"Today's expert systems deal with domains of narrow specialization. For expert systems to perform competently over a broad range of tasks, they will have to be given very much more knowledge. ... The next generation of expert systems ... will require large knowledge bases. How will we get them?"
– Edward Feigenbaum, Pamela McCorduck, H. Penny Nii, from The Rise of the Expert Company. New York: Times Books, 1988.
Are we destined to be replaced by computers? People are trying to create a computer than can diagnose and ultimately treat diseases, essentially obliterating the need for us humans to be involved in the process. Take, for example, the field of radiology. Many have been reading the literature on the use of AI in diagnostic radiology, and they speak with fear about the ability of these computerized systems to be ultimately faster and more accurate readers of x-rays, CT scans, ultrasounds, and more. True, this feels like a good use of technology, as scanning a static image is probably a good place to start using this AI.
The interest in Artificial Intelligence (AI) is constantly growing, especially in industries characterized by repetitive processes and manual tasks. One benefit of the effective application of AI is that humans, freed from these repetitive tasks, are able to focus on higher-value activities. This is clear to the 41% of decision makers who are using cognitive and AI tools in their business, according to a recnet a Forrester survey, "TITLE," as shown in Figure 1. In fact, many decision makers consider the technological progression to AI a major priority and already understand the potential of AI for their business. Automation is an opportunity for financial industry, where AI can reduce the efforts of finance professionals in traditional activities such as transaction processing, auditing and compliance.
I am by no means a particularly good example of study habits, but generally I tend to read what I need and go from there... Basically this in practice often means starting somewhere relevant to whatever work/assignment/project I'm trying to do, and then going backwards building a recursive stack of readings that seem important to understanding the previous thing until I reach a point where I am familiar with the material already. Then I work through the stack until I'm back to wherever I started. Essentially this is the backward chaining algorithm. I also, if I need to learn a lot from a book for some reason (i.e. a course) or have no particular goal in mind but find my self with a text that piques my interest, then I tend to skim from cover to cover everything that actually attracts my attention, occasionally flipping back to something that I realize is important for understanding later stuff. If it seems especially critical and I can't understand it, then I'll look through exercises and maybe do them if it seems worthwhile.
Recent use includes the assassination with VX of Kim Jong Nam, half-brother of North Korean leader Kim Jong Un, in Kuala Lumpur airport in February 2017 and the attempted murder of Sergei Skripal, a 66-year-old former Russian double agent, and his daughter with a Novichok nerve agent in March in England.
A British computer expert who helped shut down the NHS'WannaCry' cyber attack has been charged in the US with creating banking malware. Marcus Hutchins, 23, has been charged with six counts of creating and distributing malware known as Kronos. Hutchins made a telephone call from jail hours after his arrest last August to an unidentified individual - which was recorded and filed by US prosecutors, according to court documents. He said he had written code as a youngster which was turned into malicious software that prosecutors say harvested banking details. According to court documents seen by The Washington Post, Hutchins said in the phone call: 'So I wrote code for a guy a while back who then incorporated it into a banking malware, so they have logs of that, and essentially they want to know my part of the banking operation or if I just sold the code on to some guy... once they found I sold the code to someone, they wanted me to give them his name, and I don't actually know anything about him.'
The overall team performance will obviously get much better as we click on at least two of those cylinders. When we get some of our guys back in the next week, we're confident our offense is going to perform better. It's incumbent upon us, with our bullpen, to get back to what we were doing last year. We're confident we have the guys down there to perform way better than we have.
You've probably been to a supermarket that printed coupons for you at checkout. Or listened to a playlist that your streaming service generated for you. Or gone shopping online and seen a list of products labeled "you might be interested in…." that did indeed contain some stuff that you were interested in. Recommendation engines take data about you, similar consumers, and available products, and use that to figure out what you might be interested in and therefore deliver those coupons, playlists, and suggestions. Recommendation engines can be extremely complex.
R9B's APCs are 24x7x365 security-as-a-service operations centers designed to deliver both managed detection and response services as well as threat hunting to a growing customer set. The ORION HUNT platform enables cyber defenders to stealthily maneuver in a client network to proactively search for adversaries that defeat passive and automated security products. Leveraging DarkLight will enable APC HUNT operations to find correlations and patterns within any size dataset, rapidly evaluating millions of events. The combination of DarkLight and APC operations delivers a new and powerful network security-based data analysis model. Combining the analytics engine with the ORION platform creates a dynamic, targeted collection and response mechanism.
Machines are exceptionally good students. We can see that in the rapid and widespread adoption of machine learning -- also known as auto-learning -- to automate and improve the collection and processing of large amounts of data. A version of artificial intelligence, machine learning refers to the ability of computers to adapt and learn new things without having to be specifically programmed with new software. This capacity to continuously build a knowledge base and to analyze data and identify patterns is particularly useful in industries that generate large volumes of documents, such as healthcare and finance. But it also has important applications in any business situation that requires a company to accurately process sales orders.
Empirical testing is a very popular evaluation method for the development of intelligent systems. Here, previously solved problems with correct solutions are given as cases to the system. Validity is tested by comparing the expected results with the derived solutions. Besides classic forms of boolean testing of occurring solutions more refined methods are required for a thorough evaluation of real world knowledge systems. We present extended precision and recall functions for interactive knowledge systems that are generalizations of the existing measures. Additionally, we propose a visualization method for inspecting the validation result for interactive systems. A case study with a second-opinion system from the medical domain demonstrates the usefulness of the approach.