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This post is reposted from the Microsoft Azure Blog : What is Artificial Intelligence? <azure.microsoft.com/blog/what-is-artificial-intelligence/> Aug 9th 2018, 12:00, by Theo van Kraay It has been said that Artificial Intelligence will define the next generation of software solutions. If you are even remotely involved with technology, you will almost certainly have heard the term with increasing regularity over the last few years. It is likely that you will also have heard different definitions for Artificial Intelligence offered, such as: *“The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.”* – Encyclopedia Britannica *“Intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans.”* – Wikipedia How useful are these definitions? What exactly are “tasks commonly associated with intelligent beings”? For many people, such definitions can seem too broad or nebulous. After all, there are many tasks that we can associate with human beings! What exactly do we mean by “intelligence” in the context of machines, and how is this different from the tasks that many traditional computer systems are able to perform, some of which may already seem to have some level of *intelligence* in their sophistication? What exactly makes the *Artificial Intelligence* systems of today different from sophisticated software systems of the past? It could be argued that any attempt to try to define “Artificial Intelligence” is somewhat futile, since we would first have to properly define “intelligence”, a word which conjures a wide variety of connotations. Nonetheless, this article attempts to offer a more accessible definition for what passes as Artificial Intelligence in the current vernacular, as well as some commentary on the nature of today’s AI systems, and why they might be more aptly referred to as “intelligent” than previous incarnations. Firstly, it is interesting and important to note that the technical difference between what used to be referred to as Artificial Intelligence over 20 years ago and traditional computer systems, is close to zero. Prior attempts to create intelligent systems known as *expert systems* at the time, involved the complex implementation of exhaustive rules that were intended to approximate* intelligent behavior*. For all intents and purposes, these systems did not differ from traditional computers in any drastic way other than having many thousands more lines of code. The problem with trying to replicate human intelligence in this way was that it requires far too many rules and ignores something very fundamental to the way *intelligent beings* make *decisions*, which is very different from the way traditional computers process information. Let me illustrate with a simple example. Suppose I walk into your office and I say the words “Good Weekend?” Your immediate response is likely to be something like “yes” or “fine thanks”. This may seem like very trivial behavior, but in this simple action you will have immediately demonstrated a behavior that a traditional computer system is completely incapable of. In responding to my question, you have effectively dealt with ambiguity by making a prediction about the correct way to respond. It is not certain that by saying “Good Weekend” I actually intended to ask you whether you had a good weekend. Here are just a few possible* intents* behind that utterance: – Did you have a good weekend? – Weekends are good (generally). – I had a good weekend. – It was a good football game at the weekend, wasn’t it? – Will the coming weekend be a good weekend for you? And more. The most likely intended meaning may seem obvious, but suppose that when you respond with “yes”, I had responded with “No, I mean it was a good football game at the weekend, wasn’t it?”. It would have been a surprise, but without even thinking, you will absorb that information into a mental model, correlate the fact that there was an important game last weekend with the fact that I said “Good Weekend?” and adjust the probability of the expected response for next time accordingly so that you can respond correctly next time you are asked the same question. Granted, those aren’t the thoughts that will pass through your head! You happen to have a neural network (aka “your brain”) that will absorb this information automatically and *learn* to respond differently next time. The key point is that even when you do respond next time, you will still be making a prediction about the correct way in which to respond. As before, you won’t be certain, but if your prediction *fails* again, you will gather new data which leads to my definition of Artificial Intelligence: “Artificial Intelligence is the ability of a computer system to deal with ambiguity, by making predictions using previously gathered *data*, and learning from errors in those predictions in order to generate newer, more accurate predictions about how to behave in the future”. This is a somewhat appropriate definition of Artificial Intelligence because it is exactly what AI systems today are doing, and more importantly, it reflects an important characteristic of human beings which separates us from traditional computer systems: human beings are prediction machines. We deal with ambiguity all day long, from very trivial scenarios such as the above, to more convoluted scenarios that involve *playing the odds* on a larger scale. This is in one sense the essence of *reasoning*. We very rarely know whether the way we respond to different scenarios is absolutely correct, but we make reasonable predictions based on past experience. Just for fun, let’s illustrate the earlier example with some code in R! First, lets start with some data that represents information in your mind about when a particular person has said “good weekend?” to you. In this example, we are saying that *GoodWeekendResponse* is our *score label* (i.e. it denotes the appropriate response that we want to predict). For modelling purposes, there have to be at least two possible values in this case “yes” and “no”. For brevity, the response in most cases is “yes”. We can fit the data to a logistic regression model: library(VGAM) greetings=read.csv(‘c:/AI/greetings.csv’,header=TRUE) fit <- vglm(GoodWeekendResponse~., family=multinomial, data=greetings) Now what happens if we try to make a prediction on that model, where the expected response is different than we have previously recorded? In this case, I am expecting the response to be “Go England!”. Below, some more code to add the prediction. For illustration we just hardcode the new input data, output is shown in bold: response <- data.frame(FootballGamePlayed=”Yes”, WorldCup=”Yes”, EnglandPlaying=”Yes”, GoodWeekendResponse=”Go England!!”) greetings <- rbind(greetings, response) fit <- vglm(GoodWeekendResponse~., family=multinomial, data=greetings) prediction <- predict(fit, response, type=”response”) prediction index <- which.max(prediction) df <- colnames(prediction) df[index] * No Yes Go England!! 1 3.901506e-09 0.5 0.5 > index <- which.max(prediction) > df <- colnames(prediction) > df[index] [1] “Yes”* The initial prediction “yes” was wrong, but note that in addition to predicting against the new data, we also incorporated the actual response back into our existing model. Also note, that the new response value “Go England!” has been *learnt*, with a probability of 50 percent based on current data. If we run the same piece of code again, the probability that “Go England!” is the right response based on prior data increases, so this time our model *chooses* to respond with “Go England!”, because it has finally learnt that this is most likely the correct response! * No Yes Go England!! 1 3.478377e-09 0.3333333 0.6666667 > index <- which.max(prediction) > df <- colnames(prediction) > df[index] [1] “Go England!!”* Do we have Artificial Intelligence here? Well, clearly there are different *levels* of intelligence, just as there are with human beings. There is, of course, a good deal of nuance that may be missing here, but nonetheless this very simple program will be able to react, with limited accuracy, to data coming in related to one very specific topic, as well as learn from its mistakes and make adjustments based on predictions, without the need to develop exhaustive rules to account for different responses that are expected for different combinations of data. This is this same principle that underpins many AI systems today, which, like human beings, are mostly sophisticated prediction machines. The more sophisticated the machine, the more it is able to make accurate predictions based on a complex array of data used to *train* various models, and the most sophisticated AI systems of all are able to continually learn from faulty assertions in order to improve the accuracy of their predictions, thus exhibiting something approximating human *intelligence*. Machine learning You may be wondering, based on this definition, what the difference is between *machine learning* and *Artificial intelligence*? After all, isn’t this exactly what machine learning algorithms do, make predictions based on data using statistical models? This very much depends on the definition of *machine learning*, but ultimately most machine learning algorithms are* trained* on static data sets to produce predictive models, so machine learning algorithms only facilitate part of the dynamic in the definition of AI offered above. Additionally, machine learning algorithms, much like the contrived example above typically focus on specific scenarios, rather than working together to create the ability to deal with *ambiguity* as part of an *intelligent system*. In many ways, machine learning is to AI what neurons are to the brain. A building block of intelligence that can perform a discreet task, but that may need to be part of a composite *system* of predictive models in order to really exhibit the ability to deal with ambiguity across an array of behaviors that might approximate to *intelligent behavior*. Practical applications There are number of practical advantages in building AI systems, but as discussed and illustrated above, many of these advantages are pivoted around “time to market”. AI systems enable the embedding of complex decision making without the need to build exhaustive rules, which traditionally can be very time consuming to procure, engineer and maintain. Developing systems that can “learn” and “build their own rules” can significantly accelerate organizational growth. Microsoft’s Azure cloud platform offers an array of discreet and granular services in the AI and Machine Learning domain <docs.microsoft.com/en-us/azure/#pivot=products&panel=ai>, that allow AI developers and Data Engineers to avoid re-inventing wheels, and consume re-usable APIs. These APIs allow AI developers to build systems which display the type of *intelligent behavior* discussed above. If you want to dive in and learn how to start building intelligence into your solutions with the Microsoft AI platform, including pre-trained AI services like Cognitive Services and the Bot Framework, as well as deep learning tools like Azure Machine Learning, Visual Studio Code Tools for AI, and Cognitive Toolkit, visit AI School <aischool.microsoft.com/learning-paths>.

έχει τύχει να αλλάζει κωδικός σε Active Directory ( συνεπώς και MS Exchange password ) και το ActiveSync handheld να συνεχίζει να συγχρονίζει το mailbox ώρες μετά με τον παλιό .... πρακτικά τα δύο παρακάτω άρθρα το επεξηγούν, ειδικά το 1ο να πως υποτίθεται πως λειτουργεί και να πως ειναι http://www.techrepublic.com/article/why-does-my-old-password-work-via-activesync/ https://social.technet.microsoft.com/Forums/office/en-US/e792fd57-84b2-4e48-8281-99b062b8a014/activesync-can-sync-6-hours-after-changing-password-in-ad-before-client-asks-for-new-credentials?forum=exchangesvrclientslegacy

Outlook.com Smart Network Data Services Deliverability to Outlook.com is based on your reputation. The Outlook.com Smart Network Data Services (SNDS) gives you the data you need to understand and improve your reputation at Outlook.com. But just looking at the data isn't enough! Maintaining a good reputation is a lot of work. You should use this data to keep your mailing lists clean and to monitor the IPs you control for unusual behavior. Reputation is always the responsibility of the sender. SNDS gives senders access to detailed data about individual IPs, and it also includes our Junk Email Reporting Program, which lets you receive reports when users junk your messages. Now you can view IP data and manage feedback loop settings from one convenient website. Getting started To access SNDS, please log in with a Microsoft Account and then request access to the IPs for which you are responsible. You'll be taken through a simple authorization process, and then you'll soon have access to a wealth of information about those IPs. Help! I have a problem sending mail to Outlook.com Building & maintaining good reputation is a long-term proposition. The data on this site can help you do that, but if you have an urgent deliverability issue please have the person most familiar with the issue and your email infrastructure contact sender support. Other Benefits SNDS is useful for far more than just monitoring email reputation. It can help IP owners to detect compromised servers, malware, viruses, and botnets. We help network administrators detect these problems so that they can clean them up and make the internet a safer place. https://postmaster.live.com/snds

We’re happy to announce the availability of our newest free ebook, Introducing Windows Server 2016 (ISBN 9780735697744), by John McCabe and the Windows Server team. Enjoy! Windows Server has powered a generation of organizations, from small businesses to large enterprises. No matter what your area of expertise, this book will introduce you to the latest developments in Windows Server 2016. Each chapter has been written by either field experts or members of the product group, giving you the latest information on every improvement or new feature that is included in this version of Windows Server. Introduction Windows Server has powered a generation of organizations, from small businesses to large enterprises. No matter what your role in IT, you can be guaranteed you that have touched Windows Server at some point in your career or at very least you have seen it from afar! This book introduces you to Windows Server 2016, which is the next version of Windows Server. No matter what your area of expertise, this book will introduce you to the latest developments in Windows Server 2016. Each chapter has been written by either field experts or members of the product group, giving you the latest information on every improvement or new feature that is included in this version of Windows Server. This information will help you to prepare for Windows Server 2016 and give you the means to develop and design a path to introduce Windows Server 2016 into your environment and take full advantage of what is to come. This book is being written at a time when the product is still evolving and it should be noted that things might change or not appear in the final version of Windows Server 2016 when released. All guidance in the chapters is meant to be tried and evaluated in a test environment; you should not implement it in a production environment. This book assumes that you are familiar with key concepts surrounding Windows Server (i.e., Microsoft Hyper-V, Networking, and Storage) as well as cloud technologies such as Microsoft Azure. In this book, we cover a variety of concepts related to the technology and present scenarios with a customer focus, but it is not intended as a how-to or design manual. You can use other sources, including the online Microsoft resources, to stay up to date with the latest developments on the roles and features of Windows Server 2016. The online resources will also contain the latest how-to procedures and information about designing a Windows Server 2016 infrastructure for your business. About the author John McCabe works for Microsoft as a senior premier field engineer. In this role, he has worked with the largest customers around the world, supporting and implementing cutting-edge solutions on Microsoft Technologies. In this role, he is responsible for developing core services for the Enterprise Services Teams. John has been a contributing author to several books, including Mastering Windows Server 2012 R2 from Sybex, Mastering Lync 2013 from Sybex, and Introducing Microsoft System Center 2012 from Microsoft Press. John has spoken at many conferences around Europe, including TechEd and TechReady. Prior to joining Microsoft, John was an MVP in Unified Communications with 15 years of consulting experience across many different technologies such as networking, security, and architecture. https://blogs.msdn.microsoft.com/microsoft_press/2016/09/26/free-ebook-introducing-windows-server-2016/