Master the Basics of Pragmatic AI

Table of Contents

1. Introduction
2. The Growing Importance of Artificial Intelligence
3. The Role of Storage and Compute in AI
4. Defining Artificial Intelligence
5. The Evolution of AI Over Time
6. The Challenges of Unstructured Data
7. The Pillars of Natural Language Processing
8. Applying AI to Inbound Channel Correspondence
9. Magellan: An AI Solution for Natural Language Processing
10. Examples of AI Applications
11. Conclusion

The Growing Importance of Artificial Intelligence

Artificial Intelligence (AI) has become a topic of significant interest and importance in organizations across various industries. As storage capacity has increased and become more affordable, businesses now have the ability to Gather and store vast amounts of data. Additionally, advancements in computing power, with processors capable of handling multiple Threads of work, have made it possible to Scale AI solutions to enterprise levels.

However, defining what AI actually means can be a challenge, as different individuals and organizations may have varied interpretations of the term. AI encompasses a range of disciplines, including deep neural networks, machine learning, and natural language processing. It has experienced peaks and valleys of popularity over the years but has recently gained traction as a key focus in the field of computing.

The Role of Storage and Compute in AI

The exponential growth of data, coupled with the affordability and ease of storage, has played a crucial role in driving the conversation around AI. A 16 terabyte hard drive, once a luxury, is now available for as low as $400. This ease of storage has resulted in organizations accumulating and retaining massive amounts of data that were previously unthinkable.

Similarly, computing power has also significantly improved, with processors now capable of handling an enormous amount of work. For example, the latest Intel processors boast 56 physical cores on a single socket, each capable of managing two threads. This level of compute power enables organizations to process and analyze large datasets more efficiently, making AI implementation feasible at an enterprise level.

Defining Artificial Intelligence

Artificial Intelligence is an umbrella term that encompasses various disciplines and sub-disciplines. It goes beyond traditional programming logic and incorporates sophisticated algorithms and models to mimic human behavior and thinking. Instead of relying on strict "if-then" rules, AI utilizes machine learning paradigms to train models with vast amounts of data examples.

The key sub-discipline within AI that enables machines to understand and interpret human language is natural language processing (NLP). NLP consists of six primary pillars that enable machines to extract concepts, entities, sentiment, perform summarization, and work across different languages. NLP helps unlock the insights Hidden within unstructured data, such as emails, chat conversations, and documents.

The Evolution of AI Over Time

AI has a rich history that dates back to its inception in 1950. It has experienced periods of rapid advancements as well as periods of disillusionment. The late '60s and '70s, with their focus on batch-Based mainframes and limited computing power, stalled AI progress. However, with the advent of open systems and increased computing power in the mid to late '90s, AI saw a resurgence.

Today, AI is making significant strides in practical applications, such as language understanding and human-like responses. It has become a specialty business with different disciplines focusing on specific areas, such as deep learning, machine vision, and natural language processing.

The Challenges of Unstructured Data

One of the major obstacles in leveraging AI is the abundance of unstructured data. This data, which includes emails, social media posts, and handwritten documents, presents challenges in terms of searchability and analysis. Traditional analytics solutions are ill-equipped to handle such unstructured data, making natural language processing an essential component of AI implementation.

Additionally, the proliferation of communication channels, such as email, chatbots, and social media, further complicates the analysis of unstructured data. Each channel introduces its own set of complexities, such as different file formats and languages. AI solutions need to adapt and incorporate these channels to provide valuable insights and automate processes effectively.

The Pillars of Natural Language Processing

Natural language processing is built on six primary pillars:

1. Concept Extraction: Identifying simple and complex concepts within unstructured data. This allows for auto-categorization and analysis based on Relevant concepts.

2. Entity Extraction: Recognizing proper nouns, such as names, organizations, and locations, to provide Context and relevance.

3. Sentiment Analysis: Evaluating the positive or negative tone of a text, enabling organizations to gauge sentiment in customer interactions and identify potential risks or opportunities.

4. Summarization: Automatically extracting key points from lengthy documents, enabling quick and precise understanding of information.

5. Language Translation: Enabling the understanding and translation of text across different languages, enhancing communication and analysis capabilities.

6. Analysis Across Languages: Developing NLP models that can comprehend and interpret languages beyond English, catering to diverse global markets.

Applying AI to Inbound Channel Correspondence

Inbound channel correspondence, consisting of emails, chat conversations, and other forms of customer communication, poses unique challenges for organizations. The sheer volume and variety of incoming messages make it difficult to extract relevant information and automate processes effectively.

AI solutions, such as Magellan, can play a crucial role in addressing these challenges. Magellan leverages natural language processing to automatically extract vital data, classify messages, and prioritize responses. By training the system with specific criteria, organizations can manage incoming correspondence efficiently, improving customer satisfaction and streamlining operations.

Magellan: An AI Solution for Natural Language Processing

Magellan is an AI solution built on open foundation technologies that enable powerful natural language processing capabilities. It leverages advanced algorithms, data repositories, and visualization tools to analyze and interpret large volumes of unstructured data.

Magellan excels in processing inbound channel correspondence, providing organizations with insights and automation opportunities. It offers features like sentiment analysis, entity extraction, and automated summarization, streamlining customer engagement processes and enhancing decision-making.

Examples of AI Applications

AI applications span various industries and use cases. For instance, organizations can use AI to analyze customer feedback from social media, identify sentiments, and take proactive measures based on the insights gained. AI-powered chatbots and automated email responders can handle customer inquiries swiftly, providing personalized and relevant responses.

AI can also assist in regulatory compliance by detecting fraud, extracting personally identifiable information (PII), and automatically categorizing sensitive data. Furthermore, AI can aid in document processing, legal contract review, and risk management, streamlining operations and reducing manual effort.

Conclusion

As AI continues to evolve, it has become an indispensable tool for organizations aiming to automate processes, gain insights from unstructured data, and improve customer experiences. The advancements in storage, compute power, and natural language processing have paved the way for AI to tackle complex challenges across industries.

By leveraging AI solutions like Magellan, organizations can harness the power of natural language processing to extract valuable insights from vast amounts of unstructured data. With AI, organizations can streamline operations, make data-driven decisions, and unlock new possibilities in today's data-centric world.

Highlights:

• AI is becoming increasingly important in organizations, thanks to cheaper storage and improved computing power.
• Defining AI is challenging due to various interpretations, but it encompasses disciplines like machine learning and natural language processing.
• Unstructured data poses a challenge, but AI can effectively analyze and extract insights from it.
• Natural language processing involves six primary pillars, such as concept extraction and sentiment analysis.
• AI can be applied to inbound channel correspondence, automating processes and improving customer satisfaction.
• Magellan is an AI solution that excels in natural language processing, providing organizations with valuable insights and automation capabilities.
• AI applications range from analyzing social media feedback to regulatory compliance and document processing.
• AI enables organizations to streamline operations, make data-driven decisions, and capitalize on the growing amounts of unstructured data