1: Is it too early to establish a regulatory framework for Internet/OTT services, since internet penetration is still evolving, access speeds are generally low and there is limited coverage of high-speed broadband in the country? Or, should some beginning be made now with a regulatory framework that could be adapted to changes in the future? Please comment with justifications.Answer: We object to the telco invented word OTT. There is no definition possible for what constitutes an OTT. If every application on Internet is an OTT that includes personal blogs, curated content websites run by SME's then we have a major issue with regulation. Such regulation would require humungous enforcement machinery.Question 2: Should the Internet/OTT players offering communication services (voice, messaging and video call services through applications (resident either in the country or outside) be brought under the licensing regime? Please comment with justifications.Answer: There are potentially millions of applications out there that would have some overlap with some functionality offered by a Telecom player in India. It would be an ill thought move to even think this is possible without damaging the Internet experience for everyone in India. How do you know the future ? Are you going to kill the next big thing from India with the potential to add a few %age points to the GDP by bringing back the license-quota raj in this country and that too in a sunrise industry ?Question 3: Is the growth of Internet/OTT impacting the traditional revenue stream of Telecom operators/Telecom operators? If so, is the increase in data revenues of the Telecom Operators sufficient to compensate for this impact? Please comment with reasons.Answer: Why is it the responsibility of TRAI to ensure revenues for Telco's at the expense of everybody else in the ecosystem. More application creators in India would result in more data usage via telecom networks and result in sufficient revenue for data services provisioning to remain profitable. Nobody has asked telco's to sell their services at a loss. There is not sufficient wired last mile by pure play ISPs in India for people to power Internet on their smart phones it is mostly going via the telcos. Let us assume for a moment that the voice revenue becomes zero, nothing prevents telcos from recovering the entire cost of their operations from selling data packs for Internet access at a price at which this cost is recoverable. In this oligopoly of operators the end customers have no choice but to pay for the price charged.Question 4: Should the Internet/OTT players pay for use of the Telecom Operators network over and above data charges paid by consumers? If yes, what pricing options can be adopted? Could such options include prices based on bandwidth consumption? Can prices be used as a means of product/service differentiation? Please comment with justifications.Answer: Telecom operators already sell Internet bandwidth to cloud computing players like us in India who provide the Internet-accessible compute infrastructure to Internet centric workloads. The end users already pay for Internet bandwidth by buying data packs from telcos. Where is the question of additionally charging a website/mobile application service operator additional arise ? Internet is a collection of Autonomous Systems ( network of networks ). Where everyone agrees to connect with each other either directly or via a routed path via one or more backbone providers in the world. While it is the right of AS'es to choose to connect with one another, in general everyone agrees that unless the entire Internet is made accessible to end users in a non-discriminatory fashion it would significantly reduce the net-benefit derived out of Internet-access for an end customer if a telco providing Internet access were to interfere with the content/applications being accessed by their subscribers. The market power of a largish telco over a single subscriber getting a much crappier version of Internet is a situation that a regulatory body must strive to avoid by nipping such ideas in the bud.Question 5: Do you agree that imbalances exist in the regulatory environment in the operation of Internet/OTT players? If so, what should be the framework to address these issues? How can the prevailing laws and regulations be applied to Internet/OTT players (who operate in the virtual world) and compliance enforced? What could be the impact on the economy? Please comment with justifications.Answer: No, we don't agree. If a web/mobile application is illegal in India then get the competent court to ban it. The ISPs including telcos are already capable of doing that.Question 6: How should the security concerns be addressed with regard to Internet/OTT players providing communication services? What security conditions such as maintaining data records, logs etc. need to be mandated for such Internet/OTT players? And, how can compliance with these conditions be ensured if the applications of such Internet/OTT players reside outside the country? Please comment with justifications.Answer: There is not much that can be done to prevent two terrorists from communicating securely via their own proprietary protocols running on top of Internet, Postal communications, voice telephony ( via use of modems transmitting what could be considered gibberish ) in a reasonable amount of time. What the law enforcement agencies should invest into is instead signal intelligence capabilities which can look at the publicly available data sources from the front door of social media/news media/online forums etc. and start making sense of it by investing into big data technology.The open source encryption and repudiation technology relying on open general purpose computing hardware today is sufficiently advanced that government projects like carnivore ( US ) are useless against it. It is pointless to put a massive regulatory burden onto very small startups for possibility of future abuse of services. There is only result of a huge regulatory burden on any industry as shown by the ISP industry in India. OSS'ification into a bunch of liasoning players instead of a vibrant and dynamic bunch who lead the nation into greatness.Question 7: How should the Internet/OTT players offering app services ensure security, safety and privacy of the consumer? How should they ensure protection of consumer interest? Please comment with justificationsAnswer: None. The regulator must first show a market failure before proceeding with an intervention. Do we see egregious failure of market in protecting privacy of end customers. Issue non-compulsory guidelines first for the Internet industry to get its act together if there is substantive failure to protect the privacy of end customers.Question 8: In what manner can the proposals for a regulatory framework for OTTs in India draw from those of ETNO, referred to in para 4.23 or the best practices summarised in para 4.29? And, what practices should be proscribed by regulatory fiat? Please comment with justifications.Answer: Who is ETNO and HOW is it a stakeholder in India ? Why should any stakeholder be required to comment on whatever ETNO is proposing ?Question 9: What are your views on net-neutrality in the Indian context? How should the various principles discussed in para 5.47 be dealt with? Please comment with justifications.Answer: Without net-neutrality participants with substantial market-power win automatically and the end customer loses. Net-Neutrality is a fairly simple concept. Don't interfere with the data coming through the pipe. There is a finite low cost today of reaching out to vast audiences on Internet in theory making it possible for a student sitting in a hostel to compete in a large niche market for SAAS/Mobile applications against large corporations with deep pockets. Preferential treatment to certain Internet applications based on a telco who is not direct service provider for an application by merely by an act of interfering with data passing through its network is a recipe for abuse of market power. While on the other hand it is perfectly fine for an Internet application provider to take direct services from any Autonomous System ( network ) on the Internet with a view to reduce number of routing hops to make its applications reach end customers faster or use a CDN service that does the same on its behalf as long as that doesn't result in others becoming slower ( not comparatively but actually ) or in-accessible ( e.g. Airtel-Zero, Internet.org) due its collusion with ISPs serving end customers.Question 10: What forms of discrimination or traffic management practices are reasonable and consistent with a pragmatic approach? What should or can be permitted? Please comment with justifications.Answer: What is generally considered Abusive Traffic which is not requested by actual customers e.g. Malware being served from a compromised server accessible on public Internet, DDoS traffic choking networks, email spam at large volumes which affects network's primary function of providing Internet Access to the end customer should be dropped from the network without further reference to any regulation. Most ISPs/Telcos already do that. Further prioritisation of traffic by use of deep packet inspection should be prohibited by law.Question 11: Should the Telecom Operators be mandated to publish various traffic management techniques used for different OTT applications? Is this a sufficient condition to ensure transparency and a fair regulatory regime?Answer: It is desirable that Telecom Operators public traffic management techniques they use with a view to describe their actual services to their end customers and interconnecting networks but it shouldn't be for the purpose mentioned here and not due to force of laws. Nor does such transparency even if enabled by force for laws results in a free and fair market.Question 12: How should the conducive and balanced environment be created such that Telecom Operators are able to invest in network infrastructure and CAPs are able to innovate and grow? Who should bear the network upgradation costs? Please comment with justifications.Answer: In a free market there would be players who figure out the unit economics better than others. Some might be profitable others would make losses. How are you as a regulator concerned with these questions. If the network usage becomes higher then so do the charges paid by end customers. The end users who are customers of telcos are eventually bearing the network upgradation costs.Question 13: Should Telecom Operators be allowed to implement non-price based discrimination of services? If so, under what circumstances are such practices acceptable? What restrictions, if any, need to be placed so that such measures are not abused? What measures should be adopted to ensure transparency to consumers? Please comment with justifications.Answer: Same answer as answer to question number 9.Question 14: Is there a justification for allowing differential pricing for data access and OTT communication services? If so, what changes need to be brought about in the present tariff and regulatory framework for telecommunication services in the country? Please comment with justifications.Answer: Same answer as answer to question number 9.Question 15: Should OTT communication service players be treated as Bulk User of Telecom Services (BuTS)? How should the framework be structured to prevent any discrimination and protect stakeholders interest? Please comment with justification.Answer: Same answer as answer to question number 9.Question 16: What framework should be adopted to encourage India specific OTT apps? Please comment with justifications.Answer: Reduce the regulatory burden on startups in India. Here I am founder of a small startup trying to pre-empt a regulatory event of the sort that pre-maturely caused Taxiforsure 's acquisition by their well-funded competitor. Why should I not be spending my time in generating growth for my cloud computing business instead. Liberalisation and reforms hold the answer to the question.Question 17: If the App based/OTT communication service players are to be licensed, should they be categorised as ASP or CSP? If so, what should be the framework? Please comment with justifications.Answer: Same as answer to the questions number 2. Communication between people is an essential part of majority of Internet applications today. Should every application ever built have to decide to kill off the network effects by removing all inter-end-user communication component from it to avoid licensing then you my dear friends at TRAI have dug the biggest graveyard for Indian startups.Question 18: Is there a need to regulate subscription charges for App based/OTT communication services? Please comment with justifications.Answer: Where do you see the market failure. There is no need for heavy hand of regulation unless there is a market failure due to which one particular player in the entire segment controls 90% of the market due to anti-competitive behaviour e.g. By violating network neutrality.Question 19: What steps should be taken by the Government for regulation of non-communication App based/OTT players? Please comment with justifications.Answer: Please justify why do you want to regulate every engineer who can write a few lines of code or use ready made open source code to produce something useful for other people by making it accessible over Internet. The government/regulators should really stay out of creating additional regulation unless there is a demonstrated market failure. Do you even realise how many %age points of GDP growth you are going to shave off from Indian economy by coming up with such ridiculous questions.Question 20: Are there any other issues that have a bearing on the subject discussed?Answer: India has one of the slowest Internet speeds in Asia region and the most expensive bandwidth even for bulk customers like us in Cloud Computing Space. Lack of network neutrality in India would lead players like us who can and are bringing content/applications back to Indian datacenter locations to shutdown shop and work for providing services for other global cloud computing providers instead of making cloud computing in India. We already work with serious disadvantages like high cost of power and cooling in India. The oligopolic mess in International Internet transit and domestic landscape dominated by liasoning players who most end users hate is the result of incompetent regulation and lack of forward thinking for last decade and a half since Internet was introduced into India.]]>Check the pricing of our offerings here
E2E Networks' comments on TRAI’s Paper on Regulatory Framework for Over-the-top (OTT) services dated March 27, 2015
Table of Contents
A Complete Guide To Customer Acquisition For Startups
Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.
So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.
The problem with customer acquisition
As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.
To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.
So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.
How can you create the ideal customer acquisition strategy for your business?
- Define what your goals are
You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –
- MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
- CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.
- CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
- Churn rate – It tells you the rate at which customers stop doing business.
All these metrics tell you how well you will be able to grow your business and revenue.
- Identify your ideal customers
You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.
- Choose your channels for customer acquisition
How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels.
- Communicate with your customers
If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.
Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.
Reference Links
https://www.helpscout.com/customer-acquisition/
https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/
Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era
3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.
The Main Objective of the 3D Object Reconstruction
Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:
- Highly calibrated cameras that take a photograph of the image from various angles.
- Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.
By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.
State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects
The technology used for this purpose needs to stick to the following parameters:
- Input
Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.
The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.
- Output
The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.
- Network architecture used
The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.
- Training used
The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.
- Practical applications and use cases
Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.
Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:
- 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
- It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
- They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
- It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
- It can also help in completing DNA sequences.
So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website.
Reference Links
https://tongtianta.site/paper/68922
https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods
A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts
For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.
So, read on to know more.
What is Deep Q-Learning?
Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:
State> Next state> Action> Reward
The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.
Now, any understanding of Deep Q-Learning is incomplete without talking about Reinforcement Learning.
What is Reinforcement Learning?
Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.
Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.
For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.
What is Q-Learning Algorithm?
The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.
The 4 steps that are involved in Q-Learning:
- Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
- Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
- Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
- Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.
In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.
Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.
Reference Links
https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/
https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f
GAUDI: A Neural Architect for Immersive 3D Scene Generation
The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.
An introduction to GAUDI
The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.
What does GAUDI do?
GAUDI can perform multiple functions –
- The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
- Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
- GAUDI also removes GANs pathologies like mode collapse and improved GAN.
- GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.
How is GAUDI applied to the content?
The steps of application for GAUDI have been given below:
- Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
- This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
- The scenes are thus synthesized by interpolation within the hidden space.
- The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
- A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.
To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.
Reference Links
https://www.technology.org/2022/07/31/gaudi-a-neural-architect-for-immersive-3d-scene-generation/
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