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Microsoft has introduced a brand unique initiative with a view to notice 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 tender AI working towards in unreasonable college lessons. during the announcement, Nadella cited that governments should still accelerate the adoption of automation however create unique capabilities on the identical time.
"we exist pleased a massive haphazard 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 structure of solutions the usage of Azure, Bot and IoT. other lessons geared on the structure of functions and chatbots using Microsoft's cognitive features can exist provided within the following yoke 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 standard at SENAI and director-superintendent at SESI.
"qualified authorities are the key for the country to exist 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 influence 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 institute that automation technologies equivalent to AI may originate more than half of total formal employment vacancies inappropriate in Brazil. in keeping with the analysis, if Brazilian businesses continue to originate consume of automation technologies corresponding to synthetic intelligence on the present rate, it could denote 30 million jobs may exist 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 perquisite 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 comprise particulars about a number cloud capabilities enjoy computing, analytics, storage and networking. The primary expostulate of the workshop is to assist college students to stand out and accumulate hired sooner.The state of Andhra Pradesh is in severe need of a talented team of workers to fullfil the requirements of the unique IT parks arising within the capital metropolis of Amravati making it faultfinding for working towards unique comers in accordance with technological advancement. leading Tech industry – 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 exist industry in a position. understanding the discipline exist counted isn't adequate. we've been conducting industry visits to factories for electrical and mechanical students. This time they need to teach students what total works and rig are used within the IT trade”.
NEC has additionally been in industry 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 subject to change. gladden summon the venue in advance. To submit items to the calendar, travel to newssunonline.com/community.Monday
Income Tax Prep: for Low and qualify 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 industry 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, considerable 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: originate 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 considerable 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 heart 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 – accumulate 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 mode and implement it successfully. Become close with key marketing concepts. Learn how to engage an audience that will multiply 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 heart 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 industry owners need to exist awake of. Learn about the different types of industry entities and the tax requirements, benefits, and disadvantages of each, followed by a 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 Part of their everyday lives. This workshop will provide you with techniques for protecting you, your networks, devices and home. Topics comprise 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 engage 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 total 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 hearten 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 hearten Net Security podcast I want to talk about machine learning fundamentals that I assume they total need 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 exist pleased to start asking yourself what is it really, what makes a machine knowing 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 benevolent of a quick understanding of the different algorithms or understanding when you need to consume sure algorithms for specific jobs.
There has always been a very muddled consume 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 yoke of decades ago, they used to talk quite often of something they summon an expert system. Data science doesn’t need to consume machine learning, machine learning is really just the consume of algorithms to attract import out of that data and machine learning expert systems differ in the quantity of human erudition needed and how they are used. What I denote 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 enjoy a series of if then statements.
Machine learning is total about having an algorithm learn from the data rather than having a human encode the logic such as that sample and with machine learning people constantly talk about AI that their jobs are going to exist lost. The reality of it is that there is plenty of work for humans to set up data, they exist pleased to label data, they need to select features for these algorithms and then they exist pleased 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 shape 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 unique in point, jabber X here, the model can show you the Y. From X it understands Y and the model doesn’t execute this simply by memorizing the sample data, but it actually learns the relationship between each X and Y within the data. What they denote is that it starts to understand the behaviors and what things Look enjoy and what they execute instead of what they are.
When they talk about machine learning, the two major types you will tend to notice are supervised and unsupervised, and then there is too the concept of deep 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 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 unique things in true time in sure environments.
Deep learning is a model of learning that can exist applied to either supervised or unsupervised, it’s not specific to either, but it’s used to originate 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 true life human learning examples. I’m not going to cover total topics for every type 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 accumulate sick or they accumulate cancer. By that observation that you made as a human, you originate a conclusion that neither you nor your kids will ever smoke because you learnt in life that smoking kills. Mathematically, as a human you exist pleased observed a ton of data, which is lots and lots of smokers and that they accumulate cancer and they die, and you came up with the rule for classification. You decided that a sure 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 execute is prognosticate 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 sample of regression is, jabber you accumulate the data for a house charge versus the district of where total these houses are, and then you sit down and you plot it on a graph and this is a pretty common thing people execute when they want to buy a house. When you Look at the data you notice 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 total these houses placed on it, you can prognosticate the charge of a house as someone tells you the district so that you start to understand the perquisite 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 originate predictions on future homes in those neighborhoods. This is exactly how true estate insurance and things enjoy that actually work, they prognosticate these quantities. The contrast between a human and a machine is a machine does it in a more mathematical and formal way, where humans consume 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 true world scenario for that and how they execute it as bona fide people, let’s jabber that an advertising platform (we total understand advertising – Google does it, Facebook, total the major companies) wants to segment the population into smaller groups for similar demographics and purchasing habits, and they execute this so that the advertisers can compass a specific target market with apposite ads.
Vectra as a cyber security company, they advertise with Google and they travel to Google and jabber “hey, they are looking for people who are interested in cybersecurity or looking for incident response” or things enjoy that. They gave a sure criteria and then Google will prefer those criteria and start to cluster and prefer these groups of people and jabber “I don’t really know who these people are, but I know some habits about them, so I can originate 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 final area, and then I want to just benevolent of give a quick sample of how this applies to security, is deep learning. The major thing they supervision 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 true world an facile sample that is: jabber your mom show you how to elect oranges, she sends you to the grocery store to buy apples that you don’t know how to, you can consume some common sense to originate this decision, or you know how to play tennis, it shouldn’t prefer 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 consume deep learning to start to learn about unique 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 total 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 total the threats observed in the past and the present and as things happen to proactively identify unique threats that haven’t been seen before.
Just for one more example, assume of it as a student learning a unique subject at school. Memorizing the answers to a test might result on a passing grade but that approach misses the imprint when it comes to learning how to solve a problem and that’s where they exist pleased been in security with signatures. It’s a critically notable 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 unique problems that exist pleased not been encountered before, and for the traditional signature model to continue working and secure how they exist pleased been doing it, total the answers exist pleased to exist known ahead of time which is absolutely unrealistic. For sample the domain atme.com has been seen behaving badly in the past, therefore it’s bad.
Data science expects to exist asked true questions and it applies collective learning to evaluate an unknown. For the collective erudition of threats gathered from true world it’s practicable to identify the domain is acting injurious based on its behavior 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 exist pleased a number of resources there. There is a twenty minute video from a presentation I gave at Infosecurity Europe about this topic and too in their blog series one of their data principle scientist has been writing a series on understanding AI that explains the mechanics on how they execute it and how they consume it here, and if you want to compass out directly you can compass us at email@example.com and with that thank you and exist pleased a apt 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 charge volatility) in order to understand how the stock could impact your portfolio. Modern finance theory considers volatility to exist a measure of risk, and there are two main types of charge volatility. First, they exist pleased company specific volatility, which is the charge gyrations of an individual stock. Holding at least 8 stocks can reduce this benevolent 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 total the stocks on the market.
Some stocks notice their prices slip in concert with the market. Others tend towards stronger, gentler or unrelated charge movements. Some investors consume beta as a measure of how much a sure stock is impacted by market risk (volatility). While they should hold in intellect that Warren Buffett has cautioned that ‘Volatility is far from synonymous with risk’, beta is still a useful factor to consider. To originate apt consume 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 exist slip 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 charge has not been strongly impacted by broader market volatility (over the final 5 years). If history is a apt guide, owning the stock should hearten ensure that your portfolio is not overly sensitive to market volatility. Beta is worth considering, but it’s too notable to consider whether secunet Security Networks is growing earnings and revenue. You can prefer 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 exist pleased a tall beta value, but they can exist heavily influenced by company-specific events. This might warrant why this stock has a low beta.What this means for you:
The secunet Security Networks doesn’t usually exhibit much sensitivity to the broader market. This could exist for a variety of reasons. Typically, smaller companies exist pleased a low beta if their share charge tends to slip a lot due to company specific developments. Alternatively, an strong dividend payer might slip 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 notable 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 plane 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 oversight that warrants correction, gladden contact the editor at firstname.lastname@example.org. This article by Simply Wall St is common in nature. It does not constitute a recommendation to buy or sell any stock, and does not prefer 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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