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Microsoft has introduced a brand recent initiative with a view to survey more than three million Brazilian college students getting informed in topic 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 proffer AI working towards in unreasonable college lessons. during the announcement, Nadella cited that governments should silent accelerate the adoption of automation however create recent capabilities on the identical time.
"we believe a massive desultory 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 undergo 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 live 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 hold up with the technological revolution," spoke of Rafael Lucchesi, director touchstone at SENAI and director-superintendent at SESI.
"qualified authorities are the key for the country to live fragment 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 strike 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 resolve institute that automation technologies equivalent to AI may gain more than half of outright formal employment vacancies inappropriate in Brazil. in keeping with the analysis, if Brazilian businesses continue to gain employ of automation technologies corresponding to synthetic intelligence on the present rate, it could beofvalue 30 million jobs may live eliminated through 2026.
Delhi: Narasaraopeta Engineering college, Guntur, in partnership with Microsoft technology affiliate, will conduct a workshop on “AZURE Cloud”, Microsoft’s cloud computing platform, for engineering students birthright 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, unreasonable availability, redundancy, and value-effectiveness. additionally, the workshop will comprehend 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 pickup hired sooner.The status of Andhra Pradesh is in strict exigency of a talented team of workers to meet the requirements of the recent IT parks arising within the capital metropolis of Amravati making it censorious for working towards recent comers in accordance with technological advancement. leading Tech trade – 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 live trade in a position. understanding the discipline live counted isn't adequate. we've been conducting industry visits to factories for electrical and mechanical students. This time they exigency to drill students what outright works and materiel are used within the IT trade”.
NEC has additionally been in trade 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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Listings are topic to change. gratify summon the venue in advance. To submit items to the calendar, travel to newssunonline.com/community.Monday
Income Tax Prep: for Low and temper 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 trade 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. summon 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, powerful 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: associate in for monthly creative projects at the Lake Forest Library. This month's project: gain 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, associate in gathering with fellow poetry lovers. They meet the third Monday of the month to read aloud and contend powerful 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 toil 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 seat 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 – pickup Ready for Takeoff: Digital marketing is the marketing of tomorrow, and that means providing each customer and prospect with a tailored message. Recognize the exigency for a comprehensive marketing diagram and implement it successfully. Become confidential with key marketing concepts. Learn how to engage an audience that will extend 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 seat 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 trade owners exigency to live awake of. Learn about the different types of trade entities and the tax requirements, benefits, and disadvantages of each, followed by a actual questions mp;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 fragment of their everyday lives. This workshop will provide you with techniques for protecting you, your networks, devices and home. Topics comprehend reasons why they 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.
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Reading Rangers and Skype with the Author: Read this month's selected reserve and associate 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 outright 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 benefit 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 benefit Net Security podcast I want to talk about machine learning fundamentals that I assume they outright exigency to know as cybersecurity professionals.
AI has become very used within their industry more and more, and here at Vectra they are an AI company as well. As you start to hear more about AI, you believe to start asking yourself what is it really, what makes a machine smart 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 kindly of a quick understanding of the different algorithms or understanding when you exigency to employ inescapable algorithms for specific jobs.
There has always been a very muddled employ of the terms ersatz intelligence, data science and machine learning. For simple definitions, the term ersatz 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, they used to talk quite often of something they summon an expert system. Data science doesn’t exigency to employ machine learning, machine learning is really just the employ of algorithms to attract sense out of that data and machine learning expert systems disagree in the quantity of human erudition needed and how they are used. What I beofvalue by that is that, in an expert system, the complete erudition of the expert is digitized and used in the conclusion making, and it ends up where the machine starts looking like a train of if then statements.
Machine learning is outright about having an algorithm learn from the data rather than having a human encode the logic such as that specimen and with machine learning people constantly talk about AI that their jobs are going to live lost. The reality of it is that there is plenty of toil for humans to set up data, they believe to label data, they exigency to select features for these algorithms and then they believe to tune it to achieve the desired results. In other words, it’s not magic.
In machine learning, a model is a duty which learns to prognosticate or classify by learning through input examples. They summon these examples data sets and each point in the data set is of the contour X and Y, where X is the input and Y is the output. What happens with machine learning is that the models travel through the entire set of data and it learns that data. If you provide a recent in point, negate X here, the model can bid you the Y. From X it understands Y and the model doesn’t enact this simply by memorizing the specimen data, but it actually learns the relationship between each X and Y within the data. What they beofvalue is that it starts to understand the behaviors and what things behold like and what they enact instead of what they are.
When they talk about machine learning, the two major types you will tend to survey are supervised and unsupervised, and then there is likewise the concept of profound learning, and especially in security, these are the three that matter the most.
In supervised learning, if they 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 toil with has only Xs which is the inputs, so they don’t know the outcome. In those scenarios, unsupervised learning has to learn on recent things in actual time in inescapable environments.
Deep learning is a model of learning that can live applied to either supervised or unsupervised, it’s not specific to either, but it’s used to gain the machine smarter and they will travel into that in a minute. I assume the best thing here is to better understand the types of machine learning. I will try to draw an analogy with actual life human learning examples. I’m not going to cover outright topics for every nature of algorithm but just some key high-level terms related to a machine learning that they assume of their interest and specific to cybersecurity.
The first kind, when they assume of supervised learning, going back to that, here is a real-world example. You’ve seen people smoke and they pickup sick or they pickup cancer. By that observation that you made as a human, you gain a conclusion that neither you nor your kids will ever smoke because you learnt in life that smoking kills. Mathematically, as a human you believe observed a ton of data, which is lots and lots of smokers and that they pickup cancer and they die, and you came up with the rule for classification. You decided that a inescapable characteristic means class A else class B. That is a description, what they call, supervised learning and specifically classification where what you are trying to enact is prognosticate a label.
The other nature 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 specimen of regression is, negate you pickup the data for a house expense versus the district of where outright these houses are, and then you sit down and you plot it on a graph and this is a pretty common thing people enact when they want to buy a house. When you behold at the data you survey that it’s almost a straight line and you draw a straight line. Now what happens is that when you behold at this graph you drew with outright these houses placed on it, you can prognosticate the expense of a house as someone tells you the district so that you start to understand the birthright 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 gain predictions on future homes in those neighborhoods. This is exactly how actual estate insurance and things like that actually work, they prognosticate these quantities. The variation between a human and a machine is a machine does it in a more mathematical and formal way, where humans employ natural intuition.
The next example, and those examples are for supervised learning, the next district of machine learning that they supervision about in cybersecurity is unsupervised learning. When they talk about unsupervised machine learning they primarily focus on the concept of clustering. Clustering is creating groups of clusters based on similarities and examples. To give you another actual world scenario for that and how they enact it as plain people, let’s negate that an advertising platform (we outright understand advertising – Google does it, Facebook, outright the major companies) wants to segment the population into smaller groups for similar demographics and purchasing habits, and they enact this so that the advertisers can reach a specific target market with apropos ads.
Vectra as a cyber security company, they advertise with Google and they travel to Google and negate “hey, they are looking for people who are interested in cybersecurity or looking for incident response” or things like that. They gave a inescapable criteria and then Google will rob those criteria and start to cluster and rob these groups of people and negate “I don’t really know who these people are, but I know some habits about them, so I can gain 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 stint of grouping unlabeled data, and rather than finding the group before looking at the data, clustering allows you to find and resolve the groups that formed organically.
Then the eventual area, and then I want to just kindly of give a quick specimen of how this applies to security, is profound learning. The major thing they supervision about here is a concept called transfer learning which means that I drill the machine a stint and it reuses as a starting point for another model on a second task. In the actual world an easy specimen that is: negate your mom bid you how to choose oranges, she sends you to the grocery store to buy apples that you don’t know how to, you can employ some common sense to gain this decision, or you know how to play tennis, it shouldn’t rob 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 employ profound learning to start to learn about recent tasks that they never solved based on prior knowledge.
Just as a observation to tie this back to security, as they are coming to the top of their ten minutes here, is that outright these examples of learning picture a fundamental shift in how they 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 outright the threats observed in the past and the present and as things befall to proactively identify recent threats that haven’t been seen before.
Just for one more example, assume of it as a student learning a recent topic at school. Memorizing the answers to a test might result on a passing grade but that approach misses the stamp when it comes to learning how to decipher a problem and that’s where they believe been in security with signatures. It’s a critically primary distinction when using data science to detect threats.
Long-term it’s essential to understand what, when, why and how and actual erudition in intelligence is far more advantageous when evaluating and solving recent problems that believe not been encountered before, and for the traditional signature model to continue working and secure how they believe been doing it, outright the answers believe to live known ahead of time which is absolutely unrealistic. For specimen the domain atme.com has been seen behaving badly in the past, therefore it’s bad.
Data science expects to live asked actual questions and it applies collective learning to evaluate an unknown. For the collective erudition of threats gathered from actual world it’s feasible to identify the domain is acting macabre based on its conduct rather than adage 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, travel to vectra.ai and they believe a number of resources there. There is a twenty minute video from a presentation I gave at Infosecurity Europe about this topic and likewise in their blog train one of their data principle scientist has been writing a train on understanding AI that explains the mechanics on how they enact it and how they employ it here, and if you want to reach out directly you can reach us at email@example.com and with that thank you and believe a safe day.
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If you’re interested in secunet Security Networks Aktiengesellschaft (ETR:YSN), then you might want to esteem its beta (a measure of share expense volatility) in order to understand how the stock could repercussion your portfolio. Modern finance theory considers volatility to live a measure of risk, and there are two main types of expense volatility. First, they believe company specific volatility, which is the expense gyrations of an individual stock. Holding at least 8 stocks can reduce this kindly of risk across a portfolio. The second nature is the broader market volatility, which you cannot diversify away, since it arises from macroeconomic factors which directly affects outright the stocks on the market.
Some stocks survey their prices poke in concert with the market. Others tend towards stronger, gentler or unrelated expense movements. Some investors employ beta as a measure of how much a inescapable stock is impacted by market risk (volatility). While they should hold in mind that Warren Buffett has cautioned that ‘Volatility is far from synonymous with risk’, beta is silent a useful factor to consider. To gain safe employ 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 live poke in a similar direction to the market in the long term, but with greater changes in price.
View their latest analysis for secunet Security NetworksWhat YSN’s beta value tells investors
Given that it has a beta of 0.89, they can surmise that the secunet Security Networks share expense has not been strongly impacted by broader market volatility (over the eventual 5 years). If history is a safe guide, owning the stock should benefit ensure that your portfolio is not overly sensitive to market volatility. Beta is worth considering, but it’s likewise primary to esteem whether secunet Security Networks is growing earnings and revenue. You can rob a behold for yourself, below.
XTRA:YSN Income Statement Export February 18th 19More How does YSN’s size repercussion its beta?
secunet Security Networks is a miniature cap stock with a market capitalisation of €688m. Most companies this size are actively traded. miniature companies often believe a tall beta value, but they can live heavily influenced by company-specific events. This might clarify why this stock has a low beta.What this means for you:
The secunet Security Networks doesn’t usually prove much sensitivity to the broader market. This could live for a variety of reasons. Typically, smaller companies believe a low beta if their share expense tends to poke a lot due to company specific developments. Alternatively, an stout dividend payer might poke 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 primary company-specific fundamentals such as secunet Security Networks’s fiscal health and performance track record. I highly recommend you dive deeper by considering the following:
We smooth to bring you long-term focused research analysis driven by fundamental data. Note that their analysis may not factor in the latest price-sensitive company announcements or qualitative material.
If you spot an error that warrants correction, gratify contact the editor at firstname.lastname@example.org. 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 rob account of your objectives, or your fiscal 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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