Our last month’s edition of the Dataspace newsletter looked at the growing applications of AI for cybersecurity, and explored how some of the major players in tech are incorporating AI based security tools into the suite of products they offer to business clients. However, it is also important to note that hacking can present a […]
This month’s Dataspace newsletter looks at the applications of AI for cybersecurity, and some potential limitations.
Image alteration is all around us nowadays. This post looks at a new generation of tools that “predict” if an image has been altered.
Beyond the tech specs of how these algorithms actually work, the growing capabilities of predictive text beg a multitude of questions regarding the potential implications for humans.
Don’t worry. In a lot of areas AI is just not ready to make human workers redundant.
Predictive analytics is about making educated guesses regarding the future based on things you know about the past. But, what if the future doesn’t look anything like the past?
Much like an actual cloud that can look like whatever you imagine it to be, this virtual cloud is amorphous in its own right, and ever changing.
Fortunately for those who want to leverage cloud computing to improve their data sharing, storage, and analytics capabilities it is less important to understand what “The Cloud” looks like than what it does. In this issue of our newsletter, we dive into some of the silver linings of cloud computing and its applications.
While we may yet be a long way from the threat of truly sentient computers such as Hal 9000 and ARIIA, the current capability of AI technologies still offers plenty of power for those who wish to harness it for malicious purposes. These types of crimes go beyond the grey ethical areas that we discussed […]
Hashing is simply passing some data through a formula that produces a result, called a hash. That hash is usually a string of characters and the hashes generated by a formula are always the same length, regardless of how much data you feed into it.
In data science and predictive analytics we determine the likelihood of something by looking at data about it. We do this simply by looking for similarities between that data and data from past cases where we actually know the outcome.
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