AI-Alerts


NeurIPS

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Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.


How AI is helping in the fight against cybercrime Newsflash

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Organisations are becoming so overwhelmed with data relating to cybersecurity that they are having to turn to artificial intelligence (AI) in order to keep abreast of it all. More than half of them reported that they were using or looking to use AI because their organisations had too much data to deal with. The machine-learning systems can help by processing huge volumes of data in a way that would be impossible for human analysts. Some cyber-attacks can be identified and blocked automatically. The AI can also alert human analysts to areas of data that they should be paying particular attention to, allowing them to respond to threats more effectively.



Artificial Intelligence – What implications for EU security and defence?

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Consider a world where human decision-making and thought processes play less of a role in the day-to-day functioning of society. Think now of the implications this would have for the security and defence sector. Over the next few decades, it is likely that Artificial Intelligence (AI) will not only have major implications for most areas of society such as healthcare, communications and transport, but also for security and defence. AI can be broadly defined as systems that display intelligent behaviour and perform cognitive tasks by analysing their environment, taking actions and even sometimes learning from experience. The complex attributes of the human mind are well known, but to replicate most of these abilities in machine or algorithmic form has given policymakers and scholars pause for thought.


How hackers are using Deepfakes to trick people Packt Hub

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Cybersecurity analysts have warned that spoofing using artificial intelligence is within the realm of possibility and that people should be aware of the possibility of getting fooled with such voice or picture-based deepfakes. Deepfakes rely on a branch of AI called Generative Adversarial Networks (GANs). It requires two machine learning networks that teach each other with an ongoing feedback loop. The first one takes real content and alters it. Then, the second machine learning network, known as the discriminator, tests the authenticity of the changes.


How Computer Vision Is Disrupting Different Industries

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Computer vision (CV) refers to the processes and technologies involved in helping machines "see" the world much like humans do by interpreting and understanding context. The difference between a machine and a human is that algorithms process information by transforming it into numerical models. Although CV originated in the late fifties, it has grown exponentially in the last decade due to increased computational power offered by cloud technologies, dedicated hardware, and more advancements. Computer vision has numerous applications; in healthcare, security, automotive, robotics, sports, and others. It's a market that is expected to reach close to $22 billion by 2026.


How do you design ML models for malicious network detection?

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Machine Learning (ML) has found its place into cybersecurity a long time ago and usage of ML has given cybersecurity teams much-needed insights into the malware network and effective ways to curb cyber attacks. Most ML-based solutions are proprietary or designed for specific feature representations. In 2017, one of the most prominent credit reporting agencies (CRA) of the United States– Equifax, suffered a huge malicious attack that led to a data breach that is famous for all the wrong reasons. Personal and sensitive data worth 148 million was lost to a data breach. Such data breach and data risks are still prevalent irrespective of the endpoint protection and other monitoring techniques deployed by enterprises worldwide.


Artificial Intelligence and Cybersecurity

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Currently, AI is expensive and difficult to implement fully into businesses, and, at this time, AI is not ready to fully meet the demands of cybersecurity. The science fiction style concept of AI, the ability for a machine to mimic intelligent human behavior, does not exist at this time. However, machine learning can still be leveraged to support cybersecurity initiatives. The technology stack using machine learning is growing. Large tech companies rely on machine intelligence and have products that depend upon AI or machine learning.


Mining software development history: Approaches and challenges

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Software development history, typically represented as a Version Control System log, is a rich source of insights into how the project evolved as well as how its developers work. What's probably more important is events from the past can predict the future. Vadim Markovtsev is a Google Developer Expert in Machine Learning and a Lead Machine Learning Engineer at source {d} (sourced.tech) His academic background is compiler technologies and system programming. Vadim is also author of several published papers about Machine Learning on Source Code.


Deconstructing the diagnostic reasoning of human versus artificial intelligence

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Artificial intelligence (AI) is expected to occupy an increasingly important place in diagnostic tasks in health care. The principles underlying learning are similar for human and artificial intelligences, but the respective approaches to diagnosis are markedly different. Clinicians approach diagnosis in an intuitive and deductive manner, whereas AI is chiefly analytical and inductive. The wholesale replacement of human intelligence by AI in diagnostic tasks is unlikely, apart from some highly targeted tasks; instead, AI should be considered as a tool to help clinicians in their reasoning. Artificial intelligence (AI) is often presented as the future of medical practice.