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AI and NLP are integral components of future technologies like Web 3.0
Applications continue to evolve and play an essential role in nearly every aspect of life. As the next iteration of the web unfolds, web 3.0, intelligence is more integral to application evolution than ever before. Before we examine how intelligence is shaping the app world, it is crucial to provide a base understanding of artificial intelligence and natural language processing.
Introduction to Artificial Intelligence (AI)
IBM, a global leader in artificial intelligence (AI), defines it as “human-like intelligence that is exhibited by a computer, robot, or other machine.” AI’s capabilities involve learning from examples, understanding and responding to language, understanding and responding to visual cues, decision making, problem-solving, and others, including combinations of the above.
According to the consulting firm McKinsey, AI comprises three primary segments: (1) Machine Learning, (2) Robotics, and (3) Artificial Neural Networks (ANNs). Each segment consists of multiple subsegments.
Key AI Definitions
Machine learning involves designing and improving algorithms that allow computers to analyze large amounts of structured and unstructured data to make predictions and recognize patterns in the data sets.
Machine learning comprises supervised learning, unsupervised learning, and reinforcement learning.
Pattern recognition is a branch of supervised and unsupervised machine learning that analyzes data sets to uncover categorical patterns.
Robotics is a branch of AI technology concerned with developing and training robots to interact with people in predictable ways. Robotics is now combining efforts with deep learning to train robots to manipulate situations and act with varied self-awareness.
Robotics comprises five fields: soft robotics, swarm robotics, touch robotics, serpentine robotics, and humanoid robotics.
Artificial neural networks (ANNs) are an area in AI that leverages networking capabilities to facilitate machine learning and deep learning. ANNs can extract significant insights from data sets too complex for humans to decipher.
Deep learning is a segment of ANNs that enables AI to continuously learn and improve its ability to make predictions and decisions with layers of hidden neural networks.
Computer vision enables computers to capture, identify, and process images to understand the image and its context and extract any relevant meaning.
Natural language processing (NLP) is a subfield of AI that teaches computers to interpret, contextualize, and understand human language through speech and text.
Open-source platforms (software frameworks) are software frameworks or platforms for developing software applications. They provide libraries and functionalities to reduce time regarding software delivery and propel innovation.
A chatbot is an artificial intelligence-powered application that uses machine learning and NLP to converse with humans to solve problems and answer queries.
Natural Language Processing: A Deeper look
Natural Language Processing (NLP) is a sub-branch of AI technology enabling computers to understand, interpret, and manipulate human language. Computers use NLP to process human speech and text inputs. Though NLP has been around since the 1950s, significant advancements in deep learning in combination with NLP allow computers to mimic neuronal activity in the human brain. The result is continuous learning from examples and experiences.
In combination with AI, NLP has the potential to offer solutions across a wide net of sectors and industries as it fills the gap between human and computer communication.
Key NLP Definitions
Speech to text enables NLP-assisted AI to turn speech (spoken language) into text so it can be further processed. It is also referred to as speech recognition.
Text to speech enables NLP-assisted AI to convert text into spoken word. It is also referred to as speech synthesis.
Text processing derives meaningful insights from extracted text segments.
Natural Language Generation (NLG) combines automated text processing with big data. As a result, it requires less human intervention.
A Chatbot with NLP can understand speech and converse with humans to solve problems and answer questions. Chatbots learn to respond to and provide answers to specific queries by utilizing training models.
Machine Translation automatically translates text in one language into another language. Coupled with ANNs and complex statistical models, machine translation is continuously improving its ability to make more contextual and colloquial translations.
AI and NLP are integral components of future technologies like Web 3.0
Web 3.0 facilitates AI and makes it easier to execute NLP providing many benefits to users and businesses. With the common thread of intelligence, search results will be faster and more accurate. As web 3.0 takes form, the list of uses and benefits grows larger.
Email applications and writing applications use NLP to make corrections and predictions as you type. Applications like Gmail, Grammarly, and others, will tell a user to remove a comma here, highlight a misspelled word there, suggest how to complete this line in an email, and more.
Most of us have some familiarity with chatbots integrated into websites. Declarative chatbots are less evolved than their more advanced counterparts, predictive and prescriptive chatbots or virtual assistants. Declarative chatbots utilize NLP with simple machine learning, whereas virtual assistants use NLP with robust machine learning, enabling them to provide predictive responses to queries. These virtual assistants can begin to offer prescriptive responses based on contextualization through continuous learning. Prescriptive, contextualized communication is where the lines blur, and AI starts to become more and more human.
One area of AI-enabled NLP that is soaring in popularity is market intelligence. Through text extraction and sentiments analysis, AI-enabled NLP applications provide useful trends from unstructured data. User and customer behaviors provide trends that businesses use to make better decisions through robust data analysis. The trends also illuminate new opportunities. Long gone are the days of simply forecasting historical data.
Artificial Intelligence is an essential piece of what Web 3.0 is becoming. As Web 3.0 continues to take shape, more and more examples of AI-enabled technology will play a part. Accuracy and speed benefits will permeate much of the newest web iteration; all predicated on a foundation of intelligence.
- 2019 Chatbot Statistics All The Data You Need | Intellectyx2019 State of Service research report – Salesforce Blog
- AI investment by country – survey | Deloitte Insights
- AI report from McKinsey Global Institute: AI to spur economic growthArtificial Intelligence Market Size & Share Report, 2020-2027Artificial Intelligence: Privacy and Legal Issues – CPO MagazineASGARD – human Venture Capital for Artificial Intelligence
- What is Artificial Intelligence (AI)? | IBM