Application Of Machine Learning and Deep Learning In the Hospitality Industry – An In-Depth Analysis

application of machine learning

“We are going to completely change what it means to do advanced analytics with our data solutions. We have machine-learning stuff that is about really bringing advanced analytics and statistical machine learning into data-science departments everywhere.” – Satya Nadella

“Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.” —Larry Page

Machine Learning, Deep learning, Artificial intelligence, and related technologies have definitely been the front runners as far as technological revolution is concerned. Every Tech giant has acknowledged the power of these technologies and the potential to impact the human race in a huge way.

Now, let us understand what these terms mean.

What Is Machine Learning?

Simply put, Machine Learning refers to machines capable of evolving and improving by themselves by “Learning.” Here, machines can “Learn” to be better. Artificial Intelligence and related fields help to evolve Machine Learning which can then take machines beyond their cliched role of programmable equipment and give them the capability to think for themselves. They can recognize patterns, build skills, and knowledge to make themselves better, and do most of the currently programmed work on their own. This process eliminates the need for constant human supervision and improves efficiency.

What Is Deep Learning

This method is related to learning that allows a system to find out the means to make raw data organized. It is much more Data Analysis than conventional Machine Learning involving job specific algorithms, even though it belongs to the same family as machine learning. There are two types of Deep Learning, Supervised and Unsupervised.

Both Machine Learning and Deep Learning are futuristic fields of technology that have untapped potential. They have applications in many disciplines, mainly Artificial Intelligence, Data Analysis, and Automation.

Let’s understand their application in the Hospitality industry.

The Hospitality Industry – At A Glance

The Hospitality industry is a humungous service based industry which includes tourism, event planning, transport, lodging, and similar services. In the broad sense, it can be considered as any product or service related to hosting. It is a complex ecosystem in which billions of dollars change hands every day. So, just like every other multibillion-dollar industry, it is dependant on technology for its day to day operations. In such a vast sector, there is a constant need for fresh and innovative technology to make business processes more efficient and increase profitability.

Applications of Machine and Deep Learning In The Hospitality Industry

Hospitality sector needs popular demand for its survival. It is an industry that caters to the wants of the customers more than their needs. Hence, competing firms in this sector employ methods and machines to ensure that they can deliver better service at a lesser price to their clientele. The application of ML and DL can improve some of these strategies.

Predicting Seasonal Demands for Services

The Hospitality industry has certain ‘seasons’ in which some of their services are at a higher demand than others. These seasons may or may not be linked to actual climatic seasons. Whatever be the case, these are the times when service providers can make the most money, and they want to capitalize on this opportunity.

Deep learning can be applied to do this job. A computer can easily find the correlation between factors that cause this seasonal demand by analyzing raw data from the past and predicting the future trend accurately. This process is called Predictive Analysis where patterns of the past are used to predict events of the future.

Competitive Pricing

Hospitality Service Providers use competitive pricing as one of their most significant strategies to attract customers. Companies try to provide services at the best price without compromising their profits to attract maximum customers. Here, Machine Learning can be helpful.

Based on seasonality, hotel history, local events, local competition, 3rd party promotions, and external real-time events, the data can be run through predictive models and analyzed which can then be used to give the best possible pricing for any service and provide companies an edge in the market.

Hospitality Service Providers

Personalized Recommendations

Recommendation engines are being used by prominent tourism sites like TripAdvisor and Expedia since a decade to provide users with the best tour packages. Here, the engines collect data specific to the budget, preferences, and details of a customer to give him personalized recommendations for trips. Information acquired from various sources and service providers is used to come up with suitable alternatives by comparing options using a Deep Learning program.

Custom Services and Customer Satisfaction

People who use Hospitality Services come from a variety of backgrounds, and their demands and expectations may vary. Catering to them so that every customer is satisfied is the measure of success for any company in this sector. All customers want to be treated according to their preferences, and to ensure this, a process called Market segmentation is employed in every industry which caters to a broad audience. In the Hospitality industry also, this process is very efficient.

Here, the whole customer spectrum is divided into segments which have similar characteristics and more or less the same demands and expectations. This makes service much more personalized and specialized.

Using Machine Learning, the classification becomes more diverse into smaller and smaller groups. This may include subgroups that are unseen before, and the quality of service improves as it becomes much more tailored for individual customers. This process leads to happier customers, and in turn a better, and more profitable business.

Machine Learning

These are a few places where machine learning and deep learning is currently used in the industry. There is a multitude of futuristic applications which can revolutionize the industry.

Robotics, for example, can lead to automation of almost all processes, and make everything infinitely faster.

Conclusion

Deep Learning and Machine Learning are leading the way for innovation in all fields. They continue to be explored and applied in new sectors every day. sIt is a promising field with substantial untapped potential, and the capability to change the face of the industry.

Machine Learning, Deep learning, and Artificial Intelligence in Politics

Machine Learning Service Providers

“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.” —Larry Page

When we talk about machine learning, deep learning, and Artificial Intelligence (AI), it throws an idyllic image of Russian dolls that are nested inside each other where the largest being the artificial intelligence which fits both machine learning and its subset – deep learning within. Artificial Intelligence defines the human-to-machine relationship where when a machine becomes intelligent, they can connect data points, understand requests, and draw conclusions. Let’s consider the following –

  • You are leaving for a business trip, and your intelligent device automatically gives you the weather report and other travel alerts for the place you are traveling to.
  • You are planning a surprise anniversary dinner for your spouse, and your smart bot will help you in making a reservation and even remind you to pick up the cake.

In the above examples, the machine understands what information is required and thus looks at relationships between all the variables, derives an answer and automatically communicates to you.

In simple terms, machine learning and deep learning are subfields of artificial intelligence. Today, let’s talk about how these smart technologies apart from being a part of daily life have entered the political arena as well.

When you need to make sense out of huge volumes of data, it might become difficult to decide where to begin from to look out for interesting trends so you start exploring the data and for this task, we turn to machine learning.

Machine learning enables computers to get into a self-learning mode without being explicitly programmed. In other words, it is the most basic way of using algorithms to parse data and then make a prediction about something. The model of machine learning has been there in the system for some time but its capability to relate complex algorithms automatically to the big data has gained momentum over the last couple of years, and it has made its way into politics as well.

Computer Vision Consulting

If you go through the modern history, most of the political parties have had a limited number of tools to monitor their electoral campaign. They have been relying more on instincts rather than insights when running their campaigns. But now more and more political campaigns are relying on big data to maximize the effectiveness of their campaign.

The statistical techniques used by machine learning systems can automatically identify the patterns in massive amounts of data. In fact, it is now watchfully deployed in electoral campaigns to engage voters and to make them aware of important political issues.

With the improvement in both visual and audio technology and an explosion of usage of various social media channels, text posts and images have become a new norm through which political parties interact with the voters. The use of neural-network techniques or deep learning gives these political parties an unparalleled ability to factually study how these texts and images can shape the public opinion.

The political images are created with an intention to persuade the voters to vote for or against any political party. The use of neural network can accomplish this task for the politicians. It provides the key to extract features from people and objects which are politically relevant.

Machine Learning

This neural network or better known as Deep Learning is a subset of machine learning. It is a kind of computing system that is made up of interconnected units. The information is processed by responding to external inputs, and this process requires multiple passes at the data to derive the meaning. This methodology is used to learn complex patterns in vast volumes of data. Image and speech recognition are some of the typical applications.

There seems to be a wide variety of avenues for AI’s integration into the politics. From providing sets of computer algorithms to target certain sections of voters basis their internet activity, to more advanced operative work in politics, to automating public sector, to right up to the decision making role in the government, the possibilities are limitless.

The most crucial role that AI plays in the political system is during the party campaigning. Many political parties now use these algorithms to identify specific segments of the population which can be the probable voters for them by using tailor-made advertising campaigns for them. The most common example is analyzing online behavior of Facebook users and then engaging them with ads that pick their interest.

However, the use of technology can raise ethical issues as AI can be used to manipulate the voters. Since AI uses internet footprints to build voters profiles and then sends tailored messages. Another downside could be that political parties can use AI to spread fake news on social media and play with voter’s sentiments. Political parties have been using smart bots with autonomous accounts which are programmed to insistently spread a political message which is one-sided so as to create an illusion amongst the voters. A bitter political climate is created on social media platforms such as Facebook and Twitter to highlight negative messages about a party or candidate.

Machine Learning Providers

However, the problem is not technology but the covert nature of the whole political system. An ethical approach to AI can work wonders for the political system. The algorithms which are used to mislead and confuse the voters can very well be used positively to support democracy. In fact, AI can be used to make sure that their elected candidates hear people’s voices.

Experts feel that as the technology behind AI would advance further, it is likely that more and more political groups will invest their funds into it.

Conclusion –

As machine learning, deep learning, and AI continue to mature and evolve with time it’s time to start experimenting as to how these technologies can help political parties to work smarter, better and faster. It can be used to gain meaningful insights from data and automate existing operations. There has been a significant leap in AI capabilities and will continue to happen further, and it will be an exciting thing to see how politics will incorporate AI into their operations and how big it can become.

Technology and Evolution

Technology and Evolution

Technology is progressing rapidly, and it is changing the way we live, work and play. New inventions are happening, and new paradigms are born almost every day. Conventional concepts we are used to as a human race for decades or even centuries are being disrupted by cutting edge technology.

Just half a century ago, computers were only surfacing and were used for particular scientific or research work, but today they are found all around us. We see, and experience computing power around us in all shapes, sizes, and types. There are small and portable ones like in smartphones. There are big ones like desktops and mainframes. And lately, with innovative devices like Google Glass or Snap Spectacles – this computing power has also invaded our wearable devices.

Whether we like this technological revolution or not – we can clearly see how it is affecting our daily lives. But an interesting question arises – what are long-term effects of this revolution? I am not talking a decade or a century from now – instead what are the implicit Individuals who win the actions on a human evolutionary timeline – which could span millennia.

Technology is changing the patterns of our life. There was a time when we were very physically active, but now we rely on devices to do most of our work. The changes in our behavioral patterns and adaptations that are caused by these technological advances could prove to be the primary driving forces behind the next stage of evolution for our species.

Charles Darwin

Charles Darwin proposed that evolution is the change in the characteristics of a species over several generations – which could happen over eons or within few centuries. This process of evolution relies on the process of natural selection. Charles’ theory of evolution is based on the idea that all species gradually change over time. The complex process of evolution relies on there being a genetic variation in a population which affects the physical characteristics of an organism. Some of these characteristics may give the individual an advantage over other members of the same species which they can then pass on to their offspring. This process of continuously adopting advantageous characteristics by subsequent generations is called “natural selection.”

Individuals who win the evolutionary lottery and have the characteristics best suited to their environment are more likely to survive, finding food, avoiding predators and resisting disease. These members of the species are more likely to reproduce and pass their genes on to their children.

But the losers in this race that are poorly adapted to their environment are less likely to survive and reproduce. Therefore their genes are less likely to be passed on to the next generation.

As a result, the individual members of a particular species who are most suited to their environment survive and, given enough time, the species will gradually evolve.

Darwin termed this process “survival of the fittest.”

Even though Darwin’s theory has been able to explain the evolutionary cycles of many species including homo sapiens – it seems like it is proving to be ineffective as of late especially when it comes to humans. And this phenomenon could be directly attributed to the increasing power of technology in the field of healthcare. Multiple deadly diseases have been completely eradicated by the use of sophisticated cures like anti-biotics. As a result, even the weakest and most vulnerable amongst us can live their lives relatively comfortably. In some case, they can also reproduce with advanced techniques like in vitro fertilization (IVF). So the phenomenon of the survival of the fittest is no longer at work here.

Instead, the evolution is occurring in two other ways

1. Efficiency-Based

2. Transhumanism

Efficiency-Based:

Most of the mainstream medical cures, drugs, and surgical procedures are available and accessible to a vast majority of the population (except for an unfortunate segment of the global population who still do not have access to affordable and reliable healthcare). But for the sake of argument – let’s assume every human being is given an environment to survive and live a healthy life.

Even with these universal advantages – we see every individual is slightly different. Some of us can adapt, and trust technology much better and faster while others are slower to embrace these changes. These traits that make one person more efficient in the modern world as compared to others. These adaptability traits could affect the quality of life of the individuals and other factors like their financial status and place in the contemporary world. As a result, even a person with a slight physical disadvantage but with robust mental capabilities can flourish in this world.

On the other hand, a lot of the physically demanding obligations have been taken off our plates. We no longer have to hunt for our food. We no longer have to fight off formidable predators on a daily basis. The physical stresses and demands on our bodies have greatly decreased. Machines have taken the intensive work role in industries, agriculture, and transport.

The mental capabilities are the distinguishing factors in the current world. With the decreased emphasis on physical prowess, the mind is the center of attention. It is the human mind that designs and operates the automatic machines. Our minds are processing information at ever increasing speeds. Through the generations – we can bank upon this knowledge and pass it from generation to generation in the form of written or oral word and text. Thus the human race is progressively building upon its collective experience at an accelerated pace. So it wouldn’t be wrong to say that the human mind is slowly evolving to adapt to this world of automation, complexity and know-how.

Evolution itself is a slow natural process. But by observing this phenomenon in several species over extensive periods of time – there are predictions we can make about its likely course.

As we determined above, the human mind is the organ that is being used most extensively in the current world.

From complex computations in the workplace to the rapid decision making we need to execute on a daily basis.

Read: HOW MACHINE LEARNING IS CHANGING THE FINANCIAL INDUSTRY

While the analyzing part of the brain is used for a multitude of reasons, the information retaining a portion of the brain is facing many issues. The amount of information which is thrown at us is growing at an astonishing rate. The various sensors which surround us informing us about our current surroundings; news and social media which are bombarding us with information about current affairs in the world; a plethora of choices we have to make about what to eat, drink and stay healthy. All this information is like drinking from a big fat firehose. This explosion in information is quite detrimental to comprehend, store and analyze all this information.

It is, therefore, probable that our human brain may evolve based on these two factors. Over time human brain may actually start to favor its analyzing power and rely less on its capability to retain information (because this capability is already provided to us by computers). This is analogous to saying that humans will get better CPUs and RAM but relatively smaller hard disk space.

Additionally, there may also be a considerable effect on other organs and limbs. For instance, our hands, legs, and feet may undergo a huge change. Our past generations used hands for physical labor to hunt for food and build a shelter. We used our legs and feet to migrate and follow our food from continent to continent. But now we are using these ambulatory organs for much less tedious chores. So it is highly probable that they may come changes to these organs to make them more efficient in their use.

A great example of this was put forward by Melbourne artist Patricia Piccinini who built “Graham” – an interactive, life-size sculpture of a future human who has adapted well to travel in fast vehicles and have unique abilities to survive crashes.

Transhumanism:

Transhumanism is a futurist concept which is analogous to artificial evolutionary process. It involves the studying of internal software of human body and tinkering with it for better results.

Humans didn’t just appear out of nowhere. They are products of evolution of millions of years. The current human genome is the result of countless mutations that occurred before the appearance of modern humans some 200,000 years and after it. Some of those mutations were really beneficial and lasted for a long time while some were outright bad or only situationally good.

Read: TEACHING COMPUTERS HOW TO SEE LIKE HUMANS WITH CONVOLUTION NEURAL NETWORKS

The tinkering with the genetic setup of a human for removal of any inefficient mutations and insertion of useful and better genes comes under transhumanism. In addition to ineffective mutations, there are also some mutations which are outright harmful and can cause different diseases and conditions. Transhumanism deals with these mutations and increases the lifespan of a person. Transhumanism is an active counter to aging. It makes a person smarter, stronger and energetic.

Genetic engineering is not all that there is to Transhumanism. There are many other technologies that are enhancing the physical and mental capabilities of human beyond what is biologically possible. Google Glass and bionic body parts are good examples of this. Google Glass gave cognitive information to one’s mind that is not humanly possible to gather in such rapid time frame in real time. On the other hand, bionic arms and legs allow a person to tolerate loads that aren’t achievable by normal humans.

In the end, we can say that human evolutionary cycles will definitely be affected by this rapid growth in technological advancements. These technologies have already significantly changed the patterns of life thus making the standard evolution considerably fast. People of all physical strengths and capabilities can live healthy and fruitful lives; making it possible for their mental genes to mutate and create a better result. Additionally, artificial evolution is also not too far off in the future, and soon people might be able to select the different characteristics of their offspring like mental capabilities, physique, and energy levels.

How machine learning is changing the financial industry

machine learning

A lot of talks, innovation, news and information about Machine learning has been prevalent in the mainstream media for quite a long time. We see new machine learning algorithms being devised for tackling new sets of problems from time to time.

Machine learning is slowly but surely becoming employed in each and every field of life. And there is a good reason behind it – these advanced learning algorithms, learning methods, and computational ways make many problems a lot easier to solve. Many mind intensive and energy consuming tasks are being replaced by simple learning algorithms of Machine learning; thus making them more accurate, faster and efficient.

The field of Finance is not void from its presence either. Instead, it would be fair to say that machine learning is becoming an integral part of the mega financial industry. It is slowly revolutionizing the finance that was once known to the world. Here are some of the ways how machine learning is having a significant impact on finance:

Underwriting:

Whenever a customer wants to get a loan or a specific type of insurance from a bank, there is usually a specific set of procedures followed to determine the risk involved and the amount of loan and interest rate at which it can be given. This risk assessment is done by a financial agent who compares the demographics of the customer with the preset standard for loans. It is an inefficient method as it not only involves humans input, human error but also ignores the bigger picture i.e. trends of the area and other factors affecting the person.

Instead, machine learning algorithms are designed to allow computers to have access to the multitude of data points related to: macro and micro-economic trends, housing market, interest rates, trends in the geographical area where the loan is originating and the demographics of the customer. These algorithms can infer a much better and detailed picture of the underlying risk than any manual method could. They allow computers to analyze that data further on to make much better decisions for increased output.

Read: CLOUD COMPUTING: BUSINESSES ARE EMBRACING THE CLOUD

Security Service

Security and Fraud:

Software is eating the world, and the Internet is the driving force behind it. We all are connected to wireless networks which expose us to many security threats. Similar is the case in financial organizations. There are constants cases of hacked credits cards, fraudulent transactions and compromised accounts. In the past, these problems were tackled by security teams which overviewed the processes which were marked by the system as abnormal. But that process was very slow, lack-luster and full of false-positives. Therefore, it is necessary that use of machine learning is employed to improve the security and to combat fraud.

Machine learning algorithms implemented in these financial institutions benefit from huge databases and form patterns of the processes. With the help of these patterns, any anomaly or abnormality is easily identified in almost real time, the root cause is surfaced for the experts to make sound decisions and the issues are dealt with very very quickly. It also makes sure that amount of false-positives stays low as it uses learning instead of a set standard, therefore, the customers are allowed to do their work in peace without undue stress.

software and cloud computing services

Risk assessment:

2008 financial crisis was a major mishap in the world of finance and economy. Faulty risk assessment techniques actually caused it.

The 2008 economic crisis stemmed from the crisis in the mortgage market of US. It started out with many banks giving mortgage loans to subprime clients in hopes of making money off it as the housing sector was on the boom. The problem was with the risk assessment methods which didn’t consider the risk of giving loans to subprime clients. The people in charge of assessing these risks were blinded by human errors like greed, fear, and uncertainty. As a result, when prices of house sector started going downhill due to unpopularity, many financial organizations started to collapse. Many banks went bankrupt. The economy of US and worldwide suffered a significant decline. It was because the risks involved weren’t evaluated on the major scale.

To prevent any such crises for happening again, companies are opting for machine learning techniques. The advanced learning algorithms associated with it are free from human error. These ever powerful machine learning algorithms gather the data about the matter at hand, and assess risks and dangers associated with the transaction and only then take action. This obviously decreases the probability of any mishap.

Machine learning also allows easy compliance for regulatory reasons. Different types of data of multiple sources are collected regarding an issue or a client and are then analyzed to give a bigger picture of the entire relationship with a particular entity or client. It allows us to quickly and easily understand the situation at hand and decide the required action necessary for it.

Read: CTO AS A SERVICE

Bit Coin

Investing and trading:

Trade and investing is often affected by human emotions. Lack of emotional intelligence is a major cause of financial losses when it comes to investing and trading. Machine learning algorithms suited for trade can be used to remove this barrier. These algorithms can use various techniques to not only optimize the trading activity in one’s account – but can also affect the psychology of other market participants. For instance, a particular algorithm can be used to divide a large trade into smaller trades so as to cause minimal fluctuation in the prices – which are primarily controlled by supply and demand. Another application of machine learning algorithms is quick arbitrage opportunities; where machines can look for prices of one product which vary from one geographical area to another and benefit from this price difference. Moreover, we can say that machine learning is the most optimal thing for trading and investment activities as very powerful machines can use and benefit from a large number the data elements simultaneously. Comprehending this complexity with such clarify by a normal human mind is next to impossible.

Customer service

Customer service is an important part of any business. But it is also a hard one to get it right. An excellent customer service can elevate an organization or company to a beloved company, but on the flip side – a few unfortunate instances of poor customer service – can hurt a business and its brand.

Recently, the widely reported incidents by certain airlines and their behavior with the customers and passengers has caused the airline industry to step back and re-evaluate their business practices.
But getting the customer service is not an easy task. First and foremost you need a really knowledgeable and dedicated team – who not only possess the technical know-how about your product or service; but must also possess soft skills like empathy and ability to listen.

Therefore, usually most of the companies only reserve customer services for the people who have invested large sums of money or are using most of the financial services of an organization. This, however, repels the newer customers as their questions and complains are left unanswered.

Machine learning offers algorithms for dealing with this problem too. There are algorithms which analyze words, compare them with past interactions and respond accordingly. These algorithms are integral part of live chat feature offered by many companies lately.

Conclusion:

These were some of the direct aspects of machine learning on finance. We can easily say that machine learning is radically changing the finance industry as we once knew.

The companies and organizations which are employing machine learning in their working and processes, are succeeding while the vice versa are having a hard time in attaining their goals. So it seems quite straightforward that companies should start adopting the advanced techniques of machine learning if they want to remain competitive in the upcoming years.