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Overcome the skills shortage and alert fatigue with advanced machine learning and automation technology.

Advanced threats, a shortage of security experts and the rise in work-from-home together form a catalyst for MSPs to enhance cybersecurity effectiveness for their customers. As MSPs seek ways to increase efficiency and do more with less, theyre turning to advanced analytical capabilities like machine learning, security analytics and automation. All of these have moved past their initial hype cycle and are now adopted and delivering enhanced ROI and outcomes in IT and cybersecurity.

The future of your business is Big Data and Machine Learningtied to the business opportunities and customer challenges before you.

Eric Schmidt, then CEO of GoogleCloudNext Conference in 2017

Machine learning and automation are more than popular buzzwords in the cybersecurity industry. These analytic capabilities make sense of large volumes of raw data to create context and find unknown attacks that speed up decision making. When combined with cybersecurity experts, they hold real promise for their ability to transform IT and security operations for organizations of all sizes. While not a magic potion that instantly perfects data security, these advanced tools offer MSPs a way to augment limited staff in the ongoing battle against cyber criminals.

The Value of Machine Learning and Automation in Cybersecurity

With digital transformation serving as a catalyst for larger volumes of data and technology, use cases for ML and automation in IT and security operations are growing. While not exhaustive, key use cases include:

Analyzing vast reams of data for suspicious activity: Its challenging to process billions of logs with an all-manual approach. Machine learning does the initial correlation work to process incoming log streams, reduce false positives and alert security operations center (SOC) analysts who perform a second level of triage and potential threat hunting.

Improving SOC efficiency and effectiveness: Machine learning and automation manage repetitive and potentially error-prone tasks that can overwhelm security teams. The result is higher job satisfaction and retention of hard-to-find cybersecurity professionals.

Increasing speed, accuracy and scale of threat detection: Automated incident response can launch a set of corrective actions, open a ticket for SOC triage and even block suspicious processes. Faster detection and remediation reduce the potential damage of attackers.

Detecting anomalous behavior by users and supply chain partners: Detect insider threats and advanced attacks with machine learning to understand and predict normal baseline system activity and identify exceptions that signal a cybersecurity risk. A SIEM (security information and event management) solution provides user and entity behavior analysis (UEBA) to detect insider threats, lateral movement and advanced attacks.

Through advancements and adoption of machine learning and security automation, MSPs are harnessing the vast reams of device and client data to foster better cyber decision making.

Cyber Criminals Also Embrace Advanced Tools

Defenders arent the only ones looking at emerging technologies. Global cybercrime damages are predicted to reach $6 trillion annually by 2021, according to the2019 Annual Cybercrime Report by Cybersecurity Ventures. Cybercriminals are upping their game to use the latest tools and technology to improve outcomes for their exploits. Hackers are using

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MSPs are Bolstering Security Programs with Machine Learning and Automation - Channel Futures

Surviving and thriving with data science and machine learning means not only having the right platforms, tools and skills, but identifying use cases and implementing processes that can deliver repeatable, scalable business value.

However, the challenges are numerous, from selecting data sets and data platforms, to architecting and optimizing data pipelines, and model training and deployment.

In response, new solutions have emerged to deliver key capabilities in areas including visualization, self-service, and real-time analytics. Along with the rise of DataOps, greater collaboration, and automation have been identified as key success factors.

DBTA recently hosted a special roundtable webinar featuring Alyssa Simpson Rochwerger, VP of AI and data, Appen; Doug Freud, SAP platform and technology global center of excellence, VP of data science; and Robert Stanley, senior director, special projects, Melissa Informatics, who discussed new technologies and strategies for expanding data science and machine learning capabilities.

According to a Gartner 2020 CIO survey, only 20% of AI projects deploy, Rochwerger said. The top challenges are skills of staff, understanding the benefits and uses of AI, and the data scope and quality.

She said businesses need to start out by clarifying a goal so they can then know where the data is coming from. Once organizations know where the data is coming from, they can find and fill in the gaps. Having a diverse team of humans can make it easier to sift and combine data.

According to Data2020: State of Big Data Study Regina Corso Consulting 2017, 86% of companies arent getting the most out of their data and they are limited by data complexity and sprawl, Freud explained.

SAP Data Intelligence can meet companies in the middle, Freud said. The platform boasts that its enterprise AI meets intelligent information management.

The platform features benefits that include:

Stanley took another approach by introducing the concept of data quality (DQ) fundamentals with AI. AI can be useful for DQ, particularly with unstructured or more complex data, bringing competitive advantage.

Using AI (MR and ML), more efficient methods for identification, extraction and normalization has been developed. AI on clean data enables pattern recognition, discovery and intelligent action.

Machine reasoning (MR) relies on knowledge captured and applied within ontologies using graph database technologies - most formally, using SDBs, he explained.

Machine reasoning can make sense out of incomplete or noisy data, making it possible to answer difficult questions. MR delivers highly confident decision-making by applying existing knowledge and ontology-enable logic to data, Stanley noted.

An archived on-demand replay of this webinar is available here.

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Going Deeper with Data Science and Machine Learning - Database Trends and Applications

Researchers involved in aWestern University-led international study have found that the most reliable predictor of a relationships success is partners belief that the other person is fully committed.

A statement issued by the university, which is located in London Ont., said this is the first-ever systematic attempt at using machine-learning algorithms to predict peoples relationship satisfaction.

Satisfaction with romantic relationships has important implications for health, well-being and work productivity, said Western psychology professor Samantha Joel.

But research on predictors of relationship quality is often limited in scope and scale, and carried out separately in individual laboratories.

The machine-learning study is conducted by Joel, Paul Eastwick from University of California, Davis, as well as 84 other scholars internationally.

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More than 11,000 couples participated.

In the study, an application of artificial intelligence (AI) is used to comb through various combinations of predictors to find the most robust predictors of relationship satisfaction.

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It provides answers to the question: What predicts how happy I will be with my relationship partner?

According to the study, relationship-specific predictors such as perceived partner commitment, appreciation, and sexual satisfaction account for nearly half of variance in relationship quality.

Individual characteristics, which describe a partner rather than a relationship, explains 21 per cent of variance in relationship quality, the study said.

The top five individual characteristics with the strongest predictive power for relationship quality are satisfaction with life, negative affect, depression, avoidant attachment and anxious attachment.

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Joel notes she was surprised the study showed that one partners individual differences predictors like life satisfaction, depression or agreeableness explained only five per cent of variance in the other partners relationship satisfaction.

In other words, relationship satisfaction is not well-explained by your partners own self-reported characteristics, Joel said.

The current datasets were sampled from Canada, the United States, Israel, the Netherlands, Switzerland and New Zealand.

2020 Global News, a division of Corus Entertainment Inc.

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Bees do it, machines know it: Western University-led study hints at key to relationship satisfaction - Globalnews.ca

Private learning group AdvTech has announced the launch of a new online school for grades R to 9.

AdvTech is the largest private education provider in Africa, and its schools division includes major brands such as Crawford Schools, Trinityhouse and Abbotts.

Its new school, which is called Evolve Online School (Evolve), will begin operations from 1 January 2021 and will offer a curriculum mapping system developed by MIT.

This IEB-aligned mapping curriculum allows learners to progress at their own deliberate or accelerated pace, Evolve states.

In this rapidly changing society, the one-size-fits-all method of teaching no longer makes any sense, said Principal Colin Northmore. Evolve starts by answering the question of how we can make learning an adventure for each child?

This system places students within subjects according to their abilities, letting them progress up to their potential in each subject.

The result is that each students learning experience is tailored to their specific needs, and they are encouraged to grow at a pace that suits their ability and enthusiasm, the school states.

One of the key features touted by the Evolve Online School is its use of machine learning, which it says is employed to:

Evolve also offers a range of forward-looking subjects that differ depending on which phase the student is in.

The school separates students into three phases Foundation Phase, Intermediate Phase, and Senior Phase. These comprise students from Grades R-3, Grades 4-6, and Grades 7-9, respectively.

Evolve said that it plans to add a phase which caters to Grades 10-12 from 2022.

The subjects included in each phase are described as follows, according to the schools website:

Instead of teachers, Evolve states that its students will be taught by learning activators, which draw from master teachers across the country to develop curriculum content.

There will be a strong focus on foundational, social, and emotional learning skills. Our team of life coaches will focus exclusively on these skills. Our children are growing up in a world very different from the one in which we grew up, Northmore said.

Things that we, as adults, deal with and take in our stride they are already facing at a very young age. Our life coaches will play a very important role in teaching students how to deal with issues such as stress and anxiety, helping them develop coping mechanisms, resilience and a growth mindset.

Registrations for the 2021 academic year open in September, with Evolves school year set to start in 2021.

The Evolve 2021 fee structure is shown below.

It should be noted that a non-refundable registration fee of R300 is payable at the start of the online application process, and the school will supply each childs iPad with all the required books and apps they will need.

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New South African online school uses machine learning to teach children Here is how much it costs - MyBroadband

In recent years, entire industries have popped up that rely on the delicate interplay between human workers and automated software. Companies like Facebook work to keep hateful and violent content off their platforms usinga combination of automated filtering and human moderators. In the medical field, researchers at MIT and elsewhere have used machine learning to help radiologistsbetter detect different forms of cancer.

What can be tricky about these hybrid approaches is understanding when to rely on the expertise of people versus programs. This isnt always merely a question of who does a task better; indeed, if a person has limited bandwidth, the system may have to be trained to minimize how often it asks for help.

To tackle this complex issue, researchers from MITs Computer Science and Artificial Intelligence Lab (CSAIL) have developed a machine learning system that can either make a prediction about a task, or defer the decision to an expert. Most importantly, it can adapt when and how often it defers to its human collaborator, based on factors such as its teammates availability and level of experience.

The team trained the system on multiple tasks, including looking at chest X-rays to diagnose specific conditions such as atelectasis (lung collapse) and cardiomegaly (an enlarged heart). In the case of cardiomegaly, they found that their human-AI hybrid model performed 8 percent better than either could on their own (based on AU-ROC scores).

In medical environments where doctors dont have many extra cycles, its not the best use of their time to have them look at every single data point from a given patients file, says PhD student Hussein Mozannar, lead author with David Sontag, the Von Helmholtz Associate Professor of Medical Engineering in the Department of Electrical Engineering and Computer Science, of a new paper about the system that was recently presented at the International Conference of Machine Learning. In that sort of scenario, its important for the system to be especially sensitive to their time and only ask for their help when absolutely necessary.

The system has two parts: a classifier that can predict a certain subset of tasks, and a rejector that decides whether a given task should be handled by either its own classifier or the human expert.

Through experiments on tasks in medical diagnosis and text/image classification, the team showed that their approach not only achieves better accuracy than baselines, but does so with a lower computational cost and with far fewer training data samples.

Our algorithms allow you to optimize for whatever choice you want, whether thats the specific prediction accuracy or the cost of the experts time and effort, says Sontag, who is also a member of MITs Institute for Medical Engineering and Science. Moreover, by interpreting the learned rejector, the system provides insights into how experts make decisions, and in which settings AI may be more appropriate, or vice-versa.

The systems particular ability to help detect offensive text and images could also have interesting implications for content moderation. Mozanner suggests that it could be used at companies like Facebook in conjunction with a team of human moderators. (He is hopeful that such systems could minimize the amount of hateful or traumatic posts that human moderators have to review every day.)

Sontag clarified that the team has not yet tested the system with human experts, but instead developed a series of synthetic experts so that they could tweak parameters such as experience and availability. In order to work with a new expert its never seen before, the system would need some minimal onboarding to get trained on the persons particular strengths and weaknesses.

In future work, the team plans to test their approach with real human experts, such as radiologists for X-ray diagnosis. They will also explore how to develop systems that can learn from biased expert data, as well as systems that can work with and defer to several experts at once.For example, Sontag imagines a hospital scenario where the system could collaborate with different radiologists who are more experienced with different patient populations.

There are many obstacles that understandably prohibit full automation in clinical settings, including issues of trust and accountability, says Sontag. We hope that our method will inspire machine learning practitioners to get more creative in integrating real-time human expertise into their algorithms.

Mozanner is affiliated with both CSAIL and the MIT Institute for Data, Systems and Society (IDSS). The teams work was supported, in part, by the National Science Foundation.

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An automated health care system that understands when to step in - MIT News