The Key Differences Between AI vs Gen AI

AI has been a buzzword for a while now, but have you heard of Gen AI (generative artificial intelligence)? It doesn’t have anything to do with Gen Z, except that they certainly use it in their daily lives. If you’ve heard about writing their essays with ChatGPT, well, that’s an example of generative AI.

Today we are going to talk about artificial intelligence and its generative variable in the context of business. Which is better: AI vs Gen AI? How can you benefit from AI in your business? What are the risks? How can you get the most out of it?

At Droids On Roids, we’re not just observers of this evolution, we’re active participants. Our work in developing AI and Gen AI solutions spans industries, driving innovation and solving real-world challenges. So, maybe we can help you find the answers to these questions.

Read on and let’s find out.

What is Artificial Intelligence (AI)?

Making it as simple as possible…

Such systems are able to analyze data, recognize patterns, and make predictions or decisions based on the information they process. AI can also understand and respond to human language, enabling tasks such as language translation or speech recognition. From virtual assistants like Siri and Alexa to advanced algorithms in healthcare and finance, AI powers many everyday tools and complex systems, helping machines perform tasks that typically require human intelligence.

What does this have to do with your business?

• AI can solve your customer’s problems
• AI can speed up the development of your product

Artificial Intelligence includes subsets such as:

Machine Learning (ML) that you can use for:

• Predictions of changes over time (weather, prices)
• Personalized recommendations
• Content and ad selection
• Data analysis

Computer Vision (CV) that you can use for:

• Image analysis
• Object detection
• Text recognition
• Face recognition
• Gesture control
• Eye movement tracking
• Augmented reality

Generative AI (Gen AI) that you can use for:

• Text generation
• Image generation
• Video generation
• Music generation

What is Gen AI?

As you can see, the term Generative AI comes from its ability to generate original text, images, video, and other forms of content. Let’s take a closer look at what Gen AI is all about.

Generative AI (Gen AI) is a branch of AI that focuses on creating new and original content, such as text, images, and music. Unlike traditional AI, which follows predetermined rules and uses existing data, Generative AI can generate new data and ideas. It uses advanced machine learning techniques such as deep learning to produce creative and contextually relevant content, making it capable of writing, designing, or creating art from scratch. Generative AI models analyze patterns in existing data to mimic human creativity, but they also face challenges in the accuracy and reliability of their output. These models are becoming increasingly complex and have the potential to significantly impact multiple industries.

Examples of generative AI tools include:

• ChatGPT: A language model developed by OpenAI that generates human-like text responses for tasks such as conversation, writing, and answering questions.

• GitHub Copilot: An AI-powered coding assistant that helps developers by suggesting code snippets and completing programming tasks based on context.

• DALL-E2: An AI model from OpenAI that creates unique images from text descriptions, enabling users to create digital art based on their prompts (or its more advanced version DALL-E3)

• MidJourney: An AI tool similar to DALL-E that generates artistic images from text descriptions, often focusing on creative and imaginative imagery.

• Gemini: A cutting-edge AI system developed by Google DeepMind that aims to advance conversational AI and integrate deep learning techniques for more powerful and context-aware responses.

• Sora: An AI model developed by OpenAI that specializes in text-to-video generation. It generates short video clips in response to user prompts. Sora can also extend existing short videos.

Now, let’s explore the areas where you can benefit from introducing generative AI in your business.

Where does Gen AI work well?

Generative AI excels in a variety of areas, providing powerful support for tasks that require creativity, language understanding, and automation. Here are some key categories where Gen AI is proving highly effective:

Text processing and analysis

• Summarizing long documents
• Extraction of key information from texts
• Analysis of customer feedback

Generating content

• Writing articles, reports or blog posts
• Creating product descriptions
• Assistance with creative writing

Translation

• Translating texts between multiple languages
• Localization of content for different markets

Customer support

• Chatbots and virtual assistants
• Automatic answers to frequently asked questions

Programming

• Code generation
• Debugging and clarifying existing code
• Code documentation

Education

• Personalized educational materials
• Interactive learning systems
• Answering students’ questions

Research and data analysis

• Researching and synthesizing information from multiple sources
• Assisting in data analysis and hypothesis formulation

Creativity and innovation

• Generating ideas and concepts
• Support in brainstorming processes

Personalization

• Tailoring content to user preferences
• Recommendations of products or services

Natural language processing

• Recognizing intent in user queries
• Classification and categorization of texts

Gen AI limitations: What still needs to be improved?

While generative AI is incredibly powerful, there are areas where it is still limited. Here are some key areas that need improvement.

Timeliness of information

• Models are trained on past data, so they may not have up-to-date information
• They do not have access to current events or the latest discoveries

Factual accuracy

• They can sometimes generate “hallucinations,” or, in other words, convincing-sounding but false information
• They cannot always distinguish fact from fiction in their training bases
• Lack of cause-and-effect reasoning ability
• Difficulties with higher-order logic and complex reasoning
• Problems solving tasks that require multi-step planning

Security and vulnerability to attacks

• May be susceptible to various forms of attacks, such as “prompt injection”
• Risk of inadvertent disclosure of sensitive information

AI vs Gen AI: Key differences

The main difference between traditional AI and generative AI is what they can do. Traditional AI is mostly used to look at data and make predictions, while generative AI can create new things like text or images that are similar to what it has learned from its training data. In the context of generative AI vs Gen AI, the former goes beyond simply analyzing data by creating new content, while the latter is limited to predefined algorithms and structured data.

Simply put, traditional AI is good at recognizing patterns, while generative AI is good at creating new patterns. Traditional AI can look at data and explain what it finds, but generative AI can take that data and create something entirely new.

Here are some differences between AI and Gen AI, broken down by category.

Programming & intelligence

• Traditional AI: Works on the basis of pre-programmed rules and algorithms. It follows instructions set by programmers to solve tasks within its limitations.
• Generative AI: Uses advanced neural networks such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to create new and original content, adapting its output based on learned patterns.

Use cases

• Traditional AI: Designed for specific tasks such as data analysis or prediction, meaning it can only be used in a limited number of applications.
• Generative AI: Flexible and versatile, it can be used in many industries to create a wide variety of content, such as images, music, or text.

Data usage

• Traditional AI: Focuses on analyzing data and making predictions. It’s useful for tasks such as forecasting and data-driven decision making.
• Generative AI: Instead of just analyzing data, it uses that data and prompts to generate entirely new content, producing unique outputs based on the input provided.

Learning capabilities:

• Traditional AI: Has limited learning capabilities, relying heavily on data sets provided by human programmers.
• Generative AI: Learns from large data sets and can create original content, adapting more effectively to different types of input and prompts.

Which is better for use in app development? AI vs Gen AI

Whether AI or Generative AI is better to use in apps depends on the specific purpose and functionality of the app. Here’s a breakdown to help you decide which might be more appropriate:

Use traditional AI when:

• Efficiency and task-specific operations are required: Traditional AI excels at tasks such as data analysis, prediction, recommendation systems, fraud detection, and automation.
• Clear rules and visibility are required: If your application requires decision-making based on predefined rules, where the process must be transparent and easy to explain, traditional AI is more appropriate.
• Applications include customer support, recommendations, and personalization: AI-powered apps that provide recommendations (think Netflix or Spotify) or automate customer service (think chatbots) work well with traditional AI.
• Stability and accuracy take precedence over creativity: For applications such as banking, healthcare, or logistics, where precision and reliability are critical, traditional AI is better suited.

Use generative AI when:

• Creative content is needed: If your app is focused on content creation (such as writing, designing, or creating music), Generative AI can generate original and customized output based on user input.
• Personalization and interactive experiences are a priority: Generative AI can deliver dynamic, personalized experiences by generating unique responses, content, or designs tailored to the user’s preferences.
• Innovation and creative support required: For applications that require creative brainstorming, visual generation, or idea development (e.g., marketing, design, or entertainment), Generative AI offers significant advantages.
• Flexibility in output is important: If your app needs to generate unique, non-repetitive content (such as personalized recommendations or dynamic content), generative AI may be a better choice.

AI and Gen AI at Droids On Roids

Using both AI and Generative AI in applications brings a wide range of benefits. AI improves efficiency, automates routine tasks, and improves decision-making by analyzing data, providing personalized experiences, and offering real-time assistance through chatbots. Generative AI adds creativity by generating unique content such as text, images, or music, as well as by enabling more dynamic and personalized interactions. Together, they can create smarter, more adaptive applications that offer both intelligent automation and innovative content generation.

At Droids On Roids, we are actively working to improve the field. Our team of specialists is skilled in integrating AI into applications, ensuring that your product takes advantage of the latest advances in the technology. Whether you need enhanced automation or creative generative features, we have the expertise to help you bring AI-powered solutions to life in your apps – don’t hesitate to contact us!

Read also:

• 20 Best AI Apps | Artificial Intelligence in Action
• 10 Best AI Coding Assistant Tools in 2024
• 10 Ways AI Can Speed Up your Mobile App Development | Guide for Developers
• How to develop an AI app with a local model in Kotlin Multiplatform

Conclusion

In summary, the main difference between AI and Generative AI is how they work and what they aim to do. Traditional AI is great at analyzing data, making decisions, and predicting outcomes, excelling in situations where goals and rules are clearly defined. This type of AI is often used in data analytics and automated systems.

Generative AI, on the other hand, is designed for more creative tasks. It not only analyzes existing data, but also generates new, original content, making it a more flexible and advanced tool for innovation and creativity.

If you’re considering using AI or Generative AI in your application, it all depends on the goals and requirements of your project. Feel free to contact us and we’ll advise you on the best solution for your needs.

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