If you’ve tried general-purpose models and been underwhelmed by their performance, this article will walk you through a practical, step-by-step path to creating your own AI solution. The key lies in moving away from one-size-fits-all models and focusing on building small, specialized systems that do one thing—exceptionally well.

Section 1: The Limitations of Off-the-Shelf LLMs

Large language models are powerful, but they’re not a silver bullet. In many scenarios, particularly those that require real-time responses, fine-grained customization, or precise domain knowledge, general-purpose LLMs struggle. They can be:

• Incredibly slow, making real-time applications impractical.
• Insanely expensive, with API costs quickly ballooning as usage scales.
• Highly unpredictable, generating inconsistent or irrelevant results.
• Difficult to customize, offering limited control over the model’s internal workings or outputs.

For example, attempts to convert Figma designs into React code using GPT-3 or GPT-4 yielded disappointing outcomes—slow, inaccurate, and unreliable code generation. Even with GPT-4 Vision and image-based prompts, results were erratic and far from production-ready.

This inefficiency opens the door to a better alternative: building your own specialized model.

Section 2: Rethinking the Problem—From Giant Models to Micro-Solutions

The initial instinct for many developers is to solve complex problems with equally complex AI systems. One model, many inputs, and a magical output—that’s the dream. But in practice, trying to train a massive model to handle everything (like turning Figma designs into fully styled code) is fraught with challenges:

• High cost of training on large datasets
• Slow iteration cycles due to long training times
• Data scarcity for niche or domain-specific tasks
• Complexity of gathering labeled examples at massive scales

The smarter approach is to flip the script and remove AI from the equation altogether—at first. Break the problem into discrete, manageable pieces. See how far you can get with traditional code and reserve AI for the parts where it adds the most value.

This decomposition often reveals that large swaths of the workflow can be handled by simple scripts, business logic, or rule-based systems. Then, and only then, focus your AI efforts on solving the remaining bottlenecks.

Section 3: A Real-World Use Case—Detecting Images in Figma Designs

Let’s look at one practical example: identifying images within a Figma design to properly structure and generate corresponding code. Traditional LLMs failed to deliver meaningful results when interpreting raw Figma JSON or image screenshots.

Instead of building a monolithic model, the team broke the task into smaller goals and zeroed in on just detecting image regions in a Figma layout. This narrowed focus allowed them to train a simple, efficient object detection model—the same type of model used to locate cats in pictures, now repurposed to locate grouped image regions in design files.

Object detection models take an image as input and return bounding boxes around recognized objects. In this case, those objects are clusters of vectors in Figma that function as a single image. By identifying and compressing them into a single unit, the system can more accurately generate structured code.

Section 4: Gathering and Generating Quality Data

Every successful AI model relies on one thing: great data. The quality, accuracy, and volume of training data define the performance ceiling of any machine learning system.

So how do you get enough training data for a niche use case like detecting image regions in UI designs?

Rather than hiring developers to hand-label thousands of design files, the team took inspiration from OpenAI and others who used web-scale data. They built a custom crawler using a headless browser, which loaded real websites, ran JavaScript to find images, and extracted their bounding boxes.

This approach not only automated the collection of high-quality examples but also scaled rapidly. The data was:

• Public and freely available
• Programmatically gathered and labeled
• Manually verified for accuracy, using visual tools to correct errors

This attention to data integrity is essential. Even the smartest model will fail if trained on poor or inconsistent data. That’s why quality assurance—automated and manual—is as important as the training process itself.

Section 5: Using the Right Tools—Vertex AI and Beyond

Training your own model doesn’t mean reinventing the wheel. Thanks to modern platforms, many of the previously complex steps in ML development are now streamlined and accessible.

In this case, Google Vertex AI was the tool of choice. It offered:

• A visual, no-code interface for model training
• Built-in support for object detection tasks
• Dataset management and quality tools
• Easy deployment and inference options

Developers uploaded the labeled image data, selected the object detection model type, and let Vertex AI handle the rest—from training to evaluation. This low-friction process allowed them to focus on the problem, not the infrastructure.

Section 6: Benefits of a Specialized Model

Once trained, the custom model delivered outcomes that dramatically outpaced the generic LLMs in every critical dimension:

• Over 1,000x faster responses compared to GPT-4
• Dramatically lower costs due to lightweight inference
• Increased reliability with predictable, testable outputs
• Greater control over how and when AI is applied
• Tailored customization for specific UI design conventions

Instead of relying on probabilistic, generalist systems, this model became a deterministic, focused tool—optimized for one purpose and delivering outstanding results.

Section 7: When (and Why) You Should Build Your Own Model

If you’re considering whether to build your own AI model, here’s when it makes the most sense:

• Your task is narrow and repetitive, such as object classification, detection, or data transformation.
• Off-the-shelf models are underperforming in speed, accuracy, or cost.
• You need full control over your model’s behavior, architecture, and outputs.
• Your data is unique or proprietary, and not well-represented in public models.

That said, the journey begins with experimentation. Try existing APIs first. If they work, great—you can move fast. If they don’t, you’ll know exactly where to focus your AI training efforts.

The key takeaway is that AI isn’t monolithic. You don’t need a billion-dollar data center or a team of PhDs to train a model. In fact, a lean, focused, and clever implementation can yield results that beat the biggest names in the industry—for your specific needs.

Conclusion: The New Era of AI is Small, Smart, and Specialized

The myth that training your own AI model is difficult, expensive, and inaccessible is rapidly being debunked. As this case shows, with the right mindset, smart problem decomposition, and the right tools (like Vertex AI), even developers with modest machine learning experience can build powerful, reliable, and efficient AI systems.

By focusing on solving just the parts of your problem that truly require AI, and leaning on well-understood tools and cloud platforms, you can unlock enormous value—without the overhead of giant LLMs.

This is the future of AI: not just big and general, but small, nimble, and deeply purposeful.

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Despite their growing presence in our lives, there’s still widespread confusion about what LLMs really are and how they function. Are they simply “autocomplete on steroids,” or is there something more sophisticated at play? This article breaks down the complex inner workings of large language models into clear, digestible concepts—demystifying the layers, mechanisms, and logic that drive these powerful tools.

From Autocomplete to Intelligence: The Basic Premise of LLMs

At their core, LLMs are systems that predict the next word in a sequence, given all the words that came before. If you type “The Eiffel Tower is located in,” an LLM might suggest “Paris.” This seems straightforward—but when extended to billions of sentences and nuanced language usage, it becomes much more powerful.

By learning to predict the next word, LLMs inadvertently absorb the structure of language, facts about the world, reasoning patterns, and even stylistic nuances. This simple mechanism, scaled to unprecedented levels, is what enables them to write essays, answer legal questions, or mimic different writing styles.

The core task—predicting the next word—might sound like a trivial autocomplete function. But scale it up with immense amounts of data and sophisticated architecture, and you get behavior that looks remarkably like intelligence.

Tokens: The Building Blocks of Language Understanding

Before diving deeper, it’s important to understand how LLMs perceive language. They don’t operate directly on words or letters but on tokens. Tokens are chunks of text—ranging from single characters to entire words or subwords—depending on the model and its tokenizer.

For example, the word “unhappiness” might be broken into “un,” “happi,” and “ness.” This tokenization helps models manage vocabulary size while still representing complex linguistic structures. Each token is then transformed into a numerical vector through a process called embedding—essentially translating language into math.

This math-first approach allows the model to perform operations on the abstract representation of language, opening the door to nuanced understanding and generation.

Neural Networks: Layers of Abstraction

Once tokens are converted into vectors, they are passed into a neural network, specifically a type called a Transformer. This architecture was introduced in 2017 by Google researchers in a landmark paper titled “Attention is All You Need.”

Here’s how it works at a high level:

• Each layer in the Transformer processes token vectors, capturing increasingly abstract patterns.
• Initial layers may focus on syntax (e.g., sentence structure), while deeper layers grasp semantics (e.g., meaning) and context.
• These layers use mechanisms called attention heads to weigh the importance of different tokens relative to each other.

Imagine you’re processing the sentence “The trophy wouldn’t fit in the suitcase because it was too small.” The word “it” could refer to either the suitcase or the trophy. Attention mechanisms help the model decide which interpretation makes more sense in context.

Attention Mechanism: The Heart of the Transformer

The attention mechanism is what allows Transformers to outperform older models. Instead of reading a sentence word by word in a sequence, the attention system enables the model to look at all tokens simultaneously and decide which ones are most relevant when predicting the next word.

This is like how humans process language. When you read a sentence, you don’t just consider the last word—you often consider the entire sentence, or even the paragraph, to understand what comes next.

LLMs do this with scaled dot-product attention. In simple terms, for every token, the model calculates:

• Query: What am I looking for?
• Key: What information do I have?
• Value: What should I remember?

Each token’s query is compared with every other token’s key to compute attention weights, determining how much influence each other token should have in shaping the final output.

Training the Beast: Learning from Billions of Words

Large language models are trained using self-supervised learning on massive text corpora—often including everything from books and Wikipedia to social media posts and coding repositories. They aren’t taught using labeled data like “this is a dog, this is a cat.” Instead, they learn by trying to predict masked or missing tokens in real-world text.

This training process involves:

• Tokenizing billions of sentences into manageable chunks.
• Feeding them through the model, where it predicts the next token in the sequence.
• Comparing the prediction to the actual token using a loss function.
• Adjusting internal weights through a process called backpropagation to reduce the error.

Do this billions of times, and the model starts to pick up on deep patterns in language—how ideas are expressed, how arguments are structured, and what facts commonly co-occur.

Emergent Abilities: Intelligence from Scale

One of the most fascinating aspects of LLMs is that they exhibit emergent behaviors—abilities that weren’t explicitly programmed or anticipated but appear naturally once the model reaches a certain size and training depth.

Examples of emergent abilities include:

• Translation between languages without direct training.
• Arithmetic reasoning, like solving math problems.
• Code generation, even when the model wasn’t trained specifically on code.

These capabilities arise from the sheer scale of training and the universal patterns present in human communication. The model doesn’t “understand” in a conscious way, but it becomes remarkably good at mimicking understanding by statistically modeling vast data.

Limitations and Misconceptions

Despite their capabilities, LLMs are not infallible. They can generate hallucinations—plausible-sounding but incorrect or made-up information. This happens because the model doesn’t “know” facts; it generates outputs based on patterns in training data.

Moreover:

• They lack memory and awareness. LLMs don’t have a persistent sense of identity or memory across conversations unless specifically engineered to simulate it.
• They are sensitive to input phrasing. Slight changes in wording can lead to drastically different responses.
• They don’t reason like humans. Their “reasoning” is the byproduct of pattern recognition, not logical deduction or critical thinking.

Understanding these limitations is critical for using LLMs responsibly and setting realistic expectations.

Reinforcement Learning from Human Feedback (RLHF)

To make models more useful and less prone to generating harmful or irrelevant content, a technique called Reinforcement Learning from Human Feedback (RLHF) is often used.

Here’s how it works:

• After pretraining, the model is fine-tuned using example prompts and human preferences.
• Humans rank different responses, which helps train a reward model.
• This reward model is then used to further train the language model through reinforcement learning.

RLHF helps align the model’s behavior with human values and expectations—improving tone, helpfulness, and appropriateness.

Conclusion: A New Paradigm of Human-Machine Interaction

Large language models represent a seismic shift in how machines process language, knowledge, and logic. They’re not merely tools—they’re platforms for human expression, exploration, and collaboration. While they don’t possess consciousness, sentience, or intent, they simulate language-based intelligence in a way that’s proving transformative across industries.

Understanding how they work—tokens, transformers, attention mechanisms, training processes, and their limits—helps demystify the “magic” and puts the power back in human hands.

As we move forward, the challenge is not just to build bigger models, but to make them safer, more efficient, and better aligned with our goals as a society. In doing so, we unlock not just better machines—but new dimensions of human potential.

The post How large language models actually work: Unpacking the intelligence behind AI appeared first on RoboticsBiz.

As we delve into the intricate workings of retrieval-augmented generation, this blog post will unravel the key concepts, explore their applications across various industries, and illuminate its potential for revolutionizing content creation, education, customer support, and technical writing.

Join us on a journey to unlock the power of retrieval-augmented generation, where information retrieval meets generative prowess, ushering in a new era of language understanding and expression.

Understanding Retrieval-Augmented Generation

Standing at the intersection of two dominant paradigms in NLP—retrieval-based models and generative models—RAG retrieval augmented generation is transforming how AI models generate text.

Retrieval-based models excel at fetching relevant information from a predefined knowledge base, while generative models are adept at creating coherent and contextually relevant text. By combining these two approaches, researchers have unlocked a new language understanding and generation level.

How It Works

At its core, RAG involves integrating a retrieval mechanism into a generative model. The retrieval component sifts through a knowledge base to extract relevant information, which is then used to augment the generative model’s output. This dual-action approach enables the model to leverage existing information while generating contextually rich and coherent responses.

The marriage of retrieval and generation enhances the model’s factual accuracy and allows it to capture nuances and context in a way that traditional generative models struggle to achieve. This breakthrough has far-reaching implications across various domains.

Applications Across Industries

Content Creation and Copywriting

The content creation market is predicted to register a remarkable CAGR of 12.4% from 2023 to 2033, underlining the increasing importance and adoption of innovative technologies like RAG in reshaping how content is generated, curated, and delivered. By tapping into an expansive repository of information, RAG becomes an indispensable assistant for writers, helping develop well-informed and captivating pieces.

Its significance becomes particularly pronounced when navigating the intricacies of complex or niche topics, where a profound understanding is crucial. The amalgamation of retrieval-based capabilities and generative prowess equips writers with the tools to craft informative and compelling content, opening new dimensions in the art of communication and storytelling.

Educational Assistance

RAG emerges as a potent ally for students and educators within the educational domain. RAG fosters an interactive and dynamic learning environment that transcends traditional boundaries by offering instantaneous, contextually relevant information to students and helping educators generate supplementary materials, quizzes, and explanations.

Through this innovative approach, educational experiences are enriched, promoting a symbiotic relationship between technology and learning that prepares students for the complexities of the modern world.

Customer Support and Chatbots

RAG’s prowess extends seamlessly into enhancing customer support interactions. The fusion of precise information retrieval and generative capabilities empowers RAG-driven chatbots to deliver tailored responses, ensuring accuracy and effectiveness in addressing user queries.

This transformative synergy not only streamlines customer support processes but also elevates the overall user experience by providing nuanced and informed assistance, marking a significant advancement in automated customer service.

Legal and Technical Writing

RAG emerges as a valuable asset in precision-demanding domains like legal and technical writing. It helps professionals by furnishing up-to-date information and enabling the generation of highly specific documents.

This cuts down on research time and empowers experts to concentrate on the nuanced refinement and customization of the content they produce. The integration of RAG in these sectors thus represents a pivotal stride toward efficiency and excellence in crafting documents that adhere to the exacting standards of these specialized fields.

Challenges and Considerations

While RAG holds immense promise, it is not without its challenges. Critical considerations include privacy concerns, potential biases in the underlying knowledge base, and the need for fine-tuning to specific domains.

Additionally, striking the right balance between retrieval and generation to avoid over-reliance on pre-existing information is a delicate task that researchers and developers must navigate.

The Future of Retrieval-Augmented Generation

As research in RAG advances, we can expect to see even more sophisticated models with enhanced capabilities. Fine-tuning mechanisms, improved training methodologies, and the integration of ethical considerations will be pivotal in shaping the future trajectory of this technology.

Moreover, the open nature of RAG allows for collaborative efforts and community-driven improvements. Developers and researchers worldwide can contribute to refining knowledge bases, optimizing algorithms, and addressing the ethical dimensions of this technology.

Final Words

RAG represents a paradigm shift in natural language processing, unlocking the potential to bridge the gap between information retrieval and generative text generation. Its applications across diverse industries promise to revolutionize how we interact with technology, learn, and create content.

While challenges exist, ongoing research and collaborative efforts will undoubtedly contribute to refining and expanding the capabilities of the retrieval-augmented generation, paving the way for a future where our interactions with machines are more informed, nuanced, and contextually relevant.

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With breakthroughs in computing power, cloud computing services, growth in big data, and advancements in machine learning (ML) and related processes, AI is automating functions and enabling new services worldwide. AI has the potential to transform the way governments, organizations, and individuals deliver services, access information, and plan and operate.

Machine learning is an important concept of AI that enables a system to cumulatively and automatically learn and improve from experience, generally with more data. ML enables applications such as deep learning, natural language processing (NLP), speech generation, computer vision, AI-optimised hardware, decision management (including categorization and predictive analytics), biometrics, robotic process automation (RPA), virtual agents, and more.

This post will explore the nine key AI applications of machine learning.

1. Computer vision

Computer vision aims to mimic parts of the complex human vision system, allowing computers to recognize and process objects in images and videos the same way humans do. Medical diagnostics, face recognition, automated vehicles, and a wide range of monitoring systems, including satellite monitoring of crops, livestock, environmental conditions, and CCTV, all use computer vision.

2. Natural language processing (NLP)

NLP allows computers to read text, hear and interpret speech, assess sentiment, prioritize, and link to relevant subjects and resources. The most well-known application is an automated call center that categorizes calls and routes them to pre-recorded responses. With voice assistants like Siri and Alexa, NLP has become more popular. NLP employs machine learning to improve these voice assistants and deliver personalization at scale.

3. Speech generation (synthesis)

Speech generation is the delivery of language by a computer that combines recorded elements of speech. Assistive technologies for people with various disabilities use speech generation or synthesis. Text to speech enables people with visual impairments and those who struggle with literacy and reading to read text. It has the potential to give people with speech impairments a new voice. It’s also used in translation software.

4. Biometrics

People’s physical characteristics and behaviors are called biometrics, and AI uses them to analyze them. Biometrics is based on the idea that people can be accurately identified by intrinsic physical or behavioral traits, commonly used for identification, assurance, and access control.

Biometrics can be used to support large-scale identity assurance systems, which can be extremely useful in situations where there are no other reliable identity systems. The personal nature of the data, on the other hand, necessitates effective data protection. If a system performs unevenly across groups, ethical issues can arise if it does not perform equally for all skin tones. Biometrics can also be used to help livestock and wildlife, as well as to monitor them.

5. Robotic process automation (RPA)

RPA is a technology that automates business processes. RPA tools are used to create software or a “robot” that analyses and captures a process before delivering the same transaction. RPA can be used to deliver a simple response to a specific type of email or application process, or it can be combined with other systems to create more complex systems.

RPA allows businesses to cut labor costs and errors while being less expensive and easier to implement than other AI applications. RPA can also benefit from higher-level AI applications such as natural language processing (NLP). RPA’s widespread implementation poses social risks because it tends to directly replace human functions (and even jobs). An example is the creation of appropriate business forms or guidelines based on the user’s needs and the likely outcomes that someone in their profile would require.

6. Virtual agents/chatbots

A virtual agent is a person-interactive software system used in a team, service, or information interface. Virtual agents are most commonly associated with an automated representation of a customer service representative. Virtual agents identify appropriate responses and deliver them in an informative and entertaining conversation.

Chatbots are typically designed to automate a specific set of processes for a company or government agency. While personalization of individual needs and interests improves over time with virtual assistants like Siri and Alexa, virtual agents help deliver personalization at scale, greatly reducing healthcare and financial advice costs.

7. Decision management (predictive analytics)

Automated systems that accept inputs, analyze them, and make decisions based on them are called decision management systems. These systems make decisions without human intervention, which reduces the need for concentrated human labor. It’s especially useful when dealing with complex but routine decisions, such as financial services. Big data (for example, on customer behavior) can be converted into trends by decision management systems. Decision-making, when combined with machine learning, can help businesses make better decisions.

8. AI-optimized hardware

AI-optimized hardware is a subset of microprocessors or microchips designed to make AI applications run faster. This hardware is specifically designed or adapted for AI workloads. Graphic processing units (GPUs), originally designed to manage complex gaming visuals, are among the most common AI hardware architectures. Because GPUs can be optimized for neural network operations, they have been particularly useful in speeding up training and inference.

9. Deep learning platforms

Deep learning platforms are machine learning used to solve difficult problems. The term ‘deep’ refers to the system’s structure, which includes multiple layers of machine learning processing known as neural networks. An input layer, multiple ‘hidden’ layers, and an output layer are all present. Because deep learning systems are more interconnected and sophisticated than simpler machine learning systems. They are better at dealing with unlabeled and unstructured data, such as data from multiple real-world sources like sensor systems or internet traffic. Deep learning enables complex applications such as autonomous movement, spoken language translation, price forecasting, and image-based medical diagnosis.

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A virtual voice assistant is a software agent that can interpret human speech and respond via synthesized voices. It communicates with the users in natural language. The most popular voice assistants are Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana, and Google’s Assistant, incorporated in smartphones and dedicated home speakers.

Voice assistants use technologies like voice recognition, speech synthesis, and NLP to provide services to the users. Voice recognition is the heart of a voice application and is a rapidly evolving technology that provides an alternative to keyboard typing. Voice recognition is an important component for the user as a gateway to use his or her voice as an input component. It is expected to become the default input form for smartphones and cars, and other home appliances.

What can voice assistants do?

Some key elements distinguish voice assistants from ordinary programs. First, it has NLP, an ability to understand and process human languages by filling the gaps in communication between humans and machines. Second, it can use stored information and data and use it to draw new conclusions. Third, it is powered by machine learning that allows one to adapt to new things by identifying patterns.

Voice assistants have several interesting capabilities. They allow users to ask questions, control home automation devices, media playback and manage other basic tasks like email, to-do lists, and calendars with verbal commands.

There are a wide variety of services provided by the voice-enabled devices, ranging from simple commands like providing information about the weather of a place, general information from Wikipedia, movie rating from IMDB, setting the alarm or reminder, creating a to-do list, and adding items to the shopping list so that we don’t forget when we go shopping. Depending on the device provider or user preference, it can also read books for the user or play music from any streaming service. It can also play videos from YouTube or else from any streaming service.

In a recent study, voice assistants are also being used to assist public interactions with the Government, and a decrease of 30% work-load on humans when voice assistants are used in call centers. Although each currently available voice assistant has unique features, they share some similarities and can perform the following basic tasks:

• Answer to questions asked by users.
• Send and read text messages
• Make phone calls, and send and read email messages.
• Play music from streaming music services such as Amazon, Google Play, iTunes, Pandora, Netflix, and Spotify.
• Set reminders, timers, alarms, and calendar entries
• Make lists, and do basic math calculations.
• Play games.
• Make purchases.
• Provide information about the weather.
• Control Internet-of-Things-enabled smart devices such as thermostats, lights, locks, vacuum cleaners, switches).

Limitations

While voice assistants have interesting and useful features, they also pose several unique problems. One main issue with these voice-activated devices is security. Anyone with access to a voice-activated device can ask it questions, gather information about the accounts and services associated with the device. This poses a major security risk since the devices will read out calendar contents, emails, and highly personal information.

Voice assistants are also vulnerable to several other attacks. Researchers have recently proven that voice assistants will respond to inaudible commands delivered at ultrasonic frequencies. This would allow an attacker to approach a victim, play the ultrasonic command, and the victim’s device would respond.

Privacy is another big concern for voice assistant users. By their very nature, these devices must be listening at all times to respond to users. Amazon, Apple, Google, and Microsoft insist that their devices are not recording unless users speak the command to wake the assistant. Still, there has been at least one case where a malfunctioning device was recording at all times and sending those recordings back to Google’s servers. Even if the companies developing these voice assistants are being careful and conscientious, there is a potential for data to be stolen, leaked, or used to incriminate people.

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The current interest in this technology is spurred by recent developments in artificial intelligence (AI) and machine learning. In 2016, Facebook and Microsoft started providing resources to create chatbots integrated into their respective messaging platforms – Facebook Messenger and Skype. Within a year, more than 30,000 chatbots went live on Facebook Messenger alone. This exponential growth followed in other messaging platforms, including Slack, Kik, and Viber, which have also seen a substantial increase in chatbots.

Chatbots serve a broad range of purposes, such as customer service, social and emotional support, information, and entertainment. In particular, they are seen as a promising alternative to traditional customer service. They serve as virtual assistants, helping users perform specific tasks, such as booking a taxi, ordering food for delivery, etc. In most scenarios, chatbots are the preferable means of assistance, compared to a phone call or web search, due to their convenience and immediacy.

In this article, we explore the key motivational factors for people to use chatbots. The most frequently reported motivational factor is productivity since chatbots help users obtain timely and efficient assistance or information. Beyond productivity, people also use chatbots for entertainment, social and relational purposes, and eve out of curiosity to know the novel phenomenon.

We hope to provide insight into why people choose to interact with automated agents online and help developers facilitate better human–chatbots interaction experiences in the future.

Productivity

As mentioned earlier, most people use chatbots for better productivity because using chatbots is easy, fast, and convenient while looking for assistance or information. Chatbots provides the answers users looking for, quickly and accurately. It avoids the hassle of having to place a call, wait to speak to a person, and then try to get the necessary information. It also saves time in having to look through tons of text to find answers. Chatbots are as fast as searching on the internet. The chatbots can answer basic questions and are ready 24/7 whenever the user needs a solution.

Entertainment

Many people use chatbots for positive and entertainment value, and they describe chatbots as “fun” and “entertaining.” People like chatbots that have funny things to say, and they like to ask a question and be entertained with an answer. Others use chatbots to kill time when bored, have nothing to do, or want to talk to someone.

Social and relational purposes

Social and relational purposes are the third most frequently reported reason for people to use chatbots. It is noteworthy that chatbots can enhance interactions between humans and provide potential social and relational benefits such as avoiding loneliness and fulfilling a desire for socialization. Chatbots can also improve the social experiences with other individuals, especially in a group chat, using a chatbot with a child, or to improve one’s conversational skills. They are like personal assistants, and it is almost like you are talking to a real person.

Novelty and curiosity

The fourth reason is the novelty and curiosity to explore chatbots and the limits of their abilities. Some people want to understand what’s unique about the user experience provided by this new technology and feel how natural and efficient it is than interacting with a mobile app or person as they look for answers.

Here are some of the essential strengths of chatbots for both users/customers and businesses:

• 24/7 customer service and support (anytime/anywhere)
• Direct customer contact points and one-to-one conversation on personal devices
• Time and cost-savings
• Better personalization and offers based on user preferences
• Better automation of communication
• Better data collection and high amount of personal user/usage data
• Better user experience through human-like conversation
• Efficient service provisions, security, and privacy

The post Why do people use chatbots? Four motivational factors to know! appeared first on RoboticsBiz.

They use natural language processing (NLP), a subfield of linguistics, data science, data science and artificial intelligence (AI) and machine learning (ML) concerned with the interactions between computers and human languages, to teach machines how to understand human languages and extract meaning.

NLP tools make it simple to handle tasks such as document classification, topic modeling, part-of-speech (POS) tagging, word vectors, and sentiment analysis. NLP is part of our lives for decades. In fact, we interact with NLP daily without even realizing it.

NLP technology is essential for scientific, economic, social, and cultural reasons, including understanding context and emotions from verbal and nonverbal communication that involves a nonlinguistic transmission of information through visual, auditory, tactile, and kinesthetic (physical) channels.

Below is a list of basic functions NLP is used to analyze language for its meaning.

• Text Classification (e.g. document categorization).
• Understanding how much time it takes to read a text.
• Finding words with the same meaning for search.
• Understanding how difficult it is to read is a text.
• Generating a summary of a text.
• Identifying the language of a text.
• Identifying entities (e.g., cities, people, locations) in a text.
• Finding similar documents.
• Text Generation.
• Translating a text.

This post will present a list of the most important Natural Language Processing (NLP) frameworks you need to know.

1. AllenNLP

AllenNLP is an NLP research library, built on PyTorch, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks. It makes it easy for researchers to design, evaluate, and build novel language understanding models quickly and easily. It provides a flexible data API that handles intelligent batching and padding, high-level abstractions for common operations in working with text, and a modular and extensible experiment framework that makes doing good science easy. A flexible framework for interpreting NLP models, AllenNLP is hyper-modular, lightweight, extensively tested, experiment friendly, and easy to extend. It can run reproducible experiments from a json specification with comprehensive logging.

2. Apache OpenNLP

The Apache OpenNLP library is a machine learning-based toolkit for the processing of natural language text. This open-source Java library supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, parsing, chunking, and coreference resolution. Usually, these tasks are required to build more advanced text processing services. OpenNLP also includes maximum entropy and perceptron based machine learning. The goal is to create a mature toolkit for the abovementioned tasks. An additional goal is providing a large number of pre-built models for a variety of languages, as well as the annotated text resources that those models are derived from.

3. Apache Tika

The Apache Tika is a content analysis toolkit used to parse the documents in PDF, Open Document, Excel, and many other well-known binary and text formats using a simple uniform API. It can detect and extract metadata and text from over a thousand different file types. All file types can be parsed via a single interface, making Tika useful for indexing the search engine, analyzing content, translating, and much more. There are several wrappers available for using Tika in another programming language, like Julia or Python.

4. BERT

BERT (Bidirectional Encoder Representations from Transformers) is a new method of pre-training language representations that achieves state-of-the-art results on a wide range of tasks related to NLP. BERT is conceptually simple and empirically powerful and can obtain new state-of-the-art results on eleven natural language processing tasks such as question answering and language inference, without substantial task-specific architecture modifications. It is designed to pre-train deep bidirectional representations from the unlabeled text by jointly conditioning on both left and right context in all layers.

5. Bling Fire

Bling Fire is a lightning-fast Finite State machine and Regular expression manipulation library, designed for fast-speed and quality tokenization of Natural Language text. Bling Fire Tokenizer provides state-of-the-art performance for Natural Language text tokenization. It supports four tokenization algorithms: pattern-based tokenization, WordPiece tokenization, Sentence Piece Unigram LM, and Sentence Piece BPE. Bling Fire provides a uniform interface for all four algorithms to work with, so there is no difference while using XLNET, BERT, or your own custom model tokenizer. Bling Fire API is designed to require minimal or no configuration, initialization, or additional files, and is user friendly from languages such as Python, Ruby, Rust, C #, JavaScript (via WASM), and so on.

6. ERNIE

ERNIE (Enhanced Language Representation with Informative Entities) is a continual pre-training framework for language understanding in which multi-task learning incrementally builds up and learns pre-training tasks. It can take full advantage of lexical, syntactic, and knowledge information simultaneously. Within this framework, various custom tasks can be introduced incrementally at any time. For example, the tasks, including the prediction of named entities, recognition of discourse relationships, prediction of sentence order, are leveraged to enable the models to learn representations of languages. Based on the alignments between text and KGs, ERNIE integrates entity representations in the knowledge module into the semantic module’s underlying layers.

7. FastText

FastText is a library for efficient learning of word representations and sentence classification. It is on par with state-of-the-art deep learning classifiers in terms of accuracy. It can train on more than one billion words in less than ten minutes using a standard multicore CPU and classify nearly 500K sentences among 312K classes in less than a minute. Created by Facebook Opensource, FastText is available for all.

8. FLAIR

FLAIR is a simple, unified, and easy-to-use framework for state-of-the-art NLP, developed by Zalando Research. This NLP framework is designed to facilitate the training and distribution of sequence labeling, text classification, and language models. This powerful NLP library allows you to apply NLP models to text, such as named entity recognition (NER), part-of-speech tagging (PoS), sense disambiguation, and classification. It has simple interfaces that allow you to use and combine different word and document embeddings, including our proposed Flair embeddings87, BERT embeddings, and ELMo embeddings. This Pytorch NLP framework builds directly on Pytorch, making it easy to train your own models and experiment with new approaches using Flair embeddings and classes.

9. Gensim

Gensim is a Python library for topic modeling, document indexing, and similarity retrieval with large corpora. It aims at processing raw, unstructured digital texts. The algorithms in gensim, such as Latent Semantic Analysis, Latent Dirichlet Allocation, or Random Projections, discover the semantic structure of documents, by examining word statistical co-occurrence patterns within a corpus of training documents. These algorithms are unsupervised, which means no human input is necessary – you only need a corpus of plain text documents. Once these statistical patterns are found, any plain text documents can be succinctly expressed in the new, semantic representation, and queried for topical similarity against other documents.

10. Microsoft Icecaps

Microsoft Icecaps is an open-source toolkit for building conversational neural systems. Within a flexible paradigm, Icecaps provides a range of tools from recent conversation modeling and general NLP literature that enables complex multi-task learning setups. This conversation modeling toolkit was developed on top of TensorFlow functionality to bring together these desirable characteristics. Users can build agents with induced personalities, generate diverse responses, ground those responses in external knowledge, and avoid particular phrases.

11. jiant

jiant, an open-source toolkit for conducting multi-task and transfer learning experiments on English NLU tasks. jiant enables modular and configuration-driven experimentation with state-of-the-art models and implements a broad set of tasks for probing, transfer learning, and multi-task training experiments. jiant implements over 50 NLU tasks, including all GLUE and SuperGLUE benchmark tasks. This software can be used for evaluating and analyzing natural language understanding systems. jiant allows users to run a variety of experiments using state-of-the-art models via an easy to use configuration-driven interface.

12. Neuralcoref

NeuralCoref is a pipeline extension for spaCy 2.0, which uses a neural network to annotate and solve coreference clusters. NeuralCoref is ready for production, integrated into the NLP pipeline of spaCy, and can be easily extended to new training datasets. This coreference resolution module is based on the superfast spaCy parser and uses the model of neural net scoring as described by Kevin Clark and Christopher D. Manning in Deep Reinforcement Learning for Mention-Ranking Coreference Models.

13. NLP Architect

NLP Architect is an open-source Python library to explore topologies and techniques for natural language processing and understanding of natural languages. It is meant to be a platform for future collaboration and research. Key features include Core NLP models that are useful in many NLP applications, Novel NLU models featuring novel topologies and techniques, optimized NLP / NLU models featuring various optimization algorithms on neural NLP / NLU models, model-oriented design, and essential tools for working with NLP models – pre-processing text/string, IO, data manipulation, metrics, embedding.

14. NLTK (Natural Language Toolkit)

NLTK is a leading platform for creating Python programs that work with data in the human language. It provides easy-to-use interfaces for more than 50 corpora and lexical resources, along with a suite of text processing libraries for tokenization, classification, stemming, tagging, parsing, and semance reasoning, wrappers for NLP libraries of industrial strength. NLTK operates on all Python-supported platforms, including Windows, OS X, Linux, and Unix.

15. Pattern

Pattern is a web mining module for the Python programming language. It bundles tools for data retrieval (Google, Twitter, Wikipedia API, web spider, HTML DOM parser), text analysis (rule-based shallow parser, WordNet interface, syntactical + semantical n-gram search algorithm, tf-idf + cosine similarity + LSA metrics) and data visualization (graph networks).

16. Rant

Rant is an all-purpose, procedural text engine that includes a variety of features to handle everything from the most basic tasks of string generation to advanced dialog generation, code templating, and automatic formatting. The features include recursive, weighted branching with multiple selection modes, queryable dictionaries, automatic capitalization, rhyming, indefinite English articles, and verbalization of multi-lingual numbers, printing to multiple separate outputs, modifier probability for pattern elements, loops, conditional statements, and subroutines, fully-functional object model, etc.

17. SpaCy

SpaCy is a free, open-source, advanced natural language processing ( NLP) library in Python. It is specifically designed for use in production and helps you build applications that process and “understand” large volumes of text. It can be used to construct systems for information extraction or natural language understanding or to pre-process text for deep learning. The features include non-destructive tokenization, named entity recognition, support for 26+ languages, 13 statistical models for eight languages, pre-trained word vectors, easy deep learning integration, part-of-speech tagging, labeled dependency parsing, syntax-driven sentence segmentation, built-in visualizers for syntax and NER, convenient string-to-hash mapping, etc.

18. Stanford CoreNLP

The Stanford CoreNLP Natural Language Processing Toolkit is an extensible pipeline that provides core natural language analysis. It can give the basic forms of words, their parts of speech, whether they are names of companies, individuals, etc., normalize dates, times and numerical quantities, mark the sentence structure in terms of syntactic dependencies and phrases, indicate which noun phrases refer to the same entities, indicate feelings, extract particular or open-class relationships between entities, obtain quotes. This integrated NLP toolkit with a broad range of grammatical analysis tools is a fast, robust annotator for arbitrary texts, used in production. It has a modern, regularly updated package, with the overall highest quality text analytics.

19. Texar-PyTorch

Texar-PyTorch is a toolkit that aims to support a wide range of machine learning tasks, in particular the processing of natural languages and the generation of text. Texar offers an easy-to-use library of ML modules and functionalities to compose whatever models and algorithms. The tool is designed for fast prototyping and experimentation, for both researchers and practitioners. Texar-PyTorch integrates many of TensorFlow’s best features into PyTorch, delivering superior to native PyTorch modules that are highly usable and customizable. Texar-PyTorch combines many useful functions and features TensorFlow and PyTorch in. It is highly customizable, providing a different level of abstraction API to facilitate rich novice and experienced users.

20. TextBlob: Simplified Text Processing

TextBlob is a Python library for processing textual data. It provides a simple API for diving into common NLP tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, tokenization (splitting text into words and sentences), detection powered by Google Translate, word and phrase frequencies, parsing, n-grams, word inflection (pluralization and singularization) and lemmatization, WordNet integration, etc.

21. Thinc

Thinc is a lightweight deep learning library powering spaCy. It offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow, and MXNet. It features a battle-tested linear model designed for large sparse learning problems and a flexible neural network model under development for spaCy v2.0. You can use Thinc as an interface layer, standalone toolkit, or a flexible way to develop new models. It is designed to be easy to install, efficient for CPU usage, and optimized for NLP and deep learning with text – in particular, hierarchically structured input and variable-length sequences. Thinc is a practical toolkit for implementing models that follow the “Embed, encode, attend, predict” architecture.

22. Transformers

Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) is a novel architecture that aims to solve sequence-to-sequence tasks while handling long-range dependencies with ease. It provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet) for NLU and NLG with over 32+ pre-trained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch. It is as easy to use as pytorch-transformers and as powerful and concise as Keras. Researchers can share trained models instead of always retraining, reducing compute time, and production costs.

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It transforms how we interact, consume information, and purchase goods and services. Healthcare is no exception!

The impact of AI in healthcare through natural language processing (NLP) and machine learning (ML) is transforming care delivery and patient journey in ways never before possible.

Its application has a vital role in enhancing patient engagement, quality, and access to care in healthcare. It improves provider and clinician productivity, accelerates the speed at which new pharmaceutical treatments can be developed while reducing the cost, and personalizes medical treatments by leveraging analytics to mine the enormous amounts of noncodified clinical data that currently exist.

Creating patient profiles

As a method to convert human language into a structured and understandable format that computers can then use to perform various computational analysis, the use of natural language processing (NLP) is particularly important in healthcare where there is still a significant amount of clinical information being documented via a variety of unstructured methods, including dictation, typing, and writing. Even though this unstructured “free text” can provide valuable information to a human who reads it, any valuable information contained within it cannot be presently analyzed and used by a computer until it has been codified and structured.

Here is where NLP comes in. It allows free text information entered into the patient record to be turned into potentially useful data that a computer can use. NLP applied in a clinical setting can convert the information from transcribed history and physical dictation into data representing the patient’s problem list, medication list, allergies, past medical and surgical history, family history, and social history. When supplemented with the use of speech-to-text applications that convert spoken words into text, NLP can be used to turn dictated speech into structured and codified information that is usable by a computer application.

Effective diagnoses, treatment, and prevention

At its core, machine learning (ML) is a branch of AI that uses algorithms to parse data, learns from it, and then makes a determination or prediction. This learning capability enables systems to act without any explicit programs involved. In healthcare, the technology can enable faster and more accurate analysis of massive quantities of health data from various sources (e.g., research and development, physicians and clinics, non-physician clinical workers, wearables, patients, etc.) and unearth insights for more effective prevention of illness and better treatment of individuals, as well as populations.

ML also has the potential to be used for a variety of health care goals, including better drug discovery and manufacturing, clinical trials and research, improved accuracy of radiology and radiotherapy diagnoses and treatments, the development of smarter electronic health record (EHR) and health information exchange (HIE) systems, and the prediction of epidemic outbreaks.

Better patient self-service

Patient self-service emphasizes the patients’ choice and convenience in rapidly and easily completing tasks such as scheduling appointments, paying bills and filling out or updating forms, and using devices such as phones, tablets, and laptops. Implementing self-service programs helps hospitals to realize benefits such as reduced cost, reduced patient waiting times, fewer errors, easier payment options, and increased patient satisfaction.

ML and NLP further increase the convenience and efficiency of patient self-service with virtual health assistants (VHAs) and chatbots that can interact with the patients and complete simple administrative tasks and medication refills at anytime and anywhere. Patient self-service can also streamline several administrative tasks like registration, appointments, payment collection, and billing, freeing staff to do higher-level work.

Reduced time and cost for drug discovery

Pharmaceutical development has historically been a long and expensive process. It has many layers to the process of drug discovery and development. The sheer volume of tests to understand the biological systems and their adverse reactions to compounds keeps the costs high and the pace slow. ML, coupled with NLP, machine vision, and image analysis is well suited to sort through thousands of pages of research results to make the process more efficient.

The AI system then draws connections between relevant data points and can narrow the number of candidate molecules by an order of magnitude. Many drug companies are using AI to study the deep chemistry of drug interactions and to probe entire biological systems to see how a drug might affect a patient’s tissues. By analyzing large amounts of data and using machine vision, AI promises to help reduce the time and cost of drug discovery by identifying candidate molecules.

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