Netra Amin

Is Your Phone Really Listening, or is it just Smart Advertising?

Computer Science, Editors Pick, Technology / By

Have you ever had that eerie feeling that your phone is listening to your conversations? Has it ever happened that you’re making ice skating plans with your friends, and the moment you open Instagram, your eyes lay upon an ice skating ad? It has happened to me, and I’m sure I’m not alone. But amidst the bewilderment, the question lingers: Is our phone genuinely listening to us, or is it all just a series of bizarre coincidences?

Although Apple claims that it doesn’t listen to users, voice assistants such as Siri and Alexa listen for wake-up words such as “Hey Siri” and “Alexa” and record the user’s speech, contributing to the creation of a user’s profile for targeted advertisements. A user’s profile includes their demographics, browsing history, online purchases, social media interactions, app usage, and much more. Additionally, Ad networks buy data from many sources and track a user’s online activity. They seem to know everything about us – our age, gender, likes, dislikes, location, hobbies, and even the time we spend on different websites. Through the data from the profile and algorithms, advertisers effectively target specific audiences for their ads. Now sometimes the ad isn’t completely in line with the user’s preferences, but there is a process that involves customization to make the ad as precise as possible with the user’s interests. 

I mentioned that Apple claims that it doesn’t listen to users; however, that statement is contradicted by a report that revealed how Siri can “sometimes be mistakenly activated and record private matters,” raising privacy concerns. For the most part, the data gathered by advertisers is used anonymously to respect privacy, but it’s essential to read the terms and conditions before agreeing to them. 

Because of these specific ads, one gets the impression that their phone is actively listening to them 24/7, but it’s mostly due to the role of data collection and network algorithms. Sometimes confirmation bias – the tendency of individuals to support information that aligns with their opinions and ignore information that does not – plays a role here. For example, if you’re talking about chocolate ice cream and receive an ad about it, you’ll instantly think you’re phone has been listening to you all this time, but other times when you get an ad unrelated to your conversation, you disregard it or don’t notice it.

In conclusion, while your phone does listen to you, it does through voice assistants and mostly in harmless ways. So the next time you experience the ice skating situation, you’ll know the reasons behind it.

Kevin Ku Unsplash

The AI Paradox: Enhancing Cybersecurity while Raising Concerns

Artificial Intelligence, Computer Science / By

In today’s interconnected and digitized world, numerous cybersecurity attacks such as malware attacks, phishing attacks, and data breaches occur, increasing the demand for cybersecurity professionals and advanced technologies such as Artificial Intelligence. However, AI acts as a defender and as a challenger to cybersecurity. 

Cybersecurity involves analyzing patterns from massive amounts of data to protect systems and networks from digital attacks and identify threats. The traditional approach relied heavily on signature-based detection systems, which were effective against known threats but incapable of detecting new or unknown threats, resulting in frequent cybersecurity attacks. However, AI-based solutions use machine learning algorithms that are trained using vast amounts of historical and current data to detect and respond to unknown and new threats. According to Jon Olstik’s Artificial Intelligence and Cybersecurity, approximately “27 percent” of security professionals in 2018 wanted to use AI for “improving operations, prioritizing the right incidents, and even automating remediation tasks,” implying that the reasons for AI in cybersecurity continue to increase over time.

However, there’s another side to AI’s role in cybersecurity, and it’s more dangerous than beneficial. While it may appear that AI systems are unlikely to be hacked, this is not the case because hackers can manipulate these systems. As previously said, AI solutions employ machine learning algorithms to detect threats; nevertheless, a fundamental flaw is that vulnerability is that these models are fully dependent on the dataset. A poisoning attack occurs when an attacker modifies the dataset to fulfill their malicious goals, forcing the model to learn from the modified data and leading to more attacks. 

As these rising concerns and vulnerabilities, AI-based solutions will need to be not only fast, but also safe and risk-free. AI and Cybersecurity share a complex relationship and it’s safe to conclude that Artificial Intelligence will continue to play a paradoxical yet essential role in the growing field of cybersecurity.

  • Comiter, Marcus, et al. “Attacking Artificial Intelligence: AI’s Security Vulnerability and What Policymakers Can Do About It.” Belfer Center, August 2019, https://www.belfercenter.org/publication/AttackingAI#toc-3-0-0. Accessed 7 July 2023.
  • Moisset, Sonya. “How Security Analysts Can Use AI in Cybersecurity.” freeCodeCamp, 24 May 2023, https://www.freecodecamp.org/news/how-to-use-artificial-intelligence-in -cybersecurity/. Accessed 7 July 2023.
  • Oltsik, Jon. “Artificial intelligence and cybersecurity: The real deal.” CSO Online, 25 January 2018, https://www.csoonline.com/article/564385/artificial-intelligence-and -cybersecurity-the-re al-deal.html. Accessed 7 July 2023.