Generative AI vs Traditional Machine Learning: What’s the Difference?

Generative AI starts with a prompt that could be in the form of a text, an image, a video, a design, musical notes, or any input that the AI system can process. Content can include essays, solutions to problems, or realistic fakes created from pictures or audio of a person. Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data. The recent buzz around generative AI has been driven by the simplicity of new user interfaces for creating high-quality text, graphics and videos in a matter of seconds. This is a field of AI that focuses on understanding, manipulating, and processing human language that is spoken and written. NLP algorithms can be used to analyze and respond to customer queries, translate between languages, and generate human-like text or speech.

So, let’s jump on board the bandwagon and dive into the realm of artificial intelligence and data-led outputs. Generative AI and machine learning are both subfields of artificial intelligence, but they differ in their approaches and objectives. The primary objective of predictive AI is to extract valuable insights and make informed predictions based on available data. It aids decision-making processes, allowing businesses to optimize operations, identify potential risks, and develop data-driven strategies. Predictive AI is widely used in finance, marketing, healthcare, and numerous other industries where accurate predictions can drive competitive advantage and operational efficiency.

Generative Design & Generative AI: Definition, 10 Use Cases, Challenges

ML algorithms typically require a large amount of structured data to be trained effectively. Structured data is organized in a predefined format, such as a table with columns and rows. For example, a machine learning algorithm used for credit scoring would require a large dataset of historical credit data to make accurate predictions. With more innovation in the AI space, we expect that predictive AI and generative AI will see more improvement in reducing the risk of using these technologies and improving opportunities.

• These products and platforms abstract away the complexities of setting up the models and running them at scale.
• Taking the same example from earlier, we could group pictures of pizzas, burgers and tacos into their respective categories based on the similarities or differences identified in the images.
• Predictive AI is a type of machine learning which enables machines to understand patterns in data and make predictions based on those insights.
• Generative AI can be used for a wide range of applications, such as creating art, music, or even writing stories.
• It’s why companies like Salesforce, Microsoft and Google are all scrambling to incorporate generative AI across their products, and why businesses are eager to find ways to fold it into their operations.

As Artificial Intelligence (AI) technology evolves, the key difference between Generative AI and Predictive AI should be understood. Predictive AI is a type of machine learning which enables machines to understand patterns in data and make predictions based on those insights. It allows for more accurate forecasting, diagnosis, or decisions by considering prior events like Yakov Livshits customer trends when making future predictions. Conversational AI systems are generally trained on smaller datasets of dialogues and conversations to understand user inputs, process them, and generate responses in text/voice. Therefore, output generation is a byproduct of their main purpose, which is facilitating interactive communications between machines and humans.

Conversational AI Vs. Generative AI: Purpose, Functionality, and Technology

However, leveraging generative AI’s full potential requires a deep understanding of its capabilities and limitations, along with the right strategy for integration and use. Machine learning, therefore, is employed to find needles in haystacks consisting of massive quantities of data. It ties into big data in that these algorithms can be utilized to scan structured and unstructured data, social media feeds, and other essential key data in large repositories.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

Machine learning models train on large amounts of data, gradually learning and improving their accuracy rates over time. Machine learning uses artificial intelligence to learn and adapt automatically without the need for continual instruction. Machine learning is based on algorithms and statistical AI models that analyze and draw inferences from patterns discovered within data. While algorithms help automate these processes, building a generative AI model is incredibly complex due to the massive amounts of data and compute resources they require. People and organizations need large datasets to train these models, and generating high-quality data can be time-consuming and expensive.

Using AI for business

It’s also worth noting that generative AI capabilities will increasingly be built into the software products you likely use everyday, like Bing, Office 365, Microsoft 365 Copilot and Google Workspace. This is effectively a “free” tier, though vendors will ultimately pass on costs to customers as part of bundled incremental price increases to their products. A common example of generative AI is ChatGPT, which is a chatbot that responds to statements, requests and questions by tapping into its large pool of training data that goes up to 2021. OpenAI also unveiled its much-anticipated GPT-4 in March 2023, which will be used as the underlying engine for ChatGPT going forward. In addition, the company has started selling access to GPT-4’s API so that businesses and individuals can build their own applications on top of it. Unlike with MusicLM or DALL-E, LLMs are trained on textual data and then used to output new text, whether that be a sales email or an ongoing dialogue with a customer.

As with using generative AI in images, creating artificial musical tracks in the style of popular artists has already sparked legal controversies. A particularly memorable example occurred just recently when a TikTok user supposedly created an AI-generated collaboration between Drake and The Weeknd, which then promptly went viral. To create intelligent systems, such as chatbots, Yakov Livshits voice bots, and intelligent assistants, capable of engaging in natural language conversations and providing human like responses. This versatility means conversational AI has numerous use cases across industries and business functionalities. Alibaba, a leading player in the retail and e-commerce space, has also dipped its toe into AI and predictive analytics.

That being said, generative AI as we understand it now is much more complicated than what it was half a century ago. Raw images can be transformed into visual elements, too, also expressed as vectors. A full discussion of how large language models are trained is beyond the scope of this piece, but it’s easy enough to get a high-level view of the process. In essence, an LLM like GPT-4 is fed a huge amount of textual data from the internet.

This is because deep neural networks can learn complex patterns and relationships in the data that may be difficult for other algorithms to detect. However, DL algorithms can be computationally expensive and may require specialized hardware to achieve high accuracy and performance. AI techniques are employed in natural language processing, virtual assistants, robotics, autonomous vehicles and recommendation systems. Machine learning algorithms power personalized recommendations, fraud detection, medical diagnoses and speech recognition. Generative AI has gained prominence in areas such as image synthesis, text generation, summarization and video production. Generative AI has emerged as a powerful technology with remarkable capabilities across diverse domains, as evidenced by recent recent ChatGPT and Generative AI statistics.