How does generative AI differ from other forms of AI?
As its name suggests, generative AI creates new content. The content may be text, images, audio or visual content, typically similar to existing examples. Other types of AI, such as machine learning for classification or regression, focus on tasks like pattern recognition, decision-making, or predictive modeling. While generative AI can create diverse and creative outputs based on the patterns it has learned, other types of AI produce more limited results based on learned patterns from existing data, such as classifying objects, making recommendations, or predicting values.
Why has generative AI taken off so quickly?
Productivity boosts that nearly double previous increases are driving genAI’s rapid growth. Functioning as a co-pilot for hyper-automation and hyper-creation, the technology allows people to save time on a variety of tasks. This form of AI works with human input to produce unique results spanning various formats, unlike previous applications that were designed to perform specific functions repeatedly with limited human ability to influence the output. GenAI has the potential for broad application across almost all industries.
What are some of the main use cases for generative AI?
In a recent webinar, experts outlined eight key use cases for genAI:
text-to-text: writing assistance in creating content in a variety of formats ranging from poems and blog posts to cover letters, college entrance essays, video titles and SEO meta descriptions.
text-to-image: art solutions such as Midjourney or Stable Diffusion. Theaters have used these tools to create playbills for performances; musicians have created album art, graphic designers can use them to create images of people that do not exist to avoid overuse of stock images.
summarization: effectively processes unstructured natural language data. By extracting key points and essential information from a longer piece of content, generative AI can create a short, coherent, and informative summary used for search, SEO, social media posts, content descriptions and similar situations.
creation: applying AI to development, testing and features for chatbots and voice assistants. GenAI can help develop large amounts of natural language training data, or simulate user queries and generate test cases with various language patterns, intents, and entities. Generative models can also suggest new features or responses based on user feedback and emerging trends. These suggestions can inform the development roadmap.
text-to-code: software development assistance, generating an estimated 40-55% higher productivity. Applause’s test case management solution uses generative AI to improve the quality of written test cases using the new Smart Suggestion feature — clear and well-written test cases provide faster time to testing and more efficient test cycle execution.
search: conversational search that draws on summarization, allowing users to more effectively refine results. In recent years, people have become accustomed to searches that auto-suggest queries, correctly interpret natural language queries, and generate concise and coherent summaries to help quickly determine if results are relevant.
production: driving chatbots and voice assistants with LLMs and generative AI to improve run time. Model optimization, reliance on user-activated wake words, caching and short-form responses for frequently asked questions are just a few ways to serve up answers faster.
text-to-speech: synthetic voice and speech, taking a digital format and presenting to the user in a new way. Think of a voice assistant reading text messages aloud, or the screen readers used as adaptive technology for people with disabilities.
How important are prompts in generative AI?
Prompts play a significant role in defining genAI’s output – they provide a way to specify the desired task or context for the model and inform it what type of output is expected. Prompts offer the user some degree of control over the output, helping them influence the content, tone, and style. They can also clarify intent, making it easier for the AI to provide a relevant response. Prompts can be customized to align with specific applications or user preferences. For example, in chatbots, prompts can be designed to mimic a particular persona or communication style. Prompts can guide users in interacting with generative AI systems effectively, by instructing users how to frame their requests for better results.
The prompt can make the entire difference in whether the output from genAI is what the user wants or expects. For instance, a response can be 100% correct on its specific outcomes, but if things are left out of the question or request, that can cause an issue.
Is there legislation governing genAI’s use?
Though the technology is relatively new, questions have arisen about potential privacy violations, copyright infringements, and other ethical and legal implications. A February 2023 Applause survey found that the vast majority of users believe that AI technology and use should be regulated: Of 4,398 respondents, just 6% did not think AI should be regulated at all. More than half (53%) said AI should be regulated depending on its use and 35% said it should always be regulated.
In a few instances, straightforward regulations are already in effect. Laws that govern privacy, data protection and copyright apply to AI. The United States Federal Trade Commission (FTC) has issued fines and called for algorithms to be deleted or destroyed when an organization has violated privacy in its data collection or where consumers did not consent to their data being used to train AI algorithms.
Earlier this month, the UN Educational, Scientific and Cultural Organization (UNESCO) called for governments to regulate genAI in education and research. The EU’s proposed AI Act includes transparency requirements for genAI, and the Chinese government has issued Interim Measures for the Management of Generative Artificial Intelligence Services.
Are there unique testing considerations for generative AI applications?
There are some differences between testing genAI and other types of software. Some key areas to test for genAI include:
Diversity of inputs and outputs: Generative AI applications often produce diverse outputs based on different inputs. Testing should cover a wide range of input scenarios and assess the diversity, quality, and relevance of generated outputs.
Content quality and coherence: Testers need to ensure that output is contextually relevant, coherent, and free from errors, including grammatical and factual errors.
Bias and fairness: Without proper training and testing, generative AI models can produce biased or inappropriate content. Testing should check for biases, offensive language, and adherence to ethical guidelines.
Abuse and toxicity: Along the lines of testing for bias and fairness, it’s crucial to test for potential misuse and toxicity of the system. Ensure that the AI application does not generate harmful or offensive content when prompted with inappropriate inputs.
Real-time feedback loops: Some generative AI systems incorporate real-time feedback to improve responses over time. Testing should account for this feedback loop and assess how well the system adapts and learns from user interactions.
Edge cases: Explore edge cases and rare scenarios that may result in unexpected or unhandled responses. Generative AI systems should handle such cases gracefully.
User experience (UX): Test the user experience and interface design for applications that involve user interaction. Ensure that the user interface effectively communicates how the generative AI system works.
User acceptance testing (UAT): Involve end-users or representatives from the target audience to gather feedback and ensure that the generative AI application meets user expectations.
Testing generative AI applications requires a holistic approach that addresses the uniqueness of content generation, ethical considerations, and the dynamic nature of user interactions. It’s essential to combine automated testing with manual review and user feedback to ensure the reliability, safety, and effectiveness of generative AI systems.
This ebook examines genAI use cases, inherent risks and challenges in developing generative AI apps and how they can be mitigated by a thoughtful and deliberate approach to development.
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