AI has two approaches in programming intelligent machines: supervised learning and unsupervised learning. Supervised learning requires data with defined input/output relationships (labeled data). The comparison being taught by a supervisor or teacher. The resulting supervised learning algorithm uses the learning to predict outcomes from new input data. Over time the model must be maintained to ensure that the labeled data is both current and complete.

Unsupervised machine learning requires no supervision. Using this approach, the model works on its own to infer information from unlabeled data. There is no information on the outputs, the model identifies patterns from the data. This approach supports more complex processing tasks when compared to supervised learning. Unsupervised learning can be more unpredictable when compared to other learning methods.

Supervised Learning Advantages

Supervised learning takes advantage of collecting/developing data from existing experiences. This provides an approach for optimizing a model’s performance.  Supervised learning is valuable in addressing computational problems.

Unsupervised Learning Advantages

Unsupervised learning is adept at discovering unknown patterns in data. The identification of the patterns occurs in real-time and labeling is completed in the presence of the learners. Using unlabeled data, unsupervised learning does not require the data labeling effort. 

Supervised Learning in Action

Supervised learning trains the machine to complete a task. Suppose you wanted to predict how many games a pitcher will win in an upcoming season using prior year performance. The process requires the collection of a data set of pitching performance by pitcher. Example data could be:

• Games won
• Games lost
• Strike outs
• Walks
• Ground balls
• Fly balls
• Home runs
• Runs allowed

These inputs for a particular pitcher would be collected and the model determine the output, number of games won.

The labeled data defines a training data set used as an input for training the model. The model may conclude that more strikes and less walks are desirable. Similarly, more ground balls and fewer flyballs. The learning process takes this training data, isolates attributes and develops an algorithm(s) which become the model. 

Unsupervised Learning in Action

Unsupervised learning uses data with no labels. An example for unsupervised learning would be if you went to a baseball game and had no idea how the game is played, you would watch and make observations to develop an understanding of how the game is played. You would notice

• There are 9 players on the field
• Each team puts 9 players on the field while the other team’s players take turns hitting the ball
• If the batter misses hitting the ball three times the next batter comes up
• When the batting team has three players who swung and missed three times the team in the field gets to bat.
• And so forth

You would be learning baseball without any assistance. The learning would have occurred by identifying patterns that were not previously known.

Summary – Supervised vs. Unsupervised Learning

The learning methods differ on how data is used. Input data is labeled for supervised learning and unlabeled for unsupervised learning. Supervised learning uses the output data to learn and outputs to new inputs. Unsupervised learning does not use output data. Supervised learning is a simpler method with learning performed offline versus unsupervised learning being computationally more complex and occurring in real time.

The major unsupervised learning drawback is that without labels, complete information on data grouping and output data is not available. Supervised learning requires the classification of the data. Supervised learning is considered a trusted process with accurate results, whereas, unsupervised learning in more unpredictable.

Both of these processes and more contribute to the fascinating power of artificial intelligence. In the workplace, Yalo is using our new sentiment analysis services to leverage AI for social media monitoring and actionable insights for our clients. Request a demo in order to understand how these amazing tools can help to build and boost your brand.

What is Intelligence

First, some agreement on what constitutes intelligence is necessary. Depending on the discipline different attributes are valued and therefore constitute intelligence.

• Effective speakers select words considering the sounds, grammar and meaning
• Musicians create and communicate based on sound affect, pitch and rhythm.
• Mathematicians utilize relationships without actions and abstract complex ideas.
• Physicists can visualize/change/recreate images independent of referencing physical objects
• Athletes/dancers control part of or the entire body using motor skills to manipulate objects

A person’s intelligence within a discipline would be recognized by their mastery of the discipline’s attributes. An argument can be made that a machine can be intelligent by mastering one set or multiple sets of attributes.

Elements of Intelligence

When evaluating something as intangible as intelligence, what elements should be considered? It starts with reasoning processes that support extending observable facts/events to general beliefs and/or take general beliefs consider alternatives in coming to specific conclusions. For a baseball player it is baseball IQ; the musician it is a sense of timing or beat; physicist the thought experiment in visualizing the abstract. It includes learning by training, observing and experience which expands the understanding of the discipline. 

Depending on the discipline, listening, recalling experiences, duplicating physical activities and drawing conclusions by relating environmental cues are all techniques for expanding knowledge. These techniques make a decision or choose a process which resolves an issue. They must also consider their surroundings, sensing valid inputs and providing structure. Generating value and knowledge, machines must also have communications skills to understand inputs and document results.

Machine Intelligence

Humans rely on recognizing patterns in applying their intelligence. Each pattern, learned or experienced, has its information and heuristics for a situation. Familiar or stressful situations are facilitated by quick thinking in applying the most familiar pattern. New or complex situations require more thoughtful thinking in perceiving differences, altering existing or developing new patterns. 

Machine intelligence is programmed with attributes focused on a specific discipline. This intelligence relies on taking general beliefs, considering alternatives and arriving at conclusions. This can include understanding that a mistake has been made and avoiding it in the future. 

Intelligent machines evaluate inputs as data by applying sets of rules. While humans search for patterns, machines learning relies on rules/algorithms. Two different techniques are used to develop learning in machines. Supervised learning uses sample data with matched outputs (labelled data) to teach machines to approximate outcomes for new inputs. Unsupervised learning does not rely on labeled outputs – the learning occurs by inferring the natural structure of a set of data points. A differentiator between machine intelligence and human intelligence is in situations with incomplete or distorted inputs – humans can still determine outcomes where intelligent machines can have inconsistent results.

Yalo is embracing machine intelligence/AI with the arrival of our new sentiment analysis services that can recognize language patterns in order to determine social media feedback and popularity for a brand, amongst several other tasks. We are bringing AI to bear on the Web 2.0 world in our relentless pursuit of excellence for our customers. AI rocks!

The world is fast evolving, with Artificial intelligence (AI) at the forefront in changing the world and the way we live. AI is everywhere — at our workplace, in our homes, cars, in our phones and laptops — in short, in the things that have become integral in our lives. Moreover, AI devices know what kind of TV shows and movies we like, what we buy, and how we operate. Let’s explore this world a little deeper…

AI By Definition

It is the ability of a computer to imitate how we think. It is accomplished by learning from previous experience, object identification, language mastery, decision making and problem resolution. These and other capabilities can be combined to perform functions that would be performed by a human. Like a human, with AI, machines can combine input from multiple sources (sensors, digital), analyze information and act on analysis results. Designed by humans, these machines are expected to reach conclusions based on real-time analysis.

AI Explained

Today AI applications are not “artificial,” but focused and contained in functions and features we experience daily such as: word completion while typing, travel directions, eCommerce buying, and television watching recommendations. Although the value of these AI applications is taken for granted, there’s some degree of confusion on terminology. Key terminology that is used includes:

• Artificial Intelligence –used when discussing any computing alternative that demonstrates some human intelligence capability.
• Machine Learning – a subset of AI applying input data to a machine improving its mastery in completing a specific task.
• Deep Learning – a type of machine learning, without human intervention, is self-teaching in completing a specific task with greater proficiency.
  

AI runs the gamut of specific task completion to duplication of human activities by choosing and solving multiple problems without human intervention. The former we experience daily with new applications enabling humans to complete tasks with ever increasing efficiency and accuracy. The latter is currently theoretical and exists only in the realm of the TV series Next.

AI Applied

AI has found its way into our daily living. Some great examples include:

• Speech Recognition – enabling fast and accurate speech transcription in multiple languages for a variety of use cases as seen with IBM Watson
• Natural Language Processing – understanding, interpreting, and generating text, think of email filters, smart assistants, search results, and predictive texts. Learn about a more few prominent examples.
• Machine Vision – identifying/classifying visual images. As a fun example, consider a fill-level inspection system at a brewery. Each bottle of beer passes through an inspection sensor, which triggers a vision system to flash a strobe light and take a picture of the bottle. If the system detects an improperly filled bottle—a fail—it signals a diverter to reject the bottle to then be filled properly.
• Recommendations – suggest purchases/media based on past purchases/viewing. Do you ever wonder why your favorite stores or Amazon suggest certain products or why Netfllix curates certain shows for you? Recommendation engines play a huge role with collaboration filtering.

These use cases depend on machine learning and data analytics combined to deliver intelligent decisions. To remain relevant, they learn from and adapt to information changes in their environment.

What’s Next

Imagination is the only limitation where AI can improve lives but many questions remain.. What is machine intelligence? What tools are most appropriate for different AI use cases? What is the right balance of replacing humans performing tasks vs. supporting them? Want to know what comes next and how to balance the future of machine learning?

Let us help you navigate your business with AI-driven solutions. Yalo uses AI with our new Sentiment Analysis services to help your brand build content, segment customers, and leverage social media more successfully across the board. You don’t need a computer to know a smart idea when you hear one.