AI Agents Market Size, Share & Trends | Industry Report 2030
These Platforms Will Make Your Bots Language-intelligent
Natural language is a fundamental element of bot technologies. As a result, there has been a direct correlation between the evolution of bot platforms and natural language processing platforms. While the evolution of bot technologies has mostly been driven by messaging platform vendors such as Facebook or WeChat, the main advancements in natural language processing technologies seem to be coming from cloud platform and service providers like Google or IBM. Consequently, most bot developers spend time integrating their front-end bot applications with natural language processing services provided by a different platform.
From a conceptual standpoint, there are two main natural language programming techniques that have become popular with bot technologies: Natural language processing (NLP) and natural language understanding (NLU). Here's a look at their basic features:
The combination of NLP and NLU technologies is becoming increasingly relevant in different software areas today, including bot technologies. While there are many vendors and platforms focused on NLP/NLU technologies, the following technologies are becoming extremely popular within the bot developer community.
Understanding NLP/NLUThe popularity of emerging technologies like bots and artificial intelligence has led to the terms "natural language processing" and "natural language understanding" being used loosely and out of context. Broadly construed, NLP/NLU technologies should include the following elements:
Natural language generation
Natural language generation (NLG) plays an important role in enabling bot technologies to generate meaningful conversations between users and systems. Conceptually, NLG systems are responsible for understanding and maintaining the context of a conversation and then producing language-rich responses as if they were generated by a human. In order to produce language-intelligent responses, NLG techniques leverage elements that simulate human behavior such as beliefs, desires, commitment, intentions, etc.
Some NLP/NLU technologies that will make your bots language-intelligentIn recent years, we have seen impressive progress in NLP/NLU technologies, particularly accelerated by the rise in popularity of technologies like bots, the internet of things (IoT) and artificial intelligence. As a result, several platforms have emerged providing sophisticated NLP/NLU capabilities. Some of the most popular NLP/NLU platforms in the market include these:
Watson Conversation Service
The Watson Developer Cloud provides several services focused on language processing. IBM's Watson Conversation Service (WCS) is specially focused on automating interactions between systems and end users. Utilizing WCS, users can define NLP aspects such as intents and entities, and simulate entire conversations. WCS is typically used in conjunction with other Watson NLP services such as AlchemyLanguage or Natural Language Classifier.
Microsoft's Language Understanding Intelligence Service
Microsoft's Language Understanding Intelligence Service (LUIS) is a component of the Microsoft Cognitive Services (MCS) focused on creating and processing natural language models. LUIS provides a sophisticated toolkit that allows developers to train the platform in new conversation models. LUIS can also be used in conjunction with other text processing APIs in MCS such as linguistic analysis and text analytics. The platform provides a deep integration with the Microsoft Bot Framework and can be used by other bot platforms.
Google Natural Language API
Google Natural Language (NL) API is a recent addition to Google Cloud focused on NLP and NLU capabilities. The NL API enables capabilities such as intent-entity detection, sentiment analysis, content classification and relationship graphs. The NL API also includes sophisticated tooling for training and authoring new NL models. The Google NL platform is actively used by several high-profile services, such as Google Assistant.
Wit.Ai
Wit.Ai is the platform behind the NLP/NLU capabilities of the Facebook Messenger platform. Facebook acquired Wit.Ai in January 2015 and, since then, has rolled out major updates to the platform. One of the best capabilities of Wit.Ai is the sophisticated toolkit that can be used to train the platform in new conversation models as well as monitor the interactions between users and the platform.
Api.Ai
Api.Ai provides a platform that allows developers to design and implement conversational interfaces that can be integrated into external applications like bots. Functionally, Api.Ai includes capabilities such as speech recognition, fulfillment and NLU, as well as a robust management toolkit. Api.Ai provides integration with several bot platforms and is particularly popular within the Slack community.
Alexa Skills Kit
Amazon Alexa can be considered one of the simplest language processing technologies when compared with the other platforms listed in this article. However, the volume of users leveraging Alexa Services on a daily basis also makes it one of the most popular NLP engines in the market. Functionally, the Alexa Skills Kit enables the definition of intents and entities relevant in conversational interactions. One of the greatest advantages of Alexa is its integration with other Amazon Web Services offerings like AWS Lambda.
Recast.AI
Recast.AI is a platform for implementing bot solutions with sophisticated NLP/NLU capabilities. The platform provides developer-friendly interfaces to determine intent and entities in natural language sentences. Additionally, Recast.AI includes a robust toolkit for training and improving NLP models based on user interactions.
Pat
Pat is a newcomer to the NLP/NLU platform market focused on humanizing human-machine interactions. Functionally, Pat deviates from traditional statistical NLP models and focuses on leveraging neural network algorithms to correctly assign meaning to words in a sentence. As a result, the Pat platform is able to correctly analyze extremely complex natural language interactions.
It's just getting startedRegardless of recent developments in NLP/NLU technologies, we are still in the very early stages of the market. In the next few years, we can expect to see new language intelligence techniques that will streamline the conversational models between humans and systems. Bot technologies have been the immediate benefactors of the advancements in NLP/NLU platforms. As NLP/NLU platforms become smarter and more robust, bots will be able to leverage conversations as a new form of user interface for modern technology solutions.
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Unlock the world of AI by exploring key terms and concepts to better understand artificial intelligence, machine learning and their subfields.
The GistWith all the recent buzz about artificial intelligence, it's tough to keep up with all the terms that are used to define AI, and even more challenging to understand how AI works. Here are some key terms related to AI, machine learning, natural language processing (NLP), natural language understanding (NLU), as well as subfields like generative AI, conversational AI, ethical AI and explainable AI.
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AI DefinitionsActivation Function: Activation functions are important because they help neural networks learn complex things better. They transform simple outputs into more complicated ones.
Artificial Intelligence (AI): Artificial Intelligence is a subdomain of computer science focused on developing systems or machines that exhibit humanlike intelligence in tasks such as problem-solving, learning, reasoning, perception and natural language understanding.
Artificial Neural Network: Inspired by the biological neural networks found in animal brains, artificial neural networks (ANN) serve as the basis for numerous machine learning and deep learning models. They consist of interconnected nodes, or neurons, arranged into layers to create complex computing systems.
Attention Mechanism: Attention mechanisms improve neural networks by focusing on important input elements, which enhances performance in tasks such as machine translation, text summarization and image captioning.
Backpropagation: Backpropagation is a crucial algorithm for training neural networks, especially deep learning models, by adjusting weights to minimize the error between actual and predicted outputs. It computes the loss function gradient (a mathematical concept that is used to optimize the performance of neural networks) for each weight using the chain rule and iterates backward through layers for optimization.
Bag of Words (BoW): A document is represented as an unordered word set, ignoring grammar and word order but retaining frequency information.
Bias: For neural networks, bias is an extra parameter that shifts the activation function along the input axis. In the broader AI context, bias refers to systematic errors in model predictions stemming from prejudices or assumptions in the training data.
Convolutional Neural Networks (CNNs): A CNN is a deep learning model for processing gridlike data such as images using convolutional layers with filters to detect spatial hierarchies and recognize patterns at various scales.
Cross-Entropy Loss: A loss function often employed in classification tasks for assessing the difference between predicted and true probabilities.
Conversational AI: Conversational AI involves technologies that allow computers to engage in humanlike conversations, using NLP, NLU and natural language generation (NLG) techniques. Common applications that use conversational AI include chatbots, voice assistants or other applications enabling natural language-based human-machine interactions.
Clustering: An unsupervised machine learning method that clusters data points by similarity without relying on pre-existing labels. Popular algorithms include K-means, hierarchical clustering and density-based clustering algorithm (DBSCAN).
DALL-E: An AI model developed by OpenAI, which combines the capabilities of generative models and large language models to create images from text descriptions. DALL-E is based on a variant of the GPT-3 architecture, with modifications that enable it to generate images instead of text.
Deep Learning: Deep learning is a subfield of machine learning that emphasizes multilayered artificial neural networks that learn intricate patterns from large datasets, advancing applications like image recognition, speech recognition and natural language processing.
Dimensionality Reduction: Dimensionality reduction techniques minimize dataset features while maintaining essential information, enhancing computational efficiency and addressing the curse of dimensionality.
Ethical AI: The practice of designing artificial intelligence systems that adhere to ethical principles and values, including considerations of fairness, accountability, transparency and the impact of AI on society.
Feature Engineering: Feature engineering involves creating or modifying input variables to enhance machine learning model performance, including tasks such as scaling, normalization and encoding categorical variables, often requiring domain knowledge for selecting relevant and informative features.
Feature Selection: Identifying and selecting crucial features from a dataset for machine learning model building, reducing overfitting, enhancing performance and minimizing computational complexity. Feature selection techniques include filter, wrapper and embedded methods.
Generative Adversarial Networks (GANs): A deep-learning model that includes two neural networks: a generator that creates fake data and a discriminator that distinguishes between real and fake data. The networks compete with each other in a gamelike way, with the generator trying to create more realistic data to fool the discriminator.
Generative AI: A class of machine learning models that can generate new data samples (i.E. A conversation, an answer to a question, or an image) resembling the training data. These models, such as generative adversarial networks have been used in various applications, including image synthesis, text generation, data augmentation and chatbots.
Gradient Descent: An optimization algorithm that minimizes a function, often used in training machine learning models to reduce the loss function. It iteratively adjusts model parameters following the negative gradient, converging to a local minimum.
Hyperparameters: User-defined model parameters, such as learning rate, batch size and neural network hidden layers, not learned from data. Hyperparameter tuning seeks optimal values for a specific model and dataset.
Image-Text Pairs: These consist of images and their related textual descriptions or labels, used to train AI models like image captioning systems or visual question-answering models, enabling them to grasp the connection between visual and textual data.
Input Token: Units of meaning in text for training AI models or NLP tasks, can be words, phrases, or characters. They are processed by encoders in Seq2Seq models or other NLP architectures to capture sequence structure and meaning.
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View allLarge Language Model: AI models that often use deep learning, aimed at understanding and generating humanlike text. Trained on extensive text data sets, they capture complex language patterns. Examples include OpenAI's GPT series (GPT-3, ChatGPT-4), Microsoft Bing and Google Bard.
Loss Function: A function that measures the difference between the predicted output of a machine learning model and the actual output or target. The training aims to minimize the loss function, achieved using optimization algorithms such as gradient descent or stochastic (randomly determined) gradient descent.
Output Token: Single units of meaning in the text generated by sequence-to-sequence models or other NLP tasks. They can be words, phrases or characters, and a Seq2Seq model's decoder creates them one by one to form the complete output sequence.
Overfitting: Overfitting occurs when a machine learning model learns the training data too well, including noise and random changes, rather than just the main pattern. This causes the model to perform poorly on new data.
Reinforcement Learning (RL): This is when a computer program learns to make decisions by interacting with a virtual environment. It gets feedback as rewards or penalties and tries to get the highest total reward over time. It's used in areas like video games, robots and recommendation systems.
Sequence-to-Sequence Models (Seq2Seq): Deep learning structures used to convert an input series of data into an output series. They have an encoder to process the input and a decoder to create the output.
Soft Weights: Soft weights refer to how within neural networks, there are probabilities assigned to elements when calculating attention scores. They help the model pay attention to multiple inputs instead of just one, making the attention process smoother and more adaptable.
Stable Diffusion: An open-source deep learning model released in 2022, developed by Stability AI in collaboration with academic researchers. This model is mainly used for generating detailed images from text descriptions, but it can also be applied to other tasks including inpainting, outpainting and image-to-image translations as guided by a text prompt. The current version of Stable Diffusion is called Dream Studio.
Supervised Learning: Supervised learning is a machine learning approach where models are trained using labeled data and learn to map inputs to outputs by minimizing the difference between predicted and actual targets. It includes common tasks such as classification and regression.
Tokenization: Tokenization refers to the process of breaking text into tokens, which are the smallest units of meaning. These tokens can be words, subwords, phrases or characters, depending on the level of granularity chosen. Tokenization is a crucial preprocessing step for many natural language processing tasks.
Tokens: As mentioned above, tokens are the smallest units of meaning in a large language model. Tokens serve as the input for AI models that process text, such as large language models, sequence-to-sequence models and classifiers.
Unsupervised Learning: A machine learning approach where models are trained using only input data, without corresponding target outputs. The goal of unsupervised learning is to discover patterns, structures, or relationships within the data on its own, without any prior knowledge or guidance.
Value Vector: The attention mechanism in neural networks includes value vectors that store information about input elements. These vectors combine with attention scores to create a context vector, which is used to generate the output. The attention scores determine the importance of input elements, allowing the model to focus on the most relevant information.
Weight: Parameters in a neural network that influence neuron connections, and are adjusted during training to optimize output generation that closely matches the target data distribution.
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AI Articles for a Deeper DiveNow that we understand the terms that are used to define the inner workings of AI applications, if you'd like a deeper dive into some of the specific aspects of AI, and specifically how they can or will affect marketing, advertising, content creation and SEO, as well as issues such as AI's impact on privacy, legal ramifications and regulations, here are some articles to whet your appetite.
Quick AI Links for Additional InformationHere are some quick links to some of the most popular AI organizations and generative AI models:
How Natural Language Programming And Conversational AI Are Taking On ...
This article will look at how NLP and conversational AI are being used to improve and enhance the call center operations.
Natural language processing (NLP) and conversational AI are often used together with machine learning, natural language understanding (NLU) to create sophisticated applications that enable machines to communicate with human beings. This article will look at how NLP and conversational AI are being used to improve and enhance the Call Center.
NLP is a technological process that facilitates the ability to convert text or speech into encoded, structured information. By using NLP and NLU, machines are able to understand human speech and can respond appropriately, which, in turn, enables humans to interact with them using conversational, natural speech patterns.
Predictive Forecasting Isn't Just for WeatherPredictive algorithmic forecasting is a method of AI-based estimation in which statistical algorithms are provided with historical data in order to predict what is likely to happen in the future. The more data that goes into the algorithmic model, the more the model is able to learn about the scenario, and over time, the predictions course correct automatically and become more and more accurate.
Workforce management software that utilizes AI is able to analyze huge amounts of historical volume data and recommend the best forecasting algorithm that will result in more accurate forecasts. For instance, Amazon's AutoML functionality creates a predictor which trains the optimal model for a brand's datasets. Additionally, Amazon Forecast includes six built-in algorithms which can be selected, but ultimately, using Forecast to train the model for the most optimum algorithm produces the best results.
A 2019 paper by ResearchGate on predicting call center performance with machine learning indicated that one of the most commonly used and powerful machine learning algorithms for predictive forecasting is Gradient Boosted Decision Trees (GBDT). Gradient boosting works through the creation of weak prediction models sequentially in which each model attempts to predict the errors left over from the previous model. GBDT, more specifically, is an iterative algorithm that works by training a new regression tree for every iteration, which minimizes the residual that has been made by the previous iteration. The predictions that come from each new iteration are then the sum of the predictions made by the previous one, along with the prediction of the residual that was made by the newly trained regression tree (from the new iteration). Although it sounds (and is) complicated, it is this methodology that has been used to win the majority of the recent predictive analytics competitions.
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Customers and Agents Work Better TogetherPuneet Mehta, founder and CEO of Netomi, an omnichannel AI-based customer service solution provider, shared that conversational AI agents are being used within call centers to reduce costs, enhance agent productivity and improve the customer experience. "By sitting alongside human agents within platforms like Zendesk, Salesforce, Gladly, Freshworks, etc., virtual agents act as the first line of defense when a ticket comes in," explained Mehta. "Netomi's AI agents take the best course of action based on the specific ticket. Highly repeatable tickets like order status or refunds can be automatically resolved without human intervention."
NLP and AI are able to provide detailed information to agents who handle more complex queries, and even to suggest the most appropriate agent for each scenario. "For more complex questions, the AI gathers information from the customer and back-end systems and drafts a response for an agent to quickly review and send," said Mehta. "For the most complex queries, AI agents summarize and route tickets to the right agent based on experience, bandwidth, sentiment, or other specific business rules."
By using natural language understanding (NLU), conversational AI bots are able to gain a better understanding of each customer's interactions and goals, which means that customers are taken care of more quickly and efficiently. "NLU-powered AI agents are making a significant impact on support teams. Netomi's NLU automatically resolved 87% of chat tickets for WestJet, deflecting tens of thousands of calls during the period of increased volume at the onset of COVID-19 travel restrictions," said Mehta.
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NLP & NLU Enable Customers to Solve Problems in Their Own WordsThe use of AI-based Interactive voice response (IVR) systems, NLP, and NLU enable customers to solve problems using their own words. Today's IVR systems are vastly different from the clunky, "if you want to know our hours of operation, press 1" systems of yesterday. Jared Stern, founder and CEO of Uplift Legal Funding, shared his thoughts on the IVR systems that are being used in the call center today.
"NLP has revolutionized IVR systems and has made routing very effective. Conversational IVR enhances customer experience as it is easier than the traditional methods. NLP can also be used for data analysis," said Stern. "Based on customer interaction, content that will push them to an advanced stage in the sales funnel can be identified. Call centers can use NLP for speech-to-text applications. Generic data like name and address can be collected quickly. Agent data processing can be reduced, and security can be increased."
NLU converts unstructured text and speech into structured data which allows the AI to more precisely understand intent and context. NLU is able to achieve this through the combination of three different technologies: Syntactic analysis applies rules that are specific to sentence structure, i.E. Syntax, to determine part of the meaning of what's being said; semantic analysis looks at the relationship between words in order to understand meaning; pragmatic analysis determines the context of sentences to more fully understand intent.
"Natural language understanding enables customers to speak naturally, as they would with a human, and semantics look at the context of what a person is saying. For instance, 'Buy me an apple' means something different from a mobile phone store, a grocery store and a trading platform. Combining NLU with semantics looks at the content of a conversation within the right context to think and act as a human agent would," suggested Mehta.
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Conversational Intelligence Facilitates Smarter AIRaj Gupta, chief engineering officer at Cogito, an AI coaching system provider, thinks that with customer and employee expectations so high, and call center complexity increasing exponentially, emerging technologies such as conversational intelligence and NLP have become vitally important. "Conversational intelligence combines forms of artificial intelligence (AI), including machine learning (ML) and NLP technology," said Gupta. "It is used to create and train algorithms to deduce intent and emotional sentiment from customer speech or text. This analysis can then provide customer support to human agents to improve interactions and customer experiences to quickly and efficiently resolve customer needs and issues, improve satisfaction, and even simplify coaching and onboarding agents."
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View allConversational intelligence is typically focused on human-to-human and human-to-machine speech, which makes it perfect for customer support channels, call centers, and chatbots. "Yet, the actual value of conversational intelligence and NLP comes when it reveals the sentiment and intent behind customer interactions to help augment a human agent versus a chatbot, as consumers overwhelmingly prefer to interact with people today," suggested Gupta. "Chatbots in call centers are limited to using these tools for highly repetitive tasks in well-defined, closed interactions. Augmented intelligence has far more possibilities by focusing on human-aware technologies for machine collaboration with human control."
Final ThoughtsAI technologies such as natural language programming, along with natural language understanding, machine learning, and natural language generation, allows machines and their associated applications to have conversations with humans in a manner that is natural — either through text or speech. By using algorithmic forecasting and conversational intelligence, AI technologies enable customers and agents to work together more effectively and efficiently, improving and enhancing the call center experience for both customers and employees.
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