Data privacy!

It’s been another busy year for hackers. According to the Central Statistics Office, nearly 1 in 5 (18 %) of Irish businesses experienced ICT-related incidents, 87 per cent of which resulted in the unavailability of ICT services, and 41% which resulted in either the destruction, corruption or disclosure of data.

Noel O’Grady, writer of this piece, is the head of Sungard Availability Services Ireland and has over 20 years of experience working with leading technology firms including HP, Vodafone and Dell in providing critical production and recovery services to enterprise-level organisations.

Last year saw a number of high-profile security incidents making the headlines. In April, 3,600 accounts belonging to former customers of Ulster Bank were compromised, resulting in some customers’ personal details being released. In July, the Football Association of Ireland confirmed that malware was discovered on its payroll server following an attempted hack on IT systems.

Entering a new decade, digital technologies will continue to permeate every aspect of modern life, and the security of IT systems will come under increasing scrutiny. This will be driven by two major consequences of today’s hyper-connected world. Firstly, the sheer number of systems and devices which have now become digitalised has vastly expanded the cybersecurity threat landscape, potentially multiplying vulnerabilities or points of entry for hackers. Simultaneously, consumers and businesses alike demand constant availability in the products and services they use, reducing the tolerance for periods of downtime.

As a result, the security of data is no less than a global issue on par with national security, economic stability and even the physical security of citizens. It is with this in mind that Data Privacy Day is observed on this day (28th January 2020), a global initiative which aims to spread awareness of the hugely fundamental role that cybersecurity plays.

One of the most important developments in the field of data privacy was the establishment of the General Data Protection Regulation (GDPR) in May 2018. Nearly two years on, it’s timely to review how the new regulatory environment has succeeded in achieving its goals, especially in the light that almost one in three European businesses are still not compliant.

Data Privacy Day 2020

GDPR works by penalising organisations with inadequate data protection through sizeable fines. While this has established an ethical framework from which European organisations can set out strategies for protecting personal data, one issue that is still often overseen is the result of an IT outage, which prevents businesses from keeping its services running. As a server or organisation’s infrastructure is down, data is then at risk to exposure and therefore a company is at risk of failing compliance. IT and business teams will need to locate and close any vulnerabilities in IT systems or business processes, and switch over to disaster recovery arrangements if they believe there has been a data corruption.

This is especially pertinent in Ireland, where, according to a spokesperson for the Department of Business, Enterprise and Innovation (DoBEI), “Data centre presence…raises our visibility internationally as a technology-rich, innovative economy.” A strategic European hub for many multi-national technology giants, Ireland is currently home to 54 data centres, with another 10 under construction and planning permission for a further 31. While this growth in Ireland’s data centre market is a huge advantage for the national economy, Irish businesses must also tread with caution as they shoulder the responsibility for the security and availability of the countless mission-critical applications and processes which rely on them.

An organisation’s speed and effectiveness of response will be greatly improved if it has at its fingertips the results of a Data Protection Impact Assessment (DPIA) that details all the personal data that an organisation collects, processes and stores, categorised by level of sensitivity. Data Privacy Day is a great opportunity to expose unknown risks that organisations face, but moving forward, it is vital that business leaders embed privacy into every operation. This is the only sustainable way to ensure compliance on an ongoing basis.

#Cybersecurity @SungardASUK @brands2life

The next-generation inspection!

Combining machine vision and deep learning will give companies a powerful mean on both operational and ROI axles. So, catching the differences between traditional machine vision and deep learning, and understanding how these technologies complement each other – rather than compete or replace – are essential to maximizing investments. In this article Bruno Forgue of Cognex helps to clarify things.

Machine Vision vs Deep Learning

Over the last decade, technology changes and improvement have been so much various: device mobility… big data… artificial intelligence (AI)… internet-of-things… robotics… blockchain… 3D printing… machine vision… In all these domains, novel things came out of R&D-labs to improve our daily lives.

Engineers like to adopt and adapt technologies to their tough environment and constraints. Strategically planning for the adoption and leveraging of some or all these technologies will be crucial in the manufacturing industry.

Let’s focus here on AI, and specifically deep learning-based image analysis or example-based machine vision. Combined with traditional rule-based machine vision, it can help robotic assemblers identify the correct parts, help detect if a part was present or missing or assembled improperly on the product, and more quickly determine if those were problems. And this can be done with high precision.

Figure 1 – The first differences between traditional machine vision and deep learning include:
1. The development process (tool-by-tool rule-based programming vs. example-based training);
2. The hardware investments (deep learning requires more processing and storage);
3. The factory automation use cases.

Let’s first see what deep learning is
Without getting too deep (may I say?) in details, let’s talk about GPU hardware. GPUs (graphics processing units) gather thousands of relatively simple processing-cores on a single chip. Their architecture looks like neural networks. They allow to deploy biology-inspired and multi-layered “deep” neural networks which mimic the human brain.

By using such architecture, deep learning allows for solving specific tasks without being explicitly programmed to do so. In other words, classical computer applications are programmed by humans for being “task-specific”, but deep learning uses data (images, speeches, texts, numbers…) and trains it via neural networks. Starting from a primary logic developed during initial training, deep neural networks will continuously refine their performance as they receive new data.

It is based on detecting differences: it permanently looks for alterations and irregularities in a set of data. It is sensitive/reactive to unpredictable defects. Humans do this naturally well. Computer systems based on rigid programming aren’t good at this. (But unlike human inspectors on production lines, computers do not get tired because of constantly doing the same iteration.)

In daily life, typical applications of deep learning are facial recognition (to unlock computers or identify people on photos)… recommendation engines (on streaming video/music services or when shopping at ecommerce sites)… spam filtering in emails… disease diagnostics… credit card fraud detection…

Deep learning technology makes very accurate outputs based on the trained data. It is being used to predict patterns, detect variance and anomalies, and make critical business decisions. This same technology is now migrating into advanced manufacturing practices for quality inspection and other judgment-based use cases.

When implemented for the right types of factory applications, in conjunction with machine vision, deep learning will scale-up profits in manufacturing (especially when compared with investments in other emerging technologies that might take years to payoff).

How does deep learning complement machine vision?
A machine vision system relies on a digital sensor placed inside an industrial camera with specific optics. It acquires images. Those images are fed to a PC. Specialized software processes, analyzes, measures various characteristics for decision making. Machine vision systems perform reliably with consistent and well-manufactured parts. They operate via step-by-step filtering and rule-based algorithms.

On a production line, a rule-based machine vision system can inspect hundreds, or even thousands, of parts per minute with high accuracy. It’s more cost-effective than human inspection. The output of that visual data is based on a programmatic, rule-based approach to solving inspection problems.

On a factory floor, traditional rule-based machine vision is ideal for: guidance (position, orientation…), identification (barcodes, data-matrix codes, marks, characters…), gauging (comparison of distances with specified values…), inspection (flaws and other problems such as missing safety-seal, broken part…).

Rule-based machine vision is great with a known set of variables: Is a part present or absent? Exactly how far apart is this object from that one? Where does this robot need to pick up this part? These jobs are easy to deploy on the assembly line in a controlled environment. But what happens when things aren’t so clear cut?

This is where deep learning enters the game:

• Solve vision applications too difficult to program with rule-based algorithms.
• Handle confusing backgrounds and variations in part appearance.
• Maintain applications and re-train with new image data on the factory floor.
• Adapt to new examples without re-programming core networks.

A typical industrial example: looking for scratches on electronic device screens. Those defects will all differ in size, scope, location, or across screens with different backgrounds. Considering such variations, deep learning will tell the difference between good and defective parts. Plus, training the network on a new target (like a different kind of screen) is as easy as taking a new set of reference pictures.

Figure 2 – Typical industrial example: looking for defects which are all different in size, scope, location, or across various surfaces with different backgrounds.

Inspecting visually similar parts with complex surface textures and variations in appearance are serious challenges for traditional rule-based machine vision systems. “Functional” defaults, which affect a utility, are almost always rejected, but “cosmetic” anomalies may not be, depending upon the manufacturer’s needs and preference. And even more: these defects are difficult for a traditional machine vision system to distinguish between.

Due to multiple variables that can be hard to isolate (lighting, changes in color, curvature, or field of view), some defect detections, are notoriously difficult to program and solve with a traditional machine vision system. Here again, deep learning brings other appropriate tools.

In short, traditional machine vision systems perform reliably with consistent and well-manufactured parts, and the applications become challenging to program as exceptions and defect libraries grow. For the complex situations that need human-like vision with the speed and reliability of a computer, deep learning will prove to be a truly game-changing option.

Figure 3 – Compared to Traditional Machine Vision, Deep Learning is:
1. Designed for hard-to-solve applications;
2. Easier to configure;
3. Tolerant to variations.

Deep learning’s benefits for industrial manufacturing
Rule-based machine vision and deep learning-based image analysis are a complement to each other instead of an either/or choice when adopting next generation factory automation tools. In some applications, like measurement, rule-based machine vision will still be the preferred and cost-effective choice. For complex inspections involving wide deviation and unpredictable defects—too numerous and complicated to program and maintain within a traditional machine vision system—deep learning-based tools offer an excellent alternative.

• Learn more about Cognex deep learning solutions

#Machinehealth #PAuto @Cognex_Corp @CognexEurope