The growing adoption of AI and machine learning

April 26, 2021

Artificial Intelligence and Machine Learning are two disruptive technologies that are changing business, education, healthcare and finance in productive ways. Reports of the International Data Corporation (IDC) survey say that companies are already spending more on AI. Over half of the businesses have adopted AI in one form or other. To implement such powerful technologies and empower your business, these are the basic requirements.

Find out the commonalities across your process flow

In order to be proactive in implementing these advanced technologies, we have to identify commonalities throughout the business. These commonalities or process flow has to be grouped under a leader like Chief Information Officer. The leader must identify the right technology to be implemented.

Research on topics and tools

Learn every nuance in technology and develop skills that suit the AI potential. Since this stream is exponentially growing, there is a huge skill gap. To overcome this disadvantage, train your talent pool to face AI potential.

AI-based analytics

The first place where you can seamlessly implement AI in your business is in analytics. If you are a communication service provider, you could implement AI in data gathering and analysing. If you are a manufacturer or product based company, you could implement AI in customer interaction and services.

After implementing AI in analytics, standardise those data implementation without any prior judgement. Through this approach, you let the technology partner unleash their full potential in analysing the data you gathered. 

Set realistic goals

As mentioned earlier, there is a huge demand for AI professionals. Even if certain organisations have skilled AI professionals, they are naive about creating interesting and challenging roles for them. This stresses both the organisation and the professional who is employed. To reduce this stress, you need to set realistic goals for the AI professional. On the other hand, the AI specialist should also understand that they should be working with system integrators and vendors for a while. 

By implementing automation in networks and systems, we avoid huge operational costs. These cloud-based solutions are easy to implement and allow a flexible working environment.

Artificial Intelligence and Machine Learning are two disruptive technologies that are changing business, education, healthcare and finance in productive ways. Reports of the International Data Corporation (IDC) survey say that companies are already spending more on AI. Over half of the businesses have adopted AI in one form or other. To implement such powerful technologies and empower your business, these are the basic requirements.

Find out the commonalities across your process flow

In order to be proactive in implementing these advanced technologies, we have to identify commonalities throughout the business. These commonalities or process flow has to be grouped under a leader like Chief Information Officer. The leader must identify the right technology to be implemented.

Research on topics and tools

Learn every nuance in technology and develop skills that suit the AI potential. Since this stream is exponentially growing, there is a huge skill gap. To overcome this disadvantage, train your talent pool to face AI potential.

AI-based analytics

The first place where you can seamlessly implement AI in your business is in analytics. If you are a communication service provider, you could implement AI in data gathering and analysing. If you are a manufacturer or product based company, you could implement AI in customer interaction and services.

After implementing AI in analytics, standardise those data implementation without any prior judgement. Through this approach, you let the technology partner unleash their full potential in analysing the data you gathered. 

Set realistic goals

As mentioned earlier, there is a huge demand for AI professionals. Even if certain organisations have skilled AI professionals, they are naive about creating interesting and challenging roles for them. This stresses both the organisation and the professional who is employed. To reduce this stress, you need to set realistic goals for the AI professional. On the other hand, the AI specialist should also understand that they should be working with system integrators and vendors for a while. 

By implementing automation in networks and systems, we avoid huge operational costs. These cloud-based solutions are easy to implement and allow a flexible working environment.

Al and Machine Learning are the most advanced technology for software-defined networking and network function virtualisation. 

Be the first in the industry

Showcase your technology capabilities by being the guinea pig in implementing advanced potential available. However, keep your options open to centralise the process and technology. AI and ML are capable of handling inventory,  governance, maintenance, and common analytics. Collaborate with others to establish a project that is profitable and advanced.

Application of AI and ML

Some of the readily available AI and ML technologies are in: 

A) data analysis 

B) communication management 

C) process automation, and 

D) customer care

These technologies are found to be useful across various verticals of business. From healthcare to manufacturing AI is revolutionising the business world. 

  1. ML is finding immense opportunity in running models for healthcare providers and insurance companies in the healthcare domain. Besides, these technologies have promising results in disease diagnosis equipment. This will enable doctors to predict and treat patients at the right time. 
  2. ML models are already proving helpful in the finance domain by finding the irregularities in customer payment and behaviour. Mountainous transactions happen in a day and these data can easily be compromised.  By strengthening these technologies, we create a cyber safe. 
  3. In the retail market, AI and ML are used to find new combinations and customer preferences. This helps in providing the customer with a wide range of options.

Challenges in adopting AI

Even though they are very advanced and can be the most helpful technology solutions you could afford, there are four challenges involved. 

  1. Data storage is a big issue when you are planning for on-site storage. Cloud storage solutions are the best recommended, particularly a public cloud solution will be the best.
  2. If the data is not processed properly, the AI model may fail to interpret them.
  3. Without proper data organisation, it is likely sending the AI model into a sea of information without any direction instructions.
  4. To adopt such advanced technology, the organisation must have maturity in handling these technologies.

But all these drawbacks can be overcome by implementing the right solution with the right attitude.

How can E2E Cloud help you in implementing these advanced technologies?

The life-changing technology solutions we discussed are all cloud-based. The E2E cloud server can help you in implementing these solutions. You can also migrate from other cloud service providers to the E2E cloud. The steps are easy, and you could do it in a few hours. 

The E2E cloud is trusted by many top brands globally because of its prompt service and high uptime

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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.

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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?

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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 –

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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/

https://blog.hubspot.com/service/customer-acquisition

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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:

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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:

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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:

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  • 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

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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:

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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

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