AI has an impressive repertoire with many advancements that have become essential to its success. One such advancement is deep learning algorithms. This helps the AI remember common scenarios in order to process the answer more quickly. These could range from the average amount of people driving on a road per hour to what you’re watching on Netflix.
Some of the best uses for these algorithms are already implemented for our pleasures, with many working towards our overall convenience. Through some research, we have found some of the best uses of deep learning in various fields. Through these advancements, we will be able to see how deep learning will progress and improve in the future.
Deep learning has allowed AI to store large quantities of data for the use of doctors. Many issues in this field arise throughout the diagnosis process. Having to wait for the doctor to get all the details in order to know what you are afflicted with is necessary but tedious when you feel sick. This is where deep learning can help.
Through the AI, symptoms are able to be collected and analyzed quickly through the AI’s “memory”. It could essentially build a profile for any person that the AI encounters, remembering their risks and genetic factors in order to create the best diagnosis or prognosis for your situation.
Though these developments might increase the overall health of those who are being treated by AI, this can go even further. Through further developments, we could see AI creating better diagnostics and implementation that could aid in the development of prophylactic strategies.
For more on how AI can be implemented in the healthcare field, check out this podcast episode with Varun Ganapathi.
One of the best uses for deep learning is in the entertainment industry. Streaming services have heavily implemented AI usage. These services have a wide range of content, so filtering this content is necessary for making sure the consumer is getting what they want from that service.
Many services have implemented deep learning algorithms in their systems in order to find content that relates to a user’s search query. Through this process, the AI is able to look through your history in order to find similar movies or shows based on tags attached to said movies and shows. The AI essentially makes a persona for your account which is enhanced by the use of likes and dislikes to make sure you get specifically what you are looking for.
Cars are one of the most important developments in the modern era. Cars have gone from simple tools to get around to having full-blown computers inside. One of the most recent changes to this field has been the ability for cars to drive themselves.
One company working towards the development of self-driving cars is Uber. Not only are they making the deep learning algorithms in self-driving cars more able to map routes for the car to drive, but they are also looking to improve routes to enhance their food delivery services.
One thing that the deep learning process is working toward fixing is unprecedented scenarios. These would be unfortunate situations where someone would be injured or worse depending on factors both within and outside of the AI’s control. Running these scenarios on multiple different sets on the computer will work towards figuring out the best way for no one to get hurt.
This does lead to the ever-prevalent question of if the AI could make this call or even if it should, but this is why training is necessary. Making these calls is never good, so making an AI do it might seem bad, but it will fundamentally make the best choice. That will cause the least amount of deaths and injuries as the AI trains more internally so that it will be the best externally.
Within the world of AI, one of the most prevalent technologies is chatbots. Whether it be customer support robots directing you to an option for help or you asking your phone a question to search, chatbots are probably the most widespread use of AI there is.
Deep learning is helping make sure this process is more efficient for users as it will make the process far more fluent. Originally, chatbots merely went through set messages based on what button you pressed, thus giving the person on the other end a service that seemed very non-interactive and almost like the company does not want to help. Through the years this has been slowly changing, powered by Apple’s Siri or Google’s Google Assistant. Through these uses, the algorithm learns how to better interact with people and will be far more helpful when dealing with people.
For more on how chatbots and similar AI work in business, check out this article.
When dealing with human interaction, AI is quick to give you simplistic messages about how to deal with certain issues. Something that AI has difficulties in is translating those messages from one language to another. The main issue is that it does not entirely get the nuances between languages and what certain words mean in different language contexts.
AI has slowly been fixing this issue through the use of applications such as Google Translate. Google has been working tirelessly to make the most accurate translation software possible. The team has been working through multiple models of machine learning and one of these is deep learning.
Deep learning has become quite an important part of this process. By taking the letters that make up the word, as well as other words that could be context clues, the words are translated. The AI uses key parts of language in order to decipher and understand the words. This has even progressed to translate working with pictures.
Kyle Fritz graduated from Florida Gulf Coast University in the Spring of 2021 with a Bachelor’s Degree in Communication.