Machine Learning Algorithm

There is one thing about the Machine Learning algorithm and that is there is no one approach or one solution that caters to all your problems. But you can always pick an algorithm that nearly solves your problems and then you can customize it to make it one perfect solution for your problem.

Here we are stating some factors that will help you narrow down your list of machine learning algorithm options.

But first things first, you need to have clarity of the data, your constraints, and your exact problem. For achieving clarity of data, do the following:

a) Know your data

To understand your data you need to look at summary statistics and try to point out the central tendency of data. For doing this, you will require to study the averages, medians, and correlation that indicates a strong relationship in data. The next thing to figure out is ‘what to do with outliers’. You can use box plots that can identify outliers. Apart from this, ‘clean your data’. Sort it for relevancy and segregate it on the basis of the problem at hand.

b) Categorize the problem

Once you know your data, you need to categorize your problem, which can be done in two steps:

• Categorize by input:

A supervised learning program is when the data is labeled. If the data in unlabelled and you desire to find an appropriate structure then it is an unsupervised learning program. One should know the type of inputs they can offer in order to choose an appropriate machine learning algorithm.

• Categorize by output.

Now, if the output of your model is in number form then it will be called a regression problem. If you desire classification of data as an output, it’s a classification problem. Another type of problem is clustering problem when the model required to set groups for the inputs given.

c) Find the available algorithms

After proper evaluation of your problems, you can opt to identify the applicable algorithms which are practical to implement using the available tools.

Most commonly used Machine Learning Algorithms

In this blog, we have listed out some of the commonly used Machine Learning Algorithms just to give you a heads up. Follow us for more intriguing updates on Machine Learning.

1. Linear Regression

This is the simplest Machine Learning algorithm. It can be used to compute continuous input data as compared to classification in which the output is categoric. In simple words, linear regression can be used to predict some future value of a process that is currently going on. It should be kept in mind that in case of multicollinearity the linear regressions are unstable.

Examples, where linear regression can be used, are:

• Predicting sales for the coming month
• The time required in commuting from one place to another

2. Logistic Regression

Logistic Regression can be used as a probabilistic framework or to incorporate more training data into the model in future. It is not just a black box method but it will help you to understand the factors behind the predictive outcome and so forth.

Examples, where logistic regression can be used, are:

• Fraud detection and credit scoring
• Estimating the effectiveness of marketing campaigns

3. Decision trees

Using decision trees alone is done very rarely. Usually, they are combined with others machine learning algorithm to build an efficient algorithm like Gradient Tree or Random Forest.

Examples, where decision trees can be used, are:

• Investment decisions
• Buy or build decisions
• Banks loan defaulters

4. K-means

K-means is used for the unlabelled data where the task is to cluster and label them. It is used when the user group is very large and you wish to categorize them on the basis of common attributes.

5. Principal component analysis (PCA)

The principal component analysis is used when the data has a high range of features and is highly correlated. In such a situation PCA will help you in dimension reduction.

6. Support Vector Machines

Support Vector Machine (SVM) is used on labeled data and is used widely in pattern recognition and classification problems when the input data has exactly two classes.

Examples, where SVM can be used, are:

• Text categorization
• Stock market predictions

7. Naive Bayes

Naive Bayes is based on Bayes’ theorem. It is a classification technique that is easy to build and works great with large datasets. It is a better classifier than discriminative models like logistic regression because it is quicker and requires less training data.  

Examples, where Naive Bayes can be used, are:

• Text classification
• To mark an email as spam or not
• Face recognition

8. Random Forest

Random Forest can solve both classification and regression problems on large data sets. Basically, it is a collection of decision trees. It is highly scalable to any number of dimensions and has usually quite acceptable performances.

Examples, where Random Forest can be used, are:

• Predict credit loan defaulters
• Predict patients with high health risks

9. Neural networks

Neural networks can be used to train extremely complex models and these models can be utilized as a black box. For example, object recognition is enormously enhanced by deep neural networks only.

Summing up

The above pointers will be a great help to shortlist a few algorithms but it is hard to figure out which algorithm will work best for your problem. Therefore, it is suggested to work iteratively. For picking the best one among the shortlisted alternatives, test the input data with all of them and at the end evaluate the performance of the algorithm.

Also, to develop a perfect solution to a real-life problem you need to be aware of rules and regulations, business demands, and stakeholders’ concerns and you should have considerable expertise in applied mathematics.

How to build a career in Data Science

Today, data scientists are one among the highest paid professionals. Technology is soon advancing and it is necessary that you constantly pay attention to upgrade your skills and expertise.

Tech giants such as Google, Facebook, Apple etc, all of them are looking for data science experts to build intelligent and path-breaking products.

If you are planning to become a data scientist, then you need to be well-versed in some programming languages. In this blog, we list the top 11 skills that you must possess to become a successful data scientist.

1. Education

Data scientists are usually from the highly educated crowd in the college. As a matter of fact, 46% of them have PhDs while 88% of them have a Master’s degree.

You could be from any stream like social science, physical science, computer science,  or statistics in order to be a data scientist. The common field of studies are as follows:

• Mathematics and Statistics (32%),
• Computer Science (19%)
• Engineering (16%).

A degree course in the above fields helps you to develop skills you need to analyze big data. It is highly recommended to obtain a Master’s or Ph.D. after successful completion of the Bachelor’s program. To transit into the data science field, you will require to pursue your master’s degree in Mathematics, Data Science, Astrophysics or any such related field.

2. R Programming

R programming is specially designed for data science needs. Any problem in the field of data science can be solved with R. Currently, 43% of data scientists use R to solve statistical problems. Therefore, it is recommended to learn R.

However, R is tricky to learn especially if you have already mastered a programming language. An online learning program should be taken up to learn R.

3. Python Coding

Along with Java, C/C++, Perl, Python is the most common coding language and is perfect for data scientists. Around 40% of the data scientists use Python as their major programming language. Python is a versatile language and can be used in almost all the steps of the data science processes.

With Python, you can easily import SQL tables into your code and also process various forms of data. Further, it allows you to create your own datasets.

4. Hadoop Platform

This is not a pressing requirement but it is highly preferred in many cases. Also, if you have experience with Pig or Hive or familiarity with cloud tools such as Amazon S3, you will be preferred over other applicants.

Why Hadoop platform is important?

There might be a situation when the volume of data to be processed exceeds your system’s memory and you will require to send data to different servers. In such a situation, you can use Hadoop to transfer your data to various points. Also, Hadoop can be used in data sampling, data exploration, data filtration, and summarization.

5. Apache Spark

Apache Spark is faster than Hadoop with the same big data computation framework. The reason why Apache spark is faster than Hadoop is that Spark caches the computations in memory while Hadoop reads and writes to disk.

Apache Spark helps data scientists to handle complex unstructured data sets and saves time by processing the data faster. It can be used on one machine or a bunch of machines, at once.

6. SQL Database/Coding

SQL stands for Structured Query Language. SQL is a programming language which enables you to carry out operations like delete, add, and extract data from a database. Also, it helps in transforming database structures and carrying out analytical functions.

For becoming a successful scientist, you need to be proficient in SQL. SQL will help you to access, communicate and also work on data. It has brief commands that can help you lessen the amount of programming you need to perform. Additionally, it will help you comprehend relational databases and boost your experience profile.

7. Data Visualization

For a data scientist, it is essential to visualize data to make it easier to understand. This can be done with data visualization tools such as d3.js, Tableau, ggplot, and Matplottlib. These tools can convert data into easy formats.

Data visualization is the need of the contemporary corporate world because of the insights delivered. These insights indicate which business opportunities to grab and how to stay ahead of the competition.

8. Machine Learning and AI

Machine Learning can give you an edge over others as with this you can transform the way data science is functioning. Most data scientists are not proficient in this field. To stand ahead of others, you must learn decision tree, supervised machine learning, logistic regression, etc. Read here for more information on which Machine Learning Algorithm to pick. 

A proficiency in Machine Learning helps you in solving complex data science problems that are based on predictions.

Other examples of advanced machine learning skills that you should consider are Unsupervised machine learning, Natural language processing, Outlier detection, Time series, Recommendation engines, Survival analysis, Reinforcement learning, Computer vision, and Adversarial learning.

9. Unstructured data

A data scientist must essentially be able to work with unstructured data. Basically, the unstructured data are undefined content that can not be put into database tables.

For instance, customer reviews, videos, blog posts,  video feeds, social media posts, audio etc. Such heavy data is difficult to sort because they have no order.

Unstructured data is also referred to as ‘dark analytics’ because of its complex nature. Ability to comprehend and discern unstructured data from several platforms is the prime attribute of a data scientist. It helps you interpret the insights that are useful for decision making.

Apart from the above mentioned technical skills, following non-technical skills will help you to achieve your goals faster.

10. Intellectual curiosity

Curiosity provides you with the thirst to learn something new every day. As a data scientist, you will counter new problems every now and then, at this moment, curiosity will motivate you to find solutions to your problems.

On average, data scientists spend about 80% time in discovering and preparing data. In order to keep pace with the evolving world of data science, you need to keep learning.

11. Communication skills

Data scientists make complex data understandable for normal people which is why it is essential for them to have smooth communication skills. With fluent communication skills, they will be able to explain their technical findings to non-technical teams such as Sales or marketing department.

Thus, with these 11 skills, you will be able to launch your career as a Data Scientist. Even if you are someone who is planning to shift technologies, just spend some time to learn programming languages such as R, Python and the Apache suite and you will be in a good position to start off a career in data science.

How Deep Learning is Personalizing the Internet for Better Engagement

Working as a secondary field of machine learning, Deep learning focuses on the interpretation of data. This technology uses multiple layers of processing to understand a set of data. Every layer allows machines to evaluate and present data in a meaningful manner. Hence, the performance capacity of algorithms increases with the increment in data.

Advanced techniques such as NLP and image classification become possible with deep learning. Businesses are using it to enhance their communication patterns with clients and customers. The successful use of such algorithms brings personalization to the communication and hence boost engagement.

Personalization in the internet-driven market

Publishing companies, marketing agencies, e-commerce and many other businesses work through the internet. Deep learning offers an error-free system to enhance engagement in such business models. This is beneficial for users as well as businesses.

Fueled by personalization, businesses can reach a higher level of productivity in terms of communication and online experience.

Here are most valuable ways, in which, deep learning is personalizing the online world.

1 – Making recommendations more effective and relevant

On the internet, sites recommend a different set of content to the site visitors. An e-commerce site does the same with their products. However, a visitor will engage with the recommended content or product if it is relevant to him or her.

Traditionally, metadata is used to help with recommendations. However, poor metadata quality always presented issues of content relevancy in this process.

Now, deep learning is bringing effective changes in this department. Advanced algorithms use intrinsic characteristics of a content and evaluate it with visitor’s interests. These intrinsic qualities can be text, images or videos, which changes from content to content. Such technologies are now capable of creating a comprehensive view of content on their own and incorporating it when recommending products or content pieces.

2 – Focusing on interests for new visitors

When a visitor comes to a platform, it seems difficult to learn preferences and understand behavior. This happens due to the lack of layered data evaluation. Deep learning algorithms divide the clutter of interactional and transactional data.

Rather than just focusing on data gathering, deep learning allows business platforms to understand and evaluate customers’ behavior. Hence, every visitor sees what he or she desires to see.

For example, if a customer visits an e-store for the first time to purchase an oven, algorithms will collect that data. However, the use of this data will depend on the interests of the same customer when visiting the second time. If the customer looks for a mobile phone, then, the algorithms will leverage that interest to present personalized data.

3 – Leading customers towards decisions

A customer goes through various steps before deciding to purchase a product or service. This decision depends on brand loyalty, personal goals, and preferences. Deep learning understands these factors and personalizes image and other forms of content. Hence, businesses get to increase sales.

Personalization is the key to winning the business game on the internet. And DL algorithms can offer strength to this goal of your business.

What you Should know about Google AutoML

One of the highlights of last year’s Google I/O was the announcement of Google’s Automated Machine Learning or AutoML which according to founder Sundar Pichai was their ‘AI Inception’. The Machine learning Cloud software suite is finally about to hit the alpha stage and as of now, it seems like there is a lot that developer and designers alike can leverage from this tool. So let us explore this new addition to the Google family.

What is AutoML?

AutoML is Google’s Cloud software suite for Machine Learning tools based on Google’s Neural Architecture Search (NAS). Using AutoML a user can train deep networks without having any expertise in deep learning or Artificial Intelligence. This is achieved by leveraging NAS to derive the most suitable data network for the task at hand. This is a huge step for businesses as they will be able to harness the power of machine learning without using experts with years of experience in any field of automation. Customization of various AI product models meant for mundane as well as complex tasks.

How Will Businesses be able to Leverage AutoML?

Today, with deep learning still being in its infancy, most business use, AI for simplistic tasks. And considering the fact that AI and Machine learning experts are hard to come by and are quite expensive to hire, most companies try to keep their deep learning efforts to a minimum. This is where AutoML makes a world of difference. One of the most common applications of deep learning for businesses today is Image processing. Today companies rely on Image classification networks that run on neural matrices. This although running on machine learning tools, requires quite a bit of manual effort to isolate the proper network for the image datasets and then train the models to adapt to the task they intend to accomplish. With AutoML companies can skip the tedious processes of research and get directly into the transfer process. As a matter of fact, Google’s first AutoML tool is intended for exactly this aspect for visual companies.

AutoML Vision

This is AutoML’s first tool which will help developers create custom image recognition models. According to Google this tool will allow developers to upload their labeled data and the system will automatically create a custom machine learning model for them. This, in turn, will allow them to focus more on product development, design and other important aspects.  Although still in its alpha stage, this tool is being used by many big-time players such as the animation giant Disney.

Conclusion

As the concept of ‘Software 2.0’ catches on, developers around the world are getting used to the new tools largely built upon their predecessors, to write software without actually having to code each and every part of it. Tools like AutoML are a good example of how this is being applied to every aspect of software irrespective of the purpose. With AI the scope is further enhanced. Yet, for such tools to achieve any degree of comprehensiveness, they themselves need to be further developed to accommodate the needs of the upcoming generations of developers who will be relying upon them to do most of the work. So the road ahead will be the chicken and egg paradox where the efforts of both the man and the machine will craft the future. Today we have that sense of direction as to where we need to focus our efforts in terms of research, which is great news for AI tools such as AutoML.

How Machine Learning can affect Economics

Machine learning has found its way into multiple business applications. It makes computing the process more efficient, cost effective and trustworthy. Machine Learning is no longer a fantasy but a set of authentic business technologies that will help the management take better decisions in the long run. Be it Facebook recommending friends tag based on photos or voice recognition systems like Siri and Cortana, Machine Learning helps us to make smarter and better decisions.

The power of machine learning goes mainstream

Studies show that in 2017, ¼ of the respondent companies used 15% of their IT budget on machine learning and have seen phenomenal ROI emanate from it. These numbers are expected to rise in the future because of fast infusion of machine learning into different and diverse industry verticals.

Machine learning is broadly used in the field of life sciences, healthcare, hospitality, and retail. Interestingly, there is one more vertical that machine learning can impact but it is not that popular yet.

It is the field of Economics!

Research shows that machine learning is yet to revolutionize economics the same way it has done for other fields. But it will majorly expand its possibilities and more economists should start to implement machine learning in their studies.

Want to see how machine learning can impact the field of economics? Read on…

How Machine Learning and Economics come together?

When an economist analyzes economic data, they try to figure out the relationship between two dynamic and seemingly unrelated conditions. For example, an economist might sort through real estate data to figure out how much the size, location, or other factors will determine the price people are willing to pay for a home.

Machine learning will not only help an economist figure out the relationship between the user and external factors but also will use that data to predict on how much that same house is worth and what should be expected from potential buyers.

Machine learning will not directly influence economic research but will help economists with the research data and predications.  Machine learning is primarily useful in collecting new sources of data. For example, economists have already been able to convert satellite data into estimates of economic growth, as well as to measure neighborhood income levels in Boston and New York using Google Street View, Yelp, and Twitter.

Another feature of machine learning is it allows economists to analyze language as data. Algorithms can be used to identify news articles too. It can gauge if the sentiment of the text is negative or positive. Brand marketers can then utilize this information to roll out campaigns accordingly.

Machine learning is also useful for testing out economic theories. Economists are working on the theory that share prices should incorporate the most relevant information, thus making it possible to predict future stock market fluctuations.

To conclude

Although machine learning is a new concept, it has a promising future in the world of economics. Some of the above examples show succinctly why machine learning has the (nearly) perfect answer to some of the most complex queries faced by an economist.

How AI will lead to the era of Robotics

The concept of robots has been around for so long that it precedes the roots of most modern technology. Automated machines that are capable of doing more menial tasks could be dated back to as early as 4th century BC with steam-powered automatons doing menial tasks.

Back then scholars and philosophers like Homer saw robots as a means of human salvation as it presented the possibility of human equality, slavery being a huge issue at the time. However, a couple of millennia later the take on robotics had a slightly darker undertone. Works of post-industrial novelists such as James Orwell and movies such as ‘The Terminator’ depicted robotics as the end of mankind.

Even early 20th-century technology experts believed that robotics could lead to massive layoffs and could destroy working-class communities and they were not wrong. The first sign of mainstream robotics came in the early 70s when hydraulic arms started taking over production lines at a car and heavy machines factories. Cities such as Detroit and Munich suffered massive layoffs. Yet the use of automated machinery continues with several verticals such as AI built around them.

The State of Robotics Today

Robots have come a long way from their early hydraulic single motion ancestors and are surprisingly doing a lot a lot of things that most humans could only dream of achieving in their entire lifetimes. From Toyota’s Kirobo having a conversation in space to Sophia’s Saudi Arabia Citizenship, robots are going places and bringing certain science fiction theories that were once dismissed as hogwash, to life. With the exception of breaking Asimov’s three laws of course. Nonetheless, robotics is going through huge advances today especially with technologies like AI and Machine Learning catching up quite fast. Certain industries have become so accustomed to robotics that industry veterans now wonder how they survived without them. So, let us take a closer look at the some of those things.

1. The Space Bots:

When the seven Mercury Astronauts were under the threat of being replaced by a monkey, the last thing they would’ve been thinking would be that after 50 years people have to go through the same thing with robots. Well even if they did think that, they would not have been wrong. Because today most of the transplanetary missions are being carried out by rovers and the robots aboard the ISS are beginning to function more and more like their human counterparts. For astronomers and researchers, it is truly hard to imagine sending another human to the moon let alone to Mars.

So, in that respect robots have allowed us to go farther than we would have ever imagined possible and they aren’t just there planting a flag, instead they are drilling on its surface, running tests and sending in chunks of data that would have taken a human a lifetime to collect.

2. Drones:

There is no better field to measure the impact of robotics than the defense sector. Robots have been a huge part of many important military operations in the past two decades. Particularly the Drones guided by AI are capable of flying, targeting and even firing from long range as a staple of the U.S. air force.

However it is not just the terminator style drones that are making the headlines, but recent years have also seen a hike in the number of shopping drones and transport drones. The use of flight-capable drones guided by AI could be argued as one of the largest prospects for the retail industry.

3. Transportation:

Automated transport is a field that has been picking up pace off late. Self-driven cars, locomotives and aircrafts are thought by many experts to be the future of transportation. Every day, AI advancements in transport is showing promising results that could in the near future be translated to mainstream modes of transport.

4. Machine Learning:

One of the most recognizable features in robotics today is the technology known as machine learning. Machines and programmes that are capable of analyzing various patterns of the tasks that they are assigned to and create their own set of algorithms to function around more effectively. The introduction of machine learning to robotics has been one of the largest leaps in the industry. Humanoid robots are making a mark in several areas of the industry with many acting in movies, working as astronauts, therapists, nurses, yes that’s right nurses! Soon the caring feminine touch will be replaced by the cold metal claws of a robot named after the tiny metal gerbils from ‘Thundercats’.

Is the ‘Storm’ really Coming?

Well back in 1984, that line could be dismissed as James Cameron just being crazy but today we are really not in a position to tell. As a matter of fact, something big is bound to happen by the year 2029. Not on Skynet proportions but more on the lines of an AI-based global network that will function all by itself. Coming to think of it that sounds exactly like Skynet but on a more nerdy tone.

Levity aside in the decade to come organizations like SpaceX, Tesla and Google are going to put most of their resources into developing AI technology to such an extent that total automation would be possible. Elon Musk’s dream of setting up a space colony on Mars revolves largely around the prospect of AI technology that would help us from the designing phase of the space crafts to the setting up of habitats on the red planet.

Backed by AI, the possibilities of advancements in robotics are endless. Even the hardware capabilities are going through an overhaul with robots now being equipped to mimic human-like body physics. At this point the possibilities seem endless, but, only time can tell how far we can go with this.   

How AI will shape the future of Internet of Things (IoT)

The Internet of Things (IoT) has been the topic of discussion for the past few years. It seems as though everyday the IT universe is finding new applications for IoT and its mainstream plausibility is becoming more. While considered a brand new vertical with endless possibilities IoT is just an extension of Artificial Intelligence. The very idea of IoT spawned from the prospects that AI has shown in the past. The idea of devices being connected with each other and communicating is something that is truly a remarkable point in human civilization. When we take a closer look, it sheds light on the extent to which AI has grown and the development it has brought about in other verticals.

While the application of AI in other verticals such as robotics, automobile, marketing etc. create reason for argument due to the various threats they pose, IoT is a vertical that at the moment poses no such threat, unless they start transforming into little killer robots and tearing your house apart.

Device Development and AI

Today we have more machines around us than we have human beings. If we introspect it might seem that we are spending more time with our smartphones than we are with other human beings. While a frightening realization, it is the future that we have been building since we first sprouted on this earth. At the brink of achieving that reality, we are now at the stage where we are exploring choices and trying to make the right steps towards them. The devices that we are coming up with reflect these steps and that is where the concept of AI raises some interesting questions for IoT.

While AI is primarily used as the cornerstone of devices, in IoT it plays several roles. There is much there that could influence how the IoT would react with our world. Further along the way it would boil down to the popular paradox of the chicken and the egg. Which technology would shape our future- AI or IoT?

It is easy to argue that advances in both these technologies would be of equal consequence. However, that is not the case. The very correlation between these two verticals is just as defined as how contrastingly they could influence each other. Machine learning is a key aspect of the progress that IoT is making. An IoT network that would consist of devices with sensors, video surveillance tools etc. will be capable of monitoring the functioning of the other devices. For software related issues certain devices will be equipped with troubleshooting tools both for themselves as well as other devices. Data is the instigating factor that could influence all these technologies and it is data that will continue to govern them in the future. The expectations would again fall upon AI to make the best out of the data.

IoT in Data Analytics

The idea of developing actionable insights is something that in recent years has provided a huge update for the use of AI and IoT services. As these technologies function using data, the uses become well defined and the margin of error depends only on the validity of the data. This creates avenue for wearable ‘smart’ devices to actually function in a sentient manner. Devices such as the heart rate monitor watches, various goggles allow provide vital data that could be relayed to your doctor, your banker, even your barber, who could avail the analyzed output that they could use to customise the service they provide.

Deep Learning

Deep learning is a breakthrough in IoT. This technology facilitates devices to go beyond the prosaic machine learning algorithm. Deep learning draws from a plethora of sources to arrive at a solution on any given subject. This comprehensive approach to producing solutions could become a key driving force for IoT and how the various devices around us function under it.

Conclusion

The many exabytes of data that is being produced now allow for further proliferation on the IoT front. Going ahead, it is AI’s data analytics capabilities that could facilitate this growth. Both machine learning and deep learning both function on the data that is procured through AI data analytics.

With the AI data analytics process being non-stop, big data and other verticals are proving to be vital resources for IoT. Many industry experts believe, actionable insights will be the key to the future. The possibilities with actionable insights are endless and investments in AI have been made to speed up and increase the productivity.   

Machine Learning enhances User Experience for Music

When creativity meets technology, you get incredible outcomes. And that is what the music streaming industry is investing in these days to improve user experience amidst brutal competition. They push new boundaries with technology and diversify the music genre so that everyone can appreciate it.

The era of personalized music with machine learning

In the latest news, the music discovery process is getting personalized results with revolutionary machine learning. Nowadays, almost every big name of the industry is leveraging AI to create better and more personalized music lists.

So, you should not get surprised if the suggested music from Spotify, Pandora and Apple Music seems exactly what you want to hear. All these music-streaming providers implement complex algorithms to pick subtle cues and create personalized music list for you.

• Pandora combines the same technology with data analytics to make suggested playlists for listeners. The algorithms used by Pandora evaluate the songs or artists selected by a user. With that, it creates a playlist that has similar attributes, matching the personal preferences of that user.

• Spotify is probably the most enthusiastic player when it comes to using algorithm technology in music streaming. The company uses a collaborative filtering approach. The algorithms collect music streaming data from multiple users and compares it together. This comparison is conducted with Echo Nest, which is considered best in this technology for music search. Apart from collaborative filtering, Spotify also includes NLP and audio models in its method of providing personalized music.

How Machine learning is evolving music streaming personalization

As mentioned earlier, music-streaming companies are using a variety of AI technologies to make song discovery advanced and personalized.

Here are three major technologies revolutionizing the music-streaming industry.

1. NLP or Natural Language Processing

NLP enables algorithms to understand human language. APIs are used for sentiment analysis, which harnesses the meaning behind spoken and written words. The model of NLP allows music streaming providers to collect data from a variety of resources all over the internet. Algorithms collect data from articles, news, blogs and other resources available on the internet. Using the written text regarding a music, the machines understand the characteristics and provide them with the right playlists.

2. Collaborative filtering

Collaborative filtering is a comparative study of the users’ music listening behavior. The technology helps in understanding the popularity and characteristics of songs. Algorithms collect data from a wide range of users. These datasets include information regarding stream counts, saved tracks, page visits and many others.

By incorporating all kinds of streaming data together, algorithms create a personalized list of tracks for the listeners.

3. AI audio models

Companies like Spotify understand that NLP and collaborative filtering cannot offer justice for new songs. That is why they use another form of AI-Integrated audio model. This technology works just like the face recognition technology. However, the algorithms inspect the audio models instead of pixels. With raw audio evaluation, companies provide new songs to the users in their playlist.

Thus, it would not be wrong to say that machine learning has found a strong place in the large ecosystem of music discovery. With proven phenomenal outcomes, the justification of marrying AI with music does make total business sense!

How AI is changing the Advertising and Media industry

Over the past decade or so advertising has changed drastically. From the humble copywriter/editor complement, advertising today has turned into a multidimensional effort with professionals from multiple verticals pitching in to achieve the end result. This, of course, is no surprise considering the deep impact that IT has had on almost anything and everything. If a copy editor were to tell you 15 years ago that his computer will be taking care of your advertisement and its standing, you would have thrashed him with his keyboard and taken him to a mental asylum. Yet here we are at the pinnacle of IT (as far as we know) and computers are planning ad placement, bidding for keywords and updating you the status of their efforts. So the question we need to ask ourselves is how far can this be leveraged.

The use of AI and Machine learning for such processes is nothing new. As a matter of fact, the current usage of AI in advertising is still relatively primitive. But the inroads we are making through the use of this technology is substantial. However, for AI and machine learning to make any sort of assessment the most important thing is data and it needs lots of it.

What is Data Driven Advertising? 

Anything you do on the internet required the use of data and while you do it generates data as well. From a business perspective, one of the biggest reasons why organizations use the internet is for advertising. Advertising is a multi-billion dollar industry with many dimensions within. Among them internet today is the most prominent and offers the most comprehensive results. So what kind of data is it that floats around the internet to help out with advertising. Well, the answer is pretty much everything, from search histories to personal information, social media updates to data pertaining to behavioral attributes, the internet is a repository for all these. Big data as we all have come to know it is what drives this process. While in the past data-driven advertising was largely based on manual analytics, the vertical today relies on automated technology.

The AI and Machine Learning Influence on Advertising     

While still in their inception stage, both machine learning and AI are being used more than we might have anticipated.

• Search History: Most of the data that is available today on the internet comes from search history. For advertisers, tools like Google AdWords offer keyword suggestions that tend to draw in more viewers based on their activity. This largely automated service provides an edge over competitors to place your ads with the right keywords. Be it product, service or information, anything you search for on the internet gets registered irrespective of the search engine. This information is then transferred to the highest bidder like in the case of Google as part of the google analytics tool. So advertisers who are registered with the tool gain access to your location, the products you were searching for, your brand preference if you have purchased anything and so on.

• Voice Recognition: Online shopping’s next frontier-voice recognition devices like Amazon’s Alexa and Google Home are currently taking the entire e-commerce sector by storm. The ability of these devices to relay your requests as well as make suggestions based on your activity is truly something that will be influencing e-commerce in the years to come.

• Social Media Bots: While being the cause of much controversy recently, the use of bots in social media has made the process of gathering information lightning fast. Social media is the source for a plethora of sensitive information and since inception has been exploited by advertisers and marketing companies to plug their products.

• AI Content Creation: The use of AI for content creation particularly for social media and BuzzFeed, have truly revolutionized advertising and marketing. Several types of content on multiple platforms are being written by AI today. Still, in a primitive stage, this is a technology that will surely pick up in the years to come and who knows maybe be even replace human writers. It is predicted that by 2043 we might have the first number one best-seller authored by AI.

Conclusion

While the current state of AI does leave much to be desired for advertisers, we are not that far from perfecting it for that purpose. There are plenty of prospects to be had on the advertisements themselves as AI and machine learning develops. Machine learning, in particular, could be leveraged to design ads without any human involvement. Currently, technologies such as deep learning are being used in the imaging process in games and movies, which point to good prospects on that front.

There are many more technologies that are still in the prototype stage being tested under various scenarios to eventually integrate into the mainstream processes within advertising. So let us wait and watch as this bit of technology evolves and its story unfolds.