Manufacturing the products is very costly as well as a complex process for businesses that are not having the right tools, resources, and equipment to develop their quality products. In the current time, machine learning has become more popular in producing and assembling items, helping in cost reduction and time reduction of the production of a particular product. As a matter of fact, around 40% of the potential value that can be created using analytics today comes from machine learning techniques.

It has been noticed that from the last 5-6 years exceptional technologies can help build rapid and robust models that drive a lot of functional improvements.

Benefits that ML provides

Here are some ways through which machine learning is positively impacting the production process and providing benefits to it:

Improving the process:

There are three aspects of business namely operation, production, and post-production. Most of the manufacturers are successful in adding machine learning into these three aspects of their businesses. For example – Fanuc is a Japanese manufacturer of robotics and automation technology that included the machine learning process. It uses deep reinforcement learning that is a machine learning solution. Also, it enables and helps the robots to learn new skills very quickly and effectively. It doesn’t need precise and complex programming anymore.

Product Development:

Data has proved to be a very important entity as it brought nig opportunities for all the manufacturing companies in their product development process. It helps their businesses to understand customers and their needs and successfully meet their demands. This way, it helped in the overall development of a new and better product for the customer base. This data is valuable as the manufacturers are now able to create a product with an increasing customer value and it reduces the risks that are connected with the introduction of some new products to the potential market. Several actionable insights are taken as they help in strengthening the company’s decision-making process while planning, strategizing, and then modeling the final product.

Robot:

Robots are machines that can bring a lot of change in manufacturing. They are very helpful in performing all the daily routine tasks that are complex or too dangerous for humans to perform. Manufacturers these days put more money into robotizing so that they can meet their demands and reduce human errors. These machines end to contribute a lot to the best quality product manufacturing. Every year, the products come to the baseline in order to enhance the product lines.

Security:

Machine learning has developed such platforms that have made mobility a lot more secure in a company. The machine learning algorithms make their processes secure and also empower business innovation along with ensuring the smooth development of devices, mobile apps, etc, and data is protected across the organization. It gives on-device security and repairs device and network threats on any device.

Quality Control:

Machine learning plays a vital role and acts as a pillar in enhancing the overall quality of every manufacturing process. Its deep-learning neural networks help in the performance, weakness, availability, and quality of assembly equipment of the machine. For example – Siemens is using a neural network so that they can monitor their steel manufacturing and improve their overall efficiency. These kinds of companies invest a lot in machine learning to improve the quality of their operations.

Supply Chain Management:

Machine learning technology lends a helping hand to companies. It helps them so that they can maximize their value by improving their inventory management, supply chain management, logistics process, and asset management. It is better if machine learning, IoT, and artificial intelligence integrate. They will then ensure the high-level quality of products. The manufacturers today are finding new ways to combine all these emerging technologies with supply chain visibility, inventory optimization, accuracy, and asset tracking. The machine learning development companies and organizations have developed a supply chain management (SCM) suite. It can monitor each and every step of manufacturing, delivering, and packaging.

Summarizing the whole scenario

It is very obvious now at this point that the manufacturing industry is a technically advancing sector. The advantages of the adoption are increased productivity, reduced equipment failures, better distribution, and the introduction of enhanced products. Looking at all these benefits of machine learning in manufacturing, manufacturers around the world have already started investing a large sum of amounts in machine learning solutions. They did it for better efficiency and to empower their processes. The widespread adoption of these solutions will take place slowly. It’s taking place and in a few years, we will definitely see numerous companies leading the way to a smarter way of manufacturing the products we generally use in our life.

Robots and machine learning will transform the manufacturing industry. But, the manufacturing workforce needs to be reskilled to work alongside the newly developed work machines. The machines being used presently need maintenance which increases the downtime and isn’t cost-efficient. Sometimes the approach also seems wrong as the actual problem still hasn’t been solved which was leading to system failure. Getting accurate insights requires a long time. It is not a day’s task and takes years to address the real problem which leads to system failure. Machine learning can be a key as it can alert operators of time before. Also sometimes it can also avoid unplanned downtime of machines which would save a lot of time. The algorithm developed through machine learning will be a huge transformation in the manufacturing sector. It would benefit a lot the manufacturers.

Offer Next-Level Personalization

Personalization is a hot trend in the year 2020. Machine learning greatly helps businesses to offer next-level personalization to their customers. It provides a more scalable and accurate way for achieving many unique and different experiences for single users. Rather than leaving everyone with just the rule-based personalization, it allows using the algorithms to deliver one-to-one experiences in the form of recommendations for content or product.

Below is a list of personalization examples that are driven by machine learning:

• Offering applicable discounts through machine learning by creating a personalized reward system.
• Personalized emails that recommend products according to the user’s interests based on their previous purchases and search history.
• Suggestions of content for blogs that are based on user’s interest that reduces the bounce rate as well as improves the time they spend on a particular website.

Provide Higher Quality Recommendations

The key for a business to always win is to provide high-quality recommendations that increase their revenue. Even it is beneficial for users as they spend less time searching for products. A study shows that in the year 2018, almost 63% of shoppers preferred product recommendations. This is even higher for millennials. The rate was 69% for those who favored product recommendations and not manually searching for the products.

Collaborative filtering is a method that provides more personalized content recommendations. It offers content suggestions that are based on users with the same taste based on purchases and reviews. Let us take an example of a student and a businessman who have given the same scores to a few restaurants. They share similar tastes. Therefore with the help of machine learning, we can recommend the same restaurant to the student that the businessman has rated 9 or 10.

Improved Customer Service Quality and Speed

Companies and businesses can drastically improve the overall user experience by improving customer service speed. Studies showed that 46% of people expect a response in 5 seconds or less when they are using a chatbot, 43% of people expect the same while using online live chat, and 33% of people when they use a phone or a video call. Thus, it is now time to start using machine learning algorithms for faster chatbots. The top use case for the chatbots is answering time-sensitive questions.

Not responding quickly to emergency questions can leave the company behind with a negative user experience. Also, it’s sometimes impossible for someone to be available all the time to answer difficult problems. Thus, chatbots can quickly learn from previous user interactions. Machine learning algorithms can detect similarities and patterns that allow them to answer the same questions faster in the future. Moreover, they are more scalable than humans. Humans are involved only when the chatbots can’t answer some complex questions. Besides, humans can always feed data into chatbots to improve their question handling.

Optimize Layout by Analyzing User Behavior

We can easily optimize the layout of the software and applications by measuring user behavior. For example – we want to optimize the overall layout of an invoice application. The toughest task will be to develop an invoice creation button. We want to check how quickly users can find this specific button. To do this, we can measure the time that users take to press this button. By measuring time only, we can detect wrongly placed buttons and try to optimize the layout.

In short, we can use machine learning to reduce the time that users spend in finding a function and to efficiently do the A/B testing. Moreover, we can spot patterns also where the users often go back to the previous page. This shows that the flow is incorrect and the user expects something better.

In a nutshell, the main aim is to find interactions that are unclear or require a lot of time to complete, those who often affect the user experience negatively. Apart from that, we need to reduce human errors when we navigate the application to create a better product experience. But, don’t change the order of your UI components a lot.

Sentiment Analysis: Emotion AI

Lastly, the use of sentiment analysis can give a better picture of the user’s emotions when they interact with a website, advertisement, blog, or product. Measuring emotions also require a facial recognition system. But, you can also use textual analysis to check their feelings. However, this strategy is not possible for checking people’s reactions to certain advertisements.

By judging their response to an advertisement or content, we can create even more engaging advertisements. Often marketing teams create different advertisements according to the user’s wealth, age, and interests.

In short, we can use sentiment analysis to create:

• Better content that answers every question
• Advertisements that interests users
• Products that meet users needs precisely

Conclusion

Machine learning is surely a very great tool to improve the user experience. But, before implementing them, we need to validate every machine learning insight. It is valuable to implement the user testing process for validating the changes. To conclude, both machine learning and user experience designs will keep growing and there is no stopping. There are a lot of benefits when we merge machine learning and user experience design.

We all know that nowadays there is a huge buzz going over the word data, such as data mining, big data, data warehouse, data science, data analysts, etc. It highlights that data plays a major role in the current era as it is influencing the everyday activities of humans. Let us understand data visualization to a greater extent.

Understanding data visualization

Every day, humans generate more than 2.5 quintillion bytes of data that range from text messages, emails, IoT devices, images, autonomous cars, etc. All this huge amount of data is readily available through which we can leverage useful information thus helping various organizations to get a clear insight into different areas. For example, with the help of this information, organizations can know about how to bring a boost for the revenue, which fields need more focus, how to seek customer’s attention, etc.

But do you think it is easy to interpret all this collected data? The answer is no! This data is present in a raw format and several steps are needed to be performed so that this raw data can be converted into useful information. This is where the job of a data scientist starts. We provide them with the raw data; they start working on the stages which include data acquisition, cleaning, visualization, building a model for predicting future information, etc. Among them, data visualization is a key step.

Data visualization is a graphical representation of raw data and information. It is the process of producing images that shows the relationship between represented data to the viewers. We can represent this data in the form of charts, graphs, or any other visualization format. We will discuss this later.

Why data visualization?

Data does not make sense sometimes until we look at it in a visual form. Its visual format allows the patterns and trends to be seen more easily rather than looking through hundreds and thousands of rows on a spreadsheet. There is a great need of interpreting large batches of data. It is important not only for data analysts and data scientists, it is necessary for almost every career. Whether you are working in tech, marketing, design, finance, or any other career, data visualization is a must.

The sole purpose of data analysis is to gain useful insights and data is much more worthy when it is visualized. Even if meaningful insights are pulled from all the data without visualization, even then it will be quite difficult to understand its meaning. Charts and graphs make it easier to understand and identify all the trends and patterns.

Types of data visualization charts

Now that we have understood what data visualization is and why is it important, let us see the different types of charts and graphs used for the same.

Line chart

A line chart is the simplest chart that illustrates the changes that happen over time. The x-axis is the period and the y-axis is the quantity. This can be helpful to illustrate the sales of a company for a month or how many units factory produce every day.

Area chart

An area chart is just the adaptation of a line chart. The area under the line is filled to show its significance. The colored area should be transparent so that the overlapping areas can be seen clearly.

Bar chart

Just like the line chart, the bar chart also illustrates the changes over time. But if there is more than one variable present, then this chart can make it easier to compare all the data for every variable at every second. For example, it can compare the company’s sales from the present year to last year.

Histogram

A histogram looks like a bar chart. But, it represents frequency rather than trends. The x-axis represents the intervals and the y-axis represents frequency. So, every bar represents the frequency for that time interval.

Scatter plot

We use scatter plot to find the correlations. Every point of scatter plot means that when x = this then y = this. In this way, if the points show trends in a particular way then there is a relation between them otherwise not.

Bubble chart

It is an adaptation of a scatter plot. Each point is a bubble. Their area has a meaning along with its placement on the axes. But, not every data will fit perfectly on this as it has limitations on the size of the bubble due to the limited amount of space on axes.

Pie chart

It is best for showing percentages. It precisely presents the pieces in proper proportions.

Gauge

It illustrates the distance between the intervals. We can use multiple gauges at a time for multiple intervals. It can be represented as a clock-like or tube-type gauge.

Heat map

A heat map is a color-coded matrix. We use a formula to color each cell of the matrix. It represents the relative value or risk of that particular cell. Colors green and red are widely used as green represents better results whereas red is the worst result.

Frame diagram

These diagrams are treemaps that consist of branches that have even more branches connecting to them. It shows a hierarchical relationship structure.

Conclusion

In data analysis, data visualization is a crucial step. Without it, important messages and insights can get lost. Once your extract data from the web, it goes through the data analysis process that gives the organization easily consumable graphs and charts to gain meaningful insights. If your organization is ready to get the most from the data, data visualization is the key.

The standard library and tools present in its ecosystem can be useful for everyone besides just a system administrator to automate various parts of their processes to make their work much more efficient. A wide range of tasks such as data wrangling and gathering market research data can be automated by Python. People use this language as a part of marketing, DevOps, Data Science, etc because of the ease of picking up and the ecosystem that it provides.

Why use Python for task automation?

Automation is supposed to remove our work. So, why not use it to ease tasks? But, why use Python and not some other language for this purpose?

It’s because Python provides great readability and approachable syntax. Its code resembles plain English which makes it an excellent choice for the users to start their journey with. When we compare Python with other languages, it stands out as the simplest language of them all. Further, it makes the learning process pleasant and fast. With a bit of time and effort, anyone can gain enough knowledge to write simple scripts on their own. Also, it speeds up the development process even for experienced developers.

Another reason why one should choose Python is that it comes with extraordinary data structure support. They enable users to store and access the data. It also offers different dictionaries, tuples, and sets. They help you to manage the data easily and efficiently and increase software performance when chosen accurately. Moreover, it stores the data securely and in a consistent manner. Even better, this language lets the users create their own data structures which make the language super flexible. Whatever the idea or task is, you can very easily pull it off with the help of Python along with its modules and tools. It is used in many professions and industries like data analysis, networking, science, mathematics, and more.

What can you automate using Python?

Almost everything! Almost any tedious task can be automated with a small amount of work. For that, you need to have Python and relevant libraries on your computer. Let us go through some examples where we use Python for automation.

Reading and writing files

This is a task that you can easily and efficiently automate with the help of Python. To begin, you just need to know the location of the files in the system, their names, and which mode should be used to open them. It is super easy to do with Python. However, the modes of opening files can be mixed and extended. Writing with web scraping or interacting with the APIs can provide a lot of automating possibilities. You should also check a good library csv helping with reading and writing further.

Sending emails

Sending emails is another task that can be automated using Python. This language comes with a great smtplib library. You can use it to send emails via the SMTP or simple mail transfer protocol. Sending emails and replying to them has become easier than ever. It reduces the potential for errors, increases brand awareness for a business, benefits the sales teams, reduces works, saves time, reduces costs, boosts business revenues, and keeps people interested in a brand.

Web scraping

Web scraping is the method of extracting the data from Web pages and saving it on the hard drive. Picture your day at work that involves pulling data from a website that you visit almost every day. Scraping the data would be very helpful once the code is written as it can be run multiple times that makes it useful when you need to handle large amounts of data. Manually extracting the information takes a lot of time and lots of clicking and searching. It is not easy to scrape data with the help of Python. However, for analyzing and extracting data from HTML code, the target page needs to be downloaded first.

Interacting with API

It gives you superpowers when you interact with APIs! For example, there are many APIs available but Open AQ Platform API seems the best option because it doesn’t require any authentication. Interaction with API saves a lot of time, requires less code than usual, and provides faster results. Even when queried, the API gives air quality data for a given location.

Conclusion

Hopefully, after reading this article, you may have realized that there are tons of tasks in your daily life that can be automated even without much programming knowledge. However, you can always dig deeper into multiple sources on the web if you feel like creating some more automation using Python. It will not only give you a wider view of Python’s capabilities but it will also improve your knowledge of the language.

At last, always remember; never spend your precious time doing repetitive and tedious tasks that can be easily automated.

Meet Django

Django classifies itself as a high-level python web framework that is based on MVT (Model, view, and template). It encourages rapid development and clean and expedient design. Django is built by experienced developers. Thus, it very carefully takes care of much of the hassle that occurs during web development. This allows you to focus on writing the code for your application without the need of reinventing the wheel. They mean it! Django is a massive web framework that comes with a lot of batteries included which is why often it can be a mystery during development how everything manages to work together. It is extremely popular among developers and is a fully-featured server-side framework.

In addition to it being a large framework, its community is entirely massive. As a matter of fact, it is so big and active that there is a whole website developed by them that is devoted to the third party packages people. They are the people who were behind the designing of Django for making it do a whole list of things. It includes everything such as authentication, authorization, content management systems, e-commerce add ons, and integration with stripes. Moreover, when we talk about reinventing, there are considerable chances that if you want something done with the help of Django, someone might’ve already done it. You just have to pull it into your project.

For this objective, we will have to build a REST API with Django. So, we will use the Django REST framework. It has the responsibility to turn the Django framework into a system specifically made for effectively handling REST interactions. It was made especially to serve fully rendered HTML pages that are built with Django’s templating engine.

What can we use Django for?

Django framework uses a powerful ORM layer. It simplifies dealing with a database and the data. It also accelerates the development process. Without ORM (Object-relational mapping), developers would have to create the tables themselves and define all the queries and procedures which occasionally translates to a sturdy amount of SQL that is prone to hard to track and complex.

ORM is an advantage as it lets the developers write the table definitions in a simple python code. It further takes care of translating them to appropriate query language and facilitating the CRUD operations. Moreover, the developer doesn’t even need to know the complex SQL. However, understanding SQL can always help you write better codes and faster queries making your website even more secure.

Unlike other frameworks, in Django, all the models are placed in one file that is models.py. Django supports several database systems. For this purpose, SQLite is very good for development and testing since it could be used right away without the need to install further software. You can go for MySQL or PostgreSQL for production. You can use MongoDB with Django if you are looking for NoSQL database.

Why is it a good choice?

Since the Django framework’s creation, its stability, community, and performance have increased immensely over the past decade. Its detailed tutorials and good practices are available everywhere in the books and on the web. This framework further continues to add noteworthy new functionalities such as database migration with every release.

Experts highly recommend this framework for new Python web developers as its official documentation and tutorials are the best in software development. There are arguments over whether learning python by using Django is not a good idea. However, that criticism is worthless if you take your time to learn the python syntaxes and language semantics well first before diving right into web development.

Furthermore, Django has a multifaceted nature which means that it can do numerous amounts of tasks. We can use it in:

• CRM Systems
• Document administration platforms
• Emailing solutions
• Verification systems
• Machine learning
• Data analysis solutions
• Filtering systems
• Communicating platforms
• Booking engines

Thousands of websites have Django as their core. In short, it is an excellent choice for web development.

Python Django Advantages

If you will ask any Django dev about the Django framework, you will most probably get a similar reply. We have highlighted Django’s advantages that make it stand out from the rest.

1. Django is a battery-included framework. It means that Django comes with a lot of out of the box features and functions that you may or may not use in your application. Instead of writing the whole code, you just need to import several packages which will make it easy for you to develop.
2. Django uses python which is why it utilizes some of the fame and power of python itself for its benefit. Python is the easiest programming language to learn and it is quite popular too.
3. Its community is helpful and actively works on making this framework even more beginner-friendly. They frequently stabilize the framework by adding new features.
4. Picking up a scalable framework is very important for developers as it allows them to take a lot of actions regarding scalability such as running separate servers for the application and the database or use clustering and load-balancing for distributing the application across several servers. Django fulfills this requirement.
5. It offers an administrative interface that is both versatile and professional.

What is Python Numpy?

Numpy is the most powerful Python library that stands for Numerical Python. It is a general-purpose array processing package that people use in industries for array computing. Numpy provides a high-performance and multidimensional array object and several tools for working with arrays. It is an open-source project that anyone can use freely. Python Numpy is the fundamental package for computing scientifically with Python. Moreover, it can also be used as a systematic multi-dimensional container of generic data.

Numpy is gaining huge popularity and we use it in many production systems. Hence, we need to understand that what this library offers. Python has lists that serve the purpose of arrays; however, they are quite slow to process. Numpy provides array objects that are 50 times faster than traditional Python lists. Ndarray is the array object in Numpy and it offers a lot of support functions that make it easy to work with Python. We use arrays in data science where speed and resources matter a lot.

What one should use Python Numpy?

If you are a Python programmer and you haven’t run into Numpy yet then you are probably missing out. It is an open-source library that programmers use for scientific and numeric computing letting you work with many multi-dimensional arrays efficiently. Also, it is one of the top five packages in Python.

Here are 5 reasons why one should use Python:

1. It is fast because it is written in C and gives quick results. Python is a dynamic language. It is interpreted by CPython, it is then converted to bytecode and then executed.
2. There are many libraries such as SciPy, pandas, sympy, nose, etc that use Numpy. Both Numpy and SciPy are two sides of a coin. Numpy was created from two packages. It contains both ndarray type and array manipulation functions, but numeric functions as well.
3. It allows you to do matrix arithmetic. Numpy’s ndarray lets the users to do dot and inner product of two matrices and also matrix product and raising matrix to a power. Numpy can solve tensor equations and also three different types of matrix inversion.
4. It provides a lot of built-in functions. The list is long but it is sufficient to say that there are functions for searching, string operations, random sampling, financial calculations, math functions, linear algebra, statistics, indexing, polynomials, binary, logic, and sorting.
5. Numpy has universal functions, also known as ufuncs. They are applicable to each element of the input array. It stores the result in a corresponding output array respectively of the same size. Universal functions are a bit advanced. Array broadcasting is a useful feature of universal functions. It is how different arrays of different sizes and shapes can be used in a function.

Python Numpy Arrays

If you are utilizing Python for Data Science then either you have heard about Numpy or have used it. Most of the statistical analysis that requires storing data in the memory uses Numpy. You might ask now why use Numpy? Aren’t Python lists and other data structures helpful in doing the same thing?

Well, the answer to this question is both yes and no. There is nothing special in Numpy that we can’t do with the help of python lists or other data structures. But, Numpy offers efficient storage and finer handling of data for the mathematical operations that use simple API’s.

The Numpy library is used for creating homogenous n-dimensional arrays (n = 1…n). Its implication would not be possible with heterogeneous data sets. Unlike the lists in Python, all the elements of a Numpy array should be of the same type. Let us see what extra benefits Numpy provides us and how it makes our programming lives easier, especially the ones that deal with mathematical calculations.

• It uses much less memory to store the data.
• We can use Numpy for creating n-dimensional arrays.
• It makes it easy to perform mathematical operations on it.
• Numpy provides three functions named where, nonzero, and count_nonzero for finding elements in an array.

Conclusion

Over the coming decades, developers will face several challenges. New devices will be developing in the future and existing hardware will evolve to meet the reducing returns on Moore’s law. There will be a wider variety of data scientists and a large number of people will use NumPy. Further, the scale of scientific data gathering will increase continuously. Through all the things described in this article, Numpy is controlled to embrace such a changing landscape. It continues to play a significant role in interactive scientific computation. Although, it will require sustained funding from the industries, government, and academia to do so. But most importantly, it will need a new generation of community contributors and graduate students to drive it forward.

What are the variables in Python?

As the name suggests, variables and data types are the values that vary or change in python. In any programming language, a variable is a memory location where a user stores a value. The value that is stored can change according to the needs and specifications. For example, let us take Name = ‘Sakshi’. Here, Name is the variable and Sakshi is the value stored in the variable. A variable is created as soon as a value is assigned to it in python. We don’t need to give some additional commands for declaring a variable in python. However, there are several rules and regulations that a programmer needs to follow while writing a variable. Let us have a look at them in the next section.

Variable definition and declarations

The rules that we need to keep in mind while declaring a variable are as follows:

1. The variables are case sensitive.
2. Python doesn’t allow special characters in a variable.
3. The variable can only contain underscores and alphanumeric characters.
4. The name of the variable can’t start with a number. It should only start with an underscore or a character.

There are a few data types in python. Each value that the programmer declares in python has a data type. In short, data types are the classes and variables are the instances of these classes.

Data types in Python

There are mainly 6 data types in python programming language according to the properties that it possesses. However, there is one more data type range in python. We use it while working in loops in python. Now that we have known that the data stored in a variable can be of many types, we will understand this by an example. Let us take Sarah. Her age will be stored as a numeric value whereas her address will be stored as alphanumeric characters. Data types define operations that are possible on them and each one of them has a storage method.

Numerical Data Types

The data types which hold a numerical value are known as numerical data types. This data type has further four subtypes that are:

• Integers – It represents whole number values. For example, x = 24, y = 500.
• Float – It represents decimal point values. For example, x = 12.5, y = 70.8.
• Complex numbers – These numbers represent imaginary values that are denoted with ‘j’ at the end of the number. For example, x = 20 = 8j.
• Boolean – Boolean is for the categorical output. The output of Boolean will be either true or false, 1 or 0. For example –

Num = 6<10, when we will type ‘print(num)’,it will print true.

Strings

In python, strings are used to represent Unicode character values. There is no character data type in python that is why a single character is also considered as a string. We always denote the string values inside single or double-quotes. For accessing the values in a string, we use indexes and square brackets. Strings are immutable. You cannot change them once replaced. For example, take the code snippet –

Name = ‘sakshi’

Name[2] – this will give output as ‘k’ according to indexes.

Lists

Lists are a part of collection data types in python. When we choose a collection type, we need to understand the limitations and functionality of the collection. Tuples, dictionary, and sets are other collection data types. A list is changeable and ordered. We can also add duplicate values to it. To declare a list, we use square brackets. Moreover, lists can store any data types whether it be numbers, strings, or anything else. Example – mylist = [10,20,30,40,50].

Tuples

Tuples are other collection data types that are immutable or unchangeable. It is an ordered data type and we can access its values by using index values. A tuple can include duplicate values. For the declaration of tuples, we use round brackets. Tuples are unchangeable that’s why there are very few operations that one can perform on it. However, it has an advantage. You can store values in a tuple that you don’t want to be changed. You can always access the values but there won’t be any changes to be made. Example – mytuple = (10,30,50,70).

Sets

The set is an unordered collection data type. It doesn’t have any indexes. For declaring it, we use curly brackets. Example – myset = {10,60,44,76}. It doesn’t contain any duplicate values. Even if the user adds them, it will not show any error and the output will have distinct values as usual. For accessing the values of a set, we can loop through the set or can use a membership operator.

Dictionary

A dictionary is another normal collection data type but it has key-value pairs. It is changeable and not in order. We always use certain keys to access items from a dictionary. For the declaration of a dictionary, we use curly brackets. We use the keys to access items thus there can’t be any duplicate value. However, the values can always have a duplicate item. Example- mydictionary = {‘someone’, ‘anyone’, ‘no one’}.

Range

We use the range when we are using a loop. Let us understand this with an example –

For x in range(5):

Print(x)

This will print the numbers from 0-5.

Let us first look at the disastrous consequences that Artificial Intelligence brings with it.

Mass Unemployment

This is a common fear among analysts as well as workers today. Its because AI will soon result in mass global unemployment. This is because jobs are constantly becoming automated and human labor is no longer required. Job losses are the biggest worry. They are the primary reason for populism around the world. AI technology will soon lead to the creation of new and different kinds of jobs. The need for engineers will increase as the refinement of this technology will require the right talent to develop it. Although it is possible to reduce the damage to the labor market from AI with the help of upskilling and discovery of new jobs, it is clear that this issue of job losses won’t go away anytime soon.

War

The emergence of killer robots and several other uses of AI in military applications have experts worried that it may end up in war. Many analysts and campaigners claim that the use of AI in military decision making and the development of fatal autonomous weapons may lead to AI-enhanced wars. A bold prediction made is that there is a chance of a military AI system making a mistake in the analysis of a situation that can lead the countries to take potentially catastrophic decisions.

Robo doctors

Experts mostly believe that AI will provide a lot of benefits for medical practitioners, for instance, diagnosing illness very early and speeding up the health experience. But, doctors and experts also believe that it is heading in the direction of data-driven medical practices a bit too fast. One disadvantage of the application of AI in medical implications is that the privacy of patients and their data can be hacked anytime. Sometimes they even make incorrect and unsafe treatment recommendations to patients as the software is trained only to deal with a small number of cases and hypothetical situations rather than actual patient data.

Mass Surveillance

There is one more reason why experts fear AI. It’s because AI can also be used for mass surveillance. This fear is becoming a reality in China. It has 200 million surveillance cameras installed and it tracks the activities of its citizens. AI ranks them with scores that help them determine whether they can be prohibited from accessing everything such as plane flights to several dating services. It can bring a lot of security consequences.

Discrimination

AI has the potential to become prejudiced. We can understand this with an example. Microsoft created a chatbot named Tay. The bot was given a Twitter account. The users took less than a day to train it to post offensive tweets that supported white supremacy and Adolf Hitler. This happened because it was programmed to mimic users. Discrimination is one of the unexpected consequences that we can expect from technology. It can easily learn to spread sexual, racial, and other biases that the human race has spent 50+ years to remove. Experts say that AI can develop the ability to be in favor of a particular gender, sexuality, or race. For instance, facial recognition technology is better at judging white faces than black ones.

Now, let us have a look at the advantages that will make our future better in the coming days.

Lawyers and doctors in your pockets

Both virtual lawyers and virtual doctors are outperforming the real ones providing them legal information and correct diagnosis for illness quickly. After some time, there will be no need for specialists at all. Instead of that, we will always have our teacher, secretary, doctor, lawyer, and financial advisor in our pockets 24 hours a day. Moreover, scientists won’t have to sort, extract, and analyze through a large amount of published research. AI would always do it for them. Also, AI can read everything the human race has ever written and published with a basic reading comprehension skill.

Real-time models

Super intelligent artificial intelligence has a lot of global applications. It can collect data from surveillance cameras and satellites. Then, they can use this data to create a database of the whole world in real-time. With the help of this data, we can produce global system models like environmental changes and economic activities. These models help in designing effective interventions into the systems that help us to, for instance, reduce the effects of climate change. Moreover, it can start associating the races and genders with stereotypes. Researchers are finding a solution to reduce bias in AI-powered facial recognition systems.

Summary

Undoubtedly, in this article, we have gone through only a fraction of the foreseeable risks and advantages of AI. But of course, this does not mean that we should not seek the technology. There are innumerable possibilities for the future – both good and bad. But, if these super-intelligent machines from AI gain a mind of their own and can decide that we are replaceable, then it is completely on us.

Understanding AI and data science

Artificial is a perception with a large margin that recognizes patterns and unsupervised data with the help of logical discrimination and algorithm development. It mainly does so to understand robotic technology’s neural network. Besides, it uses the algorithms to perform certain autonomous tasks similar to the ones that were successfully performed in the past. Nowadays, many big companies like Google and Facebook are making use of Artificial Intelligence for developing autonomous systems.

Now, talking about data science, it is an idea to bring together information, investigations, measurements, and their related strategies to understand and analyze the wonders with data. Data science uses the systems and theories that are drawn from innumerable fields inside the regions of software engineering, computational science, machine learning, etc. We all have become a part of this data-driven society and this data has become crucial for organizations and industries to make smart and careful decisions. There are six different needs in data science which are as follows:

• First need: Deep learning and AI
• Second need: Simple ML algorithms, A/B testing, and experimentations
• Third need: Aggregates, training data, features, analytics, segments, and metrics
• Fourth need: Prep, cleaning, and anomaly detection
• Fifth need: Data pipelines, infrastructure, and structured and unstructured data storage
• Sixth need: User-generated content, logging, external data, sensors, and instrumentation

What makes data science and AI different from each other?

Let us understand this with the help of the below-mentioned points –

1. Constraints of contemporary AI: As mentioned above, both of these technologies can be used interchangeably. However, there are a lot of differences between them. The AI used nowadays is the ‘artificial narrow intelligence’. This means that computers and systems don’t have full consciousness and autonomy like us human beings. They only perform the actions and tasks for which we program them for.
2. Data science – a comprehensive procedure: Data science is the analysis, visualization, and study of data for gaining meaningful insights from it. This data is responsible for benefitting companies. The roles and responsibilities of a data scientist include preprocessing the data and cleaning and transforming it. After that, the data scientist analyzes the patterns and draws graphs that underline analytical procedures using the visualization techniques. Then, they develop prediction models to find the likelihood of the occurrence of future events.
3. Artificial Intelligence – a tool for data scientists: AI is a tool for data scientists. The data science hierarchy of needs involves collecting the data, moving/storing it, exploring/transforming, aggregating/labeling and lastly, learning/optimizing, and finding patterns in it. Companies look for AI positions such as machine learning engineers, deep learning scientists, etc for developing products based on AI. They require data scientist tools such as python and R language for performing operations but they also need some CS expertise. Data scientists, whereas, help the companies make data-driven decisions. He extracts data using SQL queries, cleans anomalies in data, analyzes the patterns, and then applies predictive models to generate insights. Moreover, he also uses AI tools such as Deep Learning algorithms for performing prediction and classification of various types of data.

Key differences between data science and AI

1. Scope – We use data science in different underlying operations whereas artificial intelligence limits only to the ML algorithms.
2. Type of data – Data science has several data types such as structured, unstructured, and semi-structured. On the other hand, AI has the kind of data that is standardized in the form of embeddings and vectors.
3. Tools – The tools used in data science are SAS, R, python, etc whereas the tools used in AI are Kaffe, Mahout, TensorFlow, etc.
4. Applications – We use data science applications widely in search engines such as Google, Yahoo, and the marketing field, banking, etc. We use artificial intelligence in several sectors such as robotics, manufacturing, healthcare, transport, and other industries.
5. Process – Data science uses the process of analysis, prediction, and visualization whereas artificial intelligence forecasts future events using a predictive model.
6. Techniques – Data science involves different methods of statistics. On the other hand, AI uses algorithms in computers for solving problems.
7. Purpose – Both technologies have different primary goals. Data science finds patterns that are hidden in the data. Artificial Intelligence automates the process and brings autonomy to the products and to the model of data which proves that both of them have different goals and purposes.
8. Different models – We construct models for producing the insights that are important for decision making in data science whereas, in artificial intelligence, we build models that have understanding and cognition similar to humans.
9. Degree of scientific processing – Data science uses less scientific processing. On the other hand, artificial intelligence uses a very high degree of scientific processing.

Summary

In this article, we understood the difference between the technologies. Artificial Intelligence is a domain that is yet to be completely explored. Data science has already started making a huge difference in the market. It uses Artificial Intelligence to generate predictions and transforming the data for visualizations and analysis. In the end, we can conclude that data science analyzes the data for better decision-making whereas artificial intelligence is a tool for creating better products and imparting those products with autonomy.

Social Media

There are a lot of social media sites such as Facebook, Instagram, Twitter, etc. They use artificial intelligence that makes it easier for users to communicate with their friends, family, colleagues, and business associates. Social media networks help marketers to run several paid ad campaigns and content creation and distribution cost them a lot. However, artificial intelligence on the other hand is capable of writing a lot of creative ads on its own. Moreover, artificial intelligence also improves the user experience on social media. For example, LinkedIn is a social media platform that provides useful recommendations to the users for their preferred jobs and contacts using AI technology. Even chatbots are based on Artificial Intelligence. It helps the users by answering them their most frequently asked questions. At times, they are so accurate that it is very hard to figure out if we are talking to a human or a robot.

Digital Assistants

In the last few years, digital assistants and similar software have become very popular such as Google Home and Amazon Alexa. They serve a wide variety of purposes such as calculations, alarms, timers, message dictation, scheduling, reminders and internet searches, etc. Some of them even provide home automation technology. With its help, people can control lights, garage doors, etc just by sitting on their couch. Again, they use AI to understand human language. They respond with natural speech. Also, they improve themselves by learning from their mistakes. They don’t need to be programmed now and then. They have made our lives a lot easier by helping us to do daily tasks easily and quickly.

Web Searches

Artificial Intelligence has completely transformed the way web search engines work. They create ranking algorithms that affect the results that a user sees on the first page of a search. Moreover, it is also crucially responsible for quality control. AI helps marketers in increasing the search ranking of their websites on web browsers. To bring their websites to the top, people often stuff keywords or invisible texts. AI penalizes such websites so that your search results are not contaminated with low-quality pages. AI is also used for predictive searches on web browsers. It helps the search engine to guess and display options that the user is trying to find making it much easier to get what they want.

Online stores and services

Artificial intelligence is greatly responsible for your e-commerce experience, product, and music recommendations, and maps and directions as well. Online retailers such as Amazon and Flipkart gather information about the user’s preferences and buying habits with the help of AI. As a result, they are easily able to improvise the user’s shopping experience by suggesting and displaying the services and the products that he/she is most likely to buy on their screen. The same is the case with songs and music preference. For instance, Spotify provides great suggestions to its users based on their music choices for new releases and songs and old favorites.
In helping people with maps and directions, the artificial intelligence calculates the traffic and construction. Based on that, it provides them with the fastest and easiest route to their destination.

Email communications

Email communications are made easy as a consequence of the spread of AI technology. Smart replies and mail filters features in the email have made the importance of AI further inevitable. Smart replies provide the users the ease to respond to emails with simple phrases such as “Yes, I will do it.” or “No, I haven’t yet started.” with only one click. They can choose whether they want to manually type the response or reply with the phrase that Artificial intelligence created for them. Now, talking about the filtration, AI filters, and sorts the emails according to the needs of the users. It separates them into categories such as spam, updates, primary, etc. This helps them to find important communications quicker.

Conclusion

These are not the only domains that have been greatly affected by AI. Other domains such as smartphones, smart homes, security and surveillance, the banking and finance sector, autonomous vehicles, and many others are also greatly benefitting from it. Thus, it has proven itself to be very useful for humans as they won’t have to worry anymore about doing small tasks on their own every day. AI makes our everyday lives much easier and efficient as it powers many services and programs that help us do everyday tasks quickly like connecting with friends, using ride-share service, etc. In conclusion, we have been using artificial intelligence for many years. As a result of it, our lives have been impacted positively. It will help us even more in the coming days too as it is affecting our everyday decisions somehow.