Getting started with AI & ML- 10 plus use cases

June 22, 2022


Artificial Intelligence (AI) and Machine Learning (ML) are the two most used terms amongst the organizations in the tech industry today. The reason being their vast use among diverse industries for cost-cutting automation, reliability, security, and faster decision-making capacity.

But have you ever wondered what it takes to develop and master the abilities necessary for AI and ML after hearing so much about them every day?

In this blog, we'll show you how to achieve just that, as well as some current AI and machine learning use cases that may seize your attention and interest in such technologies.

#1 Learn how to program.

Initially to get started with AI and ML you should pick a programming language. R and Python, for example, are two languages created expressly for machine learning. You can do it in other languages as well, but Python makes it more easy and simple and also has the greatest machine learning and artificial intelligence modules and support. If you secure a job in this industry, you'll almost certainly be utilizing Python for the majority of your work. Python is fantastic since it can be used for more than only machine learning, and it is arguably one of the easiest languages to learn and use as a beginner.

#2 Comprehend Mathematics.

Once you have mastered a programming language, the second essential skill to gain towards being a master in AI and ML is learning Mathematics. Math is at the heart of machine learning, which aids in the development of an algorithm that can learn from data and generate an accurate prediction. A thorough understanding of the arithmetic fundamentals behind any central machine learning algorithm is critical. In this way, it assists you in selecting all of the appropriate algorithms for your data science and machine learning project. Machine learning is mostly based on mathematical preconditions, thus you'll find it more intriguing if you understand why math is utilized. You'll see why to choose one machine learning algorithm over another and how it affects the model's performance.

#3 Build Algorithmic Knowledge.

You may now go right into working with some of the fundamental algorithms to understand how things function and advance in the correct sequence after you've learned the necessary programming and mathematics. Linear regression, logistic regression, KNN, SVM, and other algorithms may be used. You can move into any field of machine learning once you've gone through these algorithms and understand how they function.

If all the stuff above sounds boring to you then maybe a few examples or use cases of AI and ML might intrigue you enough to put the effort into learning or mastering these skills.

#1 Cybersecurity

When we have a lot of data, whether in the cloud or on the endpoint, AI and machine learning perform exceptionally well, especially when combined with big data and analytics. The most appropriate application of AI would be in processing large amounts of data and in AI Cybersecurity, which would perform massive operations to identify anomalies, unusual or suspicious actions, detect and correct security flaws, strange activity, and zero-day attacks. AI and machine learning might be highly useful in spotting more complicated issues faster and more correctly than a human analyst. In the unfortunate event of an attack, AI and ML make systems ready for an automated reaction for minimizing the effects, conducting forensics, and successfully defending.

#2 Securing Personal Information

Personal information security is an ongoing problem in today's culture. People readily and voluntarily share their personal information in the digital world, whether they are ordering things online or signing up for regular news updates from news sites. Artificial intelligence (AI) and Machine Learning (ML) methods can reduce the likelihood of security breaches. They have the ability to make email platforms, and banking transactions more secure. They provide built-in threat protection for apps and also inform users about how websites handle their data. Thus providing complete security to the personal data of users.

#3 Trading

Artificial Intelligence (AI) and Machine Learning (ML) have the potential to tackle large-scale trade difficulties. These scenarios or issues almost often include optimization, analysis, or forecasting. In the trading world, machine learning and artificial intelligence are used in a variety of ways. Including the search for effective algorithmic trading strategies, historical data-based stock price prediction, and increasing the number of marketplaces that an individual must watch and respond to.

#4 Fraud Detection

Things that people used to buy in stores are now acquired online, whether it's furniture, food, or clothing. But this almost certainly prompts criminals to use the Internet to track down victims' wallets. More advanced and reliable fraud detection systems are available thanks to AI and ML algorithms' capacity to learn from previous fraud trends and spot them in future transactions. When it comes to the speed with which information is processed, machine learning algorithms appear to be more effective than people. In addition, machine learning algorithms may detect complex fraud qualities that a person cannot.

#5 Recommender Systems

To Demonstrate your understanding of your consumers to gain their trust and loyalty, the consumer data is fed to AI and ML recommender algorithms.  Then by using this data AI and ML provide personalized suggestions that are tailored to each customer's interests and preferences across all of their touchpoints. This increases consumer interaction resulting in increased sales and profits. 

#6 Healthcare

When compared to AI and ML, all traditional medical techniques, such as traditional analytics or clinical decision-making tools, have a number of drawbacks. As learning algorithms interact with this data on a regular basis, they may become more precise and accurate, providing individuals with unmatched insights into diagnosis, care processes, treatment variations, and patient outcomes. AI and ML in healthcare have the potential to improve patient health outcomes by improving preventative care and quality of life, as well as allowing for more precise diagnosis and treatment.

#7 Food Industry

Industrial automation is the appropriate solution for addressing the difficulties in the food business. Automation relies on artificial intelligence (AI) and machine learning (ML) techniques. By using an AI-based system, food manufacturing and distribution activities may be managed more efficiently and effectively. Product categorization and packaging, demand-supply chain management, revenue prediction, and self-ordering systems are just a few of the AI and ML use cases in the food sector.

#8 Logistics

The combined influence of AI and machine learning on various parts of logistics has propelled the sector to new heights. A good logistical chain necessitates a significant investment of cash, as well as the involvement of various middlemen and enterprises to speed up the process, which is where AI comes in. This is due to one of artificial intelligence's most distinguishing characteristics: its ability to reason and take actions that have the highest chance of attaining a certain objective. This partnership has several advantages, including cost, speed, safety, and convenience, to mention a few.

#9 Claim Litigation

With AI-powered judges, AI robot attorneys, and AI-powered features for contract or team management systems, AI and ML have made their way into the day-to-day work of lawyers and are revolutionizing the legal profession. The most promising feature of using AI and ML in the claims industry is the ability to automate simple and repetitive operations like legal bill review while allowing human specialists to improve outcomes beyond what machines or humans could achieve alone.

#10 Marketing

Advanced AI and ML-enabled features have opened up new marketing and narrative possibilities. AI is at the heart of a new era of marketing that focuses on achieving greater degrees of customization and targeting while remaining contextual. The focus has switched from mass advertising to a more micro-targeted approach thanks to AI and machine learning. Marketers who incorporate machine learning algorithms into their marketing processes can achieve outstanding results. There are wonderful possibilities that come with huge challenges. As customer expectations increase, marketers have the chance to deliver personalization and relevance on a large scale. This may be accomplished through customized campaigns that are based on real-time client intent. Also, AI and ML can help make marketing campaigns more relevant.

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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.

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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

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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.

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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.

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