Monthly Archives: March 2020

By: Mark Krupnik, PhD, Founder and CEO, Retalon

Since this article will probably come out during Income tax season, let me start with the following example: Suppose we would like to build a program that calculates income tax for people. According to US federal income tax rules: For single filers, all income less than $9,875 is subject to a 10% tax rate. Therefore, if you have $9,900 in taxable income, the first$9,875 is subject to the 10% rate and the remaining $25 is subject to the tax rate of the next bracket (12%).

This is an example of rules or an algorithm (set of instructions) for a computer.

Lets look at this from a formal, pragmatic point of view. A computer equipped with this program can achieve the goal (calculate tax) without human help. So technically, this can be classified as Artificial Intelligence.

But is it cool enough? No. Its not. That is why many people would not consider it part of AI. They may say that if we already know how to do a certain thing, then the process cannot be considered real intelligence. This is a phenomena that has become known as AI Effect. One of the first references is known as Teslers theorem that says: AI is whatever hasnt been done yet.

In the eyes of some people, the cool part of AI is associated with machine learning, and more specifically with deep learning which requires no instructions and utilizes Neural Nets to learn everything by itself, like a human brain.

The reality is that human development is a combination of multiple processes, including both: instructions, and Neural Net training, as well as many other things.

Lets take another simple example: If you work in a workshop on a complex project, you may need several tools, for instance a hammer, a screwdriver, plyers, etc. Of course, you can make up a task that can be solved by only using a hammer or only screwdriver, but for most real-life projects you will likely need to use various tools in combination to a certain extent.

In the same manner, AI also consists of several tools (such as algorithms, supervised and unsupervised machine learning, etc.). Solving a real-life problem requires a combination of these tools, and depending on the task, they can be used in different proportions or not used at all.

There are and there will always be situations where each of these methods will be preferred over others.

For example, the tax calculation task described in the beginning of this article will probably not be delegated to machine learning. There are good reasons to it, for example:

the solution of this problem does not depend on data the process should be controllable, observable, and 100% accurate (You cant just be 80% accurate on your income taxes)

However, the task to assess income tax submissions to identify potential fraud is a perfect application for ML technologies.

Equipped with a number of well labelled data inputs (age, gender, address, education, National Occupational Classification code, job title, salary, deductions, calculated tax, last year tax, and many others) and using the same type of information available from millions of other people, ML models can quickly identify outliers.

What happens next? The outliers in data are not necessarily all fraud. Data scientists will analyse anomalies and try to understand the reason for these individuals being flagged. It is quite possible that they will find some additional factors that had to be considered (feature engineering), for example a split between tax on salary, and tax on capital gain of investment. In this case, they would probably add an instruction to the computer to split this data set based on income type. At this very moment, we are not dealing with a pure ML model anymore (as the scientists just added an instruction), but rather with a combination of multiple AI tools.

ML is a great technology that can already solve many specific tasks. It will certainly expand to many areas, due to its ability to adapt to change without major effort on a human side.

At the same time, those segments that can be solved using specific instructions and require predictable outcome (financial calculations) or those involving high risk (human life, health, very expensive and risky projects) require more control and if the algorithmic approach can provide it, it will still be used.

For practical reasons, to solve any specific complex problem, the right combination of tools and methods of both types are required.

About the Author:

Mark Krupnik, PhD, is the founder and CEO ofRetalon, an award-winning provider of retail AI and predictive analytics solutions for planning, inventory optimization, merchandising, pricing and promotions.Mark is a leading expert on building and delivering state-of-the-art solutions for retailers.

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Is Machine Learning Always The Right Choice? - Machine Learning Times - machine learning & data science news - The Predictive Analytics Times

The complexity of files and objects with their increased breadth of file formats and sizes has presented a significant challenge to modern day organisations seeking to improve detection and response processes for advanced malware threats. What might be called a "malware blob," these threats are packed deep within data, hidden layers down and sometimes even out of sight from typical detection engines. For human analysts responsible for tracking and responding to threats, current detection engines offer only a "black box" perspective. In other words, they provide alerts, but offer little to no context as to what's happening within the "blob," causing human analysts to struggle to understand and act on the risk they present effectively. To take down the blob, analysts need a more effective way to bridge the gap between detecting malware and understanding what triggered an alert in the first place. Innovations in machine learning techniques have recently surfaced, giving security teams hope for better threat explanations and improved ability to defend against malware's growing complexity and volume.

Machine learning and other anomaly detection capabilities were developed to extend malware detection beyond blacklists or databases of known attack signatures. Anomaly-based detection systems observed the behaviour of the network, profiled the normal behaviour, and predicted new threats based on some type of anomalous behaviour or anomalous characteristics. However, while new zero-day threats started to be uncovered, these predictions were missing a critical piece to the puzzle, the WHY behind the WHAT.

While detection vendors produced a binary conviction or malware classification type, the analyst never understood what characteristics of the threats or indicators were present to fully understand the conclusion. Quite simply, signature-based, AI-based and machine learning-based threat detection came with little to no context. This lack of context resulted in analysts spending numerous hours attempting to understand why a file was identified as malicious in order to effectively support their response. And for most analysts, the same scenario plays out in todays security operations centres.

To better understand how to improve machine learning-driven results, we must first understand that machine learning is a technology that in its essence converts information and object relationships into numbers that try to quantify these properties. The very first step in implementing any such system is the conversion of human experience into a sequence that a machine understands and can learn from. Where machines are specifically built to read and interpret numbers, the people who are meant to use these models often feel limited and confused by these ML/AI systems. The most common question asked of a machine learning expert is, Why? Why did the machine present such a result? Or more specifically for those in cybersecurity, Why was this object detected as malicious?

To answer the why, lets start from the beginning. As mentioned, the very first step in implementing any such system is the conversion of human experience into a sequence of numbers that a machine understands and can learn from. But what if the first step instead was to develop a system that describes the data--or malware in this case--in a way that both human and machine can understand?

We refer to this approach as explainable machine learning. To succeed, it must be built on a static analysis system that converts objects into human readable indicators that describe the intent of the code found within them. Regardless of what the analysed object is, either a simple file or compound blob, static analysis systems can, within just a few milliseconds, go through all its components and describe them in an approachable and easy to understand way.

With a foundation of human readable indicators, explainable machine learning can detect malware with results that are always interpretable by a human analyst. Quite simply, if a system makes a classification decision it must be able to defend it with a description included with any malware it detects. The human perspective comes first, and the machine can then serve as the ultimate companion.

This is why explainable machine learning systems must be built from the bottom up instead. At ReversingLabs we believe these systems must be built on the concept that declaring which malware type has been detected is its most important feature. Combined with the human readable indicators, machine learning explainability means that the result the system provides must be logical. Human analysts must therefore be given the ability to read the list of provided indicators and agree that the detected malware type has had its functionality described correctly. This same level of transparency in an explainable machine learning model is also critical when prioritising indicators, as they are not all created equal. Only some of them are a contributing factor for the final malware detection. Understanding which indicators are at play is critical to the analyst decision making process. This final piece of the puzzle builds trust in the accuracy of the classification system and underscores the value of exposing models reasoning to the human analysts.

Today, most machine learning classifiers are built from the top down. Companies that implement them usually start by making simple classifiers that discern good from bad. Data scientists then can create millions of features extracted from millions of objects. Given enough compute power, machine learning models then find optimal curves that split these datasets based on these labels. However, results wind up losing all of their explainability in the process.

Knowing good from bad is certainly the crux of malware detection, but it isnt the most important answer a detection system must provide. The second question that an analyst will pose to a machine learning expert is exactly what did the system detect? Analyst response to the threat any piece of malware poses is hugely dependent on the answer to this question.

With explainable machine learning, interaction with indicators changes drastically. Transparency in the decision-making process highlights the most important malware family properties. That information is key for assessing the organisational impact that a malware infection has, and the starting point from which a response is planned.

Machine learning models are a great choice for the first line of defence. These signatureless heuristic systems do a great job of identifying if something is malware or not, and even pinpointing what type of malware it is. Their detection outcomes are predictive, not reactive, and that makes detecting new malware variants possible. Even brand-new malware families can be detected without models explicitly being trained on how to do so. In terms of reliability, they also require fewer updates when compared to conventional signatures, and their effective detection rates decay slower.

Tomislav Pericin co-founder, Chief Architect, ReversingLabs

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Escape the malware blob with modern machine learning - ITProPortal

Technology meets large-scale dimensions in this scenario. Once upon a time, face-recognition was just imagination. Now in use and highly recommended for various sectors to pursue the face recognition technology. There are real faces hidden behind reel looks but such technology, like face recognition, is impeccable to explore the reality. Yes. Nowadays, technology reached one step up with an innovation which converts into reality. Such programs are known as Face Recognition APIs. API means a software intermediary that allows communication between two interfaces. These kinds of software could reduce the manual efforts for verification, and it's indeed for crime branch to get criminals from its record.

What is Machine Learning?

The process to analyze the data and generate the potential outcome. The computer expert says; it is the process where the computer algorithm finds patterns in data, and predict the feasible outcomes.Machine learning is a core part of computerized reasoning as it boosts

a computer program to get into a method of self-learning without being unequivocally modified.

How did machine learning work?

There are two types of techniques to understand how machine learning works. It is supervised learning that instructs a model on filled data and output so that it can predict future outputs. And the second one is unsupervised learning which detects patterns or natural compositions in input data.

Classification technique is the subtype of supervised learning which predicts discrete responses like an email is valid or spam. It recognizes the handwriting of letters and numbers using classification. Regression technique is another subtype of supervised learning which predicts continuous responses like changes happening around like temperature or variation in power demand.

To find hidden patterns or groupings in data is commonly use clustering technique which is a subtype of unsupervised learning.

Deep Learning is a type of machine learning. It identifies digit, letters, faces and sounds. It instructs a computer to filter the higher layer to recognise observed data like text, images and sounds. Deep learning encouraged by the human brain.

How Deep learning works?

Three layers are working for deep learning input layer, hidden layers, and output layer. These layers include multiple neurons. The input layer is what we fill data in it for getting a resultant value and the output layer throws a result value that has processed before executes. We can not observe the data between the input and output layers because of the neural network has to be encoded as vectors of floating-point numbers. Each of the vector input gets a weight, and each input of the neurons gets multiplied by this weight

After processed on neurons, it further generates a predictable output.

How machine learning & deep learning helps in face recognitionLife is full of threats nowadays. A technology like Machine Learning, along with deep learning brings a stunning impact on most of the sectors for detecting unusual threat like criminals attack or thieves at the bank, society or in the market. Machine learning and deep learning gives the power to build a biometric recognition program which can identify a person.

An accurate algorithm is used for face recognition like Viola-John method for real-time face recognize faces also twisted into 30 degrees. Face recognition system requires to detect a face and focus on it. Here, the algorithm measures to determine the uniqueness of proportions, skin colour, shapes of face, the gap between the eyes, the width of the noses, the length of the nose, the height and shape of the cheekbones, the width of the chin, the height of the forehead and other parameters.

After measuring, all the resulting data compared with the available database and, if the parameters coincide the person is recognised. Not only images but live video recording can be measured out the same way.

Identify, Examine and Match Face Expressions by Amazon Face Rekognition APIs

But how? It is possible by Amazon Face Rekognition API service. Amazon's best product among all the others is Face Rekognition API which undoubtedly integrates with any of the platforms to detect, and identifies a person through image or in live video recording.

Why choose a tough route or double trouble for processing face recognition? Here, Amazon Rekognition can detect a face in an image, video, find the position of eyes, also detect emotions like happy or sad in near real-time.

Amazon Rekognition Service for Mobile Apps

Easily integrateGreat Visual Analysis into your App. You don't need computer vision or deep learning expertise. Take advantage of Rekognition's high-quality image and video analysis for your web, mobile, enterprise or device applications. Amazon Rekognition removes the complexity of building visual recognition capabilities by making robust and accurate analysis available with simple to use APIs.

Continuously Learning Amazon Rekognition has designed to use deep learning technology to analyze a load of images and videos periodic. It is continuously learning as we add support for new capabilities and learn from more data.

Integrated with AWS Services Amazon Rekognition has designed to work absolutely with other AWS services. Rekognition integrates directly with Amazon S3, and AWS Lambda so you can build scalable, affordable, and reliable visual analysis applications. You can start analyzing images and videos stored in Amazon S3 without moving any data. You can also run real-time video analysis on streams coming from Amazon Kinesis Video Streams.

For more details;

Amazon Rekognition API - aws.amazon.com/rekognition/

Amazon Rekognition pricing - aws.amazon.com/rekogni&loc=4

What iMOBDEV offers as the mobile app develoment company?

iMOBDEV Technologies upgrades its mobile app development services with advanced technologies like Machine Learning, Deep Learning, and so on. As we have discussed here in the article about machine learning and how does it use for face recognition! As Amazon's most famous for selling product in the market also has a unique product called Amazon's Face Rekognition APIs which easily integrate with face recognition mobile apps. iMOBDEV's skilled developer's team can develop the mobile app for face recognition and integrate with Amazon Face Rekognition service to get a quality benefit. Why do require Machine learning technology? It is very beneficial in foremost sectors, especially for identifying criminals face recognition by police, celebrities faces can recognise from a live video recording, and most of the companies use face recognition for employees verification and daily attendance.

Wrap up,

Artificial Intelligence, Machine Learning, Deep Learning, Internet of Things, Beacon Technology is the advancement of technology and has integration with mobile apps to utilise for easier management in various sectors. Technology itself has remarkable features which reduce manual efforts in a precise manner. Machine learning is a vast subject to expand in multiple programs, but here we have considered a face recognition APIs by Amazon. Also, iMOBDEV integrates Amazon's Face Rekognition APIs with a mobile app for betterment in the industry. Want to develop an application using face recognition API? Feel free to call us or email us your quotes on Face Recognition app development.

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Machine Learning Helps Face Recognition by Amazon Rekognition API - WhaTech

Physician, heal thyself Luke 4:23

My Thermos bottle keeps hot drinks hot and cold drinks cold. How does it know?

An electrical engineer would probably design a Thermos with a toggle switch (HOT and COLD), or a big temperature dial, or if you work in Cupertino an LCD display, touchpad, RTOS, and proprietary cable interface. Thankfully, real vacuum flasks take care of themselves with no user input at all. They just work.

It would sure be nice if new AI-enabled IoT devices could do the same thing. Instead of learning all about AI and ML (and the differences between the two), and learning how to code neural nets, and how to train them, and what type of data they require, and how to provision the hardware, etc., itd be great if they just somehow knew what to do. Now that would be real machine learning.

Guess what? A small French company thinks it has developed that very trick. It uses machine learning to teach machine learning. To machines. Without a lot of user input. It takes the mystery, mastery, and mythology out of ML, while allowing engineers and programmers to create smart devices with little or no training.

The company is Cartesiam and the product is called NanoEdge AI Studio. Its a software-only tool that cranks out learning and inference code for ARM Cortex-Mbased devices, sort of like an IDE for ML. The user interface is pretty to look at and has only a few virtual knobs and dials that you get to twist. All the rest is automatic. Under the right circumstances, its even free.

Cartesiams thesis is that ML is hard, and that developing embedded AI requires special skills that most of us dont have. You could hire a qualified data scientist to analyze your system and develop a good model, but such specialists are hard to find and expensive when theyre available. Plus, your new hire will probably need a year or so to complete their analysis and thats before you start coding or even know what sort of hardware youll need.

Instead, Cartesiam figures that most smart IoT devices have certain things in common and dont need their own full-time, dedicated data scientist to figure things out, just like you dont need a compiler expert to write C code or a physicist to draw a schematic. Let the tool do the work.

The company uses preventive motor maintenance as an example. Say you want to predict when a motor will wear out and fail. You could simply schedule replacement every few thousand hours (the equivalent of a regular 5000-mile oil change in your car), or you could be smart and instrument the motor and try to sense impending failures. But what sensors would you use, and how exactly would they detect a failure? What does a motor failure look like, anyway?

With NanoEdge AI Studio, you give it some samples of good data and some samples of bad data, and let it learn the difference. It then builds a model based on your criteria and emits code that you link into your system. Done.

You get to tweak the knobs for MCU type, RAM size, and type of sensor. In this case, a vibration sensor/accelerometer would be appropriate, and the data samples can be gathered in real-time or canned; it doesnt matter. You can also dial-in the level of accuracy and the level of confidence in the model. These last two trade off precision for memory footprint.

NanoEdge Studio includes a software simulator, so you can test out your code without burning any ROMs or downloading to a prototype board. That should make it quicker to test out various inference models to get the right balance. Cartesiam says it can produce more than 500 million different ML libraries, so its not simply a cut-and-paste tool.

As another example, Cartesiam described one customer designing a safety alarm for swimming pools. They spent days tossing small children into variously shaped pools to collect data, and then several months analyzing the data to tease out the distinguishing characteristics of a good splash versus one that should trigger the alarm. NanoEdge AI Studio accomplished the latter task in minutes and was just as accurate. Yet another customer uses it to detect when a vacuum cleaner bag needs emptying. Such is the world of smart device design.

The overarching theme here is that users dont have to know much of anything about machine learning, neural nets, inference, and other arcana. Just throw data at it and let the tool figure it out. Like any EDA tool, it trades abstraction for productivity.

In todays environment, thats a good tradeoff. Experienced data scientists are few and far between. Moreover, you probably wont need his/her talents long-term. When the project is complete and youve got your detailed model, what then?

NanoEdge AI Studio is free to try but deploying actual code in production costs money. Cartesiam describes the royalty as tens of cents to a few dollars, depending on volume. Sounds cheaper than hiring an ML specialist.

Related

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Machine Learning at the Push of a Button - EE Journal

HANCOCK COUNTY Twenty robotics teams from seven Hancock County schools are gearing up for the third annual Indiana VEX Robotics State Championship, to be held Saturday at Lucas Oil Stadium in downtown Indianapolis.

The competition will be the largest state robotics championship in the country, gathering over 385 teams from elementary, middle and high schools across the state, according to event host TechPoint Foundation For Youth.

Top finishers will advance to compete in the world robotics championship in Louisville, Kentucky, next month. In competition, teams maneuver their hand-built robots via remote control over a room-size field, using the machines to pick up items and move them to designated areas.

Story continues below gallery

Hancock County is really well represented in the state championships, said Aaron Amos, who coaches the robotics teams at Eastern Hancock High School. If you consider all the schools in the county that are going, were a good chunk of whats being represented at the state level.

This is the first year Eastern Hancock has had a team qualify to go to state.

The students are obviously very excited, said Amos, who teaches physics and computer science at the school.

The schools robotics program was started a few years ago, and its kind of exploded. This year for the first time weve had more than one team, he said.

Of the schools three teams, the one going to state includes seniors Sam Blocher, Matthew Carter, Garret Henderson and Ashton Trueblood.

Amos said the community has been supportive of the group, which was recognized at the homecoming pep rally last week. The school recognizes that this is a big accomplishment for a small school like ours, he said.

While qualifying for state is impressive enough, Amos said the best part is they did so with only $1,500 of funding, which is practically unheard of, he said.

The team couldnt afford to build its own practice field as many other schools have, so the only time they could really practice was during tournament play.

Theyve worked really hard and managed to put together a great effort at our little school, Amos said.

This year marks the third consecutive year a team from Geist Montessori Academy in McCordsville will go to the state championships.

The nine-member team includes four returning members who made it to the world robotics competition last year, including seventh-graders Caleigh Johnson and Ellen Lovold and eighth-graders Molly Kwok and Eliza Sweeny.

New team members include fifth-graders Bentley Elsts and Joy Sweeny, sixth-grader Devon Jones and seventh-graders Cameron Archie and Chloe Jones.

The entire team is looking forward to our big adventure at the state competition, said Eliza, who is the team captain.

The team qualified after winning both an Excellence Award and the Skills Driving category earlier this month at a tourney in Indianapolis, where they placed second overall.

The Excellence Award is given to a team who was close to winning in several categories and demonstrated overall teamwork, cooperation with other teams, and a quality design notebook. This is what earned them a spot in the state competition, club adviser Kathy McGuinn said.

A dozen teams from the Southern Hancock schools district are going to state, including six elementary, one middle school, two junior high and three high school teams.

It marks another strong year in robotics for the school system.

At the junior high, weve gone to state the past six consecutive years and to worlds the past two consecutive years, said junior high club adviser Curt Powers.

We have a lot of students who participate and work hard at it, and they do an amazing job. We seem to be competitive in every tournament were in, he said.

Powers credits the corporations success in robotics to the fact its introduced as early as the third grade. The elementary school does a great job of introducing robotics to the students at a young age, which allows them to already have a strong background going into middle school and junior high, he said.

This is the ninth consecutive year Greenfield-Central High School has sent at least one team to the state VEX Robotics competition, having done so every year since the first was held in 2012.

This year its sending three of its seven teams to the competition, including one that is ranked sixth in the state.

That team, consisting of Claire Bishop, Owen Bishop, John Hill, Adam Hughes and Caleb Stoefler, is also 41st in the world rankings.

Greenfields robotics club co-adviser, Julie Stoeffler, said Indianas VEX Robotics community is a tight-knit group.

These kids have all worked together for many years and often scrimmage together. Having many teams at state from our school is very exciting, but equally exciting is the number of Hancock County teams that will be there, she said.

These kids are amazing, and we are very proud of all their hard work and efforts.

According to the TechPoint Foundation For Youth, which sponsors the state championships, robotics teams help reinforce STEM skills science, technology, engineering and math which educators say are crucial for jobs of the future.

In 2016, the foundation launched the State Robotics Initiative to provide hands-on, STEM learning experiences to Indiana students through an economical, entry-level robotics program. The initiative was designed as a statewide expansion of the city of Indianapolis VEX Robotics grant program.

In four years, the foundation said, it has grown the number of state schools with robotics programs by 430 percent, with more kids in Indiana involved with robotics than in any other state.

The students of these robotics teams will become the leaders of tomorrow. By helping provide a pathway from elementary to middle school and now high school, we are able to nurture the passion the students have for STEM, said George Giltner, the foundations vice president of STEM education.

With the end goal of a robotics team in every school, the Indiana workforce of the future is looking bright.

At a glance

TechPoint Foundation For Youth play host the third annual Indiana VEX Robotics State Championship at Lucas Oil Stadium on Saturday, from 9:30 a.m. to 6:30 p.m.

The competition will be the largest such competition in the country, with over 385 teams represented, including 20 from Hancock County.

The event is free and open to the public.

For information, visit techpointyouth.org/statechamp.

Hancock County teams participating:

-Greenfield-Central High School 3 teams

-Geist Montessori Academy 1 team

-Montessori Science Academy 2 teams

-New Palestine Junior High 2 teams

-New Palestine Intermediate 7 teams

-New Palestine High School 3 teams

-Sugar Creek Elementary 2 teams

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MACHINE LEARNING: 20 robotics teams will put knowledge, skill on the line in tournament - Greenfield Daily Reporter

This is the final, part 4 of a four-part series that breaks up a talk that I gave at the Toronto AI Meetup. Part 1, Part 2 and Part 3 were all about the foundations of machine learning, optimization, models, and even machine learning in the cloud. In this video I show an actual machine learning problem (see the GitHub repo for the code) that does the important job of distinguishing between tacos and burritos (an important problem to be sure). The primary concepts included is MLOps both on the machine learning side as well as the deliver side in Azure Machine Learning and Azure DevOps respectively.

Hope you enjoy the final of the series, Part 4! As always feel free to send any feedback or add any comments below if you have any questions. If you would like to see more of this style of content let me know!

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Tying everything together Solving a Machine Learning problem in the Cloud (Part 4 of 4) - Microsoft - Channel 9

By not touching your face, you reduce the chances of getting sick from a virus or bacteria. This website, called Do Not Touch Your Face, uses your webcam to analyze your face and alert you with a tone if it catches you touching your face.

From the FAQ

How does this work?

Using your webcam, you train a machine learning algorithm (specificallyTensorflow.js) to recognize you touching your face and not touching your face. Once it's trained, it watches and alerts you when you touch your face.

Why shouldn't I touch my face?

TheCDC recommendsnot touching your face as one action you can take to prevent getting COVID-19. Other things you should do: stay home if you're sick and avoid contact with other sick people. But you probably knew that already.

The alerts aren't working!

Try refreshing the page and trying again. Every time you reload the page, the algorithm retrains itself.

Do you keep my information?

Nope. This entire site runs locallyall the calculations from your webcam and alerts are done on your computer and are never sent over the internet.

Will this stop me from getting COVID-19?

Not for sure, but it might help.

Who made this?

This was made with love and fear byMike Bodge,Brian Moore, andIsaac Blankensmith. Be safe out there.

Cleethorpes is a faded northern English resort town whose inherent grimness is leavened by low rainfall and a nice sandy beach. And now it is to become home to a giant white metal palm tree, to the dismay of some locals. Artist Wolfgang Weileder has said the sculpture will serve as a warning for the []

Since the 60s Genesis P-Orridge has been one of the masterminds behind artist collective COUM Transmissions and seminal music acts Throbbing Gristle and Psychic TV. Beyond that, P-Orridge has had an astonishing career in the visual arts, founding an artist collective called Thee Temple ov Psychick Youth, as well as helming the infamous pandrogeny project []

Colossal writes: Designed to recycle outdated electronics, multiple musical projects by Electronicos Fantasticos utilize a version of the barcode system found on every package on store shelves. When scanned, each pattern sends a signal to its audio component, emitting the corresponding sound wave. The black and white stripes produce a variety of rhythmic and tonal []

Playlists, podcasts, audiobooks, IGTV vidsyou consume it all. Youd just as soon miss the train and be late for work than commute without your headphones, but what your audio experience is lacking is comfort. Whether its obnoxious wires that are always tangled and somehow simultaneously too short and too long or annoying earbuds that fall []

Bug bounties are making some hackers rich and the companies theyre hacking are more than happy to pay them. More than 600,000 white hat hackers are members of the bug bounty site HackerOne, a community connecting those hackers with companies paying them to find security holes in their vital digital systems. And its working. []

Nobody wants to get bogged down in the details. These days, theres too much going on to get lost in the weeds of endless exposition and meandering digressions. Were all in a hurry. Were all racing the clock. Boil it down. Give me the bullet. Thats the heart of the getAbstract philosophy. Among the mountains []

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This website uses machine learning and your webcam to train you not to touch your face - Boing Boing

Much has been said about what artificial intelligence and machine learning can do for journalism: from understanding human ethics to predicting when readers are about to cancel their subscriptions.

Want to get hands on with machine learning? Quartz investigative editor John Keefe provides 15 video lessons taken from the 'Hands-on Machine Learning Solutions for Journalists' online class he lead through the Knight Center for Journalism in the Americas. It covers all the techniques that the Quartz investigative team and AI studio commonly use in their journalism.

"Machine learning is particularly good at finding patterns and that can be useful to you when you're trying to search through text documents or lots of images," Keefe explained in the introduction video.

Want to learn more about using artificial intelligence in your newsroom? Join us on the 4 June 2020 at our digital journalism conference Newsrewired at MediaCityUK, which will feature a workshop on implementing artificial intelligence into everyday journalistic work. Visit newsrewired.com for the full agenda and tickets

If you like our news and feature articles, you can sign up to receive our free daily (Mon-Fri) email newsletter (mobile friendly).

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Tip: Machine learning solutions for journalists | Tip of the day - Journalism.co.uk

BOSTON, March 05, 2020 (GLOBE NEWSWIRE) -- Ripple Effect Communications, full-service digital marketing, public relations, and social media agency, announces its growing portfolio of innovative software and technology clients that are successfully deploying artificial intelligence (AI), Machine Learning (ML) in IoT as a competitive advantage in the respective industries they serve. Ripple Effect has acquired several new clients spanning technology, manufacturing, and healthcare.

The uptick comes from the influx of intelligent data science solutions for healthcare/medical/clinical applications as well as eCommerce/banking/fintech and industrial/ manufacturing/robotics. These evolving solutions are helping companies leverage their investments in AI and ML by unifying statistics with data analysis to bring context and actionable information to the data collected.

Were proud to support these transformational technology companies that are embracing AI, IoT, big data, and analytics and which rely on our strategic understanding of their industries, their customers needs and how to best convey their strategic messaging, said Valerie Harding, Founder and CEO of Ripple Effect Communications. These companies are breaking new ground in the most critical technology areas such as data science, cybersecurity, data management. They are steadfast in deploying the right mix of digital, thought leadership, and influencer engagement to drive the most meaningful, compelling and results-driven campaigns to bring awareness to their constituents, she added.

The manufacturing sector was among the early adopters of AI and ML but today millions of consumers and businesses interact with AI directly or indirectly via virtual assistants, facial recognition technology, mapping applications, wearables and a host of other software. The use of AI in banking and finance is growing rapidly as well as applications in the healthcare industry that are improving patient diagnostics and outcomes as well as clinical trial successes. That said, these are just the early beginnings and the profit potential for companies in all areas is hard to overstate.

Ripple Effect Communications is already deeply entrenched with supporting clients within the enterprise technology landscape: ERP, mobile, PLM, security, big data, and AI. Digital marketing, thought leadership positioning, increased social engagement, public relations, and digital content marketing, are essential in devising a strategic communications roadmap with scheduled milestones to reach customers, prospects and influencers, is the very best approach to help businesses thrive in our increasing complex world, Harding continued.

About Ripple Effect Communications

Ripple Effect Communications, located in Boston, New York, and California, is an integrated digital marketing, PR and social media communications agency that delivers an out-of-the-box approach to benefit clients by using an innovative infrastructure, executive-only talent pool with client-side experience, and unique pricing model that results in lean, yet target-exceeding performance. Ripple Effect's team manages integrated digital marketing and specialized PR campaigns for enterprise-wide software and technology companies that serve manufacturing, healthcare, medical devices, biotech, fintech, and food & beverage. Our senior team of PR, digital marketing professionals, and content marketers provide strategic direction, counsel and implementation across the entire range of client requirements. The Ripple Effect team brings multi-lingual capabilities as well as global connections through its Eurocom Worldwide PR partnership. http://www.rippleeffectpr.com

Media ContactValerie Hardingvalerie@rippleeffectpr.com617-536-8887

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Ripple Effect Communications Announces Thought Leadership and PR Expertise in Artificial Intelligence, Machine Learning and IoT - GlobeNewswire

Updated: May 25, 2018:

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