DEV Community: iMrLopez

Why you should learn JavaScript

iMrLopez — Mon, 25 Jul 2022 16:36:00 +0000

Why you should learn JavaScript

One of the most widely used programming languages, JavaScript, is the foundation for contemporary web development with HTML and CSS. A website's structure is codified in HTML, its style is defined by CSS, and its interaction is made possible by JavaScript.

JavaScript experts are versatile

JavaScript is an exceptionally versatile language. Once you've learned it, the options are virtually limitless: you can use Angular for client-side (frontend) coding and Node.js for server-side (backend) coding. Utilizing React.js, React Native, and Electron, you can create web, mobile, and desktop applications and dive into machine learning.

JavaScript is a requirement if you want to work as a frontend developer. However, as a JS professional, you have other options for a career as well. You might work in full-stack development, game development, information security software engineering, machine learning, and artificial intelligence, to mention a few, after mastering this vital programming language.

It is perfect for beginners

All current web browsers are pre-installed with JavaScript, so you don't need to install anything on your computer or waste time setting up a development environment. You may go in and begin programming.

JavaScript is also simple to learn. JavaScript simplifies complex information for beginners by turning it into an abstract form. JavaScript feels more like a natural language than some other higher-level languages do.

Additionally, a sizable JavaScript community exists on websites like Stack Overflow, so you'll have no trouble finding support as you learn.

It is also a fantastic introductory language for programmers who plan to broaden their skill set into other languages eventually. JavaScript enables practical programming techniques like imperative, functional, and object-oriented programming. Once you've mastered JavaScript, you may use these techniques to study any other language you desire, including Python, Java, or C++.

JavaScript makes sites attractive and develops crazy fast servers.

Not only is JavaScript simple to learn, but there are many applications for it once you start using it. The fact that you can use JavaScript for both frontend AND backend programming contributes to its versatility (and makes it an excellent language to learn initially when considering a range of languages and debating possibilities like Python vs JavaScript). In case you need a quick refresher, backend development is the work that is done "under the hood"—building and managing the servers and databases that power websites from the backend up. Front-end development is the work done to create the elements of a website that users see and interact with.

There are numerous resources available today to begin writing software. However, JavaScript is a language that enables beginners to begin. Although this program won't necessarily be the most excellent solution to the issue at hand, the novice will have written his first lines of code and begun his career in software development.

It makes you more employable!

It's crucial to know that your time and money will be well spent if you decide to engage in developing your knowledge and skills. JavaScript training is a good idea.

One of the most in-demand talents in the developer job market is JavaScript. JavaScript talent and knowledge of JavaScript-based technologies are in high demand among employers.

JavaScript is a flexible language, and you can choose from various career routes by specializing in a particular framework or becoming an expert in pure JavaScript.

It is also a great starting point for learning other languages because you'll become comfortable with the fundamentals, which hiring managers value most in engineers.

It is central to all websites.

JavaScript is required if you want to create modern, dynamic websites. It is the foundation of contemporary web development and HTML and CSS. HTML, presentation, and formatting provide basic website structure, and layout is managed by CSS. The site "comes alive" with the addition of interactive elements like audio and video, games, scrolling capabilities, and page animations possible by JavaScript.

The JavaScript Language Is Being Improved Every Year

Since its beginnings as a programming language for the Netscape browser, JavaScript has advanced significantly! In truth, the language has advanced significantly, particularly after the release of the ECMAScript 2015 specification, often known as ES6. The JavaScript specification is updated annually by ECMA, which sets JS standards. ES7, ES8, ES9, ES10, ES11, and ES12 have since been released. Contrary to widespread assumption, JavaScript is incredibly fast because of its just-in-time compiler, even though it is an interpreted language.

High Demand

Almost everything is currently developed with JavaScript, and the demand for talented JS developers is only rising! JavaScript's necessity will become increasingly crucial as the JavaScript community continues to expand rapidly. Almost every area, including healthcare, finance and banking, entertainment and gaming, aerospace, military, and more, will have a significant demand for JavaScript developers as everything from mobile apps to desktop programs, games, websites, and more are being produced with JavaScript.

Is Learning JavaScript Worth It?

Yes, it is worthwhile to learn JavaScript. Standard programming language JavaScript has several talents that can be used in other fields. Whether you are a beginner, an experienced developer, or a full-stack developer, you will be able to code in JavaScript and use that knowledge to increase your technical skills.

Final Thoughts

This language is more than just another way of saying Coffee Handwriting; it's the ideal programming language for newcomers to start using right away in their careers and the ideal starting point for any upcoming developments. Given that it underpins almost all of the web, it is entirely future-proof. The explanations for why you should learn JavaScript are, in brief, as follows again:

· It is a language with several uses.

· It's simple to begin going with.

· There are several frameworks and libraries available to make your life simpler.

· The community is well-established and excellent.

· It's a highly valued professional competence.

· It can assist in your following programming language education.

In other words, it's a fantastic language that can help you achieve much in your personal and professional lives. JavaScript must be at the top of the list of programming languages you could begin learning in 2022.

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Marny Lopez

Software Engineer.

www.marnylopez.com

How big companies are using JavaScript

iMrLopez — Tue, 19 Jul 2022 20:27:00 +0000

JavaScript is a dynamic programming language used for creating websites, web apps, video games, and many other things. You can use it to add dynamic features to web sites that you could not do with only HTML and CSS.

What Would the Web Look Like Without JavaScript?

JavaScript's ability to create interaction is essential to how we live today. You can turn off JavaScript in your browser for a day to determine just how dependent you are on it.

• A few pages will load more quickly and be cleaner

• No advertising is present.

• However, a lot of websites do not function at all, such as those with auto-playing videos or "tell us your email address" pop-ups. It is unable to submit forms and images will not load.

Here are a few well-known websites with JavaScript disabled:

YouTube’s videos and thumbnails do not load.

Except for its logo in the top left corner, Netflix doesn't load anything.

Amazon has a weird appearance, but it still functions.

Wikipedia still works great.

How Popular Is JavaScript?

The reputation of JavaScript over the years.

According to Github's 2020 Octoverse Report, JavaScript is by far the most popular language.

According to Stackoverflow, JavaScript has one of the biggest communities among programming languages. In addition to that community, Node.JS is one of the most popular technologies and has received over a billion downloads worldwide.

These are just a few of the factors that make JavaScript so well-liked. The internet community has completely invested in the development of JavaScript because of the paradigm shift that gave rise to web applications, the standardization of web application development, cross-browser support, and the abundance of libraries and frameworks available.

List of JavaScript Companies

Microsoft

One of the largest software firms, Microsoft is well-known for a variety of items, including the Microsoft Office program and the Windows operating system. It was one of the greatest proponents of Node.js and used JavaScript in the development of its Edge browser. For both front end and back-end web development, JavaScript is also used.

Meta (formerly Facebook)

JavaScript is used by Facebook. Although PHP was the system's initial dominant language, JavaScript quickly surpassed it. As of right now, the website is unusable and has terrible graphics if you want to use it without the script. This is a result of the codebase's robust language-specific core. As a result, it is difficult to claim that Facebook hardly ever uses JavaScript when there are so many viable alternatives.

Meta gave the language something. It created and still maintains React, the most extensively used JavaScript library for front-end development. This library is used by apps like Facebook, Instagram, WhatsApp, and others. However, this particular set of instructions is widely used by numerous other important IT organizations, making it well-known.

Groupon

In the past, Groupon was infamously slow. Why? Ruby on Rails served as their engine. The Twitter fail whale was created using the same conceptual foundation. Groupon was one single, extremely complex Ruby on Rails application. They choose to switch to NodeJS due to issues with maintainability and speed. Groupon was able to rebuild their whole US website using Node by decomposing it into separate NodeJS web applications. Now that Groupon consists of over 20 Node applications, it operates faster.

Groupon has joined the NodeJS Foundation and is in the process of migrating all its international websites to NodeJS. They are content with their shift, which is obvious.

Netflix

In the beginning of its growth, Netflix employed Java everywhere, but the scale and lengthy development time of that language created a lot of issues. They consequently made the decision to switch to JavaScript, specifically Node.js. Node develops more quickly and is lighter. As a result, all of Netflix's backend services—its own services, to be clear—are developed in JavaScript. The server will benefit as well because they will have less work to complete, which will improve and simplify system scaling overall.

PayPal

One of the most popular and reliable systems for receiving, transmitting, and accepting payments is PayPal. The software enables users to communicate electronically without disclosing their debit or credit card information. More than two hundred million PayPal accounts are active as of November 2017.

It's interesting to note that Paypal did not start off using Node.js as its preferred technology. The first issue, according to @paypaleng, was the divided teams: those who independently code for a browser (using HTML, CSS, and JavaScript), and those who code for server applications (using Java). Therefore, the main barrier was the line separating the browser and the server. The truth is that initially, they were utilizing Java for the backend (much like Netflix), but PayPay later switched to Node JS.

Uber

To manage the enormous volume of queries, the Uber app needs an incredibly quick and scalable technological solution. Due to the high volume of queries, the Uber app must manage tons of data in real-time. It must act quickly to accept requests, track locations, and process payments. Thanks to Node.js and JavaScript, these features are now simpler to create and maintain. Node.js guarantees speedy iterations and quick matching of passengers and drivers.

Walmart

Walmart is a big American retailer that runs a number of supermarkets and department shops around the globe. Walmart is one of the businesses that utilizes Node.js, and it employs the framework to build its user interfaces (UIs) as well as application programming interfaces (APIs) for the company's many apps. Developers can combine several apps or services using orchestration layers. Walmart app users can access multiple diverse tasks from a single platform.

However, there are not many businesses that do not use JavaScript at all. This article has analyzed the most significant businesses that use it the most. It is challenging not to use it to create the frontend, but it is also incredibly flexible and strong for the backend. JavaScript is therefore necessary for every stack and project that involves a website. Everyone can benefit from learning JavaScript, no doubt about that.

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Marny Lopez

Software Engineer

Es momento de aprender JavaScript!

iMrLopez — Wed, 13 Jul 2022 21:02:00 +0000

¿Qué es JavaScript?

JavaScript es un lenguaje de programación basado en texto que se usa tanto en el lado del cliente como en el lado del servidor y que le permite hacer que las páginas web sean interactivas. Mientras que HTML y CSS son lenguajes que dan estructura y estilo a las páginas web, JavaScript proporciona a las páginas web elementos interactivos que atraen al usuario. Los ejemplos comunes de JavaScript que puede usar todos los días incluyen el cuadro de búsqueda en Amazon, un video de resumen de noticias incrustado en The New York Times o actualizar su cuenta de Twitter.

¿Quien utiliza JavaScript?

Javascript es utilizado por programadores de todo el mundo para crear contenido web dinámico e interactivo, como aplicaciones y navegadores.

¿Porque aprender JavaScript?

Además de las posibilidades ilimitadas, existen muchas razones para que los desarrolladores web usen JavaScript en lugar de otros lenguajes de programación:

JavaScript es el único lenguaje de programación nativo del navegador web
JavaScript es el lenguaje más popular
Hay un umbral bajo para empezar
Es un idioma divertido de aprender.

¿Como aprender JavaScript?

Existen cientos de maneras de aprender JavaScript, puedes aprenderlo por tu cuenta investigando en internet o comprar cursos en plataformas en linea...

¿Algun curso de JavaScript que recomiendes?

Si estas interesado en tener una guia para aprender JavaScript puedes adquirir mi nuevo curso de JavaScript! Si! Un curso de JavaScript barato y con el contenido curado, asi estas seguro de que estas aprendiendo la habilidad de la manera correcta y preparandote para el futuro!.

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Te espero en mi nuevo curso de JavaScript!

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Deep Learning vs. Machine Learning vs. Neural Networks – What's The Difference?

iMrLopez — Tue, 21 Jun 2022 02:08:00 +0000

What is Deep learning

Deep learning, which is effectively a three-layer neural network, is a subcategory of machine learning. These neural networks are designed to emulate the human brain's function by allowing it to "learn" from massive amounts of data, but they fall far short of its capabilities. While a single-layer neural network can only make approximations, adding hidden layers can help optimize and enhance accuracy.

Deep learning is used in artificial intelligence (AI) apps and services to improve automation by performing analytical and physical tasks without the involvement of humans. Deep learning beats traditional methods when dealing with massive amounts of data. When working with minimal data, traditional Machine Learning techniques are preferred.

What is machine learning?

Machine learning (ML) is a technique for teaching computers to make accurate predictions about future events without receiving explicit instructions.

It's a subset of Artificial Intelligence predicated on the idea that computers can learn from data, identify patterns, and make judgments with little or no human input.

These predictions can tell if an email is a spam, identify a cat in a photo, and recognize speech patterns properly.

What is a neural network?

A neural network is a cognitive system that uses interconnected nodes or neurons in a layered structure to mimic the human brain. A neural network can be trained to recognize patterns, classify data, and forecast future occurrences by learning from data.

In real-world circumstances, neural networks are also well-suited to assisting humans in tackling complex tasks. The input is broken down into levels of abstraction by a neural network. It, like the human brain, can be taught to identify patterns in speech or images using a variety of examples. Its behavior is determined by the strength, or weights, of the connections between its component pieces. These weights are automatically updated during training based on a learning algorithm until the artificial neural network appropriately completes the task.

Main Differences Between Deep Learning and Neural Network

Deep learning is a type of neural network that is highly complex. A deep learning system has many more layers than a neural network, so it's a lot more complicated.

Compared to a deep learning system, a neural network offers you poor efficiency and performance for completing your tasks, whereas a deep learning system gives you excellent efficiency and performance for completing your duties. The main components of a deep learning unit are a large power supply, a GPU, and a lot of RAM. Neurons, transmission functions, learning rate, connections, and weight are the basic parts of a neural network.

Because deep learning networks are sophisticated, they take a long time to train, whereas a neural network takes a fraction of the time.

Differences Between Deep Learning And Machine Learning

To produce outcomes, machine learning necessitates more continuing human engagement. Deep learning is more challenging to set up, but it takes very little intervention once it is up and running.

While machine learning algorithms are frequently simpler than deep learning algorithms and may be run on regular computers, deep learning systems demand substantially more powerful hardware and resources. The growing need for power has led in a rise in the utilization of graphics processing units. GPUs are valuable for their high bandwidth memory and ability to hide latency in memory transfer due to thread parallelism. The ability of a large number of operations to run smoothly at the same time.

Machine learning systems are simple to set up and operate, but their capabilities are potentially limited.

Traditional techniques such as linear regression are used in machine learning, often requiring structured data. Deep learning uses neural networks and is designed to handle vast amounts of unstructured data.

Machine learning is already in use in your email, bank, and doctor's office. Deep learning technology enables more complicated and autonomous programs, such as self-driving automobiles and surgical robots.

Machine Learning and Neural Networks: What's the Difference?

Supervised and unsupervised learning models are the two types of machine learning algorithms. Feed-forward, convolutional, recurrent, and modular Neural Networks are the four types available.

Machine learning is fed data and learns from it. The ML model gains expertise and development over time as it learns from the data. In contrast, the structure of a Neural Network is quite complicated.

Machine Learning is a collection of instruments and procedures that train on it, evaluate data, and then apply what they've learned to uncover intriguing patterns. In contrast, neural networks are based on processes found in the human brain and help it function.

A Neural Network organizes algorithms so that they can make reliable decisions on their own, whereas a Machine Learning model acts based on what it learns from the data.

Machine Learning models are adaptive, meaning they can learn from new data samples and interactions and evolve. Accordingly, the simulations may be able to detect data trends. In this situation, only one response layer is data. Even a minimal Neural Network model has numerous layers.

Machine learning and deep learning will have a long-term impact on our lives, and their capabilities will alter practically every industry. Dangerous vocations, such as space travel or employment in hostile settings, could be completely replaced by machines. Traditional statistical models' ability to forecast optimal knowledge has been enhanced by the widespread use of massive data, processing power, and deep neural network design. Businesses rely on significant data breakthroughs and difficult technologies like AI, machine learning, and IoT to be competitive in their respective industries.

Machine Learning, Deep Learning, and Neural Networks all have their own set of advantages and disadvantages. Deep Learning is a subcategory of Machine Learning, and Neural Networks are a subcategory of Deep Learning. Therefore, Neural Networks are basically a more innovative type of Machine Learning. In addition, it is now finding applications in a wide range of fields.

How to get started with machine learning & AI

iMrLopez — Sun, 12 Jun 2022 08:53:00 +0000

Most industry verticals are harnessing the potential of AI and machine learning, leveraging them for a brighter tomorrow while also offering many job prospects. This blog seeks to assist you in learning AI and Machine Learning online. Let's look at some of the most common questions that people who want to understand AI have.

What is machine learning?

Machine learning (ML) is a kind of artificial intelligence (AI) that permits software to develop its forecast accuracy without being specifically intended to do so. Machine learning algorithms use historical data as input to forecast new output values.

How Does Machine Learning Work?

To grasp entities, domains, and their connections, machine learning, like the human brain, requires input, such as training data or knowledge graphs. After entities have been defined, deep learning can begin. To begin the machine learning process, observations or data are needed, such as examples, direct experience, or instruction. It looks for patterns in data to make inferences from the cases given. Machine learning's primary goal is to allow computers to learn on their own, without the need for human interaction, and to balance their performance accordingly.

Major Machine Learning Techniques

Let’s go through with the techniques so that you can gather your ideas:

Estimation / Regression

For forecasting a continuous value, the regression / estimation technique is utilized. For example, estimating the CO2 emissions from a car's engine or projecting the price of a house based on its attributes.

Classification

A classification approach is used to determine a case's class or category, such as whether a cell is benign or malignant, or whether a customer would churn.

Clustering

Groups of similar cases can be used to locate similar patients or for client segmentation in the banking industry, for example.

Association

The technique is used to locate products or occurrences that frequently occur together, for as supermarket goods that are frequently purchased together by a specific customer.

Detecting Anomalies

Anomaly detection is a technique for detecting anomalous and unexpected situations, such as credit card theft.

Mining Sequences

Sequence mining is used to forecast the next occurrence, such as a website's clickstream.

Reduced Dimensions

Dimension reduction is a technique for reducing data size.

Systems of Recommendation

Finally, recommendation systems connect people's choices with those of others who share them, and suggest new goods to them, such as books or movies.

How to start learning ML?

Prerequisites For Machine Learning

To begin with Machine Learning, you must understand the following concepts:

Statistics

Statistics include tools that can be used to extract a result from data. Descriptive statistics is a type of statistics that is used to turn raw data into useful information. Inferential statistics can also be used to extract crucial information from a sample of data rather of the entire dataset.

Linear Algebra

Vectors, matrices, and linear transformations are all dealt with in linear algebra. It is crucial in machine learning since it may be used to transform and operate on datasets.

Calculus

Calculus is an essential branch of mathematics that is used in a variety of machine learning algorithms. Machine learning models are built from data sets that have various features. In order to develop a machine learning model, multivariable calculus is essential. Differentiations and integrations are also required.

Probability

Probability aids in predicting the possibility of events, It helps us in determining whether or not the situation will recur. Probability is a foundation stone for machine learning.

Programming language

To implement the entire Machine Learning process, you'll need to know programming languages like R and Python. Both Python and R include built-in libraries that make implementing Machine Learning algorithms very simple. Apart from having a fundamental understanding of programming, you should also be able to extract, process, and analyze data. One of the most significant talents for Machine Learning is this one.

Types of Learning

Machine learning can be divided into four categories:

Supervised learning: (This is also known as inductive learning) The intended outputs are included in the training data. This is not spam; learning takes place under supervision.

Unsupervised learning: Occurs when the training data does not contain the desired outputs. Clustering is a good example. It's difficult to distinguish what is good learning and what does not.

Semi-supervised learning: Incorporates a few desired outputs in the training data.

Reinforcement learning: Reinforcement learning is when a series of actions results in a reward. It is the most ambitious sort of learning, according to AI categories.

Most machine learning algorithms use supervised learning, which is the most mature, well-studied, and extensively used type of learning. Learning with supervision is significantly easier than learning without it.

Machine Learning in Practice

Machine learning algorithms are only a small part of what a data analyst or data scientist can do with machine learning in practice. In practice, the procedure looks somewhat like this:

### Start Loop

Know the domain, prior knowledge, and objectives. Speak with professionals in the field. The objectives are frequently ambiguous. You frequently have more ideas to try than you can possibly implement.

Data integration, selection, cleaning, and pre-processing. These are all steps in the process. This is the component that takes the most time. It is critical to use high-quality data. Because the data is unclean, the more data you have, the worse it is. Garbage goes in, garbage goes out.

Models for learning. This is the exciting part. This section is quite advanced. The tools are all-purpose.

Interpreting the outcomes. It doesn't always matter how the model works as long as it produces outcomes. The model must be understandable in other domains. Human specialists will put you to the test.

Strengthening and implementing discovered knowledge. Most innovations that succeed in the lab are never implemented in real life. It is difficult to get something used.

### End Loop

It is a continuous process, not a one-time process. You must repeat the loop until you have a result that can be used in practice. Furthermore, the data may change, necessitating a new loop.

Machine learning for dummies, what is machine learning?

iMrLopez — Thu, 26 May 2022 18:49:00 +0000

What is machine learning?

Machine learning (ML) is a sort of artificial intelligence (AI) that permits software to improve its accuracy at foreseeing outcomes without being clearly programmed to do so. To forecast new output values, machine learning algorithms utilize historical data as input.

Machine learning is frequently applied to recommendation engines. spam filtering, Fraud detection, malware threat recognition, business process automation (BPA), and predictive upkeep are just a few of the other common applications.

Why Is Machine Learning Important?

The application of Machine Learning in numerous disciplines is growing in popularity as the amount of data available grows. Machine learning presents a slew of ways for extracting information from data and turning it into actionable goals.

In general, a single trip takes longer than usual to accomplish because it involves several means of transportation and traffic timing to get to the destination.

Google Maps can check the agility of shifting traffic at any time using location data from cellphones, and it can also categorize user-reported traffic such as construction, traffic, and accidents.

Gmail divides emails into categories like Primary, Promotions, Social, and Update, and labels them as important.

When you upload a photo to Facebook, it automatically recognizes faces and provides tags for friends. Face recognition on Facebook is done with the help of AI and machine learning.

A plagiarism detector can be built using machine learning. Many institutions and universities require plagiarism checkers to assess students' writing abilities.

Machine learning can also be used to predict disease breakthroughs, drive medical data for outcomes research, plan and support therapy, and manage the complete patient.

Machine learning and artificial intelligence have made our lives a lot easier, and we may expect even more technical advancements in the future thanks to some AI and ML developments. As we've seen in a number of apps here, machine learning is being applied in the real world to improve our lives. For industries that wish to stand out in the market, it also allows us to make business decisions, streamline processes, and boost productivity.

How machine learning works

UC Berkeley (link outside IBM) divides a machine learning algorithm's learning system into three components.

A model optimization process

Weights are modified to lessen the difference between the known example and the model estimate if the model can match the data points in the training set better. The algorithm will repeat this assess and optimize the procedure, changing weights on its own until an accuracy criterion is reached.

a decision process:

Machine learning algorithms are frequently used to make predictions or categorize data. Your algorithm will produce an estimate of a pattern in the data based on certain input data, which can be unlabeled or labeled.

An error functions

An error function is used to evaluate the model's prediction. If known examples exist, an error function can be used to compare the model's accuracy.

Training Methods for Machine Learning Differ

Machine learning has obvious advantages for AI systems. But which machine learning strategy is best for your company? There are numerous ML training methods available, including:

· supervised learning

· unsupervised learning

· semi-supervised learning

Let's have a look at what they have to offer.

More control, less bias: Supervised learning

Supervised learning, also referred to as supervised machine learning, is a subcategory of machine learning and artificial intelligence. It is defined by the use of labeled datasets to train algorithms that correctly classify data or predict outcomes. Unsupervised machine learning uses unlabeled or raw data, whereas supervised machine learning uses labelled input and output training data. It can also compare its output to the exact, predicted result to find flaws and make necessary adjustments to the model.

Scale and Speed: Unsupervised Learning

When the material used to train isn't classified or labeled, unsupervised machine learning algorithms are used. Unsupervised learning is the study of how systems infer a function from unlabeled input to explain a hidden structure. The system can never be guaranteed that the output is correct. Rather, it infers the output from datasets.

Reinforcement Learning: Rewards Outcomes

Reinforcement learning is a machine learning training approach that rewards desirable behaviors while punishing those that are undesired. In general, a reinforcement learning agent can observe and grasp its environment, act, and learn through trial and error.

What are some of the difficulties we face when it comes to machine learning?

· Insufficient Data

· Poor-Quality Data

· Irrelevant Features

Insufficient Data

We need massive datasets to train a machine learning model. We sometimes need thousands of photos and millions of records to forecast complex tasks like object detection.

Poor-Quality Data

Our machine learning method will always provide surprising results if the input data is incorrect, no matter how robust it is. To improve our system's performance, we must minimize noise and eliminate outliers from our dataset. An outlier is a phenomenon that is unique to every member of a group.

Irrelevant Features

Sometimes the dataset we have doesn't have all the features we need to train our model. In that case, we can either remove the unnecessary features or combine them to create a useful feature. Feature engineering is the term for the process of selecting and extracting features.

Machine Learning in the Future

Machine learning, despite its imperfections, remains vital to AI's development. However, this achievement will be dependent on a different approach to AI that addresses its shortcomings, such as the "black box" problem that arises when machines learn unsupervised. Symbolic AI, or a rule-based data processing methodology, is that approach. To define concepts and semantic links, a symbolic method uses an open box called a knowledge graph.

Hybrid AI is a technique that combines machine learning and symbolic AI to enable AI to understand language rather than just data. This powerful method is redefining how data is used across the company by providing deeper insight into what was learned and why.

Machine learning and artificial intelligence are already having a significant impact on our lives, and their importance is only growing. The more you know, the more equipped you'll be to deal with these changes as they occur.

You should be using Cloudflare for your analytics and this is why

iMrLopez — Fri, 20 May 2022 15:44:00 +0000

You should be using Cloudflare for your analytics and this is why

A slow page load time can negatively affect your website's visitors. In fact, Google estimates that if your pages take longer than 3 seconds to load, 53% of your visitors will leave your site. A slow website is also generally known to have a detrimental impact on your site's SEO ranking. This is especially true now that Google has introduced Core Web Vitals signals as ranking considerations.

As a result, you must improve the speed and performance of your website. Always begin this process from the inside out, optimizing your website code first, then your multimedia assets, software, server components, cache systems, and so on. Cloudflare provides a fantastic analytics interface that tells you about the CDN's performance, the number of page visits and pages crawled, the amount of bandwidth saved by using Cloudflare, and many other things like information on attack prevention.

All of this is accomplished without the need for any additional script to be installed on your website. Cloudflare has developed, and continues to evolve, many ways for clients to access and acquire insights from Cloudflare-hosted Internet assets in an effort to make analytics a ubiquitous component of all Cloudflare products. You may use the GraphQL API to get more control over how you see the analytics and log information available on the Cloudflare dashboard, as well as root-level analytics that offers you an overview of metadata linked to your Cloudflare account.

Cloudflare Analytics captures and analyses datasets that have the following characteristics:

All metrics represent traffic that is proxied through the Cloudflare network which is also known as orange-clouded, as specified by DNS entries in the Cloudflare DNS app. Cloudflare cannot provide DNS stats for a CNAME setup.
Unproxied DNS entries are not counted by Cloudflare. However, we can gather DNS metrics if your site is not proxied over Cloudflare, but Cloudflare is your authoritative DNS server.
Cloudflare can only proxy traffic directed at certain ports.
Cloudflare looks at the IP address associated with each request to determine the country of origin. Configure Cloudflare IP Geolocation for more information. The external link should be opened.

A visitor's browser sends a request to your web server when they request a page from your website. All assets connected to your web page (HTML, CSS, JS, IMG) will be downloaded from your server by the visitor's browser. This process will be repeated every time a browser accesses that same web page if no cache solution is in place, adding overhead to your web server. Cloudflare saves a copy of your website content in their internationally scattered data centers when you add it to their service.This implies that when a user visits a page on your website, they will most likely obtain a copy of your website assets stored in the nearby data center instead of having to request it from your actual web server. As a result, the user can see the content of your website much faster, and your web server is spared from the additional requests for static assets that it would otherwise get. The finest aspect is that you are not required to do anything. This is taken care of by Cloudflare. The primary records in your DNS area will have an orange cloud next to them when you add your site to Cloudflare. Cloudflare speeds up and protects your site when your DNS records are orange clouded. Cloudflare hides your server's real IP address from the public, which is the first degree of protection. Your server is more vulnerable to direct attacks if its IP address is disclosed. When traffic is routed through Cloudflare, your true server IP is concealed from the rest of the world, reducing the likelihood of a direct attack on your server. Cloudflare also blocks threats and restricts the use of bandwidth and server resources by abusive bots and trackers.

The worldwide network of Cloudflare is frequently faster than the Internet. We connect directly to all major cloud providers and interconnect with almost every service provider on the planet. We have data centers in more than 250 places that provide sub-50 millisecond latency to 95% of the world's Internet users. Google Analytics, like any other HTML analytics, relies on users having JavaScript enabled and the page loading to the point where the tracking code is visible. Cloudflare analytics are far more precise than JavaScript-based statistics since they capture all of the traffic statistics that JavaScript may miss owing to restricted JavaScript content or a page that hasn't fully loaded.

Cloudflare protects against DDoS and DoS attacks. DDoS is an attack on a website that sends out an excessive number of automated requests from multiple places in order to bring it down. Because the attacker targets IP addresses that deliberately bypass Cloudflare, Cloudflare can't fully protect the server from DDoS if the hacker knows your IP address. Change the IP address and solely use the new IP address in Cloudflare as a workaround. Using Cloudflare makes sense if a platform experiences too many DDoS attacks.

You gain access to a growing international user community while also increasing your website hosting experience. An attack on any individual is quickly discovered and, as a result, the entire community is prohibited. Cloudflare protects you from a variety of dangers, such as cross-site scripting, spam comments, SQL injection, bot crawling in excess, harvester emails, and so on. It will prevent most assaults without the need for complicated configurations that other security plugins and solutions require.

What is Cloudflare used for and why should you use for your websites?

iMrLopez — Sun, 24 Apr 2022 17:03:00 +0000

What is Cloudflare?

Cloudflare is a Content Delivery Network (CDN) (CDN). Cloudflare is a firm that provides CDNs, web security, and other services. Cloudflare's main goal is to assist small and large enterprises in securing and speeding up their internet assets. It was founded in 2009 with the goal of locating and eradicating the source of email spam. It has evolved over time and today serves millions of consumers with DNS and security services. It is now one of the largest cloud network platforms, with over 12 million domains trusting it. Furthermore, it is responsible for roughly 10% of all internet traffic. We look at how Cloudflare works and the primary advantages of adopting it for your company.

Cloudflare is on a mission to make the Internet a better place. Cloudflare is one of the largest networks in the world. Thanks to Cloudflare, businesses, non-profits, bloggers and anybody with an online presence now have quicker, more secure websites and apps. Millions of websites use Cloudflare, and our network is growing by tens of thousands every day. Cloudflare serves 32 million HTTP requests per second on average and powers Internet requests for millions of websites.

Features of Cloudflare

Defend your site from a DDoS attack

Cloudflare protects websites, applications, and entire networks from DDoS attacks, blocking over 57 billion assaults per day.

Load-balanced CDN on a Global Scale

Intelligent load balancing mechanisms deployed across 200 data centers worldwide ensure that there are no bottlenecks.

Static Content Caching

Content is cached on strategically situated edge servers for faster delivery and lower latency.

Argo Smart Routing

Using Argo's intelligent routing algorithm, Cloudflare intelligently routes traffic through the fastest network paths.

Geo-based Routing

Thanks to global load balancing, visitors to your web application will be served content from the servers nearest to them.

Streaming

Cloudflare's on-demand streaming offering is both cost-effective and simple to use.

Optimizing for Mobile

Cloudflare Mirage recognizes the browser type on the user's workstation and adapts automatically, optimizing for performance, thanks to its virtualized and lazy-loaded images.

Argo Tunnel

Cloudflare's Argo Tunnel service can assist you in establishing an encrypted tunnel between your nearest data center and your origin server, ensuring complete security.

Intelligence should be used to detect new threats.

Cloudflare has intelligent technologies that can learn from attacks on any site and instantaneously extend better protection to all other sites based on what they've learned.

Network of China

Cloudflare is the only truly global network with strong connections in China that provides quick, secure, and reliable experiences for your customers both inside and outside of China.

Analytics

Deep insights and intelligence takes a detailed approach to your cached data, with comprehensive product-specific dashboards and the ability to create custom dashboards using GraphQL and the Analytics API.

How Cloudflare works

Cloudflare is a plug-and-play security layer that can be added to any website to help protect it from threats and optimize its resources to be delivered to users swiftly and efficiently. Thanks to its global anycast network, Cloudflare acts as a protective gateway between your website's webserver and the internet. So, once a request is received, Cloudflare inspects it for numerous criteria such as location, browser from which the request is coming, the number of requests coming from the same IP address, and the resources required. As a result, Cloudflare can quickly distinguish between good and bad traffic, eliminate threats and spamming requests from bots, and allow only legitimate user traffic to reach your origin servers in this manner. As a result, the burden on your server is reduced, and service disruption is avoided. In addition, Cloudflare will replace your original website server IP address with a Cloudflare IP address if you enable a Cloudflare proxy (orange cloud). This protects your webserver from threats and attacks that specifically target its IP address. This has the extra benefit of concealing your actual hosting provider.

Then... Why use Cloudflare?

· Cloudflare provides a DNS service that is entirely free.

With its enormous edge network availability worldwide, Cloudflare provides a free content delivery network (CDN).

· It provides a free SSL certificate, which allows you to convert an HTTP website to an HTTPS compatible webpage in seconds.

· Cloudflare provides DDoS protection for free.

· It provides free content caching, which speeds up page loading times.

· It includes a firewall service that is included in the Cloudflare free plan. You can use this to block bots, traffic from specified countries, and IP addresses, among other things.

· It makes it simple to export and import DNS settings.

· Cloudflare also provides DNS-based load balancing for as little as $5 per month, including two origin servers and 500K free DNS queries. All included active health monitoring for each origin server, automated failover, and geolocation-based routing.

· It also lets you modify DNS settings for several domains from a single account, even if you're on the free plan, and there are no limits on the number of DNS queries you can do.

A** dvantages**

Cloudflare is primarily a content delivery network (CDN), with over 200 data centers worldwide. It improves site speed while also providing rich security features.

Enjoy advanced analytics with comprehensive dashboards and in-depth granular cache data analysis insights.

It is a favorite in the business since it is simple to set up and has an easy user interface.

Serve a cached version of your website to your clients even if the origin server is offline, ensuring that your site is always available to them.

Cloudflare offers a free trial edition that allows you to try out some of the essential functions for your setup.

Disadvantages

DNS zones are managed, which may not be ideal for some users. In addition, only one level of SSL certificate is issued to sni.cloudflaressl.com. Most of the other drawbacks are connected to the feature's lack of availability in the free version.

Conclusion

Overall, CloudFlare is worth a go if you're looking for a content delivery network that won't jeopardize your website's security or cost you a fortune. CloudFlare is a good option. In less than 5 minutes, you can boost the performance of your website.

Framework wars: React Vs Vue

iMrLopez — Tue, 12 Apr 2022 17:29:00 +0000

Framework fight: React vs Vue

Introduction

JavaScript frameworks are essential for app development nowadays, and many individuals are fighting between React and Vue for their projects. Despite years of debate, picking a clear winner is difficult because both have advantages and disadvantages. We've put together this article to assist you to decide between React and Vue and figure out which one is right for you.

What is React?

Components are at the core of every React application. Modules that produce output on their own are known as components. React components can be used to create user interface elements such as buttons and input fields. Components are composable. The output of a component may contain one or more other components.

To put it another way, we write React apps by writing React components that represent various elements of the user interface. The structure of our application is defined by how we organize these components within higher-level components.

There are strict data management rules that must be followed by each component in any React app. Complex data and application states are common in user interfaces with complex interactive capabilities. There is a limited amount of surface area in React, and its purpose is to give us the tools to anticipate how our app will look under various conditions.

What is Vue?

Vue is a front-end framework that can be easily integrated into an existing app. From this, a single-page app could be created as well. In other words, we can use it to extend the functionality of current apps or build entirely new ones. Vue is a component-based framework, which means applications are built by nesting components and passing data between them.

The similarities between React and Vue?

Given that both frameworks share some tools, features, and communication capabilities, let's take a quick look at the points.

· The most remarkable similarity between these frameworks is that both make use of virtual DOM as the browser rendering system.

· Can be used in conjunction with any other website application.

· A responsive, lightweight approach enables developers to respond more quickly.

· Both are adaptable, quick, and powerful.

· While the community support structure is distinct for each of these, it is sufficiently large.

· Each of them includes a vast library and a set of tools.

Key differences between React and Vue

While there are some similarities between the top two JavaScript frameworks, there are some significant differences in terms of performance, architecture, documentation, and other characteristics that distinguish them from one another in their application domain.

The main difference is in the approach taken by the React and Vue frameworks. Vue makes use of both HTML templates and JSX, whereas React makes use of only JSX, which can make things faster and easier, but frequently complicates simple things. On the other hand, react provides expert developers with DOM manipulation, components, and component state management. New developers may dislike such difficulties and opt for simpler frameworks such as Vue.

Major advantages of React

· In the documentation for React, there are detailed solutions to common issues. It's easy to customize because of the simple design.

· Faster application performance (virtual DOM support).

· easier component understanding due to the linking of JavaScript and HTML in JSX.

· server-side rendering opportunities.

· implementation of functional programming concepts through the creation of testable and reusable code.

· TypeScript support. There will be fewer bugs, easier project support, and the ability to quickly build an app from scratch.

· A prototype application can be developed in a much shorter period. Applications can be developed quickly by experienced programmers.

· There is no need to develop separate apps for iOS and Android. Intermediary libraries are used to launch your application.

Major disadvantages of React

· Because of the rapidity with which technology advances, the environment is constantly changing, making it difficult to keep up. As a result, the developer's skills must be constantly updated.

· It's difficult to keep up with the rapid pace of change and acceleration, making proper documentation difficult.

· React only covers the app's UI Layers, so you'll need to use other technologies for other parts of the development.

· React uses JavaScript XML (JSX), which is an XML or HTML-like JavaScript syntax extension. It extends ES6 by being processed into React Framework JavaScript calls. This allows HTML-like text and JavaScript react code to coexist. However, because of the learning curve's complexity, it also acts as a barrier.

Major Advantages of Vue

· Vue.'s simplicity is the first and most important advantage. It's simple to learn for new developers, so once you've mastered the fundamentals of JavaScript, you can start building simple applications.

· It's also worth noting the tool's speed, which is due not only to its superb optimization but also to its small size of about 23 Kb.

· Vue is an open-source framework that can be developed and customized by anyone. Furthermore, unlike Angular and React, Vue is not owned by a corporation and is continually developed by a community of Vue enthusiasts.

· Finally, the Vue documentation justifies the special mention because it is very clear and explains the framework's various functions in a simple manner.

Major Disadvantages of Vue

· Although it may appear strange, having too much freedom can be damaging. Over-flexibility causes developers to make more decisions, which is a problem. This can slow down production and cause development styles to conflict if there isn't a consensus among the team.

· Many of the resources for documentation, discussions, and tutorials are written in Chinese, making it difficult for developers who only work in English to use the framework.

· Vue's ecosystem is small in comparison to its competitors because it is still a young framework developed by a small team of developers.

Which one is better?

Currently, the most popular frameworks are React and Vue, each of which has its own set of advantages and disadvantages, as well as a slew of other considerations that aren't covered here. Overall, both frameworks allow for the creation of complex functionality using templates and are suitable for a wide range of project types.

React has a larger community than Vue, so you'll probably get an answer sooner. Vue.js has a simpler learning curve, allowing developers to jump right into their projects. It appears that Vue.js has improved upon many of React's concepts.

I hope you found this article helpful in deciding which framework to use for your next project.

Framework wars: React vs Angular

iMrLopez — Mon, 28 Mar 2022 16:37:00 +0000

A question always came to anyone’s mind like which to choose for web development. Instead of answering this dilemma, I have a discussion about them about their comparison of each option. React and Angular got their place in 10 JS frameworks for 2022. So, we’ll dive into the details of these two. So, let’s get started.

React

React, or React.js is a front-end library based on JavaScript that aids in the creation of user interfaces (UI). It uses declarative programming to represent the state of the user interface.

React allows programmers to visualize how their applications will appear to users and how they will interact with them once they have been developed.

Although React is best recognized for developing web apps, it can also be used to create mobile apps and native-rendered apps.

React is used in projects by companies including Facebook, Instagram, Netflix, and the New York Times.

Angular

Angular is an open-source framework for developing online applications, single-page web applications, and hybrid applications. Google developed and maintained TypeScript, which is used to build Angular.

Angular is a full-featured toolkit that includes everything a developer needs to create large-scale applications. It's no surprise that major companies like Xbox, Forbes, BMW, and others choose Angular to React for frontend development.

Websites may be rendered rapidly and efficiently with Angular. It's an all-in-one framework with built-in routing, state management, and form validation capabilities. TypeScript, a superset of JavaScript, is used to build Angular.

Angular is a MEAN stack component that is compatible with most code editors and is used to create dynamic websites and web applications.

Advantages of Angular

Let's go through some of Angular's key advantages:

· TypeScript functionalities like dependency injection and routing are used to create an effective cross-platform UI solution.

· Improved application performance and faster application loading

· Angular CLI, good community support, two-way data binding, and differential loading all contribute to faster development.

· Modules and components make code more readable, fix, and testable.

· Dependency injection and Angular services are two powerful design patterns.

Advantages of React

To gain a better understanding of the React vs. Angular argument, we must consider the advantages that React also.

· Most editors don't support full code completion. For the developers, this is extremely inconvenient because they don't have comprehensive references to the component's variables and functions. React, on the other hand, gets simpler, faster, and more elegant using JSX. React also lacks run-time failures, colorless coding, and code completion support for restricted code.

· React has an Excellent high user interface. React, unlike other frameworks, is primarily focused on the user interface. Through JavaScript interactions between the device and the program, it gives users an incredibly responsive interface. This improves the total load time of your program and allows it to function without interruptions.

· For storing components, react provides its own virtual DOM. React automatically analyzes the required modifications in the virtual DOM, allowing developers to work with greater flexibility and reliability. It also refreshes the DOM trees to reflect the latest changes. Furthermore, react does not necessitate costly DOM operations, and all updates are executed systematically.

Disadvantages of Angular

Debugging is difficult due to the deep scopes and hierarchical design of the system. Because of its layered architecture, Angular is challenging to debug, especially for those unfamiliar with n-tier architectures. Similarly, debugging scopes in heavy apps is a difficult task. Furthermore, all Angular injectors are arranged in a hierarchy, resulting in deep nesting.

Old devices are not supported by Angular-based apps since the browser is busy controlling the DOM elements and requires a lot of external resources to do so. Modern computers, mobile phones, and other technologies, on the other hand, do not have the performance tradeoff aspect. As a result, as long as the end-gadgets users are up to date, there will be no problem.

Angular is a top choice for many developers today, despite some of its disadvantages. It is ideal for a wide range of projects.

Disadvantages of React

Developers can use JSX to render HTML in JS. However, if the component is too large, such as form validation, the code may become complex and difficult to debug, making it difficult for beginners to understand. Furthermore, react solely addresses the user interface and does not offer an end-to-end road map.

Is Angular easier than React?

React is simple to use and provides developers with several options for completing the same task in different ways. React, in contrast to Angular, is a faster and easier-to-learn framework. React also provides a unidirectional data flow and is less than Angular (just 46KB).

How to Choose Between React and Angular?

I do agree that it's a difficult decision. Because both frameworks may be used to create similar web apps, deciding which one to employ requires a thorough examination of your requirements. As a result, you must be aware of the differences between React and Angular. The Angular framework, for example, can be used to create a minimal viable product with limited functionality. This is because the framework includes all of the MVC stack components needed for developers to create a fully functional SPA.

On the other hand, if you want to build a large-scale single-page application with multiple features, React app developers will take care of everything. This is because you'll be able to reuse your components across your application. As a result, it will drastically reduce turnaround time while also removing difficulty from the development procedure.

Which is Better?

In terms of single-page applications, React and Angular are both excellent choices. Both instruments, however, are rather different. There may be arguments that React is superior to Angular, or vice versa.

After considering everything, it's clear that react is better than Angular in a variety of ways. This isn't to say that Angular isn't worth your time and effort. Both have advantages and disadvantages, as stated above. What you should do relies entirely on your needs and goals. Whatever your viewpoint on the React Vs. Angular discussion is, you must make decisions based on your functional and usability requirements.

¿Qué tecnologías se aplican en la inteligencia artificial?

iMrLopez — Mon, 21 Mar 2022 22:31:00 +0000

Anteriormente hablamos sobre lo que era la tecnología artificial, sus mitos, sus usos y su importancia en la sociedad actual y futura.

Hoy te hablare sobre el tipo de tecnologías que se aplican en ella, ¿Cómo se utilizan? ¿Cuál es la mejor para usar si quieres dedicarte a la IA? ¿Cuáles serán las últimas tendencias de Inteligencia Artificial que imperarán en el mercado? Sin más preámbulos ¡Vamos a averiguarlo!

A diferencia de otras tecnologías, la IA puede representar muchas cosas para muchas personas diferentes. IA es esa voz que te habla desde tu altavoz Amazon Echo, pero también es ese robot que automatiza un proceso industrial, el asistente virtual que te ahorra tiempo en gestiones con tu banco o el cerebro del GPS que te ayuda a guiarte en una ciudad que no conoces bien. Por esto mismo es necesario afirmar que en la Inteligencia se encuentran un amplio conjunto de tecnologías que van desde el aprendizaje automático a la visión artificial, desde las redes neuronales al modelado de datos.

Ø Deep Learning : Es una subdisciplina del Machine Learning. Es un sistema de aprendizaje que se inspira en el funcionamiento de las redes neuronales del cerebro humano para procesar la información, con una base matemática muy compleja detrás. Aunque sí se apoya en la experiencia (ya sean datos previos, generados por el entorno o autogenerados), no parte de indicaciones estrictas que determinen qué es correcto y qué no, de forma que el sistema pueda determinar conclusiones por sí sólo. A diferencia del _machine learning, _el aprendizaje profundo crea nuevas capas de aprendizaje automático más allá del primer punto de decisión. Estas nuevas capas reciben el nombre de redes neuronales y teóricamente simulan la forma en la que operan los cerebros humanos.

Ø Visión artificial: La visión artificial supone conseguir que un ordenador sea capaz de reconocer imágenes de la misma forma en que lo haría un ser humano. Es decir no se trata tanto de recoger un conjunto de píxeles, sino entender qué representan en el mundo real y cómo unos objetos se relacionan con otros. En estos momentos, dos son las áreas en las que más se está profundizando en este campo: el reconocimiento facial y la navegación autónoma.

Ø Inteligencia Cognitiva: Esta es una combinación de las tecnologías mencionadas previamente con el objetivo de crear servicios de Inteligencia Artificial capaces de tener comprensión humana. Los agentes virtuales hacen referencia a aquel mecanismo informático capaz de interactuar con los humanos, básicamente Es la unión de reconocimiento visual, sonoro, comprensión lectora, NLP y Machine Learning para crear sistemas capaces de comprender la información relacionada a la interacción humana y responder en consecuencia.

Ø Sistema de reconocimiento de voz: Estos son sistemas que entienden lo que transmite la voz humana. Es una de las tecnologías más usadas y al alcance de todo el mundo, ya que dan respuesta a las transcripciones de lenguaje humano por medio de sistemas interactivos de voz que hoy en día podemos encontrar en las app para móviles, en altavoces inteligentes o algunos vehículos entre otros

Ø Realidad aumentada y generación de contenidos : La Realidad aumentada se basa en una tecnología que superpone los elementos virtuales en la visión de las personas, provocando una visión mucho más atractiva y súper desarrollada, lo cual traerá consigo que la mayoría de las empresas inviertan en esta tecnología para modernizar su estilo de negocio. Ya sea en videos, anuncios, blogs, infografías u otros recursos visuales.

Ahora bien, acabamos de ver las tecnologías más utilizadas en la IA, pero a un nivel más práctico ¿Cuáles son los programas específicos o lenguajes de programación que facilitan el desarrollo de algoritmos y aplicaciones para la IA?

Para responder esta pregunta es preciso tener en claro que la programación orientada a la inteligencia artificial es muy diferente a la programación convencional y, por lo tanto, los lenguajes de programación se emplean de una manera muy distinta.

Sabiendo eso, podemos decir que el principal y más usado es Phyton , esto es debido a que los paquetes de aprendizaje automático y deep learning más conocidos y utilizados del mundo, como Pandas, NumPy, entre otros están escritos en Python. Es el más completo para esta tecnología y te da un soporte sobresaliente para cada uno de los pasos del proceso de aprendizaje automático.

Luego le sigue R , este es otro lenguaje de programación popular en esta disciplina. Se trata de un lenguaje específico para trabajar en análisis de datos y aprendizaje automático , lo cual lo dota de una gran ventaja frente a otros lenguajes de propósito general, Es un lenguaje multiplataforma, funcional y también orientado a objetos, y fácil de aprender.

Y finalmente tenemos al lenguaje SQL , Este es un lenguaje declarativo pensado para facilitar la consulta de datos de forma parecida al habla natural (en inglés) y es la manera principal de comunicarse con cualquier gestor de datos relacional, se enfoca por sobre todo en base de datos.

En conclusión, estas son solo algunas de las diferentes tecnologías que se usan en la actualidad, pero existen muchas más que a través de la Inteligencia Artificial ayudan a las empresas a seguir creciendo e invirtiendo en el futuro. Recuerda que a muchas todavía les queda mucho camino por recorrer, sobre todo si tenemos en cuenta que la I.A Todavía es joven.

Ahora que ya sabes las tecnologías y los lenguajes más usados, ¡Pruébalos! se parte del cambio y ¡Anímate a apostar por ella!

“Mitos sobre la Inteligencia Artificial, ¿Nos dominaran las computadoras en el futuro?"

iMrLopez — Thu, 17 Mar 2022 16:39:00 +0000

Anteriormente te hable sobre la inteligencia artificial, su concepto, su origen y sus usos. Pudimos ver que en la actualidad gran parte de las actividades que realizamos en nuestro día a día son ejecutadas y facilitadas por esta misma, desde las búsquedas que realizamos en Google, Los bots que nos orientan de forma online en atención al cliente hasta grandes avances médicos que mejoran la calidad de vida de cientos de personas en el mundo. Pero, a pesar de sus bondades aun hay muchos mitos, dudas y desconfianza en torno a ellas, de hecho un gran porcentaje de personas consideran que en el futuro esta inteligencia nos dominara a nosotros o que acabara con grandes puestos de trabajo.

¿Es realmente así? ¿Hay alguna justificación real ante esas dudas?

¡Ven conmigo a averiguarlo! Vamos a dar un repaso por los mitos más comunes sobre esta tecnología y su realidad.

Mito 1- Las máquinas reemplazarán a los humanos en los puestos de trabajo

Este es uno de los mitos más controvertidos. Mantiene la noción de que existe una competencia entre empleados y máquinas.

Una respuesta evidente a esa pregunta es que *las empresas de todo el mundo buscan constantemente formas de reducir la carga de trabajo * de sus empleados. No es para reemplazarlos, como a menudo se cree erróneamente. La tecnología no está hecha para sustituir la capacidad humana, sino complementarla para evolucionar. De la misma manera, la IA no podrá reemplazar trabajos de los humanos, más bien transformará los existentes y creará nuevos. Aumentará la forma en que las personas completan su trabajo al hacer cosas como extraer y analizar datos para ayudar en la toma de decisiones en tiempo real.

En un sentido práctico, es más una alianza entre humanos e inteligencia artificial. Es algo erróneo estar de acuerdo con tal mito. La inteligencia artificial no te quitará tu puesto de trabajo, solo cambiará la forma en que trabajas y lo que haces en el

Mito 2 La inteligencia artificial dominará el mundo

La idea de la creación de máquinas más inteligentes que hombres y mujeres es controvertida. Personas como Stephen Hawking y Nick Bilton creen que la IA podría salirse del control humano en el futuro. Esto, sumado a las películas de ciencia ficción que muestran un apocalipsis robótico ha causado en muchos “pánico” ante esta tecnología

Pero aunque la eficiencia y la precisión son puntos fuertes de la IA, sigue siendo un hecho evidente que la IA nunca puede alcanzar el nivel de intuición y emoción de una persona. Por esto l as máquinas no constituyen de ningún modo una amenaza existencial para la humanidad, ya que su autonomía es de carácter meramente técnico, en el sentido de que corresponde sólo a las cadenas materiales de causalidades, que van desde la búsqueda de información hasta la toma de decisiones. Las máquinas no tienen autonomía moral, no poseen voluntad propia y permanecen al servicio de los objetivos que les hemos fijado.

Mito 3 «súper humanos» creados con Inteligencia Artificial

Si bien la inteligencia Artificial ahora influye en los procesos de toma de decisiones a nivel empresarial, astronomía, medicina y farmacia, el hecho es que no importa lo bien que «entrenes» a una máquina (porque al fin y al cabo su aprendizaje depende del entrenamiento que se le dé), ya que no puede pensar por sí misma al fin y al cabo. Esta es una limitación de la IA que tardará muchísimos años en superar, y de hecho no son pocos los que opinan que es una barrera que nunca se logrará superar. Como tal, la mayoría de procesos que la utilizan siempre dependerán de un veredicto final de las personas para tomar las decisiones.

Por lo tanto, el pensar que el potencial de la IA puede llegar a crear súper humanos o a dominar el mundo es algo actualmente impensable. Esta puede ser tan inteligente como la programemos es una realidad y puede volverse competente con los objetivos. Pero, sin humanos, no habrá inteligencia artificial, esta no es capaz de encenderse por sí misma, ni de motivarse y decidir por sí misma, ni de hacer preguntas alternativas.

Mito 4 Las tecnologías de datos e IA son difíciles de adoptar porque son extremadamente complejas.

La existente oferta de opciones para poner en marcha sistemas basados en la nube hoy en día hace que esto sea extremadamente fácil y rentable para cualquier organización. Una inversión razonable y capacitar a algunos empleados para aprovechar al máximo los datos son suficiente para poner en práctica una robusta estructura de retroalimentación de datos. Si se necesita una mentalidad lógica, buenos datos analíticos y capacidad estratégica pero no es necesario un doctorado en estos campos.

Mito 5 La mayoría de las empresas no tiene los recursos ni la necesidad de inteligencia artificial

Hoy en día, el uso de inteligencia artificial se ve facilitado en todo tipo de entornos. Algunas tecnologías están diseñadas para tareas específicas, tales como atrapar estafadores con datos biométricos de comportamiento. Si bien todavía no estamos en un nivel donde con tan sólo apuntar y hacer clic se podrá implementar un sistema de inteligencia artificial, estas herramientas ya están accesibles para cualquiera que desee aprender y muchas son de código abierto. Incluso pequeños negocios y entornos de limitado alcance y escala pueden verse beneficiados a partir de las lecciones provistas por la inteligencia artificial. Se puede recurrir a inteligencia artificial para, por ejemplo, automatizar y mejorar los pedidos de servicio al cliente o buscar patrones sobre cuándo y por qué los competidores van cambiando precios u ofertas de producto.

Mito 6 La IA son ifs anidados

No. no lo son...

En resumen, la inteligencia artificial aunque siga en crecimiento llego para quedarse, y aunque para muchos sea una amenaza más que una solución, esta visión es consecuencia del gran desconocimiento que hay sobre ella. Seguro después de leer sobre estos mitos (ahora refutados) ya estarás en una mejor posición para comprender la verdad detrás de la IA y ¡Apostar por ella!