It’s safe to say that AI and machine learning have been the words on everyone’s lips for the past year. With the launch of ChatGPT and multiple other AI models like Google’s Gemini, it’s no surprise why.
For newcomers, there are many questions about this field. What is generative AI? How does it differ from traditional machine learning? Then there is the question of use cases like predictive analytics in real estate, or generative AI in the legal system. How will these work in the future? These are the questions we'll answer in this article.
Knowing the difference between these technologies is key when you’re considering AI as a solution for your next application.
Here is everything you need to know.
Generative AI is the newest iteration of machine learning, focusing specifically on content generation. Whether it’s code, text, images, or videos, generative AI has your back. Generative AI creates this content based on user inputs using deep learning. Deep learning is a computing process that analyzes large datasets. The more data there is, the more human-like the outputs become.
This type of AI varies based on its training models: transformer-based models are associated with text generation, while Generative Adversarial Networks (GAN) are often used for image generation.
There are many ways that this technology is already being used. Content planning, editing, code generation, improving health records - the list goes on and on.
The most promising part of Generative AI is the fact that so many tech giants have gotten behind it. Google, Meta, Amazon, and Microsoft have all made investments in this technology, making it likely that we’ll see widespread adoption by the public in the near future.
Prisonology is a good example of the use of generative AI. Prisonology uses artificial intelligence to generate a Security Designation Scorecard for defendants’ lawyers.
Next, we have machine learning. Unlike traditional coding approaches, machine learning models learn and adapt much like humans do through experience. Machine learning is typically divided into three different categories: supervised, unsupervised, and reinforcement learning.
Each type of machine learning has its unique applications. Supervised learning is good for data categorization, while unsupervised learning is better for things like customer segmentation in marketing. Reinforcement learning, however, is used in more futuristic technologies such as self-driving cars.
Our project DataFlik is an example of machine learning being used for predictive analytics in real estate prospecting. It analyzes large datasets to identify patterns and trends, helping real estate professionals predict market behaviors and identify potential properties for investment or sale.
AI's impact on the workforce is set to be a mix of good and bad. On the plus side, it's creating new jobs in AI development, data analysis, and cybersecurity, which requires new skills.
On the downside, AI can do many tasks by itself, threatening jobs in manufacturing, customer service, and other professional areas that can be automated. This means that companies are key to using AI ethically by offering training for their employees.
Machine learning similarly creates demand for new kinds of jobs, such as in data science and AI development. For example, marketers and healthcare professionals are using machine learning for better targeting and diagnostics purposes. In short, AI and machine learning are shaking up the job market, making it important for people and companies to keep learning and adapting.
AI is set to play a unique role in our future and the exciting part is that these technologies are still evolving. We’re discovering new approaches every day and these fields are set to change completely as time goes on.
Whether it’s generative AI or machine learning as a whole, these technologies are poised to alter the entire business environment as we know it. But to apply them to your business, you need the help of an expert.
NineTwoThree Studio has been building machine learning applications since 2016. Our data scientists are well-versed in large language models, generative AI, and much more. You have a business objective, and our job is to get you closer to that goal in 30 days. Build with confidence with our in-house experts who use college-taught methods and rapid prototyping to achieve real-world solutions - used by hundreds of thousands of users.
