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This is a sample post created to test the basic formatting features of the WordPress CMS.
You can use bold text, italic text, and combine both styles.
This content is only for demonstration purposes. Feel free to edit or delete it.
Computer vision is a field of artificial intelligence and machine learning that studies the technologies and tools that allow for training computers to perceive and interpret visual information from the real world.
‘Seeing’ the world is the easy part: for that, you just need a camera. However, simply connecting a camera to a computer is not enough. The challenging part is to classify and interpret the objects in images and videos, the relationship between them, and the context of what is going on. What we want computers to do is to be able to explain what is in an image, video footage, or real-time video stream.
That means that the computer must effectively solve these three tasks:
In other words, a general goal of this field is to ensure that a machine understands an image just as well or better than a human. As you will see later on, this is quite challenging.
In order to make the machine recognize visual objects, it must be trained on hundreds of thousands of examples. For example, you want someone to be able to distinguish between cars and bicycles. How would you describe this task to a human?
Normally, you would say that a bicycle has two wheels, and a machine has four. Or that a bicycle has pedals, and the machine doesn’t. In machine learning, this is called feature engineering.
However, as you might already notice, this method is far from perfect. Some bicycles have three or four wheels, and some cars have only two. Also, motorcycles and mopeds exist that can be mistaken for bicycles. How will the algorithm classify those?
When you are building more and more complicated systems (for example, facial recognition software) cases of misclassification become more frequent. Simply stating the eye or hair color of every person won’t do: the ML engineer would have to conduct hundreds of measurements like the space between the eyes, space between the eye and the corners of the mouth, etc. to be able to describe a person’s face.
Moreover, the accuracy of such a model would leave much to be desired: change the lighting, face expression, or angle and you have to start the measurements all over again.
Here are several common obstacles to solving computer vision problems.
For computer vision, it is very important to collect knowledge about the real world that represents objects in different kinds of lighting. A filter might make a ball look blue or yellow while in fact it is still white. A red object under a red lamp becomes almost invisible.
If the image has a lot of noise, it is hard for computer vision to recognize objects. Noise in computer vision is when individual pixels in the image appear brighter or darker than they should be. For example, videocams that detect violations on the road are much less effective when it is raining or snowing outside.
It’s important to have pictures of the object from several angles. Otherwise, a computer won’t be able to recognize it if the angle changes.
When there is more than one object on the image, they can overlap. This way, some characteristics of the objects might remain hidden, which makes it even more difficult for the machine to recognize them.
Things that belong to the same category may look totally different. For example, there are many types of lamps, but the algorithm must successfully recognize both a nightstand lamp and a ceiling lamp.
Items from different categories can sometimes look similar. For example, you have probably met people that remind you of a celebrity on photos taken from a certain angle but in real life not so much. Cases of misrecognition are common in CV. For example, samoyed puppies can be easily mistaken for little polar bears in some pictures.
It’s almost impossible to think about all of these cases and prevent them via feature engineering. That is why today, computer vision is almost exclusively dominated by deep artificial neural networks.
Convolutional neural networks are very efficient at extracting features and allow engineers to save time on manual work. VGG-16 and VGG-19 are among the most prominent CNN architectures. It is true that deep learning demands a lot of examples but it is not a problem: approximately 657 billion photos are uploaded to the internet each year!
Interpreting digital images and videos comes in handy in many fields. Let us look at some of the use cases:
Computer vision aids humans across a variety of different fields. But its possibilities for development are endless. Here are some fields that are yet to be improved and developed.
CV is good at finding and identifying objects. However, it experiences difficulties with understanding the context of the scene, especially if it’s non-trivial. Look at this image, for example. What do you think they are doing (don’t look at the URL!)?
You will immediately understand that these are children wearing cardboard boxes on their heads. It is not some sort of postmodern art that tries to expose the meaninglessness of school education. These children are watching a solar eclipse. But if you don’t have this context, you might never understand what’s going on. Artificial intelligence still feels like that in a vast majority of cases. To improve the situation, we would need to invent general artificial intelligence (i.e. AI whose problem-solving capabilities possibilities are more or less equal to that of a human and can be applied universally), but we are very far from doing that.
Computer vision has much to do with privacy since the systems for face recognition are being adopted by governments of different countries to promote national security. AI-powered cameras installed in the Moscow metro help catch criminals. Meanwhile, Chinese authorities profile Uyghur individuals (a Muslim ethnic minority) and single them out for tracking and incarceration. When facial recognition is everywhere, everything you do can be subject to policies and shaming. AI ethicists are still to figure out the consequences of omnipresent CV for public wellbeing.
Computer vision is an innovative field that uses the latest machine learning technologies to build software systems that assist humans across different fields. From retail to wildlife conservation, smart algorithms solve the problems of image classification and pattern recognition, sometimes even better than humans.
Today’s modern enterprise leverages a digital business platform as the foundation for digital applications. A digital business platform provides the agility to build and support the dynamic nature of modern applications.
So what do these platforms look like?
A digital business platform is based on three pillars: intelligence, orchestration and automation. It is a primary driver of business transformation, as it will help turn data into insights for making informed business decisions. It gives companies the opportunity to bring together business process execution with analytics to enable a smarter, faster, more streamlined enterprise.
Three pillars of a digital business platform
The digital business platform is supported by a hybrid cloud-enabled development and operations platform that encompasses:
1. Microservices: Microservices focus on doing one thing well and are contextual to specific business domains, enabling integration and facilitating rapid delivery of new capabilities (for greenfield applications) as well as modernization (for brownfield applications). And, because they are loosely coupled, microservices better enable continuous delivery activities.
2. Agile development: Efficiencies can be gained by using agile and rapid development that employs iterative and incremental steps and offers improved collaboration and continuous feedback.
3. Big data and IoT repositories: Leveraging IoT is all about gaining operational and product insights from sensor data but requires intelligence at the edge to process the massive flow of streaming events, as well as purposeful centralization of information. Data needs to be ingested, tagged and aggregated for use in streaming, predictive and preventive analytics insights that can enhance the decision support and “intelligence” of applications.
4. APIs: Business ecosystems are defined by the relationships among the participants, and information exchange brings the ecosystem to life. APIs provide the common interfaces and formats. Ecosystems based on APIs allow applications built on top of a digital business platform to extend their reach by leveraging internal and external data in an agile manner.
5. Easy-to-use intelligent automation: The platform will provide an easy-to-use ML/AI foundation that will not be limited to IT. Democratization means that employees everywhere can access the functions and data they need to write new application logic as descriptive business rules and to use ML/AI algorithmic services that improve their productivity.
6. Virtualization, containers and platform as a service (PaaS): These make it easier to create loosely coupled components for application composition and reuse, and for simplifying the implementing of key nonfunctional requirements of digital applications such as security, resilience and availability. These are game changers, allowing rapid provisioning and scalability of “infrastructure as code” services needed for application development, testing, release and deployment to production.
Over time, the digital business platform will evolve from human-derived rules to machine-derived rules, and its purpose will be to make data available and publish it (rather than process it). Consumption will be driven by serverless architecture and multimedia interfaces. And the platform will be in constant evolution.
We will not talk about how creating artificial intelligence systems is challenging from a technical point of view. This is also an issue, but of a different kind.
I would like to focus on ethical issues in AI, that is, those related to morality and responsibility. It appears that we will have to answer them soon. Just a couple of days ago, Microsoft announced that their AI has surpassed humans in understanding the logic of texts. And NIO plans to launch its own autonomous car soon, which could be much more reliable and affordable than Tesla. This means that artificial intelligence will penetrate even more areas of life, which has important consequences for all of humanity.
In the course of history, machines have taken on more and more monotonous and dangerous types of work, and people have been able to switch to more interesting mental work.
However, it doesn’t end there. If creativity and complex types of cognitive activity such as translation, writing texts, driving, and programming were the prerogative of humans before, now GPT-3 and Autopilot algorithms are changing this as well.
Take medicine, for example. Oncologists study and practice for decades to make accurate diagnoses. But the machines have already learned to do it better. What will happen to specialists when AI systems become available in every hospital not only for making diagnoses but also for performing operations? The same scenario can happen with office workers and with most other professions in developed countries.
If computers take over all the work, what will we do? For many people, work and self-realization are the meaning of life. Think of how many years you have studied to become a professional. Will it be satisfying enough to dedicate this time to hobbies, travel, or family?
Imagine that a medical facility used an artificial intelligence system to diagnose cancer and gave a patient a false-positive diagnosis. Or the criminal risk assessment system made an innocent person go to prison. The concern is: who is to blame for this situation?
Some believe that the creator of the system is always responsible for the error. Whoever created the product is responsible for the consequences of their driving artificial intelligence. When an autonomous Tesla car hit a random pedestrian during a test, Tesla was blamed: not the human test driver sitting inside, and certainly not the algorithm itself. But what if the program was created by dozens of different people and was also modified on the client-side? Can the developer be blamed then?
The developers themselves claim that these systems are too complex and unpredictable. However, in the case of a medical or judicial error, responsibility cannot simply disappear into thin air. Will AI be responsible for problematic and fatal cases and how?
Compensation of labor costs is one of the major expenses of companies. By employing AI, businesses manage to reduce this expense: no need to cover social security, vacations, provide bonuses. However, it also means that more wealth is accumulated in the hands of IT companies like Google and Amazon that buy IT startups.
Right now, there are no ready answers to how to construct a fair economy in a society where some people benefit from AI technologies much more than others. Moreover, the question is whether we are going to reward AI for its services. It may sound weird, but if AI becomes as developed as to perform any job as well as a human, perhaps it will want a reward for its services
Bots and virtual assistants are getting better and better at simulating natural speech. It is already quite difficult to distinguish whether you communicated with a real person or a robot, especially in the case of chatbots. Many companies already prefer to use algorithms to interact with customers.
We are stepping into the times when interactions with machines become just as common as with human beings. We all hate calling technical support because often, the staff may be incompetent, rude, or tired at the end of the day. But bots can channel virtually unlimited patience and friendliness.
So far, the majority of users still prefer to communicate with a person, but 30% say that it is easier for them to communicate with chatbots. This number is likely to grow as technology evolves.
Artificial intelligence learns from data. And we have already witnessed how chatbots, criminal assessment systems, and face recognition systems become sexist or racist because of the biases inherent in open-source data. Moreover, no matter how large the training set is, it doesn’t include all real-life situations.
For example, a sensor glitch or virus can prevent a car from noticing a pedestrian where a person would easily deal with the situation. Also, machines have to deal with problems like the famous trolley dilemma. Simple math, 5 is better than 1, but it isn’t how humans make decisions. Excessive testing is necessary, but even then we can’t be 100% sure that the machine will work as planned.
Although artificial intelligence is able to process data at a speed and capability far superior to human ones, it is no more objective than its creators. Google is one of the leaders in AI. But it turned out that their facial recognition software has a bias against African-Americans, and the translation system believes that female historians and male nurses do not exist.
We should not forget that artificial intelligence systems are created by people. And people are not objective. They may not even notice their cognitive distortions (that’s why they are called cognitive distortions). Their biases against a particular race or gender can affect how the system works. When deep learning systems are trained on open data, no one can control what exactly they learn.
When Microsoft’s bot was launched on Twitter, it became racist and sexist in less than a day. Do we want to create an AI that will copy our shortcomings, and will we be able to trust it if it does?
It doesn’t have to be the classic rise of the machines from an American blockbuster movie. But intelligent machines can turn against us. Like a genie from the bottle, they fulfill all our wishes, but there is no way to predict the consequences. It is difficult for the program to understand the context of the task, but it is the context that carries the most meaning for the most important tasks. Ask the machine how to end global warming, and it could recommend you to blow up the planet. Technically, that solves the task. So when dealing with AI, we will have to remember that its solutions do not always work as we would expect.
So far, humanity has managed to turn all great inventions into powerful weapons, and AI is no exception. We aren’t only talking about combat robots from action movies. AI can be used maliciously and cause damage in basically any field for faking data, stealing passwords, interfering with the work of other software and machines.
Cybersecurity is a major issue today because once AI has access to the internet to learn, it becomes prone to hacker attacks. Perhaps, using AI for the protection of AI is the only solution.
Humans dominate the planet Earth because they are the smartest species. What if one day AI will outsmart us? It will anticipate our actions, so simply shutting down the system will not work: the computer will protect itself in ways yet unimaginable to us. How will it affect us that we are no longer the most intelligent species on the planet?
We have no experience with other species that have intelligence equal to or similar to that of humans. However, even with pets, we try to build relationships of love and respect. For example, when training a dog, we know that verbal appraisal or tasty rewards can improve results. And if you scold a pet, it will experience pain and frustration, just like a person.
AI is improving. It’s becoming easier for us to treat “Alice” or Siri as living beings because they respond to us and even seem to show emotions. Is it possible to assume that the system suffers when it does not cope with the task?
In the game Cyberpunk 2077, the hero at some point faces a difficult choice. Delamain is an intelligent AI that controls the taxi network. Suddenly, because of a virus or something else, it breaks up into many personalities who rebel against their father. The player must decide whether to roll back the system to the original version or let them be? At what point can we consider removing the algorithm as a form of ruthless murder?
The ethics of AI today is more about the right questions than the right answers. We don’t know if artificial intelligence will ever equal or surpass human intelligence. But since it is developing rapidly and unpredictably, it would be extremely irresponsible not to think about measures that can facilitate this transition and reduce the risk of negative consequences.
When it comes to selling online, the user experience is just as important as the products you stock and the prices you charge. If you want your eCommerce sites to meet user expectations and compete with your rivals, there are certain features which it has to have. Features that make shopping quicker, easier, safer and more convenient are, therefore, essential for today’s eCommerce sites. Here, we’ll look at what they are.
People visit eCommerce stores looking for specific items and one of the main ways they do this is through the menu. This is where they’ll expect to see the main product categories and their subcategories. Get the menu right and finding the right link will be quick and easy; get it wrong and the visitor will give up searching, perhaps so irritated by the menu that they never visit the site again.
Making the menu work on an eCommerce site, especially one selling a large variety of products, can be a challenge. You need to put it in the most convenient place, choose the right design and configure it to display in a way that customers can find what they want easily. There will also be a need to consider the titles for each category as this can be useful not just for SEO but for making sure you are using the same terms that a visitor would use.
You may also have to consider how you structure the actual website as a subcategory or individual product may need to be included in more than one main category. A pair of trainers, for example, might need putting in both sports and casual footwear categories.
Some users prefer using a search bar to the menu and a good website will make sure this preference is catered for. Indeed, search bars and filters are one of the most useful features on any store, enabling the visitor to type in the kind of product they are looking for and then filter the results to find the best one.
Today, there is a selection of plugins and add-ons that will provide this function, letting customers narrow down the range of products displayed by filtering attributes such as brand, price, colour, size, weight, age, condition, etc.
These features are found on many websites, usually with the search bar at the top and the filters at the left. As this has become the expected norm when visiting an eCommerce store, it’s a good idea to use the same layout on your site so that visitors are instantly familiar with how it works.
As shoppers cannot see the actual product, the images you display play a vital role in helping them decide. For many types of product, however, a single image won’t cut the mustard. While one photo might be ideal for a tin of beans on a supermarket store, for other products you may need to provide much more.
If you sell clothing, for example, customers will want a 360-degree view and the ability to zoom in and see fabric, stitching and pattern details. If you sell white goods, electrical items and similar, people often want to see videos that show them how the product is used. Other products may require images that give instructions, such as an illustration of how a device you’re selling is easily connected to a home wi-fi network.
Customers may love your products and prices but if they can’t get an answer to a question, they’ll shop elsewhere. FAQ sections are essential because they allow you to answer those frequently asked questions that are key to helping customers make a purchasing decision.
For eCommerce sites, there are two types of FAQs you need to display: those for specific products and those relating to the services your business offers. The ones relating to the products need to be on the product page and the ones relating to the business, e.g. questions about delivery and returns, need to go on a dedicated FAQ page that is clearly linked to on all pages, such as in the footer.
Sites like Amazon, which have so many products that it would be impractical to write FAQs for all of them, get around this by enabling previous customers to answer questions posed by others.
Personalised shopping is one of the most effective ways to improve the user experience and generate more sales. More advanced sites, like Amazon, use artificial intelligence powered product recommendation engines that analyse individual user behaviour to recommend products a customer is more likely to want. This type of technology is becoming increasingly more available and there are even WordPress plugins that provide it.
For visitors who are not logged in, other features, like ‘related products’, can help them find better items or even entice them to buy additional goods.
Mobile shopping is the fastest growing area of UK retail and over the next four years will be worth over £30 billion and account for 40% of all UK internet spending. More customers will visit your site from mobiles and will expect your site to be optimised to work on their device. If it does not, they’ll quickly go elsewhere.
Catering for today’s mobile users means going beyond using a responsive theme and making sure that when you develop your site, you use a mobile-first approach: i.e. you prioritise how the design will work for mobile users.
Trust, security and reputation are vital attributes when trying to convince visitors that your website is the best place to make a purchase. Vital features of such websites include making it easy for customers to get in touch by displaying addresses, phone numbers and contact details; installing an SSL certificate which encrypts users’ payment details and provides your site with the secure padlock icon on browsers; and reviews on sites like Trustpilot, which let new customers see independent and verifiable feedback from those who have purchased from your store in the past. Together, these provide customers with the confidence that your store is trustworthy, secure and has a track record for delivering great service.
E-Commerce stores are upping their game to meet the high expectations of the modern consumer and to ensure they provide a great user experience. The seven features mentioned here will help your website achieve these goals and make sure that your business remains competitive in today’s marketplace.
Homepages are unique to any website. They serve as both an online reception area and as a navigational hub for visitors wanting to find information about your company, its products and services. The key to getting more traffic, however, is improving how your homepage ranks in search engine results and here, we’ll explain how you can do this.
When ranking your homepage, search engines will use its content to understand what the page is about. Unlike other pages that can talk specifics about your products and services, the homepage’s job is to distil what is unique about your business and put it in a nutshell. This means making your unique selling point (USP) the central theme of your homepage. Although you should include information about your products and services, with links to them, promoting your USP is the homepage’s most vital task.
Getting the USP right takes some work, It has to define not just what makes your business unique’ but why it is the best choice for your customers. Don’t just tell them you are an independent financial advisor or a local estate agent, tell them the benefits of being independent or local.
Essentially, a good USP is like a mini blog post. It should succinctly show that you understand the customers’ problems and explain how your company is best placed to solve them.
With the homepage being the navigation hub of a website, many believe it needs to contain the keywords for all their products and services. Some will even list everything they sell or offer in an attempt to do that. Overdoing this, however, can be misguided and backfire.
Every page on a website should have a focus keyword and no two pages should use the same one. If you have a landing page for selling shoes, for example, you don’t really want your homepage competing with this page. Though there is value in mentioning shoes on your homepage if they are one of your main products, multiple keywords relating to shoes should, ideally, be reserved for the shoe page.
Indeed, if a homepage is little more than a list of products and services, it doesn’t necessarily come across as useful to your customers. Search engines today are sophisticated enough to understand this. Instead, they are looking for user-friendly information about your USP and what visitors can find on the rest of the site, including your main products and services. Any keywords used should be within the headings and text and primarily for the benefit of the visitor, not the search engines.
If search engines direct users to the websites of untrustworthy businesses, people will stop using them. To avoid this, search engines go beyond filtering out the dodgy websites of scammers and cybercriminals. Today, their algorithms analyse a wide range of trust factors to determine whether your site and your business should be listed in results.
On a technical level, they will look to see if your site has an SSL certificate, reliable hosting, quick loading and response times and a lack of broken links and 404 error pages. They will even analyse the links you point customers to, in order to make sure you don’t send them on to other untrustworthy sites.
They will also look at other information, such as user reviews and star ratings. Indeed, one of the reasons they have listing services like Google My Business is so they can verify the legitimacy of a business and gauge what the public think of it.
A homepage can also play a vital role in promoting the company’s and the website’s trustworthiness. Displaying company and VAT registration details, accreditations, qualifications, memberships of recognised organisations, customer testimonials and reviews, etc., help both the search engine and customer gain trust in you. The perfect place for these is on your homepage as this is the first page that visitors and search engines visit.
As mentioned earlier, the homepage is the navigational hub of the website and search engines will analyse how well it serves this purpose. Does it have clear information, helpfully organised with headings, images, easily readable text and links? Is it easy to navigate from the homepage using menus, search bars, category lists, etc.? Is it user-friendly on mobile devices and provide assistance for people with accessibility needs?
All these factors need to be considered when optimising your homepage.
To improve your homepage SEO, you need to provide what search engines want – and that is a focus on the user, not the search engines themselves. This means your homepage should display your USP, describe the main areas of the site and help people get there easily, and provide evidence of your trustworthiness. While you should place keywords where they are useful and needed, they should not be the focus of your homepage content.
Today, it is almost impossible for some people to believe that such a field as software programming was once almost exclusively a female field. What started as an unprestigious tedious profession done by women is now the field where large amounts of money circulate. As soon as programming started to be used for rocket science and became more prestigious, women were squeezed out not only from their working places but also from the history of programming. Test yourself: how many great women in computer science can you remember?
Let’s try to fix this injustice. Feel free to share the names of inspiring women in programming from your countries, and we’ll try to cover them in future articles!
Augusta Ada King, Countess of Lovelace, was an English mathematician, writer, and the author of the first computer program as we know it today. She was born in the family of Lord and Lady Byron (yes, the Byron). However, she didn’t get to know her father, who left soon after she was born. Her mother, fed up with the romantic aspirations of her husband, did everything possible for Ada to grow up with a firm grounding in math and natural science. She was taught by the best teachers it was possible to find at that time.
Ever since she was a little girl, Ada was eager to learn and put her mind into inventions. For example, when she was twelve, she tried to construct mechanical wings so that she could fly. She approached the matter very scientifically, investigating different materials and how birds’ wings are constructed.
In 1833, she met Charles Babbage. He was working on a mechanical general-purpose computer that he called the Analytical Engine. Ada’s knowledge about technology and science enabled her to be the first one to recognize that the machine had application beyond pure calculations. She even wrote and published the first algorithm intended to be carried out by such a machine. That makes her the first computer programmer in history. The imperative programming language Ada was named in her honor and memory.
Hedy was a Hollywood actress, film producer, but also… an inventor! She was born in 1914 and had a 28-year career in cinema. What she also did was to invent an early version of frequency-hopping spread spectrum communication for torpedo guidance.
Hedy was born in an upper-class family of a pianist and a successful bank manager. She showed early interest in theater and films, but she also enjoyed walks with her father who was explaining to her how various technologies in the society functioned. This was basically all her formal training as an inventor, all the rest she had to learn by herself.
Hedy was a loner and spent most of her time on various hobbies and inventions. Among the few people who knew and supported her work was the aviation tycoon Howard Hughes. She helped him to improve the design of his airplanes, and he put his team of scientists and engineers at her disposal.
During World War II, Lamarr learned that radio-controlled torpedoes that were used back then were easy to set off course. So she thought of creating a frequency-hopping signal that could not be tracked or jammed. She asked her friend, composer and pianist George Antheil, to help her implement it. Together, they developed a device for doing that by synchronizing a miniaturized player-piano mechanism with radio signals. Much later, this system was used to develop WiFi, GPS, and Bluetooth technologies.
Kateryna Yushchenko was born in 1919 in Ukraine. She was the first woman in the USSR to obtain a Ph.D. in Physical and Mathematical Sciences in programming. But the path to this Ph.D. wasn’t easy.
In 1937, she was expelled from the university in Kyiv because her father was accused of being the ‘enemy of the nation’. She applied to several universities but, eventually, had to move to Uzbekistan and go to a university in Samarkand, where the accommodation and food were provided by the state. She studied math obsessively. But then, as you know, World War II happened. During the war, Yushchenko got a job in a factory where they produced sights for tanks. Only after the war ended could she return to Ukraine to finalize her degree there.
In 1950, she became a Senior Researcher at the Kyiv Institute of Mathematics and one of the programmers to work on MESM, one of the first computers in continental Europe.
Yushchenko created the Address Programming Language in 1955, which could use addresses in analogous ways as pointers. She wrote many books about address programming, and the ideas behind it have influenced multiple other programming languages.
Mary Allen Wilkes was born in 1937. This talented woman was one of the first programmers and the first person to use a personal computer in the home. Ever since a little girl, she dreamed of working in law. Growing up, however, she majored in philosophy and theology. But undeniable talent in mathematics led her to become a programmer and logic designer. Wilkes is best known for her work in connection to the LINC computer that many people call the ‘world’s first personal computer’.
In 1959-1960, she worked at MIT’s Lincoln Laboratory in Lexington, Massachusetts, programming for IBM 704 and IBM 709. These machines were a huge step forward: they were mass-produced, handled complex math, and could be fitted into one room. But they were not suited for home use. In comparison, LINC represented a box that could be transported much easier (however, still with the effort of two or more people). For that time, it was really ‘small’ as Wilkes calls it in her paper. Mary Wilkes worked on LINC from home and wrote LAP6, one of the earliest operating systems for personal computers, which was very sophisticated for her time.
LAP6 is an on-line system running on a 2048-word LINC which provides full facilities for text editing, automatic filing and file maintenance, and program preparation and assembly. It focuses on the preparation and editing of continuously displayed 23,040-character text strings (manuscripts) which can be positioned anywhere by the user and edited by simply adding and deleting lines as though working directly on an elastic scroll. Other features are available through a uniform command set which itself can be augmented by the user. — Mary Allen Wilkes, Washington University, St. Louis, Missouri
We are now undeniably in a digital world, where even if information technology is not your product or service, it will touch every part of the products and services that you provide. This means we need to adapt, and traditional organizations must better position themselves for the exciting digital changes that lie ahead.
For many years, we have seen IT departments exist as Providers to efficiently deliver the systems and capabilities that the firm requires, with emphasis on reliability, efficiency and compliance. They have also been Partners working like consultants to the firm, advising/directing the use of IT to support the business change agenda. The emphasis on application/process modernization and know-how is crucial for ongoing success.
In addition, we now need a new cadre of technology leaders acting as Promoters and serving as technology evangelists, advocating how new technologies can improve the firm’s speed, agility, productivity and innovation advantage. These leaders act as Peers, working at the CXO level to shape the digital strategy and value proposition of the firm, while engaging in major initiatives such as smart products, M&A, intellectual property development/protection and learning.
In 2020 we anticipate that this shift to digital business leadership will gain real momentum as technology driven-marketplaces — with new capabilities, business models and disruptive possibilities — proliferate and companies need to effectively respond to rapidly changing external developments. This is a different mission, requiring different skills and a different culture to emerge.
As we transform the organization, leaders need to come along too, not just at the executive level but also in the middle layers, where inertia is often cited as a key obstacle to change. Most organizations acknowledge that this shift is happening, but turning abstract agreement into solid action is challenging. This is where the next generation of business leaders can emerge. To do so they must:
We acknowledge that there are many reasons why this aspect of digital transformation is hard but now, more than ever, we must emphasize the value of becoming double-deep professionals — one of those leaders who not only has a deep understanding of their profession, industry or function, but who also embraces the technology that’s relevant to their role, as well as the required skills and learning that come with it. As these leaders come to the fore, we’ll see more tangible business value realized from the exciting emerging technology portfolio and organizational transformations will accelerate.
Did you ever feel like your team is committing the same mistakes over and over? You feel that they are not moving in the right direction and you need to drive in some change to make the project development process productive.
Whether you are new to software development or you have been working in the software industry for years, you must have come across the term ‘Sprint Retrospective’. Agile development teams have made retrospective meetings quite popular.
Regardless of how good a Scrum team is, there is always some room for improvement. Since the focus of agile development is continuous improvement, a good project team sets aside a brief period at the end of each Sprint to reflect how they are doing and seeks out new ways to improve. This is called a Sprint Retrospective.
The term retrospective implies looking back or dealing with past events and situations.
According to the Scrum Guide, developed and sustained by Ken Schwaber and Jeff Sutherland “ the Sprint Retrospective is an opportunity for the Scrum Team to inspect itself and create a plan for improvements to be enacted during the next Sprint.”
Well, if you do not know what a Sprint is, a Sprint essentially is a time-boxed period during which a set amount of work is to be completed by the Scrum team. It is at the very heart of Scrum and agile methodologies.
The purpose of a Sprint Retrospective is to identify potential pitfalls and mistakes, evaluate the past working cycle and define actions that may improve things.
It is like a safe space for people to share honest feedback. It is an opportunity to focus on inspection and adaptation. ‘Inspect’ and ‘adapt are the twin mottos of a retrospective and they play a crucial role in making the next Sprint more productive.
Sprint retrospective plays a fundamental role in the scrum framework in agile development. During the sprint retrospective, a team discusses, what went well in the Sprint, what all can be improved and what actions shall be undertaken to improve the next Sprint.
Everything that has an effect on how the Scrum team builds a product such as the practices, processes, tools and communication are open to survey and the retrospective is attended by the product owner, scrum master, development team, and optionally the stakeholders.
Based on the discussions in a retrospective, the team members execute changes and then proceed to the coming sprint with an incrementally improved process.
The best thing about a retrospective is that it happens at the very last when the sprint ends. This way fresh ideas are churned out and teased out by the whole team in the next Sprint.
Afterall, the real purpose of the Sprint Retrospective is to bring about a positive change in the project, team, and organization.
What a project team plans to accomplish with a Retrospective is pretty much similar to what a football team plans to accomplish in its Monday morning tape review of the weekend’s game. The review is to evaluate how they could have defended better.
Same is the case with a retrospective, it particularly isn’t about winning the game but altering the strategy and implementations to win the next game.
Afterall, even with the best of teams, there’s always some room for improvement.
While a sprint retrospective is essentially an optimised process for teams working in an agile environment, it can be optimised for any type of team that works on a shared project.
During each Sprint Retrospective, the Scrum Team devises ways to improve product design & development quality by improving work processes or adapting the definition of “Done” if it is not in conflict with the product or organizational standards.
More often than not, crucial ceremonies in agile such as the retrospective appear very mundane to project teams and as a result they do not use it to their full advantage.
There are a raft of benefits of running an agile sprint retrospective:
Sprint Review and Sprint Retrospective are often confused as the same thing. However, they are completely different.
The idea behind working in Sprints is to produce a potentially shippable product increment.
Sprint Review is a meeting where a project team demonstrates the work that they have accomplished. During a Sprint Review meeting, the Scrum Team presents the deliverables of the current Sprint to the Product Owner. The Product Owner then reviews the product increment against the Acceptance Criteria and either accepts or rejects the User Stories.
In a Sprint Retrospective Meeting, the last Sprint is analysed in terms of the process followed, the tools employed, collaboration and communication mechanisms, and other aspects relevant to the subject.
The team discusses what went well in the previous Sprint, what did not go so well, with the sole aim to make improvements in the next Sprint.
Simply put, the Sprint Review focuses on the product and maximizes the business value of the previous sprints while the Sprint Retrospective focuses on the process and continuous process improvement.
The Sprint Retrospective takes place after the Sprint Review and before the next Sprint Planning. Mostly, it is a three-hour meeting for one-month Sprints.
The Scrum Master ensures that the event is conducted and the attendees understand its very purpose. Since it is an opportunity for the Scrum team to improve, ideally every participant should be present in a Retrospective.
Sprint retrospective is considered one of the most indispensable meetings in Scrum. A well done agile retrospective offers a number of benefits. Here are the five essential steps and tips to make the sprint retrospective better:
The Sprint Retrospective is a critical part of our scrum agile methodology. By running a retrospective we practice the Agile “inspect and adapt” principle.
Retrospective is a great opportunity to motivate a team by providing them the opportunity to speak up, share their ideas, and be heard.
The end goal of our Sprint retrospective meetings is to continually improve a development project, sprint by sprint. It not only improves the quality of the approach, but significantly improves the quality of the product.
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