Anteelo Master, Author at anteelo

Blockchain vs DLT – An Explanatory Guide You Can’t Miss On

Posted on 14/10/202113/11/2021 by Anteelo Master

As blockchain is evolving, many startups and developers are exploring and dissecting the potential of the technology in all aspects. They are not solely taking interest in knowing how the technology can revamp their existing business models.But, are also entertaining all the originating buzzwords. While some are using them as synonyms, others are taking an interest in finding the differences between them.

One such newly originated name is Distributed Ledger Technology (DLT).

What is DLT has become one of the buzz questions of the current time, with what makes it similar/different from Blockchain being the close second

Let’s cover both in this article. But first, let’s have a sneak peek of Blockchain vs DLT by considering a platform example of both, i.e, Ethereum (Blockchain) and R3 Corda (DLT).

Now while this would have given you some insights on the difference between blockchain and DLT, let’s jump into the decentralized world and study them in detail – starting with the basics of Blockchain technology.

A simple definition to Blockchain technology

Blockchain is a decentralized, distributed and often public database type where data is saved in blocks, such that the hashcode present in any block is created using the data of the previous block. These blocks offer a complete set of characteristics like transparency, immutability, and scalability that makes every brand and developer interested in investing their time and effort into Blockchain development guide.

Advantages of Blockchain:

Blockchain technology enables businesses to verify any transaction without involving any intermediaries.
Since transaction stored in the blocks are stored on millions of devices participating in the Blockchain ecosystem, the risk of data recovery is minimal.
As consensus protocols are used to verify every entry, there is no chance of double entry or fraud.
Another benefit of blockchain technology is that it offers transparency in the network, which makes it easier for anyone to be familiar with transactions in real-time.

The technology came into limelight as backend support force for cryptocurrencies, but soon made its place in different business verticals including Healthcare, Travel, Real Estate, Retail, Finance, and On-demand. A complete information about different industries that Blockchain is disrupting can be taken from this image:-

With this attended to, let’s turn towards DLT.

A Brief Introduction to Distributed Ledger Technology (DLT)

The answer to what is Distributed Ledger technology (DLT) is that it is a digital system used for storing the transaction of assets, even when the data is stored at multiple places simultaneously. It might sound like a traditional database, but is different because of the fact that there is no centralized storage place or administration functionality. Meaning, every node of the ledger processes and validates every item, and this way, contribute to generating a record of each item and building a consensus on each item’s veracity.

A timeline of DLT’s movement can be seen in this image –

The concept is attracting almost every app development company with a complete set of advantages as shared below.

Advantages of DLT:-

In DLT, data is 100% tamper proof till the database ledger is distributed.
It offers highly secure and trustworthy experience.
A decentralized private distributed network enhances the robustness of the system and assures continuous operation without any interruption.

Now, as we have looked into what the two terms mean, let’s look into the DLT and Blockchain relationship before turning towards the comparison of two.

Relation between Blockchain and DLT

As depicted in the above image, Blockchain is just a piece of the vast ecosystem of DLT. It is a type of DLT where records are stored in blocks after being validated cryptographically. That implies, a hash created using the data stored in a block is fed in the next block added such that it gives an impression of a chain.

“Every blockchain is a distributed ledger, but not every distributed ledger is a blockchain.”

Now, while this might be giving you a rush to compare Blockchain and DLT at once, let’s take a twisted path. Here, we will first learn about other types of DLT besides Blockchain, and then move to the core part of the article, i.e, Blockchain vs DLT.

Here are the popular forms of DLT that exists apart from Blockchain.

1. Holochain

Holochain, in simplified terms, is a type of DLT which does not rely upon consensus model or on the concept of tokenization.

Here, each participating node has its own secure ledger and can act independently, while also interacting with other devices on the network to meet the basic needs of decentralization. This way, it lets you build more customized and scalable solutions than what Blockchain offers.

2. Hashgraph

Another form of DLT that exists in the market is Hashgraph. It is basically a patented algorithm that has the potential of delivering all the benefits of Blockchain (decentralization, security, and distribution), but without compromising at transaction speed rate. Something for which it relies upon the concept of Gossip about Gossip protocol and Virtual voting technique.

One real-life implementation of Hashgraph that has proven to hold the potential of becoming a replacement of Blockchain is Hedera Hashgraph, about which you can learn more in this blog.

3. Direct Acyclic Graph (DAG)

Direct Acyclic Graphs (DAGs) or you can say Tangle is also one of the prime types of DLT that pertains in the tech world.

Under this concept, multiple chains of nodes are created and managed at the same time and are interconnected to one another. They, unlike Blockchain, exist both in serial and parallel form.

Factors to Consider While Comparing Blockchain and DLT

1. Consensus model

The foremost factor to focus upon while checking into Blockchain vs DLT is the consensus mechanism.

Since only a limited number of nodes participate in the case of DLT, there is no need for any consensus. But, the same is not true in the case of Blockchain where anyone can participate and contribute to the addition of a new block to the chain.

2. Block structure

Another factor that you must keep a watch on while differentiating between Blockchain and DLT is block structure.

While blocks are added in the form of a chain in a Blockchain, they can be organized in different forms in the case of Distributed Ledger technology.

3. Tokens

Tokens, i.e, the programmable assets governed by Smart Contracts or underlying distributed ledger, is also one of the prime comparing factors in Blockchain vs DLT war.

While tokens are must to consider while working with Blockchain technology, they are not required when dealing with DLT. And the reason behind is that only invited (limited) nodes are allowed to participate and validate any transaction in DLT environment which reduces the size of complete ecosystem.

However, you might be requiring tokens when you wish to perform block spamming or work on anti-spamming detection process.

4. Sequence

In Blockchain environment, all the blocks are arranged in a particular sequence, i.e, in a serial mode. However, there is no as such constraint when talking about DLT. Blocks are organized in different ways in the case of DLTs.

5. Efficiency

A DLT can complete a significantly high number of transactions in a minute than what is possible by Blockchains. So, it delivers higher efficiency with minimal cost than Blockchain-based solutions.

An outcome of this is that today, various Blockchain development companies are looking ahead to enter into the DLT ecosystem.

6. Trustability

Another factor that you can consider to see the difference between Blockchain and DLT is Trustability.

In the case of DLT, trust among participating nodes is high. And it is even higher when the corporate initiates building their own internal blockchain or organizing a consortium. Also, the censorship resistance in this template is low since they can be centralized and/or private.

However, it is not so in the case of Blockchain.

In Blockchain ecosystem, the censorship resistance is too high with one vote per PC. But, with the progressive concentration of mining with upgraded hash power in the hands of fewer decision makers, the chances of trustability is low.

7. Security

To access data stored in a blockchain, users have to employ a key. If they lose the key, they would lose access to their account and funds. A real-life example of which is that the loss of access on nearly $145M of bitcoins and digital assets on the death of a cryptocurrency exchange CEO.

However, such situations are not majorly possible in the case of DLT, since the data is distributed, encrypted, and synchronized across multiple ports.

8. Real-life implementations

When talking about the comparison of blockchain and distributed ledger technology in terms of real-life implementations, blockchain leads the battle.

This is so because many entrepreneurs are slowly and gradually understanding the nature of Blockchain via some guide and use them into their traditional model for leveraging better advantages. In fact, various recognized brands like Amazon, IBM, Oracle, and Alibaba have started offering Blockchain As a Service (BaaS) solutions.

But, when it comes to Distributed Ledger technology, DLT enthusiasts and application developers have begun to explore the core of the technology. They are looking ahead to come across different use cases of DLT, but there are not many significant real-life implementations yet.

So this was all about what you should know when looking into Blockchain vs DLT. But, in case, you have some more queries or confused about which one to invest in, feel free to connect with our Blockchain consultants.

SharePoint vs OneDrive for Business

Posted on 11/10/202113/11/2021 by Anteelo Master

Most organisations today have numerous choices close by for saving their documents. Generally, you’d store your documents on an external pen drive, HDD, or other media. Obviously, the drawback to utilising these strategies is that you have to keep the storage device handy all the time to access your documents. This isn’t always possible, as they can without much of a stretch get lost. In addition, it turns out to be practically difficult to share these documents with your associates or companions, except if you plan on mailing those copies each and every time without fail.

These days, virtual storage arrangements permit us to store our records on shared servers and access them from a large number of gadgets, any time, anywhere. Two cloud-based stages that allow you to spare, share, and match up documents across devices are Microsoft’s SharePoint and OneDrive for Business. One of the most common questions that pops up in the various organisation is ”SharePoint vs OneDrive, which one is better?” Right now, assess the fundamental contrasts between these two to find the answer to this question and see which is better for your business.

Lets first begin with understanding each one of them:

What is SharePoint?

SharePoint is a web-based collaborative platform expected to improve the working procedure in large associations and is proposed for a better joint effort of clients, record work process, and automation of operations. You can make a SharePoint portal, including wiki destinations, web journals, and dashboards where you can distribute archives and remarks. The principle highlights of SharePoint are:

The capacity to make a straightforward organisation structure in the digital arrangement.
Improvement of correspondence among department and employees. You can make dashboards to examine ventures and undertakings.
Control of settling errands and control of assignments that are being taken a shot at.
Clients can team up and alter a record shared on the server at the same time, just as trade archives, news, joins and different assets.
Automation of business forms – you don’t have to play out a noteworthy rundown of activities physically, permitting your you team to spare time.
A SharePoint portal permits you to make a corporate site.

The interface of a SharePoint portal permits you to produce reports, inform teams about significant occasions, distribute news, tune business forms with structures, and make client profiles. SharePoint has extensive utility and can be incorporated with other Microsoft Office applications and messengers

What is OneDrive?

Microsoft OneDrive is a cloud stockpiling administration that permits clients to store records in the Microsoft Cloud. OneDrive was previously known as SkyDrive, FolderShare, Windows Live Office, and LiveMesh. You can access OneDrive with or without an internet browser by introducing the OneDrive application. There are two kinds of OneDrive – OneDrive and OneDrive for Business.

OneDrive for Business (previously known as SkyDrive Pro) is similar to OneDrive yet contains additional highlights planned for business purposes. An association manager oversees OneDrive for business for users in your team to collaborate on records and different documents. The overseer can confine sharing alternatives for users. Users should sign in by utilising a work account, not an individual record so as to utilise OneDrive for Business. Unique maintenance arrangements that are accessible for OneDrive for Business permit executives to recoup documents erased by users. Different users can alter a report at the same time, and record forming is likewise accessible.

OneDrive for Business can be utilised independently (Office 365 Business plan) and with SharePoint Online (Office 365 Business Premium Plan). An organisation’s SharePoint libraries can be synchronized to the nearby PC of a user by utilising OneDrive. OneDrive for Business and SharePoint Online can be incorporated with one another. You can consider OneDrive as backend stockpiling and SharePoint can be considered the frontend interface to all the more likely envision how the mix of these two stages is actualised. A connect to documents partook in OneDrive can be utilised as a connection for SharePoint Online.

SharePoint vs OneDrive for Business

The following table compares the features of SharePoint vs OneDrive:

OneDrive for Business	SharePoint Online
Available in business as well as consumer variant.	Available only for business use with no specific consumer variant.
Referred to as a storage location	Referred to as a team site
Can be perceived as a cloud version of my documents	Is more or less like a website
Al files are set to private permissions unless specified.	Uses default permissions as set by user
No shared interface	Users access through organisations domain
Appropriate for uploading private documents which are not intended to be shared	Suitable for public documents which need to be shared frequently.
OneDrive cannot include the additional security of a standalone server.	SharePoint can include the supplementary security of a standalone server.
Content In OneDrive can not be published to a webpage	Allows publishing of documents directly to the website
Evolved from SharePoint workspace 2010, which was previously called Groove 2007.	Cloud-based adaptation of the SharePoint service which belongs to the era of Office XP

Similarities between SharePoint and OneDrive for Business

Both of them are a part of Office 365 Business plans
Both are available as a stand-alone service.
The core architecture of both platforms is built on SharePoint.
Both of them manage files/data with versioning and metadata.
Both of them can be accessed by a browser as well as the local folder

CONCLUSION

So the reality, when looking at SharePoint versus OneDrive, is actually about how you intend to utilise it. Picking an answer relies upon the requirements of your organisation. In the event that you have to store records in the virtual storage, or offer documents and at times, alter records together with different users at the same time, you can utilise Microsoft OneDrive for Business. In the event that you have to make a corporate web-based interface for a coordinated effort of a high number of users in your organisation, consider utilising SharePoint Online, which is a discretionary segment of Microsoft Office 365. OneDrive for Business can be incorporated with SharePoint and can be utilised as storage for content transferred to a site dependent on the SharePoint stage. Both OneDrive and SharePoint Online work with the inalienable security of Microsoft items.

Effort Estimation in Agile

Posted on 07/10/202113/11/2021 by Anteelo Master

I believe that this quote can perfectly relate to the thought that I would like to express in this blog post about estimation in Agile. To some extent I feel that Agile nature also reflects the same behavior that you should be at-least at the edge of right instead of totally wrong, with respect to either decision making or estimation.

Here I would like to focus on Estimation, which is a very crucial part in every project. Agile has changed the concept of estimation. In traditional software development methodology, management used to estimate the project whereas in Agile, team together does the estimation of a project. Agile has introduced simple techniques for estimation – Planning poker and T-shirt sizing.

Planning poker technique has made estimations more simpler and less pressurized. Team generally uses
Fibonacci series as story points. Agile promotes standard Fibonacci series like 0 1/2 1 2 3 5 8 13 20… Fibonacci series looks like a progress bar growing from lower to higher range in terms of three factors:

Risk, Effort(not in terms of hours or days- it should be relative comparison of efforts with every user story) and Complexity.

While estimating user stories on the basis of these three factors, team makes sure to consider development, QA, Research and all other dependent tasks. All this estimation activity is done during the backlog refinement meeting.

From the bunch of user stories provided by the product owner (PO) for estimation, team first selects the easiest user story to start with and assign story points to it. Every person in the team show his/her story points for that user story. In case of differing opinions for story points between team members, they come up for discussion and present their view points for selecting that particular story point number. After discussing that user story from all the aspects team re-votes for that user story and keeps repeating the same process until all the team members converge on the same story point number, effectively agreeing on the scope and impact. And this way it becomes the benchmark for other user stories.

After analyzing the easiest user story team starts picking up the user stories from top to bottom
considering they were arranged priority wise by the Product Owner. And team finishes discussion on all those bunch of user stories given by PO. This way team ends up allocating the story points to all the user stories.

Velocity- After the team is done with estimation, during sprint planning the team decides how many story points they can pick during this sprint. Like wise team increases or decreases the story points in further sprints till they come up with the number of story points team is comfortable in picking up for the sprint. And from the experience, team keeps on improving their estimates. After two or three sprints average of story points can be considered as the velocity of the team which can be directly related to the performance of the team.

T-Shirt sizing – This is almost similar to planning poker but the only difference is instead of Fibonacci series team take sizes of t-shirts for estimation like XS, S, M, L,XL. Rest of the method of picking-up the sizes is same as in planning poker.

Team discussion on every user stories and PO presence in backlog refinement tremendously helps the team in understanding the requirements much better. Each individual’s input is important to make the estimation better and help in understanding the requirement correctly.

P.S- You can install the free “Scrum Poker” application from application stores for Android, iOS and windows smart phone which provides virtual story point cards for estimations.

List of creative agencies in Delhi

Posted on 27/09/202113/11/2021 by Anteelo Master

“Creative without strategy is called art, creative with strategy is called advertising.”

Prof. Jef I. Richards.

Building a creative user base has become like a powerhouse for the survival of a business. And to increase the face value of your website and become a magnet for your users and clients you need to hire a creative agency today and we have exactly what you’ll need, a list of top creative agencies of Gurgaon:

Iris Delhi

Starting with, we have Iris Delhi, a promising creative agency in Gurgaon. They provide their expertise across Brand Strategy, Advertising and Content as well. They have worked with renowned brands and agencies like Pizza Hut Delivery, Samsung, Adidas with a long list that follows. They help their clients outstand the others in the industry by hitching them to some of the most creatively set up interfaces.

Link: https://www.iris-worldwide.com/

Anteelo

Working with creative young minds Anteelo has never failed to stun their clients with ideas that stand out. They provide their clients with some of the most unique ideas to build up their business. Brownie points for their cost effectivity and their expertise must be added. They help their clients reach the correct audience within the best time frames.

Link: https://anteelo.com/

Lopamudra Creative Agency

Next, we have Lopamudra Creative agency, with amazing reviews from their customers and a promise of novel designs, originality and a maintenance of ethical standards, they have the best of bests to serve their users. Some of their most pleasing works include Virat FanBox and Sona commercials along with others.

Link: https://lopamudracreative.com/

Publicis India

Having worked with brands like Nescafe, Balaji wafers, ZeeTv along with others, Publicis, has formed a firm client base in the creative development industry. Their vast experience in the field adds up to the quality of the work they provide to their customers. They believe in giving digital transformation to their client’ work.

Link: http://www.publicis.in/

Blackgoat

Blackgoat, known for its creativity, novelty, and strategic visual solutions, have been working in the industry with great zeal and passion. Their goal is to create a compelling advertising campaign to make their content go viral. They are a firm believer of ‘WHAT LOOKS GOOD SELLS GOOD’.

Link: https://blackgoatcreative.com/

Design Answers.

Design Answers is a creative agency, helping businesses in achieving a stable market for themselves. They provide creative solutions that are extremely unique in their approach. Having worked with multiple renowned names: Wingreens, Sanfe, Huggies along with others, they are well versed in the field and promise their clients the best service.

Link: https://www.designanswers.in/en/

iBrandOx

iBrandOx is a full-time advertising company and a marketing agency. They promise their clients fine quality work or the clients may also avail the “money back” option. Their team carries out in-depth research which greatly helps them to deliver unique and reliable services at all times. They consider the clients pre-determined budget and excel on the lines directed.

Link: https://www.ibrandox.com/

Computer Vision : Everything You Need To Know About It

Posted on 16/06/202116/06/2021 by Anteelo Master

What is computer vision?

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:

Automatically understand what the objects in the image are and where they are located.
Categorize these objects and understand the relationships between them.
Understand the context of the scene.

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.

How does computer vision work?

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.

Different lighting

Basic 3D lighting techniques for 3D design projects

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.

Noise

What is the Solution to a Noisy Mixer Grinder? - MixerJuicer

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.

Unfamiliar angles

Free Vector | Stationery office thumbtack, realistic set of red glossy push pins for fixing on board remind

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.

Overlapping

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.

Different types of objects

Free Vector | Filament bulbs set. retro edison lamps, incandescent vintage lightbulbs of different shapes and forms with heated wire hanging

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.

Fake similarity

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!

Uses of computer vision

10 Examples of Computer Vision Applications | Wovenware Blog

Interpreting digital images and videos comes in handy in many fields. Let us look at some of the use cases:

Medical diagnosis. Image classification and pattern detection are widely used to develop software systems that assist doctors with the diagnosis of dangerous diseases such as lung cancer. A group of researchers has trained an AI system to analyze CT scans of oncology patients. The algorithm showed 95% accuracy, while humans – only 65%.
Factory management. It is important to detect defects in the manufacture with maximum accuracy, but this is challenging because it often requires monitoring on a micro-scale. For example, when you need to check the threading of hundreds of thousands of screws. A computer vision system uses real-time data from cameras and applies ML algorithms to analyze the data streams. This way it is easy to find low-quality items.
Retail. Amazon was the first company to open a store that runs without any cashiers or cashier machines. Amazon Go is fitted with hundreds of computer vision cameras. These devices track the items customers put in their shopping carts. Cameras are also able to track if the customer returns the product to the shelf and removes it from the virtual shopping cart. Customers are charged through the Amazon Go app, eliminating any necessity to stay in the line. Cameras also prevent shoplifting and prevent being out of product.
Security systems. Facial recognition is used in enterprises, schools, factories, and, basically, anywhere where security is important. Schools in the United States apply facial recognition technology to identify sex offenders and other criminals and reduce potential threats. Such software can also recognize weapons to prevent acts of violence in schools. Meanwhile, some airlines use face recognition for passenger identification and check-in, saving time and reducing the cost of checking tickets.
Animal conservation. Ecologists benefit from the use of computer vision to get data about the wildlife, including tracking the movements of rare species, their patterns of behavior, etc., without troubling the animals. CV increases the efficiency and accuracy of image review for scientific discoveries.
Self-driving vehicles. By using sensors and cameras, cars have learned to recognize bumpers, trees, poles, and parked vehicles around them. Computer vision enables them to freely move in the environment without human supervision.

Main problems in computer vision

Personal Computer Solves Complex Problems Tens of Times Faster Than Supercomputers?

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.

Scene understanding

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.

Privacy issues

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.

Summing up

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.

Machine Learning Career Paths: 8 Demanding Roles in 2021

Posted on 15/06/2021 by Anteelo Master

An Introduction to Machine Learning | DigitalOcean

In 2021, the focus on digitalization is as strong as ever before. Machine learning and AI help IT leaders and global enterprises to come out of the global pandemic with minimal loss. And the demand for professionals that know how to apply data science and ML techniques continues to grow.

In this post, you will find some career options that definitely will be in demand for decades to come. And there is a twist ― AI has stopped being an exclusively technical field. It is intertwined with law, philosophy, and social science, so we’ve included some professions from the humanities field as well.

Popular ML jobs to choose in 2021

What are the possible careers in machine learning? - Quora

Programmers and software engineers are some of the most desirable professionals of the last decade. AI and machine learning are no exception. We have conducted research to find out which professions are the most popular and what skills you need for each of them (based on data from Indeed.com and Glassdoor.com).

1. Machine learning software engineer

3 Fast Facts: What You Need to Know About Machine Learning as a Software Engineer | CodeIntelx

A machine learning software engineer is a programmer who is working in the field of artificial intelligence. Their task is to create algorithms that enable the machine to analyze input information and understand causal relationships between events. ML engineers also work on the improvement of such algorithms. To become an ML software engineer, you are required to have excellent logic, analytical thinking, and programming skills.

Employers usually expect ML software engineers to have a bachelor’s degree in computer science, engineering, mathematics, or a related field and at least 2 years of hands-on experience with the implementation of ML algorithms (can be obtained while learning). You need to be able to write code in one or more programming languages. You are expected to be familiar with relevant tools such as Flink, Spark, Sqoop, Flume, Kafka, or others.

2. Data scientist

Data Scientist Salary: Starting, Average, and Which States Pay Most

Data scientists apply machine learning algorithms and data analytics to work with big data. Quite often, they work with unstructured arrays of data that have to be cleaned and preprocessed. One of the main tasks of data scientists is to discover patterns in the data sets that can be used for predictive business intelligence. In order to successfully work as a data scientist, you need a strong mathematical background and the ability to concentrate on uncovering every small detail.

Bachelor’s degree in math, physics, statistics, or operations research is often required to work as a data scientist. You need to have strong Python and SQL skills and outstanding analytical skills. Data scientists often have to present their findings, so it is a plus if you have experience with data visualization tools (Google Charts, Tableau, Grafana, Chartist. js, FusionCharts) and excellent communication and PowerPoint skills.

3. AIOps engineer

Is AIOps the answer to DevOps teams' ops prayers?

AIOps (Artificial Intelligence for IT Operations) engineers help to develop and deploy machine learning algorithms that analyze IT data and boost the efficiency of IT operations. Middle and large-sized businesses dedicate a lot of human resources for real-time performance monitoring and anomaly detection. AI software engineering allows you to automate this process and optimize labor costs.

AIOps engineer is basically an operations role. Therefore, to be hired as an AIOps engineer, you need to have knowledge about areas like networking, cloud technologies, and security (and certifications are useful). Experience with using scripts for automation (Python, Go, shell scripts, etc) is quite necessary as well.

4. Cybersecurity analyst

A cybersecurity analyst identifies information security threats and risks of data leakages. They also implement measures to protect companies against information loss and ensure the safety and confidentiality of big data. It is important to protect this data from malicious use because AI systems are now ubiquitous.

Cybersecurity specialists often need to have a bachelor’s degree in a technical field and are expected to have general knowledge of security frameworks and areas like networking, operating systems, and software applications. Certifications like CEH, CASP+, GCED, or similar and experience in security-oriented competitions like CTFs and others are looked at favourably as well.

5. Cloud architect for ML

The majority of ML companies today prefer to save and process their data in the cloud because clouds are more reliable and scalable, This is especially important in machine learning, where machines have to deal with incredibly large amounts of data. Cloud architects are responsible for managing the cloud architecture in an organization. This profession is especially relevant as cloud technologies become more complex. Cloud computing architecture encompasses everything related to it, including ML software platforms, servers, storage, and networks.

Among useful skills for cloud architects are experience with architecting solutions in AWS and Azure and expertise with configuration management tools like Chef/Puppet/Ansible. You will need to be able to code in a language like Go and Python. Headhunters are also looking for expertise with monitoring tools like AppDynamics, Solarwinds, NewRelic, etc.

6. Computational linguist

IJCLNLP International Journal of Computational Linguistics and Natural Language Processing

Computational linguists take part in the creation of ML algorithms and programs used for developing online dictionaries, translating systems, virtual assistants, and robots. Computational linguists have a lot in common with machine learning engineers but they combine deep knowledge of linguistics with an understanding of how computer systems approach natural language processing.

Computational linguists frequently need to be able to write code in Python or other languages. They are also frequently required to show previous experience in the field of NLP, and employers expect them to provide valuable suggestions about new innovative approaches to NLP and product development.

7. Human-centered AI systems designer/researcher

Human-Centered Machine Learning. 7 steps to stay focused on the user… | by Jess Holbrook | Google Design | Medium

Human-centered artificial intelligence systems designers make sure that intelligent software is created with the end-user in mind. Human-centered AI must learn to collaborate with humans and continuously improve thanks to deep learning algorithms. This communication must be seamless and convenient for humans. A human-centered AI designer must possess not only technical knowledge but also understand cognitive science, computer science, psychology of communications, and UX/UI design.

Human-centered AI system designer is often a research-heavy position so candidates need to have or be in the process of obtaining a PhD degree in human-computer interaction, human-robot interaction, or a related field. They must provide a portfolio that features examples of research done in the field. They are often expected to have 1+ years of experience in AI or related fields.

8. Robotics engineer

An Overview of a Career as a Robotics Engineer |

A robotics engineer is someone that designs and builds robots and complex robotic systems. Robotics engineers must think about the mechanics of the future human assistant, envision how to assemble its electronic parts, and write software. Thus, to become a specialist in this field, you need to be well-versed in mechanics and electronics. Since robots frequently use artificial intelligence for things like dynamic interaction and obstacle avoidance, you will have plenty of opportunities to work with ML systems.

Employers usually require you to have a bachelor’s degree or higher in fields like computer science, engineering, robotics, and have experience with software development in programming language like C++ or Python. You also need to be familiar with hardware interfaces, including cameras, LiDAR, embedded controllers, and more.

Bonus: AI career is not only for techies

AI is NOT FOR THE TECHIES ALONE - Consulting Insight | Magazine for Consulting World | Management Consulting | Engineering Consulting

If you don’t have a technical background or want to transition to a completely new field, you can check out these emerging professions.

1. Data lawyer

Data lawyers are specialists that guarantee security and compliance with GDPR requirements to avoid millions of dollars in fines. They know how to properly protect data and also how to buy and sell this data in a way that avoids any legal complications. They also know how to manage risks arising from the processing and storing of data. Data lawyer is the professional of the future; they stand at the intersection of technology, ethics, and law.

2. AI ethicist

An AI ethicist is someone who conducts ethical audits of AI systems of companies and proposes a comprehensive strategy for improving non-technical aspects of AI. Their goal is to eliminate reputational, financial, and legal risks that AI adoption might pose to the organization. They also make sure that companies bear responsibility for their intelligent software.

3. Conversation designer

A conversation designer is someone who designs the user experience of a virtual assistant. This person is an efficient UX/UI copywriter and specialist in communication because it is up to them to translate the brand’s business requirements into a dialogue.

How much does an ML specialist make?

Machine Learning Engineer Salary | How Much Does an ML Engineer Earn? | Edureka

According to Indeed.com, salaries of ML specialists vary depending on their geographical location, role, and years of experience. However, on average an ML specialist in the USA makes around $150,00 per year. Top companies like eBay, Wish, Twitter, and AirBnB are ready to pay their developers from $200,000 to $335,000 per year.

At the time of writing, the highest paying cities in the USA are San Francisco with an average of $199,465 per year, Cupertino with $190,731, Austin with $171,757, and New York with $167,449.

Industries that require ML/AI experts

Today machine learning is used almost in every industry. However, there are industries that post more ML jobs than others:

Transportation. Self-driving vehicles starting from drones and ending up with fully autonomous vehicles rely very heavily on ML. Gartner expects that by 2025, autonomous vehicles will surround us everywhere and perform transportation operations with higher accuracy and efficiency than humans.
Healthcare. In diagnostics and drug discovery, machine learning systems allow to process huge amounts of data and detect patterns that would have been missed otherwise.
Finance. ML allows banks to enhance the security of their operations. When something goes wrong, AI-powered systems are able to identify anomalies in real-time and alert staff about potentially fraudulent transactions.
Manufacturing. In factories, AI-based machines help to automate quality control, packing, and other processes, while allowing human employees to engage in more meaningful work.
Marketing. Targeted marketing campaigns that involve a lot of customization to the needs of a particular client are reported to be much more effective across different spheres.

AI Ethics in 2021: Ethical Dilemmas which needs to be answered

Posted on 12/06/202117/06/2021 by Anteelo Master

What Are The Ethical Problems in Artificial Intelligence? - GeeksforGeeks

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.

What happens if AI replaces humans in the workplace?

Why AI Is Not a Threat to Human Jobs - Insurance Thought Leadership

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?

Who’s responsible for AI’s mistakes?

Who's to blame when artificial intelligence systems go wrong?

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?

How to distribute new wealth?

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.

How to prevent artificial intelligence errors?

Use of Artificial Intelligence to reduce Medical Errors – Carna

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?

What to do about the unintended consequences of AI?

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.

How to protect AI from hackers?

How to prevent adversarial attacks on AI systems | InfoWorld

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?

How to use artificial intelligence humanely?

AI is going to hook us – commercially and humanely - Reputation Today

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?

Conclusion

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.

Women Who Created History in the Field of Programming

Posted on 11/06/2021 by Anteelo Master

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!

Ada Lovelace

Women Who Created History in the Field of Programming

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 Lamarr

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

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

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

How to Choose the Right Software Development Company?

Posted on 11/06/2021 by Anteelo Master

The choice of the right software development company is tough. Will the vendor you choose just bash out some code and disappear in the steppes of Asia, or will they become your greatest ally in an uncertain market? How to make sure the reality is closer to the latter option than former?

The main goal is to search for a partner, not a vendor. Outsourcing is not about getting the cheapest quote on your order, but about building a useful relationship that will serve you in the future.

How to find the right partner then? Here are 4 tips for making the fateful choice:

Define your needs and goals

How to Choose the Right Software Development Company?

Proper initiation can make or break a project.

Once you have a breakthrough idea, it is easy to zero in on a few features, technologies, solutions. If everybody else is doing a simple blockchain in Java, or, god forbid, Python or Ruby, you might think you need one as well.

Furthermore, if you get unlucky with your software development team, they will just nod their heads in unison and agree.

We believe it is important to look at the big picture and question the job-to-be-done of your solution. If you communicate your high-level needs to partners, you enable them to research and find individual solutions in their fields of expertise that fit your needs better than “the next best thing”.

Once you have agreed with your future software team, make sure to work with them to draft comprehensive requirements that will help you both to understand each other better and communicate the work that should be done.

Choose quality over price

The cheapest option in the market usually isn’t the best choice. If you buy cheaply, you pay dearly. In software, these costs usually come as bugs, crashes, and a mismatch between your (and your customer’s) needs and the solution.

Of course, you need to choose the quality level that is suitable for your project, but every software development company you hire should use proper processes, have a dedicated QA team, and, preferably, use DevOps and guarantee maintenance. Modern business environment and users demand that things don’t break, and if they do, they should be fixed instantly.

If your project concerns handling large amounts of money (fintech, cryptocurrencies) or health of others (biotech), it is worthy to look into additional quality assurance for your software – look for software companies that use functional programming (benefits: reliable, more secure and easier maintainable code) and formal verification.

Keep up to date with technology

The winds of the future are uncertain, and the technologies change with every breeze. Still, it is important to sense where the tech is going and choose the correct solutions ahead of the market. Otherwise, you risk being outdated in the fast-moving, cutthroat markets of today.

For example, Rust has been voted as the most loved programming language between developers in 2016 and 2019. If you want to get the best new talent in the market, having your project in Rust is a wonderful way to attract them. (In addition to other benefits.)

If you choose the correct development partner, they should be able to communicate their programming language and solution choices to you, explain the reasons underneath and make sure the choices match your individual needs.

Choose a reliable developer

Choose a partner that can support your long-term growth and help you with infrastructure and maintenance of the code.

Once your project takes hold, it is most likely that it will need to scale. For this reason, you need to select a software development company that can provide for a diverse set of needs, prevent any bottlenecks, and, if necessary, extend their operations to offer personal service just for you.

Furthermore, software projects are always in a state of continuous improvement. Pick the company that won’t disappear after finishing the project because your software will need maintenance, improvements to accustom your shifting needs and those of the market.

Discover the technology trends driving the next wave of digital transformation

Posted on 11/06/2021 by Anteelo Master

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.

Provider, partner, promoter, peer

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.

5 steps to digital business leadership

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:

Build awareness – Scout the emerging technology scenes of Silicon Valley or China for trends and insights into the future.
Be more open – Participate in open initiatives and share with partner organizations or the wider marketplace.
Get access to R&D – Establish and maintain links to leading universities/academics or government agencies in relevant areas.
Build partnerships and alliances – Pick the right partners to help on the journey, as most organizations can’t make the changes necessary alone.
Push digital culture – Energize and engage employees and executives through immersive digital experiences such as hackathons, incubators and accelerators. Focus on multidisciplinary teams, experimentation and learning, and business outcomes.

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.

A simple definition to Blockchain technology

Advantages of Blockchain:

A Brief Introduction to Distributed Ledger Technology (DLT)

Advantages of DLT:-

Relation between Blockchain and DLT

1. Holochain

2. Hashgraph

3. Direct Acyclic Graph (DAG)

Factors to Consider While Comparing Blockchain and DLT

1. Consensus model

2. Block structure

3. Tokens

4. Sequence

5. Efficiency

6. Trustability

7. Security

8. Real-life implementations

What is SharePoint?

What is OneDrive?

SharePoint vs OneDrive for Business

Similarities between SharePoint and OneDrive for Business

CONCLUSION

Provider, partner, promoter, peer

5 steps to digital business leadership

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