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) …
Recognition of the face as an identity is a critical aspect in today's world. Facial identification and recognition find its use in many real-life contexts, whether your identity card, passport, or any other credential of significant importance. It has become quite a popular tool these days to authenticate the identity of an individual. This technology is also being used in various sectors and industries to prevent ID fraud and identity theft. Your smartphone also has a face recognition feature to unlock it.
Over recent years, advancements in artificial intelligence (AI) have greatly increased the safety of our communities. The technology helps emergency managers predict and mitigate flooding, wildfires, and other natural disasters. It improves image and video analysis, saving investigators' valuable time and reducing errors. It aids crime analysts by pouring through vast amounts of data and making connections that can empower policing. While AI has expanded its role in our everyday lives, many cities are only just beginning to scratch the surface when it comes to the benefits that the technology can provide.
This article is sponsored by IBM. SUMMARY: Organizations often miss the greatest opportunities that machine learning has to offer because tapping them requires real-time predictive scoring. In order to optimize the very largest-scale processes – which is a vital endeavor for your business – predictive scoring must take place right at the moment of each and every interaction. The good news is that you probably already have the hardware to handle this endeavor: the same system currently running your high-volume transactions – oftentimes a mainframe. But getting this done requires a specialized leadership practice and strong-willed change management. Heed this warning: The greatest opportunities with machine learning are exactly the ones that your business is most likely to miss. To be specific, there's massive potential for real-time predictive scoring to optimize your largest-scale operations. But with these particularly high stakes comes a tragic case of analysis paralysis.
Yup, that's me being plowed to the ground because the business just lost more than $500,000 with our fraud detection system by wrongly flagging fraudulent transactions as legitimate, and my boss's career is probably over. You're probably wondering how we got here… My story began with an image that you've probably seen over 1,001 times--the lifecycle of an ML project. A few months ago, we finally deployed to production after months of perfecting our model. I told myself and my colleague, "Our hard work has surely paid off, hasn't it?". Our model was serving requests in real-time and returning results in batches--good stuff! Surely that was enough, right? Well, not quite, which we got to realize in a relatively dramatic fashion. I'm not going to bore you with the cliché reasons why the typical way of deploying working software just doesn't cut it with machine learning applications. I'm still trying to recover from the bruises that my boss left on me, and the least I can do is help you not end up in a hospital bed after "successful model deployment", like me. I'll tell you all about: By the end of this article, you should know exactly what to do after deploying your model, including how to monitor your models in production, how to spot problems, how to troubleshoot, and how to approach the "life" of your model beyond monitoring. You almost don't have to worry about anything. Based on the software development lifecycle, it should work as expected because you have rigorously tested it and deployed it. In fact, your team may decide on a steady and periodic release of new versions as you mostly upgrade to meet new system requirements or new business needs.
I graduated on Warsaw University of Technology with master thesis about text mining topic (intelligent web crawling methods). I work for Polish IT consulting company (Sollers Consulting), where I develop and design various insurance industry related stuff, (one of them is insurance fraud detection platform). From time to time I try to compete in data mining contests (Netflix, competitions on Kaggle and tunedit.org) As far as I remember, the basis of the solution I defined at the very beginning: to create separate predictors for each individual loop and time interval. So my solution required me to build 61x10 610 regression models.
The UK's chief data protection regulator has warned over reckless and inappropriate use of live facial recognition (LFR) in public places. Publishing an opinion today on the use of this biometric surveillance in public -- to set out what is dubbed as the "rules of engagement" -- the information commissioner, Elizabeth Denham, also noted that a number of investigations already undertaken by her office into planned applications of the tech have found problems in all cases. "I am deeply concerned about the potential for live facial recognition (LFR) technology to be used inappropriately, excessively or even recklessly. When sensitive personal data is collected on a mass scale without people's knowledge, choice or control, the impacts could be significant," she warned in a blog post. "Uses we've seen included addressing public safety concerns and creating biometric profiles to target people with personalised advertising. "It is telling that none of the organisations involved in our completed investigations were able to fully justify the processing and, of those systems that went live, none were fully compliant with the requirements of data protection law.
The system can detect any movement that the prisoner makes, such as violence, quarrels, or anger etc. The face, hand movements or body movements can be analysed and a warning can be sent out about a prisoner even before he or he is about to commit an illegal act. It also studies and analyses facial expressions for the purpose. The Smart Monitoring' system uses AI and machine-learning algorithms.
Zimbabwe's commonest crimes include robbery, petty theft, vehicle burglary, home invasion, and smash-and-grab vehicle break-ins. The Zimbabwe security services have made a lot of efforts to make society as safe as possible but the nation's crime remains prevalent. Zimbabwe's answer to these kinds of crimes is technology; our hidden weapon. Universities in Zimbabwe have students who are getting educated in Artificial intelligence (AI) and machine learning (ML) with the Harare Institute of Technology (HIT) leading the way. These kinds of developments in AI and ML mean that technology has a growing role to play in upholding the law.
After years of failed attempts to curb surveillance technologies, Baltimore is close to enacting one of the nation's most stringent bans on facial recognition. But Baltimore's proposed ban would be very different from laws in San Francisco or Portland, Oregon: It would last for only one year, police would be exempt, and certain private uses of the tech would become illegal. City councilmember Kristerfer Burnett, who introduced the proposed ban, says it was shaped by the nuances of Baltimore, though critics complain it could unfairly penalize, or even jail, private citizens who use the tech. Last year, Burnett introduced a version of the bill that would have banned city use of facial recognition permanently. When that failed, he instead introduced this version, with a built-in one year "sunset" clause requiring council approval to be extended.
Are you a Data Science aspirant and looking forward to some challenging and real-time Data Science projects? Then you are at the right place to gain mastery in the field of Data Science. In this article, we will discuss the best Data Science projects that will boost your knowledge, skills and your Data Science career too!! These real-world Data Science projects with source code offer you a propitious way to gain hands-on experience and start your journey with your dream Data Science job. Now let's quickly jump to our best Data Science project examples with source code.