions. If you are also looking out to integrate AI in your business or AI app development, then contact a reliable AI solution provider or hire AI developers now.

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AI Disruption 2020- Which Businesses are Likely to Get Affected? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/ai-disruption-2020-which-businesses-are-likely-to-get-affected-dc9b2df76e8e?source=rss—-5e5bef33608a—4

source https://365datascience.weebly.com/the-best-data-science-blog-2020/ai-disruption-2020-which-businesses-are-likely-to-get-affected

KDnuggets News 20:n20 May 20: I Designed My Own ML and AI Degree; Automated Machine Learning: The Free eBook

How to design your own AI & ML degree; Automated ML: The free ebook; Coding habits for data scientists; Cartoon: The Worst Telemedicine? Math for Programmers; and more.

Originally from KDnuggets https://ift.tt/3cPaywa

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Google Unveils TAPAS a BERT-Based Neural Network for Querying Tables Using Natural Language

The new neural network extends BERT to interact with tabular datasets.

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source https://365datascience.weebly.com/the-best-data-science-blog-2020/google-unveils-tapas-a-bert-based-neural-network-for-querying-tables-using-natural-language

Top 5 Courses to Learn Pandas for Python and Data Science

Top 5 Courses to Learn Pandas for Python and Data Science — Best of Lot

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Pandas is one of the most potent and popular Python libraries for Data Analysis. It’s also one of the favorite tools for Data Scientist because it helps them in cleaning, transformation, manipulation, and analysis of Data. To be honest, data in the real world is messy, and before you can start with your analysis, you need to clean and transform the data in the format you want. A tool or library like Pandas really helps there.

That’s why it’s essential to learn about Pandas while doing Data Analysis with Python. It’s even more critical if you are in the field of Data Science and Data Analysis. If you know Paands, well and good, but if you don’t nevermind, as I am going to share some of the best online courses to learn Pandas and Data Analysis with Python in 2020.

Jobs in Big Data

It also offers SQL like a powerful construct for exploratory analysis. Just like in SQL you can check the data in a table using SELECT TOP 10 * from TABLE or Linux you use the head command to get a glimpse of data in a file, you can also use Pandas similarly. It offers the head() function to show a limited amount of data.

If you don’t know Pandas are closely related to NumPy (Numerical Python), one of the fundamental package for scientific calculation. Two of the main Pandas object are Series and DataFrame, which are similar to one and two-dimensional array in general programming.

It also offers commands like read_csv, which can load the data, both small and large. You can also use tips like adding nrows=10 to only read a tiny portion of the data before actually loading the whole data.

At the core of practicing practical data science is a thorough knowledge of data analysis tools, and among them, Pandas is one of the most popular. Pandas is great for working with tabular data like data organized into tables with have rows and columns. It’s similar to SQL or Excel, but Pandas adds the power of Python.

Last but not least Data Analysis with Pandas is also a very in-demand skill, just in case if you are looking for a job. After going through these hands-on courses, you would have enough knowledge to update your resume with this in-demand skill: Data analysis in Python with Pandas.

5 Best Pandas courses to learn Data Analysis with Python

In the last couple of years, Pandas has established itself as the most essential Python library for Data Analysis. It provides a robust toolkit for analyzing, filtering, manipulating, aggregating, merging, cleaning, and pivoting data.

Pandas is also great to work with tabular data, and it’s also one of the must-know tools for Data Scientist. Because of this power and its data analysis ability, many people call Pandas as “Excel for Python” or “Excel on Steroids.” Now that we have convinced with the utility of the Pandas library, let me share with you some of the best resources to learn and master both Pandas and Data Analysis with Python.

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These are the best online courses I have come across while learning Pandas. They are both hands-on and contains the right mixture of theory and practice. Though, you don’t need to join all these courses, choose a couple of ones where you can connect with Instructors.

1. Data Analysis with Pandas and Python

This is one of the best courses to learn Data Analysis with Pandas and Python. This is both hand-on and interactive. Course Material is also well organized, and delivery is fantastic.

However, It would have been even better if there is a project to get an idea of real-life exercises. It also includes a crash course on Python for those who are not comfortable with Python or those who need to refresh their Python fundamentals.

Here is the link to sign up for this course — Data Analysis with Pandas and Python

This is also one of the best Udemy course on Data analysis, Pandas, and Python, and it’ a worth much more than just $10 I paid for it.

At the moment, more than 117,679 students enrolled in this course, and it has, on average, 4.6 ratings from close to 6,158 participants. A big thanks to Boris Paskhaver for creating this excellent course.

2. Data Analysis with Pandas

This is another excellent, inter-active course to learn Data analysis with Pandas online. This course not only teaches you Pandas, a powerful python library for data analysis, but also shows you ho to ingest, clean, and aggregate large quantities of data, and then use that data with other Python modules like Scipy (for statistical analysis) or Matplotlib (for visualization).

This course also covers how to Create Pandas DataFrames, calculate aggregates, and merge multiple tables, along with interactive exercises, quizzes, and assessments.

Here is the link to sign up for this course —Data Analysis with Pandas

If you like Interactive learning and have a CodeCademy membership, I strongly recommend this course for learning Pandas.

Btw, If you don’t have CodeCademy pro membership, you can get in just $16 monthly, which provides access to many quality courses, a complete worth of your money.

3. The Complete Pandas Bootcamp: Master your Data in Python

This is one of the most practical courses you will find online to learn Pandas. The comes with real data and 150+ exercises for analyzing that data. This project and practice will make you a Pandas master.

You will learn to import, clean, and merge messy Data and prepare Data for Machine Learning by import Financial/Stock Data from Web Sources and analyze them with Pandas. Learn and practice all relevant Pandas Methods and workflows based on the lastest Pandas Version.

Analyze, visualize, and understand your Data with Matplotlib and Seaborn. This is also one of the highest-rated courses with, on average, 4.6 ratings from 2,860 students enrolled. A big thanks to instructor Alexander Hagmann for creating this excellent course.

Here is the link to sign up for this course — The Complete Pandas Bootcamp: Master your Data in Python

4. Pandas Fundamentals By Pawel Kordek

If you have a Pluralsight membership and looking for a course to learn Pandas, then this is the best course for you. In this course, you’ll explore the world of data science by learning core Pandas functionalities, including its IO capabilities, plotting methods, and DataFrames.

If you don’t have Pluralsight membership, then you can check out this course by taking their 10-day free trial, which provides 200 minutes of watch time, enough to complete this course.

If you want, you can also join Pluralsight, their membership cost around $29 per month, but they also provide access to close to 5000+ online courses on the latest technologies.

It also has quizzes, assessments, and interactive exercises to learn things quickly.

Here is the link to sign up for this course — Pandas Fundamentals By Pawel Kordek

There is also the second part of this course, Advanced Pandas, which will teach you how to handle complex data-sets and analyze your data in a principled way with Pandas.

This is like the next part of the previous course, which taught Pandas fundamentals. This will show you advanced parts of this library, including handling higher-dimensional data, time series, window operations, joins, and plotting.

5. Introduction to Data Science in Python

When it comes to learning Data Science tools and concepts, Coursera is the best platform. It has numerous detailed and quality courses to learn Data Science, and this is one of them which will also teach you Pandas.

Along with basic Python and NumPy skills, the course will introduce data manipulation and cleaning techniques using the famous panda’s library.

It will also teach you the abstraction of the Series and DataFrame as the central data structures for data analysis. It also has tutorials on how to use functions such as group by, merge, and pivot tables effectively.

By the end of this course, you will be able to take tabular data, clean it, manipulate it, and run necessary inferential statistical analyses.

Here is the link to sign up for this course —Introduction to Data Science in Python

This course is also part of the Applied Data Science with Python Specialization, which is one of the most popular Data Science Certification on Coursera and taken by more than 350K learners.

If you are looking for a Data Science Certification, I strongly suggest to make a thin one; it’s both reputed, offered by the University of Michigan, and well structured.

Btw, Pandas is just one of the many excellent Python libraries for Data Scientists like NumPy, SciPy, TensorFlow, and Matplotlib. Each of these libraries has its strengths, and Pandas’ advantage is Data Analysis like cleaning, filtering, and manipulating data.

Anytime if you want to work with a dataset, where you need to clean the data to bring it in a shape you want to or tweak it for your need, Pandas can do that for you. It’s like any spreadsheet software like Excel or Numbers on Mac or Google Spreadsheet, but it’s built on Python.

But, it’s also powerful like it can do computation with millions of rows within seconds, which is not possible with many spreadsheet software, and that’s why many people call it “Excel on Steroids.” You can also use it to deal with different types of data like string, text, integers, floats, booleans, etc. It’s like Excel but with more power, which comes from Python code.

That’s all about some of the best online courses to learn Pandas and Data Analysis with Python in general. As I told, Pandas is an industry-standard Python library for data manipulation and analysis and one of the most critical tool for Data Scientist along with SQL and NumPy.

If you aspire to become a Data Scientist or Data Analyst, then you should spend some time learning Pandas; it will go a long way in not just getting a job but also with your day-to-day work as Data Scientist.

Also, you don’t need to join all these courses, choose a couple of ones, and stick to that. A good suggestion is the first course in this list, and if you love interactive sessions, then the CodeCademy’s Pandas course is also excellent.

One of my personal tips to join the right course is watching previews. If I can go through a couple of lectures without skipping, it’s a good sign that I can connect to the instructor, and its an excellent course.

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Thanks for reading this article so far. If you like these Pandas online courses for Data Analysis with Python, then please share it with your friends and colleagues. If you have any questions or feedback, then please drop a note.

P.S. — If you are keen to learn more about Data Science and Machine Learning and just want to do one thing at this moment, go join the Data Science A-Z: Real-life Data Science course by Kirill Eremenko on Udemy. You won’t regret your decision.

Data Science A-Z™: Real-Life Data Science Exercises Included

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Top 5 Courses to Learn Pandas for Python and Data Science was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/5-best-courses-to-learn-pythons-pandas-libary-for-data-analysis-and-data-science-34b62abb0e96?source=rss—-5e5bef33608a—4

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8 Ways Hoteliers are Employing Virtual Assistants to Woo Customers

In today’s digital world, customers expect business owners and entrepreneurs to leverage the use of technology in providing top-of-the-line satisfaction and tech-driven guest experiences experience for customers.

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Just like in most industries today, artificial intelligence, machine learning, and big data analytics are impacting and reshaping the travel and hospitality industry in no small measure.

Business outfits that fail to maximize these advancements in technology will see themselves falling behind more innovative brands. One way in which brands in the travel and hospitality industry are driving positive customer experience is through the use of a virtual personal assistant.

Virtual assistant solutions are becoming one of the bedrocks for guaranteeing superb, magnificent, and personalized customer experience for most business outfits today, so much so, Gartner predicts that by 2021, about 15% of business interactions with customers will be undertaken by virtual assistants.

Jobs in Big Data

Virtual assistants, powered by AI technology, make access to travel logistics, hotel reservations, and bookings a breeze. The growth of online virtual assistants is changing the way guests purchase and consume services in the travel and hospitality industry.

Basic, routine queries are now being handled by virtual assistants, thereby allowing company employees to focus on more demanding tasks. As a matter of fact, the virtual assistant market is expected to generate a whopping $15 billion in revenue by 2021.

Hyatt, one of the leading hospitality brands in the world today, saves as much as 4.4 million dollars annually by implementing an intelligent, conversational virtual assistant to improve its sales efficiency and overall customer experience. By automating and streamlining routine tasks for on-the-go travellers, Hyatt was able to provide its customers with cost-effective, time-saving, and revenue-generating customer service solutions.

Let’s now take a look at some areas in which brands and customers in the travel and hospitality industry are benefiting from the services of virtual assistants.

How Virtual Assistant support is impacting the travel industry.

1. Booking and Reservations:

On a daily basis, hoteliers and front-desk hotel employees are faced with routine, repetitive queries. It is common to come across basic questions such as:

● Are there any available rooms tonight?

● Can I bring my dog, she is well behaved?

● Will there be any extra charges for my pet?

● Are there conference facilities available in your hotel?

● What are your prices or rates per room?

Virtual assistants can be set-up to attend to most of the day-to-day repetitive inquiries, rudimentary work, as well as, other numerous administrative tasks faced by front-desk officers in the travel and hospitality industry. When virtual assistants are recruited to give smart answers to basic questions, employees and other front-desk officers can focus on more pertinent and productive tasks that will generate more revenue for the organization.

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With a smart virtual assistant, your clients can check availability of their preferred hotels, check airline schedules, they can make or change reservations, and they can also get a heads up as regards the cost of rental cars and prices of other services that they might want to use.

2. Meal suggestions and orders:

Guests can ask questions regarding available meals or meal ideas from virtual assistants right from the comfort of their hotel rooms. Guests can as well place orders for their preferred meal without leaving their hotel room. No need to reach out to a front-desk officer to ask basic questions or order a meal.

3. Room service requests:

Hoteliers can take the hassle away from front-desk employees by engaging a virtual assistant to deal with all room service requests from guests. Some hotels provide their guests with virtual assistant functions on their mobile apps that enable guests to request for basic room service needs and amenities. Guests can also ask questions and get answers through the use of virtual assistants.

4. Cab bookings:

With the help of a virtual assistant in your hotel, your guests can book a cab; they would not need to call the front desk officer. Uber car hire services are still not present in some countries, so intelligent virtual assistant services will really come in handy when guests need to order for a cab.

Some hotels provide virtual assistant services to customers by giving them access to a mobile app where they can choose the services they want with a tap of a button or with a voice command.

5. Itinerary/Travel planning and Logistics:

For vendors in the travel and hospitality industry, there is the challenge of providing your clients or guests with information as regards travel logistics in a way that is seamless and friendly to your bottom-line.

However, with intelligent virtual assistants powered by AI technology, stakeholders in the travel and hospitality industry can provide clients with all the travel information they need for travel planning, from take-off to check-in and even departure.

6. Concierge Services:

Virtual assistants can interact with your guests to provide streamlined concierge services for them. Virtual assistants can make personalized restaurant recommendations, handle registration of guests and check out, provide information on amenities that are available in different rooms in the hotel, book tours, and respond to other basic inquiries from your guests.

With the help of a virtual assistant, guests can also make inquiries about charges, request directions, and make necessary confirmation or cancellation of reservations on-the-go without having to contact a front-desk officer.

Employees, reservation agents, and booking desk officers in the travel and hospitality industry deal with answering a lot of repetitive, basic queries over and over again. But, with a virtual customer service assistant in place, most of these queries can be dealt with seamlessly, thereby allowing employees to focus on sales and other high-value tasks.

Best of all, brands in the hospitality industry can also automate their virtual assistant tool to collect valuable customer feedback and reviews on checkout. The importance of online reviews for the growth of business outfits cannot be overemphasized; this is why most individuals and agencies who are looking to hire writers today go to Online Writers Rating to check for reviews of quality writers.

7. Voice-activated virtual assistants in hotel rooms:

Virtual assistants are not only suitable for when guests are on transit from one place to another, but also during their stay in the hotel. While in the hotel room, guests would need so many things done. Voice-activated virtual assistant services can come in handy in this situation.

Guests can use voice-enabled virtual assistants to turn the TV on or off, change the light settings, set room temperature, and so on. All that a guest needs to do is to say the words literally in the hotel room, and they get a lot of things done.

8. Dynamic Hotel tours:

With the help of virtual assistants powered by AI technology, customers can get a feel of hotel buildings, rooms, and the surrounding environment even before they arrive. Potential customers can know what the hotel looks like before they book or make reservations.

Final words

In today’s digital age, convenience is king, and virtual assistant customer support services are already in use in some agencies in the travel and hospitality industry.

The travel and hospitality industry is witnessing tremendous growth, thanks to the rapid digital transformation going on in the industry. In 2016, the travel and hospitality sector worldwide generated an income of over 500 billion dollars. By 2020, income for the travel and hospitality industry is expected to reach a total figure of about $817.54 billion.

Moreover, Travelport, a travel commerce platform, processes as much as 12 billion searches and bookings made by travellers on a daily basis. This overwhelming amount of work and data processing associated with the travel and hospitality industry sheds more light on the need for agencies to begin to embrace tech-driven customer support in order to provide quality customer experience for users in a way that is fast, timely and cost-effective.

For one thing, through the use of virtual assistants, travellers can plan their trips more efficiently, customers can order fast food easily, bookings and reservations are easier, and the overall customer satisfaction and experience is even better. Employees can also focus on other equally important high-value tasks that directly affect the bottom line of the organization.

Not to mention the lower operational cost that agencies in the travel and hospitality industry will begin to enjoy as a result of automating basic tasks. With the overwhelming benefits derived from the use of virtual assistants in the travel and hospitality industry, agencies who fail to leverage this technological transformation will be left with no choice than to embrace the “adapt or die” principle.

Author — Ana Meyer

Ana Mayer is a freelance writer who is a qualified specialist in the field of digital marketing. She writes for different news portals and thematic blogs that helps her stay at the heart of the programming and technology news. Such work gives her the opportunity to write articles on the most relevant topics of today.

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8 Ways Hoteliers are Employing Virtual Assistants to Woo Customers was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/8-ways-hoteliers-are-employing-virtual-assistants-to-woo-customers-e9e0db3ab07a?source=rss—-5e5bef33608a—4

source https://365datascience.weebly.com/the-best-data-science-blog-2020/8-ways-hoteliers-are-employing-virtual-assistants-to-woo-customers

24 Artificial Intelligence Terms You Need to Know

With Artificial intelligence services becoming less than a vague marketing buzzword and a strict ideology, it is becoming increasingly challenging to understand all the AI terms out there. So to eliminate the new AI zone.

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A

Algorithms: a set of rules or instructions given to help AI, neural network, or other machines learn on their own; Classification, clustering, recommendation, and regression are the four most popular types.

Artificial intelligence: the ability of decision-making and decision making machines to simulate human intelligence and behavior.

Artificial Neural Network (ANN): A learning paradigm has been created to act as a human brain that solves tasks that are difficult for traditional computer systems to solve.

Jobs in Big Data

Autonomic Computing: The ability of the system for adaptive self-management of its own resources for high-level computing functions without user input.

C

Chatbots: A chat robot (abbreviated chatbots) designed to simulate a conversation with human users by communicating via text chats, voice commands, or both. These are the most commonly used interface for computer programs that have AI capabilities.

Classification: Classification algorithms allow machines to assign a category to a data point based on training data.

Cluster Analysis: A type of unsupervised practice used for exploratory data analysis to find hidden patterns or group them into data; Groups are made up of a measure of similarity defined by metrics such as Euclidean or likelihood distance.

To Know More: How Artificial Intelligence is Driving Mobile App Personalization?

https://www.usmsystems.com/application-development/

Clustering: Clustering algorithms allow machines to group clusters of data points or objects with similar properties.

Cognitive Computing: A computerized model that simulates the way the human brain thinks. Self-learning involves data mining, natural language processing, and pattern recognition.

Convolutional neural networks (CNN): A kind of neural network that detects and understands images.

D

Data Mining: Examining data sets and finding mine samples from those data that are more useful.

Data Science: An interdisciplinary field that combines scientific methods, systems, and processes from statistics, information science, and computer science to provide insight into the phenomenon through structured or unstructured data.

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4. Machine Learning System Design

Decision Tree: A tree and branch-based model used to map decisions and their consequences, similar to a flow chart.

Deep learning: the ability of machines to automatically simulate human thinking through artificial neural networks with information cascading layers.

F

Fluent: A type of situation that can change over time.

G

Game AI: A pattern of AI-specific to gaming that uses an algorithm to compensate randomness. It is the human-like intelligence in non-player characters and the computational behavior used to generate the player’s reaction-based actions.

Genetic algorithm: An evolutionary algorithm based upon these principles of genetics and natural selection, used to find optimal or near-optimal solutions to difficult problems that would otherwise take decades to solve.

H

Heuristic Search Methods: Support that reduces the search for the right solutions to a problem by eliminating the wrong choices.

K

Knowledge engineering: focuses on building knowledge-based systems, including all scientific, technical, and social aspects of it.

L

Logic Programming: A type of programming example where computation is performed based on a knowledge repository of facts and rules; LISP and Prolog are two logic programming languages used for AI programming.

M

Machine Intelligence: An umbrella term that includes machine learning, deep learning, and classical learning algorithms.

To Know More: AI APPLICATIONS IN VARIOUS FIELDS

Machine learning: One aspect of AI that focuses on algorithms, which allows machines to learn and change when exposed to new data without being programmed.

Machine Awareness: The ability of a system to receive and interpret data from the external world in the same way that humans use our senses. This is usually done with the attached hardware, although the software is also usable.

N

Natural Language Processing: A program must understand its ability to detect human communication.

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24 Artificial Intelligence Terms You Need to Know was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/24-artificial-intelligence-terms-you-need-to-know-a7807b207142?source=rss—-5e5bef33608a—4

source https://365datascience.weebly.com/the-best-data-science-blog-2020/24-artificial-intelligence-terms-you-need-to-know

What they do not tell you about machine learning

There’s a lot of excitement out there about machine learning jobs. So, it’s always good to start off with a healthy dose of reality and proper expectations.

Originally from KDnuggets https://ift.tt/36ePb4W

source https://365datascience.weebly.com/the-best-data-science-blog-2020/what-they-do-not-tell-you-about-machine-learning

Sparse Matrix Representation in Python

Leveraging sparse matrix representations for your data when appropriate can spare you memory storage. Have a look at the reasons why, see how to create sparse matrices in Python using Scipy, and compare the memory requirements for standard and sparse representations of the same data.

Originally from KDnuggets https://ift.tt/36aitlg

source https://365datascience.weebly.com/the-best-data-science-blog-2020/sparse-matrix-representation-in-python

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