Recently I took part in EthIndia Hackathon that took place in Bengaluru. This time I was participating without a team after a long time and made a team on the day of the event. All three of us (Ronak, Ayush and I) had a different idea of what we should work on but we finally came to a consensus on an idea that I had got from my current workplace’s CTO (Kailash Nadh). He had discussed a problem statement where he wanted to distribute asset holding information of people who have demised to their family members. This is a common task called the Dead Mans Switch which has been covered in a lot of movies as well as various experimental ideas. This was a big problem to solve, not only in size but also in the number of question marks it raises. After a lot of discussion with various mentors from the Ethereum community we decided and implemented upon the following idea by reducing the scope (instead of covering all assets, stick to only sending videos through IPFS) and deciding to skip the big issues like (missed heartbeats)
Whistle – A platform to empower Whistleblowers and those who live under constant fear of death. Using smart contracts and the NuCypher proxy re-encryption MockNet we store the re-encrypted ipfs hash of the recorded video on the smart contract which can be interacted with using our heartbeat function interface which resets the decryption timer to a future date. In case a heartbeat is missed, the contract triggers emails containing the decrypted ipfs hash containing the video which can be streamed by anyone else.
The best part about the event was the mentorship which guided us throughout the duration of the hackathon. We learnt that any good product, needs a few use cases which it is trying to solve and it should solve those perfectly. Based on those lines, we did a bit of research and found a bit more about this issue. Recently, Latifa Al Maktoum, a woman belonging to the royal family of Dubai, ran away and came to India as she was being tortured and drugged. She released a video on youtube, where she tells her viewers that if they are watching this, she might already be dead!
Using a unique combination of heartbeat transactions and the NuCypher MockNet, we can enable them to allow decryption of the video only after their demise. We also integrated a small platform on top, through which whistleblowers can assign receipients such as news agencies. Then the recipients stored on the contract can be sent emails with the link of the data stored on IPFS once the video’s hash stored on the contract is decrypted using our method. A few other examples are people who may be related to influential families or groups, ex-members of cults, people stuck in legal loopholes, or someone who is just afraid that they may die before publishing their findings, such as a whistleblower. In India, there are multitudes of cases, one such example is the Vyapam scam where “more than 40 people associated with the scam have died since the story broke in 2013” many of whom were critical witnesses and whistleblowers whose testimony was lost due to their murder. Our platform, Whistle, hence enables users of our application, to anonymously, store information until their demise.
We needed to define our users to allow us to reduce the scope of the product. Targeting people under immediate threats who may not be able to trust any centralized organization which could censor their message. It is important for them to although, keep their message hidden until their demise, as then they can leverage this as a position. Through using our platform, the individual could essentially, release all the information even after their demise (checked by not sending a heartbeat message) by sending an email with the files stored on IPFS to all major news outlets. By limiting ourselves to whistleblowers, we were able to solidify our projects appeal. We decided that we wanted to empower such individuals and whistle blowers who live under a constant fear of death, to utilize the decentralized blockchain and store encrypted data on the blockchain.
The most critical technology of our platform is the NuCypher network. Through this network, we are able to perform proxy reencryption and run a read only function written in our contract to detect the “is alive” criteria. The smart contract stores all the details required to securely decrypt such as the policy_id which is required by the NuCypher mocknet, along with a function that can be run by the mocknet to detect if the state on the chain is in agreement with our condition, that the last heartbeat should have been before the current time. (We update this whenever the user checks in with a time in the future). We ran into a multitude of problems like understanding and going through the codebase of NuCypher Mocknet and the demos they shared. It was a challenging task but we are proud to be able to have implemented the architecture that allowed us to perform off the chain decryption based on a condition stored on chain. Being able to implement the heartbeat contract and the flow of required to perform the decryption only after the condition was met was interesting. Finally, interacting with smart contract deployed on private chain to make a consolidated product was in itself a challenge. We originally tried to use the embark platform and wanted to make a Status.im bot that would query for the heartbeat transaction directly through a message in the chat. But due to a multitude of reasons, such as deprecation of the /debug console command we are not able to go through with this.
The ridiculous effectiveness of Deep Learning has lead to research on tools that help to analyze these Deep Neural Network based “black boxes”. Recent research papers by the Information Theory community to analyze has rise to a new tool, The Information Plane, which can help analyze and answer various questions about these networks. This article, provides a brief overview of the concepts from information theory required to develop an understanding of the Information Plane, followed by a replication study of the implementation of the paper that introduces this theory with respect to Deep Neural Networks.
Information Theory has long been considered marginal to Statistical Learning theory and has usually not been studied by Machine Learning researchers. It is considered to be an integral part of Communication Engineering and is often known to be the theory of Data Compression and Error Correcting Codes. With increased compute power enabled through GPUs, a new interest in Deep Learning (LeCun et al.) has re-emerged. Although, Deep Learning provides ridiculous effectiveness, there is pretty much no fundamental theory behind these machines and they are often criticized for being used as mysterious “black boxes”. This has lead to major corporations like Intel investing in research that focuses on building an understating of why deep networks work the way they do and has resulted in the recent paper on “Opening the Black Box of Deep Neural Networks via Information Theory” by Ravid Schwartz-Ziv and Naftali Tishby  which studies these by analyzing their information-theoretic properties and tries to provide a framework to study them using the Information Plane which have been based upon the work done by Naftali Tishby earlier . The theory provides tools, such as the Information Plane, that can be used to reason about what happens during deep learning, a study of what happens during Deep Neural Network (DNN) learning during training and some hints for how the results can be applied to improve the efficiency of deep learning.
One of the observations from the paper  is that DNN training involves two distinct phases: First, the network trains to fully represent the input data and minimize the error in generalization and then, it learns to forget the irrelevant details by compressing the representation of the input.
Another observation is a potential explanation for why transfer learning works when the top most layers are retrained for similar tasks, but I skip it for further work as it is beyond the scope of this current study, although it has been mentioned while discussing the Asymptotic Equipartition Property.
From an engineering standpoint, the papers provide a very relevant theory which could help answer questions such as, if the trained model is optimal or not, if there exist any design principles for such machines, or if the layers or neurons represent anything and if the algorithms we use can be improved or not.
The following paper contributes via providing an overview of the fundamentals of Information Theory required to study these papers, followed by a detailed summary of the work related to the Information Plane and Deep Learning and finally a replication study containing a re implementation study and its results and comparison with the results of the original authors as well as the critics of the paper. The goal of the paper was to dive into cutting edge research and implement the state of the art and verify the results of both the original authors   as well as the critique  submitted to ICML 2018.
2. Concepts from Information Theory
2.1 Markov Chain
A Markov process is a “memory-less” (also called “Markov Property”) stochastic process. A Markov chain is a type of Markov process containing multiple discrete states. That is being said, the conditional probability of future states of the process is only determined by the current state and does not depend on the past states. 
2.2 KL Divergence
KL divergence measures how one probability distribution diverges from a second expected probability distribution . It is asymmetric. 
achieves the minimum zero when everywhere.
2.3 Mutual Information
Mutual information measures the mutual dependence between two variables. It quantifies the “amount of information” obtained about one random variable through the other random variable. Mutual information is symmetric. 
2.4 Data Processing Inequality
For any markov chain: , we would have 
A deep neural network can be viewed as a Markov chain, and thus when we are moving down the layers of a DNN, the mutual information between the layer and the input can only decrease.
2.5 Reparameterization Invariance
For two invertible functions , , the mutual information still holds:
For example, if we shuffle the weights in one layer of DNN, it would not affect the mutual information between this layer and another.
2.6 The Asymptotic Equipartition Property
This theorem is a simple consequence of the weak law of large numbers. It states that if a set of values is drawn independently from a random variable X distributed according to , then the joint probability satisfies 
where is the entropy of the random variable .
Although, this is out of bounds of the scope of this work, for the sake of completeness I would like to mention how the authors of  use this to argue that for a typical hypothesis class the size of is approximately . Considering an -partition, , on , the cardinality of the hypothis class, , can be written as and therefore we have,
Then the input compression bound,
The authors then further develop this to provide a general bound on learning by combining it with the Information Bottleneck theory .
3. Information Theory of Deep Learning
3.1 DNN Layers as Markov Chain
In supervised learning, the training data contains sampled observations from the joint distribution of and . The input variable and weights of hidden layers are all high-dimensional random variable. The ground truth target and the predicted value are random variables of smaller dimensions in the classification settings. Moreover, we want to efficiently learn such representations from an empirical sample of the (unknown) joint distribution , in a way that provides good generalization.
If we label the hidden layers of a DNN as as in Figure above, we can view each layer as one state of a Markov Chain: . According to DPI, we would have:
A DNN is designed to learn how to describe to predict and eventually, to compress to only hold the information related to . Tishby describes this processing as “successive refinement of relevant information” .
As long as these transformations on in about preserve information, we don’t really care which individual neurons within the layers encode which features of the input. This can be captured by finding the mutual information of with respect to and . Schwartz-Ziv and Tishby (2017) treat the whole layer, , as a single random variable, charachterized by and , the encoder and decoder distributions respectively, and use the Reparameterization Invariance given in (2) to argue that since layers related by invertible re-parameterization appear in the same point, each information path in the plane corresponds to many different DNN’s, with possibly very different architectures. 
This is to say that after training, when the trained network, the new input passes through the layers which form a Markov Chain, to the predicted output . The information plane has been discussed further in Section 3.
3.2 The Information Plane
Using the representation in Fig. 3, the encoder and decoder distributions; the encoder can be seen as a representation of , while the decoder translates the information in the current layer to the target output .
The information can be interpreted and visualized as a plot between the encoder mutual information and the decoder mutual information ;
Each dot in Fig. 3. marks the encoder/ decoder mutual information of one hidden layer of one network simulation (no regularization is applied; no weights decay, no dropout, etc.). They move up as expected because the knowledge about the true labels is increasing (accuracy increases). At the early stage, the hidden layers learn a lot about the input X, but later they start to compress to forget some information about the input. Tishby believes that “the most important part of learning is actually forgetting”. 
Early on the points shoot up and to the right, as the hidden layers learn to retain more mutual information both with the input and also as needed to predict the output. But after a while, a phase shift occurs, and points move more slowly up and to the left.
Schwartz-Ziv and Tishby name these two phases Empirical eRror Minimization (ERM) and the phase that follows as the Representation Compression Phase. Here the gradient means are much larger than their standard deviations, indicating small gradient stochasticity (high SNR). The increase in is what we expect to see from cross-entropy loss minimization. The second diffusion phase minimizes the mutual information – in other words, we’re discarding information in X that is irrelevant to the task at hand.
A consequence of this is pointed out by Schwartz-Ziv and Tishby indicating that there is a huge number of different networks with essentially optimal performance, and attempts to interpret single weights or even single neurons in such networks can be meaningless due to the randomised nature of the final weights of the DNN. 
4. Experimental Setup and Results
4.1. Experimental Setup
The experiments were done on a network with 7 fully connected hidden layers, and widths 12-10-7-5-4-3-2 neurons, similar to what had been done in the original paper. The network is trained using Stochiastic Gradient Descent and cross-entropy loss function, but no other explicit regularization. The activation functions are hyperbolic tangent in all layers but the final one, where a sigmoid function is used. The bin count was taken to be 24 for the mutual information calculation. Off the shelf python libraries such as Tensorflow, NumPy, ScikitLearn were used for the re-implementation as described by the original paper.
Variations were made to the activation function to Rectified Linear Unit (ReLu) and Sigmoidal to verify the results of a recent paper  which is under open review for ICLR 2018 under the same conditions.
The results were plotted using the experimental setup and tanh as the activation function. It is important to note that it’s the lowest layer which appears in the top-right of this plot (maintains the most mutual information), and the top-most layer which appears in the bottom-left (has retained almost no mutual information before any training). So the information path being followed goes from the top-right corner to the bottom-left traveling down the slope.
Early on the points shoot up and to the right, as the hidden layers learn to retain more mutual information both with the input and also as needed to predict the output. But after a while, a phase shift occurs, and points move more slowly up and to the left.
The results of using the hyperbolic tan function (tanh) as the choice for activation function corresponds with results obtained by Schwartz-Ziv and Tishby (2017) . Although, the same can’t be said about the results obtained when ReLu or Sigmoid function was used as the activation function. The network seems to stabilize much faster when trained with ReLu but does not show any of the charachteristics mentioned by Schwartz-Ziv and Tishby (2017) such as compression and diffusion in the information plane. This is in line with , although the authors have commented in the open review  that they have used other strategies for binning during MI calculation which give correct results. The compression and diffusion phases can be clearly seen in Fig. 4. The corresponding plot of the loss function also shows that the DNN actually learned the input variable with respect to the ground truth .
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This work has been undertaken in the Course Project component for the elective titled “Information Theory (Fall 2017)” [https://sites.google.com/a/snu.edu.in/shashi-prabh/teaching/information-theory-2017] at Shiv Nadar University under the guidance of Prof. Shashi Prabh
Use Facial Expressions to find segments of the video where engagement is above a threshold and display advertisements during those segments.
Internet Video Traffic will account for over 80% of all consumer internet traffic in the coming years. According to Cisco, by 2021, every second, a million minutes or almost 17,000 hours of video content will be uploaded on the internet. The whole video market is changing how businesses, brands or government communicate. Even though the bandwidth and internet speeds have increased, the attention span of users is still limited. With increasing number and length of videos, a system which can recommend video segments where the customers are highly engaged with the content can be used to lure customers to watch advertisements is becoming essential. Media corporations relying on getting their message to users must now rely on alternate measures to combat the information overload. Suggesting users with engaging or interesting content is one of those methods. For example, companies like Netflix who are actively pushing their own original content, need to suggest users with engaging clips to lure them into watching more of their content.
Increasing bandwidth access of users with time has lead to the average youtube video length increasing from 2-5 minutes to 10-15 minutes within just a decade. A one hour video, which was once out of question for normal viewers, is now considered normal. Although, the time of videos has increased, any user can attest that most of the video content is just “fluff” or buffer content with the main content somewhere in the middle. Thereby, finding the most engaging part of a video will result in enabling platforms to suggest the most lucrative parts of the videos to place advertisements in.
Our project solves this problem of segregating “fluff” from videos and recommending interesting parts of the video. By tagging videos with user reactions, and averaging over multiple users our system will be able to gain knowledge about the content in the video and hence, use the tagged content based on facial reactions of viewers, pick out segments inside a video to recommend to advertisers. The same segments can then be displayed as suggestions using autoplay, on the homepage, to lure viewers to click and view the whole video.
Finally, due to the large scope of the project, we were able to complete only 2 separate parts of the project.
Part 1. Training the Model for Emotion Recognition
In this part, we trained a model (accuracy was very low due to limited GPU availability on our side) for Emotion Recognition.
Part 2. Prototype UI – Applying Knowledge Engineering
In this part, we developed a prototype UI which uses Affdex API for emotion recognition and displays advertisements when user engagement with video is above a chosen threshold.
I attended HackTheNorth, which is Canada’s Biggest Hackathon and takes place at University of Waterloo campus. I was glad to join over 1000 students from around the world at the University of Waterloo to collaborate and create something extraordinary in 36 hours. I didn’t go in as a team as the visa process was a bit delayed and so none of the participants from India knew if they would be able to go. Well, eventually, we were three people from different universities from New Delhi who formed a team when we met in Canada.
We are excited to announce that Canadian Prime Minister Justin Trudeau will be giving the welcoming remarks at opening ceremonies tonight! pic.twitter.com/cWePNlFXYj
We built ToroGo, an app powered by Data Science to help newcomers decide the perfect neighborhood for someone moving to Toronto. I worked on writing the API for the backend and the entire data collection and analysis involved in the project.
We traveled all the way from India and while booking our AirBnb we were overwhelmed with the options available to us. Although, websites like tripadvisor help with reviews of an area, it is often not enough. Toronto welcomes over 40 million visitors annually, and is the leading tourism destination in Canada. So we decided to use the power of data science to help travelers like us solve where to stay.
What it does
Find a Place
In our discussions we came up with three important things that users look at while deciding where to live in a new city:
The app requests the user to enter his priority for the above mentioned features and suggests the top 5 suggested places that would suit the user based on data analysis.
Along with that we provide the user easy access to check the live currency exchange rates using XE.com powerful api instantly from the app. It supports searching from 100s of currencies and can instantly give the live exchange rate.
How we built it
We used open datasets available from the Canadian Open Dataset website and Airbnb to build an aggregate score of around 130 sub-areas of Toronto City. We chose three parameters,
Locality Ratings based on Sentiment Analysis of Reviews
Safety Ratings based on Crime Data such as Assaults
Priceyness Ratings based on the cost of listings of Airbnbs in a Neighborhood.
Objective of this project was to show as a proof of concept that we can pick up any off-the-shelf generic lamp and by implementing minor hardware changes using an arduino, convert it into a Smart Lamp. We have implemented lamp switching based on environmental factors (such as Temperature, Light intensity etc), hand gestures (such a clapping actions), which can be set up by the user by attaching triggers to various conditions in the User Dashboard which was made in Processing 3.
Wireless Sensor Network is an emerging area that shows great future prospects. Today such networks are used in many industrial and consumer applications, such as military, industrial process, monitoring health and in automated and smart homes. So far, the researchers have only focused on making WSNs useful, feasible, and less emphasis was placed on security. The sensors used are susceptible to different types of attacks, denial of service, physical tampering. In hostile scenarios, it is very important to protect WSNs from malicious attacks. This is the reason we need better security against these challenges, threats and issues in WSN. The intent of this paper is to shed light on the security related issues and challenges in wireless sensor networks investigated by researchers in recent years and that shed light on future directions for WSN security.
The Free and Open Source Software (FOSS) movement, which is now seen as an integral part of the technology sector, is now making its impact outside this domain in various dimensions. It has given rise to the three “Open” pillars – Open Source, Open Standards and Open Content. Ideas formulated during the rise of these new FOSS communities have been able to raise various questions about Intellectual Property, Information Production and other newly formulated concepts. In this paper, we will first discuss the history of FOSS and the creation of the two camps of the FOSS movement. It is important here to remind the reader that the word “Free” stands for free as in “Freedom” and not the economic freebie, but also to note that it has been carefully chosen to highlight that as well. We will analyze this bipartisan community and the implications of their ideology about freedom. Going ahead, we will discuss the copyleft licenses and it’s impact on intellectual property followed by the emerging future of the new ideologies. Although, FOSS developers and community members are agnostic about politics the aim of this paper is to bring out the underlying political thought behind this recent modern phenomenon.
History of FOSS
The concept of sharing technological information predates computers although this technology along with the internet have enabled sharing of information exponentially. Before the 1960s, most of the source code that was written was academic and usually available under the public domain. But with the advent of commercial software, came licenses for using and distributing software. In 1983, Richard Stallman started work on the GNU project to write a complete operating system free from any constraints on the usage of it’s source code. In 1985, Stallman published the GNU Manifesto and in 1989 he released the first version of the GNU General Public Licence (GPL) and it was the beginning of the Free Software Movement which would culminate with the creation of the Free Software Foundation (FSF). We should note here that the GNU GPL was a Copyleft license. It was a novel use of the existing copyright law that guaranteed the GPL licensed works to remain freely available even under derivative works and therefore saw extensive use by the community. As an aside, it is important to mention that this was one of the inspirations for the share-alike license provided by the Creative Commons which we will discuss ahead. There seems to be two major highlights for our discussion in context of this paper from Stallman’s work. First, being his definition of the “Free” in FOSS to be Free as in Freedom. The freedom he talks about is closer to the positive liberty – which according to Isaiah Berlin would be the possibility of acting and not the negative liberty. This positive liberty is attributed to the collective community behind the projects and the members of the community. Second, is his argument about the benefits of such projects. In essence, he breaks these benefits into the benefits to the contributors and the benefits to the community as a whole. His work has continued on to become one of the two major philosophies in the FOSS world.
The early 90s saw the rise of the permissive open source licenses, like the Apache License, that were commercially aligned. Permissive licenses allowed users to use these projects and modify them and earn profit without necessarily having to be bounded by the restrictions imposed by the GPL license. In 1999, Eric S Raymond published his essay about the two different software models, “The Cathedral and the Bazaar”. He describes the Cathedral model, in which source is available with each software release but code developed between the releases is restricted to an exclusive group of software developers whereas in the Bazaar model, code was being developed over the internet in view of the public. The bazaar model was only possible with the rise of the internet and now we see the emergence of a new human dynamic that is Peer to Peer which we discuss below.
In 1997, Netscape Navigator’s release of its source code, prompted Raymond and others to rethink about FSF’s social activism since it was not appealing to corporate companies and wanted to rebrand to highlight the business potential of sharing of source code. They adopted the label “open source” and the Open Source Initiative (OSI) was formed thereafter.
The Bipartisan Community
Two major philosophies exist today in the FOSS world today. Both are lead by the two major camps, the FSF camp and the OSI camp. According to the FSF, free software is meant to protect four user freedoms. They term programs that don’t give these freedoms to by “non-free”. Their argument here is that non-free programs control the users and this makes the program an instrument of unjust power. It is clearly evident how it shares a common vocabulary with Marx, when he talks about class struggle in the Communist Manifesto. Comparing the social groups identified by Marx, the Labour and the Capital, are comparable to the Users and the Developers. To give an example, computer programs developed by hobbyists in the 90’s grew into serious competitors to commercial software being produced by large companies. One such community project was the GNU/Linux operating system which is now one of the only competitors to the Microsoft Windows operating system. The question that the FSF’s philosophy was in a position to answer was what was whether the Windows ecosystem was ethical or not in its treatment of its users.
The FSF’s list of four freedoms are listed below:
● The freedom to run the program as you wish, for any purpose (freedom 0).
● The freedom to study how the program works, and change it so it does your computing as you wish (freedom 1). Access to the source code is a precondition for this.
● The freedom to redistribute copies so you can help your neighbor (freedom 2).
● The freedom to distribute copies of your modified versions to others (freedom 3). By doing this you can give the whole community a chance to benefit from your changes. Access to the source code is a precondition for this. – (FOSS A General Introduction/Intellectual Property Rights and Licensing – Wikibooks)
It is hard not to notice the similarity between how this philosophy also calls for a certain set of negative freedoms in Isaiah Berlin’s terminology, just like Rawls lists out the primary goods for his first principle. Similar to Rawls use of arguments moral in nature to justify his argument, this philosophy is also justified on the basis of free software’s commitment to prevent limiting the freedom of others. The FSF’s main contention is the ethical use and creation of software, just like Rawls talks about the ethics of justice.
Another undertone that can be brought about by classifying the user as the labourer in Marxist terms allows us to understand is how free software is in a position to reduce the alienation between the users and the developers. Technology that is designed as a “black box” makes the user to be left without any influence over the functions that the machinery imposes.
OSI’s philosophy is a bit different from the FSF. They say, “When programmers can read, redistribute, and modify the source code for a piece of software, the software evolves. People improve it, people adapt it, people fix bugs. And this can happen at a speed that, if one is used to the slow pace of conventional software development, seems astonishing.” OSI is more focused on the technical values that make software powerful, reliable and business friendly. It bypasses FSF’s moral views on the subject and focuses on the practical advantages offered by FOSS’s distributed development model. Just like how Robert Nozick critiqued Rawls work, a similar argument like Nozick’s critique of pattern based principles can be extended to critique the FSF moral stand by the OSI.
Even though both FSF and OSI differ immensely in their fundamental philosophy, they both share the same space and cooperate on their common goal. Richard Stallman has himself said that they both are like two political parties in the same community.
Analyzing Copyleft and Intellectual Property
One of the biggest achievements of the FOSS movement is the Copyleft license. The purpose of the license as discussed above was to protect the four essential freedoms. Putting Copyleft in layman’s terms, it is a rule that when a program is redistributed, it must not add restrictions that deny others the four central freedoms. It has been successful in rupturing the naturalized form of intellectual property by inverting its singular by using intellectual property itself. This is similar to Marx’s inversion of Hegelian idealism, which retained Hegel’s dialectical method to repose history not as an expression of the “Absolute Idea” but as humanity’s collective creation through labor. By utilizing the existing copyright law, copyleft has been in a position to tell us that we are not mere subjects of an unchangeable law but in turn we can actually create and modify the laws to serve other ends. FOSS licenses can be understood as constitutions that serve to credit the researchers, protect them from liability, and then let people do what they want with the product.
Nowadays, a free software user and developer is confronted with a choice of licenses and moral codes. This choice, although, requires prioritizing one group’s freedoms over another. This ethical dilemma can be resolved by favoring the copyleft licenses as they have a better facilitation of freedoms, a broader appeal to the community, and their commitment to an ethical vision for the future. Hobbes is known to have famously defined freedom as the absence of restriction. Political thought from Aristotle to Heidegger has been at odds to differentiate freedom from license. Isaiah Berlin’s characterization of positive freedom as an alternative and negative freedom as the absence of restriction is again valuable here. There is often a critique of positive freedoms called the “paradox of economic freedom” where unfettered freedoms in the form of removing all restrictions on the rich would result in the total exploitation of the poor, and thereby their subsequent loss of economic freedoms. Similarly, non-copyleft licenses, although operating to grant protection, inevitably fail to provide any means against the exploitation possible.
Peer Production and the Gift Economy
Peer to Peer (P2P) is a newly emerging human dynamic which is giving rise to a third mode of production, governance and property. It is poised to have deeper impacts, even more than the impact of Marx’s identification of the manufacturing plants of Manchester as the blueprint for the new capitalist society. To understand what P2P is, we must first understand P2P processes. These processes produce use-value through the free cooperation of producers who have access to distributed capital and it is termed as the P2P production mode. These processes are governed by the producers themselves and make the use-value freely accessible universally. These processes occur in distributed networks where there is decentralization like the internet. P2P is often incorrectly described as a Gift Economy. This is because it is not based on equality matching but on reciprocity. Taking inspiration from Marx’s slogan “From each according to his ability, to each according to his needs” P2P does not involve obligatory reciprocity. Each contributes according to his capacities and willingness and each takes according to one’s needs. In the purest form of Peer Production, producers do not get any payment. P2P processes and P2P as a model itself might be important to read and understand and question as it might slowly but eventually be a factor for future economies and social policies. Take into account the emerging welfare states which might be able to sustain such individuals who partake in P2P processes since the current form of the economy cannot.
The commons movement, another much recent phenomenon, is centered on the idea of creating public goods to reinvigorate democratic principles. FOSS has enabled like-minded projects in art, law, and science to release all their archives under a Creative Commons license. Commons are universally available and regulated by global cyber collectives. A license called the “Share-Alike” licenses provides a similar to the Copyleft licenses that allows redistribution and derivative works only if they are also released under a commons license. This commons phenomenon is in contrast to the Communal lands of the past which were localized and regulated by communities usually limited by location. Creative Commons have therefore given an example of the spreading of the ideology generated by the FOSS movement might eventually lead to a different future.
The Open Source Initiative’s efforts to popularize free software by divorcing it from politics seek to increase greater acceptance among corporate developers has although increased the exposure of FOSS to the world. Although, with this divorce, the underlying and foundational political and ethical message have been lost into the underground. Therefore, it is important to not forget these underlying message of liberty and revolution created by the origins of the FOSS movement.
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This was a term paper I submitted for a course titled “Intro to Political Thought” at SNU under Prof. Shekhar Singh.
This product was built during the DigitalOcean Cloud Hack 2016 at 91springboard, Okhla New Delhi. These days, every cloud developer is using Docker. Docker has become the de-facto way for developers and system administrators to create lightweight images and deploy to cloud. A quick search on github returned more than 300,000 public projects with Dockerfiles. The idea behind OctoShark is to simplify the workflow of deploying and testing cloud projects. OctoShark aims to provide a one click solution to deploy any Docker Project directly to a new DigitalOcean Droplet. The OctoShark button would be visible on such github projects and it would allow users to spin up a server for that project in a jiffy. No developer now needs to think twice to test a project! Since OctoShark is a browser extension, it also provides real-time information about your existing droplets and enables you to perform actions on them. The most popular extension available online is deprecated because it was built to work with DO’s API V1 and not API V2. We believe with OctoShark, developers will be able to click and run the projects and not worry about anything else! We placed first in the Cloud Track in this hackathon.