Definition: A rather subjective, but fascinating definition of AI is: “The ability of machines to perform tasks, which humans consider out of their performing ability, is called Artificial Intelligence. ” What makes a machine artificially intelligent is its ability to do something that would astonish humans.
While the understanding of this definition would depend upon when, how, and who is studying it, it still manages to convey a lay man’s perspective of AI. The media and common people often judge a new advancement in AI on the basis of their own intellect and technical aptitude.
A general definition would be: Artificial Intelligence is the technology, which imparts human intelligence to machines. This technology overlaps psychology, cognitive science, and sociology, even though it is majorly related to computer science. This means that machines will be able to think, comprehend, perform, and address challenges just like humans do. It is a vast field that deals with a number of different areas, like natural language comprehension, computer vision, robotics, logic, and planning.
Machine Learning
Many people misinterpret machine learning as programming. While a computer program is simply the set of instructions fed into a system to make it perform a particular task, Machine learning refers to a system’s ability to learn, self-update, and grow by observing and communicating with its surroundings. To comprehend this basic difference, let’s use an analogy. A human learns and writes a program for a computer, which in turn helps the human to print a picture. Machine learning is not a replacement for the program; instead, it is taking the place of the human programmer. This means that a machine will use large amounts of data to learn to write programs.
To further simplify it, machine learning algorithms work in two steps, which sometimes occur simultaneously. The first step is training. Data accumulated from human experiences and knowledge is fed into the machine to train it and make it understand things like humans do. A machine receives training and creates a program for itself based on the model or data stored. For instance, the machine will learn how the human mind differentiates between a cat and a dog even though they both are mammals, walk on four legs, and are pet animals.
Training is further sub-divided into supervised and unsupervised learning. Supervised learning means to have someone to teach you, whereas unsupervised learning entails self-learning with patterns and features in data that the machine observes itself. A renowned method of machine learning is Deep Learning, which is based on a neural network. Deep learning can be used to learn with audio, video, linguistics, etc.
Deep Learning
While neural networks served as the foundation for deep learning, the technology has now exceeded this framework and using a lot of other frameworks, including deep neural networks, convolutional neural networks, deep belief networks, and recurrent neural networks. Currently, these frameworks are concentrating on vision, speech recognition, natural language processing, audio recognition, and bioinformatics.
Deep learning is used to generate a neural network by taking inspiration from a human brain and its natural ability to analyze and learn. It tries to emulate the function of a brain and the way it derives meaning from images, text, and sound. The model used is termed “deep” because of the fact that you have a degree of control over how many hidden layer nodes you want. A typical model would have from 5 to 10 layers of hidden nodes.
Data representation is also enhanced through deep learning as it is a polynomial multiple of the number of input nodes and has strong expressive power. One can work upon a basic network framework and use concepts of deep nonlinear network structure to perform complex functions to a great extent. Moreover, doing so will enable one to exhibit the impressive ability to extract useful information from the data gathered from numerous unknown samples.
History of Artificial Intelligence
The rich history of Artificial Intelligence can be studied in three phases. Each phase had its own set of advancements and distinctive features. Still, all three phases are interlinked. The first phase began in the 1950s when John McCarthy officially coined the term “Artificial Intelligence” for the first time at a conference held at Dartmouth in 1956. This became the stepping stone for this technology and Joseph McCarthy and Marvin Lee Minsky emerged as “fathers of artificial intelligence”.
The earliest computer scientists were of the belief that the computer is a human invention and can never have the basic abilities of its inventors. This thought was manifested in the Turing test and the Turing machine. On the other hand, there were some experts who had grasped the potential of a computer system from the very beginning, and those with this mindset originally laid down the foundation of artificial intelligence. The initial questions which shaped this revolutionary technology were:
- What is reasoning and how can a machine reason?
- What is meant by understanding and how does a machine understand?
- What is knowledge and how can a machine obtain and use it?
- What blurs the line of difference between humans and machines?
This phase was all about exploring the fundamental theories of computer science in general and artificial intelligence in particular.
Logic became the basis of the earliest breakthrough in Artificial Intelligence. Joseph McCarthy introduced the LISP – the logical language in 1958. Based on this, computers were made to demonstrate the ability to play games and understand human language to some extent. This continued till the 1980s and robots were designed for logical decision making and block building. For example, robotic mice decided which way to go and how to overcome hurdles and self-driving cars worked under supervision. As time passed, neural networks were built and machines developed the ability to understand simple human language and recognize objects.
Although it was acknowledged as a valuable field, Artificial Intelligence did not offer everything it could for the initial two to three decades. Limited applications of AI made it a dormant domain. The change started in the late 1980s and early 1990s and a new approach was adopted by the scientists. They started using this technology to solve simpler problems in specific fields instead of complex challenges requiring advanced intelligence. The advent of the “expert system” brought AI in limelight as a potential business resource.
Meanwhile, computer technology continued to expand and data storage and applications found stability in the past 30 years. This gave scientists the idea of integrating AI with data, which led to the advancement of an expert system. This means a machine fed with the relevant data and a little logic would become an expert in a field. For instance, providing a machine ample data on cancer and giving it reasoning and analytical skills would make it an oncologist, which can be referred by cancer patients.
It was apparent that expert systems would prove to be highly capable and useful in various fields, such as healthcare and weather forecasting. Once the doors of practical applications opened, the demand for academic research on AI increased manifold. One key challenge faced by the expert systems in making intelligent diagnoses at that time was the lack of digitalization of the huge amounts of data. When a patient visited a healthcare practitioner, most of the history was manually recorded or saved in a machine, which was either isolated or obsolete.
Getting automated diagnostics necessitated some initial effort from the user’s end. It required a single task: Digitize the data. To fulfill this need, the concept of Big Data came into being. All the information, which was once collected manually, now shifted on the computer, and people from around the globe continuously fed data to the internet with every passing second. Efforts to boost the computational performance, as per the predictions done in Moore’s law, increased simultaneously. With faster, cheaper, and more efficient computers, the machines started to take over most of the positions that required labor and it was expected that computers would surpass humans sooner than imagined.
AI entered its third phase with two major advancements. Firstly, the computational ability has been increased by building modern hardware, distributed systems, and cloud computing. Hardware and software have been recently integrated with AI by using neural-network-based computing. Secondly, an incomprehensibly large quantity of data has been gathered due to the widespread use of the Internet. Use of smartphones brought in data related to individual habits. In 2001, GPS was launched which helped with travel data accumulation. With the increase in computational ability and accumulated data, machine learning algorithms have experienced an incredible boost.
Ten glorious years have passed since AI came into being and in this short amount of time, the technology has offered a myriad of opportunities and applications. What makes the third phase of advancement stand out among all the phases is the fact that it brought AI out of the pages of research papers and made it accessible to the mainstream population.
It was the first time in history when computers became the talk of the town by mastering the basic and easily understandable tasks which had always been performed by humans such as image recognition, video understanding, translating machines, or driving cars. It is fascinating to see that a single technology serves various applications, such as navigation, search, map search, and stock trading, and performs tasks, which humans find difficult to do.
These days, humans are very comfortable in using their voice to provide straightforward instructions to machines but these technologies are generally meant for completing a task without any involvement of emotions, thoughts, complex judgments, or human perception.
Over the years, humans and machines have appeared to be closer with respect to complexity and structure. For instance, the self-driving technology based on machine learning has developed and transformed not only the method of traveling for humans, but also how they perceive their surroundings, and live in this world. Because of this technology, people no longer have to buy a car, let alone drive it. Because of this speedy arrival of new and unique technologies, humans are fascinated by the convenience they offer and apprehensive since they are clueless about what to do if these developments happen too quickly.
With this enhancement in the self-learning capability of a computer, the growth and development of modern machine learning algorithms, particularly the machine with deep learning algorithms, has converted machines to think more like “black box” instead of comparatively conventional “programs” and “logic”.
The second fact is that AI has made immense advancements in some specific areas but after a detailed examination, it was discovered that technology is still a long way from the comprehensive intelligence on which the founders were working in the initial wave. Although the machine is designed to do a particular task in a specific situation now the task is more complex. Still, AI is unable to comprehend simple emotions like fear or panic and machines are still lacking some fundamental human intelligence such as common sense.
The third fact is that the circumstances for AI and machine learning are incredibly broad. In the past few years, because of this speedy development and improvement of artificial intelligence and machine learning applications, these technologies have now been taken from the research laboratory and provided to the masses. The application layer comprises computer vision, deep learning, robotics, and natural language understanding.
Face recognition, automatic driving, medical diagnosis, machine assistant, smart city, new media, and games are recognized by people all around the world which is evidence that applications of algorithm class are not restricted to academics but have made their way to daily life activities of humans. However, there are some significant fields that have been overlooked such as agriculture, the children and aged peoples’ care, dangerous circumstances, and traffic flow, etc. This shows that every part of society is touched by this technology.
One of the leading artificial intelligence applications is in the local and international media industry, as media have a tendency to reach billions of consumers. Viewers are interested in watching a variety of content such as news, sports, and movies. This calls for quality content and interactive layouts. In this way, numerous commercial benefits are reaped.
The phone is not just a phone anymore. It has now become a smart secretary in the palm of your hand. Various applications, such as Apple’s Siri, Baidu’s Secret Service, Google Assistant, Microsoft’s Little Ice, Amazon’s Alexa, and other smart assistant and smart chat apps, have added more value to your smartphone.
Moreover, AI has modernized traditional news transmission by introducing news apps with the latest stories and press releases. AI is used to beautify photos in apps like Meituxiu, and photos and videos are artistically altered by using Prisma and Philm. Object identification feature in Google Photos helps the users organize the images once the objects are identified by the AI.
E-shopping platforms like Amazon and Taobao often recommend products to users by using AI. Online shopping business is also thriving by using advanced warehouse robots, logistics robots, and logistics drones to make the delivery process smooth. Google and Baidu, two extensively used search engines, have used AI to successfully answer questions, assist, and search.
Google translation also uses deep learning to translate a number of languages. AI algorithms are also used in apps like Uber and Didi to guide the drivers regarding favorable routes and vehicle schedules. In fact, the transportation industry will soon be unrecognizable with self-driving cars, smart mobility options, and smart cities surfacing.
Mobile phones are often the go-to devices when someone wants to get the latest updates regarding what is going around the world. Apps using AI, like News Reader, have gained immense popularity among common people. The technology helps the apps to gather data about the viewer’s likes and habits and then recommend content accordingly. If viewers are satisfied with the working of an app, they will use it more frequently and provide more data, which will boost the app’s performance tremendously.
The most important section of artificial intelligence, which extensively uses machine-vision technology, is facial recognition. The precision of face recognition with the help of artificial intelligent programs has surpassed the standard level of human beings, all because of the growth and advancement of deep learning technology.
Moreover, you can use facial recognition for overall safety as well as for bank transactions. Almost all the mobile phone banks will authenticate their business agents by turning the front camera of the phone to get a real-time image of their face so that the smart face recognition program can perform an identity comparison to verify their identity, but make sure you keep your mobile banking applications safe from criminals.
Along with facial recognition, all smart algorithms such as image and video object recognition, scene recognition, location recognition, and even semantic understanding are also part of machine vision technology and they are easily available in average mobile applications.
Just about all the major photo management programs provide automatic categorization and retrieval of photos. The most powerful example of this is the Google photos, which allows you to upload all your photos and videos to the cloud whether it was from yesterday or from million years ago and without any tagging or manual cataloging, it identifies every individual and every location or landscape in your photos along with quick and correct search results.
With Google Photos, you can access your great moments from past years effortlessly and can surf through the famous places you once visited. In the summer of 2016, a mobile drawing app was introduced called Prisma which can convert your photos into a specific style of painting to your likings. By using the highly developed artificial intelligence algorithms, Prisma recognizes each color and edge of the photograph and then apply the techniques learned such as classic painting techniques, brush techniques, and dry and wet painting techniques to it.
This entire process shares many similarities with the way a child learns to draw a dog in an art class. Prisma is like a child and has already received high-level art training. Chinese and Western classical artwork has been fed into it and it is now able to create paintings using diverse media, such as oil paint, watercolors, and even pencil sketches. It uses an intelligent brush to produce masterpieces in 20 different painting styles.
A large population believes that search engines are a 20th-century invention, which used Internet core technology. However, the fact is that the first Page Rank algorithm was launched in 1996 by Google’s founders, Sergey Brin, and Larry Page. This enhanced search engine ranking to a great extent. What role did AI play in this scenario?
Machine learning has been an integral part of Google’s approach to rank search engine results, making it different from the typical algorithm. It employs a mathematical model to sort web pages. This model is not fully designed by humans; instead, a computer using big data learns via an intricate iterative process and builds it. AI algorithm’s self-learning defines the importance of each feature and its participation in the calculation of final rank. The success of the Google Brain project and the advancements in deep learning since 2011 have encouraged Google to continue using deep learning for the search engine’s web ranking algorithms. An additional advantage is that the results have now become increasingly relevant and precise.
In other words, Artificial Intelligence has transformed Google into a new generation search engine.
The ranks of the results generated by a search engine are just a small example of the use of this technology. Nowadays we can witness the glory of artificial intelligence wherever we look. We can get an intelligent answer to any of our questions directly from the Google search engine.
Currently, artificial intelligence, with its speech recognition, natural language understanding, Knowledge Atlas, personalized recommendation, and web ranking features, is used by several leading search engines like Google and Baidu. This has made them ‘smart’ in the true sense of the word and changed simple web search into a globally recognized engine and simple web navigation tools into an efficient personal assistant.
AI is indeed the realization of an age-old dream of mankind: breaking language barriers. With the help of smart translation tools, we are now more connected than ever before. The discovery of the Rosetta Stone in 1799 has helped us rise above the limitations of time and space. We can now use the Rosetta Stone and linguistic experts to understand text written thousands of years ago by ancient Egyptians. People who belong to different nations, follow different cultures and speak different languages are able to interact with each other with this technology.
These days, translating tools based on artificial intelligence are making global communication easier. Out of all the translating tools, Google is the most powerful as it has nearly all the languages along with tremendous results.
Without any doubt, for a normal individual, the most fascinating example of artificial intelligence is autopilot. Almost every science fiction movie and novel played with the idea of automatically driven cars, planes, and spaceships, but now, realizing that one day it will all come true seems almost dreamlike.
However, the technology behind this self-driving car and planes is already functioning around us and producing great commercial value.
In promoting self-driving technology, Tesla has been more energetic in comparison with Google. In early 2014, Tesla started promoting and selling electric cars with an optional driver assistance program called autopilot. With the assistance of on-board sensors, the computer-controlled the speed and power of the motor along with adjusting the braking system. But the fundamental technology of preventing any collisions from any side is similar to Google’s autopilot.
Apart from the public sector, self-driving cars would find extensive application in most private industries. For instance, the companies providing rental transport, such as Uber and Didi, would love to make use of self-driving technology to boost their financial status. You can witness the test driverless cars from Uber cruising through the roads in the US. Similarly, the logistics industry would invest in self-driving trucks and we might witness its widespread use even before self-driving cars. Some R&D departments are even considering self-driving vans as a travel option on motorways.
Another ground of artificial intelligence is robotics which is a revolutionary field. It’s been years since the manufacturing industries started using robots for car production lines and mobile phone production lines. The use of robots has become so common that now it would seem almost strange to not use them.
In 2012, Amazon purchased a company called Kiva only to obtain the skill to design and construct warehouse robots. Built on kiva, Amazon produced little, orange robots to transfer goods to a selected location by shifting them swiftly through Amazon’s large storage centers.
You must have seen drones flying across the sky and landing perfectly on your rooftop. Taking inspiration from them, entrepreneurs are trying to design driverless vehicles or smart robots for delivery. The famous pizza company, Dominos, has even tested car-like robots for pizza delivery. Starship Technologies has also designed a robot that resembles a car and has the ability to carry as much as 20 pounds of cargo. The robot is loaded with features like safety locks, intelligent driving, precise positioning, and intelligent communication. Moreover, it can cover a mile on foot and act as a door-to-door courier service or as your personal helper to carry stuff after shopping.
The idea of AI is also getting sold like hot cakes in the fields of education and home robotics. However, the home robots that are currently in use are not perfect replicas of humans like one might imagine. To look at things practically, designing a robot that looks, talks, and behaves as a human would only add to the cost and not the efficiency. In fact, users will often unintentionally compare the robot to a real person and get disappointed. In comparison to a real person, the robot might seem not-so-smart.
For users at home, a simple yet smart appliance makes a substantial impact rather than a robot-like appliance. Amazon Echo is an ideal example of this which is a small and simple appliance with basic communication skills and is more preferred than a fully loaded but inconsistent android device.
Similarly, there are educational robots that make learning fun and easy. A workshop called Start-up Wonder Workshop produced tiny robots called Dash and Dot which helped children over the age of five in developing their practical skills and imagination.
Future of Artificial Intelligence
As machine learning is reaching new heights, the term “Artificial Intelligence” has taken the world by storm. Everything from computer-run vehicles to smart appliances, from intelligent robots to one-click language translations, is making an impact on our daily lives. Artificial intelligence has gradually penetrated our homes, our businesses, and our surroundings.
Computational Advertising
E-commerce thrives due to advertising, which is one of the most influential parts of it. Huge online companies are earning big with digital advertising. The statistics presented by Google’s parent company, Alphabet, showed that they were able to earn $21.411 billion just with the advertisement. This accounted for 85% of their total revenue for the first quarter of 2017. Similarly, Facebook claimed to have earned a whopping 98% of its revenue by advertisements. Other internet giants like Alibaba and Tencent are also relying heavily on the cash flow generated through digital advertising.
Digital advertisements first appeared in 1995. During that time, Yahoo was a web portal representative and an online magazine, which was delivered as per the selected mode. Back then, digital advertisements were not much different from the traditional advertisement on the mainstream media, except for the fact that they were online. Search engines like Google have been leading the digital advertisement industry since 1998.
Search engines are commercially monetized by combining paid search and ads that showcase products that correspond with the search terms, which isn’t the case with portals. Such advertisements are retailed by competitive bidding and the advertiser has the freedom to adjust the ads as per the immediate user interest. This gives ads on search engines better accuracy than portals. Another trend, which began in 2005, is business using videos. Websites like YouTube and Hulu have ad formats similar to television and this has enabled them to take over the advertisement world.
There are numerous reasons why advertisers are getting increasingly interested in the Internet advertisement format. One main reason is the amount of time the current and upcoming generations spend on the internet. Companies wish to reach more audiences and they can do that by investing in internet advertisements. Another reason is the low threshold for online advertisements. You can spend as low as $100 to register a self-service account with Google and start placing Ads. Moreover, it allows you to customize, monitor, and enhance the ads through AB testing.
As internet advertisement is gaining popularity, there is a need for more refined algorithm models for it. Andrei Broder, senior researcher at Yahoo Research Institute, proposed the idea of computational advertising in 2008 at the 19th ACM-SIAM symposium. In his opinion, computational advertising is a modern branch of science, which encompasses information science, statistics, computer science, and microeconomics. Computational advertising aims to bring context, advertisement, and audience together in the best possible manner.
Search advertising is undoubtedly the most significant form of auction advertising. A search keyword acts as the determining factor for an ad and advertisers can bid to place their advertisement on the page when that keyword is used in the search. As soon as a user types a query in the search engine, the keywords are matched to the ads that bid for them, and relevant ads are retrieved. The engine selects one of the ads and places it preferably at the top of the page with the search results. Search ads work on a pay-by-click basis. This means that when the user inputs the keyword selected by the advertiser, no fee is charged. This shows the importance of the click-rate estimation algorithm in making the advertisement more competitive.
With the help of procedural transaction advertising, advertisers get more freedom in terms of viewer selection and time of exposure. Whenever a display opportunity appears, appropriate traffic data is sent by the ad trading platform. Soon after, a bidding request is sent to the demand-side platform (DSP). DSP bids for an advertiser as per the real-time scenario of the traffic. The advertiser with the highest bid gets the spot. Cost per Action or CPA is typically used to settle procedural transaction advertisements. Therefore, click rate, conversion rate, and other factors are taken into account.
While the overall architecture of the advertising system is pretty basic, the design of the advertising systems defines the type of ads. For instance, the real effect of ads is often overlooked when dealing with contract ads; hence no CTR module is required. On the other hand, the interaction between procedural transactional ads and third-party information such as ad trading platforms is necessary. For this purpose, more data docking modules are required. The system is made up of a distributed computing platform, a streaming computing platform, and an advertising machine.
Batch calculations are carried out by the distributed computing by using large quantities of posting logs and generating outcomes after algorithm analysis and modeling. Tasks like user profiling and CTR modeling are carried out by distributed computing and user labels, model features and parameters, and additional data are updated into the database. They are then synchronized into the database in real-time. As soon as the request is sent, the advertiser uses the knowledge of the user, context, and the immediate database state to find, sort, and choose the ads to be displayed. Once the ad is positioned, the streaming computing platform captures and processes the appropriate data on time. Afterward, the records are gathered in the placement log and can be used by the distributed computing in the future.
Extensive machine learning concepts are needed for each algorithm module of the advertising system. The system is also affiliated with Spark, HDFS, Kafka, and other big data programs. One needs to develop a profound understanding of the business function of each module to establish a strong base for the algorithm and evolve as an advertising algorithm engineer. The algorithms and the associated machine learning for each module of the advertising system are detailed below.
User Portraits
The fundamental component of computational advertising is the user profile, which is extensively used in contract advertising, search advertising, procedural transaction advertising, and other forms of product. In the contract advertisement, the appropriate orientation condition is specified by the promoter, keeping the audience of his brand in mind to minimize the expenditure. The profile of the user can be used to estimate the click rate and conversion rate more accurately. This in turn will serve as the basis for search advertising and procedural transaction advertising and help with the optimization of the entire delivery effect.
User profiles frequently use supervised learning as well as unsupervised learning. For instance, the issue with gender prediction is a common problem faced with supervised learning. While the sexual orientation of the users who provide their data can be easily understood, but for those who don’t, it is hard to predict their gender accurately. This becomes problematic when advertisements request explicitly for gender-specific branding. For instance, a company selling men’s apparel may find it useless to place ads on female user’s profiles. Hence, to cater to the advertiser’s expectations, the system needs to model and predict a user’s gender on the basis of their previous behavior, habits, and preferences. For instance, if a user uses certain hashtags, it may influence the probability of the user belonging to one of the two genders. Supervised learning can be used to model and predict user characteristics with sufficient annotation samples.
There are higher chances of guessing that the person is a male and the chances are comparable to other hashtags. To represent and predict user tags, supervised learning can be used as long as annotation samples are available.
Logistic regression can be applied for the supervised learning model. The model is compatible with the vector machine, decision tree, random forest, gradient lifting decision tree, feed-forward neural network, and others. For instance, search engines can predict the gender of a person on the basis of their search text and browsing data. This helps the engine place relevant ads. This was proved with the help of experiments where browsing and search history was used by a prominent website. The website treated each term as a unique one-dimensional feature. In this way, important text features are learned by the final classifier.
In predicting women, the significant attributes are children, food and family whereas for men, sports, internet and cars are more important. This shows that the outcomes of feature learning are more instinctive.
The use of unsupervised learning is another main category of user profiling method in which the major function is to determine the rule of data itself without any use of labeled data.
In light of the previous habits and current traits, users can be classified into particular groups. While it is not easy to put each user in a single category, it has been observed that users belonging to a single group share tastes and prefer certain advertisements. Hence, the results can be greatly improved by applying clustering technology and grouping the results for click-through rate estimation, advertising ranking, and selection. Some common clustering methods are k-means, Gaussian mixture model, and subject model. All these methods are associated with unsupervised learning.
Click-Through Rate Estimates
For effect advertising, click-through rate estimates are considered as highly remarkable algorithms. The advertising effect can be initially enhanced by accurately weighing the effects of the advertising display, such as click rate, conversion rate, etc. When dealing with search advertising, results are analyzed and chosen on the basis of the number of clicks an ad receives. This shows the role of click-through rate estimation in optimization. The conversion rate after the click should also be estimated if the final outcome is given in the form of a conversion.
However, the real conversion data is hard to find. Since it is hard to use the conversion data for model training, it needs to be condensed in the next step, the second jump, add to the shopping cart, and another behavior modeling. We will be discussing the algorithm flow and common models as the only examples of the click rate estimation because its modeling principle is quite similar to that of the second jump and add to the shopping cart.
When a request is made, the system matches the ads to it. It also predicts the probability of a user clicking on the ad when posted. Tagging can be acquired from real drop data, and in historical drop outcomes. The label 1 represents a click and 0 represents otherwise. Click-through rate estimation is carried out in multiple steps, like sampling, feature extraction, and combination, model training, model evaluation, etc.
Advertising Search
During the phase of advertisement retrieval, all advertisements relevant to the criteria are retrieved. The criteria are designed by keeping the search, the viewer, and other aspects in mind. In case the search is not clear, advertisements similar to the search terms will be proposed on the basis of semantics. The proposed ads must be as relevant as possible so that they can be sorted and algorithms can be selected. The determining factor of this stage is the rate of retrieval since the ads left will not be placed in the future. This pattern matching problem can be resolved through query expansion.
For query expansion, the system finds a group of queries, which are semantically similar to the original query. The advertisements retrieved by using any one of the queries of this group are added to the substitute set. This helps to work out the similarity in the text of both the queries using the topic model and Word2Vec algorithm and calculate the probability of producing a new query for the original one by using a deep neural network and other methods.
AD Sorting/Selection
In the ad sorting phase, there are different advertising business situations having dissimilar decision-making styles. For contract advertisings, fulfilling the daily exposure requirement is the main objective (the advertiser will be charged in case of failure to provide the required exposure to ads) without the involvement of CTR estimation. Hence the advertising ranking and selection problem can be modeled as a controlled optimization problem. As a result, the option of advertisement can be expressed as a bipartite graph matching problem; the goal of optimization aims to increase the total income.
Machine Translation
Acknowledged by Wikipedia, the internet is packed with hundreds of languages. Half of the internet’s content is in the English language whereas a quarter of all the internet users is English speaking. With the increase in the use of the internet, the demand for language translation is also escalating. The concept of Machine translation was first introduced in the 1950s. It is a division of computational linguistics and is a significant application of artificial intelligence.
Machine translation refers to the ability of a machine to translate a text from one language to another. This aspect of machine learning serves as a great solution for problems involving a language barrier. In 2013, people used Google Translate to get billions of translations per day, which is equal to one million books or the global human translations are done in one year.
The rules of translation specified by the linguistic experts make up the basis of the Machine translation system – a mechanical process. Machine translation research is divided into three parts. The earliest primary method in terms of use was the rule-based method. This method had certain limitations, such as the quality and amount of hand-written regulations. The method was lengthy and strenuous, as all rules of translation had to be written. Also, the rules did not apply to any two language pairs. Another issue was the ever-increasing list of rules and the contradictions in them and this made it impossible to encompass all human languages. This is definitely a bottleneck for the machine translation system.
The Statistical Machine Translation (SMT) method was first presented in the 1990s. After which, it was adopted as the primary method used for machine training. The method obtains data for training from a bilingual parallel corpus, which contains the source language as well as the target-language text. The famous Rosetta Stone can be considered a prehistoric parallel corpus with identical texts written in the sacred script, secular script, and ancient Greek script. In fact, the Stone served as the inspiration for the linguists who were then able to decipher the script.
A statistical machine translation model searches the parallel corpora for the alignment links between the words of different languages and extracts regulations for translation on the basis of these links. Typically, a statistical machine translation model comprises of three main components. First is the translation model, which predicts the probability of words and phrases to be translated. Second is the reordering model, which arranged the language fragments after translation. The third is the language model, which minimizes the role of a human in the process. The efficiency and applications of machine translation are improved exponentially due to the model’s lack of dependence on the training process.
Recently, machine translation has experienced a boost in its performance due to the advent of neural network-based methods in this domain. Neural network models are responsible to generate over 50% of translation traffic. Google’s machine translation team shared that the China UK neural network model was released in September 2016 by Google Translate and by May 2017, it has been successful in backing up 41 pairs of bilingual translation modules.
While machine translation has not yet accomplished the same efficiency as that of human translation, the advancements in machine translation have led to considerable improvements and increased applications. Just about 14 years have passed since Google launched Google Translate in 2006, but today it is able to translate hundreds of languages and is even integrated with web pages, mobile clients, app APIs, to name a few.
A number of international and national companies like Microsoft, Baidu, and NetEase are striving to enhance their machine translation services for the common people. Google Translate was used by 500 million people per day in May 2017. While many apps providing machine translation services are still struggling to automatically provide written translation, they have been successful in bridging the communication gap caused by foreign languages to a great extent.
Language can be the biggest communication barrier when traveling across the world. However, this issue has been largely resolved by the range of mobile phone apps that use the camera to take the image input and translate it into the chosen language.
There is no doubt in the fact that machine learning is an incredibly attractive and fruitful field. However, there are still some obstacles in the path. There is room for improvement in areas such as interpretability of the neural network model and performance of machine translation services. The apps used for machine translation are useful for simple translations or auxiliary help only.
But there is no end to the machine translation technology as more and more updates and improvements are pouring in each day. It can be expected that the Tower of Babel will once again be established in near future.
Another feature of Artificial intelligence is the integration of human-computer interaction. Human-computer interaction encompasses all the aspects of AI. Such an interaction allows the machine to use speech and image recognition features to comprehend input signals from the user’s end. The machine in turn uses prediction models and learning models for apt decision making and self-learning. With the help of intelligent controls, humans can now specify certain actions for the machines and the machine can generate useful feedback. The growth of human-computer interaction is directly proportional to the development of Artificial Intelligence.
The past few years have seen unprecedented growth in the domain of human-computer interaction. These advancements can be understood as three major phases. The idea of the human-computer interface was introduced officially for the first time in the 1970s when the First International Conference catering to a man-machine system took place. The same year, the first relevant professional journal (IJMMS) was announced, and numerous research-oriented educational organizations incorporated the human-machine Interaction Research Center.
The next phase began in the 1980s when research, both theoretical and experimental, started on human-computer interaction. The main focus was on learning theories related to cognitive psychology along with behavioral science and sociology. The experimental work revolved around feedback interaction between man and machine, and the concept of human-computer interaction replaced the term human-computer interface. In the last phase, high-speed processing chip, multimedia technology, and Internet web technology flourished and the research shifted towards human-computer interaction, multi-modal (multi-channel) and multi-media interaction, virtual interaction, and intelligent interaction.
How to Excel Yourself in Artificial Intelligence Era
Learning the traditional skills involving memorization and practice would not be much useful in this era of artificial intelligence, especially when machines are capable of demonstrating the same skills easily and far more efficiently. However, a well-rounded personality with the right interpersonal and technical skills will turn out to be valuable in the long run. A person intending to thrive in this age should develop critical analytical skills to deal with complex systems and intuitive decision-making ability. Moreover, knowledge of art and culture and aesthetic sense are also required. Another quality, which would be handy, is the common sense gained through experience and cultural influence. A high emotional quotient or EQ would give you an upper hand over the machines with a high IQ in the age of AI.
The simple yet effective ways to excel oneself in the era of AI are summarized below:
Push Yourself by Taking Initiatives: Self-improvement is only possible when you break the barriers, face the challenges head-on, and take initiatives. The only way to compete with intelligent machines is to keep a steady pace to match them.
Learn and Practice Simultaneously: We need to change the approach of learning before doing. Professionals need to learn while solving real-life problems, applying strategies in practical situations, and working on self-development along with professional growth.
Use Experimental work creatively and Be a Problem-Solver: Leave the technical, order-based tasks for the machines and concentrate on innovation. Learners need to rise above the memorization and utilize their inherent potential to think out-of-the-box. In this way, you will be able to carry out investigative research.
Engage in the Futuristic E-learning: On-site learning will never cease to exist, but e-learning is the talk of the town now. E-learning platforms like VIPKid and Boxfish are revolutionizing education through machine-assisted learning. It enables the learners to maximize the available educational tools and maintain a high degree of credibility.
Learn from Machines: The future will be all about “man meets the machine”. Humans and machines will each have their own domains, but work collectively to move forward. For instance, AlphaGo’s work is used by Go pros to improve the patterns learned. This shows that the findings of the AI computational models can also be used by humans to build ideas and think critically.
Learn to communicate with Humans as well as Machines: Interaction with machines will be as important as interaction with humans in the upcoming years. By communicating with machines, humans will learn faster and better. Learners will benefit from communicating with other students, humans, and machines.
Play to Your Interests: A person’s interest and passion often help him excel in his field. Chances are that machines won’t be able to take your place if you feel passionate about it. Human working in their field of interest would most likely be more valuable than machines.
