Targeted disinformation poses a significant threat to societal trust, democratic processes, and individual well-being. The use of AI in these disinformation campaigns enhances their precision, persuasiveness, and impact, making them more dangerous than ever before. By understanding the mechanisms of targeted disinformation and implementing comprehensive strategies to combat it, society can better protect itself against these sophisticated threats.
In simplest terms, a multimodal model is a type of machine learning algorithm designed to process more than one type of data, be it text, images, audio, or even video. Traditional models often specialize in one form of data; for example, text models focus solely on textual information, while image recognition models zero in on visual data. In contrast, a multimodal model combines these specializations, allowing it to analyze and make predictions based on a diverse range of data inputs.
While APIs serve as secure data conduits, they are not impervious to cyber threats. Vulnerabilities can range from unauthorized data access and leakage to more severe threats like remote code execution attacks. Therefore, it's crucial to integrate a robust security architecture that involves multiple layers of protection. Transport Layer Security (TLS) should be implemented to ensure data confidentiality and integrity during transmission. On the authentication front, OAuth 2.0 offers a secure and flexible framework for token-based authentication. Additionally, API keys should never be hardcoded into source repositories but should be managed through environment variables or secure key vaults. Other security practices such as network-level firewall configurations, IP whitelisting, and rate-limiting should be employed to defend against DDoS (Distributed Denial of Service) attacks and unauthorized data scraping.
In the summer of 1956, a small gathering of researchers and scientists at Dartmouth College, a small yet prestigious Ivy League school in Hanover, New Hampshire, ignited a spark that would forever change the course of human history. This historic event, known as the Dartmouth Workshop, is widely regarded as the birthplace of artificial intelligence (AI) and marked the inception of a new field of study that has since started revolutionizing countless aspects of our lives.
IntroductionTrustworthy vs Responsible AITrustworthy AIAttributes of trustworthy AI1. Transparent, interpretable and explainable2. Accountable3. Reliable, resilient, safe and secure4. Fair and non-discriminatory5. Committed to privacy...
Cybersecurity strategies need to change in order to address the new issues that Machine Learning (ML) and Artificial Intelligence (AI) bring into the equation. Although those issues have not yet reached crisis stage, signs are clear that they will need to be addressed – and soon – if cyberattackers are to be prevented from obtaining a decided advantage in the continuing arms race between hackers and those who keep organizations’ systems secure.
Model Evasion in the context of machine learning for cybersecurity refers to the tactical manipulation of input data, algorithmic processes, or outputs to mislead or subvert the intended operations of a machine learning model. In mathematical terms, evasion can be considered an optimization problem, where the objective is to minimize or maximize a certain loss function without altering the essential characteristics of the input data. This could involve modifying the input data x such that f(x) does not equal the true label y, where f is the classifier and x is the input vector.
Homomorphic Encryption has transitioned from being a mathematical curiosity to a linchpin in fortifying machine learning workflows against data vulnerabilities. Its complex nature notwithstanding, the unparalleled privacy and security benefits it offers are compelling enough to warrant its growing ubiquity. As machine learning integrates increasingly with sensitive sectors like healthcare, finance, and national security, the imperative for employing encryption techniques that are both potent and efficient becomes inescapable.
In 2013, George F. Young and colleagues completed a fascinating study into the science behind starling murmurations. These breathtaking displays of thousands – sometimes...
With AI’s breakneck expansion, the distinctions between ‘cybersecurity’ and ‘AI security’ are becoming increasingly pronounced. While both disciplines aim to safeguard digital assets, their focus and the challenges they address diverge in significant ways. Traditional cybersecurity is primarily about defending digital infrastructures from external threats, breaches, and unauthorized access. On the other hand, AI security has to address unique challenges posed by artificial intelligence systems, ensuring not just their robustness but also their ethical and transparent operation as well as unique internal vulnerabilities intrinsic to AI models and algorithms.
Artificial Intelligence (AI) is no longer just a buzzword; it’s an integral part of our daily lives, powering everything from our search for a...
Data masking, also known as data obfuscation or data anonymization, serves as a crucial technique for ensuring data confidentiality and integrity, particularly in non-production environments like development, testing, and analytics. It operates by replacing actual sensitive data with a sanitized version, rendering the data ineffective for malicious exploitation while retaining its functional utility for testing or analysis.
Trust comes through understanding. As AI models grow in complexity, they often resemble a "black box," where their decision-making processes become increasingly opaque. This lack of transparency can be a roadblock, especially when we need to trust and understand these decisions. Explainable AI (XAI) is the approach that aims to make AI's decisions more transparent, interpretable, and understandable. As the demand for transparency in AI systems intensifies, a number of frameworks have emerged to bridge the gap between machine complexity and human interpretability. Some of the leading Explainable AI Frameworks include:
Where AI, robots, IoT and the so-called Fourth Industrial Revolution are taking us, and how we should prepare for it are some of the hottest topics being discussed today. Perhaps the most striking thing about these discussions is how different people’s conclusions are. Some picture a utopia where machines do all work, where all people receive a universal basic income from the revenues machines generate and where, being freed from a need to work for wages, all people devote their time to altruism, art and culture. Others picture a dystopia where a tiny elite class uses their control of AI to horde all the world’s wealth and trap everyone else in inescapable poverty. Others take a broad view that sees minimal disruption beyond adopting new workplace paradigms.
As early as the mid-19th century, Charles Babbage and Ada Lovelace created the Analytical Engine, a mechanical general-purpose computer. Lovelace is often credited with the idea of a machine that could manipulate symbols in accordance with rules and that it might act upon other than just numbers, touching upon concepts central to AI.