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Microsoft has introduced a brand new initiative with a view to see more than three million Brazilian college students getting informed in subject matters round synthetic intelligence (AI).
all through the Microsoft AI+Tour adventure in São Paulo, the firm's chief govt Satya Nadella introduced the professional-bono partnership with school networks SESI and SENAI to offer AI working towards in excessive college lessons. during the announcement, Nadella cited that governments should still accelerate the adoption of automation however create new capabilities on the identical time.
"we have a massive chance to generate advances in digital technologies - and principally in AI - to empower each and every person and each corporation in Brazil to achieve greater," Nadella informed delegates on the experience on Tuesday (12).
four courses are available during the college community's online platform, protecting the introduction to AI and records science, information science fundamentals building of solutions the usage of Azure, Bot and IoT. other lessons geared on the building of functions and chatbots using Microsoft's cognitive features can be provided within the following couple of months, in addition to courses around cloud and innovation.
"teaching younger Brazilians with the applied sciences of the so-known as industry four.0 equivalent to AI is simple for Brazil to keep up with the technological revolution," spoke of Rafael Lucchesi, director standard at SENAI and director-superintendent at SESI.
"qualified authorities are the key for the country to be part of this global stream, for you to determine the generation of latest jobs and people's exceptional of lifestyles," he delivered.
in accordance with a examine performed with the aid of Brazilian think-tank Fundação Getúlio Vargas commissioned with the aid of Microsoft, which investigated the affect of AI on key markets in Brazil - agriculture, transport, communications, oil and gasoline and government - which jointly signify 36.four % of GDP and located that AI might boost GDP boom throughout these sectors by using up to 6,forty three percent over the subsequent 15 years.
A recent analyze found that automation technologies equivalent to AI may make more than half of all formal employment vacancies inappropriate in Brazil. in keeping with the analysis, if Brazilian businesses continue to make use of automation technologies corresponding to synthetic intelligence on the present rate, it could mean 30 million jobs may be eliminated through 2026.
Delhi: Narasaraopeta Engineering college, Guntur, in partnership with Microsoft technology affiliate, will behavior a workshop on “AZURE Cloud”, Microsoft’s cloud computing platform, for engineering students right through 14-15 February 2019. The goal of the workshop, sponsored by Microsoft, is to train the members in the ideas and working at the back of Azure. The cloud carrier is ordinary for its superior security, privateness electricity, excessive availability, redundancy, and value-effectiveness. additionally, the workshop will include particulars about a number cloud capabilities like computing, analytics, storage and networking. The primary intention of the workshop is to assist college students to stand out and get hired sooner.The state of Andhra Pradesh is in severe need of a talented team of workers to satisfy the requirements of the new IT parks arising within the capital metropolis of Amravati making it critical for working towards new comers in accordance with technological advancement. leading Tech business – Microsoft has been within the forefront in imparting technical schooling within the nation. Microsoft technology affiliate is a program that gives a role-primarily based certification to students, trying out them in quite a lot of technical roles and areas.speaking concerning the value of “IT” workshops, NEC Vice-chair- Mr M Chakravarthi mentioned, “students should be business in a position. understanding the discipline be counted isn't adequate. we've been conducting industry visits to factories for electrical and mechanical students. This time we need to teach students what all works and equipment are used within the IT trade”.
NEC has additionally been in business partnership with Microsoft hosting “Microsoft Innovation Centre” on campus and other scholar evaluation courses. A observe-up certification examination was performed in a while.
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Income Tax Prep: for Low and Moderate Income Taxpayers: From Feb. 2 through April 6, Moraine Township offers free assistance to low-and-moderate-income taxpayers filing their 2018 income tax returns through the IRS VITA Program (Volunteer Income Tax Assistance). Volunteers are trained and certified by the Internal Revenue Service (IRS) to prepare basic returns for taxpayers earning less than $55,000 in 2018. Appointments are available weekdays, evenings and weekends at the Moraine Township Office 800 Central Avenue Highland Park. The Township Office's regular business hours, are from 8:30 a.m. to 4:30 p.m. Monday through Friday. Taxpayers are welcome to walk-in Saturdays from 9 a.m. to noon. Call today to schedule an appointment. 8:30 a.m. Monday, Moraine Township Office, 800 Central Ave., Highland Park, free, 847-780-6644.
Trivia Night at Buffo's Restaurant: Bring family and friends every Monday night for a fun trivia night. Live host, great prizes and free to play. 7 p.m. Monday, Buffo's, 431 Sheridan Rd., Highwood, free, 847-432-0301.
Tween Creativity Club: Homemade Bath Bombs: Join in for monthly creative projects at the Lake Forest Library. This month's project: Make a splash with Homemade Bath Bombs. 4 p.m. Monday, Lake Forest Library, 360 E. Deerpath Rd., Lake Forest, free.
Poetry Appreciation Club: If you are a fan of Dickinson, Poe, Alexie, Angelou, Frost, Whitman, or other poets, join in gathering with fellow poetry lovers. They meet the third Monday of the month to read aloud and discuss great works of poetry. 7 p.m. Monday, Lake Forest Library, 360 E. Deerpath Rd., Lake Forest, free, 847-810-4610.
Elemental: Contemporary Sculpture Exhibit- Adler Center: Five outstanding sculptural artists who work in metal, clay, wood and glass are featured and this exhibit. Elemental features prominent regional artists Ben Bates, Darlys Ewoldt, John Hatlestad, Michelle Rial and Roberta Ulrich de-Oliveira. Two free meet-the-artists events featuring Elemental are at: Gallery Night: Feb. 13, 7-8:30 p.m. The artists lead a gallery tour and share stories about their creative process. Elemental is open for viewing at the Adler Center between Jan. 28-Feb. 25. Gallery hours are M-F, 9:30-6:30 and Sat. 9:30-1:30. 9:30 a.m. Monday, David Adler Music and Arts Center, 1700 N. Milwaukee Ave., Libertyville, free, 847-367-0707.
Digital Marketing – Get Ready for Takeoff: Digital marketing is the marketing of tomorrow, and that means providing each customer and prospect with a tailored message. Recognize the need for a comprehensive marketing plan and implement it successfully. Become familiar with key marketing concepts. Learn how to engage an audience that will increase sales and provide valuable metrics that you can analyze. noon Monday, College of Lake County - Southlake Campus, 1120 South Milwaukee Ave., Vernon Hills, $25, 847-543-2033.
Chicago's Black Metropolis: Highland Park Historical Society presents 'Chicago's Black Metropolis,' focusing on African-Americans whose energy and ingenuity helped Chicago become the commercial and cultural center of the American Midwest. The program was initially presented to an audience in Paramaribo, Suriname in 2013 by Highland Park Historical Society Board Member Jeff Stern at the request of his daughter, who was then the Public Affairs Officer at the U.S. Embassy there. 7 p.m. Tuesday, Highland Park Public Library, 494 Laurel Ave., Highland Park, free, 847-432-7090.
Federal Income Taxes For Your Business: SCORE volunteer Joel Shabsin demystifies tax laws that business owners need to be aware of. Learn about the different types of business entities and the tax requirements, benefits, and disadvantages of each, followed by a Q&A session. 6:30 p.m. Tuesday, Lake Forest Library, 360 E. Deerpath Rd., Lake Forest, free.
Cyber Security Fundamentals: From computer viruses to phishing attacks to ransomware, cyber security threats are everywhere and are quickly becoming a more prevalent part of our everyday lives. This workshop will provide you with techniques for protecting you, your networks, devices and home. Topics include reasons why we are targets, common threats that exist "out in the wild" and protective measures. 11:30 a.m. Wednesday, College of Lake County, 19351 W. Washington St., Grayslake, $25, 847-543-2033.
Jim Cornelison with Frank Catalano Quartet at 210 Live : Jim Cornelison, National Anthem singer for the Chicago Blackhawks, joins forces with Grammy winning saxophonist Frank Catalano to perform music from their new release "Steppin' Out." Hear them perform the music that Johnny Hartman, Frank Sinatra and many others made famous. 7:30 p.m. Wednesday, 210 Live, 210 Green Bay Rd., Highwood, $15 – $35, 847-433-0304.
Reading Rangers and Skype with the Author: Read this month's selected book and join in with activities and to share your thoughts. Visit the Children's Library to check out your copy of this month's book, which is: The Sixty-Eight Rooms by Marianne Malone. Reading Rangers is typically for 8–12-year-olds in 2nd–5th grade, but tonight fans of all ages are welcome to attend, as the author, Marianne Malone joins via Skype. 7 p.m. Wednesday, Lake Forest Library, 360 E. Deerpath Rd., Lake Forest, free.
In this Help Net Security podcast, Chris Morales, Head of Security Analytics at Vectra, talks about machine learning fundamentals, and illustrates what cybersecurity professionals should know.
Here’s a transcript of the podcast for your convenience.
Hi, this is Chris Morales and I’m Head of Security Analytics at Vectra, and in this Help Net Security podcast I want to talk about machine learning fundamentals that I think we all need to know as cybersecurity professionals.
AI has become very used within our industry more and more, and here at Vectra we are an AI company as well. As you start to hear more about AI, you have to start asking yourself what is it really, what makes a machine intelligent and in the next ten minutes I just want to give a quick overview so that you can understand some of the principle operations and applications of how machine learnings apply to build AI, and just kind of a quick understanding of the different algorithms or understanding when you need to use certain algorithms for specific jobs.
There has always been a very muddled use of the terms artificial intelligence, data science and machine learning. For simple definitions, the term artificial intelligence is applied when a machine mimics cognitive functions that humans associate with other human minds. These are functions such as learning and problem solving.
Where it gets confusing is that AI can exist without machine learning, and just a couple of decades ago, we used to talk quite often of something we call an expert system. Data science doesn’t need to use machine learning, machine learning is really just the use of algorithms to attract meaning out of that data and machine learning expert systems differ in the quantity of human knowledge needed and how they are used. What I mean by that is that, in an expert system, the full knowledge of the expert is digitized and used in the decision making, and it ends up where the machine starts looking like a series of if then statements.
Machine learning is all about having an algorithm learn from the data rather than having a human encode the logic such as that example and with machine learning people constantly talk about AI that their jobs are going to be lost. The reality of it is that there is plenty of work for humans to set up data, they have to label data, they need to select features for these algorithms and then they have to tune it to achieve the desired results. In other words, it’s not magic.
In machine learning, a model is a function which learns to predict or classify by learning through input examples. We call these examples data sets and each point in the data set is of the form X and Y, where X is the input and Y is the output. What happens with machine learning is that the models go through the entire set of data and it learns that data. If you provide a new in point, say X here, the model can tell you the Y. From X it understands Y and the model doesn’t do this simply by memorizing the example data, but it actually learns the relationship between each X and Y within the data. What we mean is that it starts to understand the behaviors and what things look like and what they do instead of what they are.
When we talk about machine learning, the two major types you will tend to see are supervised and unsupervised, and then there is also the concept of deep learning, and especially in security, these are the three that matter the most.
In supervised learning, if we talk about the X and the Ys, the data set has both X and Ys, which means that a data scientist already understood the inputs and the outputs and what the expected results were. He just wanted to train the data to learn from that so that it can find more of the same. In unsupervised learning there is no Ys provided for learning and the data that the data scientist work with has only Xs which is the inputs, so they don’t know the outcome. In those scenarios, unsupervised learning has to learn on new things in real time in certain environments.
Deep learning is a model of learning that can be applied to either supervised or unsupervised, it’s not specific to either, but it’s used to make the machine smarter and we will go into that in a minute. I think the best thing here is to better understand the types of machine learning. I will try to draw an analogy with real life human learning examples. I’m not going to cover all topics for every type of algorithm but just some key high-level terms related to a machine learning that we think of our interest and specific to cybersecurity.
The first kind, when we think of supervised learning, going back to that, here is a real-world example. You’ve seen people smoke and they get sick or they get cancer. By that observation that you made as a human, you make a decision that neither you nor your kids will ever smoke because you learnt in life that smoking kills. Mathematically, as a human you have observed a ton of data, which is lots and lots of smokers and that they get cancer and they die, and you came up with the rule for classification. You decided that a certain characteristic means class A else class B. That is a description, what we call, supervised learning and specifically classification where what you are trying to do is predict a label.
The other type of supervised machine learning is regression learning, which is predicting the quantity. Fundamentally, classification is about predicting a label and regression is about predicting the quantity, but they are both supervised. An example of regression is, say you get the data for a house price versus the area of where all these houses are, and then you sit down and you plot it on a graph and this is a pretty common thing people do when they want to buy a house. When you look at the data you see that it’s almost a straight line and you draw a straight line. Now what happens is that when you look at this graph you drew with all these houses placed on it, you can predict the price of a house as someone tells you the area so that you start to understand the right areas you want to live in or where you want to buy a house because neighborhood A is a million dollar homes and neighborhood B is five hundred thousand dollar homes without knowing every single home, and with the data you had you can make predictions on future homes in those neighborhoods. This is exactly how real estate insurance and things like that actually work, they predict these quantities. The difference between a human and a machine is a machine does it in a more mathematical and formal way, where humans use natural intuition.
The next example, and those examples are for supervised learning, the next area of machine learning that we care about in cybersecurity is unsupervised learning. When we talk about unsupervised machine learning we primarily focus on the concept of clustering. Clustering is creating groups of clusters based on similarities and examples. To give you another real world scenario for that and how we do it as normal people, let’s say that an advertising platform (we all understand advertising – Google does it, Facebook, all the major companies) wants to segment the population into smaller groups for similar demographics and purchasing habits, and they do this so that the advertisers can reach a specific target market with relevant ads.
Vectra as a cyber security company, we advertise with Google and we go to Google and say “hey, we are looking for people who are interested in cybersecurity or looking for incident response” or things like that. We gave a certain criteria and then Google will take those criteria and start to cluster and take these groups of people and say “I don’t really know who these people are, but I know some habits about them, so I can make a guess that these people are interested in security and that’s what they give us”. What happens here, from a machine perspective, is that mathematically you are faced with the task of grouping unlabeled data, and rather than finding the group before looking at the data, clustering allows you to find and analyze the groups that formed organically.
Then the last area, and then I want to just kind of give a quick example of how this applies to security, is deep learning. The major thing we care about here is a concept called transfer learning which means that I teach the machine a task and it reuses as a starting point for another model on a second task. In the real world an easy example that is: say your mom tell you how to choose oranges, she sends you to the grocery store to buy apples that you don’t know how to, you can use some common sense to make this decision, or you know how to play tennis, it shouldn’t take you long to pick up squash. This is essentially what an HR of a company terms as a transferable skill, and it’s this skill set that allows us to use deep learning to start to learn about new tasks that we never solved based on prior knowledge.
Just as a comment to tie this back to security, as we are coming to the top of our ten minutes here, is that all these examples of learning represent a fundamental shift in how we are able to perform security and, unlike a signature based approach that delivers a one for one mapping of threats to countermeasures, data science and machine learning uses the collective learning of all the threats observed in the past and the present and as things happen to proactively identify new threats that haven’t been seen before.
Just for one more example, think of it as a student learning a new subject at school. Memorizing the answers to a test might result on a passing grade but that approach misses the mark when it comes to learning how to solve a problem and that’s where we have been in security with signatures. It’s a critically important distinction when using data science to detect threats.
Long-term it’s essential to understand what, when, why and how and actual knowledge in intelligence is far more advantageous when evaluating and solving new problems that have not been encountered before, and for the traditional signature model to continue working and secure how we have been doing it, all the answers have to be known ahead of time which is absolutely unrealistic. For example the domain atme.com has been seen behaving badly in the past, therefore it’s bad.
Data science expects to be asked real questions and it applies collective learning to evaluate an unknown. For the collective knowledge of threats gathered from real world it’s possible to identify the domain is acting bad based on its behavior rather than saying I saw it in the past. A scenario for that is atmeonetwothree.com has never been seen behaving badly in the past but traffic tuned from it showing different behaviors. Those combinations the machine learns or actually command in control with no prior knowledge.
To learn more, go to vectra.ai and we have a number of resources there. There is a twenty minute video from a presentation I gave at Infosecurity Europe about this topic and also in our blog series one of our data principle scientist has been writing a series on understanding AI that explains the mechanics on how we do it and how we use it here, and if you want to reach out directly you can reach us at firstname.lastname@example.org and with that thank you and have a good day.
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If you’re interested in secunet Security Networks Aktiengesellschaft (ETR:YSN), then you might want to consider its beta (a measure of share price volatility) in order to understand how the stock could impact your portfolio. Modern finance theory considers volatility to be a measure of risk, and there are two main types of price volatility. First, we have company specific volatility, which is the price gyrations of an individual stock. Holding at least 8 stocks can reduce this kind of risk across a portfolio. The second type is the broader market volatility, which you cannot diversify away, since it arises from macroeconomic factors which directly affects all the stocks on the market.
Some stocks see their prices move in concert with the market. Others tend towards stronger, gentler or unrelated price movements. Some investors use beta as a measure of how much a certain stock is impacted by market risk (volatility). While we should keep in mind that Warren Buffett has cautioned that ‘Volatility is far from synonymous with risk’, beta is still a useful factor to consider. To make good use of it you must first know that the beta of the overall market is one. A stock with a beta below one is either less volatile than the market, or more volatile but not corellated with the overall market. In comparison a stock with a beta of over one tends to be move in a similar direction to the market in the long term, but with greater changes in price.
View our latest analysis for secunet Security NetworksWhat YSN’s beta value tells investors
Given that it has a beta of 0.89, we can surmise that the secunet Security Networks share price has not been strongly impacted by broader market volatility (over the last 5 years). If history is a good guide, owning the stock should help ensure that your portfolio is not overly sensitive to market volatility. Beta is worth considering, but it’s also important to consider whether secunet Security Networks is growing earnings and revenue. You can take a look for yourself, below.
XTRA:YSN Income Statement Export February 18th 19More How does YSN’s size impact its beta?
secunet Security Networks is a small cap stock with a market capitalisation of €688m. Most companies this size are actively traded. Small companies often have a high beta value, but they can be heavily influenced by company-specific events. This might explain why this stock has a low beta.What this means for you:
The secunet Security Networks doesn’t usually show much sensitivity to the broader market. This could be for a variety of reasons. Typically, smaller companies have a low beta if their share price tends to move a lot due to company specific developments. Alternatively, an strong dividend payer might move less than the market because investors are valuing it for its income stream. This article aims to educate investors about beta values, but it’s well worth looking at important company-specific fundamentals such as secunet Security Networks’s financial health and performance track record. I highly recommend you dive deeper by considering the following:
We aim to bring you long-term focused research analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material.
If you spot an error that warrants correction, please contact the editor at email@example.com. This article by Simply Wall St is general in nature. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. Simply Wall St has no position in the stocks mentioned. On rare occasion, data errors may occur. Thank you for reading.
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