Conversational AI is a highly lucrative technology for enterprises, helping businesses prevent customer attrition, enhance experience, and become more profitable.   

While AI-powered chatbots and virtual assistants are the most popular forms of conversational AI, many other use cases exist across industries.    

Discover business applications, benefits, and strategies for implementing Conversational AI within an organization, peer-reviewed by the global academic community.   

Conversational AI for Business Success: How to Amplify Artificial Intelligence 

On January 4, Pavlo Yalovol, Intetics VP of Innovation, presented the scientific paper ‘Conversational AI for Business Success: How to Amplify Artificial Intelligence’ written by Boris Kontsevoi, Intetics CEO and President, at the virtual stage of the eighth edition of International Conference on Information System Design and Intelligent Applications (ISDIA). 

Read also: AI-Powered Coding Assistants—Friend or Foe for Developers? Pavlo Yalovol, Intetics Vice President of Innovation for ITID Lviv 

The ISDIA 2024 conference brought together researchers, scientists, engineers, students, and industry practitioners to exchange theories, methodologies, new ideas, experiences, products, and applications in all areas of intelligent computing methodologies. Participants explored how AI, ML, and other innovative technologies enhance and safeguard our interconnected world.  

Get a glimpse of the key presentation insights, and feel free to access the full paper by reaching out. 

AI in Action: Advancements, Capabilities, and Transformative Benefits 

Conversational AI and virtual voice assistants with natural language processing (NLP) and machine learning under the hood have emerged to simplify daily routines by handling tedious, time-consuming, or complex tasks. NLP deciphers human language, while ML enables learning from data, making these technologies proficient in human-like interactions.  

The adoption of AI experienced a 10% surge from 2021 to 2023, attributed to the proactive efforts of IT professionals. Projections indicate a substantial annual increase of 38% in the global AI market value from 2022 to 2030.   

Recent research by McKinsey highlights the financial advantages of incorporating conversational AI, positioning companies with a competitive edge, and driving heightened investments in AI development. 

Conversational AI’s Potential across Industries 

AI-driven chatbots and virtual assistants enable seamless conversations, automate mundane tasks, and gather valuable customer insights across Healthcare, Finance, E-commerce, Education, and other industries:

Featured Case Study: AI-Powered Virtual Nurse Helps Over 100,000 Patients Worldwide to Monitor Their Health Daily 

Advancements in NLP: Models & Algorithms 

Recurrent neural networks for generating word sequences enable tasks like text prediction, handwriting recognition, and speech recognition. Explore the newer NLP models, which can answer intricate research questions and process information across multiple languages. 

Different Types of NLP Models 

There are four key neural network architectures:  

• Feedforward Neural Networks (FNNs): Process data unidirectionally from input to output. Suited for tasks without sequential/temporal dependencies. Applied in classification and regression.  
• Convolutional Neural Networks (CNNs): Ideal for grid-structured data like images. Use convolutional layers to detect patterns. CNNs are used in image recognition and object detection.  
• Generative Adversarial Networks (GANs): Composed of a generator and discriminator network. The generator creates data; the discriminator differentiates from accurate data. GANs are employed in image generation and data augmentation.  
• Recurrent Neural Networks (RNNs): Designed for sequential data processing. Feedback loops allow the learning of patterns in sequences. RNNs are applied in language processing and time series analysis. Challenges like vanishing gradients are addressed by LSTM and GRU variants. 

The Ethical and Reliability Challenges of AI 

Discover core challenges faced by conversational AI that businesses should be aware of: 

• Understanding Context: AI may struggle with broad contexts and subtle nuances, leading to misinterpretations.  
• Data Bias Risks: Reliance on incomplete or biased training data can lead to skewed and unfair interaction outcomes. 
• Human Oversight Necessity: Expert review is essential to mitigate risks of unexpected consequences from AI autonomy.  
• Vulnerabilities to Exploitation. AI systems can be exploited for disinformation and propaganda. 
• Consumer Trust. McKinsey survey reveals high consumer trust in AI, often surpassing human expertise. 
• Realism and Validation. AI should not be the sole decision-making tool; rigorous human testing and validation are crucial. 

NLP API Options: A Look into Leading Choices 

There are various APIs (Application Programming Interfaces), which encompass a range of functionalities, including sentiment analysis, entity recognition, text categorization, language detection, text generation, and more.  

Therefore, when selecting an API, several key factors should be taken into consideration: customization and control, complexity of use case, platform compatibility, and integration ease. Some of the prominent NLP APIs are OpenAI GPT, Google Cloud Natural Language API, Google Dialogflow, Amazon Comprehend & Lex, IBM Watson NLU & Assistant, Microsoft Azure Cognitive Services, Wit.ai, spaCy, Algorithmia, and others. 

Key Steps to Developing a Successful Conversational AI Business Model 

Formulating a successful conversational AI business model implies these crucial steps:  

• Defining the niche. Identify a specific domain where conversational AI solutions can provide value. The niche should align with the expertise of the business and cater to a genuine market need.  
• Creating a high-quality product. The product must possess natural communication, complex query understanding, accurate responses, and scalability.  
• Leveraging NLP tools. Integrating NLP enhances the AI’s ability to engage with users intuitively.  
• Integration with existing systems. Seamless integration with established business systems (CRM, ERP, help desk software, etc.) bolsters the AI’s capabilities and elevates the overall customer experience.  
• Developing a go-to-market strategy. This strategy should encompass the following elements: target audience definition, needs and preferences analysis, and marketing plan.  
• Monitoring and optimization. Utilize analytics tools to glean insights from customer inquiries and employ these insights to drive continuous improvements that enhance system performance. 

Conversational AI can enhance lives when used appropriately and aligned with user expectations. Here are a few examples and numbers:  

• Business Process Optimization. Up to 25% cost reduction and 30% productivity growth with record management, task management, auto-scheduling, email filtering, and fast onboarding and training powered by CAI.  
• Enhanced Decision-Making. Up to 95% of information accuracy thanks to easy report generation, advanced analytics, and smart recommendations.  
• Advanced Customer Interactions. Customer satisfaction increases by up to 50% with service/product recommendations, intelligent comparison, and a robust sales funnel.  

Successful implementation demands meticulous planning, experimentation, and continuous monitoring to achieve desired outcomes.  

Are you considering starting to plan and implement conversational AI technologies for your niche? Get started with advanced approaches and industry solutions now. 

More about Conversational AI Business Applications: 

• Conversational AI. The New Era of Human-like Automated Communication. 
• AI Transforming Healthcare Solutions with Conversational Intelligence in Medical Virtual Assistants 
• Chatbot Center of Excellence: Chatbots Turnkey Development 

The post Conversational AI for Business Success: Peer Reviewed Strategy to Build Profitable AI Model [Paper Presentation at ISDIA 2024] appeared first on Intetics.

Let’s break it down and see why it’s a handy tool for programmers to speed up the coding process, learn new frameworks or libraries, or explore different ways of solving problems.  

What Is GitHub Copilot? 

GitHub Copilot is an AI pair programmer providing auto-complete style recommendations as you code. It accelerates your coding process by analyzing contextual elements like variable names, surrounding code, and function signatures, generating relevant suggestions in real time.  

The concept of an “AI pair programmer” underscores its collaborative nature, emphasizing that it complements and augments your coding efforts—it cannot work without you. 

GitHub Copilot is powered by a generative AI model developed by GitHub, OpenAI, and Microsoft. 

Once installed, the coding assistant analyzes the developer’s code, providing suggestions for completing code snippets, suggesting relevant functions, and even refactoring existing code. 

Read also: ChatGPT for Software Developers: Better Code, Increased Productivity, and Premier Product Quality [With Examples of Prompts]

Installing GitHub Copilot: Step-by-Step Guide 

GitHub Copilot integrates with various popular integrated development environments (IDEs), such as Visual Studio Code, Visual Studio, Neovim, and JetBrains IDEs.  

GitHub Copilot can be accessed through personal accounts with GitHub Copilot Individual or organizational accounts with GitHub Copilot Business. GitHub Copilot is free for verified students, teachers, and maintainers of popular open-source projects. 

You can try GitHub Copilot for free with a one-time 30-day trial. After the free trial, you will need a paid subscription for continued use. Billing plans are available at GitHub Docs. 

To get started, you’ll need the following: 

• An active GitHub Copilot subscription; 
• Microsoft Visual Studio Code (or another preferred IDE); 
• The GitHub Copilot extension installed in the Visual Studio Code. 

How to Install GitHub Copilot? 

• Find the GitHub Copilot extension page in the Visual Studio Code Marketplace and click Install. 
• Click Open Visual Studio Code in the pop-up window that appeared.  
• Click Install in the “Extension: GitHub Copilot” tab in Visual Studio Code.  
  • If you have not previously authorized Visual Studio Code in your GitHub account, you will be prompted to sign into GitHub in Visual Studio Code.  
  • If you have previously authorized Visual Studio Code for your account on GitHub, GitHub Copilot will be automatically authorized.  
• If you don’t get the prompt to authorize, click the bell icon in the bottom panel of the Visual Studio Code window. 

• In your browser, GitHub will request the necessary permissions for GitHub Copilot. To approve these permissions, click Authorize Visual Studio Code.  
• To confirm the authentication, in Visual Studio Code, in the “Visual Studio Code” dialog box, click Open. 

GitHub Copilot can be customized to align with your coding style. Configure settings like indentation, line length limits, and suggestion verbosity to make Copilot an extension of your coding preferences. 

Creation of UI Elements with Prompts in GitHub Copilot 

GitHub Copilot’s capability to generate code suggestions extends to creating user interface (UI) elements. By providing clear and concise prompts, developers can elicit relevant code snippets for various UI components, such as buttons, dropdown menus, and text fields.  

Example: We need to create a tracker of monthly income and expenses.

Let’s generate a prompt for GitHub Copilot: 

<!–bootstrap grid with 12 rows and 1 column for small screens, and 6 rows and 2 columns for medium screens and above, each cell contains label of the month from January to December, and 2 bootstrap number inputs side by side with labels, 1 for income and 1 for expenses –>) 

Based on the input, GitHub Copilot starts to suggest the code to generate the desired output. 

To accept the suggested code, just press Tab, and the assistant will write the code. 

It’s crucial to understand that Copilot isn’t flawless enough to write code independently, as we highlighted earlier, its “pair programmer” nature. You are responsible for crafting the overall logic, guiding the tool with well-thought-out prompts, and overseeing the suggestions it generates. You steer the process through practical and precise instructions while the tool accelerates the code-writing process for you.  

So, after a brief review, the developer reviews and improves the proposed structure and creates the desired table faster. 

GitHub Copilot proves to be a valuable tool for frontend developers to elicit relevant code snippets for various UI components efficiently. The example illustrates the importance of reviewing Copilot’s suggestions by an experienced developer to ensure correctness and code optimization.   

While Copilot accelerates the code-writing process, user intervention remains essential for ensuring logic, accuracy, and adherence to specific requirements. Let’s focus on how to engineer prompts that guarantee Copilot’s efficient assistance. 

Getting Desired Outcomes with Prompts: Best Practices 

Once we have reviewed the capabilities to generate entire functions, classes, or blocks of code faster, let’s touch on the best practices for prompt engineering to give clear instructions to GitHub Copilot and generate desired results. 

• High-level context in comments: Provide high-level context in a comment at the top of the file and leave more detailed instructions in the form of comments and code. 
• Provide specific details: For precise code suggestions from GitHub Copilot, provide specific details. If you aim, for example, to fetch data from an API, specify the type of data, processing method, and the target API endpoint. 
• Provide examples: Apart from instructing GitHub Copilot verbally, you can also demonstrate desired actions using examples in your preferred coding style.  
• Iterate and refine: Continuously refine your code with Copilot’s suggestions. Start with its recommendations and customize them to meet your specific requirements. Regularly review and adjust the generated code to match your coding style and project needs. 
• Validate generated code: Validate Copilot-generated code for correctness, efficiency, and adherence to standards. Conduct thorough code reviews, run comprehensive tests, and make necessary adjustments to maintain codebase quality. 

In turn, GitHub is experimenting with LLMs to evolve Copilot, focusing on creating a developer-friendly AI experience that is predictable, tolerable, steerable, and verifiable to enhance overall productivity and effectiveness. All this is done to extend GitHub Copilot across the developer lifecycle. 

Other Ways to Enhance Your Coding Workflow with GitHub Copilot 

Explore additional capabilities of GitHub Copilot to refine your coding process:  

• Code refactoring: GitHub Copilot aids in refactoring by suggesting concise and efficient alternatives, identifying redundancies, simplifying complexity, and recommending improved coding patterns for cleaner, more maintainable code.  
• Error & exception handling: Copilot assists in error handling by suggesting mechanisms and blocks for exceptions, enhancing code robustness and resilience to unexpected scenarios.  
• Customization: Enhance GitHub Copilot by customizing its training with your codebases, allowing it to generate more accurate, context-aware suggestions tailored to your specific application domain.  
• Consistency across teams: GitHub Copilot is valuable for team development, aiding in maintaining coding standards by suggesting consistent styles and practices. Its suggestions are discussion starters in code reviews, fostering collaboration, and knowledge sharing among team members. 

Summing Up 

It still requires profound expertise to build software. Even with Copilot’s help, a developer should verify and understand generated code. Non-programmers can’t jump on Copilot and make whatever they want. Or is it so yet?   

Keep a keen eye on the dynamic AI assistant’s landscape—experiment with emerging options in your environment to identify the ones that align best with your requirements. Explore tools like GitHub Copilot, and stay tuned to learn more about alternatives: Bito, Amazon CodeWhisperer, Tabnine, etc.  

Join us to empower developers to be more productive at every stage of the software development lifecycle. 

The post Why GitHub Copilot Is the Ultimate Game-Changer for Software Developers appeared first on Intetics.

This data was obtained through research by Albert Mehrabian, Professor Emeritus of Psychology at the University of California, Los Angeles, and American anthropologist Ray Birdwhistell.  

That’s all clear. But you may ask: how does this apply to my business?  

It’s all about the missed opportunity. Getting the correct meaning of the emotions of your clients or partners brings many benefits. Here emotion recognition technology also referred to as Affective Computing, comes in handy. 

Emotion recognition software powered by Artificial Intelligence and Machine Learning interprets human emotions from non-verbal visual data. By leveraging those unspoken reactions, businesses can better understand their customers. As a result, they can improve the customer experience and increase their profits.  

In the article, we will find out what emotion recognition is, how it works, and where one can use it. Let’s start! 

What Is Emotion AI? 

As a natural progression of facial recognition technology, emotion recognition is a field of computer vision that is gaining more and more attention in mass media. It involves facial emotion detection and the automatic assessment of sentiment from body language, voice patterns, gestures, facial expressions, and other non-verbal signals.  

The technology comes from the model of six universal expressions, proposed by Paul Ekman, an American psychologist and a pioneer in studying emotions and their relation to facial expressions. According to the model, there are seven universal facial expressions of emotions: anger, contempt, disgust, enjoyment, fear, sadness, and surprise.  

Emotional AI Use Cases 

Emotion AI products and solutions are incorporated into many industries to help deliver an emotionally rich experience. It can aid in the diagnosis of mental and neurological disorders in healthcare. Help educators engage students more in the lessons they are teaching. Assist talent acquisition specialists in locating the best prospects for hiring.  

Let’s explore the most common industries utilizing emotion detection solutions. 

Healthcare

Medical centers use AI facial recognition to determine patients’ emotions in the waiting rooms. That helps doctors prioritize those patients who are feeling worse and get them to appointments sooner.

Another project is designed to help children with autism spectrum disorder understand the feelings of others. The system is installed on Google Glass. When another person is near the child, the glasses use graphics and sound to suggest that person’s emotions. Tests have shown that children socialize faster with such a “digital advisor”.

Finance & Banking 

The typical use case in the finance sector is banking apps with the integrated functionality of Emotion AI. It uses the already embedded sensors to detect facial expressions. You can utilize eye tracking, as front-facing cameras on recent smartphones may now be used to precisely track a user’s gaze position. If the customers’ attention is lost and they begin to roll their eyes away, then that is something to notice and make changes.  

Recruitment

Another area in which emotion detection technology is in demand is recruiting. Large companies are implementing artificial intelligence to monitor employees’ behavior and psychological state. Cameras with video analytics modules installed in the office can detect signs of stress among employees and give alerts to HR departments.

Current State

Despite the many benefits of technology, there are some concerns too. The emotion recognition disputes growth has been influenced by privacy and transparency issues, racial bias danger, and ethical considerations.  

In May, Microsoft announced it would stop allowing broad access to cloud-based AI technology that infers people’s emotions. But it turns out that the corporation will keep its emotion identification capacity in an app used by persons with eyesight loss, despite their acknowledgment that technology has “risks.”  

Microsoft and Google will continue to include AI-based capabilities in their products despite rising worries over creating and applying “controversial” emotion recognition in software applications. 

Since we are aware of these issues, why is there such a massive demand for emotion detection technology? Remember a rule: only how technology is utilized determines whether it is intrinsically good or bad. And many other sectors, as mentioned above, are already gaining from the use of emotion recognition software. 

So, does emotion recognition matter to your industry? Despite doubts around the overall technology concept, this does not imply that the technology isn’t worth the effort. In fact, the opposite is true: as the demand for emotion recognition expands, facial recognition models and algorithms will evolve and keep getting advanced, secure, and more applicable to real-life situations. 

How to Build an AI Emotion Recognition Solution? 

Here comes the Intetics AI and ML Center of Excellence, created to help you find a solution to your bold technological ideas. 

We accumulated expertise in various technologies, unique customer project experiences, efficient methodologies, and best practices. Let’s train your program to investigate, assess, foresee, identify, and communicate! 

Here is the tech stack we use in AI/ML projects for our clients:

• TensorFlow
• StanfordCoreNLP
• CatBoost
• Scikit-Learn
• NLTK
• Keras
• GPT-2

And this is how your machine will learn:

Check Your Emotions and Get Advice with Our Fun Demo

Just follow the link emotions detection AI demo. 

Follow the path of companies prioritizing AI breakthroughs that recognize, understand, and react to human emotions while fostering more robust customer relationships.

Still wondering how to apply emotion recognition solutions for your industry to add extra value to customer experience? Reach out today and step up your Emotion AI journey.

Related Materials:

Intetics Created a Machine Learning Algorithm that Recognizes Human Emotions to Improve Wearables for Sport Fans

The post How AI Facial Recognition and Emotion Detection Helps Businesses. Check Yourself with Fun Demo. appeared first on Intetics.

The rating is represented by a list of the 500 North and South American companies with the highest growth in publicly disclosed revenues between 2017 and 2020. 

It is the third annual FT ranking, capturing the resilience of businesses as they adapted to the initial onslaught of the Covid pandemic in 2020. Those are companies across multiple industries: technology, healthcare, energy, retail, etc. We are delighted to be one of them.

We are honored to be recognized among the top 500 Americas’ fastest-growing companies by Financial Times, one of the most credible and respected media reporting financial and economic issues. Intetics team will continue to support IT industry development and tech innovations in all regions of our presence by providing top-notch solutions to our customers.

Boris Kontsevoi, Ceo and President, Intetics Inc.

The post Intetics Entering the Ranking of the Americas’ Fastest Growing Companies 2022 by Financial Times and Statista appeared first on Intetics.