How AI Is Changing The Future

Artificial Intelligence (AI) was first seen in the science fiction world—with talking houses and futuristic assistants. Those are now a reality, and the future of AI remains to be seen. Now, automated processes, hybrid work forces, machine learning, and computer vision are becoming more prominent. The consequences of these innovations will change the future no matter what. I explore what those changes could be, and the positive and negative effects of AI.

One of the most prominent ways that AI is being explored is for assisting already automated processes to take human intervention out of the way. Systems such as Robotics Process Automation and Intelligent Process Automation (RPA and IPA) can be made better by AI. According to V Soft Consulting, “Integrating AI with RPA … can take automation and task-handling to the next level.” One of the biggest faults with RPA is that it relies on the data provided to it. If that data is biased, then the RPA will be biased. Parsing the data through an AI can decrease the bias in the data, and clean the data. Bias will also be discussed later. 

In the workforce, people already use AI to automate certain tasks that don’t require a person to complete. Creating a “strong AI foundation,” according to Venture Beat, is the key to implementing an AI system in the workplace. AI can be used to support communications, make collaboration easier, manage workflows, and increase security. The biggest weakness of this is that you cannot simply download an AI and continue. Rather, the entire system would need to have AI integrated so that it would work correctly, which is very expensive to do. Overall, the pandemic has revealed the need for better systems of working virtually, and AI can enable faster communication and increased productivity in the workplace in the future.

Machine learning is how computer systems learn and adapt without being explicitly programmed. Instead they are given lots of data and statistical models, and use different algorithms to create connections and find patterns. Although machine learning has been around for many years, such as human computer processing big data, the speed of modern-day machine learning is revolutionary. Computers can now process incredible amounts of data in seconds, and find patterns between the data that they process. This means that the computer can deliver more accurate results, at a much larger scale. According to Analytics Vidhya, the “5V’s are dominating the current digital world (Volume, Variety, Variation Visibility, and Value), so most of the industries are developing various models for analyzing their presence and opportunities in the market.” Machine learning can be used to quickly find where companies can find value and what opportunities they have, as well as predict where they can profit based on current trends. The greatest strength of machine learning is that it constantly adapts, taking new data and staying with the current trends. However, like with most kinds of AI, the data can be corrupted or biased, which would change the results and make the model inaccurate.

One final innovation is in computer vision, a subject that I have been exploring personally. Computer vision is “a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information,” from IBM. For example, computer vision can be used to help the blind identify objects and avoid obstacles that they may not have detected with a cane. If you are interested in this field, please see this video from Microsoft. I also made a simulation about computer vision, which you can watch by clicking on the image below. Computer vision also has applications in cars, and new innovations to create self-driving cars usually implement some form of computer vision to identify parts of the road, pedestrians, and road signs.

All these innovations have wonderful applications and will hopefully change the future in a positive way. They all have one weakness in common, though. They rely on data that could be biased by human interference. For example, if a scientist is making a model to predict what consumers buy from all grocery stores, but only supplies data from one store, this would be biased. Thankfully, computer scientists are making efforts to decrease the bias in their data and models. One of the biggest ways that AI developers are decreasing bias is finding datasets that already have a lot of diversity in the data. One example of how the data set makes a difference is in facial recognition models. For more information, check out this video from Ted X.

In conclusion, although we wonder what the future may hold, AI has been working to bring the future to us. And as developments emerge, so do the risks. Constantly working to bring more innovations also requires constant efforts to decrease bias and limit the misuses of the technology. Whether you believe AI is helping us, hurting us, or isn’t far enough to judge, I hope that computer science as a field keeps innovating, creating, and sharing technology with the world.