Digital Transformation Project – Keadyn – Team 66

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October

2016

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Disruption of the Venture Capital Industry

For the purpose of this assignment, Keadyn, a venture capital firm from Rotterdam was analyzed. For privacy reasons, the technological solution cannot be fully revealed, but the way Keadyn is disrupting the Dutch VC market is interesting on its own.

Keadyn operates in the technology ventures industry, with a focus on early stage and seed-stage startups. The firm aims to invest a total of up to €50 million in 3 years’ time. The company operates primarily the Netherlands, but sourcing across Europe and EFTA. Expected timeframe per investment is 3 to 6 years.

Traditional VC model

Before we delve into how the industry is disrupted, we can look into how traditional VCs work. Investors, also called Limited Partners (LPs), invest in a fund that is operated by a General Partner. Investors put money in the fund but typically cannot decide what will happen with their money. The decision belongs to the board of that particular VC. Thus, this type of investing is suitable for investors that do not want to be involved in the investment process and only pick up the profits at the maturity. It is worth mentioning that traditional VC’s return on investment often underperforms the market.

The commitment from investors in a venture capital firm is usually 10 years. The General Partner commits only 1%-2% to the fund, but is eligible to around 20% of the profits. On top of that, VC’s charge yearly management fees to the LPs of around 2% of the investments by those LPs.

New approach to investing

Keadyn’s business model differs from the traditional Venture Capital (VC) model. Keadyn’s model is more similar to “angel investing”. The partners of Keadyn currently invest in startups with their own capital. In the future they are planning to do investments in which key keep at least 20% of the stake they buy to themselves and offer the rest to external co-investors. Hence co-investors will be able to decide whether they want to join a particular investment, deal by deal. Keadyn charges investors a 6% deal fee and then 25% on the upside when the investment is liquidated. For example, when the initial stake of a co-investor in the investment was €20,000 and the investment is sold for €100,000, Keadyn will charge a carry fee of 25% from €80,000.

The value proposition of Keadyn is that it works “With the money, not for the money”. Since Keadyn’s partners commit funds to a startup upfront, there are no agency problems. Investors can then decide for themselves, whether or not they think the deal is a good one.

The role of Keadyn does not end with the investment. It is in Keadyn’s interest to advise the startups, so they have better chance of success in the market. For this, Keadyn utilizes its network of professionals in different fields, such as legal and tax law, marketing, team performance, and financial analysis.

Industry development

Keadyn is not the only VC fund that changes their approach to investing. Next to traditional VCs, there are new competitors with an interesting business model. Two competitors in particular are worth mentioning:

AngelList

AngelList is a platform where investors and startups meet. Investors that are part of this network can find a deal to invest in, which makes him a “lead” in that investment. Then, the investor can find other leads to help him to perform the due diligence, but he also has to find followers that will join in on the deal. AngelList itself helps with administrative procedures. For that, AngelList takes a fixed charge of 8,000 dollars for completing the deal and administration. Additionally, AngelList takes 5% carry of the investment profit, with the investment lead taking 15%.

Gust

Gust is a SaaS platform that connects investors (VC firms and Angel groups) with startups. It offers a CRM tool both for Investors and startups to help monitor the deal flow. Investors are able to track their portfolio, collaborate with other investors and track their investment related calendar. Startups are able to upload their business plan and track their pitches.

The future of VC industry

The VC industry is more and more affected by technological shifts. There is a trend of VC firms starting to make use of data science to speed up the deal making process as well as bring consistency into their decision-making.

Some examples of this new approach include Google Ventures and Correlation Ventures. While the former uses various data-driven algorithms to help them make investment decisions, the latter has built the “world’s largest, most comprehensive database of U.S. venture capital financings”, which includes key information about most of the pursued investments made over the past two decades and allows them to make predictive models as a guide for their own investments decisions in less than two weeks’ time.

These examples illustrate new trends that are becoming more popular within the venture capital industry, however, for the moment only big companies with huge investment funds can actually benefit from this data-driven approach.

 

By Adrianna Henc 463511, Eva Novotna 370931, Stephan Verhoeve 459523, Joanna Małkowicz 462688

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Is the internet of things destroying the internet?

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October

2016

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With the rise of the internet of things, the internet and its advantages as well as dangers have become much more integrated with our devices. This also creates an opportunity for hackers to launch cyberattacks targeted to those connected devices. Due to the inherent properties of software, the internet of things can never be 100% secure.

Lately a botnet has been created out of a large array of cameras and other devices that fit into the internet of things. This so-called Mirai botnet consist of more than half a million nodes. The targets of this botnet and the consequences are not small with recently reported DDoS attacks to Dyn’s Domain Name System management services (DNS) infrastructure, resulting in outages of websites such as Twitter, Spotify and Reddit. It is estimated that just around 10% of the nodes of the botnet were used for this attack.

Examples of Internet of Things devices that are used in this Mirai botnet are for example security cameras. Ironically, many of those cameras cannot be easily updated to increase their data security. The amount of devices that can be used for such a bonnet is ever increasing. As it is impossible to control for the security of all software that is put on internet of things devices, the problem is only likely to become worse.

After a hacker put the source code to this botnet online on a hacking forum, more DDoS attacks were predicted by CERT, the US Computer Emergency Readiness Team. Given that the source code was published before the outages of e.g. Spotify and Twitter, this is also what happened.

What do you think about the future of this development? If software (or accompanying hardware) can never be 100% secure and the amount of connect devices increases how secure is the future even? Apart from DDoS attacks, how about all the internet connected sensors of these connected devices and its effect of the inherent properties of software on security and privacy? Let me know what you think!

 

https://motherboard.vice.com/tag/The+Internet+of+Hackable+Things

https://motherboard.vice.com/read/criminal-hackers-have-launched-a-turf-war-over-the-internet-of-shit

http://motherboard.vice.com/read/internet-of-things-malware-mirai-ddos

https://motherboard.vice.com/read/twitter-reddit-spotify-were-collateral-damage-in-major-internet-attack

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Halloween theme: Try out this AI powered algorithm generating scary imagery!

23

October

2016

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Just in time for Halloween MIT has started a project to generate scary imagery using an image processing algorithm based on artificial intelligence, specifically through deep learning. The algorithm can recognize and generate scary faces and make places look scarily. It is interesting to think about the purpose of this experiment, because can machines make us scared? The result is scarily accurate.

The image generation works through several steps. First it uses deep learning to generate completely new faces. Subsequently a filter with extra imagery is applied to scarify to the faces. After that the system learns from a voting system about the extent of scariness. The system that is behind this algorithm is based on unsupervised learning with convolutional networks (CNNs) and convolutional generative adversarial networks (DCGANs). If you want to read more about this, find the very interesting and recent conference paper by Chintala, Metz and Radford (2016) at https://arxiv.org/abs/1511.06434.

The cool thing is that you can help training the algorithm by going to http://nightmare.mit.edu/faces. After trying it myself I do not think that the machine still needs a big increment in learning anymore as the pictures already look quite scary. What do you think?

It is also interesting to think about the possibilities of this development in the future. Chintala, Metz and Redford (2016) imagine future applicabilities in video prediction as well as for audio. I imagine implications in for example virtual reality in which the environment is automatically generated with the intent to make you scared. The system could learn from how you react on the input and personalize the algorithm to make you even more scared.  Moreover just think about how this can be applied to theme parks to make those experiences even more immersive!

All in all this development is a step towards a scary but also very exciting future!

If you have any comments or other ideas for similar applications in artificial intelligence, let me know! If you want to read more about the algorithm go to http://nightmare.mit.edu.

http://boingboing.net/2016/10/23/using-machine-learning-to-auto.html

http://nightmare.mit.edu/faces

http://nightmare.mit.edu

https://arxiv.org/abs/1511.06434

Image from http://boingboing.net/2016/10/23/using-machine-learning-to-auto.html

 

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DeepDrumpf for the win!

22

October

2016

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The fact that posts coming from Twitter bots are becoming more realistic is exemplified by Deep Drumpf. The Deep Drumpf account on Twitter is built on a deep-learning algorithm known as a recurrent neural network, that is trained on speech transcripts, tweets and debate quotes. The Twitter bot has over 20,000 followers as well as 12 million views. The bot is named after a remark from John Oliver about the laste name of Trump’s ancestors.

The bot was created by Bradley Hayes, a robotics researcher at MIT. Trump was specifically chosen to study as a response to a training model that can simulate Shakespeare, as Hayes argued that it would be a fun process to model while learning modeling techniques. How it works is that the bot creates tweets starting with a random letter and consequently adds the letters that are most likely to follow, until it hits Twitter’s word limit. While the tweets that the bot produces do not always make sense, most of the produced sentences are coherent and recognizable as Trump’s. The bot has even sent replies to messages posted through Trump’s real account, while taking the context of those messages in account.

Hayes has recently added another purpose to the bot. Deep Drumpf campaign website www.deepdrumpf2016.com is now used to raise money for GirlsWhoCode, an organization with the aim of bridging the gender gap in fields such as technology and mathematics.

In the future Hayes wants to develop Twitter accounts for other presidential candidates and make them talk to each other to simulate debates. If you want to judge yourself how good the bot’s algorithm works, go to https://twitter.com/DeepDrumpf. Also feel free to let me know what you  think about this development in the field of Twitter bots and its future.

https://www.technologyreview.com/s/602682/why-im-backing-deep-drumpf-and-you-should-too/

http://www.deepdrumpf2016.com/about.html

http://cs.stanford.edu/people/karpathy/char-rnn/shakespear.txt

 

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