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All the reason why kids need to learn AI and how DcodeAI can help them

Artificial intelligence, dubbed the "skill of the century," allows humanity to create intelligent machines and systems capable of executing tasks that would otherwise necessitate human intelligence. It has really become a part of our daily lives, to the point that when a machine plays games with us or recognizes our natural language, it feels absolutely natural. Because of the benefits of artificial intelligence, an increasing number of businesses are using the technology to optimise their goods and services, analyse their business models, and develop their decision-making process. We've all heard of AI's position in chess-playing machines and self-driving vehicles, but the advantages of AI extend beyond the computing and space industries.

It is also important in sectors that are closely connected to our daily lives, such as healthcare, automobiles, and even banking and finance. It will now work its magic on nearly every market, taking in previously unseen improvements.

The nuanced integration of AI into the way the environment works, as well as the possibility of further AI technologies mushrooming in the future, further emphasise the importance of artificial intelligence and machine learning for children. Let's take a closer look at how these details can contribute to your children's general success and prepare them to perform well in the world of tomorrow.

The expectation that millions of positions will be generated in the field of artificial intelligence over the next decade significantly increases the value of artificial intelligence for children and provides a compelling argument for them to be exposed to it. Even today, AI is used directly or indirectly in a wide range of manufacturing and service fields, and the number of domains can grow with each passing day, providing new job opportunities.

AI applications are so diverse that your child will be enthralled by their application in an area that affects them, and their knowledge of these applications will help them progress constructively in it. An understanding of Artificial Intelligence and Machine Learning for children will not only provide them with career stability, it will also land them in the highest-paying career in today's world.

Undirected coding is tedious, and children lose interest after a bit, but AI puts a real-life challenge in front of them, making it more interesting and allowing them to practice coding from a new perspective. Thus, coding with AI applications in mind will not only help them conceive of a new challenge, but will also allow them to be the developer of the solution rather than a mere user.

Data drives the twenty-first century. Exposing children to big data at a young age and teaching them how to capture, examine, and evaluate data sets would familiarise them with processes that shape the foundation of the modern environment. This is where artificial intelligence and machine learning come into play. Learning about these innovations introduces children to the field of data, which is not only changing the IT world but also yielding better and more exciting outcomes in the healthcare and exercise industries.

DcodeAI – The AI Learning App will help them master AI principles without requiring any coding knowledge. It will render learning AI basics easy, intuitive, and customizable by using low code/no code tools. We will also assist you in researching and developing AI models without the need for specialised computational tools or GPUs (Graphics Processing Units). So, instead of allowing your children to play games or watch videos, give them the opportunity to use their screen time to learn ideas and skills that can make their future stronger and brighter. Go ahead, get in touch with us as soon as possible!

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Learn everything about AI with DecodeAI

Over the last two decades, people have come to value artificial intelligence and its subsequent elements. Everyone takes them to the ultimate tools and strategies for making the world a better place. If you really think about it and take a look around you, you’d already feel its presence. Take Alexa or Siri, or even Google assistant for instance, which surely have managed to make our lives far simpler than they already were.

 Such inventions have been proved to benefit humans as they have been specifically designed to minimise human effort as much as possible. Artificial intelligence typically has the opportunity to work in an automated fashion. As a result, manual interference is the last thing that should be required or seen while using parts associated with this technology. These devices strive to speed up the tasks and processes while retaining a high degree of precision and accuracy, which is what makes them a useful and important tool, without which, soon enough, nobody would be able to imagine their lives. Apart from making the world a more error-free place by easy and everyday techniques, these innovations and applications are absolutely important to our general and everyday lives.

 It has been noted that this year many students preferred a BTech course in AI over a standard CSE (computer sciences) course despite having a higher ranking. According to Vineeth N Balasubramanian, associate professor at IIT Hyderabad, “it is a sign that students have realized the promise of AI as a career prospect given the reach of research and employment in the industry.”

 AI has the power to influence any part of our lives. The area of artificial intelligence seeks to comprehend patterns and actions of individuals. We want to use AI to create smart systems while also understanding the principle of intelligence. The intelligent systems we create are incredibly useful in learning how an intelligent system, such as our brain, goes about building another intelligent system.

 In comparison to fields like Mathematics or Physics, which have been around for decades, Artificial Intelligence is still in its infancy. Based on our current course, it is clear that attaining knowledge would have a huge effect on our lives in the coming years. Even though the human brain is exceptional at evaluating the world around us, it is incapable of keeping up with the preceding circumstances. For instance, the amount of data that we are generating every day is simply overwhelming because of which there is an urgent need of designing and building intelligent machines that are capable of supporting us as we evolve and take a big next step as a human race. 

 We need artificial intelligence systems that, as we said, handles vast volumes of data in a timely and effective manner. Techniques are currently being used to make current machines smarter, allowing them to perform more quickly and efficiently. 

 Lucky for you, Dcodeai promises to educate your children about Artificial intelligence that would further help them decoding the success formula early in life. With us, your kids will get an opportunity to learn the fundamentals of data science, understand the basics of statistics using simple tools, learn python using DIY activities, learn about the technology that powers bots, Explore the parallel world of CV applications.

DcodeAI – The AI Learning App will assist them in mastering AI concepts without the need for any coding experience, make learning AI fundamentals simple, intuitive, and customized by using low code/no code resources. We will also help you in studying and implementing AI models without the use of advanced computing resources or GPUs (Graphics Processing Units).

So, go to our website and instead of letting your kids play games or watch videos, allow them a chance to use that very screen time in learning concepts and skills that will make their future better and brighter.

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Integration of Cloud and AI

As per the research conducted by Accenture, 85 % of the businesses and IT organizations are planning to invest in AI-based technologies over the next 3 years. This increase in AI will lead to an increase in usage and processing of data which is leveraging the cloud-based platforms for fetching, storing, and managing this data at a faster rate and significantly affordable rates. Now, the question arises how cloud impact AI and vice-versa, and how the integration of AI and cloud-based technology is used? Let’s find out.

First and foremost, AI consists of various complex deep learning algorithms which have high software and hardware requirements to generate the insights related to data, therefore cloud service providers like AWS, Microsoft Azure, Google Cloud, etc. supplement these requirements by offering GPUs (Graphical Processing Units) of high computational power enabling the processing of data in a seamless way. Therefore, these service providers provide various compute instances or virtual machines to ensure that all workloads must be running all the time by auto-scaling these instances. Secondly, data aggregation is considered as one of the most essential factors for industries using AI to establish the correlations among the various sets of data obtained within the organization as well as outside the organization. Therefore, cloud computing helps in maintaining the data quality and aggregating the data in one place. For instance, AWS provides a Simple Storage Service most commonly known as S3 bucket for storing the data of all formats and performing secure data transfer services. Moreover, AWS has come up with their cloud machine-learning platform that helps users in building, training, tuning and deploying machine-learning models in a production-ready hosted environment. It supports various frameworks for working on various deep learning models like TensorFlow, Apache MXNet, PyTorch, Chainer, and more. Here, we have seen how the cloud is supporting various AI-based activities. But there is a vice-versa situation where there is a great possibility for AI to reshape the cloud-based platforms. AI can be used to monitor the workflows of all the virtual machines present in the cloud infrastructure by upgrading or downgrading the instances of the cloud infrastructure depending on its daily processes. Apart from this, AI tools are integrated with SaaS based platform and these tools help in enriching the functionality of these cloud application services. For instance, Salesforce is a CRM platform present on cloud which gathers the customer related data and helps in tracking the relationships of the customers.  It consists of Einstein, an AI tool which uses this data to generate actionable insights to derive the company’s sales strategies. So with the amalgamation of AI and cloud, the business processes are transforming at a granular level in terms of resource utilization, minimizing infrastructure cost and workload of IT teams in the organization.

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L earnings by Machine in Machine Learning

AI was designed to perform tasks for which it was programmed to do but with the advancement in AI, Machine Learning (an application of AI) came into existence with the ability to learn automatically from the past data to solve complex problems without being explicitly programmed. Therefore, ML-based models have to be trained over the data and there are various methods to do so. Let’s discuss. Supervised Learning – This type of machine learning algorithm can be considered as the learning that is under the guidance of a teacher. Here, the data set acts as a teacher, therefore ML-based models learn under the guidance and are being trained over the labeled data i.e. we know what we want to predict. Moreover, a small portion of the data set is used for training the model, and the cause and effect relationship between the variables of the data set and output is predicted. For example – To predict the house price, an ML-based model is trained over a small portion of the data set which possesses labeled data. This data includes input variables locality, size of a house and collector rate, and the relationship is established between the variables and the house price is predicted as the output variable. Therefore, the model gets improved with the future data fed into it. Supervised learning algorithms include regression, classification, decision tree, and random forest. Unsupervised Learning – In this learning technique, the machine learning algorithm acts without any guidance or any direction. For instance – an adult does not need any kind of supervision or guidance to make decisions related to daily activities. Therefore, in this technique, the Ml-based model is fed with an unlabeled data set for training to make any prediction as output. A model itself identifies the trends or patterns from the given data set by creating a relationship in it and further producing the output. Unlike supervised learning, in unsupervised learning, only input data is given, the output variable is not provided and unsupervised learning more computationally complex compared to supervised learning. Unsupervised learning algorithms include clustering and association analysis. Reinforcement Learning – It is a learning method in which an agent interacts with the environment and learns through experiences further producing the actions and discovering the errors or rewards. Moreover, in reinforcement learning, there is no predefined data set, so it works on the hit & trial concept by taking action. For instance – initially, a robot (an agent) does not know anything where to move i.e. forward, backward, right, or left, so it learns more about its surroundings or environment and then moves. Reinforcement learning algorithms include Q-learning and SARSA (State Action Reward State Action) algorithm. So, these are the mainly used types of machine learning algorithms. Hence, depending on the use cases, learning patterns, and the rapidly increasing data, these ML-based algorithms and models are evolving and producing the desired outcomes with accuracy.

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Tailoring Data Science with Analytics Spectrum

Data is everywhere and is surpassing the boundaries of traditional databases. But this data is of no use if you don’t know how to derive meaning from it and use it to your advantage. Therefore, to comprehend this big data into a useful format, there are various analytic techniques. Let’s find out.

Descriptive Analytics It is the kind of analytics that involves the usage of historical raw data, decomposing it into smaller chunks, summarizing it, and comprehending what exactly has happened. It uses data aggregation and data mining techniques to provide insight into the past and then it answers what is happening now based on the incoming data. Descriptive analytics is mainly associated with business intelligence tools and dashboards. For instance – Companies use descriptive analytics for creating reports regarding sales, profits, revenue and can determine the performance by analyzing these metrics. Diagnostic Analytics This type of analysis is done to understand why something happened and to identify the root cause of the problem. It helps in determining what factors and events contributed to a particular outcome. It uses various techniques like drill-down, data discovery, data mining and correlations. It uses probabilities, likelihoods and the distribution of data for establishing the cause and effect relationship within the data. For instance – In e-commerce, many customers don’t buy the products after adding them to the cart, therefore diagnostic analytics will identify the key factors i.e. high shipping fees, lack of availability of payment options, etc. which leads to this problem. Predictive Analytics As the name implies, predictive analytics predicts the future by analyzing historical facts and current trends. So, the main behind this analytics is to determine what will happen next. It uses data mining, artificial intelligence, various statistical models and machine learning algorithms like random forests, SVM, classification, etc. Therefore, predictive analytics involves building and validating these models on the data for forecasting purposes. For instance – Businesses use this analysis for determining consumer behavior, market trends, forecasting future demand and prices of products, etc. Moreover, American Express records past historical transactions for predicting the customers who are at high risk and take precautions before any kind of fraudulent activity. Prescriptive Analytics This analytics helps in prescribing the solution to a specific problem. In other words, it suggests what action should be taken. It uses optimization and simulation algorithms and learns from continuous feedback through various machine learning and neural network algorithms to provide the appropriate recommendations. Therefore, prescriptive analytics works with both descriptive as well as predictive analytics. Google self-driving car is one of the best examples of prescriptive analytics which uses various business rules, machine learning models and computational models to analyze the real-time environment and decide the direction and the best route to be followed. So predictive and prescriptive analytics are future-oriented, while descriptive and diagnostic analytics are present-oriented. The application of these analytics depends on the usage and availability of data. Therefore, considering this scenario, descriptive analytics is one of the widely used techniques among all other kinds of analytics because it is least driven by data in decision-making while prescriptive analytics is highly data-driven and requires advanced knowledge and availability of machine learning models and AI-based tools. Hence, the exponential increase in data and the scope of AI are paving the way for organizations and businesses to harness these techniques of analytics at the utmost level.

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CAN I ALSO LEARN AI?

Artificial intelligence is one of the most significant breakthroughs of the 21st century. Experts from different industries study its capabilities and discover new ways of its application, which is why AI is called an evolving science. Artificial intelligence systems represent a pretty exciting area of study: There is a good-sized call for people with the skills needed, and the technology is still developing and growing.

However, we know that it can be a little difficult to figure out how best to get involved with the tech, especially if you’re wanting to learn on your own. Fortunately, there are plenty of resources available for beginners to build up their knowledge and skills—or even figure out whether this path is for them, and through this article, we help you do the same.

Before you get into the nitty-gritty of this learning process, you must start with the basics – programming. Even if you don’t have any prior experience in engineering, you can learn artificial intelligence from home and start applying your knowledge in practice, creating simple machine learning solutions and making first steps towards your new profession.

Learn a programming language first. Java (link to Java and Play framework article???), Python, C++, JavaScript, and Ruby on Rails, are probably the most common and useful ones. Better yet, it’s easier than ever to learn how to code for free with the help of many applications available at a click. And the little homework you’re supposed to do is work in these areas- Advanced Math (e.g. correlation algorithms) and Stats Programming language Machine Learning

With a strong background in the same, you can then start learning AI online by enrolling in a course. However, to be ahead in the game, make sure you are well aware too with the latest developments in the field. In addition to courses on artificial intelligence, passionate programmers, software developers, and computer science students can also read books on the topic. There are quite a few out there that are both puzzling and incredibly interesting. All of them will help broaden your knowledge of AI and its potential.

Artificial intelligence is no longer a product of our imagination. AI is real and we use it daily. Cortana, Siri and Google Now have become our most valued virtual personal assistants. The gaming industry uses artificial intelligence to create seamless online experiences and the auto industry currently focuses on smart cars that drive themselves. Fraud detection and purchase prediction software programs are artificially intelligent products too. In layman’s terms, your house, bank, smartphone and car all use AI on a daily basis.

AI has gone mainstream, and today’s savvy software developers and programmers are fascinated by its potential. Still asking the question: ‘How to start learning Artificial Intelligence?’, then our advice is to start with the basics first – advanced math. Move up and take a step further by learning a programming language. Get more familiar with machine learning and you’ll be ready to understand artificial intelligence too. Advanced technologies like AI slowly move out from the data center and out into the world.

The understanding of artificial intelligence opens lots of opportunities. It’s enough to master the basics of this technology to understand how simple tools work. There are endless possibilities in this field. Studying AI is necessary for a career in software engineering, in case you want to work with human-machine interfaces, neural networks, and quantum artificial intelligence.

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What is weak Artificial intelligence (AI)?

Weak artificial intelligence (weak AI) is a way to deal with man-made consciousness innovative work with the thought that AI is and will consistently be a re-enactment of human intellectual capacity, and that PCs can just seem to think they are not really cognizant in any feeling of the word. Weak AI essentially follows upon and is limited by the standards forced on it and it couldn't go past those guidelines. A genuine case of weak  AI is characters in a PC game that demonstrate conceivably inside the setting of their game character, yet can't do anything past that. Point to be Remember : "Weak artificial intelligence is also known as narrow artificial intelligence."

Let's Discuss Weak Artificial Intelligence (Weak AI): Weak AI needs human awareness, however, it might have the option to mimic it. The exemplary outline of Weak AI is John Searle's Chinese room psychological study. This test says that an individual external to a room might have the option to have what has all the earmarks of being a discussion in Chinese with an individual inside a room who is given guidelines on the most proficient method to react to discussions in Chinese. The individual inside the room would seem to speak Chinese, however truly, they couldn't really talk or comprehend an expression of it missing the guidelines they're being taken care of. That is on the grounds that the individual is acceptable at adhering to guidelines, not at speaking Chinese. They may seem to have Strong AI – machine insight identical to human knowledge – yet they truly just have Weak AI. Weak frameworks don't have general knowledge; they have explicit insight. An AI that is a specialist at disclosing to you how to drive from direct A toward point B is typically unequipped for moving you to a round of chess. Furthermore, an AI that can claim to speak Chinese with you most likely can't clean your floors.

A car using Artificial intelligence

Weak AI helps transform enormous information into usable data by recognizing examples and making expectations. Models incorporate Facebook's news channel, Amazon's proposed buys and Apple's Siri, the iPhone innovation that addresses clients' expressed inquiries. Email spam channels are another case of Weak AI where a PC utilizes a calculation to realize which messages are probably going to be spam, at that point diverts them from the inbox to the spam envelope.

The weakness of weak AI : Issues with Weak AI other than its restricted capacities incorporate the likelihood to cause hurt if a framework fails – think about a driver less vehicle that miscounts the area of an approaching vehicle and causes a severe crash – and the likelihood to cause hurt if the framework is utilized by somebody who wishes to cause hurt –, for example, a psychological militant who utilizes a self-driving vehicle to convey explosives in a jam-packed region. Another issue with it is figuring out who is to blame for a breakdown or a plan blemish.

A further concern is the loss of occupations brought about by the mechanization of an expanding number of undertakings. Will joblessness soar or will society concoct new ways for people to be financially beneficial? In spite of the fact that the possibility of a huge level of labourers losing their positions might be unnerving, it is sensible to expect that should this occur, new openings will arise that we can't yet anticipate, as the utilization of AI turns out to be progressively far-reaching.

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Applications of Artificial Intelligence In Use Today

Artificial Intelligence(AI) is a Man-made brainpower (AI) alludes to the reenactment of human insight in machines that are modified to think like people and copy their activities. The term may likewise be applied to any machine that displays attributes related with a human brain, for example, learning and critical thinking. Lets begin with some interesting uses and application of Artificial Intelligence(AI):

1. Alexa Alexa's ascent to turn into the savvy home's center, has been to some degree fleeting. At the point when Amazon previously presented Alexa, it surprised a great part of the world. Notwithstanding, it's helpfulness and its uncanny capacity to decode discourse from anyplace in the room has made it a progressive item that can assist us with scouring the web for data, shop, plan arrangements, set alerts and 1,000,000 different things, yet additionally help power our savvy homes and be a course for those that may have restricted versatility.

2. Siri Everybody knows about Apple's own associate, Siri. She's the well disposed voice-enacted PC that we cooperate with consistently. She helps us to discover data, gives us headings, adds occasions to our schedules, encourages us to send messages, etc. Siri is a pseudo-keen computerized individual right hand. She utilizes AI innovation to improve ready to anticipate and comprehend our characteristic language questions and demands.

3.  Tesla If you don't own a Tesla, you have no idea what you're missing. This is quite possibly one of the best cars ever made. Not only for the fact that it received so many accolades, but because of its predictive capabilities, self-driving features and sheer technological "coolness." Anyone that's into technology and cars needs to own a Tesla, and these vehicles are only getting smarter and smarter thanks to their over-the-air updates.

4. Cogito Originally co-founded by CEO, Joshua Feast and, Dr. Sandy Pentland, Cogito is potentially one of the most remarkable instances of social transformation to improve the passionate knowledge of client care delegates that are available today. The organization is a combination of AI and conduct science to improve the client cooperation for telephone experts. This applies to tons of voice calls that are happening consistently.

5. Netflix Netflix gives exceptionally precise prescient innovation dependent on client's responses to films. It examines billions of records to propose films that you may like dependent on your past responses and selections of movies. This tech is getting more brilliant and more intelligent continuously as the data set develops. Nonetheless, the tech's just downside is that most little named motion pictures go unnoticed while large named films develop and swell on the stage.

6.Amazon.com Amazon's conditional A.I. is something that has been in presence for a long while, permitting it to make cosmic measures of cash on the web. With its calculations refined increasingly more as time passes, the organization has gotten intensely brilliant at foreseeing exactly what we're keen on buying depending on our online conduct. While Amazon intends to deliver items to us before we even realize we need them, it hasn't exactly arrived at this point. Be that as it may, it's unquestionably in its sights.