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Machine Learning and Cognitive Computing

Though a buzzword, machine learning continues to create an excited flutter in the heart of all those who are hooked to new-age technologies. Having a computer or a machine absorb something deeply and utilize the learning to make intelligent analysis is truly amazing. This is one of the application areas of big data and artificial intelligence.

Machines such as computers can be made to do a deep analysis of situations and provide insights on complex matters. Such insights are normally precise and have been adopted successfully into domains such as healthcare, transportation and e-commerce. We have seen learning by social media platforms like Facebook. Google does this too.

Examples

Other examples are the self-driving cars on trial the world around, and product recommendations given by shopping sites like Amazon. An interesting aspect of machine learning is the mass appeal it holds. Machine learning or the more complex artificial intelligence is no longer reserved for intelligent professionals. We have seen natural language assistants such as Siri dealing with questions in a better way than search engines. There are similar applications providing contextual information based on inputs. Wearables tracking personal health parameters are examples.

Businesses must make use of the predictive capabilities granted by machine learning so that they can gain access to customer insights. A few leading machine learning service platforms makes this a possibility. Such platforms provide an opportunity to create mobile applications incorporating APIs , while serving domains such as retail, healthcare, education, banking and transportation.

Machine Learning APIs

So which machine learning APIs are we speaking about here? There are several in the market, specifically those from leaders such as IBM Watson, Azure ML and Google. Let us take the case of IBM Watson. IBM provides a set of APIs which makes it easier to integrate many cognitive computing features (text processing and analysis, speech processing and analysis) into mobile applications.

The Personality Insights API

The IBM Watson API for personality insights uses textual information from specific sources to throw more light on the personalities of people. Since social media is where one finds a wide range of personalities, it becomes a natural hunting ground for this activity. It is a typical example of cognitive computing utilizing machine learning.

IBM scores human personality traits under its Big Five categorization, one that includes parameters like openness, conscientiousness, extroversion, agreeableness and emotional range. The results are compiled onto a pie chart for a clearer picture towards the end. The test also unveils the values and needs important to people.

Potential Applications of the Personality Insights API

Business organizations are likely to be the greatest beneficiaries of this API. In this age of customer-centric service, personality analysis will play a greater role in designing marketing campaigns, collecting product features, and personalizing customer service. Recruitment is another area of potential use. Insights into employee behavior will help human resources design targeted programs and engagement practices. In fact, such applications will be valid for all situations warranting a psychological analysis of people and their behavior.

How Experion tested this API We developed a web application by utilizing the personality insight API along with a graph API (Facebook API to pull public data from the Facebook database). After getting into the web application, the user logs in to their Facebook account. Their public posts will be sent along with the personality insight API call, which will be analyzed and a personality summary of the people concerned will be displayed. The use of this test will be its inclusion into many possible areas mentioned earlier.

Challenges Depending on social media to derive accurate insights about personalities is not easy, as people always portray or learn to portray their best qualities online. This can lead to error-prone insights that are not consistent with reality.

Other Examples of APIs

There are other APIs that offer services in areas such as fraud detection, credit services, speech recognition, predictive maintenance and customer sentiment analysis. All such APIs are likely to have positive effects on domains such as aviation, banking, e-commerce and healthcare. Some APIs also offer interesting tools such as Conversation bots that can answer questions based on machine learning Tone analysing APIs that can be used to identify customer sentiments Visual insight tools that can predict personality of people by analysing pictures Speech to text and text to speech APIs that can be used in conversation bots

Conclusion

The future of mobile applications and artificial intelligence looks attractive, based on predictive capabilities provided by external tools and APIs. Every day, we are advancing by leaps and bounds inside the realm of mobile technologies. One of the most important objectives will be simplifying some of the complex concepts of AI to benefit humanity. Having said that, most of this effort depends on data, making security an important component in the scheme of things.

Experion Global

Experion Global

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