AI/ML – DevopsCurry

Computer Vision: How do Computers ‘See’?

Mon, 16 Sep 2024 05:29:55 +0000

In this article, we will be talking about what is computer vision, the technologies that support it, and its applications.

Introduction to Computer Vision

Image credits: The Power of Computer Vision in AI: Unlocking the Future!

Computer vision, to speak in simplest terms, is how computers see. One of the most common examples is the face unlock feature on your mobile. You first register your face with your mobile, where it captures some of the facial features unique to you. It then tries to match this stored facial data to your face the next time you try to face-unlock it. If your face matches it, it unlocks itself, otherwise it doesn’t. The whole of this process requires your mobile to process visual data although it appears as if it is ‘seeing’. This capability is enabled by computer vision.

IBM defines computer vision as “…a field of artificial intelligence (AI) that uses machine learning and neural networks to teach computers and systems to derive meaningful information from digital images, videos and other visual inputs—and to make recommendations or take actions when they see defects or issues. “ It’s a long and technical definition. To understand this field better, let’s learn about how it works first.

How does Computer Vision Work

Computer vision functions using AI and machine learning algorithms like CNN. Let’s get a brief idea of them one by one.

Deep Learning

Deep learning is a subset of machine learning which further is a subset of artificial intelligence. It is an advanced version of machine learning that can mimic the human brain and its decision-making process. Deep learning works using an interconnected network of nodes that resembel the network of neurons in a human brain. It enables CV models to work autonomously and gain context for the visual data once sufficient training data is provided.

Convolutional Neural Network (CNN)

Convolutional neural networks or ConvNets or CNNs are a type of deep learning model that are specially designed to support Computer Vision. It allows CV models to extract features associated with an object, thus helping them identify an object. Before CNNs, these features were extracted manually and provided to the CV model in the form of labeled data. Therefore, CNNs help save a lot of time and manual effort.

The steps involved in computer vision processing can be summarized as follows:

Image credits: Computer Vision .vs Machine Learning .vs Deep Learning | Guide to AI applications

Computer Vision Tasks

CV models can perform one or more of the following tasks…

Image classification: In image classification, a computer is able to classify images or objects in an image into different classes. For example, a computer with CV capabilities may be able to distinguish humans from animals or non-living objects in an image.
Object detection: Object detection uses previously classified objects to detect them in images. It is used in smart factories to detect damage to equipment. It is also used in surveillance cameras to detect suspicious persons or activities.
Object tracking: Object tracking uses object classification and detection to locate an object belonging to a particular class, and track it in real-time videos. It is used, for example, in monitoring traffic.

Real-world Applications of Computer Vision

Healthcare: Computer vision is used for diagnostic analysis of medical images like X-rays, MRI, or CT scans. It helps in detecting potential tumors (cancerous cells) or any other anomalies.
Manufacturing: In the manufacturing industry, computer vision may be utilized for automated inspection of equipment. It may also be used for monitoring adherence to safety protocols like detecting helmets or masks.
Autonomous cars: Computer vision is a key component of self-driving cars. It enables such vehicles to scan their environment for pedestrians, traffic, and potential hazards.
Augmented Reality and Virtual Reality: Computer vision enables AR/VR systems to integrate virtual objects into the real world, thus, improving the gaming experience

Challenges in Computer Vision

Real-word images are far more complicated than training data. Variations in lighting and occlusion (i.e. the object is partially hidden from the camera’s view) can affect the accuracy of computer vision.
CV models require large amounts of labeled training data. Labeling can be a labor-intensive task that requires a considerable amount of time.
Facial recognition and AI-powered computer vision in surveillance cameras can raise concerns regarding privacy and data breaches.

Conclusion

Computer vision is a rapidly advancing field that allows machines to ‘see’ and interpret the world visually, almost like humans. By using technologies like deep learning, CNNs, and AI, computer vision has found its applications across industries, from healthcare and manufacturing to autonomous vehicles and augmented reality. However, as impressive as it sounds, computer vision has its own set of limitations. Real-world environments which are much more dynamic and complex than training data, can still be difficult for CV models to process. Privacy and data leakage concerns are also important challenges that need to be addressed.

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How do Voice Assistants Work? A Comprehensive Guide

Fri, 13 Sep 2024 06:16:01 +0000

Voice assistants are certainly not something unheard of. You must have used Siri if you have an iPhone or Google Assistant if you’re using Android. Google’s voice search is also a familiar example of voice assistant software. But if you’re wondering how they work, and what technologies are working behind the scenes to make it possible, then this article is for you.

In this article, we will be talking about what a voice assistant is, a brief history of its evolution, how it works, pros and cons, and much more…

What are Voice Assistants?

Voice assistants can be grouped under a wider category of digital assistants which include all software’s capable of performing simple tasks like answering questions, scheduling events, setting up reminders, etc. However, they can even include AI softwares that work exactly like voice assistants but uses textual data instead of audio. That said, voice assistants specifically use voice-activated commands with speech-to-text or text-to-speech capability.

In technical terms, a voice assistants can be defined as “…a digital assistant that uses voice recognition, language processing algorithms, and voice synthesis to listen to specific voice commands and return relevant information or perform specific functions as requested by the user.” (Alan AI) Apple’s Siri, Google’s Google Assistant, Amazon’s Alexa, and Microsoft’s Cortana are popular examples of voice assistants. Out of these, Siri was the first voice assistant to be publicly available with its launch in 2010.

Surprisingly, voice assistance technology has existed since the 1960s with a few traces dating to the 1920s. That said, now let’s get a brief overview of the evolution of voice assistance technology…

Evolution of Voice Assistants

Speech recognition technology can be traced back to the 1920s when a voice-activated product called ‘Radio Rex’ was invented in 1922. It looked like a dog house with a toy dog (named ‘Rex’) attached to a spring inside the house. Whenever you called its name, by which I mean shouted ‘Rex’, the toy dog would spring out of its house. This was however a crude technology that mostly recognized only adult male voices. Thus, women and children had to either shout out loud or pronounce it differently for the device to sense their voice.

Image credits: Speech Recognition in 1920s: Radio Rex – The first speech recognition machine?

This was followed by Audrey, the ‘automatic digital recognizer’, invented by New York’s Bell Laboratories in 1952. It could recognize the 10 numbers, from ‘0’ to ‘9’, for which it required a 6-foot tall casing to house all of its circuitry system.

IBM Shoebox was launched in 1962 and could perform simple mathematical operations like addition, subtraction, multiplication, etc on numbers from 0-9. It could recognize 16 spoken words in total – including the numbers (zero, one, two, etc.) and operations (plus, minus, etc). It was named so because of its size which was similar to that of a standard American shoebox.

Next in the line was the Dragon Dictate which was invented by Dr. James Baker in 1977. It was the first speech recognition software that was commercially available at a startling price of $9000! Designed for DOS-based computers, Dragon Dictate required the user to dictate one word at a time perfectly and pause for the computer to process it before moving on to the next one. That said, it was frustrating to use, unlike today’s natural voice typing programs.

Then in 2010 came our familiar Siri, developed by SRI International as a dedicated app on the iOS app store. It was acquired by Apple Inc. in April in the same year. In 2011, a beta version of Siri was introduced as an integrated program in iPhone 4S. Now, Siri has advanced to all Apple products including iPhones, iPads, Apple TV, Mac, etc.

Soon other famous voice assistant models began coming up – like Google Voice Search in 2011 and Google Assistant in 2016. Amazon’s Alexa was announced in 2015 which popularized smart devices with integrated speech recognition technology.

Here’s a comprehensive timeline showing the evolution of voice assistance technology…

(WARNING: requires a lot of scrolling)

Image credits: Voice Assistant Timeline

How do voice assistants work?

Voice assistants work using a combination of various technologies – like speech recognition, STT (speech-to-text), machine learning, etc. Let’s understand each one of them and their role one by one…

Speech recognition

Speech recognition, also known as automatic speech recognition (ASR), helps a computer to interpret and process spoken words. It may involve steps like preprocessing, feature extraction, pattern matching, etc. Speech-to-text (STT) involves the conversion of spoken words (audio) into written words (text) to make it readable to the computer. Speech recognition utilizes 2 models to work:

The acoustic model matches audio signals to its corresponding phonemes (basic sound units).
The language model ensures the recognized words form sensible and meaningful sentences.

Natural Language Processing (NLP)

Natural Language Processing or NLP is the technology that helps computers to interpret and generate data in natural human language. It involves Natural Language Understanding (which is the comprehension aspect) and Natural Language Generation (which is the generative aspect).

In voice assistant technology, NLP ensures that the computer understands the intended meaning of the user’s input and responds in a humanistic way.

Artificial Intelligence (AI) and Machine Learning (ML)

AI and machine learning play a crucial role in the working of voice assistants. It enables features like:

Identifying the user’s voice in particular
Providing personalized responses and recommendations
Tone modulation
Making natural human-like conversations (NLP), etc.

Text-to-speech (TTS)

Opposite to Speech-to-text, which converts spoken words to text format, Text-to-speech (TTS) converts written text into audio. It is through TTS technology that the voice assistant conveys the output or response to the user.

All the steps involved in the working of voice assistants can be summarized in the following illustration…

Image credits: Voice AI: What is it and How Does it Work?

Benefits

Work efficiency: Voice assistants can take up various tasks in the work environment – like setting reminders, scheduling calls, writing emails, etc. They are much quicker than manually performing these tasks. Moreover, writing using voice assistance technology is more natural along with fewer spelling errors.
Easy to learn: Voice assistants are simple and quick to use. You only need to speak a command and they will perform the tasks on their own.
Device integration: Smart home devices are the best examples of device integration using voice assistants. House lighting and music player systems are some devices that can be integrated with voice assistance technology.
Personalization: AI and machine learning enable voice assistants to suit a specific individual’s needs and behavior based on past interactions.
Accessibility: Voice assistants are a blessing for people with disabilities. It can help visually impaired people to use computers through voice navigation and screen reading.

Challenges & Limitations

Accuracy: Although they are continuously improving, voice assistants may still struggle with understanding accents, dialects, slang, and incorrect pronunciation. Their accuracy can also be affected by background noise.
Privacy and security: Voice assistants continuously collect data from their environment to detect wake words. This can cause concerns related to data breaches and constant recording.
Bias: A major issue in AI/ML-supported technology like voice assistants is dependence on training or past data. That said, any bias or inaccuracy in the training data may be reflected in the voice assistant’s output.

Conclusion

In conclusion, Siri and Google Assistant are powerful examples of how voice assistants have transformed the way we interact with technology. Using natural language processing, artificial intelligence, and machine learning, they understand our commands, learn from our behavior, and improve over time. These assistants are more than just tools; they are becoming integral parts of our daily lives, helping us manage tasks, find information, and stay organized with just a few spoken words. As technology continues to evolve, we can expect voice assistants to become even smarter and more intuitive in the future.

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Deep learning: Teaching Machines How to be Human

Mon, 09 Sep 2024 03:42:02 +0000

In this article, we will be diving into the topic of deep learning and how it is different from machine learning. We will also talk about the basic architecture of deep learning models, their types, their applications, and more.

Introduction to deep learning

Machine learning (ML) is a branch of artificial intelligence that focuses on building machines that can learn on their own. However, ML models cannot learn from just any data. They can process only structured and well-labeled data, and any unstructured or unlabeled data needs to be refined before feeding it to ML models.

Deep learning is a subset of machine learning and AI

A more evolved subset of machine learning is deep learning which closely resembles the human brain and its decision-making process. Deep learning models do not necessarily require structured data and can work with a variety of data effortlessly. Moreover, this ability to learn autonomously and respond in a humanistic way has opened up several interesting possibilities such as – AI image recognition, natural language processing (NLP), generative AI, etc. However, although deep learning seems more advanced and powerful than machine learning, both have their advantages and disadvantages. That said, let’s learn about the major differences between the two…

Deep learning vs Machine learning

Deep learning and machine learning differ in terms of their functionality and complexity. Following is a table highlighting the major differences between both of them…

Major differences between deep learning and ML

How does deep learning work

Deep learning works on a network of interconnected nodes (or neurons) called artificial neural networks (ANNs). It is similar to how a human brain comprises millions of interconnected neurons and nervous tissue. A typical ANN is composed of 3 layers:

Input layer: This layer introduces the data into the neural network.
Hidden layer: The number of hidden layers can vary from one to many. If more than one hidden layer is present within a neural network, it is called a deep neural network. Most of the processing happens in this layer.
Output layer: The output layer presents the final output. The number of nodes in an output layer can vary based on the diversity of output – for example, a yes or no output requires 2 nodes.

Different types of deep learning models perform various functions using a similar layered architecture. Let’s discuss some of them now…

Types of deep learning models

Convolutional neural networks: CNNs or ConvNets are designed mainly for computer vision tasks such as face recognition, object detection, and image classification. To identify an object in an image, computers look for specific features associated with that object. Before CNNs, these features were extracted manually through a process of feature engineering. However, with the development of CNNs, feature extraction became an autonomous process, thus saving time and effort. Although they are more powerful than other neural networks, they require high computational power to match their performance and highly trained experts for their maintenance.
Recurrent neural networks: Just like how CNNs mostly deal with images, RNNs mostly deal with text and language. They are used in NLP, speech recognition, and language translation softwares. They support Google Translate, voice search, and voice assistants like Alexa and Siri. They are also used in predictive analysis such as in stock market predictions. RNNs are of 3 types – one-to-many, many-to-many, and many-to-one. Some of the limitations of this model include slow training time and complexity in optimization, especially with a high number of hidden layers.
Generative adversarial networks: GANs are made up of 2 components – a generator and a discriminator. The generator uses training data to produce fake but realistic data while the discriminator tries to identify whether this data is real or fake. As long as the discriminator can identify the fake data, the generator keeps on creating even more realistic data. Because of this back and forth between the two, it is referred to as adversarial. The GAN model has 2 major advantages – it can train itself and it can produce very realistic data.

Benefits

Deep learning models require less human interference. They can perform feature extraction on their own and do not necessarily require structured or labeled data.
They can learn on their own as they are fed more and more data.
These models are also highly accurate. For example, they are better at grasping the intended meaning in a text than the literal meaning.
Deep learning models have a wider range of functionalities than traditional machine learning models.

Limitations

Deep learning models are totally dependent on the data they are trained on. Hence, any bias or inaccuracy in the training data may also be reflected in its output.
They require large amounts of data to produce accurate results. Consequently, they need high computational power and efficient hardware systems.
A major disadvantage of deep learning models is they operate within black boxes. Black boxes refer to the hidden calculations and decision-making process of deep learning models through which they arrive at a particular conclusion. Hence, their results are unexplainable which can lead to a lack of trust between the model and the user.

Conclusion

Deep learning has narrowed the gap between humans and computers. By mimicking natural human intelligence and decision-making, deep learning models continue to replace the manual workforce in various industries. However, being more advanced, it cannot replace ML completely, which is still useful for tasks requiring mathematical or objective analysis rather than deep learning’s subjective analysis. Additionally, the output produced by deep learning models is not entirely trustworthy due to their ‘black box’ nature. This has led to the development of explainable AI which can trace an AI model’s output to its sources and thus, uncover the hidden black boxes.

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Understanding the buzz around GPUs in 2023 ?

Shiwani Sharma — Thu, 21 Sep 2023 05:14:44 +0000

buzz around GPUs ; The server market for the near future is going to be all about GPUs, GPUs, and more GPUs. The Graphic Processing Unit (GPU) Market size was valued at USD 25.41 Billion in 2020 and is projected to reach USD 246.51 Billion by 2028, growing at a CAGR of 32.82% from 2021 to 2028.

According to the latest research report by ZMR (Zion Market Research),“According to the latest research study, the demand of global GPU As A Service Market size & share in terms of revenue was valued at USD 2.31 billion in 2022 and it is expected to surpass around USD 28.7 billion mark by 2030, growing at a compound annual growth rate (CAGR) of approximately 28.78% during the forecast period 2023 to 2030.”

The key market players are listed in the report with their sales, revenues and strategies are DigitalOcean, Amazon Web Services (AWS), Oracle Cloud, Microsoft Azure, OVHcloud, Google Cloud, Qarnot Computing, IBM Cloud, Packet, INVIDIA Cloud, Vultr, Scaleway, Igneous, HPE GreenLake, Paperspace, NetApp, Alibaba Cloud, Hetzner, Rescale, Linode, and others.

IMAGE CREDIT: https://www.zionmarketresearch.com/report/gpu-as-a-service-market

This is the reason we see a lot of buzz around the GPUs and the GPU manufacturers and vendors.

But as a layman, do we have an idea, what is GPU, how does it work, what the the different GPU components etc.

What is GPU?

A Graphics Processing Unit (GPU) is an electronic device or circuit designed primarily for rendering and processing video and image data on a computer screen. GPUs are also known as Visual Processing Units (VPUs) because they excel at handling visual data. They are optimized for parallel processing, allowing them to perform multiple tasks simultaneously, making them exceptionally fast at handling mathematical and graphical workloads. GPUs are instrumental in providing 2D and 3D graphics in computer games and various other visual applications. In recent years, GPUs have become a critical component in fields such as Artificial Intelligence (AI), machine learning, scientific research, and simulations, as they significantly reduce the time required to solve complex mathematical problems.

Additionally, GPUs are used in cryptocurrency mining to perform the intensive calculations necessary for securing blockchain networks, as seen in cryptocurrencies like Bitcoin.

Examples of Applications that need GPUs:

Productivity
Medical Imaging
Gaming
Cryptocurrency Mining
Data Science & Analytics
Machine Learning
Media & Entertainment

Types of GPUs

There are several types of GPUs, each tailored to specific applications:

Integrated GPUs: An integrated graphics card shares power between the GPU and CPU, because the graphics card is built directly into the computer’s processor. Integrated GPUs are best for typical PC processes like web browsing, social media, and resource-light work such as spreadsheets, editing documents, and project management software.

An integrated GPU (iGPU) does not have its own video memory bank (also known as VRAM), and instead uses the same system memory that is utilized by the CPU. Integrated graphics cards are typically a low-power, low-heat solution that still lets your computer boot and perform all kinds of day-to-day tasks without any issues

Discrete GPUs: Discrete GPUs are individually embedded on the motherboard instead and are capable of dealing with more complex graphic processing.For the users playing high resolutions games and video editing may need an external component i.e graphics card for their purpose.

Discrete graphics cards also come with their own memory in the form of VRAM (video RAM, or video random access memory), which gives the dedicated GPU quick access to relevant image data. As a comparison, integrated graphics do not have a dedicated set of memory to pull image data from, but, rather, use the system’s memory to pull image data from.

There are two major providers of discrete GPU for laptops: AMD & NVIDIA.

What Components Does a Graphics Card Have?

GPU: The GPU is an actual hardware component, similar to a CPU
Memory: Also known as VRAM, the graphics card comes with dedicated memory to assist operations
Interface: Most GPUs use PCI Express, found at the bottom of the card
Outputs: You’ll find various video outputs, often comprising HDMI, DisplayPort, DVI, or VGA
Fans/Heat Sink: All GPUs come with fans and a heat sink to help dissipate heat build-up during usage
Voltage Regulator Module (VRM): the VRM is responsible for ensuring that the GPU receives all of the necessary power at a constant steady voltage. Modern GPUs require a six or eight-pin power connector, sometimes even requiring two or three
BIOS: The GPU BIOS holds initial setup and program information, retaining data on voltages, memory, and more when you power down your machine

IMAGE Credit: https://www.akshatblog.com/graphics-card-components-explained-in-detail/

How does a GPUs work?

GPU generate the images and for that it needs more space to collect the image data, so GPU collect their data in RAM (Random Access Memory) and RAM is connected though DAC (Digital-to-analog convertor) and from that the image will be interpret from the analog signal that regulate for uses.

A Graphics Card is primarily responsible for rendering images on the display, be it images, videos, games, documents, the regular desktop environment, or a file folder, and anything else. All of these things, from tasks that require tremendous computing power, like a video game, to something we consider “simple” like opening a new text document all require the use of a graphics card.

A graphics card is a PC component intended to permit the computer to read different graphic pictures.

A graphics card maps the instructions issued by other programs on our computer into a visual rendering on the screen. But, a modern graphics card is capable of processing a phenomenal number of instructions simultaneously, drawing and redrawing images tens or even hundreds of times every second to ensure whatever we are looking at, whatever tasks we are attempting to complete remains smooth.

Difference Between GPUs & CPU

S.NO.	GPUs	CPU
01	It stands for Graphics Processor Unit.	CPU stands for Central Processing Unit.
02	It has many numbers of cores.	It has less number of cores as compared to GPU.
03	It has few image processing libraries.	It has many image processing libraries.
04	GPU has simple instruction cycle.	The instruction cycle of CPU is difficult.
05	GPU has faster speed then CPU.	CPU has less speed.
06	GPU is utilized for 3D graphics.	It is utilized for a wide range processing.
07	It has parallel processing.	It has serial process.
08	Easy to programming with imaging libraries.	Easy to program with C++ language.
09	It has less numbers of tools for debug.	It has many numbers of tools available for debug.
10	It provide the good performance-per-watt.	It provide low heat output.

Conclusion

We would like to conclude on a note that the buzz around the GPUs is all real , with ever increasing demand from AI,ML, Blockcahin, Gaming and other similar domains and GPUs would the hot selling cakes for then next few years.

Manufacturers like NVIDIA, AMD, Intel, ASUS, Apple, GIGABYTE, ZOTAC, EVGA etc are all facing high demands from their customers and a supply crisis to match those numbers, again making the demand supply chain imbalanced.

So through this post we made a short attempt to share basic understanding and knowledge with our customers about what is a GPU and how does it work. We will deifinetly do more detailed posts on GPUs in future so please do subscribe us and follow us.

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Exploring the Buzz around ChatGPT in 2023

Shiwani Sharma — Fri, 11 Aug 2023 09:29:42 +0000

So what is ChatGPT?

ChatGPT , launched in November 2022, by AI & research company & open AI that specializes in developing conversational AI models. The abbreviation “GPT” stands for “Generative Pre-trained Transformer.” This tool utilizes AI technology to enable human-like conversations with chatbots.

To better understand this concept, let’s consider an example. Imagine you are searching for a real estate company and you come across a website. Upon visiting the website, you encounter a chatbot designed to assist you and address your queries. This chatbot operates on a similar principle as ChatGPT, as it is constructed as a question-answering model.

In summary, ChatGPT leverages advanced AI techniques to create Chatbot models capable of engaging in realistic conversations, making it a valuable tool for facilitating user interactions and providing information.

Image Credits: https://medium.com/geekculture/chatgpt-what-is-it-and-how-does-it-work-exactly-62e7010524d3

Characteristics of ChatGPT

Formation of New Content: ChatGPT is capable of generating new content in a human-like manner using a question-answer model. When you pose a question, it can provide you with an exact answer to your query.
Huge Vocabulary: It possesses a vast word dictionary, which is highly beneficial for users. With approximately 570 GB of data, ChatGPT has access to an extensive vocabulary.
Self-Learning Process: ChatGPT can learn and adapt to new information, implementing it when necessary. When users provide feedback, ChatGPT actively tries to incorporate and address i
Translation in any Language: One of the important characteristics of ChatGPT is its translation features. If you want to learn any language, you can easily learn it without paying anything. This tool can translate text into a maximum of 26 different languages.
Code Generation: One of the amazing features of ChatGPT is its ability to create code. If you are stuck while writing any code for a website or anything else, you can take help from ChatGPT and it will easily write the whole code structure for you.
Content Creation Assistance: If you are a writer and you need some content for social media, blogs, or articles, then you can take the help of ChatGPT. It will also assist you in writing SEO content and provide keyword suggestions for SEO writing.
Helpful for Homemakers: This tool is not only for working people in offices but also helpful for homemakers who want to make some special dinner or lunch for their family. It can also be used to learn cooking.
General Research: Another important feature is that anyone of any age, whether it’s a school-going child, college student, working professional, or homemaker, can use this tool to search for anything and get the answers they need.

Importance Of ChatGPT

Effective Interaction & Communication ChatGPT is a unique and effective tool that facilitates direct interaction between humans and machines. With the assistance of ChatGPT, users can ask questions and receive the desired information through conversational text or messaging.

Access to Vast Information ChatGPT collects data from external sources and training data, providing it with access to a vast amount of information. Users can easily obtain answers to their questions and be satisfied with the detailed explanations and information provided.
Customer Satisfaction ChatGPT ensures quick responses and helps customers resolve their queries. Users can ask any type of question, and ChatGPT always strives to satisfy customers by providing relevant and extensive information that precisely addresses their needs.

Benefits of ChatGPT

Saves Time This tool helps you save time searching for the data you want; you can get it within seconds. When making a website, you would have to write hundreds of lines of code, but with this tool, it becomes easy as it writes the code quickly. This tool not only saves the time of web developers but also benefits writers and SEO experts by saving their time.
Best Free Tool for Learning Whatever you want to learn, you can get your answers quickly without having to pay a certain amount to anyone. Therefore, this tool is the best for learning, enabling you to improve your knowledge and easily navigate through complicated situations.
Provides Best Content This tool also helps you create the best content. Whether you need to write an email or any other content, content creators nowadays use this tool to produce top-notch content. It also assists in sentence formation and rectifies grammatical mistakes.
Available 24 Hours It’s a 24-hour open platform to solve your queries, always available for your help.
Provides Instant Response Additionally, it provides an instant response to your problems.

How To Use ChatGPT

It is very easy to use ChatGPT, so sharing it in a step by step manner:

Step-1: As a first step you need to go to their webpage by the link chat.openai.com on your any device( smartphones, computer, laptops) and create an account, if you do not a have it already.

Step-2: Now once your account is created, it’s free to use. After the account is created you can login to the home page of ChatGPT

Step-3: Now you can ask your queries and ChatGPT will reply back to your questions.

Some limitations with ChatGPT

Some important limitations of ChatGPT are as follow:

Not run without internet service
Lack of common sense
Reply one answer at one time, Not a multi-tasker tool
Sufficient knowledge with less amount of creativity
ChatGPT is a free product, but running this technology is an expensive affair that all cannot afford. The running cost is estimated to be around $100,000 per day or $3 million per month
Emotionless
Problem while solving some Mathematical problems

Conclusion

After its launch, ChatGPT had crossed over a Million users just within the first week. The world is amazed with infinite possibilities and can’t stop talking about this one-of-its-kind AI and are sharing their experiences on social media. It seems like from generating video ideas for content creators to helping developers spot errors in their code, the ChatGPT seems to have a little something for everyone.

What is different this time is that unlike the traditional Chatbots that connect keywords with intents, LLMs like ChatGPT are text predictors. Which means they fundamentally learn about the relationship between texts, words and sentences and then they use these relationships to predict the following string of characters.

But despite the fact that such developments are clearly ground-breaking, there seems to be a long way to go for it to become standard for general NLP purposes. Current studies show that even though the ChatGPT model is impressive given its do-it-all ability, it still might be underperforming compared to existing state-of-the-art solutions for modern NLP tasks.

Hence we would like to conclude with the thought that even though ChatGPT has taken the world by storm and not to deny the fact that ChatGPT is an exciting advancement in AI technology, it is yet to be seen if this is sustainable model in the long term both from cost and technology perspective.

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Top 8 technology trends to keep an eye on in 2021

Namrata Joshi — Mon, 12 Oct 2020 11:04:23 +0000

Top Technology Trends for Future

Currently more than 3.8 billion population on the globe use the Internet today, which is 40% of the world’s population. We are being introduced to new gadgets and technologies every day. Now as old technologies continue to work as a base for new ones, let us talk about some of the future technology trends that are going to rule the world for good.

These trends are going to take over the internet by storm in 2021.

This post talks about some of the latest key technology trends in the software industry like Artificial Intelligence, VR ,AR, IoT, and many more. Let us dive into these trends to know about what makes them so special for the future of technology.

1. Cloud Computing 

In the simplest words, cloud computing will let you store your data over the internet, instead of your hard drive. The most common example of cloud computing is Google Drive. The data you store in the Google drive is delivered to you via Google cloud. This data is stored in the cloud only.

The majority of companies are now turning towards Cloud Technology certainly because of the great benefits of it. Companies do not need to invest in hardware and servers. Instead, they can rely on Cloud providers and save a huge amount of money and time.

But, there something even more advanced than the Cloud and everyone talking about it. It is Edge computing. Edge computing ,a concept that started from the mobile and telecom sectors has now quickly spread to almost all domains. Edge computing transformed the way data is being handled, processed, and delivered from the devices all over the world.

2. Artificial Intelligence/Machine Learning 

Everyone has heard of Artificial Intelligence, especially in those Sci-fi movies. So, it is already a well-known term but it is yet to become present everywhere in our everyday life. There is no limit to the potential that Artificial Intelligence can provide us in the future.

Artificial Intelligence is present everywhere if you notice. It is in home appliances, hospitals, smartphones, military, government, and many other sectors. It is at the root of technologies like face recognition and speech recognition.

Machine Learning is an application of AI. It provides the system, the ability to automatically learn and self-improve from experience without being explicitly programmed. So machine learning is basically a part of AI. We are surrounded by Artificial Intelligence daily. That day is not far when we will be relying on AI for most of our needs.

3. Blockchain 

Blockchain has its own place in the most important technology trends for the future.Blockchain simply creates a chain of data that cannot be changed. It is data that you can only add to, not take away from or change. The center point of its security is that the data blocks cannot be altered once it is added into the chain.

As Blockchain is highly secure, it is going to be used to stop internet fraud and hacking. By 2021, institutes are developing a blockchain for preventing internet fraud and information leakage at a high scale.

Blockchain is also a good solution for providing security to IoT devices. IoT devices are not good at securing your data at a high level. Blockchain will turn the tables in the future for IoT.

4. Data Science 

Data Science helps to manage complicated data that does not make sense otherwise. This includes business data, sales data, customer profiles, server data, and financial figures.

Most of the time, this data belongs to huge data sets that are unstructured. Data Scientists convert these unstructured data sets into structured data sets. So this data can be analyzed to identify patterns and trends. These patterns will work greatly to understand everything about a company. From business performance to customer retention.

Companies are relying more on the use of these unstructured big data to leverage data science technology. Data science technology is cost-efficient, innovative, and quick if used appropriately.

5. Augmented Reality (AR)

Augmented Reality or AR is one of the top technology trends of 2021. Many times people confuse AR with VR, but both are different from the core. People are more familiar with virtual reality but don’t have much knowledge of what is Augmented Reality(AR).

Augmented Reality enhances real-life views with digital images and effects. AR works with camera lenses. Popular examples of AR applications are Snapchat and Pokemon Go.

Customers are loving AR because of its infinite real-life applications. For ex. Lenskart.com provides a feature to upload your image on your website. You can try different glares and glasses and decide which suits you the best. You don’t need to go to the shop and try glasses on your face.

AR is used in many other sectors like an advanced navigation system to show the route on a real-time view of the road. In the military, pilots use AR helmets to see status of their speed and altitude.

6. Virtual Reality

Everyone is well-aware of VR. Virtual Reality has to be one of the top technology trends for 2021. It immerses the user in an virtual environment. It makes them feel as if they are experiencing the simulated environment by stimulating their hearing and vision.

The most popular VR tools are PlayStation VR and Facebook Oculus.VR is also used for educational purposes. For ex. Virtual museums, galleries, discovery centers, and theaters. Some theme parks offer a more engaging experience with the use of VR. Another widespread use of Virtual Reality is training and simulation.

But there are so many things that can be improved in VR technology. In the future, we will have an even better experience in VR.

7. Internet of Things (IoT)

Another buzzwrod, Internet of Things is the future. But what exactly is IoT? Well, it means things that are built with WiFi and can be connected to the Internet and with each other. IoT is growing every day and something new is being added to it constantly.

It is assumed by 2021, every person will have at least 4 connected devices, which will increase to 15 devices by 2030. The total market value of the IoT industry is approximately $150 billion.

IoT can be seen in smart lights, smart fridge, etc. It is also a part of our daily life in the form of smart wearables such as smart glass and activity trackers. Many small cities are adopting the Internet of Things for traffic and waste management, urban safety monitoring, and water distribution.

8. Cybersecurity

Cybersecurity is an oldie but it has made a place in one of the trending technology for the future. It never went out of trend. Why? Because as long as there are hackers, there will be a high demand for cybersecurity. Hackers are always trying to perform new malicious activities.

The global market of Cybersecurity is worth $173 billion and it will increase to $270 Billion by 2026. Also, jobs in this industry are growing at a faster rate of 3x.
It is not just about businesses that are at risk. Every individual who connects to the network is at a risk. Also, small businesses are at a higher risk of cyberattacks. As per research, 61% of data crimes are targeted towards companies with less than a thousand employees.

Cybersecurity is emerging day by day. Some advanced features of cybersecurity include blockchain security, homomorphic encryption, and zero-knowledge proofs.

So these are some of the trends which we feel, are going to rule the world of technology in the coming years. That is why there are greater chances of jobs in each of these fields.

Do let us know what is your favorite technology trend so far in the comments section.

The post Top 8 technology trends to keep an eye on in 2021 appeared first on DevopsCurry.

Devops Trends to watch in a post-COVID era

Fri, 09 Oct 2020 13:49:35 +0000

DevOps Trends to Watch in a Post Covid Era

COVID-19 has not only pushed the digital adaption by the customers immensely, but the industry at large has also seen the digital transformation accelerate by at least twice the rate at which it was planned. This technology advancement is possible only because of various next-gen coding practices are being practiced and DevOps is one of them.

So, what are the trends that we will get to see in DevOps going further? Let us have a look.

Cloud-native to be a big trend
The term is interchangeably used to describe container-based environments will be a key trend to watch in the months to come. The cloud native applications will be software agnostic, they will see more of microservices based architecture being used and will pave way for more innovative approaches while building the applications, leading to a lighter code.

Betting heavy on Artificial intelligence
AI has been playing a pivotal role in many development practices right from voice-based commands to chatbots and DevOps is no exception. Companies across the globe are betting big on optimizing test cases to say the least on how artificial intelligence will impact the DevOps teams.AI alongwith ML(Machine Learning) capabilities would prove revolutionary for the future software ecosystem.AI/ML based solutions have use cases in all industries and verticals across be it Retail, Banking, Education, Heatlhcare etc.

Security
Going forward DevSecOps is going to be the flavour of the year. DevSecOps or Security in DevOps is a critical and major trend to watch out for as there is significantly higher push towards digitalisation because of the pandemic. The teams will start deploying security features early in the development process. This will avert the re-testing and re-deployment of the code when a vulnerability is found.

Kubernetes becomes more stronger
A container management tool developed by Google is gaining lot more importance and acceptance in the developer circles. In the year 2020-21, Kubernetes will see a lot more usage such as effectively creating staging environments, move applications to production with minimal downtime and much more. The developer community will see the rise of community-based learning process when it comes to adapting this tool.In short going forward post-covid era, kubernetes as a technology will continue to dominate the Devops toolchain and will see more adoption from organisations across the globe.

Automation
Continuous integration,continuous testing and continuous delivery are key aspects of any DevOps based software delivery process. Deploying code to automate the above-mentioned aspects could lead to a huge amount of savings in time and money. Hence, we believe that most of the organizations will put their energies on automating the processes. There will be greater number of tools that will be developed and launched in the market which will not only make automation easy but will also make testing of these applications easier.

Microservices
DevOps and microservices go hand in hand. Especially because of the current pandemic situation, the companies worldwide will start crunching their monolithic software chunks into smaller snackable microservices based architecture. This is important trend as in the recent past, we have seen multiple attacks on various organizations which were using legacy monolithic applications.

These are the trends that we think will be on top of the minds of the developer community, as the world is slowly started to live with the virus and will learn to live post pandemic.

It is important that before companies commit themselves to DevOps based software development processes, they get an understanding and acceptance from all the relevant stakeholders from within and outside the company. Jumping the bandwagon without proper strategic planning could prove to be disastrous.

The post Devops Trends to watch in a post-COVID era appeared first on DevopsCurry.