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The Future of Computing

Andrew Li

August 14th, 2022

Artificial Intelligence is a rapidly growing field that is becoming more and more valuable in today’s world. Compared to the field of computer science as a whole, which has been around for decades, AI is relatively new, and AI techniques are constantly being tweaked and improved. In addition, the foundations of AI ethics are still being laid, and lots of studies are currently focused on resolving the controversies within this tough issue.


AI Innovations

Despite the fact that AI is relatively new, it is already embedded into a lot of the technology we use in our everyday lives. For example: when you type something into Google Search, the engine uses AI—specifically, it uses Natural Language Processing(NLP) to understand the meaning of the text. NLP is a very popular field in AI, used in applications ranging from voice assistants to autocomplete on your phone. Below are some examples of relevant projects that rely heavily on the capabilities of AI.



Self-Driving Cars:

I’m sure everyone has heard about this one. Self-driving cars have quickly gone from fiction to a soon-to-be reality in the past few years. But how do these cars know how to navigate the road? AI plays a big role—these cars “learn” from data they collect on the road. As companies continue to work on these cars, more data will be collected, helping the cars recognize patterns on the road and become more reliable.

  • Impact: Self-driving cars could have massive benefits for society, as they would eliminate human errors that lead to incidents. However, companies have to be certain that self-driving cars are absolutely perfect and will not cause any accidents so that we don’t replace faulty drivers with faulty machines.

  • Ethics: the same link from above captures some of the ethical questions raised by the prospect of self-driving cars. One of the main questions is: if an accident is unavoidable, what’s the “correct” action the car should take to minimize the damage?

DALL-E generated this image from the text prompt “an armchair in the shape of an avocado”


DALL-E:

DALL-E is a neural network(a computing system that uses AI to analyze patterns in data) that generates images based on text input. It’s completely different from the self-driving cars discussed above, but that just goes to show the wide range of applications that AI has. By “training” DALL-E with image and text data, they are able to produce shockingly accurate results(check out the link for examples).

  • Impact: The impact of DALL-E is probably not as monumental as self-driving cars. But still, it’s pretty impressive to see in action, and can be used for things such as AI Art.

  • Ethics: The accuracy of image-generating tools like DALL-E, while impressive, can also be destructive, especially in the context of deep fakes. Deep fakes refer to fake images or videos that are very realistic and can be used to spread fake information.

So how does it work?

With the examples above, you might be wondering how computers are capable of doing these human tasks. I’ve hinted at it a bit already, but in basic terms, these projects all involve “training” the machines. AI engineers collect massive amounts of data and run their machines, AKA models, many times on this data to try to determine patterns in the model. In a sense, the models are just trying to mimic human intelligence, since our brains are naturally very good at recognizing and using patterns—hence the term artificial intelligence.


There is also another field that is very valuable to the AI process: Data Science. As I mentioned above, data is incredibly important for AI because the more data a model has, the more accurate it becomes. Data Scientists work with tons of data, and their job is essentially to organize the data. This might involve extracting useful parts of data and getting rid of “noisy” data that could be harmful to the AI training process. For this reason, AI and Data Science go hand in hand an extremely powerful pair.


AI is an incredibly complex field with many subfields and applications, and this blog is only an introduction. For those that are interested in AI and want to gain skills in the field, there are many online courses that can help you on your journey, such as Coursera’s AI For Everyone. There are a lot of complex topics in AI and it may be challenging; as someone with computer science experience, learning AI was certainly difficult for me since it’s a pretty unique field. But once you get over these challenges, you’ll have gained a valuable key that unlocks many opportunities in today’s world.


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