So let's understand what is Generative AI? Basically, Gen AI is a subset of artificial intelligence that holds the ability to generate many different types of content, including but not limited to audio, imagery, text, and synthetic data. It employs various machine learning (ML) techniques such as semi-supervised and unsupervised learning algorithms to work its magic. These algorithms are constantly supplied with humongous datasets of input/ output instances to extract patterns from the input leading to the conclusions around the desired output. This is a form of training.
Currently, generative AI models are used in generative AI applications for creating content as a response to natural language requests.
What has led to generative AI being so popular over the internet in such a short span of time? Why is it that we have started to constantly hear this buzzing word everywhere in the past couple of years?
Somewhere around the end of 2022, the world witnessed a phenomenon that shook the world - arrival of ChatGPT. This led to something crazy and that was everyone talking about the new thing in the market - generative AI.
The World Wide Web reached 100m users in 7 years - ChatGPT did it in less than 2 weeks!!
The numbers popping up around the current use of generative AI and how it will look like by the end of this decade is mind-blowing. Over 116.9 million people are expected to start using generative AI by 2025. And this number is not going to stop or even slow down any time soon. So, all we can do is adapt to it too!
Generative AI is giving its users left and right. Be it expediting product development, deploying innovative and customized content, enhancing customer experiences, or boosting employee productivity, there are very few things this technology is not offering.
Generative artificial intelligence is not an entirely new concept. It has been around for many years, arguably since ELIZA was developed at MIT in 1996. Generative AI began to take its current shape somewhere around 2006 when Geoffrey E. Hinton and Simon Osindero published the first significant paper in this field, "A Fast Learning Algorithm for Deep Belief Nets". Here is reintroduced RBM or Restricted Boltzmann Machines along with the context of deep learning.
However, it is now, after years of rigorous work on machine learning and AI that the new generative AI systems have stemmed and proved fruitful. Be it ChatGPT, DALL-E, or Stable Diffusion, generative AI has come a long way since its first known development.
There are a lot of questions that revolve around the concept of generative AI. The questions regarding its ethics or its impact on human jobs are being raised constantly. But before we move onto those topics, let us see what is the use of gen AI.
If you have only ever heard of or used ChatGPT, then here are some other brilliant known uses of gen AI. Let's begin!
One of the most common uses of gen AI is image generation, majorly text-to-image conversion. The user enters a textual prompt regarding the kind of image they want, the input is processed by AI, and a realistic image is produced. The user can get as specific as they wish to.
Some functions performed by these tools are-
One of the finest examples of text generating AI tools is ChatGPT, one that made gen AI such a common name everywhere. Using user prompts, this tool produces textual content as it is trained on humongous data sets to help generate authentic and updated content. Here is how gen AI is employed for text generation and summarization -
Gen AI is bringing about a transformation in the coding and software development field via innovative solutions that help streamline coding. Here is how generative AI impacts software and coding -
As gen AI progresses, organizations are able to implement it to enhance organizational collaboration too. Here are two competitive gen AI tools that have enabled this -
Gen AI aids in simplifying video production via highly efficient and flexible features being used for generating high-quality video content. With gen AI models, apps are able to automate tedious and time consuming tasks like adding special effects, animations, editing video snippets, video compositions, etc. Video generate creation AI tools are also able to make video style transfers and video predictions.
Chatbots are on the list of prominent gen AI uses and as this technology advances, chatbots become better. This positive impact has mostly been rendered due to NLP (natural language processing) and generative models. Here is how generative AI is being used for chatbot performance improvement -
Audio generation is another way where Gen AI is used extensively. Top three cases where it is being used as of now includes -
Enterprise search has proved to be a great benefit of generative AI. Organizations are now able to access information faster by training AI models to securely go through organizational documents, highlight important parts, and even allow quick access to enterprise members.
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Generative artificial intelligence makes use of machine learning (ML) to process a gigantic amount of textual or visual data to determine what should or can appear near another thing. Most of this data is scraped from the internet itself. A major portion of programming work in terms of gen AI goes towards creating algorithms for distinguishing what the user wants as per their prompts.
Thus, the gen AI model assesses a humongous corpus of data and then takes prompts to determine what the output should be.
We have been using a low-level form of gen AI for some time now with autocorrect. Be it on our cell phones or emails for sentence and word completion, it has been a part of our daily lives for quite some time now.
Given how rapidly gen AI is expanding and how broadening it is being used even in such a little time, there is sure to come a time when most jobs will be affected by it, some in a positive light while others in negative. Working professionals are moving towards Generative AI training to understand the AI tools and models better.
And while it might have an impact on a lot of job titles today, it will also ultimately lead to the creation of a lot of employment opportunities. After all, machines need to be run by humans!
A lot of job roles have begun to spring as the expansion of generative AI continues. Here are a few of them -
AI researchers are tasked with refining any existing algorithms, pushing the boundaries of gen AI, and developing new architectures.
Skills required to become an AI Research Scientist - advanced knowledge in deep learning, programming (TensorFlow, PyTorch, Python), and machine learning. A Masters or PhD in a related field is also recommended.
An AI Ethics Officer has become a necessity since gen AI has also led to unintended biases and deepfakes. These professionals oversee the deployment and usage of such technologies.
Skills required to become an AI Ethics Officer - knowledge of interdisciplinary expertise in humanities and tech, ethical frameworks, and AI systems.
Gen AI can also produce inappropriate or unpredictable content sometimes. As a content validator, you will review and edit to make sure that the content that is generated and put out meets ethical standards.
Skills required to become a Content Validator - domain-specific expertise, understand gen AI capabilities, an eye for details.
Gen AI requires to be fine-tuned and trained and it is the job of an AI trainer to make it happen.
Skills required to become an AI trainer - good understanding of specific AI models, patience, adaptability, and domain-specific knowledge.
A generative AI engineer is tasked with the jobs of implementing and maintaining generative models, scaling them as per the application size, and ensuring an efficient run.
Skills required to become a Generative AI Engineer - familiarity with GANs, strong programming skills, good at neural networks, and generative architectures.
An AI Data Curator sources, cleans, and manages data sets with the intention of ensuring that artificial intelligence is equipped with a good and rich foundation to learn from.
Skills required to become an AI Data Curator - understanding of basic programming, data management, and data biases.
Legal challenges are sure to accompany any new tech. Thus, the job of a generative AI legal consultant is important.
Skills required to become a Generative AI Legal Consultant - understanding of AI implications, specialization in tech law, legal expertise.
A Gen AI Solutions Marketing and Sales Specialist is required by companies to help them bridge the gap between business and their technical AI capabilities.
Skills required to become a Gen AI Solutions Marketing & Sale Specialist - business acumen, marketing and sales skills, AI skills.
ChatGPT is the name that has made generative AI a household name. But this is not the only AI application that is making revolutionary changes today. DALL-E and Google BardAI are also in the running. Let us take a closer look at DALL-E, Google BardAI, and ChatGPT.
Developed by OpenAI in 2021, DALL-E has transformed the way we search for unique and creative images online. This groundbreaking AI model is used for generation images as per the textual descriptions given to it. This AI app combines the best of GPT (Generative Pre-trained Transformer) and GANs (Generative Adversarial Networks) to bring out conceptually novel and stunning images.
Based on the GPT architecture, ChatGPT is an advanced language model that was also developed by OpenAI in November, 2022. ChatGPT generates human-like textual responses to the prompts given by the user. This application is an expert in natural language understanding as it is trained on humongous text data.
BardAI or Basic AI for Research and Development is a leading AI platform developed by Google. BardAI provides a wide array of services and tools for developers and researchers to facilitate them in building and deploying AI models easily. It gives the users access to scalable infrastructure for inference and training as well as pre-trained tools and models for data processing.
Generative artificial intelligence is a new tool that has turned out to be helpful for everyone for both personal and professional purposes. Let us take a look at some of the benefits of Generative AI.
Gen AI facilitates businesses in generating engaging and creative content at a fast pace on a large scale. AI-powered systems are being greatly used in the advertising industry to automatically generate compelling visuals, ad copies, and even video content.
Gen AI paves the path for and promotes hyper-personalization to enhance customer experience. As these apps analyze customer data and generate highly customized recommendations and offers, businesses experience enhanced benefits.
Gen AI utilizes its ability of analyzing diverse data sets and synthesizing gigantic amounts of data to generate valuable insights. This can be seen in the financial sector wherein generative artificial intelligence is used for analyzing market trends, economic indicators, and consumer behavior to further generate predictive models that facilitate businesses in making well-informed and data-based investment decisions.
Gen AI systems have the ability to continuously learn and adapt as per new data and the feedback given to them. This equips them with the ability to enhance their performance as time passes and even generate outputs that aligns better with business objectives and user preferences.
One of the early and still most important business applications of Gen AI remains improvement in customer experience. Since gen AI is highly adaptive in nature, it is able to provide more human-like responses towards customer inquiries that aids in improving customer interaction. Chatbots are now able to deliver more sophisticated and comprehensive responses that elevate the depth of customer interactions.
Businesses are now able to automate time-consuming and complex processes with the aid of gen AI, allowing them to improve efficiency, allocate resources, and optimize workflows effectively. Gen AI is also known for its use in the manufacturing industry as it generates optimized production schedules to maximize efficiency and minimize waste.
Gen AI facilitates automating tasks that were earlier centered around human intervention. This automation has led to a lot of saving in terms of time and operational costs. These AI apps also have the bandwidth to analyze stakeholder feedback as well as market trends, which further aids in generating new designs and concepts.
There are dozens of Generative AI tools that facilitate the user in accessing gigantic language models, performing LLMOps (large language model operations), training and serving models, fine-tuning models, building web applications, and indexing and storing documents, among others.
Here are some of examples of gen AI tools-
OpenAI's API gives developers advanced AI models for usage that helps in generating text via GPT models, convert speech to text via Whisper, create creative images via DALL-E models, and find similar text via embeddings. It allows anyone to easily build a complete AI startup as it has a wide array of APIs and other developer tools.
LongChain is an ecosystem, an open-source framework that facilitates the users in building AI applications easily by using OpenAPI and other LLMs. It provides prompt management, VectorStores, AI agents, modular interface, context management, access to top LLMs, and chaining various tools and multiple models together.
Transformers allow the user to access models and datasets within seconds at zero cost. The Transformers Python Library renders it easy to fine-tune humongous language models on any new dataset.
OpenCV or Open Source Computer Vision is a popular open-source library that is used for computer vision tasks. This tool offers a great many functions and tools for object detection, video and image processing, facial recognition, and much more.
NLTK or Natural Language Toolkit is a famous Python library that offers a plethora of resources and tools for NLP. IT incorporates a lot of functionalities including stemming, sentiment analysis, tokenization, text classification, and more. NLTK is most often used for educational and research purposes.
YOLO or You Only Look Once is an object detection algorithm that is popular for its real-time object detection capabilities. This facilitates accurate and efficient detection of objects in videos and images.
A widely used NLP or natural language processing library, spaCy offers brilliant text processing capabilities. These include named entity recognition, dependency parsing, tokenization, and part-of-speech tagging. spaCy is famous for its performance and ease of use.
A widely popular open-source framework for machine learning, TensorFlow offers a great deal of flexibility to build and deploy various AI models such as deep neural networks.
Traditional AI is the basis of generative AI. If you are looking for some good points around generative AI vs AI, then we are here to help you out. Let's begin!
Generative AI follows a data-driven approach wherein it learns from structures and patterns stemming from gigantic datasets by applying ML techniques such as deep neural networks. It does not rely on explicit rules and rather learns from existing data. It also generates new content by accessing underlying relationships and patterns within the data.
Traditional AI follows a rule-based approach wherein it follows predefined rules and explicit instructions to help the system in performing certain tasks.
Generative AI makes use of generative models where they learn all the data's underlying probability distribution to generate new samples resembling the original data.
Traditional AI majorly makes use of discriminative models to learn distinguishing between various categories and classes of data.
Generative AI can make use of both supervised and unsupervised learning. However, it excels in scenarios based on the latter where the model is then trained on unlabeled data to find underlying structures and patterns without human guidance.
Traditional AI mostly employs supervised learning wherein it is trained on labeled data. Thus, the inputs and the corresponding outputs are already provided.
Generative AI boasts adaptability and creativity as it is able to generate novel content. Thus, it is able to create new text, music, images, and much more.
Traditional AI has been designed to carry out specific tasks and lacks adaptability and creativity beyond its programming. It cannot create new content or even adapt itself to new situations without human intervention.
Gen AI is here to stay and in the coming years, no company will be able to stay untouched from it. The best way to incorporate it is to start by doing so today. Here are some ways in which companies can and are embracing the generative AI era.
Gen AI aids in automating a lot of time-consuming and repetitive tasks that fall within the tech domain. Whether we talk about generation of code snippets or creation of comprehensive test cases, gen AI aids in streamlining workflows to enable developers in directing their focus on more strategic and creative tasks.
Technology teams can achieve a lot more by delegating replicable, repetitive, and mundane tasks to AI.
It is extremely important for tech companies to deliver high-quality software and this goal can now be achieved brilliantly by incorporating gen AI. This new-age technology generates comprehensive test cases to bolster the verification process to ensure software's accuracy and resilience. It can also identify and rectify any potential glitches and bugs to enhance the functionality and reliability of software.
Innovation is what keeps the technology-driven industries moving and blooming. Gen AI has the potential of catalyzing creative ideation. By leveraging tools of this technology, teams have the scope of tapping into an endless sea of fresh and unique ideas for features, marketing campaigns, and products. Gen AI can be the assisting hand tech teams need for transforming raw ideas into tangible projects.
When a company wishes to progress, they must be ready to do it at a good speed. Gen AI can prove to be a great ally to have on your side to help accelerate the product & software development lifecycle. Rapid development means rapid deployment, which further means that businesses get a better competitive edge.
Generative AI goes way beyond just ChatGPT and DALL-E. It is much more than these apps and companies that have begun to use it in their everyday working know how wide its use case is.
Considering the current implications of gen AI on ethics, bias, and data security, 81% customers still wish to have a human in the loop to constantly review and validate the generative AI outputs.
Salesforce's State of IT 2023 Report considers a survey of 4,300 IT leaders and decision makers. The report states that 9 out of every 10 CIO thinks that gen AI has gone mainstream. As per this report, process automation is consistently rising to help propel businesses forward.
With its capabilities in the sphere of automating, gen AI is expected to utilize its potential of automating 40% of average everyday work.
There is no end to how far and wide generative AI's impact is going to be. Today, it is beyond our imagination but in the coming years, it will surely change the face of how things are done.
In this blog titled- what is Generative AI, we have attempted to give you a complete overview of generative artificial intelligence. And once you go through our blog, you will realize that the most sane thing to do today is to make a career using this technology. So, get started now because some of the best career opportunities will stem from this field.
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