In a blog post published today, Google CEO Sundar Pichai likened this moment to “the first rocket that successfully left Earth’s gravity to touch the edge of space,” stating “[t]hat’s what this milestone represents for the world of quantum computing: a moment of possibility.”

Google achieved this milestone with its new quantum chip that it’s dubbed “Sycamore.” This is a 54-qubit chip built on a superconducting architecture. The quantum computer takes advantage of a principle of quantum mechanics called “entanglement.” Which means that while a classical computer bit is binary—it exists as “zero” or “one”—a qubit is able to take advantage of a phenomenon known as entanglement to produce a quantum superposition. Put simply, a qubit doesn’t exist as “one” or “zero” but rather a complex linear combination of both, which enables a quantum computer (theoretically) to solve certain classes of problems faster than traditional computers.

The problem is that entanglement is a very delicate process—small environmental changes can impact them, making errors and noise a problem in quantum computing. This is why Google, IBM and others have made quantum chips that operate using superconductors at near absolute zero in an attempt to preserve these fragile entanglements.

In this particular experiment, Google’s researchers were able to connect 53 of Sycamore’s qubits and use them to detect patterns in large, seemingly random numbers. Its computer was able to solve this problem in about 3 minutes and 20 seconds, and the research team calculated it would take 10,000 years for a classical computer to solve the same problem. Though whether this is actually the case is disputed by quantum rival IBM, which wrote in a blog post earlier this week that its team estimates “an ideal simulation of the same task can be performed on a classical system in 2.5 days and with far greater fidelity.”

(If you want to take a deeper dive into the science behind Google’s quantum computer, I recommend this article by Forbes contributor Ethan Siegel.)

A Big Race To Market

Google’s news comes at a time when a lot of energy—and money—is being spent on the development of quantum computers. There are several reasons for this, but one of the biggest is that the biggest driver of computational innovation over the last few decades, Moore’s Law, is slowing down. That reliable increase in computing power over time is fundamentally constrained by physics, and the slowdown in hardware capabilities has the potential to stymie innovation in software as well.

This is where quantum computing steps in. Quantum computers have advantages over classical computing in three areas that are very important to business: logistics optimization (for example, ideal delivery routes), chemical simulation (which can speed up pharmaceutical development) and certain machine learning applications.

While Google’s paper may represent a milestone technologically, it’s facing plenty of competition to bring that technology to customers. Earlier this week, quantum hardware company IonQ announced that it raised $55 million from Samsung and others to accelerate the development of its quantum system, which can run at room temperature. Its trapped ion architecture could potentially be easier to deploy and might produce lower error rates in computation. While it’s still behind on producing chips with as many qubits as Google’s chip, its CEO, Peter Chapman, says the company plans to “double that every year” and will begin offering a quantum cloud service for its hardware in the first quarter of 2020.

Google faces another major rival in IBM, which last month announced the opening of a quantum computation center in Poughkeepsie, New York, to support its Q Network—a quantum cloud service that’s already being used by enterprise customers and software enthusiasts to develop the software for the day when quantum computers reach their potential.

For his part, though, Pichai made little reference to the competition in discussing Google’s achievement, but focused rather on moving the technology forward. “I am excited about what quantum computing means for the future of Google and the world,” he wrote. “Part of that optimism comes from the nature of the technology itself. You can trace the progress from the mega-computers of the 1950s to advances we’re making in artificial intelligence today to help people in their everyday lives.” 

I could not avoid thinking of that, when I came across the Oracle-commissioned "From Fear to Enthusiasm - AI is Winning Hearts and Minds in the Workplace" report, a couple of days ago.

Research firm Future Workplace surveyed 8,370 employees, managers and HR persons across 10 countries, clever PR followed and it all translated into headlines such as "64% of people trust a Robot more than their Manager", "Four out of five think robots are better than their managers" or "Chinese workers trust robot coworkers more than their managers".

But is this what the report says?

Here things become a little problematic.

It is true that, according to the study, 64% of respondents would trust a robot more than their managers, but the difference in feedback between French, American and UK workers on one side and Eastern workers on another, is so huge that it’s almost as if you were looking at two different surveys.

The pro-AI message of the survey relies a lot on the fact that 90% of Indian, 88% of Chinese and 84% of Singaporean interviewees answered "yes" to the "would you trust a robot more than your manager" question. By contrast, only 52% of French, 55% of Brits and 57% of Americans agreed. Still a slight majority, but quite a different picture.

What to make of it? Does it mean that the latter are less forward-looking and ready to embrace innovation in general? Or that they are more conscious and wary of the consequences of automation in the workplace? We don’t know for certain.

What we know is that the study was administered online, "respondents were recruited through a number of mechanisms, via different sources", had passed "a double opt-in process and complete, on average, 300 profiling data points" and were "invited to take part via email and are provided with a small monetary incentive for doing so."

The report does indeed offer a possible explanation for these different outcomes: the countries in which respondents say AI adoption is stronger - India, China, but also Brazil - and the chances of experimenting with it are higher, are also the ones in there’s more optimism and excitement about the opportunities involved.

According to this line of reasoning, Western workers and managers are less bullish about AI because they are much more ignorant and less aware of its capabilities than Brazilian or Indian ones.

Another possible explanation is that they are more concerned than their Asian counterparts about the need for human interaction, security and privacy in the workplace, the main factors that according to the report are preventing companies from using AI.

Making sense of the report’s results gets even more complicated if you look at the size of the samples involved. As we have seen, the impact of China, India and Singapore on the overall AI acceptance score is very significant; surprisingly, though, the samples considered for those countries are very small, especially as a percentage of the respective populations.

I could not find exact figures, but, judging from a graph, for China, fewer than 1,000 people were interviewed. Therefore, as Dan Robitzki of Futurism also notes, it’s not clear how could they be representative of the 800 million people in the country’s workforce.

My University notions of Statistics are a bit rusty, unfortunately, but I wonder, based on the above, if it’s really possible to say that "nearly 90% of Chinese workers have more trust in robots than in their human managers."

Isn’t it a bit far-fetched?

Source : https://www.forbes.com/sites/federicoguerrini/2019/10/19/us-and-uk-workers-much-less-enthusiastic-about-ai-than-their-asian-counterparts-new-study-shows/?ss=ai#35ae338a272b

Benefits of computer vision in education

The main advantage of computer vision in education is the ease and non-obstructiveness of the assessment process compared to traditional classroom education. Teachers can observe whether a pupil is motivated or disinterested in the class without interrupting their activities. Affective computing techniques, availability of low-cost cameras, and their widespread use in electronic devices like cell phones, computers, and tablets, allow educators to measure learners’ engagement levels using computer vision. Monitoring a student’s behavior continually is not possible. Computer vision eliminates the need for a teacher to continually monitor every student’s behavior, which proves to be highly beneficial. With computer vision technology, every student’s engagement in the classroom can be monitored easily. There is hope that computer vision will enhance the ability to detect, measure, and respond to individual students’ levels of engagement in the near future.

Uses of computer vision in education

Here’s how the use of computer vision will help transform education:

ALLERIN

Improve digital teaching methods

The world is coming closer due to the internet. People can easily search for any topic they might be interested in and find relevant information at their fingertips within seconds. Expanding on this, users can opt for any course or skill-oriented program from a variety of courses available online. People are not restricted when it comes to learning about something they are interested in. Capitalizing on the demand for online learning, many massively open online course (MOOC) platforms are on the rise. Many reputed institutes now offer online certification courses in addition to standard college courses. These online training courses provide audio and video lectures to the students, along with digital materials such as e-books for learning. These courses almost provide a near real-world classroom experience for the students. Students can learn from these courses as per their schedule and do not have a fear of missing out on important lectures. These courses are usually opted by students who can’t commit full-time due to full-time employment or enrolment in a different course. Online learning provides an excellent opportunity for such students who want to learn an additional skillset.

The only major drawback of these courses is that there is no direct real-time interaction and feedback between the teacher and student. Low student engagement continues to be a significant concern faced by educators in online learning contexts. Research shows that students opting for digital courses suffer from low levels of engagement and ultimately give up the course without completion. The completion rates for online courses or MOOCs are as low as 7%. This is a huge concern for educators as well as for the students. Self-reporting of doubts and unclear study material is hardly exercised by students taking these courses. It is a tedious process and doesn’t produce the desired results.

The use of computer vision can help overcome this problem. With computer vision, educators can analyze user behavior, eye movement and posture to assess engagement levels. This helps educators study their students’ behavior as to which sections were engaging and which made the student uninterested. Thus, they can provide engaging courses, which require consistent interaction to garner attention from the user. As new technologies enable us to record and collect data on everything from posture, eye movement, and even facial expression, self-reporting is no longer the only or even the best way to measure engagement.

Improve traditional teaching methods

As with online courses, computer vision can be helpful to track student engagement in traditional classrooms. Analyzing students’ postures and behaviors and eye-tracking can help monitor their interest level and attentiveness. By studying this data, teachers can analyze the behavior of every individual student and measure the spikes and slides in their interest level at every point in time. Educators can modify their teaching methods to garner attention from maximum students and maximize their interest. This helps develop an open interaction between the teachers and students. Teachers can gauge and understand student reaction to their teaching methods and ask students to provide genuine feedback which can be compared with the data collected with computer vision. This data can then be utilized to improve the teaching methods and provide customized courses and materials for students according to their understanding capabilities.

Computer vision is also helpful in improving cooperation between students. Teachers can place students into groups where they are most comfortable interacting and sharing their ideas and opinions. Introverted students can benefit significantly as they may be uncomfortable among large crowds and around loud, extroverted people. Arranging students according to their personalities helps in the growth of student as they are at their comfort level. Students can share their ideas and suggestions and discuss their doubts, if they have any, freely with other students who are a close match to their own personality and can easily interact with each other. Computer vision analysis help study behavior and interaction during diverse group tasks, how students teach others, and how comfortable they are with fellow students. This optimizes peer-to-peer interaction between students when teachers group students according to their comfort levels. Computer vision can offer valuable insights into students’ posture, their facial orientation, or gesticulation during the team activities, by recording all students’ interactions.

Concerns regarding the use of computer vision in education

The use of computer vision in education has been controversial and debatable. The ethics of placing students under continuous surveillance has long been debated upon. There is also the argument that the technology itself may not yet be ready to be implemented widely. The facial recognition tools used for computer vision are currently quite susceptible to errors and report higher error rates for both women and people of color. Concerning online courses, there is the privacy issue of user data being used without their consent. The amount of data and duration for which the website can monitor the user’s movements is highly debatable. The facial recognition data being used for other illegal purposes, and the possibility of it being sold or hacked by a third party is also a major concern. Stringent security measures need to be adopted by institutions implementing computer vision technology to protect user data from being misused. Not every institution can implement such strong security measures as it requires capital investment.

The use of computer vision in education is not without its limitations. However, these issues can be resolved with further improvements in the technology. Companies that deal in edtech can leverage the benefits of computer vision to create an engaging and intuitive atmosphere at educational institutes. The use of computer vision technology can mutually benefit the parties involved by making learning more satisfying, productive, and, most importantly, fun.

Source : https://www.forbes.com/sites/cognitiveworld/2019/10/19/computer-vision-can-transform-education/#7b4113701c4c

Today, there is a lot of hype surrounding artificial intelligence, but is AI around to stay or will it see its period of interest peak and wane as it has in the past? In order to answer this question, it's important to learn about AI’s beginnings and past. Are we headed to another AI winter? 

The First Wave of AI Adoption, and the First AI Winter

Since the invention of computers, developers have been working to build smarter computers. The term AI was actually coined in 1956 at a Dartmouth convention. Then the first major wave of AI interest and investment occurred from the early 1950s through the early 1970s. There was a lot of interest and funding from government, academic, and military sources which produced some of the earliest and most impressive advancements in AI. 

But all this advancement and development was put on hold in the mid-1970s. People started to feel more skeptical about AI’s promises, and the inevitable pullback happened when AI’s promises failed to deliver. This combined with the Lighthill report precipitated the first AI Winter. There are two major reasons for this demise. People had big plans about what AI could do and problems it would solve, but the technology failed to deliver on these grand plans. There was also a lack of diversity in funding sources so when the government and military decided to pull back investment in AI, most of the funding dried up.

The Second Wave of AI Adoption, and the Second AI Winter

Interest in AI research was rekindled in the mid 1980’s with the development of expert systems and reinvigorated research and investment in AI. Corporations started massively investing in desktop computers on knowledge workers desks, and expert systems helped to connect the emerging power of desktop computers and cheap servers to do the work that had previously been assigned to expensive mainframes. Expert systems helped automate and simplify decision-making for many industries. And this wave the funding came from businesses as well as the government, allowing for a little more diversity in funding. 

At the same time, as expert systems became increasingly hyped as people wanted to do increasingly more complicated things with them, the more problems that these AI adherents ran into. Expert systems were very dependent on data, and storage was still expensive in the 1980s. Storage and data were a few of the problems that expert systems came across in terms of technological problems. Corporations also needed to develop their own data and decision flows, but faced limitations to what they could really do with this data. In the 1980’s there wasn’t an Internet-based cloud to store data on like we can today, nor access to almost unlimited compute.  It was also harder to transmit large quantities of data from one area to another. This meant that expert systems couldn’t communicate with systems outside of the company that owned them and the data they needed to progress further wasn’t available.

These limitations along with diminished appetite for AI lead to another AI winter. In conjunction with expert systems, companies also started to develop software programs to handle various business tasks. Expert systems developed a reputation of being too brittle, depending on specific inputs to get desired outputs and businesses became wary of AI projects when they saw that there were cheaper alternatives that were just good enough to do what they wanted.

AI is hot again, but for how long

For about a decade or so, we have been in a period of a real blossoming of interest and investment in AI, an AI “summer” if you will. The innovation of AI algorithms combined with the availability and experience of working with big data is one of the biggest reasons that artificial intelligence has been able to leave hibernation. We now have almost limitless storage and have been able to successfully manage and handle large amounts of data. The development of deep learning is another reason that we have come out of the AI winter. Also investment is now quite diverse coming from enterprises, governments, academics, and venture capital. 

Additionally, dozens of countries have acknowledged that AI is going to be so important for their citizens and growth of their economies that we now have a country level investment and strategies around AI. Furthermore, AI is becoming more a part of everyday life. Today, AI is being used all around the world to accomplish any number of tasks. We have put AI into cars, phones, advanced bots, and other technology that we use every day. It’s not uncommon to interact with AI daily whether that’s through virtual assistants, hyperpersonalized offerings and recommendations, or better movie suggestions on Netflix.  Artificial intelligence now is in so many pieces of technology that we interact with every day that we almost forget that it is there. Sometimes bots are so good that we can’t tell they aren’t humans. We use AI to park our cars, and much more. 

However, with all this investment, interest, and funding in AI are we headed to another AI winter? Are we once again overpromising and underdelivering on what AI is capable of? Are we going to be disappointed with the limitations of driverless vehicles, natural language processing, and AI-powered predictive analytics? Will investors start seeing more snake oil from AI vendors than real-world implementations? 

While there certainly will be a lot of missed expectations for AI, this time around, our expectation and hope is that the next AI winter will never come. Another AI winter is always possible if we have a repeat of past circumstances such as over promising and under delivering what an AI system could do. Some experts are also concerned about the lack of research on general AI, we still haven’t figured out common sense reasoning, and that a lot of the funded research is too application specific. AI is too integrated in our daily lives for research, investment, and use to stop all together, and so it’s possible while there might be a cooling off of research and investment, as there always inevitably is with technology waves, we are hopeful this time we don’t go into hibernation.

Source : https://www.forbes.com/sites/cognitiveworld/2019/10/20/are-we-heading-for-another-ai-winter-soon/#5d50fe0256d6

One way of looking into the future that AI might bring is through Cognilytica’s four-part AI enabled vision of the future. In that vision, there are four main aspects in which AI will impact our future lives: the way we work, the way we live, the way we experience the world and our interactions with each other, and the relationship we have with data. (Disclosure: I’m a principal analyst with Cognilytica)

The New Workforce & The Always-On Organization

As technology progresses, the nature of work and labor rapidly changes. It is the purpose of technology to eliminate burdensome tasks and improve our daily lives. Technology is meant to increase our productivity, and allow us to live our lives to old age with increasing quality. We have always seen adjustments to the workforce when new technologies become available, especially in the areas of automation and augmentation. It should therefore be no surprise that organizations in industries of all types are looking to AI to change the way that they run their businesses. As such, the adoption of AI will necessarily and unavoidably change the nature of work. 

This is definitely the case for blue-collar work, which has already seen widespread change with the impact of robotics and automation. Automation has already replaced many positions in the area of manufacturing, but the bigger shift is not on the factory floor, but in the office cubicle. AI is eliminating much of the drudgery and repetitive work that has been farmed to service workers, or is otherwise taking up the time of more highly paid office workers. The use of various forms of process automation, from the less-intelligent, and ill-named robotic process automation (RPA) to the more intelligent cognitive automation forms, it’s clear that AI is here to stay in the organization. In the not-too-distant future, there will be just as many software and hardware bots operating the back and front offices of businesses as there are humans in the organization, and quite likely, many more bots than humans.

Another benefit and change that AI brings is the “always-on organization”. With the use of conversational agents and autonomous bots, there’s very little reason for businesses to close. Even after the humans go home, bots are operating, providing both the expectation and the reality that users can do business at any time they choose. This is not only limited to ecommerce and purely digital operations. The AI-enabled future means 24-hr coffee shops that are fully automated, and banks that no longer operate on “bankers hours”. In the AI-enabled future, businesses simply don’t have hours of operation. They’re always in operation. 

The future is Autonomous

AI is empowering the ability for autonomous operation of all types. Of course, the first thing that comes to mind is autonomous vehicles, but this is just one specific application of autonomous systems - and not even the best one. In the future, all modes of transportation will become autonomous, and this will bring about a change in the way we think of things like vehicles. If we think of it in conjunction with businesses like Uber or Lyft where you can essentially call a ride on demand whenever, you no longer need to own your own car if you can instead call one and it can show up whenever we need them. Our society is heading in a direction that lets us imagine these possibilities.

Autonomous everything doesn’t just mean autonomous vehicles. It means that we can have autonomous systems and operations of all sorts, from the autonomous software bots we described above performing the perfunctory work required in most processes to the vision of autonomous retail currently being innovated by Amazon Go and others. The combination of computer vision, machine learning, natural language processing, predictive analytics, and the world of intelligent sensors and devices means that we will be awash in autonomous systems of all sorts pervasive in our daily lives.

Enhancing the Human Experience

In addition to our work lives, AI will soon be a part of our overall human experience. We will come to use and sometimes rely on AI systems to enhance our daily interactions with each other, expand our creativity, and give us ways to express ourselves that we can’t otherwise do today. In the same way that spell check is part of the daily world of even the most casual of writers, AI will be used to give us powers of language translation and augmented creativity. Already, AI systems are helping those with limited artistic talents compose and produce original and powerful works of art and music.

AI systems are also having a great influence on how we communicate and socialize with each other. Humans are incredibly social creatures and one of the biggest developments to come out of machine learning technology is machine translation that can help humans understand and communicate in many languages. In the AI-enabled future, humans will be able to converse and interact with each other in the native language of choice, not having to worry about miscommunicating intentions. Machine learning models will be able to understand context, nuance, and colloquialisms that help to fill the gaps of human communication. This will make it easier for people who speak two different languages and don't understand each other to be able to carry on a conversation whether it is for business or for travel. AI is going to help us continue to meet our need to connect with others in meaningful ways. 

Changing our relationship with data: Pervasive Knowledge

The internet and mobile phones have completely changed the way we interact with others and the world around us. In the 1980s the concept of “pervasive computing” was nothing more than a pie-in-the-sky vision of everyone having computers available to them wherever they are, with instant access to data and information. It took just a few scant decades to make that vision a reality. Now we carry with us constantly-connected devices that have worldwide access to information. And now through AI, we’re moving beyond the idea of pervasive computing to pervasive knowledge. 

Pervasive knowledge is the idea that not only are we always going to be connected to the worldwide information grid, but all systems will have constant knowledge of what we are doing and how we’re doing it. This is the idea that people will already know things about you because of what is available for them through technology. On the positive side, this could mean a lot for disaster preparedness. If you think of instances such as an Amber Alert or tornado warning, 10 or 15 years ago you didn't get a text message when a kid went missing or a storm was imminent. The idea of pervasive computing made it an assumption that a text message would be sufficient to provide notification. But if you think about this, that’s a particularly non-intelligent way to do things. What if the system already knew that you were or weren’t in the path of a storm, could guide you to a safe place to seek shelter, and would additionally notify first responders if the storm hit the location where you were. This is the idea of pervasive knowledge made tangible, and it will impact everything from the way we shop to the way we live. 

The problem with pervasive knowledge is the “creepy” factor. To many, pervasive knowledge means pervasive surveillance. We don’t want systems to know where we are and what we are doing, yet we freely give this information away with our GPS-enabled phones connected to social media with cameras. While we might accuse governments or companies of tracking us without our consent, we are actually already freely giving this information in exchange for all the modern conveniences afforded by that pervasive connectivity. So too we’ll see people giving information to contribute to pervasive knowledge in exchange for even greater conveniences. 

The AI-enabled future will be a world where there is more access to all types of information and we are going to demand that those who have access to it use it smartly and use it for our benefit. This is going to mean that we're asking more of the people who have custody of this knowledge. We will demand that companies already know about us. We will demand that governments already have access to what they need to know. We won’t be patient with giving people information that we will presume they already should know. The future will be AI enabled and less patient.

We don't really know how soon or in what ways these visions of the AI-enabled future will become a reality, but even the most casual of observers can see that these are inevitable trends. The one thing that is certain is that it’s coming and we’re already heading in that direction. Companies are developing and investing in these technologies now. The future is AI enabled and it’s up to us to figure out what that means for our daily lives.

One positive aspect in this area of the AI-enabled organization is the realization that AI is making organizations much more responsive to their customer’s needs and desires. AI is making mass-customization possible for many different industries by leveraging the power of machine learning-enabled hyperpersonalization to tailor their offerings and build solutions customized for each individual customer, patient, client, or citizen. Through the use of conversational agents and autonomous systems, organizations are able to provide better customer service and better products and solutions to their customers by leveraging AI to churn through large quantities of data to build more accurate profiles of all of their customers. This impact can be felt even in the healthcare industry where AI is being used to provide personalized medicine and healthcare better tailored to individual patient needs. 

Source : https://www.forbes.com/sites/cognitiveworld/2019/10/17/the-ai-enabled-future/#d35edc03339d

Structured Data

Data that is the easiest to search and organize, because it is usually contained in rows and columns and its elements can be mapped into fixed pre-defined fields, is known as structured data. Think about what data you might store in an Excel spreadsheet and you have an example of structured data. Structured data can follow a data model a database designer creates - think of sales records by region, by product or by customer. In structured data, entities can be grouped together to form relations (‘customers’ that are also ‘satisfied with the service). This makes structured data easy to store, analyze and search and until recently was the only data easily usable for businesses. Today, most estimate structured data accounts for less than 20 percent of all data.

Often structured data is managed using Structured Query Language (SQL)—a programming software language developed by IBM in the 1970s for relational databases.

Structured data can be created by machines and humans. Examples of structured data include financial data such as accounting transactions, address details, demographic information, star ratings by customers, machines logs, location data from smart phones and smart devices, etc.

Unstructured Data

A much bigger percentage of all the data is our world is unstructured data. Unstructured data is data that cannot be contained in a row-column database and doesn’t have an associated data model. Think of the text of an email message. The lack of structure made unstructured data more difficult to search, manage and analyse, which is why companies have widely discarded unstructured data, until the recent proliferation of artificial intelligence and machine learning algorithms made it easier to process.

Other examples of unstructured data include photos, video and audio files, text files, social media content, satellite imagery, presentations, PDFs, open-ended survey responses, websites and call center transcripts/recordings.

Instead of spreadsheets or relational databases, unstructured data is usually stored in data lakes, NoSQL databases, applications and data warehouses. The wealth of information in unstructured data is now accessible and can be automatically processed with artificial intelligence algorithms today. This technology has elevated unstructured data to an extremely valuable resource for organizations.

Semi-Structured Data

Beyond structured and unstructured data, there is a third category, which basically is a mix between both of them. The type of data defined as semi-structured data has some defining or consistent characteristics but doesn’t conform to a structure as rigid as is expected with a relational database. Therefore, there are some organizational properties such as semantic tags or metadata to make it easier to organize, but there’s still fluidity in the data.

Email messages are a good example. While the actual content is unstructured, it does contain structured data such as name and email address of sender and recipient, time sent, etc. Another example is a digital photograph. The image itself is unstructured, but if the photo was taken on a smart phone, for example, it would be date and time stamped, geo tagged, and would have a device ID. Once stored, the photo could also be given tags that would provide a structure, such as ‘dog’ or ‘pet.’

A lot of what people would usually classify as unstructured data is indeed semi-structured, because it contains some classifying characteristics.

The Difference Between Structured, Unstructured, And Semi-Structured Data

To easily understand the differences between the classifications of data, let’s use this analogy to illustrate. When interviewing for a job, let’s say there are three different classifications of interviews: structured, semi-structured and unstructured.

In a structured interview, the interviewer follows a strict script that was defined by the human resources department and is followed for every candidate. Another form of interview is an unstructured interview. In an unstructured interview, it is entirely up to the interviewer to determine the questions and the order they will be asked (or even if they will be asked) for every candidate. A semi-structured interview takes elements from both structured and unstructured interview classifications. It uses the consistency and quantitative elements allowed with the structured interview but offers the freedom to customize based on the circumstances that are more in line with an unstructured interview.

So, for data, structured data is easily organizable and follows a rigid format; unstructured is complex and often qualitative information that is impossible to reduce to or organize in a relational database and semi-structured data has elements of both.

Source : https://www.forbes.com/sites/bernardmarr/2019/10/18/whats-the-difference-between-structured-semi-structured-and-unstructured-data/#33a2eb192b4d

The challenges of scaling cloud services to grow a digital business are many and are well-explained in the recent research report, Remote Office Networks Pose Business and Reliability Risk A Survey of IT Professionals (27 pp., PDF, no opt-in), published on August 2019 by Dimensional Research in collaboration with Infoblox. This report provides valuable insights into why scaling cloud services is essential for growing a digital business. The study’s findings reflect how remote branch and production locations’ lack of IT security and site personnel are one of the most challenging constraints to overcome and keep growing their business. Please see page 22 of the study for specifics on the methodology.

99% or nearly all enterprises with distributed operations suffer adverse business impacts from network interruptions. Of the many causes of network disruption, one of the most common is not directing traffic to the closest point of entry into cloud platforms. Taking a software-based approach to wide-area networking (SDWAN) is proving effective in improving cloud-based application performance, including Microsoft Office 365 cloud-based application performance. The report shows how SD-WAN is replacing outdated centralized IT models that lack the scale to flex and support new digital business models.

Key insights from the research report include the following: