Generative AI in business
If you’re reading this article, you likely have heard of Generative AI or wish to learn more. You are certainly not alone in this interest.
According to Google Trends, the search term “Generative AI” has seen a notable spike in interest this year. This trend indicates that Generative AI is a hot topic today, impacting all sectors of society. For example, did you notice that NVIDIA, the main GPU player, briefly reached a valuation of $1 trillion? (Source: Reuters)
In this article, I will explain the main buzzwords related to Generative AI in business and how they connect. Additionally, I will provide an overview of the current Generative AI offerings in Google Cloud Platform. Lastly, I will share resources for your learning journey.
Understanding key terms related to Generative AI
Currently, many AI-related terms circulate in the media without context. Therefore, let’s start with some definitions.
Artificial Intelligence (AI) refers to the study of intelligent systems that simulate human knowledge. Importantly, these systems do not need explicit programming. We should think of AI similarly to Chemistry, Physics, or History.
Machine Learning (ML) is a branch of AI where systems derive knowledge from patterns in data. Other well-known branches include Optimisation, Computer Vision, and Robotics.
Deep Learning (DL) refers to a subset of ML models based on artificial neural networks. Other subsets include tree-based models and clustering methods.
Generative AI describes a type of neural network that generates new data based on its training dataset. Other types of neural networks include discriminative networks, which classify new data based on previously learned patterns.
How can you use Generative AI models in GCP?
In May of this year, Google Cloud Platform previewed its Generative AI Studio. This feature appeared on TechRadar by Devoteam inside Vertex AI. Google defines Generative AI Studio as “a Google Cloud console tool for rapidly prototyping and testing generative AI models.” Consequently, you don’t need to develop your own Generative AI models from scratch. Instead, you can experiment and prototype within GCP using Google’s pre-trained models.
Currently, Generative AI Studio offers two main usage scenarios:
- Language: It provides models for summarisation, classification, extraction, writing, and ideation. My personal favourite is the meme generator.
- Speech: It offers models for speech-to-text and text-to-speech.
With these pre-trained models, you can solve various recurring tasks, including sentiment analysis and contract analysis. Moreover, you can create a chat agent that summarises interactions and generates follow-up lists. You can also fine-tune these pre-trained models to meet your specific needs without recreating the entire model.
Exciting developments in image generation
The preview of the new Model Garden features foundational models that are enterprise-ready and task-specific. You can find first-party models created by Google, such as PaLM 2, and open-source models like BERT. Additionally, Google plans to make third-party models available in the future.
Exciting developments are coming, especially regarding image-related scenarios. We anticipate offerings in image generation, classification, search, and recommendation. Some fortunate testers have already previewed these features, so availability is likely soon.
Fonte: Vertex AI Image Generation – Image generated using Vertex AI Image Generation from the prompt: 4K video game concept art, urban jungle, cityscape inspired by New York city, detailed rendering.
Resources for learning about Generative AI
As Generative AI becomes widely available and remains a hot topic, Google offers a free learning path to help you acquire new skills.
You can find the Generative AI Learning Path here, covering topics such as:
- Large Language Models
- Responsible AI
- Image Generation
- Attention Mechanism
- Transformer Models
This learning path allows you to delve deeply into the concepts, provided by Google. You will also have direct contact with these tools in the Google Cloud Platform environment by completing labs.
Conclusion
Thank you for spending time reading my overview of Generative AI and Google Cloud Platform’s tools. Hopefully, you now have a clearer understanding of the buzzwords and feel sparked to explore this field further.
Generative AI is growing at an incredibly fast pace. It may seem that a new product hits the market daily. This rapid development also means new and exciting advances occur regularly. However, it might also be time to reflect on ethics and responsible AI.
On that note, I will leave you with Google’s Responsible AI business case, along with Google’s Responsible AI Practices. I hope you read and implement them in your day-to-day use of Generative AI.
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