Artificial intelligence is making its way into more and more areas of business. From e-commerce recommendations to customer service chatbots, AI is now well integrated into the way that businesses operate.
AI writing software is a natural next step in this transition. However, it’s important to understand that AI writing isn’t without its limitations.
Training
AI writing tools work by employing processes like machine learning and natural language processing. They take a person’s input, be it in the form of questions or prompts, and then gather data from across the web to churn out original content. Generally speaking, they can help people write articles, blog posts, research summaries, social media content, website copy, and other marketing materials.
They can also assist with tasks such as paraphrasing, checking grammar, and identifying plagiarism. This helps to free up time for the writer so that they can focus on more important things, such as writing compelling, on-brand content.
While some people have an apocalyptic view of how AI writing tools will put professional writers out of jobs, Sharples believes that it is more likely that they will simply complement the work of human writers rather than replace it altogether. “What I think is more realistic is that the AI writing tools will give you a boost, freeing up your time so that you can spend more time on other activities such as researching, brainstorming, and editing your work,” he said.
One example of an AI writing tool is HyperWrite, which can generate high-quality and original content in just minutes. It can produce a range of content types including blogs, social media posts, YouTube scripts, and even eBooks. It can also generate content that is search engine optimized and complies with Google’s webmaster guidelines.
Deployment
Once your AI is ready for deployment, the next step is to decide how you want to integrate it into real-world environments. There are several deployment options, each with different costs and risks. It is best to involve all stakeholders in the planning process. This will ensure that the conversational AI matches company goals and reflects the brand identity.
It is also necessary to understand that the business environment is constantly changing and that the AI must be adapted accordingly. For example, new products may require new models or a change in business trends might make existing models obsolete. Either way, it is important to implement a continuous model development and deployment cycle.
Deploying your AI to production can be time consuming and resource intensive. AI Deploy automates deployment with Docker images, so you can industrialise your production AI quickly and easily. AI Deploy can also automatically scale your deployments across multiple instances for high availability, with load balancing and managed infrastructure. AI Deploy can run your production AI in either batch or online inference. Batch inference runs a set of predictions periodically. This approach is useful for generating results with low latency and is a good choice for applications that can handle periodic responses.
To deploy a prediction with AI Platform Prediction, you need to create a tarball that contains your machine learning model and its dependencies. Then you can upload the tarball to Cloud Storage and use the gcloud ai-platform local predict command to test how your model serves predictions locally before you deploy it to AI Platform Prediction.
Evaluation
In high-stakes contexts such as autonomous driving and medical diagnosis, failures of AI can have disastrous consequences. As such, it is essential that researchers and policy-makers have a full understanding of the capabilities and limitations of these systems to make well informed decisions about their use.
Unfortunately, it is not easy to evaluate AI tools objectively, in part because there are few ground truth data sets that can be fed into the algorithm. In addition, the choice of what to include in a data set has a major impact on an AI tool’s quality and value.
For example, if an AI program is designed to detect cancer on mammography images, the gold standard to evaluate the tool would be final pathology results and long-term patient health outcomes—data that are difficult and expensive to acquire. The AI developer will therefore construct a ground truth label for the tool, and may choose to select from a wide range of possibilities.
To evaluate an AI tool, users should ask vendors to openly discuss their selection of ground truth and the logic behind it, as well as any trade-offs they considered. Reticence to do so should be viewed as a red flag. In addition, buyers should look for technical research reports and method summaries that describe the ground truth used to test AI tools.
Misuse
The main issue with AI writing is that it may be used to plagiarize and cheat. For example, students might use ChatGPT to write their SAT essay or a college admissions essay. To help combat this, teachers should be clear about their expectations surrounding the use of writing ai and explain its limitations. In addition, teachers should emphasize learning objectives focused on developing skills and practices as writers. Ideally, this can reduce student incentives to turn to generative AI writing.
Another problem is that it is difficult to detect whether or not an AI has been used to generate text. While a number of companies have created AI content detection tools, they rely on comparing a piece of text to other pieces of writing to identify plagiarism. This process can be time consuming and relies on a large sample of writing known to be human to work effectively.
Additionally, many of these tools only accept text input and cannot analyze images or diagrams. This can be problematic if students respond to a prompt that requires them to provide a detailed response, such as an SAT essay question. To address this, teachers can include image-based prompts or ask students to submit their responses in a format that does not require generating text, such as a video or audio file.
