If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Have you ever been worried on a tour, if you've switched off a light bulb back home? We've all had this confusion at least once, for there was no way you could ensure that everything was perfect. Now, with AI, we don't need to leave anything to chance nor assumptions. AI provides perfect ways to ensure that use of appliances is optimized perfectly when in use and when not in use. Let's imagine some of the ways through which AI and Smart Home Automation will impact and change the way we live: Amazon's Alexa, Google Home, Apple's Siri and Microsoft's Cortana have all optimized and automated living inside a home to a great extent.
With all the buzz around big data, artificial intelligence, and machine learning (ML), enterprises are now becoming curious about the applications and benefits of machine learning in business. A lot of people have probably heard of ML, but do not really know what exactly it is, what business-related problems it can solve, or the value it can add to their business. ML is a data analysis process which leverages ML algorithms to iteratively learn from the existing data and help computers find hidden insights without being programmed for. With Google, Amazon, and Microsoft Azure launching their Cloud Machine learning platforms, we have seen artificial intelligence and ML gaining prominence in the recent years. Surprisingly, we all have witnessed ML without actually knowing it.
Predictive policing – the use of machine-learning algorithms to fight crime – risks unfairly discriminating against protected characteristics including race, sexuality and age, a security thinktank has warned. Such algorithms, used to mine insights from data collected by police, are currently deployed for various purposes including facial recognition, mobile phone data extraction, social media analysis, predictive crime mapping and individual risk assessment. Researchers at the Royal United Services Institute (RUSI), commissioned by the government's Centre for Data Ethics and Innovation, focused on predictive crime mapping and individual risk assessment and found algorithms that are trained on police data may replicate – and in some cases amplify – the existing biases inherent in the data set, such as over- or under-policing of certain communities. "The effects of a biased sample could be amplified by algorithmic predictions via a feedback loop, whereby future policing is predicted, not future crime," the authors said. The paper reveals that police officers, who were interviewed for the research, are concerned about the lack of safeguards and oversight regarding the use of predictive policing.
It's been 10 years since the first ever Mario AI Competition, so I return to the world of Super Mario level generation research and catch up one some of the more interesting examples that have arisen in recent years. This video is inspired by the following AI research papers and projects: NOOR SHAKER: http://lynura.com/publications.php It's is supported through and wouldn't be possible wthout the wonderful people who support it on Patreon. You can follow AI and Games (and me) on Facebook, Twitter and Instagram: http://www.facebook.com/AIandGames
Some days ago I was interviewing a candidate for a data-related position: after a couple of technical questions I asked him what algorithm he would have used to have a reliable starting point for a random classification problem. I was just curious to understand how used he was in doing some data science and if he knew some state-of-the-art algorithms and techniques. He told me that he would have gone with a simple decision tree because it's somehow easy to explain and interpret. That answer surprised me a little: I mean, why a decision tree in 2019 when you can get way better and, above all, more robust results using more advanced algorithms? As always happens, once you notice something you see it everywhere, and from that day I keep seeing and reading here and there blog posts about interpretability, explicability and how all of these concepts are connected to machine learning and trust.
As more and more banking institutions look to find out more about the importance of artificial intelligence, app developers are playing a much larger role. App developers are able to guide their clients in the proper direction. They are at the cutting edge of all new technologies. Banking institutions that do not take the time to meet with app builders are placing themselves behind the proverbial eight ball. They are not going to be able to enjoy all of the benefits that artificial intelligence has to offer.
As of today, lots of companies state to assist security firms, the army, in addition to consumers prevent crime and defend their private, homes, and buildings belongings. This particular article intends to offer business leaders in the security space with a concept of what they are able to presently expect from Ai in the business of theirs. We wish this report allows company leaders in security to garner insights they are able to confidently relay to the executive teams of theirs so they are able to make educated choices when thinking about AI adoption. At the minimum, this article intends to serve as a technique of decreasing the time industry leaders in physical security spend researching AI businesses with whom they might (or might not) be keen on working. Evolv Technology claims to offer a physical security system that consists of the Evolve Edgepersonnel threat screening machine that works with the Evolv Pinpoint automated facial recognition application.
CYBERSECURITY specialists have been betting on artificial intelligence (AI) to defend their organizations against sophisticated cyberattacks for quite a while now -- and it seems as though deep learning and machine learning have the potential to deliver. AI is a broad term that encompasses computer vision, machine learning, and deep learning, and generally offers the ability to mimic human actions, intelligently, and at incredible speed. For hackers trying to "guess" a password, it means AI can not only use "trial and error" to break into a victim's account much faster but also do it intelligently so that that the account doesn't get locked before the right password is guessed. On the other side of the fence, or network, cybersecurity professionals didn't immediately benefit from AI because systems in place don't automatically lend themselves to the technology -- however, experts bet on two niche elements of AI to find a solution. Those niche areas are machine learning and deep learning.
This post addresses the appropriate way to split data into a training set, validation set, and test set, and how to use each of these sets to their maximum potential. It also discusses concepts specific to medical data with the motivation that the basic unit of medical data is the patient, not the example. If you are already familiar with the philosophy behind splitting a data set into training, validation, and test sets, feel free to skip this section. Otherwise, here's how and why we split data in machine learning. A data set for supervised learning is composed of examples.
You might not know it, but deep learning already plays a part in our everyday life. When you speak to your phone via Cortana, Siri or Google Now and it fetches information, or you type in the Google search box and it predicts what you are looking for before you finish, you are doing something that has only been made possible by deep learning. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. It also is known as deep structured learning or hierarchical learning. The term Deep Learning was introduced to the machine learning community by Rina Dechter in 1986, and to Artificial Neural Networks by Igor Aizenberg and colleagues in 2000, in the context of Boolean threshold neurons.