#MachineLearning: Hacking Tools for Computer + Hacking With Kali Linux + Python Programming- The ultimate beginners guide to improve your knowledge of programming and data science
Jul 04 2021
Attackers use âoffensive AIâ to create deepfakes for phishing campaigns
Malware Analysis Using Artificial Intelligence and Deep Learning
Apr 27 2021
When AIs Start Hacking
If you donât have enough to worry about already, consider a world where AIs are hackers.
Hacking is as old as humanity. We are creative problem solvers. We exploit loopholes, manipulate systems, and strive for more influence, power, and wealth. To date, hacking has exclusively been a human activity. Not for long.
As I lay out in a report I just published, artificial intelligence will eventually find vulnerabilities in all sorts of social, economic, and political systems, and then exploit them at unprecedented speed, scale, and scope. After hacking humanity, AI systems will then hack other AI systems, and humans will be little more than collateral damage.
Okay, maybe this is a bit of hyperbole, but it requires no far-future science fiction technology. Iâm not postulating an AI âsingularity,â where the AI-learning feedback loop becomes so fast that it outstrips human understanding. Iâm not assuming intelligent androids. Iâm not assuming evil intent. Most of these hacks donât even require major research breakthroughs in AI. Theyâre already happening. As AI gets more sophisticated, though, we often wonât even know itâs happening.
AIs donât solve problems like humans do. They look at more types of solutions than us. Theyâll go down complex paths that we havenât considered. This can be an issue because of something called the explainability problem. Modern AI systems are essentially black boxes. Data goes in one end, and an answer comes out the other. It can be impossible to understand how the system reached its conclusion, even if youâre a programmer looking at the code.
In 2015, a research group fed an AI system called Deep Patient health and medical data from some 700,000 people, and tested whether it could predict diseases. It could, but Deep Patient provides no explanation for the basis of a diagnosis, and the researchers have no idea how it comes to its conclusions. A doctor either can either trust or ignore the computer, but that trust will remain blind.
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May 24 2021
AIs and Fake Comments
This month, the New York state attorney general issued a report on a scheme by âU.S. Companies and Partisans [to] Hack Democracy.â This wasnât another attempt by Republicans to make it harder for Black people and urban residents to vote. It was a concerted attack on another core element of US democracy Ââ the ability of citizens to express their voice to their political representatives. And it was carried out by generating millions of fake comments and fake emails purporting to come from real citizens.
This attack was detected because it was relatively crude. But artificial intelligence technologies are making it possible to generate genuine-seeming comments at scale, drowning out the voices of real citizens in a tidal wave of fake ones.
As political scientists like Paul Pierson have pointed out, what happens between elections is important to democracy. Politicians shape policies and they make laws. And citizens can approve or condemn what politicians are doing, through contacting their representatives or commenting on proposed rules.
Democracy and Fake News: Information Manipulation and Post-Truth Politics – the analysis of post-truth politics.
The volume sheds light on some topical questions connected to fake news, thereby contributing to a fuller understanding of its impact on democracy. In the Introduction, the editors offer some orientating definitions of post-truth politics, building a theoretical framework where various different aspects of fake news can be understood. The book is then divided into three parts: Part I helps to contextualize the phenomena investigated, offering definitions and discussing key concepts as well as aspects linked to the manipulation of information systems, especially considering its reverberation on democracy. Part II considers the phenomena of disinformation, fake news, and post-truth politics in the context of Russia, which emerges as a laboratory where the phases of creation and diffusion of fake news can be broken down and analyzed; consequently, Part II also reflects on the ways to counteract disinformation and fake news. Part III moves from case studies in Western and Central Europe to reflect on the methodological difficulty of investigating disinformation, as well as tackling the very delicate question of detection, combat, and prevention of fake news.
Tags: AIs and Fake Comments, Information Manipulation
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