Illuminating the Black Box Interpreting Deep Neural Network Models

August 2, 2022

Research often includes the confrontation with various complex dynamics and abstractions that are not exactly well-defined and easily accessible to our understanding becomes difficult to manage. This is where a deep neural network comes in handy. It can learn and show better performance than regular machine learning models. Moreover, they help in making new discoveries in different fields.

However, the usage of DNN is infamously known as Black Box, owing to its lack of transparency. The lack of insight is what derives this term.

Nonetheless, to fully understand the concept of Black Box, one needs to have a better idea of what a deep neural network is.

What is a Deep Neural Network?

A neural network is a computer architecture in which processors are interconnected in a way that replicates the connections between neurons in a human brain. This way, the neural network, both in the brain and computer, is able to learn various things by trial and error.

A deep neural network or DNN is an artificial neural network or ANN, which has several layers between the layer of input and output.

Deep neural networks (DNNs) are capable of automatically studying abstract data that can be completely original and fresh. DNNs perform superiorly compared to classical machine learning models. Therefore, they are more promising tools to make new discoveries in fields like pharmacology, psychiatry and space research. The scope of applications of DNNs is increasing with an increase in programming and research.

How Does a Deep Neural Network Behave?

Deep neural networks belong to a broader category of neural networks (NNs). Neural networks, also known as artificial neural networks and inspired by a human brain.

A biological neuron takes input from different neurons, forms the potential for action and then they output signals to other neurons through synapses. These artificial neurons are connected analogously. Synaptic strengths are chosen by weight. So, the principle is - the higher the weight, the stronger the synaptic connection. The action potential is simulated by a nonlinear activation function, which changes after the input value surpasses a certain threshold value.

Emergence of Big Data Brought Forward the Black Box Problem

The rise of Big Data and new developments in machine learning might provide insights into some of the challenges. Deep neural networks (DNNs), a specific type of ML model, can help understand these networks. Our biological brains have inspired DNN models. The neural network uses artificial neurons as units and wires a large number of units together in particular ways forming various types of architectures.

Common DNN Architectures

Neural networks typically consist of layers of artificial neurons. They take signals from their equivalent counterparts from the preceding layer. Then, after the next transformation that we mentioned earlier, it sends the output to the subsequent layer to another equivalent counterpart in that layer. Normally, there is zero connection between neurons in the same layer.

However, in real-world applications, a number of neurons input the data, link to a single output cell and apply a logistic activation function at the output cell, and send the output. This gives rise to DNN architecture.

  • Feedforward Neural Network (FNN)

A feedforward neural network is an ANN in which connections between the nodes do not form a cycle. A layer is different from its successor or the recurrent neural networks. It can be used for a variety of things like data compression, pattern recognition and voice recognition.

  • Convolutional Neural Network (CNN)

A convolutional neural network (CNN) is an artificial neural network which is used in image recognition and processing that is specifically designed to process pixel data. CNN identifies several objects on images which makes it useful in medicine or MRI diagnostics. CNN is also used in agriculture. They are feedforward neural networks which employ filters and are used for pooling layers.

  • Recurrent Neural Network (RNN)

A recurrent neural network (RNN) is a neural network in which the output from the preceding step is fed as input to the present step. Its real-world applications are language modelling and text generation, call centre analysis and video tagging.

  • Learning Model Parameters

In simple models like linear regression, the estimation of model parameters can be done with a closed-form and fixed solution. For ML models which have some degree of complexity, one would have to depend upon numerical approximations for obtaining estimates. This can be learned with gradients during model training. Gradients calculated for all parameters of DNNs use a method called back propagation, where a model output would change by a small change in the input. This method of back propagation makes it useful for model interpretation.

To sum up, the concept of Black Box, even though it is widely known, the interpretation of it is still somewhat half-hearted. Hopefully, in the coming years, researchers will be able to shed some light on this topic.

Besides, if you are using machine learning or planning to use it, then take the assistance of the potent cloud infrastructure extended by E2E Networks. A host of powerful cloud GPUs will ensure that you get the best possible performance. 

Reference Links:

https://www.frontiersin.org/articles/10.3389/fpsyt.2020.551299/full

https://pubmed.ncbi.nlm.nih.gov/33192663/

https://www.readcube.com/articles/10.3389/fpsyt.2020.551299

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Cloud GPUs vs On Premises GPUs

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Cloud platforms offer fast access to high performance compute and deep learning algorithms, which makes it simpler to start using machine learning models and get early insights into your data. 

Cloud GPUs are better for machine learning because they have lower latency, which is important because the time it takes a neural network to learn from data affects its accuracy. Furthermore, cloud GPUs allow users to take advantage of large-scale training datasets without having to build and maintain their own infrastructure.

On Premises GPUs are better for machine learning if you need high performance or require access to cutting-edge technologies not available in the public cloud. For example, on-premises hardware can be used for deep learning applications that require high memory bandwidth and low latency.

Cloud GPUs: Cloud GPUs are remote data centers where you can rent unused GPU resources. This allows you to run your models on a massive scale, without having to install and manage a local machine learning cluster.

Lower TCO: Cloud GPUs require no upfront investment, making them ideal for companies that are looking to reduce their overall capital expenses. Furthermore, the cost of maintenance and upgrades is also low since it takes place in the cloud rather than on-premises.

Scalability & Flexibility: With cloud-based GPU resources, businesses can scale up or down as needed without any penalty. This ensures that they have the resources they need when demand spikes but also saves them money when there is little or no demand for those resources at all times.

Enhanced Capacity Planning Capabilities: Cloud GPU platforms allow businesses to better plan for future demands by providing estimates of how much processing power will be required in the next 12 months and beyond based on past data points such as workloads run and successes achieved with similar models/algorithms etc... 

Security & Compliance : Since cloud GPUs reside in a remote datacenter separate from your business' core systems, you are ensured peace of mind when it comes to security and compliance matters (eigenvector scanning / firewalls / SELinux etc...) 

Reduced Total Cost Of Ownership (TCO) over time due to pay-as-you-go pricing model which allows you only spend what you actually use vs traditional software licensing models where significant upfront investments are made.

Cloud GPUs: Cloud GPUs offer significant performance benefits over on-premises GPUs. They are accessible from anywhere, and you don't need to own or manage the hardware. This makes them a great choice for data scientists who work with multiple data sets across different platforms.

Numerous Platforms Available for Use: The wide variety of available platforms (Windows, Linux) means that you can run your models using the most popular machine learning libraries and frameworks across different platforms without having to worry about compatibility issues between them.

This is a decorative image for: Impact of the Strong Dollar: Cloud Costs Increasing, Be Indian Buy Indian
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Impact of the Strong Dollar: Cloud Costs Increasing, Be Indian Buy Indian

Indian SMEs and startups are feeling the effects of the high dollar. These businesses use hyperscalers(MNC Cloud) who cannot modify their rates to account for the changing exchange rate. For certain companies, even a little shift in the currency rate may have a significant effect on their bottom line. Did you know, when the INR-USD exchange rate moved from 60 to 70 in December 2015, it had an impact of around 20% on Digital Innovation?

As the rupee is inching closer to 82 per dollar, the strong dollar has directly impacted the costs of cloud services for Indian businesses. The high cost of storage and computing power, along with bandwidth charges from overseas vendors, has led to a huge increase in the effective rate of these services. This is especially true for startups and SMEs that rely on cloud computing to store and process user data. With the strong dollar continuing to impact the cost of cloud services, it is essential for Indian companies to evaluate their options and adopt local alternatives wherever possible. This blog post will discuss how the strong dollar impacts cloud costs, as well as potential Indian alternatives you can explore in response to this global economic trend. 

What is a Strong Dollar?

A strong US dollar($) is a term used to describe a situation where a US’s currency has appreciated in value compared to other major currencies. This can be due to a variety of factors, including interest rate changes, a country’s current account deficit, and investor sentiment. When a currency appreciates, it means that it is worth more. A strong dollar makes imports more expensive, while making exports cheaper. Strong dollars have been a growing trend in the past couple of years. As the US Federal Reserve continues to hike interest rates, the dollar strengthens further. The rising value of the dollar means that the cost of cloud services, especially from hyperscalers based in the US, will rise as well. 

Increase in Cloud Costs Due to Strong Dollar

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Why are Cloud Services Becoming More Expensive?

Cloud services are priced in US dollars. When the dollar is strong, the effective price of services will be higher in Indian rupees, as the cost is not re-adjusted. There are a couple of reasons for this price discrepancy. First, Indian customers will have to pay the same prices as American customers, despite a weaker Indian rupee. Second, vendors have to ensure that they make a profit.

Possible Indian Alternatives to Cloud Services

If you're looking for a cost-effective substitute for services provided by the U.S.-based suppliers, consider E2E Cloud, an Indian cloud service provider. When it comes to cloud services, E2E Cloud provides everything that startups and SMEs could possibly need.

The table below lists some of these services and compares their cost against their US equivalents. 

According to the data in the table above, Indian E2E Cloud Services are much cheaper than their American equivalents. The difference in price between some of these options is substantial. When compared to the prices charged by suppliers in the United States, E2E Cloud's bandwidth costs are surprisingly low. Although not all E2E Cloud services will be noticeably less expensive. Using Indian services, however, has an additional, crucial perk: data sovereignty.

Conclusion

The price of cloud services will rise as the US Dollar appreciates. Indian businesses will need to find ways to counteract the strong dollar's impact on their bottom lines. To do this, one must use E2E Cloud. The availability of E2E Cloud services in INR currency is a bonus on top of the already substantial cost savings. An effective protection against the negative effects of a strong dollar.

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Actions CEOs can take to get the value in Cloud Computing

It is not a new thing to say that a major transition is on the way. The transition in which businesses will rely heavily on cloud infrastructure rather than having their own physical IT structure. All of this is due to the cost savings and increased productivity that cloud technology brings to these businesses. Each technological advancement comes with a certain level of risk. Which must be handled carefully in order to ensure the long-term viability of the technology and the benefits it provides.

And CEOs are the primary motivators and decision-makers in any major shift or technological migration in the organization. In the twenty-first century, which is a data-driven century, it is up to the company's leader to decide what and how his/her organization will perform, overcome the risk and succeed in the coming days.

In this blog, we are going to address a few of the actions that CEOs can take to get value in cloud Computing.

  1. A Coordinated Effort

As the saying goes, the more you avoid the risk, the closer it gets. So, if CEOs and their management teams have yet to take an active part or give the necessary attention that their migration journey to the cloud requires, now is the best time to start top-team support for the cloud enablement required to expedite digital strategy, digitalization of the organization, 

The CEO's position is critical because no one else can mediate between the many stakeholders involved, including the CIO, CTO, CFO, chief human-resources officer (CHRO), chief information security officer (CISO), and business-unit leaders.

The move to cloud computing is a collective-action challenge, requiring a coordinated effort throughout an organization's leadership staff. In other words, it's a question of orchestration, and only CEOs can wield the baton. To accelerate the transition to the cloud, CEOs should ask their CIO and CTO what assistance they require to guide the business on the path.

     2. Enhancing business interactions 

To achieve the speed and agility that cloud platforms offer, regular engagement is required between IT managers and their counterparts in business units and functions, particularly those who control products and competence areas. CEOs must encourage company executives to choose qualified decision-makers to serve as product owners for each business capability.

  1. Be Agile

If your organization wants to benefit from the cloud, your IT department, if it isn't already, must become more agile. This entails more than simply transitioning development teams to agile product models. Agile IT also entails bringing agility to your IT infrastructure and operations by transitioning infrastructure and security teams from reactive, "ticket-driven" operations to proactive models in which scrum teams create application programme interfaces (APIs) that service businesses and developers can consume.

  1. Recruiting new employees 

CIOs and CTOs are currently in the lead due to their outstanding efforts in the aftermath of the epidemic. The CEOs must ensure that these executives maintain their momentum while they conduct the cloud transformation. 

Also, Cloud technology necessitates the hire of a highly skilled team of engineers, who are few in number but extremely expensive. As a result, it is envisaged that the CHRO's normal hiring procedures will need to be adjusted in order to attract the proper expertise. Company CEOs may facilitate this by appropriate involvement since this will be critical in deciding the success of the cloud transition.

  1. Model of Business Sustainability 

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  1. Taking risks into consideration 

Risk is inherent in all aspects of corporate technology. Companies must be aware of the risks associated with cloud adoption in order to reduce security, resilience, and compliance problems. This includes, among other things, engaging in comprehensive talks about the appropriate procedures for matching risk appetite with technological environment decisions. Getting the business to take the correct risk tone will necessitate special attention from the CEO.

It's easy to allow concerns about security, resilience, and compliance to stall a cloud operation. Instead of allowing risks to derail progress, CEOs should insist on a realistic risk appetite that represents the company plan, while situating cloud computing risks within the context of current on-premises computing risks and demanding choices for risk mitigation in the cloud.

Conclusion

In conclusion, the benefits of cloud computing may be obtained through a high-level approach. A smooth collaboration between the CEO, CIO, and CTO may transform a digital transformation journey into a profitable avenue for the company.

CEOs must consider long-term cloud computing strategy and ensure that the organization is provided with the funding and resources for cloud adoption. The right communication is critical in cloud migration: employees should get these communications from C-suite executives in order to build confidence and guarantee adherence to governance requirements. Simply installing the cloud will not provide value for a company. Higher-level executives (particularly the CEO) must take the lead in the digital transformation path.

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