on items that are frequently bought in bulk, such as pens and notepads for office supplies, is likely to make bulk buyers log in to the online store and place purchase orders, but it might not be attractive for luxury product buyers. By identifying customer segments based on their behavioral patterns and using customized marketing campaigns, you can optimize your marketing channels.

In this article, you’ll use an online retail dataset that contains all the transactions that occurred between Jan. 12th 2010 and Sep. 12th 2011 for a UK-based online retail store to build models for customer segmentation. This dataset is available in the UCI Machine Learning Repository and can be downloaded from http://archive.ics.uci.edu/ml/datasets/online+retail#. The full code for this data analysis can be found at https://github.com/yoonhwang/c-sharp-machine-learning/blob/master/ch.6/DataAnalyzer.cs.

Data analysis for the online retail dataset

It is now time to look into the dataset. You can follow http://archive.ics.uci.edu/ml/datasets/online+retail#, click on the Data Folder link in the top-left corner, and download the Online Retail.xlsx file. You can save the file Learn how to build models for customer segmentation in this tutorial by Yoon Hyup Hwang, a seasoned data scientist with expertise in predictive modeling, machine learning, statistical analysis, and data engineering.

Whether you’re trying to send marketing emails to your customers or simply want to better understand your customers and their behaviors on your online store, you will want to analyze and identify different types and segments of your customers.

Depending on the behavioral patterns, your marketing campaigns should vary. For example, sending out emails with promotions on luxury items is likely to provoke luxury product buyers to log in to the online store and purchase certain items, but such an email campaign is not going to work well for bulk buyers.

 On the other hand, sending out emails with promotions as a CSV format and load it into a Deedle data frame.

Handling missing values

Since you’ll be aggregating the transaction data for each customer, you need to check whether there are any missing values in the Customer ID column. The following screenshot shows a few records with no Customer ID:

                                          

Drop these records with missing values from the Customer ID, Description, Quantity, Unit Price, and Country columns. The following code snippet shows how you can drop records with missing values for those columns:

// 1. Missing CustomerID ValuesecommerceDF    .Columns[new string[] { "CustomerID", "InvoiceNo", "StockCode", "Quantity", "UnitPrice", "Country" }]    .GetRowsAt(new int[] { 1440, 1441, 1442, 1443, 1444, 1445, 1446 })    .Print();Console.WriteLine("\n\n* # of values in CustomerID column: {0}", ecommerceDF["CustomerID"].ValueCount); // Drop missing valuesecommerceDF = ecommerceDF    .Columns[new string[] { "CustomerID", "Description", "Quantity", "UnitPrice", "Country" }]    .DropSparseRows(); // Per-Transaction Purchase Amount = Quantity * UnitPriceecommerceDF.AddColumn("Amount", ecommerceDF["Quantity"] * ecommerceDF["UnitPrice"]); Console.WriteLine("\n\n* Shape (After dropping missing values): {0}, {1}\n", ecommerceDF.RowCount, ecommerceDF.ColumnCount);Console.WriteLine("* After dropping missing values and unnecessary columns:");ecommerceDF.GetRowsAt(new int[] { 0, 1, 2, 3, 4 }).Print();// Export DataecommerceDF.SaveCsv(Path.Combine(dataDirPath, "data-clean.csv"));

Use the DropSparseRows method of the Deedle data frame to drop all the records with missing values in the columns of your interest. Then, append the data frame with an additional column Amount, which is the total price for the given transaction. Calculate this value by multiplying the unit price with the quantity.

As you can see, there were 541,909 records before you dropped the missing values. After dropping the records with missing values from the columns of your interest, the number of records in the data frame ends up being 406,829. Now, you have a data frame that contains the information about CustomerID, Description, Quantity, UnitPrice, and Country for all transactions.

Variable distributions

Start looking at the distributions in your dataset. First, take a look at the top five countries by the volume of transactions. The code used to aggregate the records by the countries and count the number of transactions that occurred in each country is as follows:

// 2. Number of transactions by countryvar numTransactionsByCountry = ecommerceDF    .AggregateRowsBy<string, int>(        new string[] { "Country" },        new string[] { "CustomerID" },        x => x.ValueCount    ).SortRows("CustomerID"); var top5 = numTransactionsByCountry    .GetRowsAt(new int[] {        numTransactionsByCountry.RowCount-1, numTransactionsByCountry.RowCount-2,        numTransactionsByCountry.RowCount-3, numTransactionsByCountry.RowCount-4,        numTransactionsByCountry.RowCount-5 });top5.Print(); var topTransactionByCountryBarChart = DataBarBox.Show(    top5.GetColumn<string>("Country").Values.ToArray().Select(x => x.Equals("United Kingdom") ? "UK" : x),    top5["CustomerID"].Values.ToArray());topTransactionByCountryBarChart.SetTitle(    "Top 5 Countries with the most number of transactions" );

As you can see from this code snippet, the Aggregate RowsBy method is used in the Deedle data frame to group the records by country and count the total number of transactions for each country. Then, sort the resulting data frame using the SortRows method and take the top five countries. When you run this code, you will see the following bar chart:

                                          

The number of transactions for each of the top five countries looks as follows:

                                                              

As expected, the largest number of transactions occurred in the United Kingdom. Germany and France come in as the countries with the second and third most transactions.

Start looking at the distributions of the features that you’ll use for your clustering model—purchase quantity, unit price, and net amount. Look at these distributions in three ways:

  • First, get the overall distribution of each feature, regardless of whether the transaction was for purchase or cancellation
  • Second, take a look at the purchase orders only, excluding the cancel orders
  • Third, look at the distributions for cancel orders only

The code to get distributions of transaction quantity is as follows:

// 3. Per-Transaction Quantity DistributionsConsole.WriteLine("\n\n-- Per-Transaction Order Quantity Distribution-- ");double[] quantiles = Accord.Statistics.Measures.Quantiles(    ecommerceDF["Quantity"].ValuesAll.ToArray(),    new double[] { 0, 0.25, 0.5, 0.75, 1.0 });Console.WriteLine(    "Min: \t\t\t{0:0.00}\nQ1 (25% Percentile): \t{1:0.00}\nQ2 (Median): \t\t{2:0.00}\nQ3 (75% Percentile): \t{3:0.00}\nMax: \t\t\t{4:0.00}",    quantiles[0], quantiles[1], quantiles[2], quantiles[3], quantiles[4]); Console.WriteLine("\n\n-- Per-Transaction Purchase-Order Quantity Distribution-- ");quantiles = Accord.Statistics.Measures.Quantiles(    ecommerceDF["Quantity"].Where(x => x.Value >= 0).ValuesAll.ToArray(),    new double[] { 0, 0.25, 0.5, 0.75, 1.0 });Console.WriteLine(    "Min: \t\t\t{0:0.00}\nQ1 (25% Percentile): \t{1:0.00}\nQ2 (Median): \t\t{2:0.00}\nQ3 (75% Percentile): \t{3:0.00}\nMax: \t\t\t{4:0.00}",    quantiles[0], quantiles[1], quantiles[2], quantiles[3], quantiles[4]); Console.WriteLine("\n\n-- Per-Transaction Cancel-Order Quantity Distribution-- ");quantiles = Accord.Statistics.Measures.Quantiles(    ecommerceDF["Quantity"].Where(x => x.Value < 0).ValuesAll.ToArray(),    new double[] { 0, 0.25, 0.5, 0.75, 1.0 });Console.WriteLine(    "Min: \t\t\t{0:0.00}\nQ1 (25% Percentile): \t{1:0.00}\nQ2 (Median): \t\t{2:0.00}\nQ3 (75% Percentile): \t{3:0.00}\nMax: \t\t\t{4:0.00}",    quantiles[0], quantiles[1], quantiles[2], quantiles[3], quantiles[4]);

Use the Quantiles method to compute quartiles—min, 25% percentile, median, 75% percentile, and max. Once you get the overall distribution of order quantities per transaction, look at the distribution for purchase orders and cancel orders. In your dataset, cancel orders are encoded with negative numbers in the Quantity column. In order to separate cancel orders from purchase orders, you can simply filter out positive and negative quantities from your data frame as in the following code:

// Filtering out cancel orders to get purchase orders onlyecommerceDF["Quantity"].Where(x => x.Value >= 0)// Filtering out purchase orders to get cancel orders onlyecommerceDF["Quantity"].Where(x => x.Value < 0)

In order to get the quartiles of per-transaction unit prices, use the following code:

// 4. Per-Transaction Unit Price DistributionsConsole.WriteLine("\n\n-- Per-Transaction Unit Price Distribution-- ");quantiles = Accord.Statistics.Measures.Quantiles(    ecommerceDF["UnitPrice"].ValuesAll.ToArray(),    new double[] { 0, 0.25, 0.5, 0.75, 1.0 });Console.WriteLine(    "Min: \t\t\t{0:0.00}\nQ1 (25% Percentile): \t{1:0.00}\nQ2 (Median): \t\t{2:0.00}\nQ3 (75% Percentile): \t{3:0.00}\nMax: \t\t\t{4:0.00}",    quantiles[0], quantiles[1], quantiles[2], quantiles[3], quantiles[4]);

Similarly, you can compute the quartiles of the per-transaction total amount using the following code:

// 5. Per-Transaction Purchase Price DistributionsConsole.WriteLine("\n\n-- Per-Transaction Total Amount Distribution-- ");quantiles = Accord.Statistics.Measures.Quantiles(    ecommerceDF["Amount"].ValuesAll.ToArray(),    new double[] { 0, 0.25, 0.5, 0.75, 1.0 });Console.WriteLine(    "Min: \t\t\t{0:0.00}\nQ1 (25% Percentile): \t{1:0.00}\nQ2 (Median): \t\t{2:0.00}\nQ3 (75% Percentile): \t{3:0.00}\nMax: \t\t\t{4:0.00}",    quantiles[0], quantiles[1], quantiles[2], quantiles[3], quantiles[4]); Console.WriteLine("\n\n-- Per-Transaction Purchase-Order Total Amount Distribution-- ");quantiles = Accord.Statistics.Measures.Quantiles(    ecommerceDF["Amount"].Where(x => x.Value >= 0).ValuesAll.ToArray(),    new double[] { 0, 0.25, 0.5, 0.75, 1.0 });Console.WriteLine(    "Min: \t\t\t{0:0.00}\nQ1 (25% Percentile): \t{1:0.00}\nQ2 (Median): \t\t{2:0.00}\nQ3 (75% Percentile): \t{3:0.00}\nMax: \t\t\t{4:0.00}",    quantiles[0], quantiles[1], quantiles[2], quantiles[3], quantiles[4]); Console.WriteLine("\n\n-- Per-Transaction Cancel-Order Total Amount Distribution-- ");quantiles = Accord.Statistics.Measures.Quantiles(    ecommerceDF["Amount"].Where(x => x.Value < 0).ValuesAll.ToArray(),    new double[] { 0, 0.25, 0.5, 0.75, 1.0 });Console.WriteLine(    "Min: \t\t\t{0:0.00}\nQ1 (25% Percentile): \t{1:0.00}\nQ2 (Median): \t\t{2:0.00}\nQ3 (75% Percentile): \t{3:0.00}\nMax: \t\t\t{4:0.00}",    quantiles[0], quantiles[1], quantiles[2], quantiles[3], quantiles[4]);

When you run the code, you will see the following output for the distributions of per-transaction order quantity, unit price, and total amount:

                                                              

If you look at the distribution of the overall order quantities in this output, you’ll notice that from the first quartile (25% percentile), the quantities are positive. This suggests that there are far less cancel orders than purchase orders, which is actually a good thing for an online retail store. Now, look at how the purchase orders and cancel orders are divided in your dataset.

Using the following code, you can draw a bar chart to compare the number of purchase orders against cancel orders:

// 6. # of Purchase vs. Cancelled Transactionsvar purchaseVSCancelBarChart = DataBarBox.Show(    new string[] { "Purchase", "Cancel" },    new double[] {        ecommerceDF["Quantity"].Where(x => x.Value >= 0).ValueCount ,        ecommerceDF["Quantity"].Where(x => x.Value < 0).ValueCount    });purchaseVSCancelBarChart.SetTitle(    "Purchase vs. Cancel" );

When you run this code, you will see the following bar chart:

                                                  

As expected and shown in the previous distribution output, the number of cancel orders is much less than the number of purchase orders. With these analysis results, you can start building features for your clustering model for customer segmentation in the next section.

If you found this article interesting you can explore Yoon Hyup Hwang’s C# Machine Learning Projects to power your C# and .NET applications with exciting machine learning models and modular projects. C# Machine Learning Projects will help you learn how to choose a model for your problem, how to evaluate the performance of your models, and how you can use C# to build machine learning models for your future projects.

 

What's augmented reality(AR)?

AR is the power to integrate digital information into a real-time adventure. In contrast to computer virtual reality, that is widespread among game lovers, this improvement doesn’t come with any hefty video headsets to traumatize their gaming relaxation. Virtual reality involves the use of technology to immerse oneself into a totally different world with a VR headset. Augmented reality on the other hand uses technology to overlay realistic video, images, or text on the prime of actual video or photos. Most Smartphones these days already come with augmented reality capabilities. Examples embody Pokémon Go, Snapchat and Facebook exposure filters, and constellation finders. New AR apps begin each day and Smartphone users grab them up. AR promoting takes the obtainable digital information and creates an integrated 3D image of a product. It permits customers to explore that product up close and privately. However, Ecommerce Development Company created AR experiences for various brands, as well as a virtual reality environment that covers the whole biome of brands and man.

With new technology and therefore the incorporation of apps in virtually each facet of our lives, augmented reality has advanced and enlarged. Today, it's employed in medicine, education, sports, marketing among other quarters. AR is the idea of the future starting from today.

Augmented reality has the potential to revolutionize whole businesses promotion within the next few years. Some estimates even claim that AR-oriented merchandise like camera glasses can be within our thoughts as early as next year. And as a lot of brands begin to use increased reality, there’s an honest likelihood that it'll begin to have an effect on the means through which users browse the net. With changes that are this massive on the horizon, it’s a prize to take a while to and observe what augmented reality has already accomplished within the previous few years. This will facilitate various technological countries of the world note what's seemingly needs to be alter within the future, particularly in relation to its impact on SEO agency.

So to begin with, let’s scrutinize what makes AR distinctive and then, point out what’s seemingly shall happen within the coming years which can be called The Shift.

THE GROWTH OF AR MARKET

According to Digi-Capital elementary “Augmented/Virtual Reality Report Q2 2015”, the AR/VR market has a high proportion of expanding up to £197 billion by 2020. Moreover, AR has the larger portion of this market pie with £157 billion (VR gets solely £39.3 billion, though).

The latest market study shows that enterprises and industrial markets are progressing to bite more than a chunk of the £3.14 billion buffet in 2019. Compared to £323.6 million in 2014, the distinction is amazingly impressive!

Because of the world technological explosion, computer code enhancements and preponderance of wearable, Juniper predicts extending interest within the options of Augmented Reality among businesses. Though advancing, records of the last decade shows that its adoption won`t be easily racing at bullet speed.

Augmented reality is divided into three characteristic forms:

  • Information overlay
  • Virtual objects
  • Digital packaging

Each of those has completely different applications that brands all over the world are already actively exploring.

ADVANTAGES OF AR FOR BUSINESSES

Elite and Evident Technology

For now, there are more than one way to surprise your customers and build a compulsive buzz as a result of putting your customers interest first in ways your competitors don`t have nonetheless.

Augmented reality goes viral

These same buzzing thrills create more acquisition of recent customer by word of mouths and social sharing.

AR personalization opportunities

Creation is perplexing a chance to make one thing distinctive and thereby to specific one's individuality allows for more connection than customary media content.

Content quality improvement

With AR you provide clients a tool for making the content that they couldn`t do before by themselves through virtual provision of engaging platforms that is more interactive than mere templates.

Interactivity keeps the going moving

It is celestial to think the entertainment trends ranks the leader list. The extremely exciting content motivates users to interactively acquaint you with your mobile application more and once more.

Other facts

Statistically speaking, ecommerce is going to be the foremost compact business of all. Imagine just about attempting to buy a dress from your favorite specs without the sweat of wearing out from doing it all day long, running through all the different searches on earth. Brands like Nike and Vans have already tried this technology, grading higher conversion rates, nonetheless fewer returns, once customers happy with their products.

With the assistance of AR search and promotion, ecommerce is and can be able to gain additional powerful insight and feedback from customers. Reviews, star ratings, what number times one thing was tried out, social shares, try-out span, and an entire heap additional data are going to be generated at the brands’ disposal like who and who checked your profile on LinkedIn.

GAP and hardware chain Lowes area unit already mistreatment AR and VPS (Virtual Positioning System) to meet up to their consumers’ requirement and exceed their expectations. GAP opted sure a “no physical try required anymore”, like that of Nike’s and Vans’s. Lowes came with this bright plan of associating AR app with whatever you’re shopping cart enlist, together with actual directions within the shop to understand the exact position of the merchandise you noted on the list.

Mentions, reviews, links, ratings, are placed in a new trend – this is often what AR offers to the planet of SEO.

Given the novelty and vibes of the method in AR madness, individuals are magnetically attracted to brands taking the opportunity to incorporate AR into their content marketing strategy. This is often why AR triggers quality awareness, one thing several businesses are clashing swords over.

Augmented reality cultivates a ‘wow-factor’. It presents a replacement search and information-ready scheme to AR-impacted SEO agencies that will get information and deep insight into client behavior, so knowing a way to launch further participating and thriving promotional approach across search engines.

Augmented reality isn’t purported to replace your existing SEO efforts, however its aim is to expand your audience and to have interaction with them to an extent beyond their adventurous knowledge.

A TOUCH OF AR IN ALL

  1. Citations: more mentions of your brand name, address, or the other connected data, necessary to increased reality, within the read of third-party sites referred to as citation sources as a part of ecommerce development company Business.com is one such third-party citation platform that folks from metropolis, New York, L.A., Chicago, and lots of others typically use. Expresit encourages individuals to share their expertise and leave reviews on numerous kinds of businesses like restaurants, shops, clubs, sports centers, beauty salons, home services, and a full ton a lot of.
  2. Google Business: within the AR landscape one could use his phone or wearable device to scan a building or a close-by space and find data on a selected business, pictures and every one, ratings and reviews, and conjoined rival listings of businesses close up . This is often each compelling and fascinating hence the audience are going to be beyond questions and intensely interested in it at the same time. That’s why you would like to provide comprehensive and updated details about your business which could satisfy your potential customers’ desires and curiosity.
  3. Audits: they're an enormous a part of native SEO agency and conjointly for location-based AR apps. They’re user-generated, therefore 100 % authentic and organic. With AR apps users can get time period reviews on restaurants, stores, and alternative building as long as they poise their device over such structures. The auditing system is amplified with the AR technology. To maximize secured sales businesses ought to currently use these increased feedback mechanisms.

Reviews have the facility to influence traffic to your native business and it is constantly used by Ecommerce Development Company. However, brands ought to for all intents and functions deliver impeccable services and merchandise expertise to customers, and subsequently sensible word of mouth are going to be unfold the importance.

Besides, this increased reality twist on reviews might raise awareness among business home-owners to implement or improve their negative client grievance management system, as reviews of every kind become visible in real time and might damage their prestigious status.

  1. Geo-targeting: can be likened to roaming messages that uses the geo-localization principle, imagine obtaining push notifications on your AR app once connected and viewing the road with a wearable device, and being briefed of discounts, partnering brands with the one you’re in at the instant and their services all within proximity while walking or driving.

 Nonetheless location targeting goes beyond simple geo-localization it may also be exercised through the marker-based form of AR, with objects animating to life, toggling between offline client expertise and on-line client adventure or simply entwining them. Google Daydream gets a replacement as Visual Positioning Service (VPS) that works as a more robust activity, extremely correct style of GPS. VPS, referred to as the new GPS, is taking the reins from wherever GPS stopped. It helps devices quickly and accurately perceives their location behind closed doors. GPS takes you to the position; however, VPS will take you to a selected item from a store or location.

Google Earth is a vast bosom of information with everything from satellite mental imagery, 3D buildings, terrain, maps, to the topography within the oceans and cyberspace because of its incorporation with ARCore it gives a relaxing user-friendly platform. Augmented reality can answer questions via the intellectual use of this information as it is life changing and applicable to many areas of humans.

As the researchers at ARISE (an acronym for the augmented reality information search engine) noted, the entire planet and the virtual world of AR are more and more merging into one big family. These ‘Big Shift’ of AR trend is quickly moving from a novelty to an unexpected and relied upon expertise to reinforce user engagement and to ease up selections; this becomes a must-have among prominent businesses because of all its beneficial advantages over norms.

On November 9, 2015, Google announced to a pleased and perplexed AI community that it has open-sourced TensorFlow, its proprietary machine learning system. Sundar Pichai said he hoped “this will let the machine learning community—everyone from academic researchers, to engineers, to hobbyists—exchange ideas much more quickly, through working code rather than just research papers”.

Although this brewed a lot of debate, very few people must have realized the impact of this decision at that point of time, and those who did very well knew it was a pivotal moment in Google’s strides towards an AI-first world.

The Inception of TensorFlow

TensorFlow began as part of Google’s former DeepDream product called DistBelief. The DeepDream program was built for scientists and engineers to visualize how image processing is done by deep neural networks. However, the algorithm went viral as people started visualizing psychedelic art in it, unaware that those images were powered by two of the most advanced technologies: deep learning and neural networks.

This led to the creation of TensorFlow, a comprehensive machine learning platform that enabled a plethora of deep learning and neural network-based projects. A highly flexible and scalable platform, TensorFlow has accelerated the production of several machine learning and AI-based projects across applications, including face recognition, music, art, and online content. And ever since Google has open-sourced it, one can only imagine the boom TensorFlow is set to create in the world of AI. What with it being equally accessible to scientists and talented enthusiasts alike, it is imperative that their inspired contributions will harness the yet unfathomable power of Artificial Intelligence.

Okay, agreed that the move is all set to redefine IT as we know it, but what was in it for Google when it decided to make TensorFlow open source? Let’s take a look.

Competitive Edge in the Market

A platform with such incredible potential had to go open source, as staying proprietary would have been pointless and extremely unfortunate. A majority of the deep learning core users prefer working on open source environments as they are much more convenient and certainly conducive to the process. Major competitors like Keras and Theano had already gone open source, and Google didn’t want to lose out in its vision to lead the AI boom.

Better Support for Google Brain

You all must have heard Sundar Pichai talking about Google’s transformation from Search to AI; Google Brain is the project that will drive this transformation. Google Brain is being developed by the best minds in the industry, including Jeff Dean, Geoffery Hilton, and Andrew NG among others, who are also the minds behind TensorFlow. Open-sourcing TensorFlow will only accelerate the platform’s development while also making significant inroads into relevant research areas, which will further strengthen Google’s hold on AI and Cloud.

Leveraging Academic Intelligentsia

Some of the major innovations in the past decade have come as research prototypes from universities before they went mainstream. AI is still making that transition and, consequently, requires huge investments in research. TensorFlow going open source will help a lot in this respect, as some of the best minds will collaborate towards several AI problems on Google’s platform for free, and this will add to the existing body of knowledge. They will have all the access to bleeding-edge algorithms that are not yet available in the market. Their engineers could simply pick and choose what they like and start developing commercially ready services.

TensorFlow as Platform-as-a-Service for AI

What Amazon did for storage with AWS, Google can do it for AI with TensorFlow. As mentioned earlier, open-sourcing TensorFlow will give access to a lot of brilliant minds out there. This will accelerate the time to build and test apps through collaborative development; that is, most of the basic infrastructure will be available for developers to further build on, thereby leaving a lot of scope for customization and abstraction.

Expand its Talent Pool

Hiring for AI development is competitive in the Silicon Valley as all major companies vie for attention from the same niche talent pool. With TensorFlow made freely available, Google can quickly reach out to a talent pool specifically well-versed with the technology and also save on training costs.Just look at the interest TensorFlow has generated on a forum like StackOverflow:

                                                                                  

                                                                                       

This indicates that growing number of users are asking and inquiring about TensorFlow. Some of these users will migrate into power users who the Google can tap into. A developer pool at this scale would never have been possible with a proprietary tool.

The road to AI world domination for Google is on the back of an open sourced TensorFlow platform. It appears not just exciting but also promises to be one full of exponential growth, crowdsourced innovation and learnings drawn from other highly successful Google products and services.

The storm that started three years ago is surely morphing into a hurricane. As Professor Michael Guerzhoy of University of Toronto quotes in Business Insider, “Ten years ago, it took me months to do something that for my students takes a few days with TensorFlow.”

This article is written by Packt Publishing, the leading UK provider of technology eBooks, coding eBooks, videos and blogs; helping it professionals to put the software to work.

Until a little time back, China was way behind America in this rat race of leading the Artificial intelligence market, but in the recent times, it has managed to come neck to neck with the nation. A recent report by MIT listed the Chinese AI giant Baidu as the world’s no.2 in this sector ranking after Amazon.

Below listed are some reasons for this advancement-

Major Reasons: -

  1. Investment: -

The Chinese government has recently pledged to invest a massive sum of 2.1 billion dollars in the AI sector and launch one of its kind AI industrial park in this year. It aims at locating four-hundred AI firms in the 55-hectare industrial park which is to generate an estimated income of 50 billion yuan annually.

Whereas there is no provision for an increase in the funding for research developments in AI in the United States budget.

This is a graph representing the number of robotic patents files by different countries, China can be easily noticed leading the way with over 3000+ patent applications which have surpassed the U.S. by a vast difference of over 1500 applications.

       2.Talent and Development: -

The Chinese AI companies have been very successful in managing and acquiring talented personnel for their hi-tech companies. Plus, their top quality of training and developments that are provided by them really makes an impact on the productiveness and quality of work.

  1. Competitive nature: -

The country’s never give up nature really helped it find its flow which has led to an incomparable research that the companies are able to do to provide useful innovations in the technology to lead the market.

  1. A huge supply of Data: -

Because of the weak state of the country’s privacy laws, many big giants are able to track down customer data fairly easily and add them to their database of millions which helps them in having an almost endless supply of citizen information which is very useful for the research of AI as innovations and inventions only matter to the people when they are of any use and this use can be figured out from the collected data.

  1. Larger audience: -

The Chinese innovations and inventions have a larger audience to cater to which gives them a more detailed review and feedback which helps in better developments and provides a direction for research which lacks in America as their population is way less.

  1. Upcoming firms: -

Beijing being the head of all the AI activity in China is witnessing and promoting the upcoming of new AI-based firms by various methods such as grants, data, intelligent personnel, external help etc. Their help has significantly raised the number of AI firms in China.

With the nation’s continuous endeavour in becoming the tech superpower by the year 2030, it is safe to say that China’s copying days were a past now and they are paving their path to lead the industry by an example really soon.

 

Link: - https://www.nature.com/articles/d41586-018-00604-6

https://www.ft.com/content/856753d6-8d31-11e7-a352-e46f43c5825d

http://www.digitaljournal.com/tech-and-science/technology/start-up-says-china-leads-many-areas-of-ai-development/article/511658

https://www.nytimes.com/2017/05/27/technology/china-us-ai-artificial-intelligence.html

https://www.weforum.org/agenda/2017/11/the-us-is-losing-to-china-in-the-ai-race

Artificial intelligence

In simple terms, artificial intelligence is the intelligence showcased by machines. It focusses on developing machines and bots that work and act like humans but are far more efficient and productive than them. This industry has seen some massive developments in the recent years which has resulted in the spread of word of this technology and it being adopted by tons of people and companies.

Levels of Artificial Intelligence

For easy understanding, Artificial intelligence is normally divided into 2 groups which are quite easy to understand by their names itself

  • Strong And Weak Artificial Intelligence

The basic difference between The Strong A.I and The Weak A.I is the explanation of the intelligence reports. A strong A.I not only thinks and provides the results but can also provide the proofs of the thinking but a Weak A.I could only provide results.

  • Narrow And General Artificial intelligence

As the name suggests, Narrow AI is specially set up to perform a certain kind of tasks whereas General AI has no specific objectives.

A.I in the current time is a need but a threat too, as said “A coin has 2 faces” A.I if used for good purpose may land you up in levels but would also cause threat if in dangerous hands.

Advantages of A.I

A.I have numerous Advantages but some of the basic and most important ones are mentioned below: -

  • Personalized Learning – The students are the future and this future nowadays is gadget-filled, For which A.I is a basic need for their better understanding of the work. A.I help the People to learn by themselves and become their own master.
  • Faster Decision- As A.I is a computer in layman’s term, it provides you with the results of any of your problem faster than any human.
  • Avoiding Error- NO human can Guarantee NO Error but yes Here is another Advantage of A.I which gives you an Error Free Report.
  • Increases Productivity- the work here is done by programs which are way faster than a human who helps it to complete more work in less time which will eventually increase productivity and will help to submit faster with more effective and efficient results.

 

Disadvantages of A.I

The coin is turned and here is the time for some disadvantages in comparison to the above positives: -

  • Cost- The programs are usually of high valor and not in reach of everyone’s friendly pocket.
  • Addiction- Daily use and dependency on A.I leads to its Addiction which pulls you from working and takes you to Technology Addiction.

 

As we can see the Percentage of Advantage is way more than disadvantage which makes a mark of creating new opportunities and ways of jobs in the market rather than slowing down the market. As the rate of Pro’s are taking over the Con’s, the personnel practicing A.I will be in high demand. A.I have already been asking attention and if people grab the opportunity at this very right time there is more success on their way ahead waiting for them.

Links-

https://en.wikipedia.org/wiki/Artificial_intelligence

https://www.arrkgroup.com/thought-leadership/artificial-intelligence-the-advantages-and-disadvantages/

https://www.livetiles.nyc/blog/pros-cons-artificial-intelligence-classroom/

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