AI is a key component in software engineering development. As AI is implemented in a range of systems and for a variety of purposes, it is the indispensable role of software engineers to create and maintain such systems. This article will explore the possible limitations that AI has that software engineers need to be aware of and mitigate in their role. Through processes such as data management, bias mitigation and real-time system integration, software engineers ensure that AI is efficient at meeting the needs of customers.
What is AI?
The programs and algorithms that instruct and teach AI models how to learn and think are all designed and written by software engineers. Without these engineers, AI would not have reached the advanced stages that are currently seizing attention worldwide, and work in this area is still ongoing.
Computer scientists are responsible for the deep learning component of AI, taking their inspiration from the human brain and creating artificial neural networks that process information in the same manner as our brains do. AI utilizes multiple layers of processing to extract higher levels of information from existing data.
Deep learning models recognize complex patterns in sound, pictures and text, producing accurate insights and predictions and automating tasks that typically require human input. AI adapts and improves as it learns.
AI: Opportunities and drawbacks
AI has made our lives easier, and it has become creative too. It entertains, educates and streamlines processes. However, it lacks the critical thinking of a software engineer and the insight to solve problems. Software engineers analyze the results of what AI has created and tweak their algorithms for improved results.
Mitigating biases
AI has the potential to amplify biases, create misinformation based on incorrect data and infringe on copyright and intellectual property if not designed and developed correctly.
As a result, software engineers need to mitigate the possibility of these biases, preferably during the development phases. But how are they to know?
Mitigation of bias in AI depends on the status of the application. If still in the early stages, software engineers will intervene by tweaking the algorithms accordingly. However, prevention is better than cure. Software engineers add coding to their algorithms to instruct AI on what types of data to ignore and how to recognize anomalies when searching databases for information.
Extreme care needs to be exercised when creating algorithms for specific purposes, and this should be taken care of in the planning or specification phases of development. Software engineers require an awareness of the various scenarios that may exist within the data, and they need to do a thorough analysis of the data before designing the algorithms for specific use by AI.
Given the enormous volumes of social media interaction that happens daily, how do software engineers mitigate false information?
Software testing
SEs build checks into their data mining algorithms, testing for each possible scenario and checking against other information. If an anomaly exists, the algorithm will flag it as such and ignore it if proven to be different from other, more reliable sources of information. These processes are extremely complex and almost impossible to predict and detect. It takes critical thinking and analysis skills on the part of software engineers. Once the algorithms have been written, the programs and data are tested to the point of exhaustion.
Software engineers are also instrumental in determining what categories of data are needed for specific projects and will instruct data analysts accordingly. Necessary modifications can be made to exclude any bias or issues in the future. This process is called ‘data scrubbing’ or ‘data cleansing’ and is an ongoing process.
AI and the environment
Computer scientists are constantly seeking ways to improve efficiency, both in terms of processing power used, and reducing processing times, and this will hopefully improve the status of AI processes and their impact on the environment.
This is particularly relevant in cloud-based applications as organizations store, retrieve and process data in remote locations. Software engineers work to reduce the impact of cloud processing by storing historical data locally, while using the cloud for real-time applications that require the more powerful processing abilities of cloud-based technology. This task involves constant maintenance on the part of data professionals.
Real-time system integration
The integration of AI into real-time systems has many benefits: it generates new concepts, improves efficiency and automates processes. It enables systems to diversify content and generate new content. An example of content generation is in gaming, where AI can generate new scenarios in response to players’ interactions, creating never-ending engagement for players.
Real-time integration can aid human creativity by putting forward ideas and suggestions. Conversely, AI uses human input and ideas for its own creations.
The use of AI in real-time medical systems is perhaps one of the most productive applications so far. This is because it needs to be strictly controlled, and there is no room for error. AI is used in the diagnosis of illnesses, in intricate procedures where accuracy is imperative and in monitoring patients in terms of risks and progress. Medical scientists and computer experts are constantly striving for improvements in medical technology and coming up with new inventions. Each time new technology is introduced, software engineers are involved in the programming, error detection and fine-tuning of products in the interest of patient safety.
Should software engineers be concerned?
Will AI replace software engineers? We think not. Now, more than ever, software engineers are needed to ensure that ethical standards are maintained and that our human rights are protected. Online degree programs, such as Baylor University’s Master’s in Computer Science – Software Engineering track, are training future software engineers to harness the potential of AI in relation to software engineering, with skills to design, create and maintain suitable software systems. With the 100% online coursework also covering advanced algorithms and systems development, students are prepared for an in-demand career in software engineering.
Maintaining AI systems
Software engineers must use a variety of skills and knowledge to create, maintain and implement AI systems. Managing data, mitigating bias, testing and data scrubbing are just some of the roles engineers have to ensure AI systems run correctly, efficiently and without bias.
Featured image credit: ThisisEngineering RAEng/Unsplash
