Virtual AI Assistants at the Example of Microsoft M365 Copilot

Advances in virtual AI assistants are closely linked to ChatGPT and other LLMs from US-based startup OpenAI. Microsoft has forged a partnership with OpenAI to bring the latest advances in AI to their products and services. Microsoft has announced these “Copilots” across major applications, including M365 and the Power Platform.

Here are some capabilities of these Copilots within M365:

• In PowerPoint, Copilot allows users to create presentations based on a given context, such as a Word document, for example by stating “Create a 10-slide product presentation based on the following product documentation.“
• In Word, Copilot can adjust the tone of writing a text or transform a few keywords into a complete paragraph. Simply type something like “Create a proposal for a 3-month contract for customer XYZ based on doc ADF.”
• In Excel, Copilot helps users with analyzing datasets, as well as with creating or modifying them. For example, it can summarize a dataset in natural langue and describe trends.
• Let’s not forget Outlook! Your new AI Copilot helps you organize your emails and calendar. It assists you in crafting email responses, scheduling meetings, and even provides summaries of key points from the ones you missed.

If you want to learn more about Copilot in M365, this youtube video provides an excellent overview. However, these are merely a handful of examples: Microsoft 365 Copilot Explained: How Microsoft Just Changed the Future of Work. The potential of AI copilots extends far beyond the scope of Office applications and can elevate any software or service to a new level. No wonder, large software companies like SAP, and Adobe, have announced plans to upgrade their products with copilot features.

Components of a Modern Virtual AI Assistant áka AI Copilot

By now you should have some idea of what modern virtual AI assistants are. Next, let’s look at the technical components that need to come together.

The illustration below displays the main components of an LLM-powered virtual AI assistant:

• A – Conversational UI for providing the user with a chat experience
• B – LLMs such as GPT-3.5 or GPT-4
• C – Knowledge store for grounding your bot in enterprise data and dynamically providing few-shot examples.
• D – Conversation logic for intent recognition and tracking conversations.
• E – Application API as an interface to trigger and perform application functionality.
• F – Cache for maintaining an instant mapping between often encountered user intents and structured LLM responses.

Let’s look at these components in more detail.

A) Conversational Application Frontend

Incorporating virtual AI assistants into a software application or digital service often involves the use of a conversational user interface, typically embodied in a chat window that showcases previous interactions. The seamless integration of this interface as an intrinsic part of the application is vital.

A lot of applications employ a standard chatbot methodology, where the virtual AI assistant provides feedback to users in natural language or other forms of content within the chat window. Yet, a more dynamic and efficacious approach is to merge natural language feedback with alterations in the traditional user interface (UI). This dual approach not only enhances user engagement but also improves the overall user experience.

Microsoft’s M365 Copilot is a prime example of this approach. Instead of simply feeding responses back to the user in the chat window, the virtual assistant also manipulates elements in the traditional UI based on user input. It may highlight options, auto-fill data, or direct the user’s attention to certain parts of the screen. This combination of dynamic UI manipulation and natural language processing creates a more interactive and intuitive user experience, guiding the user toward their goal in a more efficient and engaging way.

When designing the UI for a virtual AI assistant, there are several key considerations. Firstly, the interface should be intuitive, ensuring users can easily navigate and understand how to interact with the AI. Secondly, the AI should provide feedback in a timely manner, so the user isn’t left waiting for a response. Thirdly, the system should be designed to handle errors gracefully, providing helpful error messages and suggestions when things don’t go as planned. Finally, the AI should keep the human in the loop and assist him in using AI in a safe way.

Also: Building “Chat with your Data” Apps using Embeddings, ChatGPT, and Cosmos DB for Mongo DB vCore

B) Large Language Model

At the interface between users and assistant sits the large language mode. It translates users’ requests and questions into code, actions, and responses that are shown to the user. Here, we are talking about foundational models like GPT-3.5-Turbo or GPT-4. In addition, if you are working with extensive content, you may use an embedding LLM that converts text or images into mathematical vectors as part of your knowledge store. An example, of such an embedding model, is ada-text-embeddings-002.

It’s important to understand that the user is not directly interacting with the LLM. Instead, you may want to put some control logic between the user and the LLM that steers the conversation. This logic can enrich prompts with additional data from the knowledge store or an online search API such as Google or Bing. This process of injecting data into a prompt depending on the user input is known as Retrieval Augmented Generation.

Typical tasks performed by the LLM:

• Generating natural language responses based on the user’s query and the retrieved data from the knowledge store.
• Recognizing and classifying user intent.
• Generating code snippets (or API requests) that can be executed by the application or the user to achieve a desired outcome in your application.
• Converting content into embeddings to retrieve relevant information from a vector-based knowledge store.
• Generating summaries, paraphrases, translations, or explanations of the retrieved data or the generated responses.
• Generating suggestions, recommendations, or feedback for the user to improve their experience or achieve their goals.

Let’s dive into the “Knowledge Store” and why it’s vital. You might think feeding a huge prompt explaining app logic to your LLM, like ChatGPT, would work, but that’s not the case. As of June 2023, LLMs have context limits. For instance, GPT-3 can handle up to 4k tokens, roughly three pages of text. This limitation isn’t just for input, but output too. Hence, cramming everything into one prompt isn’t efficient or quick.

Instead, pair your LLM with a knowledge store, like a vector database (more on this in our article on Vector Databases). Essentially, this is your system’s information storage, which efficiently retrieves data. Whichever storage you use, a search algorithm is crucial to fetch items based on user input. For vector databases, the typical way of doing this is by using similarity search.

Token Limitations

Curious about GPT models’ token limits? Here’s a quick breakdown:

• GPT-3.5-Turbo Model (4,000 tokens): About 7-8 DIN A4 pages
• GPT-4 Standard Model (8,000 tokens): Around 14-16 DIN A4 pages
• GPT-3.5-Turbo-16K Model (16,000 tokens): Approximately 28-32 DIN A4 pages
• GPT-4-32K Model (32,000 tokens): Estimated at 56-64 DIN A4 pages

F) Cache

Implementing a cache into your virtual AI assistant can significantly boost performance and decrease response times. Particularly useful for frequently recurring user intents, caching stores the outcomes of these intents for quicker access in future instances. However, a well-designed cache shouldn’t directly store specific inputs as there is too much variety in the human language. Instead, caching could be woven into the application’s logic in the mid-layers of your OpenAI prompt flow.

This strategy ensures frequently repeated intents are handled more swiftly, enhancing user experience. It’s critical to remember that cache integration is application-specific, and thoughtful design is vital to avoid unintentionally inducing inefficiencies.

While a well-implemented cache can speed up responses, it also introduces additional complexity. Effective cache management is crucial for avoiding resource drains, requiring strategies for data storage duration, updates, and purging.

The exact impact and efficiency of this caching strategy will depend on your application specifics, including the distribution and repetition of user intents. In the upcoming articles, we’ll explore this topic further, discussing efficient cache integration in AI assistant systems.

In this article, we delved into the fascinating world of virtual AI assistants, powered by LLMs. We started by exploring how the advanced language understanding of LLMs is revolutionizing software design, easing the workload of developers, and reshaping user experiences with technology.

Next, we provided an overview of the key architectural components of a modern virtual AI assistant: the Conversational Application Frontend, Large Language Model, Knowledge Store, and Conversation Control Logic. We also introduced the concept of an Application API and the novel idea of a Cache for storing and quickly retrieving common user intents. Each component was discussed in the context of their roles and how they work together to create a seamless, interactive, and efficient user experience.

We then discussed architecture considerations, emphasizing the necessity of maintaining user control while leveraging the power of AI automation. We talked about the judicious use of different GPT models based on task requirements, the advantages of starting with simple implementations and progressively scaling up, and the benefits of user feedback in continuously refining the system.

This journey of ‘AI in Software Applications’, from concept to reality, isn’t just about innovation. It’s about unlocking ‘Innovative Business Models with AI’ and boosting user engagement and productivity. As we continue to ride the wave of ‘Natural Language Processing for Software Automation’, the opportunities for harnessing the power of virtual AI assistants are endless. Stay tuned as we explore the workflows further in the next article.

I believe we have all tested ChatGPT, and probably like me, you have been impressed by its remarkable capabilities. However, ChatGPT has a significant limitation: it can only answer questions and perform tasks based on the public knowledge base it was trained on.

Imagine having a chatbot based on ChatGPT that communicates effectively and truly understands the nuances of your business, sector, or even a particular topic of interest. That’s the power of a custom ChatGPT. A tailor-made chatbot allows for specialized conversations, providing the needed information and drawing from a unique database you’ve developed.

This becomes particularly beneficial in industries with specific terminologies or when you have a large database of knowledge that you want to make easily accessible and interactive. A custom ChatGPT, with its personalized and relevant responses, ensures a better user experience, effectively saving time and increasing productivity.

Let’s delve into how to build such a solution. Spoiler it does not work by putting all the content into the prompt. But there is a great alternative.

Understanding the Building Blocks of Custom ChatGPT with Retrieval Augmented Generation

The foundational technology behind ChatGPT is OpenAI’s Generative Pre-trained Transformer models (GPT). These models understand language by predicting the next word in a sentence and are trained on a diverse range of internet text. However, the GPT models, such as the GPT-3.5, have a limitation of processing 4096 tokens at a time. A token in this context is a chunk of text which can be as small as one character or as long as one word. For example, the phrase “ChatGPT is great” is four tokens long.

Another challenge with Foundation Models such as ChatGPT is that they are trained on large-scale datasets that were available at the time of their training. This means they are not aware of any data created after their training period. Also, because they’re trained on broad, general-domain datasets, they may be less effective for tasks requiring domain-specific knowledge.

How Retrieval Augmented Generation (RAG) Helps

Retrieval-Augmented Generation (RAG) is a method that combines the strength of transformer models with external knowledge to augment their understanding and applicability. Here’s a brief explanation:

To address this, RAG retrieves relevant information from an external data source and uses this information to augment the input to the foundation model. This can make the model’s responses more informed and relevant.

Data Sources

The external data can come from various sources like databases, document repositories, or APIs. To make this data compatible with the RAG approach, both the data and user queries are converted into numerical representations (embeddings) using language models.

Data Preparation as Embeddings

The embeddings, which are essentially vectors, need to be stored in a database that’s efficient at storing and searching through these high-dimensional data. This is where Azure’s Cosmos Mongo DB comes into play. It’s a vector search database specifically designed for this task.

To circumvent the token limitation and make your extensive data available to ChatGPT, we turn the data into embeddings. These are mathematical representations of your data, converting words, sentences, or documents into vectors. The advantage of using embeddings is that they capture the semantic meaning of the text, going beyond keywords to understand the context. In essence, similar information will have similar vectors, allowing us to cluster related information together and separate them from a semantically different text.

Storing the Data in Vector Databases

The embeddings, which are essentially vectors, need to be stored in a database that’s efficient at storing and searching through these high-dimensional data. This is where Azure’s Cosmos Mongo DB comes into play. It’s a vector search database specifically designed for this task.

Matching Queries to Knowledge

The RAG model compares the embeddings of user queries with those in the knowledge base to identify relevant information. The user’s original query is then augmented with context from similar documents in the knowledge base.

Input to the Foundation Model

This augmented input is sent to the foundation model, enhancing its understanding and response quality.

Updates

Importantly, the knowledge base and associated embeddings can be updated asynchronously, ensuring that the model remains up-to-date even as new information is added to the data sources.

In sum, RAG extends the utility of foundation models by incorporating external, up-to-date, domain-specific knowledge into their understanding and output.

By incorporating these components, you’ll be creating a robust custom ChatGPT that not only understands the user’s queries but also has access to your own information, giving it the ability to respond with precision and relevance.

Ready to dive into the technicalities? Stay tuned!

Building the Custom “Chat with Your Data” App in Python

Now that we’ve discussed the theory behind building a custom ChatGPT and seen some exciting real-world applications, it’s time to put our knowledge into action! In this practical segment of our guide, we’re going to demonstrate how you can build a custom ChatGPT solution using Python.

Our project will involve storing a sample PDF document in Cosmos Mongo DB and developing a chatbot capable of answering questions based on the content of this document. This practical exercise will guide you through the entire process, including turning your PDF content into embeddings, storing these embeddings in the Cosmos Mongo DB, and finally integrating it all with ChatGPT to build an interactive chatbot.

If you’re new to Python, don’t worry, we’ll be breaking down the code and explaining each step in a straightforward manner. Let’s roll up our sleeves, fire up our Python environments, and get coding! Stay tuned as we embark on this exciting hands-on journey into the world of custom chatbots.

The code is available on the GitHub repository.

First, you must understand that you will need a database to store the embeddings. It does not necessarily have to be a vector database. Still, this type of database will make your solution more performant and robust, particularly when you want to store large amounts of data.

Azure Cosmos DB for MongoDB vCore is the first MongoDB-compatible offering to feature Vector Search. With this feature, you can store, index, and query high-dimensional vector data directly in Azure Cosmos DB for MongoDB vCore, eliminating the need for data transfer to alternative platforms for vector similarity search capabilities. Here are the steps to set it up:

1. Choose Your Azure Cosmos DB Architecture: Azure Cosmos DB for MongoDB provides two types of architectures, RU-based and vCore-based. Each has its strengths and is best suited for certain types of applications. Choose the one that best fits your needs. If you’re looking to lift and shift existing MongoDB apps and run them as-is on a fully supported managed service, the vCore-based option could be the perfect fit.
2. Configure Your Vector Search: Once your database architecture is set up, you can integrate your AI-based applications, including those using OpenAI embeddings, with your data already stored in Cosmos DB.
3. Build and Deploy Your AI Application: With the Vector Search set up, you can now build your AI application that takes advantage of this feature. You can create a Go app using Azure Cosmos DB for MongoDB or deploy Azure Cosmos DB for MongoDB vCore using a Bicep template as suggested next steps.

Azure Cosmos DB for MongoDB vCore’s Vector Search feature is a game-changer for AI application development. It enables you to unlock new insights from your data, leading to more accurate and powerful applications.

Cosmos DB for Mongo DB Usage Models

Regarding Cosmos DB for Mongo DB, there are two options to choose from: Request Unit (RU) Database Account and vCore Cluster. Each option follows a different pricing model to suit diverse needs.

The Request Unit (RU) Database Account operates on a pay-per-use basis. With this model, you are billed based on the number of requests and the level of provisioned throughput consumed by your workload.

As of 27th Mai 2023, the brand new vector search function is only available for the vCore Cluster option, which is why we will use this setup for this tutorial. The vCore Cluster offers a reserved managed instance. Under this option, you are charged a fixed amount on a monthly basis, providing more predictable costs for your usage.

Once you have created your vCore instance, you must collect your connection string and make it available to your Python script. You can do this either by storing it in Azure Key Vault (which I would recommend) or by storing it locally on your computer or in the code (which I would not recommend for obvious security reasons).

Custom ChatGPT boosts efficiency, personalizes services, and improves experiences across industries. Here are some examples:

• Customer Support: Companies can use ChatGPT for 24/7 customer service. With data from manuals, FAQs, and support docs, it delivers fast, accurate answers, enhancing customer satisfaction and lessening staff workload.
• Healthcare: ChatGPT can respond to patient questions using medical texts and care guidelines. It offers data on symptoms, treatments, side effects, and preventive care, helping both healthcare providers and patients.
• Legal Sector: Law firms can use ChatGPT with legal texts, court decisions, and case studies for answering legal questions, offering case references, or explaining legal terms.
• Financial Services: Banks can use ChatGPT to extend their customer service and give customers advice based on their individual financial situation.
• E-Learning: Schools and e-learning platforms can use ChatGPT to tutor students. Using textbooks, notes, and research papers, it helps students understand complex topics, solve problems, or guide them through a course.

In short, any sector needing a large information database for queries or services can use custom ChatGPT. It enhances engagement and efficiency by offering personalized experiences.

Examples of How OpenAI GPT Reduces Digital Friction

With OpenAI GPT models, we can expect to see a future where we no longer have to search for information, but rather, it will be readily available to us through natural language conversations with our devices.

By leveraging the power of artificial intelligence, large generative language models are capable of reducing any friction between products and services, enabling users to access information quickly and effortlessly. The key is their ability to understand and reason over natural language.

• Users can communicate with GPT models using natural language and express their intent.
• GPT can go through large amounts of data and return an aggregated result

As we continue to see advancements in artificial intelligence, I believe we are on the cusp of a new era of technology that will redefine how we interact with the world around us. With OpenAI GPT, we can expect to see a future where accessing information is no longer a chore, but rather a seamless and intuitive experience. In the following, we will discuss four examples.

Also: 9 Powerful Applications of OpenAI’s ChatGPT and Davinci for Your Business

Customer Service ChatBots

Before OpenAI, chatbots were often limited in their ability to understand and respond to customer inquiries. They relied on keyword matching and pre-programmed responses, which could lead to frustration for customers who weren’t getting the help they needed. With OpenAI, chatbots can now use natural language processing to understand the context of a customer’s inquiry and provide more accurate and helpful responses. This reduces friction by speeding up the process of resolving customer issues and increasing customer satisfaction.

Businesses can benefit from OpenAI-powered chatbots by reducing the workload of customer service agents. With chatbots able to handle routine inquiries, agents can focus on more complex tasks that require human expertise. This results in a more efficient use of resources, allowing businesses to provide better service to their customers while maximizing their operational efficiency.

E-commerce Product Recommendations

In the past, product recommendations were often based on simple algorithms that looked at a customer’s browsing history or purchase history. However, these recommendations were often too simplistic and didn’t take into account a customer’s preferences or interests. With OpenAI, product recommendations can now be based on more complex algorithms that take into account a wider range of data, such as customer reviews and social media activity.

Furthermore, the recent release of ChatGPT plugins has enabled it to browse the web. This means that it can now take into account information from websites and aggregate the result. This allows ChatGPT to provide even more accurate and relevant recommendations to customers. In the future, there is a possibility that generative language models like ChatGPT can present a comprehensive view rather than a biased fragment.

Language Translation Services

In the past, language translation services often relied on machine translation, which had several limitations. Machine translation typically used rule-based algorithms that had difficulty with idiomatic expressions, cultural nuances, and colloquialisms. This often led to translations that were inaccurate or awkward, causing confusion or misunderstandings. In addition, machine translation was often unable to recognize and correct errors in the original text, leading to further inaccuracies in the translation.

However, with the advent of OpenAI and its advanced neural networks, language translation services can now produce more accurate and natural translations. OpenAI’s neural networks are trained on large amounts of data, allowing them to recognize and adapt to a wider range of linguistic features, such as idioms, slang, and regional variations. This makes the translations produced by OpenAI much more accurate and natural-sounding than those produced by traditional machine translation.

Improved translation capabilities can help to make content more widely accessible to people who speak different languages. This can be particularly beneficial for businesses and organizations that operate in multiple countries or regions, as it allows them to reach a wider audience and communicate more effectively with customers or stakeholders who may speak different languages.

Virtual Assistants

In the past, virtual assistants were often limited in their ability to understand and respond to users’ requests due to their reliance on pre-programmed responses. With OpenAI, virtual assistants can now use natural language processing to better understand and respond to users’ requests, reducing the friction of having to repeat requests or navigate through a confusing user interface. This can improve the user experience and increase engagement with the product or service.

With the increasing capabilities of large language models like OpenAI’s GPT, we are likely to see the development of digital assistants that can help us with a wide range of day-to-day tasks. These assistants could use natural language processing to understand our requests and preferences, and then use machine learning algorithms to generate personalized recommendations and solutions.

For example, a digital assistant based on GPT could help us to organize our schedules, book appointments, make travel arrangements, and even order groceries or meals. By reducing the friction associated with these tasks, these digital assistants could help us to save time and increase our overall productivity and efficiency.

Also: ChatGPT Style Guide: Understanding Voice and Tone Prompt Options for Engaging Conversations

Customized Content Creation

Creating high-quality content can be time-consuming and challenging, especially for businesses or individuals without extensive writing experience. With OpenAI, content creation can be made easier by providing users with AI-generated content suggestions or even full articles based on their desired topic or target audience. This is another example of how OpenAI can help reduce friction and streamline tasks, ultimately making it easier for individuals and businesses to succeed in today’s digital landscape.

With OpenAI, businesses and individuals can use AI-generated content to create highly customized products and services that stand out from the competition. For example, a t-shirt company could use AI-generated designs to create truly unique and personalized shirts for their customers. This not only reduces the time and effort required for design work, but also allows for a higher level of customization and uniqueness in the final product. This can help businesses differentiate themselves in crowded markets and appeal to customers looking for something truly one-of-a-kind.

Summary

The advancements in OpenAI technology will significantly impact the digital landscape by reducing friction between users and products/services. From customer service chatbots and e-commerce product recommendations to language translation services and content creation, OpenAI has provided new and innovative solutions to improve user experience and efficiency. And these are just a few examples. We are just beginning to understand the possibilities of OpenAI GPT to reduce friction in the digital world, and much more innovations can be expected in the coming years.

Things are already moving very fast. With the recent release of OpenAI’s ChatGPT plugins, users can now enjoy the benefits of AI-generated content and web browsing, further expanding the capabilities of digital assistants. As OpenAI continues to develop and refine its technology, we can expect to see even more exciting applications and use cases emerge, ultimately shaping the future of digital interactions.

The coming month will be exciting as we can expect to see the first wave of OpenAI use cases being implemented. So what can we expect? In the current economic situation, cost reduction is top of mind for many decision-makers. Business efforts will, therefore, likely focus on improving and optimizing the existing business processes rather than exploring new applications.

One of the critical reasons for this approach is that organizations need to gain experience with generative models to lay the foundation for working on more innovative solutions in the future. While traditional machine learning techniques are valuable for many applications, they have limitations when it comes to implementing OpenAI’s generative language models.

OpenAI’s GPT models are a form of general-purpose AI that uses a different approach to machine learning. Implementing these models requires new skills and knowledge. For example, prompt design and rethinking existing processes are crucial for effectively using OpenAI’s capabilities. This means that organizations need to invest in training and develop new expertise to leverage the new technology fully.

Before delving into use cases, it’s important to note that the categories were deliberately chosen to be generic and relevant across various industries. Additionally, to make the use cases as practical as possible, we’ve included examples for prompts and responses. For simplicity, this article doesn’t differentiate between various GPT-3 models but rather focuses on the use cases for ChatGPT and Davinci, which have similar capabilities. The primary distinction between the two models is that ChatGPT keeps track of conversation history, while Davinci treats each request separately.

Today’s fast-paced business environment confronts organizations with an overwhelming amount of text and documents to process. From customer requests to contracts and product information, employees often spend countless hours sifting through information to understand and extract relevant insights. The inability to efficiently process text-based data can cause delays in critical business processes, leading to reduced productivity and increased costs.

OpenAI’s text summarization capabilities are a game-changer for businesses that need to process large amounts of text. By summarizing dense documents into a few key points or even a single title, businesses can increase process efficiency and improve decision-making.

Text summarization is helpful in various areas. For example, customer service, where customer inquiries via email can be numerous and time-consuming to address. In many cases, only a part of a conversation is relevant to the business, and employees spend a lot of time sifting through customer emails and chat messages. By using OpenAI, customer service representatives can quickly summarize the content of customer emails and identify the key issues that need to be addressed. This form of summarization can lay the foundation for delegating tasks to teams and individuals effectively.

Prompt
Summarize the following text into three sentences, each with a maximum of 20 words: https://en.wikipedia.org/wiki/Reinforcement_learning

Response
Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Reinforcement learning is different from supervised learning as there is no labelled input/output pair. It can be applied to various real-world scenarios such as game playing, robotics, recommendation systems, and even in autonomous driving

The modern work environment is characterized by work sharing and specialization. However, this often leads to tasks landing on someone’s desk who may not be the best person to handle them, leading to delays in completing the task. This is where GPT models can be particularly useful. These models can classify text into categories, allowing organizations to automate the routing of incoming messages to the appropriate person or department. Prediction accuracy will benefit when the models are fine-tuned for a particular domain or industry.

Implementing GPT models for classification can yield significant benefits for organizations, enabling them to save time and resources while ensuring that messages and tasks are directed to the right person or department. For instance, organizations can use generative AI for email triage and prioritization by categorizing incoming messages and assigning them to specialized service teams, such as contract inquiries, relocations, claim-related issues, and general complaints.

In addition, sentiment analysis can be employed to gauge customer satisfaction or dissatisfaction in social media statements. Organizations can start with a semi-automated process, where they use OpenAI to assign labels to text, and a human then makes the final decision on who should handle the task. This can lead to increased efficiency and accuracy, as well as reduced response times and improved customer satisfaction.

Prompt
Classify the following news article into 1 of the following categories: categories: [Business, Tech, Politics, Sport, Entertainment] news article: Donna Steffensen Is Cooking Up a New Kind of Perfection. The Internet’s most beloved cooking guru has a buzzy new book and a fresh new perspective: Classified category:

Response
Entertainment

Another challenge that many businesses face when processing natural language is incomplete information. For example, companies receive customer inquiries and orders via email or telephone. Customers may submit requests via email, social media, or telephone, and while it is convenient for them to state their issue in natural language, the messages may lack the necessary information to process their request further. This may include missing product or service information when the customer states his order, address changes where the new address is missing, or inquiries with missing order or ticket numbers. In these cases, a person must first reach out to the customer to collect the missing information and then again check if the information has been completed.

OpenAI can help to identify missing information and streamline information processing. For example, when a customer submits an order request via email, social media, or telephone, OpenAI can analyze the message and identify any missing product or service information, address changes, or order and ticket numbers. This can help customer service teams quickly address the issue and fulfill the request, ultimately improving customer satisfaction and loyalty.

If the AI identifies missing information, it can directly reach out to the customer to request it, enhancing the process efficiency even further. Additionally, combining OpenAI’s capabilities with text classification can streamline the process by automatically categorizing and prioritizing requests.

Example of GPT-3 Based Completeness of Information Checks

Personalized communication is key for businesses to achieve customer experience and satisfaction because it demonstrates that the company values each customer as an individual with unique needs and preferences. By customizing communication, businesses can provide targeted messages and recommendations that are relevant to each customer, making them feel seen and understood. This can lead to increased engagement, loyalty, and improved customer satisfaction. However, creating personalized communication usually takes a lot of time and resources. This is where GPT models can step in.

ChatGPT offers businesses the opportunity to enhance customer satisfaction, engagement, and sales through personalized communication. The model can generate individualized email campaigns, social media posts, and website/app experiences, creating a tailored and unique interaction for each customer.

As businesses begin to implement personalized communication, it may be beneficial to conduct a test phase during which humans review generated messages to ensure accuracy and relevance. Additionally, businesses can provide ChatGPT with additional information on customers, such as past purchases or preferences, to personalize messages further and increase the effectiveness of the communication strategy.

Example of a GPT Prompt for Personalized Communication

ChatGPT can help businesses to identify differences and similarities between two documents. This feature can be particularly useful in situations where businesses need to ensure the authenticity of important documents or when multiple collaborators work on the same document. For instance, a law firm can use ChatGPT to compare two versions of a contract and highlight any discrepancies or unusual changes. Additionally, ChatGPT can group similar documents together and identify instances of plagiarism, redundant information, or conflicting statements, which can save time and improve accuracy.

Areas, where document comparison with GPT models is particularly useful, include underwriting, purchasing, and contract management, especially in legal and contractual matters such as comparing versions of contracts, identifying unauthorized changes or unusual deviations from the norm, and ensuring compliance with regulations.

For example, in the insurance industry, underwriters can use ChatGPT to compare policy documents and ensure that there are no inconsistencies or errors in coverage. Purchasers can use ChatGPT to compare supplier contracts and ensure that prices, terms, and conditions are consistent across multiple agreements. In contract management, ChatGPT can help businesses track changes made to a contract over time and ensure that all parties are adhering to the agreed-upon terms. By utilizing ChatGPT for document comparison, businesses can maintain document integrity and improve their overall efficiency.

Example of a Prompt for Document Comparison

OpenAI’s advanced machine learning models, such as the GPT series, have proven to be powerful tools for analyzing complex information. These can be either structured or unstructured data in individual documents or a comprehensive knowledge base.

Take financial data, for example. Balance statements can be a nightmare to interpret manually, but with OpenAI’s natural language processing, companies can automatically pinpoint trends and crucial information, making it easier to digest and act on financial data. And that’s just the beginning. By enhancing their solutions with techniques for summarization or advanced prompt engineering, organizations can generate complete reports with tables, further simplifying the digestion of complex information.

But OpenAI’s power extends beyond finance. In IoT (Internet of Things), analyzing sensor data can be a mind-bending challenge. OpenAI can help companies make sense of this data in real-time, identifying patterns and anomalies that might have otherwise gone unnoticed. This can be especially valuable in manufacturing, where even minor sensor data variations can significantly impact product quality and efficiency.

Also: Predictive Maintenance: Predicting Machine Failure using Sensor Data with XGBoost and Python

In short, OpenAI’s ability to analyze and reason over complex information is nothing short of revolutionary. With this technology, companies can make faster, more informed decisions based on large volumes of data. As technology continues to improve and evolve, the possibilities for AI are limitless. We can expect to see organizations using AI to interpret and analyze even more complex information in the years ahead, paving the way for a brighter, smarter future.

Sample Prompt for Reasoning over Structured and Unstructured Data

OpenAI ChatGPT can also assist businesses with SQL query generation. This is especially useful for non-technical users, who may not be familiar with SQL syntax or the structure of the database. With ChatGPT, users can simply ask natural language questions about the data they are interested in, and the model can generate the appropriate SQL queries to extract the relevant information.

This has the potential to greatly expand the user base that can directly interact with a database. Additionally, more advanced solutions can provide question-answering functionality based on the data residing in data warehouses (DWH). This can help businesses quickly extract insights and make data-driven decisions without the need for extensive technical knowledge. More advanced solutions will be able to provide question-answering functionality based on the data residing in a DWH, that are often perceived as black boxes by non-tech guys. This will expand the user base that can directly interact with a database and leverage the existing information.

Sample Prompt for SQL Query Generation

Another powerful feature is ChatGPT’s language translation capacity. It can translate text in near real-time, allowing businesses to communicate with customers in their preferred language. While language translation is nothing but new, what sets ChatGPT apart is its ability to seamlessly integrate with any process by simply adding a “translate to” command to a prompt. As a result, ChatGPT has the potential to significantly lower language barriers and facilitate communication across international teams and channels.

By using ChatGPT for language translation, businesses can enhance their reach and improve customer satisfaction. ChatGPT’s language translation capabilities can be used to translate various types of content, such as customer service interactions, website content, and other communication channels. What’s even more impressive is that ChatGPT supports regional dialects like Swiss-German, making it a versatile tool for organizations operating in multilingual regions.

Example of GPT-3 Based Language Translation

Content generation is a critical aspect of modern business. It is vital in attracting and engaging customers, building brand awareness, and establishing thought leadership in a particular industry. As such, businesses invest significant resources in creating high-quality content that can capture the attention of their target audience. Such content ranges from blog posts and social media updates to product descriptions and website copy.

However, creating compelling and engaging content is not always easy. Businesses need to produce a large volume of content while ensuring that it is high-quality and relevant to their target audience. This is where OpenAI ChatGPT can assist.

ChatGPT can assist businesses with content generation in a variety of ways. For instance, it can generate unique and engaging content based on specific topics or keywords provided by the user, saving businesses time and resources. Moreover, ChatGPT can be trained to understand a business’s unique tone and voice. In this way, businesses can ensure that the generated content aligns with their brand and values.

Another way that ChatGPT can assist with content generation is by automating the content curation process. By analyzing and organizing vast amounts of data from various sources, including social media, blogs, news articles, and research papers, ChatGPT can generate curated content that is highly relevant and valuable to a business’s target audience. Some examples are product recommendations, email campaigns, or chatbot interactions that are tailored to each customer’s preferences and needs.

Example of GPT-3 Based Content Generation

This article has outlined nine business applications that are likely to be the focus of the first wave of OpenAI adoption. While some of these applications, such as sentiment analysis, are not entirely new and can be accomplished with traditional NLP models, others like content generation are groundbreaking and were not previously possible.

The hype surrounding GPT models stems from their combination of versatility and performance. Unlike traditional models, generative AI can handle a variety of tasks simultaneously without requiring extensive training data. As a result, businesses can leverage generative AI to develop new use cases, as well as upgrade their existing processes and become more agile by outsourcing some of their decision logic to generative AI.

It will be fascinating to witness the development of generative AI and how it will transform how we work and communicate in the future. The emergence of generative AI is often compared to the introduction of the iPhone, and just as the iPhone has evolved over time, so too will generative AI. If we think of GPT-3 as the third generation of the iPhone, we can only imagine what the future holds.

The idea behind content-based recommenders is to generate recommendations based on user’s preferences and tastes. These preferences revolve around past user choices, for example, the number of times a user has watched a movie, purchased an item, or clicked on a link.

Content-based filtering uses domain-specific item features to measure the similarity between items. Given the user preferences, the algorithm will recommend items similar to what the user has consumed or liked before. For movie recommendations, this content can be the genre, actors, release year, director, film length, or keywords used to describe the movies. This approach works particularly well for domains with a lot of textual metadata, such as movies and videos, books, or products.

The quality of the content-based recommendations is significantly influenced by how well the algorithm succeeds in measuring the similarity of the items. There are different techniques to calculate similarity, including Cosine Similarity, Pearson Similarity, Dot Product, and Euclidian Distance. They have in common that they use numerical characteristics of the text to calculate the distance between text vectors in an n-dimensional vector space.

It is worth denoting that these techniques can only measure word-level similarity. This means the algorithms compare the word of the item for word without considering the semantic meaning of the sentences. In some instances, this can lead to errors. For example, how similar are “now that they were sitting on a bank, he noticed she stole his heart, and he was in love” and “They are gangsters who love to steal from a large bank”? By just looking at the words, one may appear similar because the words have a good overlap.

Advantages

• Content-based filtering is good at capturing a user’s specific interests and will recommend more of the same (for example, genre, actors, directors, etc.). It will also recommend niche items if they match the user preferences, even if these items draw little attention.
• Another advantage is that the model can generate recommendations for a specific user without the knowledge of other users. This is particularly helpful if you want to generate predictions for many users.

Disadvantages

• On the other hand, there are also a couple of downsides. The feature representation of the items has to be done manually to a certain extent, and the prediction quality strongly depends on whether items are described in detail. Therefore, content-based filtering requires a lot of expertise.
• Since recommendations are based on the user’s previous interests. However, the recommendations are unlikely to go beyond that and expand to areas (e.g., genres) that are still unknown to the user. Content-based models thus tend to develop some tunnel vision, so that the model recommends more and more of the same.

In the following, we will implement a content-based movie recommender using Python and Scikit-learn. We will carry out all steps necessary to create a content-based recommender. The data comes from an IMDB dataset containing more than 40k films between 1996 and 2018. Based on the data, we define the features we want to use for recommending the movies. These features include the genre, director, main actors, plot keywords, or other metadata associated with the movies. Then we preprocess the data to extract these features and create a feature matrix. The feature matrix becomes the foundation for a similarity matrix that measures the similarity between the items based on their feature vectors. Finally, we use the similarity matrix to generate recommendations for a given item.

By the end of this Python tutorial, you will have learned how to implement a content-based recommendation system for movies using Python and Scikit-learn. This knowledge can be applied to other types of recommendations, such as articles, products, or songs.

The code is available on the GitHub repository.

We will train our movie recommender on a popular Movies Dataset (you can download it from grouplens.org). The MovieLens recommendation service collected the Dataset from 610 users between 1996 and 2018. Unpack the data into the working folder of your project.

The full Dataset contains metadata on over 45,000 movies and 26 million ratings from over 270,000 users. The Dataset contains the following files (Source of the data description: Kaggle.com):

• movies_metadata.csv: The main Movies Metadata file contains information on 45,000 movies featured in the Full MovieLens Dataset. Features include posters, backdrops, budget, revenue, release dates, languages, production countries, and companies.
• ratings_small.csv: The subset of 100,000 ratings from 700 users on 9,000 movies. Each line corresponds to a 5-star movie rating with half-star increments (0.5 – 5.0 stars).
• keywords.csv: Contains the movie plot keywords for our MovieLens movies. Available in the form of a stringified JSON Object.
• credits.csv: Consists of Cast and Crew Information for all our films. Available in the form of a stringified JSON Object.

In this tutorial, you have learned to implement a simple content-based recommender system for movie recommendations in Python. We have used several movie-specific details to calculate a similarity matrix for all movies in our dataset. Finally, we have used this model to generate recommendations for two cases:

• Films that are similar to a specific movie
• Films that are recommended based on the watchlist of a particular user.

A downside of content-based recommenders is that you cannot test their performance unless you know how users perceived the recommendations. This is because content-based recommenders can only determine which items in a dataset are similar. To understand how well the suggestions work, you must include additional data about actual user preferences.

More advanced recommenders will combine content-based recommendations with user-item interactions (e.g., collaborative filtering). Such models are called hybrid recommenders, but this is something for another article.

The first attempts with recommendation systems reach back to the 1970s. The approach was relatively simple and categorized users into groups to suggest the same content to all users in the same group. However, it was a breakthrough because, for the first time, a program could make personalized recommendations. As the importance of recommendation engines increased, so did the interest in improving their predictions.

With the rise of the internet and the rapidly growing amount of data available on the web, filtering relevant content has become increasingly important. Large tech companies such as Netflix (TV shows and movies), Amazon (products), or Facebook (user content and profiles) understood early on that they could use recommendation systems to personalize the selection of user content shown to their customers. They all face the same challenge of having massive content and only limited space to display it to their users. Therefore, it has become crucial for them to select and display only the content that matches the individual interests of their users.

Internet companies are predestined for using recommender systems, as they have massive amounts of user data available. The user data is the foundation for generating personalized recommendations on a large scale by analyzing behavior patterns among larger groups of users to tailor suggestions to the taste of individuals.

Collaborative filtering searches for behavioral patterns in interactions between a group of users and a group of items to infer the interests of individuals. The input data for collaborative Filtering is typically in the form of a user/item matrix filled with ratings (as shown below).

Patterns can exist in the user/item matrix in dependencies between users and items. Some dependencies are easy to grasp. Similar dependencies exist between items in that some movies receive high ratings from the same users. For example, assume two users, Ron and Joe, have rated movies. Ron enjoyed Batman, Indiana Jones, Star Wars, and Godzilla. Joe enjoyed the same movies as Ron, except Godzilla, which he has not yet rated. Based on the similarity between Joe and Ron, we assume that Joe would also enjoy Godzilla.

Things get more complex as latent dependencies are present in the data. Imagine Bob gave a three-star rating to five different movies. Another user, Jenny, rated the same movies as Bob but always gave four stars, which is an example of latent dependency. There is some form of dependence between the two users, and although it is not as significant as in the first example, considering latent dependencies will improve predictions.

We will train our movie recommendation model on a popular Movies Dataset (you can download it from grouplens.org). The MovieLens recommendation service collected the Dataset from 610 users between 1996 and 2018. Unpack the data into the working folder of your project.

The full Dataset contains metadata on over 45,000 movies and 26 million ratings from over 270,000 users. However, we will be working with a subset of the data “ratings_small.csv,” which contains 100,836 ratings from 700 users on 9742 movies.

The Dataset contains the following files, from which we will only use the first two (Source of the data description: Kaggle.com):

• movies_metadata.csv: The main Movies Metadata file contains information on 45,000 movies featured in the Full MovieLens Dataset. Features include posters, backdrops, budget, revenue, release dates, languages, production countries, and companies.
• ratings_small.csv: The subset of 100,000 ratings from 700 users on 9,000 movies. Each line corresponds to a single 5-star movie rating with half-star increments (0.5 – 5.0 stars).

There are several other files included that we won’t use.

Sales forecasting is a crucial business strategy that involves predicting future sales volumes for a product (for example, beer) or service. It leverages sophisticated statistical and analytical techniques, such as time series analysis or machine learning algorithms, to scrutinize historical sales data. By identifying trends and patterns within this data, businesses can make informed predictions about their future sales performance.

This strategic forecasting plays a pivotal role in business operations. It is instrumental in guiding key decisions surrounding production, inventory management, staffing, and various other operational elements. By honing in on accurate sales forecasting, businesses can strike the perfect balance – maintaining enough inventory to meet customer demand without overproducing or overstocking. This equilibrium ensures a smooth flow in the supply chain and avoids unnecessary costs tied to excess production or storage.

Furthermore, sales forecasting serves as a roadmap for business growth. It aids in identifying potential market opportunities and predicting future sales revenue. This valuable foresight enables businesses to strategically plan their expansion, ensuring resources are optimally utilized and future goals are met. With this in-depth understanding of sales forecasting, businesses can stay ahead of market trends, navigate through business challenges, and ultimately steer towards success.

ARIMA models provide an alternative approach to time series forecasting that differs significantly from machine learning methods. Working with ARIMA requires a good understanding of Stationarity and knowledge of the transformations used to make time-series data stationary. The concept of Stationarity is, therefore, first on our schedule.

Stationarity is an essential concept in stochastic processes that describes the nature of a time series. We consider a time series strictly stationary if its statistical properties do not change over time. In this case, summary statistics, such as the mean and variance, do not change over time. However, the time-series data we encounter in the real world often show a trend or significant irregular fluctuations, making them non-stationary or weakly stationary.

So why is Stationarity such an essential concept for ARIMA? If a time series is stationary, we can assume that the past values of the time series are predictive of future development. In other words, a stationary time series exhibits consistent behavior that makes it predictable. On the other hand, a non-stationary time series is characterized by a kind of random behavior that will be difficult to capture in modeling. Namely, if random movements characterized the past, there is a high probability that the future will be no different.

Fortunately, in many cases, it is possible to transform a time series that is non-stationary into a stationary form and, in this way, build better prediction models.

The first step in the ARIMA modeling approach is determining whether a time series is stationary. There are different ways to determine whether a time series is stationary:

• Plotting: We can plot the time series and visually check if it shows consistent behavior or changes over a more extended period.
• Summary statistics: We can split the time series into different periods and calculate the summary statistics, such as the variance. If these metrics are subject to significant changes, the time series is non-stationary. However, the results will also depend on the respective periods, leading to false conclusions.
• Statistic tests: There are various tests to determine the stationary of a time series, such as Kwiatkowski–Phillips–Schmidt–Shin, Augmented Dickey-Fuller, or Phillips–Perron. These tests systematically check a time series and measure the results against the null hypothesis, providing an indicator of the trustworthiness of the results.

As the name implies, ARIMA uses autoregression (AR), integration (differencing), and moving averages (MA) to fit a linear regression model to a time series.

ARIMA Parameters

The default notation for ARIMA is a model with parameters p, d, and q, whereby each parameter takes an integer value:

• d (differencing): In the case of a non-stationary time series, there is a chance to remove a trend from the data by differencing once or several times, thus bringing the data to a stationary state. The model parameter d determines the order of the differentiation. A value of d = 0 simplifies the ARIMA model to an ARMA model, lacking the integration aspect. If this is the case, we do not need to integrate the function because the time series is already stationary.
• p (order of the AR terms): The autoregressive process describes the dependent relationship between an observation and several lagged observations (lags). Predictions are then based on past data from the same time series using linear functions. p = 1 means the model uses values that lag by one period.
• q (order of the MA terms): The parameter q determines the number of lagged forecast errors in the prediction equation. In contrast to the AR process, the MA process assumes that values at a future point in time depend on the errors made by predictions at current and past points in time. This means that it is not previous events that determine the predictions but rather the previous estimation or prediction errors used to calculate the following time series value.

SARIMA

In the real world, many time series have seasonal effects. Examples are monthly retail sales figures, temperature reports, weekly airline passenger data, etc. To consider this, we can specify a seasonal range (e.g., m=12 for monthly data) and additional seasonal AR or MA components for our model that deal with seasonality. Such a model is also called a SARIMA model, and we can define it as a model(p, d, q)(P, D, Q)[m].

Auto-(S)ARIMA

When working with ARIMA, we can set the model parameters manually or use auto-ARIMA and let the model search for the optimal parameters. We do this by varying the parameters and then testing against Stationarity. With the seasonal option enabled, the process tries to identify the optimal hyperparameters for the seasonal components of the model. Auto-ARIMA works by conducting differencing tests to determine the order of differencing, d and then fitting models with parameters in defined ranges, e.g., start_p, max_p as well as start_q, max_q. If our model has a seasonal component, we can also define parameter ranges for the seasonal part of the model.

Having grasped the fundamental concepts behind ARIMA (AutoRegressive Integrated Moving Average), we’re now ready to dive into the practical aspect of crafting a sales forecasting model in Python. Utilizing ARIMA for forecasting sales data is an esteemed practice owing to the algorithm’s adeptness in modeling seasonal changes combined with long-term trends – a characteristic commonly exhibited by sales data.

In this tutorial, we’ll be employing a dataset representing the monthly beer sales across the United States from 1992 through 2018, recorded in millions of US dollars. Our objective is to construct a robust time series model using ARIMA to accurately predict future sales trends.

When it comes to the technological aspect, we’ll be using the Python-based ‘statsmodels’ and ‘pmdarima’ libraries to build our ARIMA sales forecasting model. So, if you’re ready to harness the power of Python and ARIMA for sales prediction, let’s get started!

The code is available on the GitHub repository.

Prerequisites

Before we start coding, ensure you have set up your Python 3 environment and required packages. If you don’t have an environment, you can follow this tutorial to set up the Anaconda environment.

Also, make sure you install all required packages. In this tutorial, we will be working with the following standard packages: 

• pandas
• NumPy
• matplotlib

In addition, we will be using the statsmodels library and pmdarima.

You can install packages using console commands:

• pip install <package name>
• conda install <package name> (if you are using the anaconda packet manager)

This Python tutorial has shown how to use SARIMA for sales forecasting. Sales forecasting is important for businesses because it can help them to make informed decisions about production, inventory management, and staffing, among other things. By accurately forecasting sales, businesses can ensure that they have the right amount of product available to meet customer sales, avoid overproduction and excess inventory, and plan for future growth. The use cases presented were forecasting beer sales, and we have used arima to analyze seasonal sales data.

In the first part, we have learned how ARIMA works, what Stationarity is and how to check if a time series is stationary. In the second part, we developed an ARIMA model in Python to create a forecast for US beer sales. For this purpose, we created an in-sample forecast and used Auto-tARIMA to find the optimal parameters for our sales forecasting model.

If you have any questions or suggestions, please let me know in the comments, and I will do my best to answer.