5/5 (1)

Robotic Process Automation (RPA) is already a very famous niche on the information technology sector. The market well understands the need for a technology that automizes the multitude of repetitive tasks that each enterprise faces. Performing simple but time-consuming rule-based tasks, such as processing invoices, modifying documents or sending e-mails is much more efficient with an RPA algorithm.
Despite its attractive scope, RPA is still perceived merely as an emerging technology. Tailor-made robots that automize company-specific tasks are not yet a very cost-effective investment and it also requires highly skilled IT specialists to program the robot. However, Ui.Path, a rapidly growing start-up, offers a more easy-to-use and user-friendly platform that allows even non-IT specialists to design RPA algorithms to automize their tasks. The company provides not very long trainings to use the platform, which is a much more effective investment on employees compared to contracting programmers to write the algorithms.
The company is interesting from both an information technology and a financial perspective. It was founded by Daniel Dines who quickly became the richest Romanian. With its first large-scale market launch in 2015, the start-up grew immensely within these years, being currently evaluated at approximately 10 billion dollars (65% compounded annual growth rate). Ui.Path is the only RPA provider named to Forbes AI 50 in 2020, and it is planning to launch an IPO in the nearby future which will be a very interesting event for the financial market. Its effectiveness is very notable for the financial sector companies. Employees on this industry face a lot of easy and repetitive tasks, and with the digitalization trends they can be easily incentivized to learn to use this RPA platform in their daily tasks. Major giant companies have contracted Ui.Path, such as Citi Bank, PwC, EY, Deloitte, Orange, etc.
Subsequently, Ui.Path is a very interesting start-up on the RPA sector, fully deserving very careful monitoring in the next few years. Perhaps it’s the next tech giant, considering its current growth?

References
Ui.Path 2020, About Us, Ui.Path, https://www.uipath.com/company/about-us
Ohnsman, A., Kenrick, C. 2020, ‘AI 50: America’s Most Promising Artificial Intelligence Companies’, Forbes, 3 July, https://www.forbes.com/sites/alanohnsman/2020/07/03/ai-50-americas-most-promising-artificial-intelligence-companies/#63badb185c99
Ui.Path 2020, Customer Success Stories, Ui.Path, https://www.uipath.com/solutions/customer-success-stories

5/5 (1)

It is without a doubt that digitalization and the large amount of information we are exposed to has a great impact on our society. With the emergence of the digital age there is a shift from industrialisation to information age.  Our industrial society which is primarily dependent on tangible resources, is shifting towards one in which data and information are essential.  This has many implications on businesses and society as a whole in order to make the most use of it. 

As more and more information becomes available. It’s becoming increasingly important for companies and society to distinguish between use and non-useful information. Specially, as information is easily collected people are now more exposed to information than ever before, it becomes difficult to distinguish true information from fake information.  This leads to various dilemma’s. Take for instance social media, on social media platforms as Facebook, Twitter and Instagram users are allowed to express themselves, write, comment and share large amounts of information.  However, with this user created content it’s very easy to spread misinformation. How should these tech-giants deal with the spread of fake news? To what extent should companies be held accountable for the behavior of their users and  the spread of fake information?
The dilemma entails that although every user has the freedom of speech, however the spread of fake news can have severe consequences  in real life. Take for instance the American election in 2016. This showed the impact of social media information and that it can be used to manipulate people’s psychology and behavior in real life.

On the other hand, social media also requires accountability, as the world becomes more saturated with cameras and live streams that are shared on social media platforms. Your every move and action can be recorded by someone and published online. This can have good implications, if you think about catching store thieves or exposing police violence. Nevertheless, digital surveillance can also  have severe privacy concerns. 

The digital age and the intensity of surveilling human behavior calls for accountability. But who should be held accountable? Governments? Corporations? Society itself? What do you think?

5/5 (2)

After reading the blog ‘Can the Cloud save the environment?’, of fellow BIM student Joram van Rijn, I remembered an interesting news item which I saw a few months ago about the development of data centres in the Netherlands that run on renewable energy. Hence, I made a comment about this and shared my thoughts.

By chance, yesterday evening, the item about the development of giant data centres in the Netherlands using renewable energy was on the news again. In the Wieringermeer, a municipality and polder in the province of North Holland, data centres from Google and Microsoft have been built which fully run on renewable wind energy from the Prinses Ariane Windpark located nearby. For Google and Microsoft, locating data centres in the Netherlands is attractive as they can use renewable energy for making the company ‘greener’. In addition, using the Netherlands as their headquarters provides certain tax advantages.

At first glance, this seems like a very positive development. After all, using renewable energy for the powering of data centres is very sustainable and the fact that these large companies are located here could provide certain economic advantages. However, this does not seem to be the case:

While the development of the Prinses Ariane Windpark was originally meant for empowering 360.000 Dutch households, the data centres of Google and Microsoft currently use 100% of all of the wind energy generated. As a result, locals have been complaining about the fact that these companies use all of ‘their’ promised renewable energy and they only experience nuisance from the park without benefiting. The fact that these households are not able to use Dutch ‘green’ energy and an American company is, does not seem like a positive development to me. Accordingly, the question could be raised what the actual benefit is for the Netherlands if an American company builds a data centre here which uses our renewable energy sources?

One could argue that these data centres provide economic benefits for the government and the involved municipalities, through for example taxes, employment and residual heat. But apparently, Google has directly sold the acquired data centre in order to lease it back from another company via Luxemburg in order to pay as less tax in the Netherlands as possible. In addition, the Google data centre only provides 125 jobs and the temperature of the residual heat is way too low (between 25 and 35 degrees) in order to still use. Finally, the development of the Prinses Ariane Windpark has been heavily subsidized by the Dutch government; in total Vattenfall (the developer) will receive 660 million euros over 15 years. In other words, the Dutch tax payer is paying for the development of a windpark, of which the energy generated is only used by American companies whose presence here does not provide substantial economic benefits for the country as a whole.

In 2019, data centres in the whole of the Netherlands already presented around 3% (4 TWh) of the total electricity consumption (125 TWh). It is expected that by 2030, the data centres of Google and Microsoft in the Wieringermeer will use 8 times more electricity than currently, or around 3.5 TWh yearly. This could possibly provide issues for the Netherlands in concern to achieving their climate goals, as more renewable energy is needed.

Evidently, the development of giant data centres from foreign companies in the Netherlands has significant negative effects without providing significant economic benefits. Moreover, it is expected that under the current circumstances more new data centres will follow. In my opinion, the Dutch government should act as quickly as possible by changing existing regulations in concern to renewable energy use by foreign companies. Providing our own households and companies with ‘green’ energy should be a priority and without providing significant economic benefits for the Netherlands, foreign companies are not welcome.

To conclude, if our small country is to become the hard drive of the world, we certainly need to make sure that we profit from that.

Hollands Kroon. (2020). Zondag met Lubach slaat de plank mis over Prinses Ariane Windpark. Retrieved from: https://www.hollandskroonactueel.nl/2020/10/12/zondag-met-lubach-slaat-de-plank-mis-over-prinses-ariane-windpark/ (Accessed 12 October 2020).

NRC. (2020). Gebroken beloftes: hoe de Wieringermeerpolder dichtslibde met windturbines en datacentra. Retrieved from:https://www.nrc.nl/nieuws/2020/06/05/gebroken-beloftes-hoe-de-wieringermeerpolder-dichtslibde-met-windturbines-en-datacentra-a4001882 (Accessed 12 October 2020).

NRC. (2019). Datacenters verbruiken drie keer zoveel stroom als de NS. Retrieved from:
https://www.nrc.nl/nieuws/2019/05/14/datacenters-verbruiken-drie-keer-zoveel-stroom-als-de-ns-a3960091 (Accessed 12 October 2020).

Zondag met Lubach. (2020). Nederland als harde schijf. Retrieved from: https://www.youtube.com/watch?v=OiPoR9OvD0Y&feature=emb_title (Accessed 12 October 2020).

5/5 (1)

Sustainability, a buzz word you hear very often. Followed with, “you have to make an impact”, “I want you to act as if the house is on fire”, and not to forget about RSM’s “Be a Force for Positive Change”. You listen to it, but do you actually do something about it in your day-to-day life? I think we all know the answer. On a daily matter, unnecessary commuting travel is one of the things which cause excessive emission. This is still not prioritized by many people to improve and make the right regarding choices. One of the reasons is top management from companies who do not encourage their employees to travel sustainably or even work online more often. There are developments which can be used easily and should be used more frequently to deal with such problems.

To start with, big data is being integrated into sustainable mobility planning support systems in order to benefit more informed and agile decision making for citizens, and planning processes for city planners (Semanjski et al, 2016). This integration also enables efficiency in terms of spending time to search for travel options or making the optimal plans for sustainable cities. Moreover, models are developed for sustainable transportation options, such as applied gamification for tracking, managing, and encouraging prudent travel behaviour (Wells et al, 2014). This game-based model provides users to set goals, respond to challenges, and finish tasks to score points which subsequently encourages these users to continue with making responsible choices (Wells et al, 2014).

Furthermore, the travel planner ‘Green Tickets’ is a useful tool to reach the greater good. This website/application will help you make the trade-off for sustainable travel options and reduce your footprint. When searching for a certain route, it provides you with all possible combinations of transport, categorized from lowest to highest CO2 emission (Green Tickets, n.d). Travelling from Rotterdam to Moscow is a very good example: https://www.greentickets.app/from/51.9244201_4.4777326_Rotterdam%2C+Netherlands_NL/to/55.755826_37.6172999_Moscow%2C+Russia_RU/on/DEPARTURE_1602406800

So, there are many digital opportunities to help you make the right choices. Although, those must be combined with awareness, the right communication, and encouragement from people who can take the lead and should become role models in this regard. Consequently, every individual will hopefully realise his/her choices and make the Greenest decisions more often.

Do you agree with this? Next to that, would you use Green Tickets for your travels? And lastly, if the app tells you to go by train instead of by airplane because it is the most sustainable option, would you take this advice even if this option will take longer?

 

Wells et al, 2014 https://rke.abertay.ac.uk/en/publications/towards-an-applied-gamification-model-for-tracking-managing-amp-e

Semanjski et al, 2016 https://www.mdpi.com/2071-1050/8/11/1142/htm

https://www.cambridge.org/core/journals/global-sustainability/article/leveraging-digitalization-for-sustainability-in-urban-transport/9322C52E379793B7C4A41682EC99DB6A/core-reader

Green Tickets, n.d. https://www.greentickets.app/about

https://www.sciencedirect.com/science/article/pii/S2214367X1500040X

5/5 (1)

Three much talked about languages in data science are Python R and Julia. Python, created by the Dutch Guido van Rossum and named after the famous show, is tough to beat and has seen many challengers during its time on top. R, the user friendly, slower and perhaps more sophisticated data science language also won’t be gone tomorrow. Julia, the new kid in town with the odd name. What is it, what does it do well and how does it do that well? These questions I will tackled in an abstract way. The language is focused on data science. Which raises the question: will it beat the other data science languages.

Code readability spectrum

Before exploring Julia, we take a quick step back and look at the programming language dimensions. More specifically, how close a programming language is to machine code. Machine code is ultimately just ones and zeros. If we assume our machine uses the von Neumann structure. This could change when Neuromorphic computing takes off, about which you can read more in the following blog written Wesley Kruijthof: https://digitalstrategy.rsm.nl//2020/09/29/biomimicry-from-neural-networks-to-neural-architecture/. Anyway, in the world of ones and zeros, there are languages which lie closer to machine code and languages which are closer to human readable code. Broadly spoken, there are three categorizations on this readability spectrum: assembly, compiled and interpreted. Assembly languages are designed for a kind of processor, meaning they are highly adapted to the machine they’re supposed to run on. Some families of processors are x86, x64 (the 64 bit version of x86), ARM and MIPS. These families are instruction sets on which languages written to create the code more efficiently. Small sidenote, Apple’s mac’s have long used intel’s x86 processors but are switching to ARM, and will create these themselves. When we go a level higher we’ll see the compiler languages. C, C++, GO, Rust and many more. These programs, when run, are compiled, turned into machine code by the assembler and run directly on the machine. Interpreted languages find a different path to the machine. Interpreted languages are easier for beginners. I’m personally comfortable when writing in Python, R or VBA (Excel specifically). Other interpreted languages examples are Javascript, BASIC, PERL and  Ruby. When these languages are executed, the code gets interpreted line by line. The interpreter reads each line and executes it immediately. For Python the process looks a little bit as the following: CPython translates Python to a C-file. This file gets compiled by C’s compiler GCC (GNU Compiler collection), translated to machine code, which is not executed. This machine code is the actual interpreter and needs Python source code, which is ultimately executed line by line (GeeksforGeeks, 2020).

So where is Julia?

What does this mean for Julia? Or R and python for that sake? Well the interpreter is much slower, though many libraries exist to speed up the process. Both R and Python are interpreted and this is exactly where the difference with Julia lies. Julia looks like an interpreted language, swims like an interpreted language and quacks like a interpreted language, but it is a compiled one. But how? Well compiled code is checked for errors while compiling, which is called static typing. In dynamic typing the checking is done per line at run time. To be clear, the difference in typing has nothing to do with pressing the keyboard. In Julia, Just-In-Time (JIT) compiling is used (Hall, sd). Opposed to compilation, JIT doesn’t compile the entire code in one go, but does it on the fly and opposed to interpreted coding, it doesn’t interpret. But there are already many interpreted languages adjusted to use the JIT way and these languages have existed for far longer. The difference is that Julia’s got some other tricks up its sleeve. Type stability is the notion that only one type can be the output of a method (UCIDataScienceInitiative, sd). Julia scans the code, finds which type is expected and compiles code for that type (Julia, sd). If it can derive which type the output shall be, Julia achieves speeds equal to C.

Does this mean it’s better?

Well not quite. Though speed is a very important, there are many different factors which define a language as better or best, which obviously aren’t defined terms here. R for example is rather slow. It’s not that some guy from the R-team, while years into development, in a conference said: ah sh*t, we forgot to implement speed. R is for data scientists and is made simple/convenient. It’s also not made to process terabytes or produce time critical results. A complete beginner will get used to R quicker than to Julia and the same applies to Python.

Versatility is also important. When you find your way into a job application for data scientist and are asked why R is popular in the field, the answer shouldn’t be that it’s easy to learn. R’s CRAN has over ten thousand official packages which are solely made for data analysis and related fields. Dwarfing Python and demolishing Julia. The amount of mature packages is directly related to the community of a language. When more people use a language, it gets streamlined. Improvements will be made when many people spend a lot of time. If you google “Biggest R contributor” I can assure you the name Hadley Wickham will pop up very quickly. It’s safely argued that R wouldn’t nearly be as popular without his contributions. Which are both easier to use packages, faster packages and books explaining those. Python on the other hand is the most versatile. Though seemingly contradictory to the dwarfing statement earlier, PyPI has the most packages of the three with quite some distance as well. That’s because Python is also very well developed in other fields like web development, databases and webscraping. (CRAN, sd)

Future

So Julia’s prospects? Julia has got an annual growth rate of 101% (Simplylearn, 2020). Meaning the community is growing and the packages are expanding. If you were to take only one thing from this blog about Julia. It’s that Julia combines the best of two worlds: high performance and ease of use. Somebody who wants to get the best of both worlds, might actually write programs in the two different world. Prototyping in one, and implementing in the other is no longer necessary. Solving this problem will create a user base, which in turn sparks some good old positive network effects. I haven’t even named the possibility to call Python and C packages from Julia yet. Also, Julia might be able to learn from its predecessors. Creating the equivalents of popular packages. Almost forgot: like Python & R, Julia is free (looking at you Matlab and others).

 

I’m no computer scientist and currently just an aspiring data scientist, so I’m happy to hear any additions or errors in my blog. The future of Julia is discussed intensely in the science community, so I’ve heard. What I ‘d love to hear is your opinion on its prospects. Is Julia going to make it to the top or not? And why?

Bibliography

CRAN. (n.d.). Contributed Packages. Retrieved October 8, 2020, from CRAN: https://cran.r-project.org/web/packages/index.html

GeeksforGeeks. (2020, June 8). Compiler vs Interpreter. Retrieved October 8, 2020, from GeeksforGeeks: https://www.geeksforgeeks.org/compiler-vs-interpreter-2/

Hall, M. (n.d.). Julia in a nutshell. Retrieved October 8, 2020, from AgileScientific: https://agilescientific.com/blog/2014/9/4/julia-in-a-nutshell.html

Julia. (n.d.). Performance tips. Retrieved October 8, 2020, from Julia: https://docs.julialang.org/en/v1/manual/performance-tips/

Simplylearn. (2020, January 21). Things to Know About Julia Programming Language. Retrieved from Simplylearn: https://www.simplilearn.com/things-to-know-about-julia-programming-language-article#:~:text=It%20may%20seem%20like%20an,annual%20rate%20of%20101%20percent.

UCIDataScienceInitiative. (n.d.). Why Does Julia Work So Well? Retrieved October 8, 2020, from UCIDataScienceInitiative: https://ucidatascienceinitiative.github.io/IntroToJulia/Html/WhyJulia

5/5 (1)

This post aims to highlight the importance of privacy and the efforts to prevent the infringement of data privacy in the platform-based businesses.  The concept of privacy is generally defined as “an individual’s ability to control the extent by which the personal information is acquired and used” (Galanxhi-Janaqi and Fui-Hoon Nah, 2004). Platform-based businesses are inseparable from the internet since their operations more than often do not require any hardware. Then, when the internet is involved, data privacy affects aspects such as the obtaining, distribution or the non-authorized use of personal information.
.
Today, information technologies’ growing size in information processing, and its complex nature, have made privacy an important issue. Consequently, distrusts of consumers are also increasing because of the same reason the platform-based information technologies are successful. Due to the sensitive nature of the topic and the increased importance, privacy infringement is that much more harmful to the companies’ performance. Awareness for the privacy heightened more than ever, privacy related hazards and crises can be extremely devastating.
.
Therefore, a company’s damage control capacity regarding the privacy related dangers is of utmost importance. Prevention and promotion are both equally important in data privacy. One of the many ways to secure both is by maintaining a regulatory focus under difficult situations. In privacy research, such regulatory focus is widely studied in order to analyze human motivation and behavior. Based on such approach, in the work of Chang and colleagues (2018), it was attempted to provide deeper understanding of the role of privacy policy on consumers’ perceived privacy.
.
Privacy policy play an important role for consumers to build trust and reduce privacy related concerns. Due to the introduction of GDPR, implementation of privacy policy is not voluntary. Such prevalence of the privacy policies also expose consumers to repeated evaluations of such policies. This eventually conditions them into acquiring personal standards on how well a company’s privacy practices are followed and the responsiveness of the practices.
.
One of the other common criteria in evaluating the perceived effectiveness of privacy policy is notice (Wu and Huang, 2012). Along with the freedom to choose the extent of the shared data, notifying the users with the details in the executions of policy is important (Wu and Huang, 2012). Based on these, the responsiveness and the level of clarity in the communications promoting the privacy policy are employed as the tool of measuring the perceived quality of the policy.
.
.
.
.

– Galanxhi-Janaqi, H. and Fui-Hoon Nah, F. (2004), “U-commerce: emerging trends and
research
– Chang, Y., Wong, SF, Libaque-Saenz, CF, & Lee, H. (2018). The role of privacy policy
on consumers’ perceived privacy. Government Information Quarterly , 35 (3), 445-459.
– Wu, K. Huang, S.Y. (2012) The effect of online privacy policy on consumer privacy
concern and trust