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Thinking that Machine Learning Isn't Making an Impact? Featured

Thinking that Machine Learning Isn't Making an Impact? "Collide"

Artificial intelligence is a topic that we keep hearing now and then during discussions in different tech and business circles. Some people still associate it with science fiction movies, but the truth is, it is here with us and chances are, you might be interacting with it daily. The popular AI inventions include Siri and Alexa, both of which make our lives easy. These inventions have made AI a household name and Alexa is the evidence of its presence in our lives. While some people may see it as a new phenomenon, the concept is not new. It first came into existence as early as 1956, although it took decades to make progress towards making it a reality. Here are top AI and machine learning trends in 2021:

  1. AI is aiding the fight against COVID-19

According to the World Health Organization, AI and big data are playing a critical role in the fight against the coronavirus pandemic. It has helped the healthcare providers to respond to the outbreak and manage infections in a variety of ways. For instance AI systems that use infrared technology to check temperatures of people in airports or bus stations are deployed in countries such as China. Similarly, thermal cameras are used to read temperatures before people could access public transport systems, or buildings to manage infections. Robots and drones on the other hand are helping doctors and other medical personnel in delivering medication and checking if social distancing regulations are followed in streets respectively. As the search for the best candidate for coronavirus vaccine and cure continues, machine learning will be at the core of research. 

  • Transparency will be the main talking point

Although artificial intelligence has become ubiquitous, it suffers from trust issues. As businesses seek to increase the investments in this technology, they will first want to know that the technology is transparent enough and can be adopted with confidence. After all, no one wants to invest in technologies they do not understand. As such, there will be an increased push for transparency in AI projects and their deployments in 2021 going to the future. Although companies will try to understand the workings of AI models and algorithms, AI software developers and solution providers will be required to create solutions that are understandable to the users. 

  • Automated ML

AutoML is slowly becoming famous as scientists seek ways of executing repetitive and tedious modeling tasks that once required weeks or even months of data personnel effort to complete. AutoML processes raw data that is input to it, chooses a model that makes sense, and finds patterns in the data inputs. It then finds the best model that should be applied to it. These are the activities that were once done by hand. An example is the Google AutoML that is a combination of recurrent neural network (RNN) and reinforcement learning. These systems are increasing the accuracy of ML systems and will be the main area of focus in the coming days. 

  • IoT and AI are converging

IoT is becoming a critical technology in industrial processes. The advancements in AI algorithms are making it more useful and enhances efficiency. There is however a lot of room for improvement with the help of machine learning in areas such as predictive maintenance that allows users to understand machines and carry out maintenance of manufacturing equipment before it is too late. The power of predictive maintenance that is achieved as a collaboration between IoT and ML allows companies to use the technology to their advantage as a way of enhancing efficiency. An example of companies that have taken advantage of trends in IoT and ML is Rolls Royce that in partnership with Azure IoT Solutions is using it to check the health of their aircraft engines to increase uptime. Many companies are slowly adopting these technologies to keep their machines running as much as possible. 

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Scott Koegler

Scott Koegler is Executive Editor for PMG360. He is a technology writer and editor with 20+ years experience delivering high value content to readers and publishers. 

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