Artificial intelligence is becoming more and more prevalent in modern society. While a majority of Americans still don’t know exactly how AI works, they use it on a daily basis. Whether you are navigating through a new city or composing an email, AI serves as an important tool in helping you execute daily tasks. AI can present itself in numerous ways such as natural language processing, machine learning, and computer vision) which allows it to have massive potential and revolutionize a multitude of industries.
Corporations such as Google, Netflix, and Uber are all utilizing machine learning to improve their products. Similar to how AI can be presented in a variety of ways, there are different types of machine learning (ML), which is essentially a computer learning from experience to make predictions. There are three main subsets: reinforcement learning, supervised learning, and unsupervised learning. Supervised learning is sort of like having a teacher but the machines learn with a training dataset that includes a set of input and output labels. On the other hand, unsupervised learning is when machines comb through data to find patterns they can use. Reinforcement learning is when machines are rewarded or penalized depending on their answer. Uber uses these algorithms by learning from external factors like traffic and then predicting how long it will take for your ride to arrive. Spotify, Apple Music, and Netflix all use ML to give you music and TV recommendations. American Express uses these algorithms to detect fraud in the millions of credit cards they have issued.
Another type of machine learning is Natural Language Processing which is another powerhouse that helps numerous tech giants develop their products. For instance, the Hello Barbie kids play with utilizes ML and Natural Language Procession (NLP) in order to have the toy listen and respond to a child. Google Translate uses NLP to provide language translations. While Google Translate isn’t accurate in terms of grammar and sentence structure, more advanced NLP algorithms are helping to increase accuracy. Search engines such as Google and Yahoo are able to predict search results because of NLP. One of the biggest applications of NLP is email filters (spam, inbox, other folders) that sort our email so that our inboxes stay organized.
In addition to these forms of artificial intelligence, computer vision is also an extremely important asset for companies. Computer vision essentially enables a computer to process and identify information. Amazon used computer vision to change the retail industry when it launched Amazon Go. People can go to the store and walk out without having to check out because they will automatically be charged through their Amazon accounts. In the healthcare industry, hospitals around the country are having physicians spend hours going over patient data. Computer vision can help doctors by going through the data for them and flagging important details saving countless hours. One major application of computer vision is in the automotive industry with self-driving cars and autopilot. Unfortunately, computer vision is still not as accurate as a human’s vision which is why its applications and impact are limited.
More and more tasks are becoming easier to do with the help of AI. While most of us associate Artificial Intelligence with being complicated and hard to understand, we’ve all actually been using AI for years. As technology develops and evolves, more of our daily routines and tasks will involve AI.
Image credit: Sara Kurfeß on Unsplash
Have you ever “talked” to Siri? Used a spell-checker? Taken advantage of Google Translate in your foreign language class? If so, you’ve experienced the power of natural language processing.
What is natural language processing?
Natural language processing (often shortened to “NLP”) is a subfield of artificial intelligence where software is taught to interpret or replicate human language. Some of the goals of NLP include:
- Determining the meanings of words and phrases in the context of, say, an article
- Transcribing spoken words into writing
- Extracting the themes, moods, or other attributes of a piece of writing
These goals can be approached in a number of ways—for example, by using machine learning algorithms.
What are some applications of NLP?
For those with access to the technologies mentioned at the beginning of this article, NLP has been revolutionary, reshaping their everyday lives by helping them carry out their everyday errands more conveniently and efficiently. But NLP has also been the subject of promising research at the forefront of the AI field, leading to its application to a variety of tasks. Here are a few examples:
- NLP for content moderation: Many companies are already using NLP to monitor and regulate content published to their digital platforms—for example, online forums—in an effort to reduce violent or hateful speech.
- NLP for political analysis: NLP can be used to parse and evaluate large databases of political texts (including Tweets!) in order to determine trends—for example, voter behavior.
- NLP for research paper generation: A team at MIT developed a program called “SCIgen” which, using NLP, writes “random CS research papers”.
What are some limitations of NLP?
As with any other AI innovation, NLP has its limitations, along with a fair share of ethical concerns:
- NLP can perpetuate pre-existing biases. Last summer, I attended a talk by Ayanna Howard, an accomplished professor and roboticist. During the talk, Dr. Howard described the struggles her team faced in building a robot capable of travelling over snowy terrain. These struggles stemmed from a simple problem: no one on their team had much experience with snow! Dr. Howard’s anecdote illustrates an important point: when technology (including NLP software) is developed by a “biased” group of technologists, it can perpetuate those biases. NLP can reflect the biases in language pervasive in NLP datasets (for example, the association of certain adjectives to certain groups of people).
- NLP for content moderation can be ineffective or lead to censorship. Our current NLP cannot grasp many of the nuances of human language—for example, sarcasm. And some worry NLP may ban posts which would actually be protected under free speech, while missing posts crafted with truly malicious intent.
I want to get involved with NLP. What do I do?
There are a number of ways to get involved! Here are some suggestions:
- NLP-related projects: If you have the means, reach out to local professors to ask about conducting NLP research under their supervision! You can probably find a list of ongoing projects on a professor’s website; look into those that interest you. If you’re unable to pursue NLP research, consider looking into online projects, including open-source projects, that you may be able to contribute to.
- NLP-related courses: Head over to our ‘Resources’ page for a list of over 150 links to various digital learning websites! Many of these websites offer free and open NLP courses. Or, head to your local library to check out an NLP book!
Hope this article helped you learn something new about NLP, and maybe even gave you an idea for your next project. Happy coding!