Build Intelligent Applications

Machine Learning

At Azumo, we have developed our own proprietary tools to leverage the advances in machine learning and natural language process. We’ve used those tools to build intelligent applications for our customers. For the first time ever companies like ours and yours can affordably take advantage of artificial intelligence to create new businesses.  While many technologists will have a good understanding of the meaning behind machine learning and its many different variants, we thought it could be useful to provide a brief overview of machine learning, provide a few real world examples and introduce just a few of the popular frameworks available to developers.

 

Machine Learning: What it is and why it’s important

Machine learning (ML) and artificial intelligence (AI) are hot topics at the moment, and if you are in the business of improving any task or process, you may have already looked into some ML solutions in an effort to advance your cause.

If you are just joining the conversation, welcome to your future. ML, you see, is not science fiction anymore. In fact, it has been disrupting industries that include finance, retail, customer service, travel, media, advertising, and hospitality for some time now. If you don’t think you have encountered ML in your travels, think again. Anywhere there is data to be leveraged, a computer of some sort is likely standing by to make sense of it all, learn from the patterns, and make your processes more accurate and efficient.

What is Machine Learning?

So, what is machine learning, exactly? And what does it have to do with artificial intelligence?

Today, machine learning could be seen as a subset of AI, but in reality, it is today’s more common iteration of AI. That said, you can have ML without AI, but you can’t have AI without ML. In many cases, the two terms are used interchangeably. They work together, though one is largely dependent on the other.

For instance, if AI is a computational system that is designed to reproduce and automate a specific action, ML is the platform by which the data is analyzed in order to produce the resulting AI action.

 

Why Machine Learning is more important today than ever before

AI has been around for a long time. Since the 1950s, we have tasked computers with solving myriad technological and computational problems in order to deliver more efficient results and to reduce human error. Today, as a method of replacing human actions, it has streamlined everyday processes and given many aspects of our lives more economy.

Some would argue that it has effectively replaced humans in many situations, creating a vocational gap in the world’s workforce as factories and fulfillment processes become automated and this would largely be true. On the other side of the coin, however, it is making our lives easier, driving costs of consumer products down and giving us all more convenience in our everyday activities.

But it’s in business where we really see the value of ML in action. Mining big data and identifying deeper insights help us improve processes, but it is ML’s ability to learn and redirect these processes accordingly that makes it that much more valuable. Leveraging ML gives more meaning to AI’s responses. The more it learns from our input, the more useful these efficiencies will be.

Real-world examples of Machine Learning

Consumer-level examples of AI with ML include Apple’s Siri or Amazon Alexa. These virtual personal assistants or chatbots and voice-bots come to learn your preferences and, over time, will better understand what you need and what you want. For instance, you would only have to tell Alexa once what brand of coffee you would like to purchase from Amazon. When you reorder, Alexa will know when, approximately, you will be ordering again and may even remind you that you’re likely running out of coffee.

Self-driving cars are another example of ML, as is Apple Music or Netflix, which will suggest new music or media that it thinks you might like based on what you have previously listened to, watched or set in your preferences.

Machine Learning in business

In business, ML is more important today than ever before. The underlying elements of math, algebra, calculus, and statistics are largely the same, but it’s the way we are leveraging those results that allows us to predict the future.

If you look at a timeline of ML advances since the 1950s, it’s interesting to note that in the leap from teaching a computer to play games like checkers and chess to the role that it plays today, there wasn’t much going on. Since then, data availability has increased by more than 1000 times, and processing power has also increased exponentially. And with the advent of the cloud and cloud computing over the past 20 years, companies of all sizes can now take advantage of machine learning and artificial intelligence to build powerfully new software applications.

The digital revolution and the proliferation of data that we are producing is, ultimately, the key to ML’s current significance in that without it, there would be no practical way to make sense of it all. Today, ML helps us guide experiences, improve processes, and make businesses more efficient and profitable.

 

Is ML different than Deep Learning and Neural Networks

Deep learning and neural networks are components of machine learning. You might think of it this way: if artificial intelligence is the umbrella, machine learning lies within it, and deep learning within that. A neural network is a data architecture that leverages other technologies to produce an artificially intelligent action.

A neural network is a program inspired by the biological processes of the human brain itself, processes which allow a computer to learn by leveraging observational data. Simply put, under normal programming circumstances, we would tell the computer what to do via programming language. In a neural network, rather than being told what to do, the computer learns from the data and figures out its own solution.

Neural networks are often used to make predictive decisions in healthcare, finance, and even professional sports training performance. The use of this technology has helped to determine ideal courses of action based on past behavior and other variables and can even be used to “fill in the blanks”, so to speak, in motion picture sound, to colorize black and white images, or to replace pixels in those images based on the pixels around them.

Neural networks depend on contextual relationships. The larger the network is, the more accurate it will be. In other words, the larger the library of learned items that the neural network has to draw from, the better the result.

The difference between deep learning and neural networks lies in the depth of the data model. More complex data sets or problems that have several layers of data would qualify as being deep. It also implies the use of more recent technologies that simply aren’t found in traditional neural network iterations.

For instance, even though AI is not new, it has exploded in recent years. This is largely due to the wider availability of GPUs and CPUs that help to make processing that much faster and that much cheaper. Add to that the accessibility of on-demand, virtually endless cloud storage and the voluminous amounts of data we generate on a daily basis, and you’ve got a landscape tailor-made for artificial intelligence, machine learning, and deep learning.

 

How Natural Language Processing (NLP) has advanced in recent times

Natural language processing (NLP) is a subset of AI. It is the protocol by which systems can understand and interpret language, whether written or spoken, with the objective of having the computer as good as a human at understanding and language.

NLP is used to execute tasks that include automated text, automated speech, and machine translation. Each of these processes ultimately has two steps:

1. Natural language understanding

There is always a challenge here. Understanding the input and being able to make sense of it is necessary in order to put the input into context. Some of the complexities (ambiguities) include:

  • One word having several meetings (lexical)
  • A sentence having more than one parse trees (syntactic)
  • A sentence having several possible meanings (semantic)
  • Words mentioned more than once that have different meanings (anaphoric)

2. Natural language generation

Natural language generation is about generating text from data into a format that is easily understood. The process has three stages:

  • Text planning, which orders the data
  • Sentence planning, where sentences are constructed from the ordered data
  • Realization, which is the final output of properly structured sentences
  • Key applications for natural language processing and natural language understanding include generating summaries of input text or speech or translating it into terms that would then generate a specific action.

In everyday computing terms, it might translate one language into another, auto-correct, spell-check, or to conduct an online search. Any computer feature that includes language uses NLP.

From a business standpoint, we see NLP in action over a range of industries, including advertising, customer service, business intelligence, and even healthcare.

In a customer service environment, NLP leverages the customer’s spoken input to direct them to the information or department they need. Chat bots and online assistants are good examples. They are able to respond to simple needs, answer questions, translate text to speech, pick up on the meaning of emoticons, and even determine whether the person on the other end of the line is happy or dissatisfied.

In terms of business intelligence (BI), it makes relevant data more accessible and user-friendly. As the technology has matured, computers have become better at delivering meaningful answers to spoken queries.

Some common business cases for NLP include:

  • Natural language understanding (NLU): enable machines to understand domain specific language along side a data corpus of words. We developed our own natural language solution called mynlu.
  • Sentiment analysis: know what your customers are feeling when they speak to the chat bot.
  • Spam filters in emails: scanning incoming email to filter malicious or spammy content.
  • Voice recognition: processing spoken commands.
  • Information gathering: extract articles and information from the news and other sources that can then be used to help make business decisions.
  • As for the latter example, this can be extremely helpful when actions in the business world can have financially impactful repercussions or benefits – such as mergers, acquisitions, or political activity that could affect the market.

 

Popular programming languages for ML

Python is by and large the most popular programming language for machine learning applications. More than half of all developers use and prioritize Python, but according to a recent survey, the answer is mutable and really depends on what you are building and what your background is.

After Python, in order of popularity, you’ll find Java, R, C++, C, JavaScript, Scala, and Julia.

C++ and C are favored in game applications. R is used widely in bioinformatics, biostatistics, and bioengineering. Where sentiment analysis is a factor, Python is preferred, then R, then JavaScript, and Java. For developers working in network security and fraud detection, Java is more favored.

Popular libraries for developers

Libraries provide a source of data   upon which deep learning applications are built. Different libraries have relevance to different end applications; for instance, some are more scientific, and some are more business-oriented. They are all open-source.

Some of the popular frameworks include:

TensorFlow is the principle language of Python. It is the neural network library used by Google, and also in its apps like Translate, Maps, and all Google apps that run on smartphones. It is highly complex and favored by companies who have ML specialists on staff.

Theano is a simpler framework that predates TensorFlow and still has a considerable audience. It is considered the industry standard for DL R&D and plays well with other libraries.

MITIE is an open source framework that is friendly to C, C++, Python, Java, and R. It provides models in both English and Spanish.

Scikit-learn is based on SciPy, a Python framework developed specifically for scientific applications.

MXNet is a framework that has been heavily developed by Amazon and Microsoft, among others.

Caffe is a framework primarily geared towards images and video.

Torch is widely considered to be the easiest ML framework for beginners. Facebook, Google, and Twitter are all using Torch in their AI projects, primarily for algorithmic timelines and post categorizations.

Emerging applications that leverage ML and NL

With all the possibilities presented by machine learning, it is becoming easier every day for businesses to use data to their advantage. In an effort to improve efficiency, boost profits, and to generally be more effective in delivering your services, machine learning can be leveraged towards a range of business processes: 

Analytics: data analysis is a key feature of machine learning. NL will provide greater meaning to that data, resulting in deeper insights.

Productivity: machine learning supports decision making and identifying data anomalies, which enables more effective and timely business decisions.

Management: the ability to learn and to make decisions based on ever-changing data sets will support greater operational efficiency

Customer experience: engage your audience and reap the rewards of a more meaningful customer relationship.

Customer service: collect real-time data from your customer interactions to improve business processes.

If you have questions about how machine learning, neural networks or natural language processing can support your business, drop us a line today.

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