Machine learning is the process of creating patterns and modifying those patterns using algorithms. To generate patterns, you need a lot of rich data from all over the place since the data must reflect as many different outcomes from as many conceivable circumstances as possible. AI and machine learning models need to be updated and reworked throughout their lifecycle in order to do what they are designed to.
Machine learning is one of the most exciting tech developments currently and will play a major role in economic and social development moving forward. With that as our backdrop, below are 4 interesting and unusual machine learning applications happening right now.
Social Media
One of the more conventional tools for organizations trying to manage their social media accounts is social media monitoring. Some platforms, such as Twitter and Instagram, have analytics tools that may track the performance of previous postings, such as the number of likes, comments, clicks on a link, or video views.
Similar social media analysis and management services may be provided by third-party solutions, educating businesses about their consumers, including demographic information and optimal posting times. Because social media algorithms often favour current posts over older ones, companies use machine learning to carefully plan their posts during, or a few minutes before, peak hours using this data.
Businesses are also increasingly able to rely on AI and machine learning to make suggestions about which individuals to approach directly or which posts to comment on, which might lead to greater sales. These recommendations would be based in part on data obtained through existing analytics.
Image Recognition
When it comes to identifying photos, people are capable of clearly recognizing and distinguishing various elements of items. This is because our brains have been unknowingly taught with the same collection of visuals, resulting in the development of the ability to readily discern between objects.
When we interpret the real world, we are scarcely cognizant of it. We have no trouble encountering and differentiating various things of the visible world. Our subconscious mind handles all of the procedures automatically.
In contrast to human brains, computers see pictures as an array of numerical values and seek patterns in digital images, whether still, video, graphic, or even live, to detect and differentiate significant elements. Machine learning allows computers to develop large databases of visuals with which to make these same kinds of patterns and relationships and make inferences and evaluations moving forward.
Product Recommendations
A product recommendation system is a software application that generates and provides recommendations for things or content that a given user would like to buy or engage with. The system generates an advanced net of complicated connections between those things and those individuals by utilizing machine learning techniques and varied data about both specific products and individual consumers.
A product recommendation system using machine learning establishes three sorts of connections. The first is user-product interactions based on specific product preferences of users. The second is user-to-user connections — based on comparable people (e.g., persons of a similar age, background, etc.) having presumably similar product preferences. And the third is product-to-product linkages based on comparable or complementary items (e.g., printers and ink cartridges) that may be classified into relevant groupings
Cybersecurity
Machine learning is currently being used to assist cybersecurity teams in becoming more proactive when it comes to avoiding risks and responding to active assaults in real-time. It also helps firms use their resources more strategically by reducing the amount of time spent on regular tasks.
Machine learning is being used to make cybersecurity easier, more proactive, less expensive, and considerably more successful. However, it can only do so if the underlying data used to drive machine learning offers a thorough view of the environment. As cybersecurity threats continue to grow throughout the 21st Century and the use of AI to bypass network security becomes more common, the use of AI and machine learning to level the playing field becomes increasingly necessary.
Conclusion
Machine learning is one of the most exciting technological innovations to come out of the era of big data, and it will continue to affect our lives as citizens and consumers in all sorts of ways. In order for machine learning to work effectively, the programs need copious amounts of data to build relationships and make accurate predictions, but the applications are essentially limitless.
