Artificial intelligence is known as a technology that allows machines to simulate human learning. Machine learning (ML) is part of artificial intelligence technology, and the purpose of ML is to have machines learn automatically by having them store information and not intervening with coding.
It is no secret that AI and machine learning are still relatively new concepts in the modern world, but you can already see quite a few industries benefiting from this technology. And as things continue to move forward and improve, we can expect even better things from artificial intelligence.
If you are not too familiar with how machine ML and AI are applied right now, this article will take you through 7 prominent uses.
We live in an era where cybersecurity threats are quite common. The trend is even more prominent now when you have so many people spending their time at home because of the pandemic. And it should not come as a surprise that many of us are using computers, smartphones, or tablets while connected to the internet, not just for work but also for leisure.
Whether you are an individual or a company looking to improve cybersecurity, machine learning is a promising technology. Algorithms require only a few seconds to detect potential threats, meaning that security teams can respond quickly and avoid potential problems.
Since cybercriminals are developing new threats regularly, machine learning could also be used to discover attack patterns and predict what is coming next.
Ecommerce is another example of how an industry can benefit from machine learning and AI. For example, there is a recent surge in voice shopping online, and you can see more online stores optimizing their websites for the feature. Technology that recognizes voice patterns and registers them can make the online shopping experience even more hassle-free.
Chatbots are another great example of how online stores can benefit from ML. A chatbot that can respond immediately after a customer sends a question provides a lot of value. Such chatbots can be available 24/7 and are more efficient than real people working in customer support.
Healthcare workers that transition from medical school to actual work might need more practice than they expect. The problem is that you cannot really risk “practicing” on real people, and it would be much better to create simulations that can replicate the real-life experience.
With the help of virtual reality, the healthcare industry can provide its workers with an environment that allows them to polish their skills. Training on an operating table and receiving a signal as soon as the simulation detects that something is wrong is a solid approach to improve as a surgeon, for instance.
Self-driving cars are probably the first thing that comes to mind when speaking of machine learning and manufacturing. The gimmick is still relatively new, and there is still a lot of room for improvement going forward. Right now, not that drivers would be willing to leave their cars in the hands of a computer and would rather drive themselves.
Moving from the car example to a broader perspective, you can consider how machine learning can optimize manufacturing and reduce or eliminate redundant processes. From there, businesses can increase productivity and establish a better growth plan which will translate to more profits.
Machine learning can also enhance education. Adaptive learning is used to analyze a student’s performance and provide them with a modified and better-suited curriculum.
Efficiency can also be increased when using ML because different students are capable of different things, and dividing the learning material according to everyone’s potential provides better education.
The problem with applying machine learning in education is that the concept is relatively new and expensive, so most public schools and universities or colleges cannot afford it or are not interested in such a significant overhaul to already established teaching methods. However, the odds of such an approach changing in the next five to ten years are relatively high.
Marketing is one of the key things if a business is looking to be successful, especially if we are talking about online ventures.
The competition leaves little room for errors, and some industries are notorious for their well-established brands that leave next to no room for newcomers. Unless there is the right marketing plan, the odds of success are almost zero.
Utilizing machine learning can be the difference-maker between an effective marketing strategy and a waste of money.
Take personalization, for instance. These days, it is common to offer tailor-made shopping experiences so that customers can relate to the brand more and get attached to it.
Since you need to process vast amounts of data to pick out what works for different demographics and individuals, doing so manually would take too long. Instead, it would be more efficient to put machine learning to use and let it do the work.
Machine Learning can also be utilized as a means to pick the right marketing channels or influencers from the vast pool of available options.
Virtual Personal Assistants
Siri, Alexa, and other digital assistants might seem like a gimmick to some, but there is more to them. You can take advantage of these assistants and leave some of the more redundant tasks to them instead of doing it yourself.
For instance, if you are using an iPhone or MacBook, you can tell Siri to do a quick Google search for you or send someone a text. Of course, you also need to make sure that the device you are using is functioning properly, as there are instances of various issues, such as no microphone found, or the input sound too low.
If you were paying attention to the trend of these digital assistants and their development, you must have noticed how they tend to receive new features with each update. More advanced technology comes thanks to the achievements in researching machine learning and applying new discoveries to use.
Comparing what virtual personal assistants were in their early versions and what they are capable of right now is an excellent example of how much machine learning and AI advanced recently and that we can expect even greater things in the future.
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