Machine Learning (ML) is an integral part of Data Science (DS), that helps computers learn from data. This process of learning from data through machine learning techniques contributes to Artificial Intelligence (AI).Here is a quick overview and simplified steps for developing AI Models.

A Guide to Artificial Intelligence Model Development

<ol><li>Identification of Business Case</li><li>Collection of Data</li><li>Preparation of Data</li><li>Building the Model</li><li>Testing the Model</li><li>Validate the Model</li><li>Deploy the Model</li><li>Govern the Model</li></ol>

Steps Involved in AI Model Development

Ask the right questions.<ul><li>What results are you expecting from the process?</li><li>What processes are being used ?</li><li>What are the KPI's that can help track success?</li><li>What resources are required?</li><li>How do you break down the problem?</li></ul>Based on your answers, you will fall under one of the categories:<ol><li>Classification - helps categorize something into type-a/type-b/multi-type</li><li>Regression - helps predict a definite number of defined parameters. Eg: Covid-19 Projections, Holiday Seasons Sales Projections, etc.</li><li>Recommendation - analyze past data and identify patterns. Eg: video recommendations, target sales ads, etc.</li></ol>

1. Identification of Business  Case

ML Models are only as accurate as the data fed to them.Important to identify the right set & format of data to ensure accuracy & relevance of the model.Ask relevant questions:<ul><li>Data required to solve the problem. Eg. Customer data, Inventory</li><li>Quantity of the data we have, and required</li><li>Collection & Storage Mechanism</li><li>Training Methods & Data Pipelines</li></ul>Common formats of data are<ol><li>Structured - fixed rows & columns. Eg. Inventory, sales</li><li>Unstructured - like variable texts, images, videos</li><li>Static - historical - sales & call history</li><li>Streaming - continuous, real time - web site traffic</li></ol>

2. Collection of Data

It is critical, though time consuming to clean the available data and transforming it into required formats. Involves segmentation of  data sets into training, testing and validation.<ul><li>Process the available data</li><li>Clean the data set for erroneous and Irrelevant data</li><li>Transform the data into required format</li><li>Enhance and augment the dataset if applicable</li></ul>

3. Preparation of Data

<ul><li>Define the features of the model. </li><li>Use the same features for both Training and Testing the model</li><li>Collaborate closely with Subject Matter Experts </li><li>Be wary of "Curse of Dimensionality" - do not use unnecessary and irrelevant features that reduces the model accuracy</li><li>Choose suitable Algorithms</li><li>Consider Model interpretability - Predictions and Decisions should be clearly explainable</li></ul>For Improving Results:<ul><li>Tune the hyperparameters or algorithm parameters like no. of trees of Random Forest or no. of layers in a Neural Network</li><li>Use pre-trained models, reuse them to build new models</li><li>Version each iteration</li></ul>

4. Building the Model

Primary objective of Model Testing is to improve results and minimize changes in model behavior post-deployment in real world.Carry out multiple experiments using Training, Validation and Testing Datasets.If model performs poorly on Training data improve the model by <ul><li>selecting a better algorithm, </li><li>increasing quality of data, or</li><li>feeding more data</li></ul>If model performs poorly on Testing data - there might be overfitting, i.e. too closely fit with limited no. of data points. Then, add more data to the model.

5. Testing the Model

After testing the model with different datasets, validate the model performance using the business parameters defined in step 1.Analyze if KPI's and Business Objectives of the model are achieved. If not, consider changing the model, or improving the quality and quantity of the data.

6. Validate the Model

After successful validation against all defined parameters, deploy the model onto planned infrastructure - cloud, edge, or on-premises environment.Before deployment, consider the following<ul><li>plan to continuously measure and monitor the model performance</li><li>define a baseline to measure future iterations of the model</li><li>keep iterating the model to improve model performance with changing data</li></ul>

7. Deploy the Model

When a model is deployed in real-world, the data fed to it is dynamic.There can also be changes in technology, business goals or drastic real world changes like the pandemic. It is crucial to analyze how these changes affect the model, so you can reiterate.Consider monitoring the model for:<ul><li>deviations  from the pre-defined accuracy of the model</li><li>irregular decisions or predictions</li><li>drifts in the data affecting the model performance</li></ul>Continuously keep iterating the model to keep up with the changes in data, technology and business.

8. Govern the Model

AI models need time to be developed.A smooth and successful model development involves a combined effort from data engineers, data scientists, ML engineers and DevOps engineers.Proper resource allocation, project planning and management is crucial to meet the business goals and objectives.

Further Considerations

The terminologies might change, the technologies might evolve, but this is the future. And that Future is already here! 

In 2019, Venturebeat reported that almost 87% of data science projects do not get into production. Redapt, an end-to-end technology solution provider, also re…

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Technology & The Future

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Computer Science

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Artificial Intelligence

A Step By Step Guide To AI Model Development

full name in Vedic texts is Bharadvaja Barhaspatya , his father name was Brihaspati and His mother was Mamata, wife of Utathya Rishi .

3. Maharishi Bhardwaj 

<a href="https://12monthview.com/shivkar-bapuji-talpade/">Maharishi Bharadwaj </a>was one of our ancient great scientists. His life was simple. Maharishi Bharadwaj got the privilege of meeting Shri Ram twice in Tretayuga. Once in the exile period of Shri Ram and for the second time Shri Ram had met on his return to Ayodhya from Sri Lanka. It is mentioned in Maharishi Valmiki’s book Ramayana and Tulsi Das’s Ram Charit Manas. His ashram was a short distance away from Prayagraj Sangam, which is still there today..

His Meetings With Shree Ram

The names of the children (progeny) of Maharishi Bharadwaj are Drona , Garga, Ilavida, Katyani, Shruvati. Maharishi Bharadwaj had two daughters. Maharishi Bharadwaj composed more books, among them Yatra Sarvastra and Vimana Shastra, Grammar, Ayurveda, Dhanurveda Arthashastra, Political Science and Purana.

Contributions 

Even today the discussion of <a href="https://12monthview.com/shivkar-bapuji-talpade/">Vimana Shastra </a>takes place among us. Charak Rishi has called Maharishi Bharadwaj as one of infinite age. He composed the <a href="https://12monthview.com/shivkar-bapuji-talpade/">Vimana Shastra </a>before 600 Era. In this, passenger aircraft, fighter aircraft and space satellites (aircraft flying in the second planet) have also been mentioned. The first book on Vimana Shastra is the name of Maharishi Bharadwaj, which is more than 1500 years old.

VIMANA SHASTRA

Some interesting things have been written in this book which has been told by <a href="https://voices.shortpedia.com/karan-kapoor/shivkar-bapuji-talpade-first-aircraft-inventor/?amp">Shivkar Bapuji Talpade</a> that 8 chapters are written in it and it has 100 volumes in which the detail of making the aircraft is described. Maharishi Bharadwaj has written 500 principles of aircraft making. And in this book there are 32 meathod to make vimana i.e. 1 vimana can become 1 method, 2 vimana 2 meathod such that 500 vimana can become 32 mathod se. Vimana Shastra has 3000 verses, 100 sections and 8 chapters.

Shivkar Bapuji Talpade

Vaimanik Shastra is a part of Bharadwaj’s Yantra Sarvasva. It is also known as Brihadvimana Shastra. Vaimanika Shastra explains in detail about aeronautics, including how to use them for aircraft design and applications. The knowledge of aeronautics is described in Sanskrit in 100 volumes, eight chapters, 500 principles and 3000 verses. The great sage Bharadwaja explained the construction of the aircraft and how to fly it in the air, on land, over water and using a single aircraft like a sub-sea.

YATRA SARVASTRA (Brihadvimana Shastra)

He also described the construction of warplanes and fighter planes. Vaimanika Shastra explains the metals and alloys and other essential materials to make an aircraft indestructible in any condition. Along with the treatise are pictures of three types of airplanes – “Sundar”, “Shukna” and “Rukma”. The vimanas are classified into three types – Mantrika, Tantrik and Krutaka, corresponding to different eras or eras. The aircraft used in Kali Yuga, the running Yuga is called Krutakaviman, which is flown with engine power.

SUNDAR, SHUKNA, RUKMA

Bhardwaj says that there are thirty-two secrets of aeronautical science. Some of these are astonishing and some point ahead of our time. For example the mystery of the “Para Shabd Griha”, i.e. a cabin for listening to conversations in another aircraft, is explained by a detailed description of an electrically working sound-receiver that did the trick. The construction of different types of equipment and putting them together to make an aircraft is also described.

SECRETS SCIENCE

Maharishi Bhardwaja newrepublic.com Maharishi Bhardwaj full name in Vedic texts is Bharadvaja Barhaspatya, his father name was Brihaspati and His mother was Mamata, wife of Utathya Rishi . chiraan.com Maharishi Bharadwaj was one of our ancient great scientists. His life was simple. Maharishi Bharadwaj got the privilege of meeting Shri Ram twice in Tretayuga. Once […]

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Society, Arts & Culture

Maharishi Bhardwaj– First to share about Aircrafts 

We all are somewhat doomed by heavy social media consumption in one or the other way. The tools that we once thought as an effective way of sharing became a tool of weird thoughts, confusion, anxiety & a lot more for a lot of people who can't control their usage.Here are 5 quick tips that'll help in reducing social media usage:<ol><li>Turn off all social apps notifications</li><li>Enable <a href="https://www.oneplus.in/blog/product/zen-mode-explained">Zen mode</a> (Google up for your device)</li><li>Set <a href="https://support.google.com/youtube/answer/9012523?hl=en&co=GENIE.Platform%3DAndroid">break reminders</a></li><li>Make use of <a href="https://wellbeing.google/">digital wellbeing </a>on your phone</li><li>Try to create, rather than just consume!</li></ol>Do incorporate healthy tech habits!

5 Significant Things You Can Do To Minimise Social Media Usage

This helped me in forming better tech habits & spending a much more productive & mentally peaceful day.

Understand yourself and those around you.

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Psychology

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Habits

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Problem Solving

Why too much notifications?

The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.

Edsger W. Dijkstra — ‘The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.’

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Science & Nature

“The question of whether a computer can think is...”

A huge influx of capital by tech giants, especially search engines, means that AI computing power is making rapid advancements in a range of sectors from healthcare to construction to marketing and search engine optimization.However, before we get into the implications of machine learning for SEO professionals, let’s define what we mean by AI.

Search Engines And AI

This type of AI is designed to perform specialized tasks that must be “taught” to the algorithm (think Google’s search algorithms). 
While extremely specialized in scope, narrow AI (ANI) is able to quickly recognize patterns and perform tasks in a way that outpaces human ability.

The Three Types Of AI: Narrow or Weak AI

Capable of autonomously learning and solving problems, general AI (AGI) takes machine learning to the next level. 
This AI is powered by deep learning processes designed to mirror the human brain’s neural networks, allowing the algorithm to make decisions without instruction.

General or Strong AI

At the moment, artificial superintelligence (ASI) still lands fully in the category of science fiction. 
This type of AI would, theoretically, be capable of outperforming human capabilities to solve the “unsolvable” problems of our time.

Artificial Superintelligence

While companies like OpenAI and Conversion.ai are moving toward developing general AI for natural language processing, there are currently no clear-cut examples of AGI.To progress from ANI to AGI, deep learning will be the key to creating stronger AI capable of using deductive reasoning to analyze complex, unstructured data and make independent decisions.

ANI(Normal AI) To AGI(General AI)

Back in 2016, Google declared its intention to become a “machine learning first” company. Since then, they’ve made steady strides toward that goal, launching Google AI in 2017 and rolling out BERT in 2019.They want to not only make our lives easier but also use AI to find “new ways of looking at existing problems, from rethinking healthcare to advancing scientific discovery.”

Google: The AI Mission

Google has been making steady progress in the way it connects users to the content they’re searching for, including these nine ways we know search engines are using machine learning right now.

Google And How It Uses AI

Low-quality content typically has distinct similarities, such as:<ul><li>The presence of several outbound links to unrelated pages.</li><li>Lots of uses of stop words or synonyms.</li><li>The occurrence rate of identified “spammy” keywords.</li></ul>Machine learning recognizes these patterns and flags them.Even though Google still uses human quality raters, utilizing machine learning to detect these patterns drastically cuts down on the amount of manpower necessary to review the content.

Pattern Detection

RankBrain is the machine learning algorithm developed by Google that not only helps identify patterns in queries, but also helps the search engine identify possible new ranking signals.Before RankBrain, Google’s algorithm was coded entirely by hand. It depended on a team of engineers to analyze search query results, run tests to improve the quality of those results and implement the changes.

Identification of New Signals

Google’s end goal is to use technology to provide users with a better experience. They don’t want to automate the entire process if that means the user won’t have the experience they are looking for.So don’t assume machine learning will soon take over all search ranking; it is simply a small piece of the puzzle search engines have implemented to hopefully make our lives easier.

The Real Use Of AI

Google’s current privacy policies discuss how the search engine currently creates personalized search results based on a user’s behaviour.Google’s personalized search patent states that:“…personalized search generates different search results to different users of the search engine based on their interests and past behaviour.”We can clearly see this in action. Often used in conference presentations, proving this process is as simple as typing a string of queries into Google in one sitting and seeing how the results change depending on what you last searched.

Real-Time Personalization Of Search Results

It’s important for a search engine to be able to recognize how similar one piece of text is to another. This applies not just to the words being used but also their deeper meaning.Bidirectional Encoder Representations from Transformers – BERT, for short – is a natural learning processing framework that Google uses to better understand the context of a user’s search query.

Natural Language Processing and BERT

People don’t always speak like a machine would expect them to. We play with language to come up with new turns of phrases.BERT is designed to replicate human recognition as closely as possible to decode those contextual nuances by learning how users interact with the content and matching search queries with more relevant results.As language develops and transforms, machines are better able to predict our meanings behind the words we say and provide us with better information.

How Users Interact With Machines

Every second, approximately 1087 photos are uploaded to Instagram, and 4000 are uploaded to Facebook. That’s hundreds of millions of photos being uploaded to those two social networks alone every day.Analyzing and cataloging that many submissions would be an arduous (if not impossible) task for a human, but it’s perfect for machine learning.Machine learning analyzes color and shape patterns and pairs them with any existing schema data about the photograph to help the search engine understand what an image actually is.

Image Search to Understand Photos

Just like its organic search results, Google wants to provide the most relevant ads for its individual users. According to Google U.S. patents on ad quality, machine learning can be used to improve an “otherwise weak statistical model.”This means that Ad Rank can be influenced by a machine learning system.

Ad Quality & Targeting Improvements

When you see search results that don’t include the keyword in the snippet, it’s likely due to Google using RankBrain to identify synonyms.When searching for [forest preservation], you’ll see various results with the word “protection” as it can be used interchangeably with “preservation” in this case.

Giving You What You Searched For But Didn't Type

There are many reasons to fire up a search engine. Users may be searching to buy (transactional), research (informational), or find resources (navigational) for any given search.By analyzing click patterns and the content type that users engage with (e.g., CTRs by content type) a search engine can leverage machine learning to determine the intent behind the user’s search.

Query Clarification

While machine learning isn’t (and probably never will be) perfect, the more humans interact with it, the more accurate and “smarter” it will get.This could be alarming to some, creating visions of Skynet from the “Terminator” movies.However, the actual result may be a better experience with technology that solves complex problems and allows humans to focus on driving creativity and innovation

The Bottomline

Deep-dive into google's machine learning models and how it relies more on logic than data

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Software Engineering

Machine Learning With Google

We know that search engines are using machine learning in these 9 ways. Here’s what it means for SEO and digital marketing.

How Search Engines Use Machine Learning: 9 Things We Know For Sure

The development of AI is creating new opportunities to improve the lives of people around the world, from business to healthcare to education. 
It is also raising new questions about the best way to build fairness, interpretability, privacy, and security into these systems.

Responsible AI practices

Reliable, effective user-centered AI systems should be designed following <a href="https://techdevguide.withgoogle.com/">general best practices for software systems</a>, together with practices that address considerations unique to machine learning.

General recommended practices for AI

The way actual users experience your system is essential to assessing the true impact of its predictions, recommendations, and decisions.<ul><li>Design features with appropriate disclosures built-in: clarity and control are crucial to a good user experience.</li><li>Consider augmentation and assistance.</li><li>Model potential adverse feedback early in the design process, followed by specific live testing and iteration for a small fraction of traffic before full deployment.</li><li>Engage with a diverse set of users and use-case scenarios, and incorporate feedback before and throughout project development. </li></ul>

Use a human-centred design approach

The use of several metrics rather than a single one will help you to understand the tradeoffs between different kinds of errors and experiences.Consider metrics including feedback from user surveys, quantities that track overall system performance and short- and long-term product health (e.g., click-through rate and customer lifetime value, respectively), and false positive and false negative rates sliced across different subgroups.Ensure that your metrics are appropriate for the context and goals of your system.

Identify multiple metrics to assess training and monitoring

ML models will reflect the data they are trained on, so analyze your raw data carefully to ensure you understand it. <ul><li>Does your data contain any mistakes (e.g., missing values, incorrect labels)?</li><li>Is your data sampled in a way that represents your users and real-world settings?</li><li>Are any features in your model redundant or unnecessary? </li><li>If you are using a data label X as a proxy to predict a label Y, in which cases is the gap between X and Y problematic?</li></ul>

Directly examine your raw data

The difference between performance during training and performance during serving—is a persistent challenge. 
During training, try to identify potential skews and work to address them, including by adjusting your training data or objective function. During the evaluation, continue to try to get evaluation data that is as representative as possible of the deployed setting.

Training-serving skew

<ul><li>A model trained to detect correlations should not be used to make causal inferences, or imply that it can. </li><li>Machine learning models today are largely a reflection of the patterns of their training data.</li><li>Communicate limitations to users where possible.</li></ul>

Understand the limitations of your dataset and model

<ul>
<li>Conduct rigorous unit tests to test each component of the system in isolation.</li>
<li>Conduct integration tests to understand how individual ML components interact with other parts of the overall system.</li>
<li>Proactively detect input drift by testing the statistics of the inputs to the AI system to make sure they are not changing in unexpected ways.</li>
<li>Use a gold standard dataset to test the system and ensure that it continues to behave as expected.</li>
<li>Conduct iterative user testing to incorporate a diverse set of users’ needs in the development cycles.</li>
<li>Build quality checks into a system.</li>
</ul>

Test, test, test

<ul><li>Issues will occur: any model of the world is imperfect almost by definition. Build time into your product roadmap to allow you to address issues.</li><li>Consider both short- and long-term solutions to issues. A simple fix (e.g., blocklisting) may help to solve a problem quickly, but may not be the optimal solution in the long run. Balance short-term simple fixes with longer-term learned solutions.</li><li>Before updating a deployed model, analyze how the candidate and deployed models differ, and how the update will affect the overall system quality and user experience.</li></ul>

Monitor and update the system after deployment

We're committed to progress in the responsible development of AI and to sharing knowledge, research, tools, datasets, and other resources. Learn more about recommended practices and our current work.

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Philosophy

Responsible AI practices – Google AI

The transcription feature in the Google Translate app may be used to create a live, translated transcription for events like meetings and speeches, or for a story at the dinner table. In such settings, it is useful for the translated text to be displayed promptly to help keep the reader engaged.Early versions of this feature the translated text suffered from multiple real-time revisions. The non-monotonic relationship between the source and the translated text, in which words at the end of the source sentence can influence words at the beginning of the translation.

Real Time Conversion Of Languages We Don't Understand

The new version of the Google Translate app that significantly reduces translation revisions and improves the user experience. The research enabling this is presented in two papers. The <a href="https://arxiv.org/abs/2004.03643">first</a> formulates an evaluation framework tailored to live translation and develops methods to reduce instability. The <a href="https://arxiv.org/abs/2004.03643">second</a> demonstrates that these methods do very well compared to alternatives, while still retaining the simplicity of the original approach. The resulting model is much more stable and provides a noticeably improved reading experience within Google Translate.

A New Update

Erasure: Measures the additional reading burden on the user due to instability. It is the number of words that are erased and replaced for every word in the final translation.Lag: Measures the average time that has passed between when a user utters a word and when the word’s translation displayed on the screen becomes stable. Requiring stability avoids rewarding systems that can only manage to be fast due to frequent corrections.BLEU score: Measures the quality of the final translation. Quality differences in intermediate translations are captured by a combination of all metrics.

Evaluating Live Translation: The Metrics

It is important to recognize the inherent trade-offs between these different aspects of quality. Transcribe enables live-translation by stacking machine translation on top of real-time automatic speech recognition. For each update to the recognized transcript, a fresh translation is generated in real time; several updates can occur each second. This approach placed Transcribe at one extreme of the 3 dimensional quality framework: it exhibited minimal lag and the best quality, but also had high erasure. Understanding this allowed us to work towards finding a better balance.

The Performance Measure of Quality

One straightforward solution to reduce erasure is to decrease the frequency with which translations are updated. Along this line, “streaming translation” models (for example, <a href="https://arxiv.org/abs/1810.08398">STACL</a> and <a href="https://arxiv.org/abs/1906.05218">MILk</a>) intelligently learn to recognize when sufficient source information has been received to extend the translation safely, so the translation never needs to be changed. In doing so, streaming translation models are able to achieve zero erasure.

Stabilizing Re-translation

In our paper, “<a href="https://arxiv.org/abs/2004.03643">Re-translation versus Streaming for Simultaneous Translation</a>”, we show that our original “re-translation” approach to live translation can be fine-tuned to reduce erasure and achieve a more favourable erasure/lag/BLEU trade-off. Without training any specialized models, we applied a pair of inference-time heuristics to the original machine translation models — masking and biasing.

Real-Time Streaming 

The end of an on-going translation tends to flicker because it is more likely to have dependencies on source words that have yet to arrive. We reduce this by truncating some number of words from the translation until the end of the source sentence has been observed. This masking process thus trades latency for stability, without affecting quality. This is very similar to delay-based strategies used in streaming methods such as <a href="https://arxiv.org/abs/1810.08398">Wait-k</a>, but applied only during inference and not during training.

The End Game

The combination of masking and biasing, produces a re-translation system with high quality and low latency, while virtually eliminating erasure. The table below shows how the metrics react to the heuristics we introduced and how they compare to the other systems discussed above. The graph demonstrates that even with a very small erasure budget, re-translation surpasses zero-flicker streaming translation systems (MILk and Wait-k) trained specifically for live-translation.

Zero-Flicker Streaming

The solution outlined above returns a decent translation very quickly, while allowing it to be revised as more of the source sentence is spoken. The simple structure of re-translation enables the application of our best speech and translation models with minimal effort. However, reducing erasure is just one part of the story — we are also looking forward to improving the overall speech translation experience through new technology that can reduce lag when the translation is spoken, or that can enable better transcriptions when multiple people are speaking.

Stabilizing Live Speech Translation in Google Translate

A digital coin is an asset native to its own blockchain. For example, Bitcoin exists on the Bitcoin blockchain, Ether operates and functions on the Ethereum blockchain, etc. Digital coins don't move physically when you send and receive them. They exist as data on a database (or blockchain) and are tracked and verified by computers around the world. Coinmarketcap lists over 900 different examples of coins, such as Bitcoin (BTC), Bitcoin Cash (BCH), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), and Cardano (ADA).

Digital coin definition and examples

Digital coins are generally used the same way as real-life money in your wallet. For example, Bitcoin can be used to pay for goods and services all over the internet and often in real-world places too. You can store it for a long time or swap it for something of equal value. Other digital coins, such as Ether, NEO, and DASH, can be used in different ways than just as a form of money. For example, Ether (ETH) is used for transactions on the Ethereum network. It is needed to send a token and fund the mining costs.

Digital coin uses

Token are not digital coins. Tokens are created on existing blockchains. The most common blockchain token platform is Ethereum and tokens from this platform are known as ERC-20 tokens.There are also other tokens such as NEO, Waves, Lisk, and Stratis. NEO uses tokens known as NEP-5 tokens.

Tokens

Creating a token takes little technical ability, but it requires the developer to spend some native coins to the blockchain on which the token is being created. For example, a creator will need to spend some Ether to create a token on Ethereum. This is used to get the miners to validate the token transaction.

How tokens are created

Most tokens are used with decentralised applications (dApps). When developers create their tokens, they can be used to activate features of the application they were designed for. Some tokens are created to represent a physical thing. If you want to sell your house using a smart contract, you can't physically put your house into the smart contract, but you can use a token that represents your house. For example, WePower (WPR) uses a token that represents electricity. Users can buy and sell electricity on the blockchain using smart contracts.

The purpose of tokens

Tokens are used to interact with decentralised applications that are built on top of different blockchains. For example, Civic (CVC) is an application that keeps track of encrypted identities on the Ethereum blockchain. If you were to go on a foreign holiday, you'd need to confirm your identity repeatedly along the way, such as the airline, hotel, etc. If they are a partner of Civic, they can quickly identify you at various points. The CVC token is used for the transaction.

Understanding how tokens work

<ul><li>Security Tokens. Most tokens issued by Initial Coin Offerings (ICO) are security tokens. This is because the buyer invests their money in the ICO expecting a profit.</li><li>Equity Tokens. It represents some stock or equity in the company that issues it.</li><li>Utility Tokens are also known as application tokens. They are used to give people access to either a product or service. They are rare as most tokens are expected to gain in value.</li><li>Payment Tokens only serves to pay for goods and services.</li></ul>

Security Token, Equity Token, or Utility Token

Crypto? Blockchain? NFTs? Let us decrypt..

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Cryptocurrency

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Money & Investments

Getting started with Cryptocurrency

People who are new to cryptocurrency often confuse the term "token" and "coin". This guide defines the two terms and provides some examples for a better understanding.

Token vs Coin: learn the token and coin definitions & the differences between security token, utility token & equity token in this Token vs Coin guide.

Token vs Coin: What's the Difference?

Google’s main aim is to organise the world’s information and make it accessible and useful to everyone. This is where AI comes in. Google has mastered machine learning and made life easy with all its applications. It is continuously updating its platform and is one step ahead. Google applies AI to products and new domains and helps to develop tools to ensure that everyone can access AI.

Google is a machine learning-first company

Machine learning algorithms are trained with examples, where they learn from past experiences and analyse historical data. As it continues training, it can identify patterns to make predictions about the future. Google takes advantage of machine learning algorithms and systematically apply them across all their products, such as search, ads, YouTube, or Play. Machine learning is already part of services like Gmail, Google Search and Google Maps.

How Google uses machine learning

Social, promotional and primary mails are filtered through Google as it labels the email accordingly. When a user marks a message in a consistent direction, Gmail will learn your results for categorisation for the future and change the uses. With the help of smart replies, you can reply within seconds. Gmail will suggest a ‘Smart Reply’ and ‘Smart Compose’.

Gmail

When you start typing in the search box, it will anticipate what you’re looking for, then provide suggested search terms for the same based on past searches, trends, or from your present location. For example, bus traffic delays. Google gets all the real-time data on bus locations and forecasts, and can now provide predictions so you don’t have to rely on bus schedules.

Google Search and Google Maps

Google Assistant. It helps with everyday tasks. For example, it makes it easy to search for nearby restaurants, buy movie tickets while on the go and find the nearest theatre to you. It can also help to navigate to the theatre. Google Play Music have 30 million-plus songs available for streaming. It can handle up to 50,000 own songs without any subscription.

Google Assistant and Google Music

There are about 6,500 spoken languages in the world. Google Translate has helped many to decode and understand words, sentences and paragraphs. It is free and fast, although not 100% accurate with larger blocks of text or for some languages.  This is possible because of Statistical Machine Translation (SMT). Computers analyse millions of existing translated documents from the web to learn vocabulary and look for language patterns. After that, Google translates it and picks the most statistically probable translation.

Google Translate

Google Photos play an essential role for Gen X and Gen Z. The app stores your gallery online and lets you access it anytime and anywhere. It also automatically collects and organises pictures of the same people or objects into albums, such as on a vacation, office, etc., then shows each album's ‘best’ photos. The photos are tagged with well-known landmarks they depict and people that appear frequently. It tracks down space-wasting documents.

Google Photos

Speech Recognition. This feature enables a user to convert audio to text by applying neural network models in an easy to use API. The API currently recognises 120 languages and their variants. Reverse Image Search. Image search creates categories you might want. With the image search, it can be easy to search for similar images and the websites that contain these images.

Speech Recognition and Reverse Image Search

Google Adsense. It keeps track of the users’ search history and recommends advertisements to users as it is now aware of its target market. With Google Adsense, Adsense matches text and display ads to the site based on the content and the visitors, while the website owners earn money from their online content. Google Adwords. Google Ads help advertisers to pay to display short advertisements.

Google Adsense and Google Adwords

How Google uses Machine Learning to revolutionise the Internet World

Mentally replace the phrase “the metaverse” in a sentence with “cyberspace.” The Metaverse is a combination of multiple elements of technology, including virtual reality, augmented reality and video where users "live" within a digital universeRead more below to learn about the investment opportunities you can explore to make money

What is The Metaverse?

Event space is a great opportunity for booking short-term rentals on your metaverse property. If your structure is large enough to accommodate events like virtual concerts or shows, you'll be able to attract musicians and other performers who are looking for a new way to reach audiences that may have not been willing or able to attend events in person. Make sure to provide retail space for event-related swag sales. Who doesn't want to pick up a (virtual) band T-shirt on the way out of the venue?

1. Metaverse event space can bring in a diverse crowd

Long live the billboard, the giant roadside advertisement that never seems to die. Metaverse platforms also have roads and traffic, though you can think of it more as pedestrian traffic. Even so, billboards are valuable platforms to reach diverse audiences who occupy and visit the metaverse. Just like with real-life billboards, your income stream comes from renting the space out to companies who want to share their messages with passersby.Look to both real-life and virtual companies as potential billboard customers. After you all, just because someone is active in the metaverse doesn't mean th<warningtag>ey know there's a new virtual shop by a big brand going into an area they don't regularly frequent.</warningtag>

2. Virtual billboards are just as useful as real ones

Forget amusement parks - they can't hold a candle to what's possible in the metaverse. Just because there's no gravity (or physical bodies) doesn't mean that a virtual experience can't be interesting and fun for visitors. You'll need ample space and a huge imagination (or several on the payroll) to create something that will generate ticket sales, but the whole point of the metaverse is social experiences, and this is no different.Bonus points for being able to update, change, or remodel your experience space for pennies on the dollar compared to a real-life amusement park or similar facility.

3. Experiences can be totally wild in the metaverse

Just because it's not the real world doesn't mean that people aren't looking for stuff to populate their virtual life. Retail has a strong place in the metaverse, and brands will need plenty of room for showrooms and shops. Even small brands may find cult followings in the metaverse, so don't be shy about reaching out to potential tenants when setting up your well-planned rental spaces for commercial businesses.Many brands have already seen great success with promoting and selling NFT versions of their real-world products to metaverse denizens. Custom sneakers, high-end bags and so on.

4. Traditional malls and shopping centers are still needed

Want to learn where to invest in the Metaverse? Read below - two words pop out to me - real estate 🏡 

Basic lots are just the beginning when it comes to investing in the metaverse. Here are other ways you can develop there for long-term returns.

5 Interesting Ways To Invest in the Metaverse | The Motley Fool

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