Hottest Jobs in Artificial Intelligence.

May 2, 2022

There are some things that sound astonishing but they are not, A similar case goes with fetching out the most prominent artificial intelligence job for yourself. While many industries remain severely affected by the consequences of the COVID-19 crisis, there is one sector that is actively recruiting: jobs in AI are booming, and the trend is showing no sign of slowing down. 

Artificial intelligence, and machine learning are changing the face of the global economy. How much do you know about the technology and its effects?

  • Just 15% of organizations use AI today; by next year, that number will be 31%.
  • A new type of artificial intelligence will become a bioelectronic hybrid. 
  • A new report carried out by a research agency in the UK said 110,500 jobs were posted in the past year. 
  • Every month for the past three years between 8000-10000 roles were posted online. 

Table Of Content

  • Exploring Artificial Intelligence Jobs market size.
  • Industries where Artificial Intelligence is being used intensively.
  • 7 most promising jobs in the Artificial Intelligence Industry.
  • How Artificial Intelligence will impact the job market in India?
  • Conclusion

Exploring Artificial Intelligence Jobs Market Size-

The artificial intelligence market is expanding, especially in light of the pandemic and the resulting business model adjustments. AI has been used by businesses all over the world to help with automation, workforce management, and digital transformation. 

The market for AI software reached $62.3 billion in 2020 and is predicted to rise at a breakneck pace to $997.8 billion by 2028.

Industries where artificial intelligence is being used intensively-

Amazon.com, Apple Inc., Google LLC, Facebook, Microsoft, and International Business Machines Corporation are among the tech behemoths spending heavily on AI research and development. AI is being incorporated into nearly every instrument and program, from self-driving cars to life-saving medical equipment. 

AI has already been shown to be a major game-changer in the approaching digital world. These businesses are working on making AI more accessible for business applications. 

  • Healthcare
  • Banking & Financial Services
  • Retail & Ecommerce
  • Logistics & Transportation
  • Entertainment & Gaming
  • Manufacturing

7 most promising jobs in the Artificial Intelligence Industry-

These jobs are listed in descending order in accordance with the compensation paid in each role. 

1. Big Data Engineer/Architect

Big data architects are in charge of creating a framework that accurately mimics a company's big data requirements using data, hardware and software, cloud services, developers, and other IT infrastructure, to align an organization's IT assets with its business objectives. They are essential in any organization that uses big data solutions to work with massive data collections. 

They collaborate with banks, technology companies, information solutions companies, payment solutions, and consulting organizations, among other entities. 

Skills Required-

  • Knowledge of tools like Hive, Spark, HBase, Sqoop, Impala, Kafka, Flume, Oozie, MapReduce, etc. 
  • Spark Streaming, Spark, Kafka, and other Hadoop tools. 
  • Linux 
  • Python, Java, Shell Scripting, or Scala. 
  • SQL and Data modeling

2. Data Scientist 

In day-to-day operations, businesses are increasingly relying on data. A data scientist interprets raw data and pulls meaningful information from it. They then analyze the data to look for patterns and propose solutions that will help an organization grow and compete. If we had to define a data scientist, we'd say someone who extracts value from data.

Skills Required-

  • Python or R
  • SQL
  • SAS 
  • Tableau or PowerBI for Excel 
  • Deep Learning or Machine Learning
  • Apache Spark and Hadoop. 

3. User Experience

User experience (UX) designers are currently among the most in-demand creative talents. People who can help conceptualize and construct intuitive and engaging online experiences are needed across the country as businesses use AI more frequently to update their websites, and mobile apps, and to interact with customers in new ways.

Skills Required-

  • CSS and Figma 
  • Canva 
  • Javascript 
  • Miro Prototype Touchpoint Analysis 
  • Miro's User Experience Flow using Sitemaps

4. AI Engineer 

An Artificial Intelligence Engineer is a computer scientist whose goal is to create intelligent algorithms that can learn, analyze, and anticipate future occurrences. Their mission is to develop machines that can reason like a human brain. 

As a result, the AI engineer is also a researcher: he or she studies the human brain's functioning to create computer programs with human-like cognitive capacities.

Skills Required-

  • Java, Python, R, and C++.
  • Probability, Statistics, and Linear Algebra.
  • Cassandra, Hadoop, and MongoDB.
  • KNN, Support Vector Machine, linear regression, and Naive Bayes.
  • TensorFlow, Theano, PyTorch, and Caffe.

5. Natural Language Processing

By merging information, business process improvement, and technology, NLP engineers or developers are responsible for developing new solutions to meet business commitments and opportunities. 

The NLP Engineer's tasks include converting natural language input into relevant characteristics for classification algorithms utilizing NLP approaches.

Skills Required-

  • Text representation
  • Semantic extraction techniques 
  • Modeling
  • Python, Java, and R 
  • Frameworks - Keras or PyTorch 
  • Libraries - sci-kit
  • Products life cycle - Design, Development, Quality, Deployment, and Maintenance

6. Business Intelligence (BI) Developer

Business intelligence has evolved into a valuable asset for any modern company. The term "business intelligence" refers to a variety of strategies and technologies employed by businesses used to give actionable data to end-users so that they can make informed business decisions.

A business intelligence developer is an engineer who creates, delivers, and maintains business intelligence interfaces. Query tools, data visualization, interactive dashboards, ad hoc reporting, and data modeling tools are just a few examples.

Skills Required-

  • Data warehouse design - dimensional modeling.
  • Data mining.
  • Microsoft Power BI, Tableau, or Oracle BI.
  • Python or R.
  • SQL, SQL Server Integration Services (SSIS), and SQL Server Reporting Services (SSRS).

7. Data Analytics

To answer a query or solve an issue, a data analyst collects, cleans, and evaluates data sets. 

Business, finance, criminal justice, science, medical, and government are just a few of the fields where data analysts operate. 

What types of clients should a business focus on in its next marketing campaign? 

What age group is the most susceptible to disease? What behavioral patterns are linked to financial fraud?

Skills Required-

  • Google Sheets and Microsoft Excel 
  • SQL 
  • R or Python 
  • Tableau or  Microsoft Power BI 
  • Jupyter Notebooks 
  • SAS

You can check the salaries for AI sector here-

https://www.payscale.com/research/IN/Skill=Artificial_Intelligence_(AI)/Salary

How Artificial Intelligence will impact the job market in India?

“PM Modi has a goal of making India a global center for artificial intelligence. Many Indians are working on technology all across the world, and many more will in the future” . He spoke about building a conducive learning environment at the RAISE Summit, citing initiatives such as the National Educational Technology Forum (NETF).

According to The Indian Express, “Artificial Intelligence would create nearly 20 million employment by 2025. These figures reflect the positive response and the most recent technological advancements in every industry imaginable” 

Disease detection, mental health counseling, weather forecasting, crop predictions, studies, designing, urban city planning, sewage systems, traffic planning, disaster management, and fashion and space research are only a few of the fields that have been explored. It's all been touched by AI.

Conclusion-

There has been much information and research on the impact of AI on work, ranging from nature of jobs, to workplace configurations to issues around bias, privacy, ethics and more, challenging many assumptions that we have lived with in the past and creating new possibilities. The more we understand the nature of unique data sets, the better placed we will 

be to make best use of the benefits and mitigate the risks that these technologies bring to us.

So, looking for a career change/start? Grab the opportunity today.

Know more about E2E Cloud - https://bit.ly/3eaePdo
Contact no - 9599620390
Email - raju.kumar1@e2enetworks.co

Latest Blogs
This is a decorative image for: A Complete Guide To Customer Acquisition For Startups
October 18, 2022

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/

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

This is a decorative image for: Constructing 3D objects through Deep Learning
October 18, 2022

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

This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts
October 18, 2022

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:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. 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.
  3. 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.
  4. 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

Build on the most powerful infrastructure cloud

A vector illustration of a tech city using latest cloud technologies & infrastructure