Generative AI: How It Works, Use Cases & Limitations
If you've tuned into the news, picked up a newspaper or magazine, or spent any time on the internet in the past year, you've likely heard of generative artificial intelligence (AI) and its impressive capabilities. Maybe you've even wondered whether you can leverage it in your business but aren't sure where to start.
We've got you covered. In this article, we'll delve into what generative AI is, some practical ways to incorporate this emerging technology and tips for protecting your data and your business.
What is generative AI?
Generative AI refers to a type of artificial intelligence technology powered by large language models and trained on trillions of words. The technology can produce various types of content, including text, images, video, audio, and software code. It starts with a prompt—any input the system can process—and returns new content in response.
Generative AI isn't new. However, earlier versions required complex processes for submitting data. Today's generative AI tools, including OpenAI's ChatGPT, Google Bard, Bing AI, DALL-E, and StyleGAN by NVIDIA, provide a more accessible user experience and allow users to enter prompts in plain language and fine-tune results with feedback about style, tone, and other elements they want the generated content to reflect.
Gartner predicts generative AI will become "a general-purpose technology with an impact similar to that of the steam engine, electricity and the internet."
What are the practical applications of generative AI for business?
Generative AI technology has a lot of potential for businesses across various sectors.
Here are a few practical ways businesses are using it right now.
Content generation. Businesses can use generative AI to create content for websites, blogs and social media platforms. This content creation can help scale content marketing efforts, save time, and ensure consistency in brand voice and messaging.
Customer service. You've likely interacted with generative AI without realizing it. Many businesses are incorporating customer service chatbots into their websites. AI chatbots can swiftly and efficiently handle customer inquiries, improving customer service and freeing human representatives to address more complex issues.
Software development. In the tech industry, developers use AI for coding. The AI suggests code snippets or offers debugging solutions, helping improve productivity.
Data analysis. In the age of big data, generative AI can help businesses make sense of large volumes of complex data, extracting valuable insights and trends to inform strategic business decisions. For example, online retailers might apply AI to customer data to predict demand for goods and plan inventory levels.
Human resources. Many organizations are using AI to help with the hiring process. Using AI, hiring managers can analyze applicants' past work experience and skills to match them with the right roles.
Accessing and organizing knowledge. Generative AI is great at summarizing large volumes of information. Workers might use it to scan contracts and summarize crucial terms or analyze and interpret new legislation.
Limitations of generative AI
While generative AI offers compelling advantages, it's important to remember that it has its limitations. For starters, the quality of the generated content strongly depends on the quality and amount of data fed into the system. Garbage in, garbage out, as the saying goes. The output will reflect biases or errors if the AI is trained on biased or incorrect data.
Also, generative AI cannot understand context like humans do. It's essentially a complex pattern recognition system, so its outputs are based on patterns it has seen in its training data rather than on a deep understanding of the material. This can lead to outcomes that are nonsensical or inappropriate in certain contexts.
Generative AI is also prone to "hallucinations." It generates fictitious information, presenting it in a highly authoritative tone, and it's easy to believe the information is factual and accurate. It can even generate fictitious citations, statistics and publications. Two attorneys learned this lesson the hard way when they used ChatGPT to create a legal brief, and the technology cited multiple nonexistent court cases.
Many generative AI models are trained on data with a cutoff date. As a result, it can produce outdated information.
Finally, you should never input private or sensitive information into a public generative AI model like ChatGPT. These platforms function by learning from the data they process, which means they store and analyze the information you provide. You essentially hand over that data to the system if you input sensitive data, such as personally identifiable information (PII), financial details, or confidential business information.
The generative AI systems might inadvertently reveal proprietary data in their generated content. For instance, if you input a confidential company strategy into the AI system, the model could potentially generate content based on that strategy, unintentionally disclosing it.
Working with generative AI models can be beneficial for businesses, but it's crucial to approach it with a "trust but verify" mindset. You should not use it without first verifying its results and output. If you want help with business decisions that are grounded in verifiable facts, contact Slate. We can help your organization leverage data for better business decisions safely and securely.