Daily Archives: March 5, 2020

In order to help build increasingly effective care pathways in healthcare, modern artificial intelligence technologies must be adopted and embraced. Events such as the AI & Machine Learning Convention are essential in providing medical experts around the UK access to the latest technologies, products and services that are revolutionising the future of care pathways in the healthcare industry.

AI has the potential to save the lives of current and future patients and is something that is starting to be seen across healthcare services across the UK. Looking at diagnostics alone, there have been large scale developments in rapid image recognition, symptom checking and risk stratification.

AI can also be used to personalise health screening and treatments for cancer, not only benefiting the patient but clinicians too enabling them to make the best use of their skills, informing decisions and saving time.

The potential AI will have on the NHS is clear, so much so, NHS England is setting up a national artificial intelligence laboratory to enhance the care of patients and research.

The Health Secretary, Matt Hancock, commented that AI had enormous power to improve care, save lives and ensure that doctors had more time to spend with patients, so he pledged 250M to boost the role of AI within the health service.

The AI and Machine Learning Convention is a part of Mediweek, the largest healthcare event in the UK and as a new feature of the Medical Imaging Convention and the Oncology Convention, the AI and Machine Learning expo offer an effective CPD accredited education programme.

Hosting over 50 professional-led seminars, the lineup includes leading artificial intelligence and machine learning experts such as NHS Englands Dr Minai Bakhai, Faculty of Clinical Informatics Professor Jeremy Wyatt, and Professor Claudia Pagliari from the University of Edinburgh.

Other speakers in the seminar programme come from leading organisations such as the University of Oxford, Kings College London, and the School of Medicine at the University of Nottingham.

The event all takes place at the National Exhibition Centre, Birmingham on the 17th and 18th March 2020. Tickets to the AI and Machine Learning are free and gains you access to the other seven shows within MediWeek.

Health Europa is proud to be partners with the AI and Machine Learning Convention, click here to get your tickets.

Do you want the latest news and updates from Health Europa? Click here to subscribe to all the latest updates and stay connected with us here.

See the original post:

How is AI and machine learning benefiting the healthcare industry? - Health Europa

Researchers have been working to make mind-controlled prosthetics a reality for at least a decade. In theory, an artificial hand that amputees could control with their mind could restore their ability to carry out all sorts of daily tasks, and dramatically improve their standard of living.

However, until now scientists have faced a major barrier: they havent been able to access nerve signals that are strong or stable enough to send to the bionic limb. Although its possible to get this sort of signal using a brain-machine interface, the procedure to implant one is invasive and costly. And the nerve signals carried by the peripheral nerves that fan out from the brain and spinal cord are too small.

A new implant gets around this problem by using machine learning to amplify these signals. A study, published in Science Translational Medicine today, found that it worked for four amputees for almost a year. It gave them fine control of their prosthetic hands and let them pick up miniature play bricks, grasp items like soda cans, and play Rock, Paper, Scissors.

Sign up for The Algorithm artificial intelligence, demystified

Its the first time researchers have recorded millivolt signals from a nervefar stronger than any previous study.

The strength of this signal allowed the researchers to train algorithms to translate them into movements. The first time we switched it on, it worked immediately, says Paul Cederna, a biomechanics professor at the University of Michigan, who co-led the study. There was no gap between thought and movement.

The procedure for the implant requires one of the amputees peripheral nerves to be cut and stitched up to the muscle. The site heals, developing nerves and blood vessels over three months. Electrodes are then implanted into these sites, allowing a nerve signal to be recorded and passed on to a prosthetic hand in real time. The signals are turned into movements using machine-learning algorithms (the same types that are used for brain-machine interfaces).

Amputees wearing the prosthetic hand were able to control each individual finger and swivel their thumbs, regardless of how recently they had lost their limb. Their nerve signals were recorded for a few minutes to calibrate the algorithms to their individual signals, but after that each implant worked straight away, without any need to recalibrate during the 300 days of testing, according to study co-leader Cynthia Chestek, an associate professor in biomedical engineering at the University of Michigan.

Its just a proof-of-concept study, so it requires further testing to validate the results. The researchers are recruiting amputees for an ongoing clinical trial, funded by DARPA and the National Institutes of Health.

Go here to read the rest:

An implant uses machine learning to give amputees control over prosthetic hands - MIT Technology Review

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.

See more here:

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

Here is the original post:

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.

This email address is being protected from spambots. You need JavaScript enabled to view it.

For more information:

Read the original:

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

See the original post here:

Machine Learning at the Push of a Button - EE Journal