This Top-Rated App Can Help You Deal With Stress in the New Year

Stress tends to accumulate around the holiday season but it's not like it simply disappears otherwise. Life is full of stressors, from tight project deadlines to frustrating coworkers to rush hour traffic. Don't just accept the stress, deal with it appropriately by investing in your own mindfulness. Aura is a meditation app that can help you manage stress and anxiety effectively without resorting to bad or destructive habits.

Aura was created by top meditation teachers and therapists and designed to help you prioritize and improve your mental health. Personalized with AI, Aura provides short, science-backed mindfulness meditation exercises every day to give you the peace of mind you need to power through stress and anxiety to be your best self. Each day, you'll get a free 3-minute guided meditation session and have the option to choose between 3-, 7-, or 10-minute meditations throughout the day depending on your availability. By rating each meditation experience, Aura learns your preferences and can provide more specific meditations to meet your needs. Through the app, you can also track your mood patterns, save unlimited meditations, and access additional wellness content like life coaching sessions, stories, and music.

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Related: This Top-Rated App Can Help You Deal With Stress in the New Year 3 Stress-Busting Relaxation Exercises You Can Do Anywhere (60-Second Video) How to Feed Your Brain to Combat Stress

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This Top-Rated App Can Help You Deal With Stress in the New Year - Greenwich Time

Ursula didnt just dampen the Christmas spirit, it also left a trail of devastation in places that had earlier known the frightening fury of wind and rain and whose residents were then still treading the tortuous road to full recovery.

The stunning blow was doubly cruel for its timing, no matter that in these parts December is not always sweetness and light.

Occasionally, December descends or departs accompanied by a typhoon in full orchestra, so that, weather-wise, somewhere in this unhappy archipelago, there is terror even on Christmas Eve, inflicted by the unforgiving elements.

The new yeara constant symbol of hope, made more significant in that this one ushers in a new decadethus begins on a sobering fact: Ursula has set the nation back once more, and not exclusively in physical terms: lost lives and property, a new level of homelessness, damaged infrastructure, ruined agriculture.

The psychological state of its people, strained close to breaking by various disasters that leave them with unfilled needsexisting as well as newly formedis now even more fragile.

Its uncertain if there are still inner reserves with which to brace for yet another pummelling by earth, wind or fire, which invariably exposes the frailties of structures due to official corruption and greed (for example, a building that crumbled in an earthquake yields the grim discovery of substandard steel) or the brutal exigencies of impoverishment (coastal dwellings made of light materials are easily demolished by a storm surge).

After a lifetime of typhoons that grow fiercer by the year, those displaced by such calamities invariably seek shelter in schoolhouses.

A poignant question now haunts survivors in Eastern Visayas unable to quickly rebuild their dwellings: Where will they go when classes resume in 2020?

And yet, it is the disaster-weary that provide a heartening glimpse of never-say-die: television footage of six men fording chest-high floodwaters, bearing on their shoulders a prostrate neighbor requiring medical attention.

The footage, although all too brief, offers a shot in the arm for the new year, illustrating in fresh and vivid detail that Filipino trait, resilience, which politicians like to trot out in claiming credit for the survival of their constituents, even if these same constituents remain trapped in a primitive state.

Resilience is toughnessthe ability to cope with and not snap in critical conditions. Survivors of the apocalyptic Yolanda displayed it when they picked up the pieces of their lives and carried on in the face of the realization that long-term government succor would not go beyond the customary food packs, money doles and grand promises of better housing.

For the moment, the survivors of Ursula in the Visayas and in the Mindoro provinces need help, including the most basic: food, water, clothing.

The other urgency is housing materials, so they can put a roof over their heads. Now as in the aftermath of Yolanda, long-term planning is essential.

Assistance is sure to be forthcoming from the public, here and elsewhere; what is crucial is how its handled to the best advantage of target beneficiaries in the long term.

But apart from the necessity of giving, there are grim issues to address in this celebration of new beginnings.

Now more than ever, there is no room for indifference to the injustice that prevailsfrom the continuing escape of the dictator Ferdinand Marcos heirs from accountability to the continuing detention of prisoners of conscience; from the stalled trials of accused plunderers to the brazen refusal of powerful thieves to return purloined money despite official orders for them to do so; from the impunity with which fake news is manufactured and the law is violated in the highest places, to the barefaced attempts of lawmakers in the House to perpetuate themselves in power, to the unrelenting campaign against truth-tellers

Once more into the fray then, with 2020 vision marking the resistance to disempowerment, mindful of Nadine Gordimers sharp reminder: When people are deprived over years of any recourse to the provisions of civil society as a means of seeking redress for their material and spiritual deprivations, they lose the faculty of using the law when, at last, such recourse is open to them.

The result of this conditioning now is fashionably called the culture of violence; an oxymoron, for culture implies enlightenment, to aim towards attaining the fullness of life, not its destruction.

Click here for more weather related news."

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Meditation for the new year | Inquirer Opinion - INQUIRER.net

Principles of Mindfulness Leadership and Meditation

1) According to Buddhism, following Mind is the ultimate goal:

I follow spiritual and mystic traditions (mostly Hindu and Buddhist philosophies) and draw inspirations for leadership coaching . With the following article, I am endeavoring to shed light on the concept of mindfulness leadership and meditation.

In Buddhist traditions, mind is the ultimate deal. Mind is the deathless and timeless aspect of our being. Resting in its core of peace and calm is the way to countering human vices. That is the basic premise of Buddhist teachings. So, what is mind and what is its nature? The basic nature of mind is empty. This sense of base and bare awareness is transparent, clear and expansive. When we become quietin body, in mindwe feel and acknowledge our core of peace. We realize that this core of peace, basic awareness and primordial mind is non-quantifiable and yet all-knowing. It is that timeless aspect of our being that has existed before us and will remain after our worldly departure (Buddhists believe in the concept of reincarnation and the fact that our self or our Consciousness which essentially is non-self is a continuum.and empty). And acknowledgement of this aspect of our being can be brought about through the practice of mindfulness meditation.

2) Mindfulness Meditation explained:

In acknowledging the co-existence of mind and no-mind, being and non-being lay the great human challenge. Yet, arriving to a state where we witness non-duality is liberating. This state of non-duality is also called the Buddha-like-mind or the Buddha-mind. When we acknowledge and align to this illusory aspect of our being, we become more affectionate and empathic. We witness compassion imbuing our being; we are more open and creative. We love more and are loved more in return. That is the power of meditation. And mindfulness. The aforesaid analysis is also entertained and offered in the Buddhist practice of Mahamudra and is also its premise. And yet, Mahamudra can beand perhaps needs to bea non-intellectual undertaking or practice oriented more towards its practical approach and implication. Perhaps that is why it has classically gained appeal to laymen.

Now, getting back to the state of non-doing and just being:

When we are not thinking, or not indulging in relentless day-dreaming, ruminating and getting lost in the past and future, we notice, amid silence, that there is a silent knower or what I call an on-looker. This is called base awareness or in Tibetan Buddhism, rigpa. This aspect, as I mentioned before, is the non-dying core. So, this central peaceful core is what we should seek to align and acknowledge: to attain unswerving peace, balance and wisdom . Yes, mulling over our own existential inquiries, whilst aligning to our core of silence can help answer classic bigger questions of life like: Who am I? Where was I before I was born? (This particular question is also explored in a different Buddhist tradition, namely Zen Buddhism), What will happen to me after I am physically or bodily gone? But it is beyond the scope of this little article, so let me concentrate on the peace and calm generating effect of meditation.

In Mahamudra, we try to align to that state of unity. Here let me also add this: that the Buddhist meditation practice of samatha (of gently focusing ones mind in one object, mostly breaththe in-breath and out-breath to elicit mental and bodily calm and concentration ), and vipassana (the process of gaining wisdom and insight through the sheer positive effect of mental stillness), can be seamless. And, the above-said practices of samatha and vipassana complement Mahamudra.

3) How following Mindfulness Meditation has positively affected my life.

Personally, when I practice samatha and vipassana, I witness how past hurts and slights (that others have inflicted upon me and I have perhaps knowingly and unknowingly inflicted in others) have left a mark in my body through bodily aches and discomfort . Modern medical science is an evolving field: further research could elicit that many bodily ailments could have their origination in pent up hurts, slights and emotions. So, getting back to how the Insight helps me acknowledge my own mistakes (and the mistakes and hurts inflicted upon me), it also brings me to a sense of release. And calm and peace. It makes me realize how pent up emotions and hurts should be released. We need to forget our hurts in the pastand forgive. Since we cant go back in time, all we can do is not let those moments persist. Lets untie those knots. Perhaps Buddhist meditation is hinting at this very discernible effect and result of meditative and contemplative undertaking. Yes, even thoughexploring full repertoire of Buddhist practice leads to deeper understanding of our own existence, and goes beyond beckoning bodily and mental peace and calm; yet, starting with eliciting bodily and mental peace and calm through it is a great way to start. It can appeal to non-followers or non-practitioners of Buddhism too owing to its sheer power of helping attain peace and calm and transforming lives.

Link:

Principles of Mindfulness Leadership and Meditation - Thrive Global

Let your Conscience be your guide!

AVERILL PARK, N.Y. (PRWEB) December 26, 2019

The first-ever National Conscience Month is being launched January 1, 2020 by The American Meditation Institute (AMI), an organization devoted to providing comprehensive training in Yoga Sciencethe worlds oldest holistic mind/body medicine. With the mission of encouraging individuals across the nation to practice using their conscience as a guide, this month-long observance is vital educationespecially for those burdened by stress, burnout, fear, anger and the general discontent that pervades our society.

New Years Eve means the making of resolutions for millions of Americans. However, while stating a resolution can be easy, sticking to it often proves to be quite difficult. The challenge comes soon after the first of the year when old, unconscious habits kick back in, or we find we are not fully committed to the resolution and we lose our focus and will power. This year, National Conscience Month reminds us to use the inborn technology of our Conscience for creating and keeping personally meaningful and culturally beneficial resolutions.

The inspiration for National Conscience Month grew out of the Yoga Science Law of Karma which states simply that thoughts lead to words, actions and, eventually, to consequences. According to Leonard Perlmutter, AMI founder and originator of Conscience Month, This law of cause and effect is as real and unavoidable as the Law of Gravity. Once we appreciate the mechanics of the Law of Karma (that thought precedes action, and action precedes consequence), we all can start experiencing the unimagined, profound benefits to be found in relying on the unerring wisdom of the Conscience as it guides the thoughts we choose to think, words we speak and actions we take.

National Conscience Month's (January 2020) campaign will:

1. A free screening of the movie Peaceful Warrior to be followed by an engaging discussion about the importance of using your Conscience as your guide for a healthy, happy, and productive life. This two-part event will be presented at the Cohoes Music Hall, 58 Remsen Street, Cohoes, NY, on January 9, 2020 at 7 pm.

2. Four high school students from the Capital region in New York State will be awarded the first-ever National Conscience Month scholarships totaling $2,000. Recipients will be selected through original artistic creations inspired by the use or denial of the Conscience. Submissions will be accepted in four categories: Creative Writing; Visual Art (two-dimensional); Music or Song; and Video.

Public Service Announcement: "The Time is Now" https://youtu.be/zUJH4ge_320 Public Service Announcement: MORE https://youtu.be/yWFgxjXB8HA

INTERVIEW OPPORTUNITY: Leonard Perlmutter, originator of National Conscience Month and founder of The American Meditation Institute will be available for interviews. Contact Robert Washington for details.

Media Contact: Robert Washington 60 Garner Road Averill Park, NY 12018 Tel: 518-674-8714 Fax: 518-674-8714

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The American Meditation Institute Inaugurates National Conscience Month to Instruct and Inspire Humanity on How to Let Their Conscience be Their Guide...

Being a student comes with its fair share of difficulties, regardless of your age or the type of educational system you're following, for that matter. One of the most difficult aspects is the learning process itself, since it's riddled with roadblocks, high volumes of information or even information that you don't necessarily find utility for at all times. However there are methods that can help students boost their ability to slow down, focus and synthesize information in a way that streamlines the learning process and propels them closer to their end goals:

There has been a lot of talk about meditation these past years, as it gradually found a way into more and more people's daily lives. This is mostly because practicants are starting to discover its multiple benefits that tend to span over their physical, emotional and spiritual sides. Having said this, there is an abundance of meditation techniques out there, each of them with their own purpose and style.

To this end,countless studieshave proven that daily mindfulness meditation sessions, for example, will have tremendous effects on your well-being by slashing the inflammatory response caused by stress. This type of meditation also helps you become more centered, calmer and more inclined to adopt a positive attitude. Considering meditation teaches you to continuously be focused on the present moment, it also lengthens your attention span considerably. As a student you may already know these attributes or lack thereof can make or break your studying game in the long-term.

Sports are detrimental to a student's physical and psychological health, as engaging in some sort of regular sports activities will help decrease stress levels and improve your ability to focus. Since sports are mostly fast-paced and goal-oriented, they will help you get a better grasp on what tasks are more important, while also motivating you to leave those procrastinating days behind.

Sports also inherently teaches you how to deal with failures, which in turn will make you as a student become more proactive and ready to get back in the game, regardless of the difficulties you are facing. So, regardless if you are planning to enroll in an online class with reputable portals such asTraining.com.auor in a traditional type of educational system, practicing sports as little as 1-2 times a week will help you become a more relaxed and productive student.3. Declutter and Organize Your Life

Unfortunately, students often tend to underestimate just how important their living and working environment is and more often than not will let clutter fill up their lives. It's been proven that ourbrains on clutteractually become cloudier and more anxious, which in turn leads us to adapting coping mechanisms, such as watching TV shows or eating junk food. However decluttering can also feel like an impossible and painful task, especially to those suffering from a form or another of hoarding disorder.

Thankfully, science backs up the benefits of decluttering, leading us to understand that if we commit to cleaning out our personal environments, we will most likely commit to solving other problematic aspects of our lives as well. As a student, you'll see an increase in your quality of sleep and diet, as well as in your ability to focus and efficiently carry out tasks.

An inability to perform in school can also be linked to something seemingly unimportant as note-taking. Take the time to objectively assess how you take your notes while you are in class or while you browse books and you may find that sometimes they are incomplete or jotted down in a hurry.

It's also important that when you take notes, you do so mindfully so you can assimilate the information as you write it down and create a strong base to which you can later on add missing details. There are manynote-taking techniquesout there and there is certainly one that best fits your personality as well.

All in all, being a student is all about balancing your projected goals and the effort it takes to meet them with the ability to relax and have fun. By implementing the techniques above into your life, you'll most likely see how this balance is easier to attain than you ever thought was possible.

See more here:

Becoming a More Relaxed and Productive Student in 4 Engaging Ways - University Herald

Deep Learning

One of the factors often credited for this latest boom in artificial intelligence (AI) investment, research, and related cognitive technologies, is the emergence of deep learning neural networks as an evolution of machine algorithms, as well as the corresponding large volume of big data and computing power that makes deep learning a practical reality. While deep learning has been extremely popular and has shown real ability to solve many machine learning problems, deep learning is just one approach to machine learning (ML), that while having proven much capability across a wide range of problem areas, is still just one of many practical approaches. Increasingly, were starting to see news and research showing the limits of deep learning capabilities, as well as some of the downsides to the deep learning approach. So are peoples enthusiasm of AI tied to their enthusiasm of deep learning, and is deep learning really able to deliver on many of its promises?

The Origins of Deep Learning

AI researchers have struggled to understand how the brain learns from the very beginnings of the development of the field of artificial intelligence. It comes as no surprise that since the brain is primarily a collection of interconnected neurons, AI researchers sought to recreate the way the brain is structured through artificial neurons, and connections of those neurons in artificial neural networks. All the way back in 1940, Walter Pitts and Warren McCulloch built the first thresholded logic unit that was an attempt to mimic the way biological neurons worked. The Pitts and McCulloch model was just a proof of concept, but Frank Rosenblatt picked up on the idea in 1957 with the development of the Perceptron that took the concept to its logical extent. While primitive by todays standards, the Perceptron was still capable of remarkable feats - being able to recognize written numbers and letters, and even distinguish male from female faces. That was over 60 years ago!

Rosenblatt was so enthusiastic in 1959 about the Perceptrons promises that he remarked at the time that the perceptron is the embryo of an electronic computer that [we expect] will be able to walk, talk, see, write, reproduce itself and be conscious of its existence. Sound familiar? However, the enthusiasm didnt last. AI researcher Marvin Minsky noted how sensitive the perceptron was to small changes in the images, and also how easily it could be fooled. Maybe the perceptron wasnt really that smart at all. Minsky and AI researcher peer Seymour Papert basically took apart the whole perceptron idea in their Perceptrons book, and made the claim that perceptrons, and neural networks like it, are fundamentally flawed in their inability to handle certain kinds of problems notably, non-linear functions. That is to say, it was easy to train a neural network like a perceptron to put data into classifications, such as male/female, or types of numbers. For these simple neural networks, you can graph a bunch of data and draw a line and say things on one side of the line are in one category and things on the other side of the line are in a different category, thereby classifying them. But theres a whole bunch of problems where you cant draw lines like this, such as speech recognition or many forms of decision-making. These are nonlinear functions, which Minsky and Papert proved perceptrons incapable of solving.

During this period, while neural network approaches to ML settled to become an afterthought in AI, other approaches to ML were in the limelight including knowledge graphs, decision trees, genetic algorithms, similarity models, and other methods. In fact, during this period, IBMs DeepBlue purpose-built AI computer defeated Gary Kasparov in a chess match, the first computer to do so, using a brute-force alpha-beta search algorithm (so-called Good Old-Fashioned AI [GOFAI]) rather than new-fangled deep learning approaches. Yet, even this approach to learning didnt go far, as some said that this system wasnt even intelligent at all.

Yet, the neural network story doesnt end here. In 1986, AI researcher Geoff Hinton, along with David Rumelhart and Ronald Williams, published a research paper entitled Learning representations by back-propagating errors. In this paper, Hinton and crew detailed how you can use many hidden layers of neurons to get around the problems faced by perceptrons. With sufficient data and computing power, these layers can be calculated to identify specific features in the data sets they can classify on, and as a group, could learn nonlinear functions, something known as the universal approximation theorem. The approach works by backpropagating errors from higher layers of the network to lower ones (backprop), expediting training. Now, if you have enough layers, enough data to train those layers, and sufficient computing power to calculate all the interconnections, you can train a neural network to identify and classify almost anything. Researcher Yann Lecun developed LeNet-5 at AT&T Bell Labs in 1998, recognizing handwritten images on checks using an iteration of this approach known as Convolutional Neural Networks (CNNs), and researchers Yoshua Bengio and Jrgen Schmidhube further advanced the field.

Yet, just as things go in AI, research halted when these early neural networks couldnt scale. Surprisingly very little development happened until 2006, when Hinton re-emerged onto the scene with the ideas of unsupervised pre-training and deep belief nets. The idea here is to have a simple two-layer network whose parameters are trained in an unsupervised way, and then stack new layers on top of it, just training that layers parameters. Repeat for dozens, hundreds, even thousands of layers. Eventually you get a deep network with many layers that can learn and understand something complex. This is what deep learning is all about: using lots of layers of trained neural nets to learn just about anything, at least within certain constraints.

In 2010, Stanford researcher Fei-Fei Li published the release of ImageNet, a large database of millions of labeled images. The images were labeled with a hierarchy of classifications, such as animal or vehicle, down to very granular levels, such as husky or trimaran. This ImageNet database was paired with an annual competition called the Large Scale Visual Recognition Challenge (LSVRC) to see which computer vision system had the lowest number of classification and recognition errors. In 2012, Geoff Hinton, Alex Krizhevsky, and Ilya Sutskever, submitted their AlexNet entry that had almost half the number of errors as all previous winning entries. What made their approach win was that they moved from using ordinary computers with CPUs, to specialized graphical processing units (GPUs) that could train much larger models in reasonable amounts of time. They also introduced now-standard deep learning methods such as dropout to reduce a problem called overfitting (when the network is trained too tightly on the example data and cant generalize to broader data), and something called the rectified linear activation unit (ReLU) to speed training. After the success of their competition, it seems everyone took notice, and Deep Learning was off to the races.

Deep Learnings Shortcomings

The fuel that keeps the Deep Learning fires roaring is data and compute power. Specifically, large volumes of well-labeled data sets are needed to train Deep Learning networks. The more layers, the better the learning power, but to have layers you need to have data that is already well labeled to train those layers. Since deep neural networks are primarily a bunch of calculations that have to all be done at the same time, you need a lot of raw computing power, and specifically numerical computing power. Imagine youre tuning a million knobs at the same time to find the optimal combination that will make the system learn based on millions of pieces of data that are being fed into the system. This is why neural networks in the 1950s were not possible, but today they are. Today we finally have lots of data and lots of computing power to handle that data.

Deep learning is being applied successfully in a wide range of situations, such as natural language processing, computer vision, machine translation, bioinformatics, gaming, and many other applications where classification, pattern matching, and the use of this automatically tuned deep neural network approach works well. However, these same advantages have a number of disadvantages.

The most notable of these disadvantages is that since deep learning consists of many layers, each with many interconnected nodes, each configured with different weights and other parameters theres no way to inspect a deep learning network and understand how any particular decision, clustering, or classification is actually done. Its a black box, which means deep learning networks are inherently unexplainable. As many have written on the topic of Explainable AI (XAI), systems that are used to make decisions of significance need to have explainability to satisfy issues of trust, compliance, verifiability, and understandability. While DARPA and others are working on ways to possibly explain deep learning neural networks, the lack of explainability is a significant drawback for many.

The second disadvantage is that deep learning networks are really great at classification and clustering of information, but not really good at other decision-making or learning scenarios. Not every learning situation is one of classifying something in a category or grouping information together into a cluster. Sometimes you have to deduce what to do based on what youve learned before. Deduction and reasoning is not a fort of deep learning networks.

As mentioned earlier, deep learning is also very data and resource hungry. One measure of a neural networks complexity is the number of parameters that need to be learned and tuned. For deep learning neural networks, there can be hundreds of millions of parameters. Training models requires a significant amount of data to adjust these parameters. For example, a speech recognition neural net often requires terabytes of clean, labeled data to train on. The lack of a sufficient, clean, labeled data set would hinder the development of a deep neural net for that problem domain. And even if you have the data, you need to crunch on it to generate the model, which takes a significant amount of time and processing power.

Another challenge of deep learning is that the models produced are very specific to a problem domain. If its trained on a certain dataset of cats, then it will only recognize those cats and cant be used to generalize on animals or be used to identify non-cats. While this is not a problem of only deep learning approaches to machine learning, it can be particularly troublesome when factoring in the overfitting problem mentioned above. Deep learning neural nets can be so tightly constrained (fitted) to the training data that, for example, even small perturbations in the images can lead to wildly inaccurate classifications of images. There are well known examples of turtles being mis-recognized as guns or polar bears being mis-recognized as other animals due to just small changes in the image data. Clearly if youre using this network in mission critical situations, those mistakes would be significant.

Machine Learning is not (just) Deep Learning

Enterprises looking at using cognitive technologies in their business need to look at the whole picture. Machine learning is not just one approach, but rather a collection of different approaches of various different types that are applicable in different scenarios. Some machine learning algorithms are very simple, using small amounts of data and an understandable logic or deduction path thats very suitable for particular situations, while others are very complex and use lots of data and processing power to handle more complicated situations. The key thing to realize is that deep learning isnt all of machine learning, let alone AI. Even Geoff Hinton, the Einstein of deep learning is starting to rethink core elements of deep learning and its limitations.

The key for organizations is to understand which machine learning methods are most viable for which problem areas, and how to plan, develop, deploy, and manage that machine learning approach in practice. Since AI use in the enterprise is still continuing to gain adoption, especially these more advanced cognitive approaches, the best practices on how to employ cognitive technologies successfully are still maturing.

See the article here:

Are We Overly Infatuated With Deep Learning? - Forbes

Artificial Intelligence (AI) has come a long way from just being a dream to becoming an integral part of our lives. From self-driving cars to smart assistants including Alexa, every industry vertical is leveraging the capabilities of AI. The software testing industry is also leveraging AI to enhance security testing efforts while automating human testing efforts.

AI and ML-based security testing efforts are helping test engineers to save a lot of time while ensuring the delivery of robust security solutions for apps and enterprises.

During security testing, it is essential to gather as much information as you can to increase the odds of your success. Hence, it is crucial to analyze the target carefully to gather the maximum amount of information.

Manual efforts to gather such a huge amount of information could eat up a lot of time. Hence, AI is leveraged to automate the stage and deliver flawless results while saving a lot of time and resources. Security experts can use the combination of AI and ML to identify a massive variety of details including the software and hardware component of computers and the network they are deployed on.

SEE ALSO:Amazons new ML service Amazon CodeGuru: Let machine learning optimize your Java code

Applying machine learning to the application scan results can help in a significant reduction of manual labor that is used in identifying whether the issue is exploitable or not. However, findings should always be reviewed by test engineers to decide whether the findings are accurate.

The key benefit that ML offers is its capability to filter out huge chunks of information during the scanning phase. It helps focus on a smaller block of actionable data, which offers reliable results while significantly reducing scan audit times.

An ML-based security scan results audit can significantly reduce the time required for security testing services. Machine learning classifiers can be trained through knowledge and data generated through previous tests for automation of new scan results processing. It can help enterprises triage static code results. Organizations can benefit from a large pool of data collated through multiple scans ongoing on a regular basis to get more contextual results.

This stage includes controlling multiple network devices to churn out data from the target or leverage the devices to launch attacks on multiple targets. After scanning the vulnerabilities, test engineers are required to ensure that the system is free of flaws that be used by attackers to affect the system.

AI-based algorithms can help ensure the protection of network devices by suggesting multiple combinations of strong passwords. Machine learning can be programmed to identify the vulnerability of the system though observation of user data while identifying patterns to make possible suggestions about used passwords.

AI can also be used to access the network on a regular basis to ensure that any security loophole is not building up. The algorithms capability should include identification of new admin accounts, new network access channels, encrypted channels and backdoors among others.

SEE ALSO:Artificial intelligence & machine learning: The brain of a smart city

ML-backed security testing services can significantly reduce triage pain because triage takes a lot of time if organizations rely on manual efforts. Manual security testing efforts would require a large workforce to go through all the scan results only and will take a lot of time to develop efficient triage. Hence, manual security testing is neither feasible nor scalable to meet the security needs of enterprises.

Aside, application inventory numbers used to be in the hundreds before, but now enterprises are dealing with thousands of apps. With organizations scanning their apps every month, the challenges are only increasing for security testing teams. Test engineers are constantly trying to reduce the odds of potential attacks while enhancing efficiency to keep pace with agile and continuous development environment.

Embedded AI and ML can help security testing teams in delivering greater value through automation of audit processes that are more secure and reliable.

See original here:

The impact of ML and AI in security testing - JAXenter

As we dive deeper into the Fourth Industrial Revolution, there is no disputing how technology serves as a catalyst for growth and innovation for many businesses across a range of functions and industries.

But one technology that is steadily gaining prominence across organizations includes machine learning (ML).

In the simplest terms, ML is the science of getting computers to learn and act like humans do without being programmed. It is a form of artificial intelligence (AI) and entails feeding machine data, enabling the computer program to learn autonomously and enhance its accuracy in analyzing data.

The proliferation of technology means AI is now commonplace in our daily lives, with its presence in a panoply of things, such as driverless vehicles, facial recognition devices, and in the customer service industry.

Currently, asset managers are exploring the potential that AI/ML systems can bring to the finance industry; close to 60 percent of managers predict that ML will have a medium-to-large impact across businesses.

MLs ability to analyze large data sets and continuously self-develop through trial and error translates to increased speed and better performance in data analysis for financial firms.

For instance, according to the Harvard Business Review, ML can spot potentially outperforming equities by identifying new patterns in existing data sets and examine the collected responses of CEOs in quarterly earnings calls of the S&P 500 companies for the past 20 years.

Following this, ML can then formulate a review of good and bad stocks, thus providing organizations with valuable insights to drive important business decisions. This data also paves the way for the system to assess the trustworthiness of forecasts from specific company leaders and compare the performance of competitors in the industry.

Besides that, ML also has the capacity to analyze various forms of data, including sound and images. In the past, such formats of information were challenging for computers to analyze, but todays ML algorithms can process images faster and better than humans.

AUTOMATION

For example, analysts use GPS locations from mobile devices to pattern foot traffic at retail hubs or refer to the point of sale data to trace revenues during major holiday seasons. Hence, data analysts can leverage on this technological advancement to identify trends and new areas for investment.

It is evident that ML is full of potential, but it still has some big shoes to fil if it were to replace the role of an investor.

Nishant Kumar aptly explained this in Bloomberg, Financial data is very noisy, markets are not stationary and powerful tools require deep understanding and talent thats hard to get. One quantitative analyst, or quant, estimates the failure rate in live tests at about 90 percent. Man AHL, a quant unit of Man Group, needed three years of workto gain enough confidence in a machine-learning strategy to devote client money to it. It later extended its use to four of its main money pools.

In other words, human talent and supervision are still essential to developing the right algorithm and in exercising sound investment judgment. After all, the purpose of a machine is to automate repetitive tasks. In this context, ML may seek out correlations of data without understanding their underlying rationale.

One ML expert said, his team spends days evaluating if patterns by ML are sensible, predictive, consistent, and additive. Even if a pattern falls in line with all four criteria, it may not bear much significance in supporting profitable investment decisions.

The bottom line is ML can streamline data analysis steps, but it cannot replace human judgment. Thus, active equity managers should invest in ML systems to remain competitive in this innovate or die era. Financial firms that successfully recruit professionals with the right data skills and sharp investment judgment stands to be at the forefront of the digital economy.

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Can machine learning take over the role of investors? - TechHQ

Smart personal audio devices are lookingto have the strongest year in history in 2019, with true wireless stereo set to be the largest and fastest growing category, according to new data released by analyst firm Canalys.

New figures released show that in Q3 2019, the worldwide smart personal audio device market grew 53% to reach 96.7 million units. And the segment is expected to break the 100 million unit mark in the final quarter, with potential to exceed 350 million units for the full year.

Canalys latest research showed the TWS category was not only the fastest growing segment in this market, with a stellar 183% annual growth in Q3 2019, but it also overtook wireless earphones and wireless headphones to become the largest category.

The rising importance of streaming content, and the rapid uptake in a new form of social media including short videos, resulted in profound changes in mobile users audio consumption and these changes will accelerate in the next five years while technology advancements like machine learning and smart assistants will bring more radical innovations in areas such as audio content discovery and ambient computing, explainsNicole Peng, vice president of mobility at Canalys.

As users adjust their consumption habits, Peng says the TWS category enabled smartphone vendors to adapt and differentiate against traditional audio players in the market.

With 18.2 million units shipped in Q3 2019, Apple commands 43% of the TWS market share and continues to be the trend setter.

Apple is in clear leadership position and not only on the chipset technology front. The seamless integration with iPhone, unique sizing and noise cancelling features providing top of the class user experience, is where other smartphone vendors such as Samsung, Huawei and Xiaomi are aiming their TWS devices," says Peng.

"In the short-term, smart personal audio devices are seen as the best up-selling opportunities for smartphone vendors, compared with wearables and smart home devices."

Major audio brands such as Bose, Sennheiser, JBL, Sony and others are currently able to stand their ground with their respective audio signatures especially in the earphones and headphones categories, the research shows.

Canalys senior analyst Jason Low says demand for high-fidelity audio will continue to grow. However, the gap between audio players and smartphone vendors is narrowing.

"Smartphone vendors are developing proprietary technologies to not only catch up in audio quality, but also provide better integration for on-the-move user experiences, connectivity and battery life, he explains.

Traditional audio players must not underestimate the importance of the TWS category. The lack of control over any connected smart devices is the audio players biggest weakness," Low says.

"Audio players must come up with an industry standard enabling better integration with smartphones, while allowing developers to tap into the audio features to create new use cases to avoid obsoletion."

Low says the potential for TWS devices is far from being fully uncovered, and vendors must look beyond TWS as just a way to drive revenue growth.

"Coupled with information collected from sensors or provided by smart assistants via smartphones, TWS devices will become smarter and serve broader use cases beyond audio entertainments, such as payment, and health and fitness, he explains.

"Regardless of the form factor, the next challenge will be integrating smarter features and complex services on the smart personal audio platforms. Canalys expects the market of smart personal audio devices to grow exponentially in the next two years and the cake is big enough for many vendors to come in and compete for the top spots as technology leaders and volume leaders.

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Machine learning to grow innovation as smart personal device market peaks - IT Brief New Zealand

One of the most used machine learning (ML) algorithms of this year, reinforcement learning (RL) has been utilised to solve complex decision-making problems. In the present scenario, most of the researches are focussed on using RL algorithms which helps in improving the performance of the AI model in some controlled environment.

Ubisofts prototyping space, Ubisoft La Forge has been doing a lot of advancements in its AI space. The goal of this prototyping space is to bridge the gap between the theoretical academic work and the practical applications of AI in videogames as well as in the real world. In one of our articles, we discussed how Ubisoft is mainstreaming machine learning into game development. Recently, researchers from the La Forge project at Ubisoft Montreal proposed a hybrid AI algorithm known as Hybrid SAC, which is able to handle actions in a video game.

Most reinforcement learning research papers focus on environments where the agents actions are either discrete or continuous. However, when training an agent to play a video game, it is common to encounter situations where actions have both discrete and continuous components. For instance, when wanting the agent to control systems that have both discrete and continuous components, like driving a car by combining steering and acceleration (both continuous) with the usage of the hand brake (a discrete binary action).

This is where Hybrid SAC comes into play. Through this model, the researchers tried to sort out the common challenges in video game development techniques. The contribution consists of a different set of constraints which is mainly geared towards industry practitioners.

The approach in this research is based on Soft Actor-Critic which is designed for continuous action problems. Soft Actor-Critic (SAC) is a model-free algorithm which was originally proposed for continuous control tasks, however, the actions which are mostly encountered in video games are both continuous as well as discrete.

In order to deal with a mix of discrete and continuous action components, the researchers converted part of SACs continuous output into discrete actions. Thus the researchers further explored this approach and extended it to a hybrid form with both continuous and discrete actions. The researchers also introduced Hybrid SAC which is an extension to the SAC algorithm that can handle discrete, continuous and mixed actions discrete-continuous.

The researchers trained a vehicle in a Ubisoft game by using the proposed Hybrid SAC model with two continuous actions (acceleration and steering) and one binary discrete action (hand brake). The objective of the car is to follow a given path as fast as possible, and in this case, the discrete hand brake action plays a key role in staying on the road at such a high speed.

Hybrid SAC exhibits competitive performance with the state-of-the-art on parameterised actions benchmarks. The researchers showed that this hybrid model can be successfully applied to train a car on a high-speed driving task in a commercial video game, also, demonstrating the practical usefulness of such an algorithm for the video game industry.

While working with the mixed discrete-continuous actions, the researchers have gained several experiences and shared them as a piece of advice to obtain an appropriate representation for a given task.They are mentioned below

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A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box. Contact: ambika.choudhury@analyticsindiamag.com

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This AI Agent Uses Reinforcement Learning To Self-Drive In A Video Game - Analytics India Magazine