Category: Software

Top 9 Non-technical Tech Startup CEOs

Post author By Magdalena Brych
Post date 30/10/2018
No Comments on Top 9 Non-technical Tech Startup CEOs

What does it take to create a successful App? Technical skills? Well, not really. In fact, many successful founders and startup CEOs have proven otherwise. The founders and CEOs featured in this article have no technical skills and have gone out of their way to create successful and prize-winning Apps.

Creating apps without any technical knowledge is like cooking without knowing much about ingredients, it’s helpful but not necessary. Interestingly in this case, these top nine founders and CEOs with non-technical skills have turned the market to their advantage.

With modern technology, all you need is an idea – a great idea.

Arum Kang, Dawoon Kang and Soo Kang

Founders of Coffee Meets Bagel

Coffee meats bagel. Top 9 Non-technical Founders of Great Tech-companies

Famously known as the 3 Kang sisters, Arum, Dawoon and Soo are originally from South Korea. Both Arum and Dawoon aspired to study in the top-ranked MBA schools in the USA, Harvard and Stanford. Dawoon first made it to Stanford and Arum soon started studying at Harvard Business School.

After graduating from business school, the 3 Kang sisters noticed the monotony in the dating apps arena and decided to break it. They thought of an app that limits the number of profiles users can interact with each day and offers unique icebreaker information for the matches.

And so, Coffee Meets Bagel was born. In April 2012, it was launched in New York City, May in Boston and October in San Francisco. Six years down the line the company has a whopping net worth of about $150 million (Estimated Value).

Walker Williams

Founder and CEO of Teespring

Teespring. Top 9 Non-technical Founders of Great Tech-companies

At an early stage in his life, Walker had a number of paths that he desired to pursue, including a cartoonist and a writer. However, at the age of 16, he settled and set a course on becoming an entrepreneur. Walker later joined the Brown University and obtained Bachelor’s degree in Arts and History, 2007-2011.

In 2011, he joined forces with Evan and together, they created a platform for custom merchandise, Teespring. Teespring enables users to create unique custom designs, set prices for their item(s) and set a sales goal.

By 2016, Teespring was valued at over 30 million and its CEO Walker was listed in the 40 Under 40 2016 for the social commerce company.

AJ Forsythe

CEO of iCracked Inc.

iCracked. Top 9 Non-technical Founders of Great Tech-companies

AJ Forsythe has an exemplary track record in the field of business and entrepreneurship. His business endeavors have ranged from running a winery, beekeeping and founding iCracked Inc.

AJ graduated from California Polytechnic State University with a Bachelor of Science in Psychology/Biology in 2011. AJ founded iCracked in his dorm room in 2010. iCracked has grown since then to be the world’s largest and most efficient on demand repair service for smartphones and tablets with over 4000 Certified iTechs and in 11 countries.

AJ made it to be one of Forbes 30 under 30. At the moment, iCracked has expanded to the UK and Europe with offices in London and Berlin.

Stephanie Tilenius

Founder and CEO of Vida Health Inc.

Vida Helath Enterpises Inc. Top 9 Non-technical Founders of Great Tech-companies

Stephanie studied at Harvard University then joined Brandeis University to do a BA in Economics and finally an MA in International Finance. She worked at Paypal and EBay for 9 years before joining Google and helping build and launch several products.

Stephanie later founded Vida Health in 2012. Vida provides expert, personalized and on-demand health coaching together with programs from a network of experienced health care providers and leading medical institutions.

Approximately 133 million people in the U.S. alone live with chronic conditions and 70% of the $3T healthcare spend in the U.S ends up in preventable chronic conditions that Vida is now focusing on.

Sean Rad

Founder and Chairman of Tinder

Charmain of Tinder. Top 9 Non-technical Founders of Great Tech-companies

Sean Rad was brought up in the Persian community of Beverly Hills by his parents who are Iranian immigrants. Rad attended the University of Southern California in 2004. Two years later, he dropped out to pursue entrepreneurial endeavors.

Rad launched Tinder alongside other co-founders in 2012. Tinder is a dating app and it was an overnight success. Two months down the line after its launch, Tinder reached over a million matches.
On August 6, 2018, the Match Group announced Tinder had over 3.7 million paid subscribers, which is up by 81 percent over the same quarter in 2017. The company is now valued at around $3 billion and is one of the highest-grossing apps in App Store.

Jamie Wong

Founder and CEO of Vayable

Vayable. Top 9 Non-technical Founders of Great Tech-companies

Jamie’s background is in disruptive media, advocacy and communication. She attended the Universitat de Barcelona to study Art, History, Politics and later did her BA in History at Wesleyan University and finally went to Columbia University’s Graduate School of Journalism.

Jamie Wong co-founded Vayable in 2010. Her vision for a more open world that enables collaborative experiences and exchanges through travel is the driving force behind Vayable. While using the platform, you can discover, buy and sell unique travel experiences.
Vayable is currently in over 240 cities and has featured in the New York Times, CNN and The Wall Street Journal.

Tracy Young

Founder and CEO of PlanGrid

Plan Grid. Top 9 Non-technical Founders of Great Tech-companies

Tracy attended California state University and majored in Civil Engineering. In 2008, she graduated and became a Construction Project Engineer.

She helped build two hospitals. In the process, she realized that many things went wrong not because they were poor builders but because there was no technology that could enabled them to do better. This inspired the creation of PlanGrid in 2012.

PlanGrid is a field collaboration software for construction. It has grown to be the lead construction productivity software completing over 1 million projects around the world.

Evan Sharp

Co-founder and Chief Creative Officer Pinterest

pinterest. Top 9 Non-technical Founders of Great Tech-companies

Sharp studied Architecture at Columbia Graduate School of Architecture, Planning and Preservation. Prior to that, he received a Bachelor’s degree in History from the University of Chicago. Evan found inspiration from pinning interesting maps, science facts, architecture, vacation plans, and fonts for his design projects.

Sharp met Ben Silbermann, Pinterest CEO and fellow co-founder, through a mutual friend while in Columbia University’s architecture program. Pinterest was launched in 2010. An online platform for saving, searching, bookmarking creative ideas uploaded by the people from around the world.

As of 17th October 2018, Pinterest has over 250 million monthly active users with a total number of over 175 billion Pinterest Pins.

Conclusion

In the century when technology evolves every day being a tech-savvy is not crucial to make the next big app in the market. The nine founders and C.E.Os are a case in point that tech-skills are not necessary in order to thrive in tech-world!

5 Easy steps to run your C code from Angular app

Post author By Sebastian Superczynski
Post date 16/10/2018
No Comments on 5 Easy steps to run your C code from Angular app

As a front-end developer you’ve probably been hearing about WebAssembly for a long time. Below I will show you how in 5 simple steps you can start using the code written in C, calling it directly from the Angular component. I won’t write down what WebAssembly is but I will show you how to use it right away:

1. Download the Emscripten library from Github and install it

2. In the app.tsconfig.json change the module from es2015 to esnext.

3. Write your code in C

4. Compile it to .js and .wasm

5. And voila, you can already call the C code from Angular

The results are amazing. It’s two times faster than the vanilla JS Fibonacci method!

You probably don’t need WASM when you develop a simple login form, or another table on the page. But, once you have developed your own map implementation, or when you fall in love with CityBound, you will see that WASM is the right solution for you.

Software Technology

Automatic Jira Task Estimation based on the Azure Machine Learning Predictive Model

Post author By Sebastian Superczynski
Post date 27/09/2018
No Comments on Automatic Jira Task Estimation based on the Azure Machine Learning Predictive Model

Machine Learning has been increasingly entering every area of our lives over recent years. From the recognition of photos and voices to, intelligent hints and suggestions online and even in our cars. According to a study by mckinsey.com, by 2030, 30% of the work that people currently do will be taken over by machines. In software companies it will be even more so. Worse-case scenario – you’re afraid that they’ll make you redundant from your favorite job, and they’ll replace you with machines. Don’t think that. The machine will rather take over the basic work leaving you to focus on more innovative things. And this is how AI can support our daily administrative tasks…Let’s take a closer look to the Jira task estimation based on the Azure Machine Learning predictive model.

AI powered planning with Jira

We spend 2 hours twice a month estimating the backlog tasks. For a team of 5 people that is 10 hours, and on a monthly basis 20 hours of planning. It’s quite a lot, given that the programmer, hopefully, earns on average 30 USD per hour. Annually it means 8 thousands USD spent on planning alone. In this case, 4 additional people from different departments take part in the planning as they are responsible for the whole process of delivering the software to the client.

On the one hand, without planning, the project would certainly not look as it should. And inevitably, the level of complexity and structure would decline over time. On the other…few programmers like the so-called “Meeting Day”. It’s the day when you think of all the things you could be doing, but can’t because you’re at a meeting.

Jira is a well-structured tool where many processes can be simplified, but maybe Jira could give something more just by itself. You have backlog of 200 tasks. You read the content of the task. After 5 seconds you know, more or less, how much time it will take you to complete each task (as long as the content is written legibly, concisely, and illustratively). Clear situation – the task is priced, you move on to the next task. This is another task in a row that you priced to 3 Story Points. You have already done 20 similar tasks.

Azure Machine Learning Planning – Configuration Model

The first step to include Azure Machine Learning to our planning process is to export current tasks from Jira to CSV so that they can be analyzed. Unfortunately, Jira exports the CSV file in a way that is not compatible with what we expect. The file is very dirty (spaces, tabs, enters, html code). Azure ML cannot import it properly either. Additionally, the team valued the tasks according to the Fibonacci sequence on a scale of 0,1,2,3,5,8,13. For our calculations it is too big a gap – we will simplify it to the form of EASY(1), MEDIUM(3), DIFFICULT(5).
We export the data to a html file and then parse it using NodeJS to a format that we can accept.
https://gist.github.com/ssuperczynski/b08d87843674eb4be64cb0fe7f658456
After importing a new CSV file to Azure ML we get the following task distribution.

Since our JS script only helped us to pre-prepare the file, we now have to further prepare it for more analysis.
Steps we have already taken:

merge title with description,
drop html tags (this step will be explained later),
replace numbers,
remove special chars.

Steps we need to take now:

remove duplicate chars,
convert to lower case,
stem the words: driver, drive, drove, driven, drives, driving becoming drive,
remove stopwords (that, did, and, should …).

Verification process

Further steps that I have not done, but they can significantly influence the final result. In order to verify the truthfulness of the estimate it is necessary to attach it one by one and verify the final effect.

Do not cut out html tags (the project has tasks for frontenders, where html tags sometimes have a key meaning in the estimation of the task).
Some of the tasks have French words – it is questionable whether they are significant, here we should make changes at the level of project management in Jira.
Separation of frontend and backend tasks. Currently, despite the fact that each task has its own EPIC, we do not attach it, so the front and back tasks are combined into one backlog.
The number of tasks is too small – the more tasks the better model

The current Azure ML scheme is as follows

After making all the necessary modifications, the sentence that initially looked like this:
Create table to log user last logins. This omit issue with locks on user table
Is changed to:
creat tabl log user login omit issu lock user tabl
Below is a graph of word statistics for tasks larger than 5SP.

The next step is to analyze the words from two angles.
Unigram – a method that counts all single occurrences of words. In our case, the method may prove ineffective, because the content of “Change font under application form”. – having 1 SP and “add two new child forms under application form” has 5 SP points to the word “application form” which once has 1SP and another 5SP.
Bigram, Trigram, N-gram – based on N word statistics, where N is 2, 3 and so on.
I chose the N-gram method, which turned out to be much more effective.
In N-gram analysis we stop comparing strings, and switch to on hash – this comparison works faster, and because our database will continue to grow with time, comparisons will be faster.

Once the N-grams analysis is created, we can create and train our model, taking 70% of the data as the data to train, and 30% as test data.

The last step is to give our scheme the ability to introduce content into the analysis, and to show the level of difficulty as simulated by the model.

The tests used were those of my colleagues from the project, who themselves gave me the content of the tasks for analysis.
Here are the results:

oAuth Refresh token doesn’t work – Azure ML – easy – friends – easy
Add BBcode feature to the form – Azure ML – easy – friends easy
Fix the styles for upload button – Azure ML – easy – friends – easy
Message form refactor – Azure ML – difficult – friends – difficult
Random string method has many implementations – unify them into one – Azure ML – easy – friends – easy

Summing up

As you can see in the above 5 examples, the accuracy of our program was 100%, despite the fact that there are places where we can improve our model. Based on our tests overall accuracy was around 80%.

At the moment it can be used during planning – for tests, but in the near future to efficiently inform the customer how much the task will cost and whether it should be divided into pieces – and all this before planning.

The next step is to build a Jira plugin and include it right next to the task description.

Decentralized AI: Blockchain's bright future

Post author By Oleg Brytskyi
Post date 26/09/2018
No Comments on Decentralized AI: Blockchain's bright future

Blockchain and artificial intelligence are driving technological innovation worldwide and both have profound implications for the future of business as well as our personal data. How can the two technologies merge? I’ll discuss the opportunities which could arise from decentralized AI.

Before we look at the possible merging of blockchain and AI into decentralized AI, let’s look at the two separately. Let’s look at the benefits of Artificial Intelligence and blockchain.

Artificial intelligence (AI) is a field in computer science dedicated to creating intelligent machines. Also known as machine learning, AI gives machines skills traditionally reserved to humans. Problem solving, speech recognition, planning, and learning are among them.

Meanwhile, blockchain is a decentralized technology which is a global network of computers. A robust platform allows blocks of similar information to be stored over the network.
PwC predicts that by 2030 AI will add up to $15.7 trillion to the world economy, and as a result, global GDP will rise by 14%. According to Gartner’s prediction, business value added by blockchain technology will increase to $3.1 trillion by the same year. Currently, the cryptocurrency sector makes the most use of blockchain tech. So, is the integration of blockchain and AI possible? Can both merge into one and enter other sectors? Actually, that’s already happening and some businesses are beginning to see the potential of integrating blockchain and AI.

Advantages of blockchain technology

Here are some of the advantages of blockchain technology:

Blockchain is decentralized. It allows data to be shared without a central unit. This keeps transactions on a blockchain verifiable and processable independent of a central force.
Blockchain is durable and consistent due to its decentralized nature. It can resist malicious attacks on its systems because it does not have a central point vulnerable to attack.
Information, timelines, and authenticity supplied by blockchain technology are all accurate.

Benefits of Artificial Intelligence (AI)

AI, or machine intelligence, has a lower error rate compared to humans when coding. As a result, AI offers a greater level of accuracy, speed and precision.

AI is totally logical as it has no emotions and thus makes error-free rational decisions.
Machines don’t get tired and can thrive in hazardous conditions. This enables them to carry out dangerous tasks, such as space exploration, or even mining.
Trusting AI with data analysis is the best decision any company can make. AI can easily calculate unstructured data, and give results in real-time, ensuring accuracy in data analytics.

Previous collaboration between blockchain and AI

There’s been notable integration between AI and blockchain. Some examples of this include the Singularity.Net blockchain and AI program, which was created to enhance smart contract testing. Supply chain firm, Nahame has also incorporated blockchain technology and AI to help companies with auditing. There are some plans by a peer-to-peer car rental company, which have been made public, to produce a fleet of self driving cars on blockchain technology.

Decentralized AI – where AI and blockchain could intersect

The best way to use the two of the biggest technologies out there today is by looking to capitalize on one’s strength to aid the other.

Data protection

Artificial intelligence largely depends on our data and uses it to improve itself through machine learning. What’s particularly relevant to AI is the gathering of data about human interactions and other details. Blockchain is a technology that allows encryption of data storage on a decentralized system, and it runs a totally secured and protected database only authorized users can access. So when we integrate blockchain and AI, it means we have a protected decentralized AI system for sensitive data such as financial or even medical data. Therefore, blockchain technology is a great security advantage.

Let’s take a look at Spotify – it uses users’ data to recommend music based on their recent searches and preferences. Most of the time we aren’t concerned about the information as it isn’t particularly sensitive. However, when it comes to our sensitive information stored in the cloud of a company, we would be more concerned about privacy and the guarantee of that privacy.

Ensuring security

As a centralized system running on a single processor, hackers or malware can infiltrate an AI system and alter its instructions. With blockchain though, before any information is accepted and processed on a blockchain platform, it must go through several nodes or phases of the network on the system. It becomes more difficult to hack any blockchain-based technology when it has more nodes on its network. Although not impossible, it would be far more difficult to hack a blockchain-based, decentralized AI platform.

Trustworthiness

There is greater trust in the system. In order to have credibility, a system must be trustworthy. Blockchain is a more transparent technology than a closed AI system. Blockchains protect data through encryption — only authorized users can access it. This makes it impossible for unauthorized parties to view anything.

In the case of blockchain application in the healthcare sector, patients don’t want their medical information to be accessible to any unauthorized viewers. Medical information remains encrypted to prevent unauthorized third parties from accessing it. Keeping medical information on a blockchain would also allow healthcare providers to easily access patients’ files so they can provide medical aid in case of an emergency. Adding increased performance AI will bring storage to the blockchain by making it easier to access unstructured data.

Benefits of Artificial Intelligence & blockchain in the long run

There are many benefits businesses can gain from integrating blockchain with AI. Porsche automobile in partnership with XAIN AG is already working on decentralized AI applications in its advanced vehicles. JD.com, a leader in developing AI-based applications, has already started using this integration to build decentralized business applications. So it’s worth considering blockchain and AI as integrated technology. It’s not a problem if you already use blockchain or just AI in your business. You can integrate either technology through your existing website API.
Here are some benefits of Artificial Intelligence merging with blockchain:

Decentralized Intelligence

This is an obvious result of the technology integration. Blockchain is a decentralized system while AI is an intelligent system. It would enable business organizations to set up a blockchain-based architecture that allows a combination of AI design. This could be a peer-to-peer connection that has an image recognition feature or language processing.

Energy saving and cost efficient IT architecture

A 2016 report from Deloitte estimated that the annual cost of authenticating transactions on a blockchain is $600 million, most of which goes into mining operations. An AI-integrated blockchain will help organizations reduce their energy consumption. Since AI can predict and speedily calculate data, it would also make it possible for cryptocurrency miners to know when they are performing a less important transaction. This would also allow enterprises to execute transactions faster.

In fact, as AI becomes more developed, and after the integration of AI and blockchain technology becomes more common, AI may take over the mining process on blockchains. Given the fact that AI learns and adapts to its environment, combined with blockchain, there’s no doubt that it will learn the process and the architecture of the blockchain network.

Flexible AI

AI integration with blockchain will pave the way for the development of an artificial general intelligence (AGI) platform. The blockchain model can create a distributed specimen for the development of an AGI.

The integration of blockchain and AI has yet to take off fully. Combining the two technologies into decentralized AI has deep potential to use data in novel ways. A successful integration of both technologies will allow quicker and smoother data management, verification of transactions, identification of illegitimate documents, etc. Therefore, if you’re contemplating the integration of both technologies for your business, don’t hesitate, do it!