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Today, students are encouraged to go to college more than ever, with parents and teachers claiming that a post-secondary education is a necessary step towards a good job and success in the future. According to the Pew Research Center, the number of college applications increased by 21.4 percent between 2002 and 2017. Thus, as the applicant pools to competitive colleges increase, colleges become increasingly selective of students. This has prompted many students to find ways to become more competitive in the college selection process, oftentimes by self-studying certain subjects and taking courses online.
When it comes to self-studying, there are a variety of available resources. One of the most common resources for studying for tests are test guides or prep books such as Barron’s or the Princeton Review. However, these books can be very expensive, ranging from $20 to $40. To avoid the cost, books can be borrowed from libraries, bought second-hand or shared between peers taking the same tests. These prep books provide a variety of strategies to succeed in specific topics, ranging from test-taking strategies to condensed material that is easy-to-follow, often accompanied by charts and other graphic organizers. They also include practice tests which are similar to official standardized tests and are a good way to get used to the formats of different tests. Aside from tests, students also study various subjects on their own, out of interest or to get an edge in school. An effective way to study outside of school is through various tutors. Peers who have previously taken certain subjects can make good tutors; they can sometimes even have an edge on teachers, since they know the feeling of not understanding a topic and may be able to better communicate topics in an easy-to-understand manner. Tutoring services have also been on the rise, ranging in classes to supplement school curriculum to classes meant to teach the entirety of the material for a subject over the summer or on weekends.
The internet can also be a good place to find study materials. One popular online source to complement what is learned in school is Khan Academy, which offers free online courses and other tools for students. According to the New York Times, Khan Academy has over 10 million users worldwide with over 5,000 courses. The videos on the website are concise and easy-to-follow, making it a good resource for students who may be struggling with a subject or need to study before a test. There are other available resources online when it comes to learning general skills, including Udemy. Udemy is a platform where online instructors can construct courses in their topics of interest, uploading resources such as videos, Powerpoints and PDFs. While Udemy offers over 130,000 courses in a variety of categories including design, management and digital marketing tactics, though some of the courses may not be free.
Some online resources are geared towards specific subjects. Duolingo is a free language-learning platform and offers 90 courses in 22 languages. It even offers fictional languages High Valyrian from Game of Thrones and Klingon from Star Trek. Duolingo provides lessons on grammar and vocabulary then tests users on the material. It functions much like a video game, using a reward system for in-game currency that can be used for character customization. Another subject-specific learning platform is Codecademy for learning how to code. It offers free courses in programming and markup languages. It provides specific tracks for each language, starting with the ubiquitous “Hello World” lesson and moving on the more complex topics.
Students can also take full courses for credit online. Online classes provide students with opportunities to take classes that are not offered at school or can let students skip classes at school. A report from the Brookings Institute explains that online classes are beneficial in that it allows easy access to education for students. However, the report also finds that online courses have higher drop-out rates than traditional schools. While online courses currently face drawbacks, there is potential for improvements in online classes in the future. This includes the incorporation of artificial intelligence to personalize the teaching to the student; with developments in artificial intelligence, online classes can match the pace of each individual’s learning speed and account for prior knowledge in a subject.
As colleges become more competitive, there are increasing numbers of resources that are available to students to give them an edge when it comes to education, both online and in-person. Furthermore, resources like Udemy and Duolingo are good for students who just want to further develop an understanding of different subjects and skills outside of school.
Pew Research Center
New York Times
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From years of wasting away my life on YouTube, I have often heard creators complain about the “YouTube Algorithm” and how it damages their career by demonetizing them or not recommending them. But what is the YouTube Algorithm and how does it work? Is it even an algorithm? By exploring YouTube and the mechanisms by which YouTube recommends videos, these questions and more can be answered.
The YouTube Algorithm has evolved over the years. Before 2012, it focused simply on view count; videos with more views would be recommended to more viewers. However, this lead to the problem of clickbait where creators added purposefully catchy titles without actual substance in their videos. And so, YouTube changed its algorithm to account for view duration, or watch time and time spent on the platform or session time. This caused creators to delay the time taken to deliver on promises that the video’s title makes. The algorithm’s changes also led to creators being obliged to make high quality videos while increasing the rate at which they were produced. People could not make high quality, lengthy videos. It also explains why so many popular YouTubers at this time were gamers as they could produce long videos in short periods of time without a lot of editing.
From 2016 onward, YouTube changed its algorithm again, releasing a lengthy paper describing how the new process works. In their new system, YouTube employs deep learning to improve their recommendation process. YouTube is a platform with 300 hours of content uploaded every minute. To sort through all of this data and find specific recommendations for each viewer is why two neural networks are needed: one for candidate generation and one for ranking.
The candidate generation network sorts through billions of videos and provides broad personalization using collaborative filtering. This network takes events from the user’s history and retrieves a small subset of a hundred videos. Data such as IDs of video watches, search query tokens, and demographics are used.
The ranking network then has to filter through these hundreds of videos and rank them according to what the viewer is most likely to click on. It does this by assigning a score to each video using different features describing the video and the user. The highest scoring videos are then shown on recommended pages.
Even with this highly specialized system, YouTube receives criticism about it being a “misinformation engine” which radicalizes viewers by showing them conspiracy theories, fake news, and other disturbing content. YouTube keeps their algorithm close to their chest, so it is difficult to understand why this happens. However, it has become increasingly clear that disturbing videos are recommended more.
YouTube is constantly changing its model with new input from viewers and creators. In 2017, they supposedly began to improve the quality of videos by preventing inflammatory videos from popping up. In 2018, they added their controversial monetization policy, where clips can be eligible for making money depending on their content. This was meant to reduce the amount of content creators the platform had to actively monitor because YouTube has strict policies for what videos can get monetized. And yet, CNN reported that popular brands including Adidas, Cisco, and Hilton still had their ads running on extremist videos. This year, YouTube announced that it would be banning “borderline content” which could seriously harm or misinform viewers. The effects of this feature are still uncertain.
Essentially, YouTube uses an incredibly complicated “algorithm” which is made up of multiple components. Every YouTube video that you watch is delivered to you with a lot of metadata behind it. Now that’s something to think about the next time you scroll through your recommendations page.
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Artificial intelligence, or AI, has marked a revolutionary turning point within our society. This dynamic and emerging field of technology has enabled us to interact with each other in unimaginable ways. If you looked around, you would come to see just how often our routines intertwine with the possibilities and opportunities of machine learning algorithms, cloud computing platforms, virtual reality, and image processing. With everything ranging from healthcare and environmental sustainability to education and transportation, AI introduces the promise for an efficient and technologically advanced lifestyle.
Take Google Translate, a multilingual machine translation service developed by Google. Whenever we’re in need of a quick translation, we resort to pulling out our phones, recording a voice, using live translation on an image, and watching the magic happen. This is the work of deep neural networks and a method in which computers are programmed to analyze a variety of languages, called natural language processing (NLP). However, with the many prospective benefits of such advanced machinery, comes a darker side. A frightening dilemma soon emerges, commonly referred to as “deep fakes”, which gains the attention of news headlines.
Deepfakes, a form of combining existing images and videos onto source images or videos using a machine learning technique known as generative adversarial network (GAN), introduces us to the dangers and threats of media manipulation. GANs are able to receive photos and videos of a person, typically in extremely large amounts, and are “trained” based off of these inputs. They are then able to generate new images and videos that look nearly identical and indistinguishable from the original content. This new form of image altercation makes us question whether what we see is indeed real, and raises questions concerning the credibility of anything seen on the media such as news sources, online platforms, and politics. Since anyone has the ability to find resources to produce a manipulated video, deep fake technology undoubtedly opens doorways for malicious intent, public shaming, identity theft, and fraud.
The rapid pace at which deep fake production is growing is concerning, considering its capability to influence politics by unauthentically framing and exploiting one’s words and actions. Currently, there are around a whopping 14,678 deepfakes on the internet—and counting, according to CNN. It was also found that individuals and businesses have begun to make custom deepfakes for buyers and sell them for profit. So, what is being done to combat the rising exploitation of deepfake technology?
The Pentagon, through partnership with the Defense Advanced Research Projects Agency (DARPA), is working to hinder the spread of deepfakes with researchers and universities by finding ways to train computers to identify them. Additionally, organizations such as Deeptrace aim to re-establish trust in visual media by detecting and monitoring deepfakes using deep learning.
Sadly, deepfakes may be a problem that is getting too out of hand for the work of companies and organizations. In an article by Digital Trends, Luke Dormehl elaborates on why tech companies are ill-equipped to tackle this problem. He says that that deepfake technology is becoming increasingly better, and the inconsistencies present within earlier deepfakes have now been fixed. With the rate at which these visual reproductions are being created, it is nearly impossible for researchers to keep up. According to the Washington Post, Hany Farid, a computer-science professor and digital-forensics expert at the University of California at Berkeley, states that “The number of people working on the video-synthesis side, as opposed to the detector side, is 100 to 1.”
Larger companies are making an effort to combat this such as Facebook, who announced a $10 million ‘deepfakes detection challenge’ according to VICE. The challenge is expected to take place in December, where Facebook will release a data set of faces and videos for the development of methods and technologies that can detect an algorithmically generated video.
As of now, there is not much we can do as individuals to know whether the next audio we hear or the next video we see is 100% unaltered. But, educating and spreading awareness of the threats and dangers associated with such a rapid and promising period of technological growth is always important to live by.