Machine Learning Overview and Practical Applications

A Guide on Machine Learning for all tech levels

We’ll begin our machine learning overview with a brief history of computers. The very first computer was created in a German living room in the late 1930s. Soon after, businesses raced to build machines with more computing power and task diversity. The technology proved expensive and enormous, yet barely compared to the modern day iPhone.

Jump forward to 1969. NASA used computers to assist in landing a man on the moon. The smartphone in your pocket has millions of times more computing power than the entire space command and shuttle combined.

The introduction of statistical models and basic algorithms allowed machines to harness data-driven action, such as playing games of tic-tac-toe and identify basic patterns. Machine learning evolved as data scientists experimented with different methods to train machines.

Today, machine learning fuels advancements in:

  • Object detection
  • Speech recognition
  • Natural language translation
  • Rankings and recommendations

As machine learning capabilities increase alongside our understanding of human cognition, computers will be able to optimize human processes by solving problems through a constantly updating algorithmic approach.

What is Machine Learning?

This machine learning overview provides case studies and real-life applications while illustrating how the technology can produce a positive social impact.

If you aren’t familiar with The Hoeffding Confidence Interval or lineralized regularization (trade-off between error rate and complexity) for algorithmic design and training, you’ve probably familiar with the term “machine learning.”

Machine learning (ML) can be explained as the development and of-improving of software algorithms. These are used to make data-driven predictions.

The basic flow of machine learning follows:

  • Data Input
  • Data Analysis
  • Output Prediction
  • Process Optimization

Machine learning is used to make increasingly accurate predictions while minimizing dependency on humans for improvement.

In everyday use, a user gives machine data that it uses to make informed predictions. There are different ways to manage machine learning that adjust the quality and frequency of data given to a machine.

To further the capabilities of machine learning, neural networks are developed to mimic the way the human brain functions, except on a much smaller scale. Neural networks are built of layers of algorithms tasked to perform advanced computations.

These may one day challenges the processing power of the human brain. Until then, we need to continue training machines and improving the algorithms they are built on. This machine learning overview covers computer training and use cases.

Everyday Machine Learning

There is a high probability that you interact with and contribute to machine learning every day.

If you’re familiar with IBM’s Watson’s legendary Jeopardy victory in 2011, then you’re already familiar with a device that uses machine learning to complete tasks. Watson’s algorithms account for natural human language as data input by utilizing natural language understanding (NLU) and translation of speech into data.

“The computer’s techniques for unraveling Jeopardy! clues sounded just like mine.” – Ken Jennings

This allows programs to recognize speech input and search various knowledge databases to quickly locate the information needed to answer each question correctly. Watson collects speech input, parses it into keywords, and generates an output based on its analysis when it reaches a high enough confidence level.

NLU enables machines to:

  • Recognize speech patterns
  • Identify the speech of an individual user
  • Continually improving upon its performance based on user-feedback

Watson uses NLU to process inputs -in this case, the Jeopardy questions- and formulate accurate outputs in the form of answers. This voice recognition process is replicated in your homes when you ask a question to Alexa, Siri, or Google Home.

Differences Between Machine Learning and AI

It is difficult to find machine learning overviews that don’t touch on Artificial Intelligence (AI). Although it is common to see the two ideas used interchangeably, it is good to acknowledge that the two are different.

Machine learning is a set of algorithms and functions that fall underneath the wide umbrella of AI.

Machine Learning – A way to analyze data to make informed predictions using preexisting data as a baseline.

AI – The science of enabling machines to perform tasks at a human-intelligence level.

In summation, AI does the work, and machine learning enables it to improve its performance over time. You can have AI without machine learning, but you cannot have machine learning without AI.

AI can be broken down into two subcategories:

General – Well-rounded in all fields of planning, comprehension, and problem-solving.

Narrow – Extremely skilled in some fields, but is not as experienced in other areas.

Machine learning can further a device’s ability to recognize patterns in a specific area to achieve narrow AI status. Machines that are capable of NLU are considered narrow AI because they specialize in speech recognition.

The Future of Machine Learning

Data scientists are making strides in the field of machine learnings, and machines are becoming capable of carrying out more and more sophisticated tasks.

As we continue to make discoveries in the field of machine learning, the learnings are applied to both existing and developing technologies. The result will be faster, smarter, and more accurate software.

Machine learning is already being used to develop solutions to vastly improve computing processing speeds. Edge computing will allow machines to achieve optimal processing speeds during periods of time where latency levels spike.

Apply machine learning to edge computing, and we’ll be working on devices that can predict when latency is going to spike and adjust resources accordingly.

In this section of our machine learning overview, we will touch on the general uses of machine learning and how it can be used in the future to improve business.

Machine Learning Overview: Business Automation

Today’s word is fast-paced. Customers demand immediate feedback, especially as technology continues to advance. Machine learning can be applied to help business reduce the time it takes to carry out tasks as well as stay ahead of the curve.

A few examples of how machine learning provides value in automation:

  • Populate databases in anticipation of busy periods
  • Collect and store information
  • Reduce cross-departmental delays

The last point should be particularly interesting for businesses looking towards automation to reduce overhead and collect and improve on systemic processes.

Looking forward, IBM champions the integration of blockchain with artificial intelligence, signaling the evolution of blockchain-based smart contracts into cognitive blockchains. From this we will see:

  • Calculated predictions
  • Searchable and secure data
  • Self-improving smart contracts

Machine Learning Overview: Data-driven Predictions

A primary function of machine learning is to generate accurate results from a proven, reliable dataset. There are several ways businesses can implement machine learning strategies to streamline their processes and cut back on production costs.

  • Suggest outreach targets and predict responses
  • Make time-based sales suggestions
  • Predict missing data from datasets

Businesses benefit from quality leads or higher conversion rates, while customers identified from the data enjoy a better user experience directed to their interests. As machine learning improves, more businesses will use technology to do the heavy lifting for data procurement.

Have you ever made a purchase based on Amazon’s item recommendations? Thank machine learning.The technology pulls data from your shopping habits and digital shopping carts to recommend additional products that you might find interesting.

Future machine learning functions will take this concept a step further by recommending specific products during times where users are more likely to make a purchase. It also can alert a website when human interaction may be necessary.

Machine Learning Overview: Stronger Results Over Time

Machine learning will only get better over time. A core function of machine learning is the self-teaching improvement of the algorithms.

Machine learning software will:

  • Decrease margins in prediction accuracy.
  • Better train it’s algorithms using data.

These results that increase in quality as the machines continue to learn. The more data a machine has access to, the better systems and predictions they have to work off in future tasks.

Smart thesaurus QuillBot is an excellent example of a useful machine learning application that is functional but still needs to undergo many iterations of self-improvement. Users type in a sentence for QuillBot to interpret, similar to the way users interface with IBM’s Watson.

The device identifies each word in the sentence and makes active suggestions on how to improve the sentence. Users can rate the newly generated sentence, which is how QuillBot gauges if its recommendation engine is accurate or if it needs to improve its analysis.

Machine learning such applications like the QuillBot Thesaurus depends on human input and training assistance provided by a data scientist. These make sure the application algorithms don’t iterate on grammatically incorrect sentences or assume improvement based on stylistic preferences.

In the future, machine learning will find traction providing a pre-consultation step for services targeting highly specific user groups. Chatbots that utilize machine learning to navigate and improve upon their response tree will develop stronger language skills.

Future chatbots will continue to improve to:

  • Copy human language
  • Respond to user voice commands
  • Sound less robotic
  • Analyze images
  • Make smart connections between data and actions
  • Recognize objects

We can also look forward to more accurate complex, multi-decision voice input responses.

Human Interactions and Machine Learning Overview

Machines cannot improve their algorithmic systems and outputs in a black box. Software and machine learning applications must be trained with datasets fed to them by humans.

Collaboration with experienced data scientists is necessary for developing accurate, efficient machine learning applications.

If you’re worried about machines taking over the workplace, that fear is still ungrounded today. Human guidance of algorithmic learning is necessary for the continued development of machine learning for:

  • Oversight of the learning progression of individual or groups of machines.
  • Evaluating the output generated by computers.

There are plenty of formulas, weighting, and bias that come into play when developing and setting up machine learning training systems. Data scientists know how to correctly classify, label, and distribute data to the machines to ensure the device can learn successfully.

It’s always possible that the wrong data could be administered to the machine, or even that correct data could be improperly administered to machines. We won’t include data training in this machine learning overview, we can go on for hours about how to properly train a computer.

Working with an experienced data scientist minimizes the risk of receiving inaccurate outputs by developing processes to analyze output data and flag unusual findings.

Data scientists ensure machines operate at top quality by strategically handling user feedback:

  • Collection
  • Administration
  • Scrubbing

The result is optimal, unbiased training. As machine learning finds its footing under the ever-improving catch-all of artificial intelligence apps, tools, and software we depend on today will see value in incorporating machine self-improvement.

We hope this machine learning overview inspires you to explore machine learning and how AI will drive the future. If you’re looking to work with an agency experienced with designing for machine learning apps, contact us.