Write amplification effect terrorism

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Write amplification effect terrorism

National Security-Related Applications of Artificial Intelligence Introduction There are a number of direct applications of AI relevant for national security purposes, both in the United States and elsewhere.

Take X and add AI. Included below are some examples in cybersecurity, information security, economic and financial tools of statecraft, defense, intelligence, homeland security, diplomacy, and development.

This is not intended as a comprehensive list of all possible uses of AI in these fields. Rather, these are merely intended as illustrative examples to help those in the national security community begin to think through some uses of this evolving technology.

The next section covers how broader AI-driven economic and societal changes could affect international security. Cybersecurity The cyber domain represents a prominent potential usage arena for AI, something senior leaders have expressed in recent years.

Each system was capable of automatically discovering and exploiting cyber vulnerabilities write amplification effect terrorism its opponents while patching its own vulnerabilities and defending itself from external cyberattacks.

The systems in the first Cyber Grand Challenge used rule-based programming and did not make significant use of machine learning. Were a similar competition to be held today, machine learning would likely play a much larger role.

Below are several illustrative applications of machine learning in the cybersecurity domain that could be especially impactful for the international security environment.

write amplification effect terrorism

Increased Automation and Reduced Labor Requirements Cyber surveillance has tended to be less labor-intensive than the traditional human surveillance methods that it has augmented or replaced. The increased use of machine learning could accelerate this trend, potentially putting sophisticated cyber capabilities that would normally require large corporation or nation-state level resources within the reach of smaller organizations or even individuals.

Narrow AI will increase the capabilities available to such actors, lowering the bar for attacks by individuals and non-state groups and increasing the scale of potential attacks for all actors. The machine learning approach allows the system to learn from prior experience in order to predict which locations in files are most likely to be susceptible to different types of fuzzing mutations, and hence malicious inputs.

This approach will be useful in both cyber defense detecting and protecting and cyber offense detecting and exploiting. Automated Red-teaming and Software Verification and Validation While there is understandable attention given to new vulnerability discovery, many cyber attacks exploit older, well-known vulnerabilities that system designers have simply failed to secure.

SQL-injection, for example, is a decades-old attack technique to which many new software systems still fall prey.

AI technology could be used to develop new verification and validation systems that can automatically test software for known cyber vulnerabilities before the new software is operationally deployed. Email phishing to trick users into revealing their passwords is a well-known example.

The most effective phishing attacks are human-customized to target the specific victim aka spear-phishing attacks — for instance, by impersonating their coworkers, family members, or specific online services that they use. AI technology offers the potential to automate this target customization, matching targeting data to the phishing message and thereby increasing the effectiveness of social engineering attacks.

Similar to the role of AI in cyber attacks, AI provides mechanisms to narrowly tailor propaganda to a targeted audience, as well as increase its dissemination at scale — heightening its efficacy and reach. Alternatively, natural language understanding and other forms of machine learning can train computer models to detect and filter propaganda content and its amplifiers.

Yet too often the ability to create and spread disinformation outpaces AI-driven tools that detect it. Targeted Propaganda and Deep Fakes Computational propaganda inordinately affects the current information ecosystem and its distinct vulnerabilities.

Bots that amplify this content at scale, tailored messaging or ads that enforce existing biases, and algorithms that promote incendiary content to encourage clicks point to implicit vulnerabilities in this landscape. Exploitation of behavioral data — The application of AI to target specific audiences builds on behavioral data collection, with machine learning parsing through an increasing amount of data.

Metadata generated by users of online platforms — often to paint a picture of consumer behavior for targeted advertising — can be exploited for propaganda purposes as well.

This can affect political discussions, especially when coupled with anonymity that reduces attribution and therefore accountability to imitate legitimate human discourse.

Clarifying semantic interpretations of language for machines to act upon can aid in the construct of more emotionally relevant propaganda.

Private sector companies are already attempting to quantify this behavior tracking data in order to vector future microtargeting efforts for advertisers on their platforms. These efforts are inherently dual-use — instead of utilizing metadata to supply users with targeted ads, malicious actors can supply them with tailored propaganda instead.

Deep fakes — AI systems are capable of generating realistic-sounding synthetic voice recordings of any individual for whom there is a sufficiently large voice training dataset. However, at the pace these technologies are making progress, they are likely less than five years away from being able to fool the untrained ear and eye.

Countering Disinformation While no technical solution will fully counter the impact of disinformation on international security, AI can help mitigate its efficiency. AI tools to detect, analyze, and disrupt disinformation weed out nefarious content and block bots.

Some AI-focused mitigation tools and examples include: Automated vetting and fake news detection — Companies are partnering with and creating discrete organizations with the specific goal of increasing the ability to filter out fake news and reinforce known facts using AI.

Trollbot detection and blocking — Estimates indicate the bot population ranges between 9 percent and 15 percent on Twitter and is increasing in sophistication. Machine learning models like the Botometer API, a feature-based classification system for Twitter, offer an AI-driven approach to identify them for potential removal.

Additionally, blockchain technologies are being utilized at non-profits like PUBLIQ, which encrypts each story and distributes it over a peer-to-peer network to attempt to increase information reliability.

Thus, it is difficult to create a universal filter based on purely technical means, and it is essential to keep a human in the loop during AI-driven content identification. Technical tools can limit and slow disinformation, not eradicate it.File fragmentation has a negligible effect due to the elimination of electro-mechanical components (seeking) Due to the nature of the flash memory and how data is currently written, SSD reducing write amplification.

The new requirement for managing SSDs is a disk optimizer that identifies which.

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