Visual, no code tools like Altova RecordsManager have revolutionized the field of data-centric app creation, making it faster and more accessible. But now imagine expressing your database vision in a single sentence, and having it created automatically – including not just the database structure and tables, but forms and reports as well. That’s exactly what the new AI Assistant in RecordsManager does.
With a single AI prompt, users of all skill levels can turn their ideas into functional database solutions without any coding or database design expertise required. RecordsManager lets you skip the manual work of database design so you can focus more on the higher-level aspects of your project.
AI is a great productivity booster for IT projects, and working with databases is one area where AI is really making inroads for improving efficiency. By leveraging AI in database tools, DBAs and database developers of any skill level can save time and effort with AI-generated SQL scripts and sample data, for instance, as well as query optimization and troubleshooting.
Altova offers an integrated AI Assistant in DatabaseSpy to help with SQL script creation, data modeling, SQL and error explanations, and even SQL pretty-printing. This makes the multi-database tool, which supports all major databases in a single UI, even more useful.
The explosion of AI tools has made a significant impact on the field of software development – not by replacing software engineers as some have predicted – but by actually increasing their value by freeing them to focus on higher-level tasks. By automating low-level code generation, for instance, AI increases development speed and opens the doors to deeper innovation.
To give developers the AI tools they need to realize these productivity and creativity gains, Altova has integrated AI functionality in XMLSpy for XML and JSON editing tasks.
One of the most common examples of AI in our everyday lives is facial recognition. Facial recognition is the process of identifying or verifying a person’s identity based on their face. Facial recognition is used in many applications, such as unlocking our phones with FaceID, tagging our friends on social media platforms like Facebook, and checking in at airports or hotels with biometric scanners. Facial recognition can make our lives more convenient and secure, but it can also raise some privacy and ethical concerns. For instance, how can we ensure that our facial data is not misused or stolen by hackers or malicious actors? How can we prevent facial recognition from being used for surveillance or discrimination? How can we ensure that facial recognition is accurate and fair, and does not have any biases or errors?
The paragraph above was generated by ChatGPT in response to my request to describe the benefits and risks of artificial intelligence and include a real-life example. It’s interesting that ChatGPT chose FaceID as the example, since FaceID is simply one variation of image analysis and AI-powered image classification offers potential to automate many real-world tasks.
One common use-case is a product catalog, wherein a company manages product information provided by many different manufacturers. A product loaded into that database may have a name that does not necessarily include a precise description of the item. For instance, wellington is a boot, fedora is a hat, a mongoose is a bicycle, and a yellow watermelon shiny needlefish is a fishing lure. We can make use of AI-powered image classification using the Microsoft Azure Cognitive Services Computer Vision API to address this problem. The Computer Vision Service takes the image data or URL as its input and returns information about the content. One service generates image classification tags based on a training set of recognizable objects, living beings, scenery, and actions that the Azure AI has been trained on. These tags allow us to categorize products in the database accordingly and may even correspond to search terms a user might provide to find products in the catalog.
Automated sentiment analysis of text, such as user reviews, has historically been a challenge. Because of the myriad intricacies of natural language, systems faced difficulties in analyzing context and nuances. This required an inordinate amount of manual work to overcome.
One of the many useful capabilities of modern AI systems that are based on large language models (LLMs) such as OpenAI’s GPT-4 is that they are very good at sentiment analysis of natural text inputs. We can use that capability to build a very efficient database solution in MapForce that, for example, goes through all the new incoming records in a support database and automatically determines whether a particular support request or other customer feedback is positive, negative, constitutes a bug report, or should be considered as a feature request.
