Knowledge Engineering


Knowledge Engineering

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Knowledge engineering is the process of creating rules that apply to data in order to imitate the way a human thinks and approaches problems. A task and its solution are broken down to their structure, and based on that information, AI determines how the solution was reached. Often, a library of problem-solving methods and knowledge to solve a particular set of problems is fed into a system as raw data. Then, the system can diagnose the problem and find the solution without further human input. The result can be used as a self-help troubleshooting software, or as a support module to a human agent.


r/artificial - I cant conceive of a machine actually seeing colors like we do. The only thing I can see is possible is a computer simply having knowledge based on what color is what. Like having a number represent what color is there but not actually seeing it. Is this how AI works? I cant find anything on google.

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Take for instance a computer that records a video and can recognize objects in the video, sure it has a data warehouse somewhere of what each object is, and if it doesn't it could add one once it "learns" what it is. But realistically how is that different from humans? Humans don't know what a color is until they learn what it is, I didn't know red was red until someone told me, and red is only red because it is generally agreed upon what the word red represents. If I see a color and tell you it's red, and an a.i.


Help Your Multilingual Knowledge Base Thrive With AI

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Data shows that, for global businesses, providing support in multiple languages is well worth the effort. Nearly three quarters of people search online in their native language, which means that if you're only communicating in English, for example, you're probably losing customers and adding layers of inefficiencies for your agents. Easier said than done, perhaps. On average, even in one language, 20% of agent time is spent looking for information to either share directly with customers or to find the right way to resolve a problem. Providing support in multiple languages across multiple channels adds another set of variables to the mix.


PhD-student for our research division Knowledge Engineering / Data Science - MAASTRO clinic

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Conditions of employment and salary are based on the Dutch Collective Labour Agreement for Hospitals (CAO-Ziekenhuizen). You will receive a fulltime contract (36 hours/week) for an initial period of one year. Your salary will be according to the salary scale FWG 50 (starting with € 2.336,- depending on your relevant experience). Within the collective labor agreement, there is an extensive package of fringe benefits, including a good pension arrangement, a 8.33% holiday allowance and end-of-year bonus and an excellent pension provision. In addition, MAASTRO offers various discount schemes with regard to (healthcare) insurance, bicycle purchase and sports subscriptions.


AI Knowledge Map: how to classify AI technologies – Francesco Corea – Medium

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I have been in the space of artificial intelligence for a while, and I am aware that multiple classifications, distinctions, landscapes, and infographics exist to represent and track the different ways to think about AI. However, I am not a big fan of those categorization exercises, mainly because I tend to think that the effort of classifying dynamic data points into predetermined fix boxes is often not worth the benefits of having such a "clear" framework (it is a generalization of course, cause sometimes they are extremely useful). When it comes specifically to artificial intelligence, I do also think that many of the categorizations out there are either incomplete or unable to capture strong fundamental links and aspects of this new AI wave. So let me first tell you the rationale for this post. Working with strategic innovation agency Chôra, we wanted to create a visual tool for people to grasp at a glance the complexity and depth of this toolbox, as well as laying down a map that could help people orientating in the AI jungle.


Het vizier op de tech industrie

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Last week I attended the Oracle Open World Europe 2019 in London. At this event Andrew Sutherland VP of technology told us that security was one of the main reasons why customers were choosing the Oracle autonomous database. This is interesting for two reasons firstly it shows that security is now top of mind amongst the buyers of IT systems and secondly that buyers have more faith in technology than their own efforts. The first of these reasons is not surprising. The number of large data breaches disclosed by organizations continues to grow and enterprise databases contain the most valuable data.


Police are using artificial intelligence to spot written lies

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There's no foolproof way to know if someone's verbally telling lies, but scientists have developed a tool that seems remarkably accurate at judging written falsehoods. Using machine learning and text analysis, they've been able to identify false robbery reports with such accuracy that the tool is now being rolled out to police stations across Spain. Computer scientists from Cardiff University and Charles III University of Madrid developed the tool, called VeriPol, specifically to focus on robbery reports. In their paper, published in the journal Knowledge-Based Systems earlier this year, they describe how they trained a machine-learning model on more than 1000 police robbery reports from Spanish National Police, including those that were known to be false. A pilot study in Murcia and Malaga in June 2017 found that, once VeriPol identified a report as having a high probability of being false, 83% of these cases were closed after the claimants faced further questioning.


Braidio announces WorkStreams platform to optimise enterprise productivity

ZDNet

Technology is changing rapidly, information is coming at us so fast that traditional methods of learning the information you need for the job - and improving your performance - are no longer effective. The challenge is giving your employees the tools they need to be able to access the requisite knowledge, and the ability to share it, is key to business success. SaaS had a major impact on the way companies consume cloud services. This ebook looks at how the as a service trend is spreading and transforming IT jobs. San Francisco, CA-based social workplace intelligence platform Bradio has launched a new platform that aims to address these issues.


AI Knowledge Map: How To Classify AI Technologies

#artificialintelligence

I have been in the space of artificial intelligence for a while and am aware that multiple classifications, distinctions, landscapes, and infographics exist to represent and track the different ways to think about AI. However, I am not a big fan of those categorization exercises, mainly because I tend to think that the effort of classifying dynamic data points into predetermined fixed boxes is often not worth the benefits of having such a "clear" framework (this is a generalization of course as sometimes they are extremely useful). I also believe this landscape is useful for people new to the space to grasp at-a-glance the complexity and depth of this topic, as well as for those more experienced to have a reference point and to create new conversations around specific technologies. What follows is then an effort to draw an architecture to access knowledge on AI and follow emergent dynamics, a gateway of pre-existing knowledge on the topic that will allow you to scout around for additional information and eventually create new knowledge on AI. I call it the AI Knowledge Map (AIKM).


AI Knowledge Map: How To Classify AI Technologies

#artificialintelligence

I have been in the space of artificial intelligence for a while and am aware that multiple classifications, distinctions, landscapes, and infographics exist to represent and track the different ways to think about AI. However, I am not a big fan of those categorization exercises, mainly because I tend to think that the effort of classifying dynamic data points into predetermined fixed boxes is often not worth the benefits of having such a "clear" framework (this is a generalization of course as sometimes they are extremely useful). I also believe this landscape is useful for people new to the space to grasp at-a-glance the complexity and depth of this topic, as well as for those more experienced to have a reference point and to create new conversations around specific technologies. What follows is then an effort to draw an architecture to access knowledge on AI and follow emergent dynamics, a gateway of pre-existing knowledge on the topic that will allow you to scout around for additional information and eventually create new knowledge on AI. I call it the AI Knowledge Map (AIKM).