Artificial intelligence (AI) is a really innovative feat of computer science that is poised to become a critical component of all modern software in the future years and decades. This is both a threat and an opportunity. In this blog, we talk about the scope of AI.
Artificial intelligence (AI) can be used to supplement both defensive and offensive cyber operations. In addition, new methods of cyber assault will be developed to exploit AI technology’s specific flaws. Finally, AI’s demand for enormous volumes of training data will amplify the value of data, altering how we must think about data protection. Prudent global governance will be required to ensure that this game-changing technology brings about broadly shared safety and prosperity.
Big Data and AI
In general, artificial intelligence (AI) refers to computational technologies that can do particular tasks in place of human intelligence. This technology is currently evolving at breakneck speed, similar to the exponential growth seen in database technology in the late twentieth century. Databases have evolved into the critical infrastructure that powers enterprise-level software. Similarly, AI is predicted to drive the majority of new value-added from software in the future decades.
Databases have grown greatly in the previous decade to manage the new phenomena known as “big data.” This refers to the extraordinary extent and global scope of modern data sets, which are mostly derived from computer systems, which have come to mediate practically every aspect of daily life. YouTube, for example, receives almost 400 hours of video footage per minute.
What Research Says?
Researchers, for example, have trained computer models to determine an individual’s personality traits more accurately than their friends based just on what Facebook posts they liked. Big data and artificial intelligence have a specific link. Recent advances in AI development have mostly been driven by “machine learning.”
Rather than providing a fixed set of instructions for an AI to follow, this technique trains AI by utilizing vast data sets. AI chatbots, for example, can be trained on data sets containing text recordings of human conversations collected through messaging applications to learn how to interpret what humans say and respond appropriately. Big data might be thought of as the raw material that powers AI algorithms and models.
The fundamental impediment to innovation is no longer the difficulty of recording and storing information, but rather the discovery of relevant insights among the massive amounts of data that are currently being collected. AI can find patterns in massive data sets that human vision is incapable of detecting. The use of AI technology can turn even mundane and seemingly insignificant data into important information. Researchers, for example, have trained computer models to determine an individual’s personality traits more accurately than their friends can only be based on what Facebook posts the subject had liked.
Artificial Intelligence and Cyber Security
Almost every day, there is a news article about a high-profile data breach or a cyber-attack that costs millions of dollars in damages. Cyber losses are difficult to assess, but the International Monetary Fund estimates that they cost the global financial system between $100 and $250 billion every year.
Furthermore, as computers, mobile devices, servers, and smart devices become more prevalent, the aggregate danger exposure climbs by the day. While the corporate and governmental communities are still grappling with the cyber realm’s growing relevance, the application of AI to cyber security heralds even more profound shifts.
Goals of AI
One of the primary goals of AI is to automate jobs that formerly required human intelligence. Reduced labour resources required by an organization to execute a project, or the time an individual needs to dedicate to everyday duties, offers huge increases in efficiency. Chatbots, for example, can be used to answer customer service concerns, while medical assistant AI can be used to diagnose diseases based on symptoms.
In a simplified model of how AI could be used in cyber defense, log lines of recorded activity from servers and network components can be labeled as “hostile” or “non-hostile,” and an AI system can be taught with this data set to classify future observations into one of those two categories. The system can therefore function as an automated sentinel, picking up anomalous observations from the massive background noise of typical activity.
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