Applications For Artificial Intelligence

By Direct Knowledge

The 2004 reboot of the classic television show Battlestar Galactica explored a crucial aspect of what it means to be human. If machines can think and learn for themselves, then what differentiates us from them? It’s hard to decide where the line is drawn. So, are we to consider smart supercomputers to be human? No. The power of artificial intelligence grows by the year. If we continue developing better technology, the line could get even more complicated to find. When will we know the true nature of artificial intelligence, and what it means for our society? This is a question that data scientists have only begun to explore. Let’s look into applications for artificial intelligence to find out the role machines play in our lives.

Artificial Intelligence

In 1997, Gary Kasparov lost a game of chess. But this wasn’t just any chess game. Instead of a man, his opponent was a machine. To be more specific, an artificial intelligence program named Deep Blue. Gary Kasparov trudged home that night with the bitter taste of loss in his mouth. But at IBM’s offices in Armonk, NY, champagne corks flew. They had finally developed an artificial intelligence program that was superior to human intelligence – at least at chess. We could see in this moment that computers were capable of so much more than we originally imagined.

Applications in artificial intelligence now do so many things for us. We have cars that keep us from hitting others in fender benders thanks to AI break alerts. Likewise, we have AI systems on our phones we can ask questions to. It’s common for people to simply say “Hey Alexa, turn down the lights in the living room,” without a second thought. It’s miraculous, and a culmination of decades of hard work. But what does it mean for us?

If a computer behaves like a human, is it displaying artificial intelligence? Or is true artificial intelligence shown only in machines with superhuman powers? These are the questions troubling hundreds of data scientists. In fact, some people find the entire field of artificial intelligence as disconcerting. In fact, many people believe that artificial intelligence may become a malevolent force in the future.  To sum up, data in the modern world makes it possible for AI to exist today.

AI for Consumers

What would those people think if they knew that artificial intelligence was operating in their own home? There are many ways artificial intelligence has filtered into our lives,. One example is the modern air conditioner. Many air conditioners with thermostats use a form of artificial intelligence called fuzzy logic to control the temperature in the room. 

Broadly defined, artificial intelligence is any machine that is aware of its environment and makes choices that maximize its chance of reaching a goal. Artificial intelligence can imitate thought processes that are typically considered human, like advanced learning and problem solving. If you give a math expert and an artificially intelligent machine the same math problem, they will both get the same right answer. But critically, the path they take to reach that answer is completely different. For instance, Deep Blue won the chess game against Kasparov with a brute force attack. The artificial intelligence out-thought Kasparov, whose human mind was unable to fathom the stunning 200 million chess positions Deep Blue could access every second. In short, the AI surprised everyone.

Applications For Artificial Intelligence: Machine Learning

Right now, a child is fumbling through his first steps. A 4th grader is tackling long division. A professor is poring over a scientific text. A baby bird can feel the wind lifting its fledgling wings for the first time. Creature or human, we all go learn every day. Today, computers have joined the race for knowledge through a process called machine learning.  

Machine learning is a branch of computer science that allows computers to learn without being explicitly directed by a programmer. In the early days of artificial intelligence, researchers had big dreams. They wanted to make a computer that could do everything a human could, and more. Artificial intelligence researchers were trying to figure out how to make machines “think” the same way human minds did. Machine learning was discovered by researchers who were more interested in making computers get the same results that humans did (or better) than copying the mechanisms of the human mind.

How a Machine Learns to “Think”

Because machine learning became so useful, it started a field of its own. Here’s how it works. Imagine a quiz about rocket science. Now, you are given all the answers ahead of time. Does this mean you understand rocket science? Not necessarily. That being said, solely based on the questions and answers, you were now expected to figure out how to ace future quizzes about rocket science. This is how many machine learning algorithms learn. The programmer gives the machine learning algorithm an example set of inputs with the desired outputs included. Based on that, the machine has to figure out how they can find outputs for those inputs in the future. 

This makes machine learning very useful for a data analysis when all the variables aren’t available. Based on what you do know, the machine learning program can figure out what you don’t. Machine learning is an integral skill for a budding data scientist who wants to write algorithms that yield actionable analysis. The great part of machines is their ability to adapt to individual needs. For example, machines react to the data they receive. Because of this, machines are capable of doing virtually anything. That being said, it is helpful to know how to help machines think.

The capabilities of a machine to resolve data errors or write algorithms is limited to the user’s experience with the machine. For that reason, many studying computer science today sit in classrooms with science students. For a scientific researcher, knowing the machine is going to be the difference between time spent discovering and time wasted troubleshooting.

Applications For Artificial Intelligence: Deep Learning

You upload a picture of graduation day onto Facebook. As the page reloads, you see that your freshly uploaded pic now sports name-tags for each of the people in your picture. Congratulations. You’ve just become the unwitting accomplice of one of Facebook’s deep learning tools. 

Deep learning mimics the way the human mind turns light and sound into images and words. Before deep learning, researchers used machine learning to teach computers to identify images. If they wanted the computer to learn to identify cats, they would code classifiers that detected where the edges were on an image. However, the machine learning program could easily be fooled by an image that was cat-shaped but not actually a cat. In 2012, a team at Google made real progress. The team taught a deep learning program how to recognize cats. They trained it using 10 million randomly selected Youtube videos. At the end of the training, this deep learning tool was able to recognize cats 74.8% of the time without ever being given a classifier to identify cats. If you think the ramifications stop there, then you should know identification tools are quite important.

Deep Neural Networks

One of the most commonly used deep learning tools is called a deep neural network. A deep learning computer starts small, learning simple concepts and progressing to abstract ones. As opposed to traditional programs with hard-and-fast rules, deep learning programs learn on the go. It’s called “deep” learning because there are multiple stages of learning that occur between the time when it first sees an image (input) to when it figures out what the image is (output). For instance, Facebook’s deep learning algorithm named “Deepface” measures the distance between your friends’ eyes, ears, and nose (input) to successfully tag pictures with their name (output). 

When machine learning programs reach a certain point, their growth screeches to a halt. In contrast, the more data you throw at a deep learning program, the more it will learn. Deep learning is relevant to any data scientists who want to stay ahead of new advances in medicine, speech and image recognition, and intelligent personal assistants.

Applications For Artificial Intelligence: The Life Sciences

Imagine going into the doctor’s office to figure out what’s causing your persistent cough. But instead of the friendly face of your local doc, you find yourself face-to-face with a futuristic computer. It asks you to type your symptoms onto a keyboard encased in a germ-resistant layer of plasma. With a faint whirring, it processes your symptoms and spits out a results sheet with your diagnosis. It even encloses a prescription. 

Medicine is just one of the life sciences that are changing because of data science. The life sciences study living things, like humans, animals, plants, and microorganisms. You may know these fields better as biology, zoology, botany, and microbiology. While the areas they study differ greatly, they all have one thing in common – massive stores of data. Like all sciences, the life sciences need to organize their data by counting it, tracking it, and classifying it into different groupings. Today, machines provide the deep learning needed to make high level computing possible.

Impacts in Other Sciences

So far, data science has been very effective at saving time and money in the medical fields. As an example, cell counting is a time-consuming process which involves peering into a microscope and recording results in real-time. Now the same result can be accomplished using deep learning computer vision programs. Before deep learning, lab researchers had to classify each cell into groups by hand. Now, deep learning programs can use image recognition to classify each cell, saving hours of time. 

Data scientists who want to work in medical fields, wildlife conservation, or any sort of biology will do well to brush up on their knowledge of the life sciences. Since most of these fields are barely using data science, there is tremendous potential for innovation here. If given the resources, then many problems will be solvable.

Future Applications For Artificial Intelligence Robot and scientist facing Turing test

Future Applications For Artificial Intelligence

There are a handful of ways that the applications for artificial intelligence will impact our society in the future. For one, students are seeing more AI in the classroom across all ages. K-12 schools are playing with subscriptions to AI tutors. These help to assist students when teachers cannot be taking 1 on 1 time with each student. Plus, AI can show assisted lesson explanations for students who are a bit behind without requiring after school sessions. If provided to students, then test scores and comprehension could increase significantly.

The impact will be on higher education as well. AI is getting more common, and in turn, less expensive. Artificial intelligence used to be available at high level institutions. Now, you can find it in many more universities and colleges across the globe. This means more students across the world will have better access to modern tools and resources. That prepares students across institutions, regardless of financials and geography.

Will Artificial Intelligence Allow Humans to Become Digital Beings?

Sci-Fi movies always like to toy around with the concept of human life and AI. The story goes that after death, humans can one day upload their thoughts and memories into a computer. That way, after their body is past life, they can live on in a digital world. Most serious scientists are not playing with this idea in a meaningful way. The problems of the day are much more important, considering the international issues at play. If you want to be a computer one day, you might need to wait a bit longer.

One day, this possibility could be a reality. Digital beings return us to the question of what makes a human. Applications for artificial intelligence point to, among other things, questions of the human experience. If you want to be a computer human hybrid, then you might want to start studying to see the dream become reality. Furthermore, there are other issues AI can accomplish that might be worth solving first.

Conclusion

The numerous applications for artificial intelligence make it a booming field. As more and more problems become global concern, there will be a rise in AI interest. The best way to accomplish what AI is capable of is to fund these programs. Hopefully, with enough financial and intellectual attention, AI can be the resource we know it can be for humanity.

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