Artificial Intelligence conceptual understanding

Introduction

The use of Artificial Intelligence (AI) has been growing by leaps and bounds with ground-breaking use cases across all industry sectors. AI is helping experts across all industries diagnose and solve problems faster. This enables consumers to do amazing things, like find songs with just a voice command. The company Interactions offers Intelligent Virtual Assistants that impeccably assimilate conversational AI and human understanding. This enables businesses to engage with their customers in a very productive and satisfying manner.

Trustera by Interactions

Interactions has recently launched Trustera, which is a real-time, audio-sensitive redaction platform. Trustera pre-emptively detects and protects sensitive information such as credit card numbers. It aims to solve the biggest compliance challenge which is protecting a customer’s Payment Card Information (PCI) during a call. The platform is designed to make the customer experience more trustworthy, secure and seamless.

While Trustera is a great result of Artificial Intelligence, for those of us who like to look under the hood, there are four foundational elements to understand: categorization, classification, machine learning, and collaborative filtering. Read on learn more about these elements.

The Four Essential Elements of Artificial Intelligence 

Most users concentrate on the results of AI. For those who like to look under the hood, there are four foundational elements that with respect to AI. They are:

• Categorization
• Classification
• Machine Learning
• Collaborative Filtering.

These pillars also illustrate steps in an analytical process. Read on to understand more about these pillars of AI.

Categorization

AI needs a huge amount of data that is pertinent to the problem being solved. The first step to building an AI solution is creating the design intent metrics which are used to classify the problem. One may be trying to design a system that plays Jeopardy or one that helps a doctor diagnose cancer, or even help an IT administrator diagnose wireless problems. The user needs to define metrics that allows the problem to be broken into smaller pieces. For example, in wireless networking, key metrics are throughput, user connection time, coverage, and roaming. In cancer diagnosis, key metrics are X-ray scans, white cell count, and ethnic background.

Classification

After categorization, the next step is to have classifiers for each category. These classifiers point users in the direction of a meaningful conclusion. For example, in wireless networking users need to start classifying the cause of the problem. The cause could be authentication, association, dynamic host configuration protocol (DHCP), or other wireless and device factors.

Machine Learning

Once the problem is divided into domain-specific chunks of metadata, users can feed the information into Machine Learning (ML) algorithms. There are various ML techniques and algorithms with ML using neural networks (i.e. deep learning) now becoming one of the most popular approaches. With the latest increases in storage and compute capabilities, neural networks can now solve a variety of real-world problems. This ranges from natural language processing, to image recognition and to predicting network performance.

Collaborative Filtering

Most users experience collaborative filtering when they receive recommendations for movies or items they might like. Collaborative filtering also sorts through large sets of data and puts a face on an AI solution. This is where all the data collection analysis turns into meaningful action or insight. Collaborative filtering is the means to providing answers with a high degree of confidence for who ever uses it. It is like a virtual assistant that helps solve complex problems.

Conclusion

A new generation of scalable ML methods—few-shot learning—creates NLU models y without the depending on large datasets. These methods use just only a few examples to train, thereby expanding the use of NLU to applications. In such applications, large collections of labeled data may not be available.