Daily Tech Digest - December 02, 2019

Project Cortex: Microsoft aims to shake up knowledge management

Project Cortex isn't only for Office documents. Using Azure's Cognitive Services, it can use image and text recognition to work with scanned content, images, and other file formats such as PDF. It can even use rules to define form structures, so that key information can be extracted from scanned forms and other common document types, allowing you to build a model of where projects are spending money by parsing purchase orders and invoices. Extracted information is used as metadata to provide context around documents, helping users find the content they need. You're not limited to structured document types. Another Azure Cognitive Service, LUIS, forms the basis of Project Cortex's Machine Teaching. Here you can build new document models that look for key terms, allowing classification of, say, contracts which will differ from contract to contract, with different content and different formatting. Once a model is trained it can be used across your entire document store, improving search and increasing your organisation's underlying knowledge model.

Microsoft: We're creating a new Rust-based programming language for secure coding

The company recently revealed that its trials with Rust over C and C++ to remove insecure code from Windows had hit its targets. But why did Microsoft do this? The company has partially explained its security-related motives for experimenting with Rust, but hasn't gone into much detail about the reasons for its move. All Windows users know that on the second Tuesday every month, Microsoft releases patches to address security flaws in Windows. Microsoft recently revealed that the vast majority of bugs being discovered these days are memory safety flaws, which is also why Microsoft is looking at Rust to improve the situation. Rust was designed to allow developers to code without having to worry about this class of bug. 'Memory safety' is the term for coding frameworks that help protect memory space from being abused by malware. Project Verona at Microsoft is meant to progress the company's work here to close off this attack vector. Microsoft's Project Verona could turn out to be just an experiment that leads nowhere, but the company has progressed far enough to have detailed some of its ideas through the UK-based non-profit Knowledge Transfer Network.

KPMG Launches Blockchain Platform, KPMG Origins 

The platform has been developed to enable global trade. It brings together a number of emerging technologies including blockchain, internet of things sensors (IoT), as well as data and analytics tools to provide transparency and traceability to trading partners across complex industries. KPMG Origins allows these trading partners to communicate unique product information across their supply chains, and in particular to end users, while reducing operational complexities. Laszlo Peter, KPMG Head of Blockchain Services for Asia Pacific, said: “KPMG Origins is the result of several successful initial trials with clients to understand industry pain and trust points, map incentive structures, and create a platform to add real value. To move beyond the hype, it is necessary to introduce complex technology across a diverse set of corporate stakeholders. The platform is based upon in-depth work across highly specialised areas, as well as collaboration across multiple jurisdictions to deliver a multi-lingual, standards and taxonomy driven platform that accelerates the development of distributed ecosystems.”

FinTech’s Opportunity in the Coming Recession

Historically, secured credit cards have been among the most prominent solutions for people who are new to credit or have poor credit history. But secured credit cards typically require an upfront deposit, as much as $500, which can be prohibitive for the very people who need such a tool to improve their credit. The solution to helping consumers build credit without an upfront security deposit is to offer more of an installment plan, using equity from a credit builder loan as a deposit for a secured card and on-time payment history in lieu of a hard inquiry. The tool itself is not new – credit builder loans have existed in credit unions for 40-50 years. But many people are unaware of this offering and do not have the tools to use it; FinTechs provide a delivery model that reaches and resonates with today’s tech and mobile-savvy consumers, particularly Millennials. Instead of taking time away from one of several jobs (44 percent of workers aged 25-34 report taking additional jobs to make ends meet) to go to a physical bank during business hours, borrowers are empowered to manage their finances directly from their phones at any time, day or night.

Scientists developed a new AI framework to prevent machines from misbehaving

The framework uses ‘Seldonian’ algorithms, named for the protagonist of Isaac Asimov’s “Foundation” series, a continuation of the fictional universe where the author’s “Laws of Robotics” first appeared. According to the team’s research, the Seldonian architecture allows developers to define their own operating conditions in order to prevent systems from crossing certain thresholds while training or optimizing. In essence, this should allow developers to keep AI systems from harming or discriminating against humans. Deep learning systems power everything from facial recognition to stock market predictions. In most cases, such as image recognition, it doesn’t really matter how the machines come to their conclusions as long as they’re correct. If an AI can identify cats with 90 percent accuracy, we’d probably consider that successful. But when it comes matters of more importance, such as algorithms that predict recidivism or AI that automates medication dosing, there’s little to no margin for error.

The Evolution of Lean Thinking - Transitioning from Lean Thinking to FLOW Thinking

The Flow System™ is not a new Agile or Lean framework. Indeed, it is not a framework at all, and it’s certainly not a one-size-fits-all solution. What is presented is a system of understanding, a system of learning. Many project management methods and agile frameworks concentrate on taskwork and planning with no regard to how an organization is structured to support these activities, seeing them simply as a linear progression of tasks. Scaling frameworks tend to struggle or simply not work as they do not recognize that they are operating in a complex adaptive system which can only scale through continuous decomposition and recombination, which they are unable to do with their rigid doctrines. Organizations and institutions utilize teams but fall short of developing teamwork skills and fail to restructure leadership to maximize the benefits that can be obtained from the utilization of teams. These shortcomings introduce additional constraints and barriers that prevent organizations and institutions from achieving a state of flow.

What’s Holding Back Data-Driven Healthcare?

Healthcare is definitely a data-rich sector, so scarcity of information is not a problem – and the NHS database is particularly valuable with respect to other countries, since it has comprehensive records that go back decades. However, access to health data is often very difficult from a regulatory point of view, and there are extreme differences in terms of quality and accessibility. Typically, health data is messy, disperse and often siloed in a multitude of medical imaging archival systems, pathology systems, EHRs, electronic prescribing tools and insurance databases. While things are moving in the right direction, i.e., with the development of unified data formats such as Fast Healthcare Interoperability Resources, there is no easy and quick fix. No fancy algorithm can be developed without proper data collection and cleaning – and in many cases, this phase can take months. Until companies keep reinventing the wheel and developing their own internal tools for data cleaning with huge costs in terms of time and money, progress will be slow.

3 Modern Myths of Threat Intelligence

Many organizations don't know how to gain value from threat intelligence, and intelligence — cyber or not — doesn't help people who aren't willing to help themselves. If someone tells you that thieves are planning to rob your house tonight, what steps would you take to try to prevent it? You could lock the doors, hide your valuables, and maybe stay at a friend's house. However, none of that would guarantee that the crime wouldn't happen. I've noticed that organizations don't truly understand what it means to be "agile" when acting on threat intelligence. In my experience, an agile security team rapidly operationalizes and incorporates intelligence into detection processes, and deploys tools that work quickly to deliver detection. If you learn that a group is planning to hack your systems using a certain method, but you can't adjust your infrastructure or existing controls to defend against that method, intelligence is wasted. You are only as secure as the next steps you take after learning about a threat — and if you take them in the time you have before it hits.

IoT growth set to come from managed data analytics

According to CompTIA’s end-user data, there is a very slow technology adoption curve across various new trends, with only IoT and AI reaching critical mass. “Even amid all the hype, companies in the business of technology are starting to pull back on adopting new technology as part of their portfolio,” CompTIA noted in its IT industry outlook 2020 report. “This slight tap on the brakes suggests that classic situation where companies move too quickly into a new technology discipline or business model, only to have a reality check in year two or three.” CompTIA’s research also found that small and medium-sized businesses are struggling to integrate the various platforms, applications and data they need. While large businesses are able to use internal resources for integration, CompTIA noted that companies of all sizes may outsource to third parties for integration activities

Blockchain must overcome hurdles before becoming a mainstream technology

We like blockchain. At least, that's the takeaway from a recent TechRepublic Premium survey where the majority of respondents (87%) stated that blockchain will have a 'positive' effect on their industry, and 27% indicated a 'very positive' effect. However, thinking something and actually doing it are two different actions. Despite the enthusiasm for the technology, only 10% of those respondents actively use blockchain at their company. Blockchain appears on 13% of the strategic roadmaps for respondents' organizations, compared to 7% in 2018. Which industries will blockchain most likely impact? IT and technology was chosen by 58% of respondents, with professional services -- including finance, insurance, legal, and consulting -- a close second at 56%. Rounding out the top five cited industries were logistics & transport (45%), healthcare (41%), and retail & wholesale (37%). What needs to happen for the widespread adoption of blockchain? Two-thirds of respondents (66%) indicated the need for a clearly-stated business use case. A cryptocurrency operated by a government entity was suggested by 35% of respondents, while a company-controlled cryptocurrency was favored by 20%.

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Quote for the day:

"Superlative leaders are fully equipped to deliver in destiny; they locate eternally assigned destines." -- Anyaele Sam Chiyson

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Daily Tech Digest - December 01, 2019

Data Scientists: Machine Learning Skills are Key to Future Jobs

SlashData queried some 20,500 respondents from 167 countries, which means this is a pretty comprehensive survey from a global perspective. Responses were additionally weighted in order to “derive a representative distribution for platforms, segments, and types of IoT [projects],” according to the report accompanying the data. According to the survey, some 45 percent of developers want to either learn or improve their existing data science/machine learning skills. This outpaces the desire to learn UI design (33 percent of respondents), cloud native development such as containers (25 percent), project management (24 percent), and DevOps (23 percent). “The analysis of very large datasets is now made possible and, more importantly, affordable to most due to the emergence of cloud computing, open-source data science frameworks and Machine Learning as a Service (MLaaS) platforms,” the report added. “As a result, the interest of the developer community in the field is growing steadily.”

Did You Forget the Ops in DevOps?

This person with deep operational knowledge was "too busy" fighting fires in production environments, and had not been included in the devops transformation conversations for this large organization. He worked for a different legal entity in a different building, despite being part of the same group, and he was about to leave due to lack of motivation. Yet the organization was claiming to do "devops". The action we took in this case was to take offline a number of experts who were effectively bottlenecks to the flow of work (if you’ve read the book "The Phoenix Project" you will recognize the "Brent" character here). We asked them to build the new components they needed with infrastructure-as-code under a Scrum approach. We even took them to a different city so they wouldn't get disturbed by their regular coworkers. After a couple of months, they rejoined their previous teams but now had a totally new approach of working. Even the oldest Unix sysadmin had now become an agile evangelist that preached infrastructure as code rather than manually hot fixing production.

Is your approach to enterprise architecture relevant in today’s world?

In today’s fast-changing market, the role of enterprise architecture is more important than ever to prevent organisations from creating barriers to future change or expensive technical debt. To remain relevant, modern enterprise architecture approaches must be customer experience (CX)-driven, agile, and deliver the right level of detail just in time for when it needs to be consumed. Static business capabilities are no longer the only anchor point for architecting enterprise technology environments. CX is now a dominant driver of strategy and so businesses need to understand how stakeholders (customers, employees, partners, etc.) consume services and how they can be enabled by technology and platforms. The importance of capturing, managing, analysing and exposing data grows each year. Therefore, enterprise architecture needs to reinvent itself again to incorporate the needs of a rapidly evolving digital world. In a CX-driven planning approach, customer journeys are used to define the services and channels of engagement.

Edge Computing – Key Drivers and Benefits for Smart Manufacturing

Edge computing means faster response times, increased reliability and security. A lot has been said about how the Internet of Things ( IoT ) is revolutionizing the manufacturing world. Many studies have already predicted more than 50 billion devices will be connected by 2020. It is also expected over 1.44 billion data points will be collected per plant per day. This data will be aggregated, sanitized, processed, and used for critical business decisions. This means unprecedented demand and expectations on connectivity, computational power, and speed of quality of service. Can we afford any latency in critical operations such as operator hand trapped in a rotor, fire situation, or gas leakage? This is the biggest driver for edge computing. More power closer to the data source-the “Thing” in IoT. Rather than a conventional central controlling system, this distributed control architecture is gaining popularity as an alternative to the light version of data center and where control functions are placed closer to the devices.

63% Of Executives Say AI Leads To Increased Revenues And 44% Report Reduced Costs

The McKinsey global survey found a nearly 25% year-over-year increase in the use of AI in standard business processes, with a sizable jump from the past year in companies using AI across multiple areas of their business; 58% of executives surveyed report that their organizations have embedded at least one AI capability into a process or product in at least one function or business unit, up from 47% in 2018; retail has seen the largest increase in AI use, with 60% of respondents saying their companies have embedded at least one AI capability in one or more functions or business units, a 35-percentage point increase from 2018; 74% of respondents whose companies have adopted or plan to adopt AI say their organizations will increase their AI investment in the next three years; 41% say their organizations comprehensively identify and prioritize their AI risks, citing most often cybersecurity and regulatory compliance. 84% of C-suite executives believe they must leverage AI to achieve their growth objectives, yet 76% report they struggle with how to scale AI;

How Europe’s AI ecosystem could catch up with China and the U.S.

Europe edges out the U.S. in total number of software developers (5.7 million to 4.4 million), and venture capital spending in Europe continues to rise to historically high levels. Even so, the U.S. and China beat Europe in venture capital spending, startup growth, and R&D spending. The U.S. also outpaces Europe in AI, big data, and quantum computing patents. A Center for Data Innovation study released last month also concluded that the U.S. is in the lead, followed by China, with Europe lagging behind. Multiple surveys of business executives have found that businesses around the world are struggling to scale the use of AI, but European firms trail major U.S. companies in this metric too, with the exception of smart robotics companies. This trend could be in part due to lower levels of data digitization, Bughin said. About 3-4% of businesses surveyed by McKinsey were found to be using AI at scale. The majority of those are digital native companies, he said, but 38% of major companies in the U.S. are digital natives compared to 24% in Europe.

Singapore government must realise human error also a security breach

More importantly, before dismissing man-made mistakes as "not a security risk", organisations such as the SAC need to consider the stats. "Inadvertent" breaches brought about by human error and system glitches accounted for 49% of data breaches, according to an IBM Security report conducted by Ponemon Institute, which estimated that human errors alone cost companies $3.5 million. In fact, cybersecurity vendor Kaspersky described employees as a major hole in an organisation's fight against cyber attacks. Some 52% viewed their staff as the biggest weakness in IT security, where their careless actions put the company's security strategy at risk. It added that 47% of businesses were concerned most about employees sharing inappropriate data via mobile devices, while careless or uninformed staff were the second-most likely cause of a serious security breach--second only to malware. Some 46% of cybersecurity incidents in the past year were attributed to careless or uninformed staff. Kaspersky further described human error on the part of staff as the "attack vector" that businesses were falling victim to.

6-essential practices to successfully implement machine learning solutions

Here’s a golden rule to remember: a machine learning algorithm is only as good as the data it’s fed. So, to use machine learning effectively, you must have the right data for the problem you’re trying to solve. And not just a few data points. Machines need a lot of data to learn — think hundreds of thousands of data points. Your data will need to be formatted, cleaned, and organized for your algorithm, and you will need two datasets: one to train the model and one to evaluate its performance. So after picking up the use cases, filter out the ones where there is data available and the ones that can quickly generate value across the board. Go for multiple smaller wins and have a clear data strategy. ... With a worldwide shortage of trained data scientists, you need to empower your data analytics professionals and other domain information experts with the tools and support they need to become citizen data scientists.

The hardest part of AI & analytics is not AI - it’s data management

“This is going to enable organisations to train their AI and ML algorithms with a more complete, more comprehensive and less biased sets of data.” According to Hanson, this can be done by using good data engineering tools with AI built-in. “What we actually need is not just artificial intelligence in the analytics layer — in terms of generating graphical views of data and making decisions in real-time around data — we need to make sure that we’ve got artificial intelligence in the backend to ensure we’ve got well-curated data going into our analytics engines.” He warned that if organisations fail to do this, they won’t see the benefit of analytical AI going forward. “In my opinion, a lot of mistakes could be made, some serious mistakes, if we don’t make sure that we train our analytical AI with high quality, well-curated data,” said Hanson. He added, if the data sets aren’t good, then AI advocates in organisations are not going to get the results they expect. This could hinder any future investment in the technology.

How to Advance Your Enterprise Risk Management Maturity

Before you can determine whether you want to advance your ERM maturity, you must first define your appetite for risk to make a proper assessment. Not all companies require the same level of risk maturity. In fact, the highest level of maturity does not necessarily equal the best ERM program. Rather than immediately aiming for the highest level of maturity, companies need to take a step back and identify their priorities to understand what is best for their organization’s specific circumstances. ... Effective risk culture is one that empowers business functions to be intellectually honest about the risks they face and encourages them to align risks with strategic objectives. To accomplish this, companies must remain patient. Changing a culture of any sized organization takes time and is not something that can be done by any single meeting or memo to the staff. It takes time to educate team members properly and for leaders to demonstrate the importance of the change. ... Once you determine who should hold primary responsibility for the risk management program and have received the necessary buy-in, you will need to measure your progress towards greater ERM maturity. One way to measure progress is to compare yourself to your peers.

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Quote for the day:

"The science of today is the technology of tomorrow." -- Edward Teller

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Daily Tech Digest - November 30, 2019

We’ve got to regulate the application of AI — not the tech itself

Another important factor that governments and businesses will need to be aware of will be in devising methods to prevent the rise of AI used with malicious intent, i.e. for hacking or fraudulent sales. Most cyber-experts predict that cyberattacks powered by AI will be one of the biggest challenges of the 2020s, which means that regulations and preventative measures should be implemented as with any other industry: designed specifically for the application. Stringent qualification processes will also need to be addressed for certain industries. For example, Broadway show producers have been driving ticket sales through an automated chatbot, with the show Wicked boasting ROI increases of up to 700 percent. This has also allowed producers to sell tickets for 20 percent higher than the average weekly price.  Regulations will need to address the fact that AI and bots have the potential to take advantage of consumers’ wallets, which means that policymakers will need to work closely with firms that are gradually beginning to rely on chatbots to make sure that consumer rights are not being breached.

How Smart Home Tech Is Shaking Up The Insurance Industry

Through smart home devices, homeowners are able to remain connected to their property 24/7, whether at home, work or on holiday. In turn, this constant connectivity instils a psychological shift in householders, encouraging them to take a more proactive approach to home security and protection. ... For example, while water damage may not top the list of worries from homeowners, it can cost thousands of pounds to repair and is one of the most common types of domestic property damage claims. However, with a leak sensor installed, escaping water can be caught quickly and customers will even be alerted via a notification to their smartphone. This knowledge is critical, as homeowners are able to call out a plumber on the same day – at a fixed fee – and contain the damage. This proactivity benefits both sides. For insurers, responsible and safe homeowners pose less of a risk, resulting in lower premiums. It’s a win win all round. Moreover, the additional information gained from the steady stream of signals sent to the insurer from in-home sensors and monitors can allow claim handlers to remain better informed in the event of an incident.

Fintech Regulation Needs More Principles, Not More Rules

It is important to recognize that principles-based regulation is not a euphemism for “deregulation” or a “light-touch” approach—far from it. Principles-based regulation is a different way of achieving the same regulatory outcomes as rules-based regulation. But it simply does so in what is, in many cases, a more efficient and flexible manner. That flexibility also prevents subversion of those outcomes through the kind of loopholes that revealed the inherent vulnerability of rules-based regulation in the run up to the financial crisis. Of course, in practice, it is rare for to have either a purely principles-based or a purely rules-based regulation. Rather, they represent two ends of the regulatory spectrum. Every principles-based regulatory regime has some rules, and every rules-based regime has some element of principle. For this reason, we frequently see hybrid regulatory systems of principles and rules.

Singapore wants widespread AI use in smart nation drive

"Domestically, our private and public sectors will use AI decisively to generate economic gains and improve lives. Internationally, Singapore will be recognised as a global hub in innovating, piloting, test-bedding, deploying and scaling AI solutions for impact," said the SNDGO, which is part of the Prime Minister's Office. To kick off its efforts, the government identified five national projects that focused on key industry challenges, including intelligent freight planning in transport and logistics, chronic disease prediction and management in healthcare, and border clearance operations in national safety and security. These form part of nine sectors that have been earmarked for heightened deployment as AI is expected to generate high social and economic value for Singapore. These verticals include manufacturing, finance, cybersecurity, and government. The national AI strategy also outlined five key enablers that the government deemed essential in building a "vibrant and sustainable" ecosystem for AI innovation and adoption. A robust data architecture, for instance, would be necessary for the public and private sectors to manage and exchange information securely, so AI algorithms can have access to quality datasets for training and testing.

How To Thrive At Work: 10 Strategies Based On Brain Science

In his book, The Shallows, Nicholas Carr demonstrates how our internet usage has rewired our brains. We think superficially, skimming, glancing and scanning rather than reading or processing more deeply. Cal Newport, in his book Deep Work, advocates for focusing, contemplating and concentrating. His contention is this distraction-free thinking has become increasingly rare and is a skill we must learn (or relearn). In fact, empathy—so critical to our humanity—is impossible without deeply considering others’ situations. And the ability to solve problems and develop ideas cannot happen effectively without depth of thought. Tell stories. While communicating facts tends to engage limited portions of the brain, hearing a story engages multiple parts of the brain. One study in particular, using an MRI found participants had greater understanding and retention of concepts based on the engagement of multiple parts of the brain. Other researchers, including Dr. Paul Zak, have demonstrated hearing stories that include conflicts and meaningful characters tend to engage us emotionally. The resulting release of oxytocin leads us to trust the messages and morals the story is trying to convey.

3 Reasons This Stock Is a Top Cybersecurity Pick

Check Point's research and development expenses increased 20% year over year while selling and marketing expenses rose nearly 10.5%. Both of these metrics outpaced the company's actual revenue growth. In fact, Check Point has stepped up its investment in both of these line items in the past year or so, and the positive impact is visible on the company's subscription growth. The company is now looking to get into lucrative cybersecurity niches as well. Check Point recently announced the acquisition of Internet of Things (IoT)-focused cybersecurity start-up Cymplify. Check Point will integrate Cymplify's expertise into its Infinity cybersecurity architecture so that clients can protect their IoT devices -- such as smart TVs, medical devices, and IP cameras -- against cyberattacks. This should open up a big growth opportunity for Check Point because according to IHS Markit, cybersecurity is the fastest-growing IoT niche. The firm predicts that the IoT data security market will grow from $3 billion in revenue this year to $7 billion in 2022 as more original equipment manufacturers (OEMs) move to secure their IoT devices.

5G radiation no worse than microwaves or baby monitors: Australian telcos

"When we've done our tests on our 5G network, they're typically 1,000 to 10,000 times less than what we get from other devices. So when you add all of that up together, it's all very low in terms of total emission. But you're finding that 5G is in fact a lot lower than many other devices we use in our everyday lives." Wood added there is no evidence for cancer or non-thermal effects from radio frequency EME. "There's some evidence for biological effects, but none of these are non-adverse," Wood told the committee. "So they've really looked at all of the research they need to set a safety standard, and in summary what they said is that, if you follow the guidelines, they're protective of all people, including children." On the issue of governmental revenue raising from its upcoming spectrum sale, Optus said it would be wrong of government to view it as a cash cow, as every dollar spent on spectrum is not used on creating networks. "Critically, in order to achieve the coverage and deployment required, 5G networks will require significant amounts of spectrum," the Singaporean-owned telco wrote.

How can businesses stop AI from going bad?

Starting from the very beginning of the process, CIO’s can help AI be “good” by ensuring that the data being used to create the algorithms is ethical and unbiased, itself. Gathering and using data from ethical sources significantly reduces the risk of harbouring toxic datasets which may infect systems with problematic biases further down the line. This is especially crucial for highly regulated industries, which will need to identify biases already present and remedy accordingly. Using insurance as an example, CIO’s should take care not to include data that heavily features one particular demographic, gender etc., which might augment averages and inform non-representative policies. Collecting a rich sample of ethical, GDPR compliant, representative data from consenting customers actually benefits the accuracy of the AI it powers, and it also reduces the work needed to “clean” it.

INNOPHYS Develops Muscle Suit for Physical Labor

The suit can lift upwards of 30kg. While it won’t do the lifting on its own, it can take that weight off from its wearer. It offers support in the form of hydraulically-controlled artificial muscles which are housed in an aluminum backpack linked to the waist joints. The pack provides two axes of movement: one for bending at the waist and another for supporting the thighs. Controlling the suit can be done in two ways. The wearer can either blow into a tube or touch a control surface with their chin, thus creating a hands-free control system for the exoskeleton. The muscle suit is wrapped inside a custom, water-repellent bag. This protects the device from the elements and gives it a softer appearance. ... Many other Japanese companies have also taken the challenge of producing suits to assist in physical labor. Companies like HAL have already placed a stable foothold in the exoskeleton industry with their series of robotic suits. Nevertheless, the Muscle Suit is an awe-inspiring invention by this venture company from the Tokyo University of Science.

Could Artificial Intelligence Save Us From Itself?

Yes—at least in some circumstances, both researchers said. Bordes’s group, for example, is creating a benchmark test that can be used to train a machine learning algorithm to automatically detect deepfakes. And Rossi said that, in some cases, A.I. could be used to highlight potential bias in models created by other artificial intelligence algorithms. While technology could produce useful tools for detecting—and even correcting—problems with A.I. software, both scientists emphasized people should not be lulled into complacency about the need for critical human judgment. “Addressing this issue is really a process,” Rossi told me. “When you deliver an A.I. system, you cannot just think about these issues at the time the product is ready to be deployed. Every design choice ... can bring unconscious bias.” You can read more about our discussion and watch a video here. ... “Yes, it is true that A.I. is only as good as the data it has been fed,” she said. But, she argued, this potentially gave people tremendous power.

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Quote for the day:

"Whenever you see a successful business, someone once made a courageous decision." -- Peter F. Drucker

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Daily Tech Digest - November 29, 2019

Cybersecurity: The web has a padlock problem - and your internet safety is at risk

Even now, encryption is sometimes discussed as if it's a bonus when using the internet, when it needs to become the standard way of doing things everywhere on the internet, Helme explained. "We need it to become so ingrained and embedded into everything that we do that it's boring and we don't need to talk about it because it shouldn't be special. Encryption should be the boring default that we don't need to talk about," he said. The security industry therefore needs to step up and help fix the issue, Helme argued, because by doing this, it takes the responsibility for deciding if a website is safe or not away from the user – something that will help make the internet safer for everyone. "We need to take encryption and make it the default, universal – it needs to be everywhere," he said, adding: "The lack of encryption on the web is actually a bug. And what we're doing now isn't adding a new feature for an improvement or a new thing: we're going back and fixing a mistake we made in the beginning."

Simplifying a data problem can ensure better buy-in

Like many complex technical topics, an ability to share a relatable and very human story can engender action far more quickly than the most thoughtful technical arguments, or detailed integration diagrams combined. Similarly, an ability to find an impactful story can serve as a sanity check for your data-related projects. If you can't concisely articulate how gathering, sharing, or analyzing data can have a real impact on your business or its customers, then perhaps the project is not as valuable as you thought or will present an uphill battle for funding that may not have been obvious purely on the technical merits. Look for opportunities to condense your data-related endeavors into a simplified, relatable metric. Asking, "What if we had sales data a week earlier?" may more easily get funding for your data lake project than a 90-slide presentation about the merits of Hadoop. Similarly, you'll have a guiding objective for your data projects that's more readily understandable than a Gantt chart or status slide, and often is more successful at generating continued interest and excitement in the endeavor.

Will the future of work be ethical? Future leader perspectives

As a consumer of a lot of technology and as someone of the generation who has grown up with a phone in my hand, I’m aware my data is all over the internet. I’ve had conversations [with friends] about personal privacy and if I look around the classroom, most people have covers for the cameras on their computers. This generation is already aware [of] ethics whenever you’re talking about computing and the use of computers. About AI specifically, as someone who’s interested in the field and has been privileged to be able to take courses and do research projects about that, I’m hearing a lot about ethics with algorithms, whether that’s fake news or bias or about applying algorithms for social good. ... Today we had that debate about role or people’s jobs and robot taxes. That’s a very good debate to have, but it sometimes feeds a little bit into the AI hype and I think it may be a disgrace to society to try to pull back technology, which has been shown to have the power to save lives. It can be two transformations that are happening at the same time. One, that’s trying to bridge an inequality and is going to come in a lot of different and complicated solutions that happen at multiple levels and the second is allowing for a transformation in technology and AI.

An AI Debated Its Own Potential for Good vs. Harm. Here’s What It Came up With

Critical thinking, linking different lines of thought, and anticipating counter-arguments are all valuable debating skills that humans can practice and refine. While these skills are tougher for an AI to get good at since they often require deeper contextual understanding, AI does have a major edge over humans in absorbing and analyzing information. In the February debate, Project Debater used IBM’s cloud computing infrastructure to read hundreds of millions of documents and extract relevant details to construct an argument. This time around, Debater looked through 1,100 arguments for or against AI. The arguments were submitted to IBM by the public during the week prior to the debate, through a website set up for that purpose. Of the 1,100 submissions, the AI classified 570 as anti-AI, or of the opinion that the technology will bring more harm to humanity than good. 511 arguments were found to be pro-AI, and the rest were irrelevant to the topic at hand.

The power and promise of AI in the coming year and beyond

AI advancements are also happening rapidly in the area of sales productivity. Over the past year, the level at which businesses are utilising AI to grow their business has skyrocketed. It’s become standard for companies to use AI to improve predictive business software and to make more effective decisions. Using heavy duty machine learning analytics as a standard business practice is now widely accepted. Looking even farther down the road, there are those who believe that computers will be just as smart as humans in about two decades. I personally love reading about the subject of singularity and quantum computing. It’s fascinating to hear about its potential. Naturally, one could argue that humans might not want computing to become as smart as us. We’ve all watched movies centered-around apocalyptic devastation! But, in my opinion, AI stands to improve our lives in ways that we have yet to consider, especially at home. While AI is becoming commonplace in customer service and sales, we are a long way from having a robot cooking us dinner or cleaning our apartments.

CISOs and CMOs – Joined At The Hip in the Era of Big Data

Today, data is the lifeblood of business. Businesses have access to copious amounts of consumer data that can be leveraged to gain a better understanding of their market and customer base. To the CMO, this is a gold mine – more detailed insight into the wants, needs, habits and activities of their target demographics. These can result in initiatives with large scopes and larger budgets. On the flip side, the CISO sees the red flags and vulnerabilities that come along with this information. Privacy and security threats, technological limitations, and reputational risk are all on the radar. Commonly their response is to reel the scope back in to reduce risk and budget. As you may expect, this can result in internal friction as to who is truly responsible for the management of this data, making it more important than ever for the CISO and CMO to establish an effective working relationship. In order for your organization to best capitalize on the benefits of big data, the CISO and CMO must work together cohesively.

How artificial intelligence is improving automotive manufacturing standards

With AI-based technology, it’s possible to increase the efficiency, objectivity and accuracy of work on vehicle production lines, while enhancing safety and enabling a higher volume of work with the same amount of resources. By detecting faults at an early stage, we can prevent a potential breakdown and reduce maintenance costs over the lifetime of the vehicle. These faults might include loose bolts, incorrectly routed cables, damage to paintwork or underinflated tyres, to name a few examples. What’s more, with manual checks, manufacturers not only risk overlooking faults on their vehicles, but also waste time that could be more productively allocated elsewhere in the factory. An intelligent AI-based system greatly enhances speed and efficiency, improving the flow of vehicles through and out of the plant. vWith all of this in mind, we expanded the breadth and capabilities of UVeye’s technology to other areas of a vehicle’s exterior, such as the tyres and bodywork.

The Top Seven Technology Trends for 2020

The benefits of 5G are huge compared to 4G: it offers much higher data speeds (1-20 Gbit/s), much lower latency (1 ms), increased capacity as the network grows and it uses very high frequencies (3.5 GHz). The challenge with 5G is that it requires a lot more antennas than 4G networks. This is because 5G uses millimetre waves, which are a lot shorter than 4G wavelengths. As a result, it can carry a lot more data, but it means a much shorter range. As a result, to achieve a reliable 5G signal, you need a lot more 5G antennas. Placing these antennas will take time, so it will take another 2-3 years before we will have a broad, reliable 5G network. However, until then, enterprises are already building their own private 5G network to enable machine-to-machine communication. 5G will be vital for the 4th industrial revolution, and the first successful pilots have been done. Earlier this year, Ericsson, Vodafone and eGO launched the first 5G car factory in Germany. 

Palo Alto Networks Employee Data Breach Highlights Risks Posed by Third Party Vendors

Palo Alto Networks has declined to name the vendor concerned, or provide details of where on the internet the data appeared, but it has said that it has terminated the contract of their careless vendor. We would all like to think that the companies we work for would put robust demands on those external firms that provide products and services that they will be careful with our data - whether it be information about our products and services, intellectual property, customers, or employees. But however much you may demand in a contract that your providers have proper security measures and practices in place to reduce the chances of a breach or hack, you can never have 100% certainty that accidents and goofs won't happen. All you can do is limit the amount of sensitive data that your external providers have access to, ensuring that they can only access the information that they absolutely need to do their job and no more.

The Implications of Last Week's Exposure of 1.2B Records

Data enrichment is a legal but controversial practice. "The industry exists for the purpose of influencing people and giving you access to people you want to influence," says Farrow, who says he has heard both sides of the argument. On one hand, employees often use this data to ensure they're not sending mailers to or cold-calling the wrong people. They could get the same information themselves on Facebook or LinkedIn; data aggregators speed up the process. At the same time, it "feels like an intrusion on our privacy," he says. Cybercriminals can use this leaked data to influence victims to their advantage. A leak like this gives attackers access to organized and meaningful information, as opposed to a broad data dump. It forces those affected to think twice about who they trust — about whether a message is legitimate or malicious. Further, there is a difference between this data leak and other security breaches in which credit card numbers or passwords are stolen.

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Quote for the day:

"There is no 'one' way to be a perfect leader, but there are a million ways to be a good one." -- Mark W. Boyer

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Daily Tech Digest - November, 28, 2019

Cutting Cybersecurity Budgets In A Time of Growing Threats

Greater spending on cybersecurity products hasn't entailed a better organizational security posture. Despite the millions of dollars spent by organizations year after year, the average cost of a cyberattack jumped by 50% between 2018 and 2019, hitting $4.6 million per incident. The percentage of cyberattacks that cost $10 million or more nearly doubled to 13% over the same period. Enterprises are using a diverse array of endpoint agents, including decryption, AV/AM and EDR. The use of multiple security products may, in fact, weaken an organization’s security position, whereby the more agents an endpoint has, the greater the probability it will get breached. This wide deployment makes it difficult to standardize a specific test to measure security and safety without sacrificing speed. Buying more cybersecurity tools tends to plunge enterprises into a costly cycle of spending more time and resources on security solutions without experiencing any parallel increase in security. However, in a mad chicken-and-egg pursuit, this trend of spending more on security products persists due to the rising costs of a security breach.

Digital transformation: Business modernization requires a new mindset

A lot of executives actually want to share their frustrations, and one of the frustrations, especially with more, let's just say, legacy-oriented organizations, I'll hear about millennials all the time. And then also the coming of centennials. In that they do want to work differently, they do think differently, and infrastructures, and also models, don't necessarily support that way of thinking and way of working. The consumerization of technology, it hasn't just affected millennials or the younger workforce, it's affected all of us. I think, anybody who has a smartphone or uses social media, or has ordered an Uber or Lyft, or DoorDash, or Postmates, you name it, we have, as human beings, radically transformed. Our brains have radically transformed as we use more of these technologies, we're multitasking, we're doing a million things. Employees get something like 200 notifications during their work day, just from their phone and social and email. So a lot of the way that we have to think about work has to change. We have to think bigger than the millennial workforce.

Hotel front desks are now a hotbed for hackers

First spotted in 2015 but appearing to be most active this year, RevengeHotels has struck at least 20 hotels in quick succession. The threat actors focus on hotels, hostels, and hospitality & tourism companies. While the majority of the RevengeHotels campaign takes place in Brazil, infections have also been detected in Argentina, Bolivia, Chile, Costa Rica, France, Italy, Mexico, Portugal, Spain, Thailand, and Turkey. The threat group deploys a range of custom Trojans in order to steal guest credit card data from infected hotel systems as well as financial information sent from third-party booking websites such as Booking.com. The attack chain begins with a phishing email sent to a hospitality organization. Professionally-written and making use of domain typo-squatting to appear legitimate, the researchers say the messages are detailed and generally impersonate real companies.  These messages contain malicious Word, Excel or PDF documents, some of which will exploit CVE-2017-0199, a Microsoft Office RCE vulnerability patched in 2017.

Regaining ROI by reducing cloud complexity

“The first thing is admitting that there’s an issue, which is a tough thing to do,” Linthicum acknowledges. “It essentially requires creating an ad hoc organization to get things back on track and simplified, whether that’s hiring outside specialists, or doing it internally. “The good thing about that is typically you can get 10 times ROI over a two-year period if you spend the time on reducing complexity,” he says. Even with that incentive, reducing complexity involves a cultural change: shifting to a proactive, innovative, and more thoughtful culture, which many organizations are having trouble moving towards, he warned. The most effective way to do that is really retraining, replacing, or revamping. “That’s going to be a difficult thing for most organizations,” Linthicum says. “I’ve worked with existing companies that had issues like this, and I find it was the hardest problem to solve. But it’s something that has to be solved before we can get to the proactivity, before we can get to using technology as a force multiplier, before we can get to the points of innovation.”

Top 5 SD-WAN Takeaways for 2019

Auto failover, redundancy, simplified management, and cost savings topped the list of factors driving SD-WAN adoption, according to Avant Communications’ SD-WAN report. “It is Avant’s belief that SD-WAN will continue to make ongoing incursions into the higher-end enterprise, beginning at remote offices and other edges of the network, and then reaching steadily closer toward the core,” the report reads. One of the biggest promises made by many SD-WAN vendors is that the technology will reduce costs by shifting bandwidth off of — and in some cases eliminating the need for — expensive MPLS connections. And while this can be true, with more than half of companies surveyed in the aforementioned Avant report indicating that cost savings over MPLS was a key concern, the majority were still split on whether to keep or replace their MPLS connections in favor of SD-WAN and broadband internet. Roughly 40% of those surveyed said they planned to use a hybrid solution that combines the two.

Autonomous systems, aerial robotics and Game of Drones

Now, automation has basically enabled a level of productivity that you see today. But automation is very fragile, inflexible, expensive… it’s very cumbersome. Once you set them up and when everything is working well, it’s fantastic, and that is what we live with today. You know, autonomous systems, we think, can actually make that a lot easier. Now the broad industry is really still oriented toward automation. So we have to bring that industry over slowly into this autonomous world. And what’s interesting is, while these folks are experts in mechanical engineering and operations research and, you know, all those kind of important capabilities and logistics, they don’t know AI very much.  ... They don’t know how to create horizontal tool chains which enable efficient development and operations of these type of systems. So that’s the expertise we bring. I’d add one more point to it, is that the places we are seeing autonomous systems being built, like autonomous driving, they’re actually building it in a very, very vertical way.

How Machine Learning Enhances Performance Engineering and Testing

During testing, there are numerous signs that an application is producing a performance anomaly, such as delayed response time, increased latency, hanging, freezing, or crashing systems, and decreased throughput. The root cause of these issues can be traced to any number of sources, including operator errors, hardware/software failures, over- or under-provisioning of resources, or unexpected interactions between system components in different locations. There are three types of performance anomalies that performance testing experts look out for. ... Machine learning can be used to help determine statistical models of "normal" behavior in a piece of software. They are also invaluable for predicting future values and comparing them against the values being collected in real-time, which means they are constantly redefining what "normal" behavior entails. A great advantage of machine learning algorithms is that they learn over time. When new data is received, the model can adapt automatically and help define what "normal" is month-to-month or week-to-week.

How Microsoft is using hardware to secure firmware

"Given the increase in firmware attacks we've seen in the last three years alone, the goal was to remove firmware as a trusted component of the boot process, so we're preventing these kinds of advanced firmware attacks," Dave Weston, director of OS security at Microsoft, told TechRepublic. The first line of the Windows boot loader on Secured-core PCs puts the CPU into a new security state where, instead of accepting the measurements made during Secure Boot, even though they're in the TPM, it goes back and revalidates the measurement. If they don't match, the PC doesn't boot and goes into BitLocker recovery mode instead. If you're managing the PC via Intune, it also sends a signal to the service that the device can't be trusted and shouldn't be allowed to connect to your network. "These PCs use the latest silicon from AMD, Intel, and Qualcomm that have the Trusted Platform Module 2.0 and Dynamic Root of Trust (DRTM) built in. The root of trust is a set of functions in the trusted computing module that is always trusted by a computer's OS and embedded in the device," Weston explains.

It's been a bad year for female entrepreneurs in Europe

Not a single investment deal worth $100 million or more has been signed with an all-women team over the past four years, and only 7% of such deals went to mixed teams in 2019.  That's still a slight improvement on the previous year, when every single mega-round went to teams led exclusively by men. Sarah Nöckel, investment associate at VC firm Dawn Capital, told ZDNet: "Europe is lagging behind on diversity. In general, there is still an ongoing unconscious bias towards women. There needs to be a lot more education to change mentalities." The issue is not that women are absent from the tech space. Out of 1,200 European tech founders that were surveyed in the report, nearly a quarter identified as women.  As it dug further, the report also found that women and men are almost equally qualified for science and engineering careers. In fact in some countries, like Lithuania, the number of women who are scientists and engineers surpasses that of men. Women can and do found tech companies, therefore; the problem is rather that they then struggle to secure enough capital to develop their projects.

"Security campaigns do not work," says infosec professor Adam Joinson

The researchers' conclusions are based on a case study they performed with a large engineering services firm, based in the UK and employing more than 30,000 people. They found that - "whether we were talking to security practitioners or whether we were talking to employees" - security was not seen as something that supported the business; instead, it was perceived as a block. "In fact, they would see it as almost an adversary of employees," trying to catch and sanction workers for security breaches. One of the reasons for this was a misalignment between security policies and processes, and the lack of tools provided for employees to do their jobs. As part of an engineering firm, employees often had to deal with "massive" files from architects and similar, but the company limited emails to a 15MB attachment limit and did not allow workers use USB sticks. Cloud storage, in one particular case, was banned by a client's security policies. "Effectively, security stopped them from doing the core function of their role."

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Quote for the day:

"Don't necessarily avoid sharp edges. Occasionally they are necessary to leadership." -- Donald Rumsfeld

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Daily Tech Digest - November 27, 2019

10 Predictions How AI Will Improve Cybersecurity In 2020

Nicko van Someren, Ph.D. and Chief Technology Officer at Absolute Software, observes that “Keeping machines up to date is an IT management job, but it's a security outcome. Knowing what devices should be on my network is an IT management problem, but it has a security outcome. And knowing what's going on and what processes are running and what's consuming network bandwidth is an IT management problem, but it's a security outcome. I don't see these as distinct activities so much as seeing them as multiple facets of the same problem space, accelerating in 2020 as more enterprises choose greater resiliency to secure endpoints.” ... Josh Johnston, Director of AI at Kount, predicts that “the average consumer will realize that passwords are not providing enough account protection and that every account they have is vulnerable. Captcha won’t be reliable either, because while it can tell if someone is a bot, it can’t confirm that the person attempting to log in is the account holder. AI can recognize a returning user. AI will be key in protecting the entire customer journey, from account creation to account takeover, to a payment transaction. ...”

Stephen Wolfram on the future of programming and why we live in a computational universe

Wolfram Language has limitations, and has been described by some users as better suited to solving a wide range of predetermined tasks, rather than being used to build software. It also seems there is still a way to go for Wolfram Language – it didn't, for example, feature in the IEEE's recent list of top programming languages. Wolfram has said that Wolfram Language is not just a language for telling computers what to do, but a way for both computers and humans to represent computational ways of thinking about things. Of late Wolfram has been more bold in how he talks about Wolfram Language, describing it as a "computational language" that could even help bridge the gulf between ourselves and future non-human intelligences, be they artificial intelligence (AI) or extraterrestrial. As esoteric a pursuit as it might seem, Wolfram believes the need for this lingua franca is timely, as machine-learning systems increasingly make decisions about our lives -- whether that's screening loan applications today or maybe even choosing whether to kill people tomorrow.

Tech jobs: These are the skills hiring managers are looking for now

CompTIA noted that the technology workforce, in particular, has been under the microscope for its lack of diversity. Diversity in tech staffing is likely to improve due to continuing pressure, the association said, but "fully diverse and inclusive environments still lie further in the future". A wide range of research and anecdotal examples proves that there's still much work to do in achieving equity, from data on wage gaps to the makeup of executive teams to ongoing reports of abusive behaviour, CompTIA said. Although 30% of companies feel that there has been significant improvement in the diversity of the tech workforce over the past two years, previous CompTIA research shows that "sentiment tends to skew more positive than reality on this topic." "The trend may be heading in the right direction, but the chasm was so wide that it will take significant time and intentional changes to close," said CompTIA, noting that there is a long list of potential actions that could improve the situation. Flexible work arrangements, including the physical environment, can create more opportunities and a more welcoming atmosphere, especially if there is a hard look at how the existing arrangements unintentionally create barriers, the association said.

AI Is The Link Between Big Data & Persons-Level Measurement

To highlight the shortcomings of big data from a measurement perspective, we conducted an analysis in the U.S. earlier this year that compared set-top box data with set-top box data that we calibrated with Nielsen panel data. The analysis found that the uncalibrated data is inherently biased and underrepresents minority audiences. That’s not to say, however, that big data has no value. Quite the opposite. But it does need to be grounded in a foundational truth set. That’s where our panels and artificial intelligence (AI) come into play. Our panel data—the key to persons-level measurement—is the perfect truth set for training big data. Through the application of AI, we use big data to dramatically broaden our measurement capabilities while preserving quality and representativeness. Today, AI is integral in our measurement methodologies. For example, it played a pivotal role in the development of our enhanced measurement capabilities for local TV markets, which combines the scale of big data (return path data {RPD} from TV sets) with fully representative in-market panel data.

GDPR Data Regulations & Commercial Fines

The public and private sector are both impacted, although government agencies have more leeway across GDPR in general due to requirements to retain and use data to deliver services to citizens. In terms of what best practice should be in dealing with a request, the advice from the UK’s Information Commissioner’s Office is that there should be a policy for recording all “subject access requests” and that based on Recital 59 of the GDPR, organisations “provide means for requests to be made electronically, especially where personal data are processed by electronic means.” This process will start with an access request form but when it comes to identity, the guidance is unclear. A number of organisations are asking for a similar set of documents that most banks require to open an account which includes a “proof of identity” such as a passport, photo driving license or birth certificate along with a “proof of address” such as a utility bill, bank statement or credit card statement. This requirement to verify from copies or scans of electronic documents is a major weakness in this process. 

Non-functional Requirements: Examples, Types, How to Approach

Simply said, a non-functional requirement is a specification that describes the system’s operation capabilities and constraints that enhance its functionality. These may be speed, security, reliability, etc. We’ve already covered different types of software requirements, but this time we’ll focus on non-functional ones, and how to approach and document them. If you’ve ever dealt with non-functional requirements, you may know that different sources and guides use different terminology. For instance, the ISO/IEC 25000 standards framework defines non-functional requirements as system quality and software quality requirements. BABOK, one of the main knowledge sources for business analysts, suggests the term non-functional requirements (NFR), which is currently the most common definition. Nevertheless, these designations consider the same type of matter – the requirements that describe operational qualities rather than a behavior of the product. The list of them also varies depending on the source.

The Road to 2030 Must Be Circular

What gets exciting, is when you can find the perfect material match in someone else’s waste. Carbon fiber is a great example. Turns out computers use a similar grade carbon fiber as airplanes. So we reclaim aerospace material for Latitude, our commercial notebook line. To date, Dell has prevented more than 2 million pounds of carbon fiber from ending up in landfills. And in this case, the benefits go far beyond the environment. We’ve partnered with Carbon Conversions, a start-up based in South Carolina with a mission to reclaim and recycle carbon fiber. Carbon Conversions has redesigned and reengineered the papermaking process to produce carbon fiber non-woven fabrics, bringing new growth to an area historically impacted by overseas manufacturing. Finding more partners like Carbon Conversions will be important. It will also be important to increase our own recycling streams dramatically (i.e. you all have a role to play too). We must make it as easy as possible for you to recycle.

Bringing Business and IT Together, Part II: Organizational Alignment

COA is similar to other continuous improvement processes such as continuous quality improvement (CQI) and continuous process improvement (CPI). Just as CQI and CPI demand structure and metrics, so too does COA. Continuous improvement is evolutionary and incremental. It is manageable only when understood as a set of interconnected components that can be identified and measured. The COA Framework illustrated in Figure 1 provides the necessary structure. This three-dimensional structure associates the core elements of COA – those of organizational alignment and working relationships – with the activities of continuous improvement. The framework identifies the components that can be managed, measured, and modified to improve the overall alignment of business and technology organizations. ... Organization-to-organization relations are ideally structured and business-like. Conversely, person-to-person relationships are best when unstructured and friendly. Team-to-team relationships seek a balance between the two extremes.

VMware doubles up on Kubernetes play

Many of our large customers have Kubernetes clusters on vSphere, Amazon EC2 and sometimes bare metal. These are managed by different teams, making it difficult to manage and control everything. That was a problem we wanted to solve. Then comes the next question on how we can help customers build and deploy new applications. Historically, we’ve relied on Pivotal as a partner to help customers modernise their applications. While Pivotal Cloud Foundry is a great platform, Pivotal last year decided to use Kubernetes as the default runtime for their developer platform. Meanwhile, Spring Boot was becoming the de facto way by which people built microservices. So, we felt that by bringing Pivotal into the family, we could offer a very comprehensive solution to help customers build, run and manage their modern applications.

Using Kanban with Overbård to Manage Development of Red Hat JBoss EAP

Red Hat JBoss EAP (Enterprise Application Platform) has become a very complex product. As a result, planning EAP releases is also increasingly complicated. In one extreme case of the team working on the next major release while developing features for the previous minor release, the planning for that major release was ongoing for 14 months with the requirements constantly changing. However, spending more effort on planning didn't improve the end result; it didn't make us any smarter or more accurate. We'd rather spend more time doing stuff rather than talking about it. That was a major problem. In addition, there were cases in which requirements could be misunderstood or miscommunicated and we found that out late in the cycle. We had to find a way to collectively iterate over a requirement and make sure everyone understood what was to be done. In some cases we could go as far as implementing a proof-of-concept before we would be certain we fully understood the problem and the proposed solution.

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Quote for the day:

"Inspired leaders move a business beyond problems into opportunities." -- Dr. Abraham Zaleznik

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