Category: General

An Intro to RLHF What makes an ordinary text a good one? Well, that is not an easy thing to define because texts are subjective and context-dependent. In recent years, language models have demonstrated impressive capabilities in generating diverse and compelling texts from human prompts. Imagine if we could leverage human feedback on generated text as a metric for evaluating the model’s performance, or better yet, use that feedback as a form of loss to optimize the model itself. This forms the basic idea for Reinforcement Learning from Human Feedback or RLHF. The method is what the title says: it makes use of reinforcement learning to directly improve a language model with human feedback. With RLHF, language models can align models trained on a large collection of text data with complex human values. The best example to understand RLHF’s success is ChatGPT, where this technology is one of the most pivotal reasons that make this chatbot so amazing. How does RLHF apply to large language models or LLMs? A Closer Look into Reinforcement Learning from Human Feedback Reinforcement learning is a machine learning field where agents learn decision-making by interacting with the environment. Agents take actions, including choosing not to act at all, impacting the environment, and triggering state transitions and rewards. Rewards are vital for refining the agent’s decision-making strategy. Throughout the training, the agent changes its policy to maximize cumulative rewards. This approach enables continuous learning and improvement over time. RLHF enhances the RL’s training by making the process human-centred. As such, this new technique has been pivotal in some of the latest chatbots that are creating headlines, such as: OpenAI’s ChatGPT InstructGPT DeepMind’s Sparrow Anthropic’s Claude With RLHF, LLMs are not merely trained to predict the next word. Instead, it is trained to understand instructions and give appropriate responses. Why Language is a Problem in Reinforcement Learning LLMs have proven to be good at handling multiple tasks at a time, such as: Code generation Text generation Question answering Protein folding Text summary On a large scale, they can do zero, and few-shot learning, thereby doing tasks they haven’t learned yet. The transformer model, which is the architecture utilized in large language models (LLMs), has achieved a significant milestone by demonstrating its capacity for training without supervision. LLMs, while impressive in their achievements, share basic features with other ML models. They are huge prediction machines that can guess the next prompt (token in a sequence). But, the biggest challenge here is that there are more than one correct answers for one prompt. All these answers may not be desirable in specific LLM contexts, applications, and users. Also, learning without supervision on extensive text corpora, although beneficial, may not fully correspond with the diverse range of uses it will encounter. In such cases, RL can guide LLMs appropriately. To understand it better, let’s approach language as a Reinforcement Learning problem: The agent is where the Language model itself functions as an RL agent, aiming to generate optimal text output. Action space- A list of language results generated by the LLM. The state space- The environmental state comprises of prompts from the user and the LLM results. Reward measures how the LLM responds to the application context and user intent. Other than the reward system, all other elements are relatively straightforward. Defining clear and effective guidelines for rewarding the language model’s performance is not a simple task. Luckily, it is possible to design an effective reward system for the language model by using RLHF. How Does RLHF Work: 3 Steps of RLHF For Language Models There are several challenges to RLHF. It has to be trained with multiple models and go through several deployment stages. As such, Reinforcement Learning from Human Feedback is executed through three basic steps: Step 1: The Pre-trained Language Model Initially, the RLHF uses a pre-trained LM trained with classical pretraining objectives. This step is crucial because LLMs need vast training data. Such an LLM trained in unsupervised learning will have a good language model capable of generating coherent outputs. However, some output may not always be relevant to the user’s needs and goals. Further, training the model having labelled data can generate more correct and appropriate results for specific tasks or domains. Step 2: Training the reward model Reward models are trained to recognize ideal results produced by the generative model. Then it rates them on relevancy and accuracy. The main LLM receives a prompt for each training example and generates several outputs. A dataset of LLM-generated text with quality labels is generated during the training process. Next, human evaluators review and categorize the generated texts from the best ones to the worst. The reward model is then trained to make predictions about the score from the LLM text. As a result, the generative model learns more and generates better and more relevant results. Step 3: Fine tuning with RL During the last phase, the reinforcement learning loop is established, which involves fine-tuning certain or all parameters of a replicated version of the initial language model using a policy-gradient RL algorithm. In reinforcement learning, the policy takes actions from a given state to maximize rewards, enabling real-time learning and adaptation. The model interacts with the environment, receiving feedback as rewards or penalties to know the actions that yield positive outcomes. Rigorous testing done with the help of a curated group ensures its competence in actual situations and accurate predictions. 3 Ways ChatGPT Utilizes RLHF Here’s how ChatGPT utilizes the RLHF framework in every phase: Phase 1 A pre-trained GPT-3.5 model underwent supervised fine-tuning by a team of engineers. A team of writers wrote answers to several prompts using the dataset of prompt-answer pairs to refine the large language model. Phase 2 A standard reward model was created. It generated several answers to the prompts, and human annotators ranked the responses. Phase 3 The Proximal Policy Optimization or (PPO) Reinforcement Learning algorithm was used to train the main LLM. However, there is no further information

“Alexa, what will the weather be like this afternoon?” “Siri, find me a recipe for low-calorie chia pudding.” “Alexa, find me the best dentist in town.” This is how many of us across North America start our day- by talking to our AI-powered voice assistants. Alexa, Siri, Google Assistant, and Cortana are everyone’s best friends if you own an AI-powered device. These voice assistants have been around for years ( Siri since 2011 and Alexa since 2014), and we’ve asked them all sorts of questions- from the most important and informative to the most mundane. But have you noticed one thing: All AI assistants are voiced by women, and all have a feminine name? Why aren’t AI assistants answering in men’s voices or having a male name? Have you ever thought about this? As evident as it is, the female gendering of AI technology is used by all major companies. We are used to having Alexa or Siri talk to us in a soft, soothing voice and answering our queries. The burning question is, are we restricting AI assistants to a particular gender? Maybe we are, but why? Gender Bias in AI Voice Assistants: What’s Happening? While some people may want to end this argument by saying that female voices sound better, the issue is far more deep-rooted. Companies servicing customers in the USA, such as Amazon, Apple, and Google, have faced intense backlash for instigating gender bias by using feminine voices and names for their AI assistants. And the reasons are amply justifiable: Ever since man can remember, women have been tagged as the “weaker sex.” They are expected to be subservient and tolerant to degrading treatment and verbal and physical abuse. In a way, smart devices with feminine voices demonstrate these long-prevalent gender biases. Using gender-biased voices and names in AI can exacerbate women-related violence, hyper-sexualization, and objectification. AI bots are engineered to answer, by default, in a subservient, pleasing, obliging manner, irrespective of whether the tone is appropriate for the answer. Studies have revealed that the prominence of feminine AI voices showcases women as compliant and passive, thereby perpetrating misogynistic and abusive behavior from users. AI voice assistants are primarily used for domestic and administrative tasks like setting an alarm, making important payments, or remembering dates. Even human robots are being built for specific customized roles like bartenders, waiters, etc. It is no coincidence that society relegates these tasks and professions to women. This bias only reflects what already exists in society, and it must be addressed because these technologies are here to stay. Well, who would have thought that our penchant for hands & eyes-free human-computer interaction would open up a hornet’s nest about gender bias and the influence of AI in our daily lives? How did it all come to this? Have We Personified AI-Powered Machines? The technological practice of using voice assistants dates back over half a century. It was the time when engineers were trying to make machines learn how to understand and process human speech. Hence we cannot simply point fingers at Amazon, Google, or Apple. Popular among those inventions were: Phone dialler Audrey invented in 1952 Calculator Shoebox invented in 1962 Carnegie Mellon’s Harpy, a vocabulary machine designed in 1976 Dragon Dictate’s Naturally Speaking software was created in 1997 Apple introduced the world of Siri in 2011, after which followed Alexa, Google Assistant, Cortana, and many others. Of course, these AI-powered voice assistants took the digital world by storm, with adults and children gaga over them. It opened a whole new, exciting interaction with a machine and fostered a “relationship” with them that was, until then, quite impossible. So, we’ve assigned these AI assistants with a voice like how we have animated pictures, robots, and conversational AIs, to make them seem or sound more human. By doing so, we have initiated the personification process. Today, AI voice assistants have firmly embedded themselves in our society. It’s remarkable how their technological capabilities have increased almost ten years after their introduction. And the story will not end here. There will be a significant increase in voice-based AI integration. According to Juniper Research, there will be more than 8 billion voice assistants by 2024, which is less than a year away. The burgeoning number of AI voice assistants will radically change how we interact and perceive them. In a nutshell, the personification process that we initiated is now unstoppable, primarily because we gave the machine a voice. This is where the gender issue comes into play. Will we want to define the future by artificial female servitude? Not at all. There’s no reason why we can’t use male voices for AI assistants alongside female ones. Before heading into the future, let’s return to the past to understand why feminine voices were the first choice. Did it really stem from gender-biased notions? How & Why Did Feminine Voices Become the De Facto Standard? In North America, when Amazon, Apple, Google, and Microsoft launched their AI-voice assistants with a female voice and name, there was, apparently, a lot of excitement. Despite these companies creating masculine voice options, the default firmly remains feminine. For this, we can either award credit or blatantly blame the lack of diversity in the tech industry. There just haven’t been that many masculine voices for centuries! Let’s go all the way back to 1878 when Emma Nutt was appointed as the first woman telephone operator. In the years that followed, many more women joined the list, and soon the industry was dominated by women. The result is more than a century of archived women’s telephone conversations which can be used for creating and training new AI voice automation. In due course of time, what once served as a traditional choice, became more of a matter of convenience. Apart from this, there isn’t any other tangible advantage to prioritizing female voices over male voices. Both are equally capable of conveying information with the right tone of voice and diction. Blame it on

There are probably more than a dozen movies where AI-powered robots read the human mind, obey their human master, and end up ruling the world. But that’s just fiction. With AI gaining traction at lightning speed, can this advanced technology read our minds in the real world? The answer is “yes” AI can read your mind, but only if you are hooked up to an fMRI machine and only if it is trained to process visual information. How is this possible? Read on, and you will be astonished at how science has advanced to unimaginable levels. Are Bots Reading Our Minds? Conspiracy theories and AI “alarmists” have repeatedly been warning us about one thing: AI machines and bots will take over the human race the day they learn to read our minds. Now, thanks to researchers Yu Tagaki and Shinji Yoshimoto from Osaka University, Japan, the bots have successfully reconstructed high-resolution images by reading human brain activity. So, what happened? Researchers used the popular Stable Diffusion, a deep learning model, to use data from fMRI scans and translate the images in people’s minds into AI reconstructions. The new algorithm drew roughly a thousand images by taking prompts and cues from fMRI brain scans. The images that included a teddy bear and an airplane were about 80% accurate to the images that the test subjects were thinking in their minds. View the original image source here Amazing, isn’t it? What made this possible? If you’ve noticed, we’ve repeatedly used the term “fMRI scan.” This entire research and its outputs were made possible by this highly advanced machine. The interesting question is: How? Deploying the fMRI: Reconstructing of Visual Images From Brain Activity fMRI, or Functional Magnetic Resonance Imaging, is one of the most advanced tools for understanding human thinking. The fMRI scanner is a technological marvel capable of producing mesmerizing images by scanning a person’s various mental tasks. The resulting colorful photos show the person’s brain in action as they think about various images. However, this is not the first time researchers have tried this experiment. Several trials have been conducted using generative models that need to be trained from scratch using the fMRI data. This task is more herculean than it sounds. But there had to be a way to make this research easier. The solution? Diffusion models! What are Diffusion Models or DMs? DMs are Deep Generative Models capable of achieving state-of-the-art performance in image-related tasks. But, as always, technology has evolved, and now we have LDMs or Latent Diffusion Models, the most recent breakthrough. LDMs further reduce computational expenses using the latent space generated by their autoencoding components. If we analyze tech history, we can understand that for a long time, neuroscience has inspired Computer Vision. This inspiration has enabled artificial systems to see the world through the eyes of humans. The advances in neuroscience and AI have made it possible to directly compare the latent representation of the human brain and the architecture of neural networks. Hence, it should come as no surprise that the Osaka University researchers were able to create a gallery of images by combining AI-powered fMRI, Diffusion Models, and Computer Vision. Other efforts, like reconstructing visual images from brain activity and analyzing computational processes of biological and artificial systems, have also contributed to this research. But such tasks are easier said than done. Reconstructing images from brain activity is challenging as the nature of brain representations is not commonly known. Also, the available sample sizes of brain data are relatively very small for the researchers to use them conclusively. Let’s now circle back to the Osaka University research. Also Read: AI Democratization & Emerging Trends for 2023 What Does The Bot Do When It Reads Human Mind? According to the researchers, the AI model draws information from the areas of the brain (primarily the occipital and frontal lobes) involved in picture processing. Besides, fMRI can detect blood flow to the active brain regions. During cognitive or emotional behavior, fMRI can detect oxygen molecules, enabling sensors to identify where our neurons work the hardest in the brain and consume the most oxygen. The researchers used four people for the experiment. Each one of them viewed a collection of 10,000 images. The AI model first generates these images as noise (imagine something like television static). The model then builds on this by assigning unique characteristics after comparing them to the images it was trained on. For training the AI, the researchers showed about 10,000 visuals to each participant while they were inside the fMRI scanner. This process was repeated thrice, and the MRI data that was produced by the scanner were transferred to a computer to train it on how each participant’s brain processed and analyzed thes images. A surprise finding was that the AI model read some of the participant’s brain activity better than it did for the others. Even more interesting is that despite the differences, there were significant similarities observed between the resulting images and what was displayed to the participants. For example, even the objects, color schemes, and image compositions were quite similar. Take a look at these images. View the original image source here Real-life Application of the Diffusion Model A technological breakthrough in this proportion has left its researchers spellbound. In his interview to Newsweek, Takagi, who works as an assistant professor at Osaka University, said that the Diffusion Model used for the research was originally not created to understand the human brain. However, the image-generating AI model was able to predict brain activity remarkably well, which indicates that it can be used to reconstruct visual experiences from the brain. According to Takagi, this technique may, in the future, be used to construct images directly from a person’s imagination. His explanation for this theory stems from the fact that all visual information captured by the retina is processed in the occipital lobe in an area called the “visual cortex.” Since the same region is activated even when we

What is the Democratization of Artificial Intelligence? In the most simple words, the Democratization of AI means making AI available for everyone, including those who lack the knowledge and resources for the same. Why democratize AI, you may ask? Well, technology is conquering one peak after another, and Artificial Intelligence (AI) is one of the most prominent pinnacles of 21st-century technology. The role of AI in solving real-world business problems is one of the most noteworthy impacts of software engineering in recent years. In fact, AI adoption has increasingly become crucial to every organization’s digital foundation. Therefore, it won’t be long before AI becomes omnipresent, thanks to digital transformation initiatives. It is already making itself indispensable by enhancing business process efficiency and customer experience. So it’s high time this advanced technology is democratized and made available even to non-experts. What does the democratization of AI mean for the world? It is not difficult to imagine what will happen when AI is democratized. People will have more access to AI-based applications and AI tools. AI democratization will pave the way for more innovations that lessen the burden on humans and reduce the need for expert knowledge. With the democratization of AI, organizations can rely on simplified AI solutions that handle the legwork rather than sifting through mountains of data to derive the information. One classic example is when AI solves problems that analysts and data scientists would have otherwise solved. However, we are only at the cusp of AI democratization. So, rather than focusing on the big picture, let’s explore how this tech move will impact businesses in 2023. AI Democratization: Changes to Expect in 2023 AI democratization is a given. But here’s what will happen next: 1. There will be an increased application of blended AI use cases. Emerging digital trends already predict that these use cases will pivot on robust and resilient operations that can scale vertically or horizontally. 2. Gartner predicts that in the next five years, decision intelligence and Edge AI will attain mainstream acceptance. This may result in transformational business innovations like: Operational AI systems ModelOps Smart robots Natural Language Processing (NLP) AI Engineering Autonomous vehicles Decision intelligence Computer Vision (CV) Intelligent applications AI cloud services 3. Business use cases will significantly influence the end goal of AI solutions. Hence, before choosing an AI solution, organizations will consider several factors depending on how far along they are in their digital transformation journey. In most cases, it will depend on how complex the task is compared to the decision risk. 4. In most cases, companies will not hesitate to invest in AI solutions with low task complexity and decision risk. Augmented AI will become an option when the decision risk and task complexity are higher. On the other hand, highly complex tasks will require human-in-the-loop intervention, with AI solutions as a decision-support tool. Nevertheless, it is still too early to determine the right approach. Further, the 2022 Gartner AI Hype Cycle Report recommends companies to pay attention to some of the AI innovations that are expected to hit mainstream markets over the next few years. These include: Generative AI Deep learning Causal AI Composite AI Physics-informed AI Foundation models When it comes to data-centric AI systems, innovations to look out for include: Knowledge graphs Data annotation Data labeling Synthetic data 4 Sub-trends of Democratized AI Likely to Emerge in 2023 Democratization of AI will likely give birth to these following subtrends, which will emerge more robustly than others. 1. Fully automated AI solutions The most probable sub-trend to expect this year is AI solutions that will completely automate some of our daily tasks. We will likely witness AI automation in complex applications involving fewer risks, such as sending notifications and scheduling tasks. 2. Augmented AI As intelligent applications become mainstream, user adoption of Augmented AI is expected to rise. Take, for example, business applications that rely on embedded or integrated AI solutions such as intelligent automation, guided recommendations, and data-driven insights. These solutions will be more prominent in improving productivity, enabling quicker decision-making, and delivering a personalized interface. 3. No-code AI No-code AI is fast gaining traction because even engineers with little or no coding knowledge can handle it. Professionals can train or retrain existing AI models and fine-tune them for more relevancy and efficiency. 4. Human Involvement AI works best when it co-exists with humans. The human-in-the-loop approach is a mutually beneficial partnership that simplifies even the most complex applications that require unusual pattern recognition, subjective decision-making, and cognitive judgment. Thus, with the democratization of AI, we will witness many trends. But perhaps the most important one that will race ahead of others will be “composite AI.” Also Read: Learn the ideal ways to keep AI claims in check by FTC Evolution Of Composite AI in 2023: What to Expect? What is Composite AI? It is the method of using various AI techniques to achieve the best results. Also known as Multidisciplinary AI, it is the emerging trend for many use cases that are not machine-trainable due to the lack of adequate data in some fields. This “data deficiency” happens when organizations hold back from sharing too much data in the public domain for sensitive business applications. In such scenarios, sufficient domain knowledge, together with human expertise, will be required to feed adequate context to the AI models. Further, the AI models will also have to be trained and retrained by humans consistently to achieve optimal performance. Here’s where composite AI makes itself indispensable. Companies prefer the composite AI approach to tackle complex business problems holistically. This approach combines the capabilities of various AI models, such as: Machine learning Forecasting Optimization NLP Anomaly detection Traditional rules-based system Graph techniques When done right, this combined power helps improve the AI system’s overall efficiency. But that’s not all. Composite AI has several advantages for organizations daring enough to use it. It: Allows users to gain insights from small datasets. Reduces the need for large data science teams.

As humans, we have a long history of interacting with objects that blur the line between natural and artificial. Haven’t we all heard of stories of a mound of clay turning into some creature or a puppet coming to life? So, is it possible that we may be conditioned to believe advertisements for shiny new gadgets that purport to be “powered by AI”? However, the FTC disapproves of this term. The agency writes, “One thing is for sure: it’s a marketing term.” FTC officials also go on to write, “one thing we know about hot marketing terms is that some advertisers won’t be able to stop themselves from overusing and abusing them.” The FTC has investigated numerous companies in the AI (artificial intelligence) and automated decision-making space and brought numerous cases asserting law violations while enforcing AI-related regulations. The FTCs regulations and law enforcement guidelines demand that any application of AI should involve responsible, fair and explainable practices and display high levels of accountability. These laws aim to provide important guidance to businesses to better manage consumer risks of AI applications or algorithms. Let’s get to the depth of the matter and understand what FTC advises when it comes to AI claims from any organization. The FTCs Guidelines on AI Applications Very recently, on February 27, 2023, the US Federal Trade Commission (FTC) published a set of guidelines from their Division of Advertising Practices on advertising claims for AI applications. The latest FTC guidance emphasizes that AI tools must also “work as advertised,” whereas earlier posts focused on avoiding automated tools that tend to have biased or discriminatory outcomes. Just a few days earlier, on Feb 18th, Sam Altman, the CEO of ChatGPT and creator of OpenAI, tweeted that future regulation of AI is “critical” until the technology can be adequately understood. He said people would need time to adjust to “something so big” as AI. Again, on March 12, 2023, Forbes made a statement that “The Federal Trade Commission aims to bring down the hammer on those outsized unfounded claims about generative AI, ChatGPT and other AI, warns AI ethics and AI law.” All this just means that the FTC guidance this year was in response to the sudden spike in AI research and development and the burgeoning markets for generative AI products such as ChatGPT, DALL-E, Uberduck AI, Stable Diffusion, MidJourney and more. Also Read: Generative AI & Its Massive Role in Redefining Creative Processes How to Ensure You Meet the FTC Guidelines? The AI fever has caught on every possible product out there — from toy cars to chatbots and everything in between. But the reality is most of these products with tall AI claims may not even work as advertised, to begin with. Although the products may not really cause any big harm, their effectiveness is often questionable. So before you make AI claims for your products, FTC advises you to consider the following questions. 1. Are you overvaluing your AI product’s capabilities? Are you claiming that your product is capable of delivering over and beyond the existing capabilities of similar AI products or technology? For example, you should know that predicting human behavior accurately is still beyond the scope of machines. So it would be deceptive to claim that your product can make trustworthy predictions unless it’s backed by scientific evidence or if they apply conditionally, only to certain groups of users or in regulated environments. 2. Are you making claims that your AI product will outperform conventional alternatives? FTC warns that any claims that place the capabilities of an AI product as superior over a non-AI product of similar functionality must be substantiated with sufficient data. For instance, you might have to reveal comparative performance scores to prove superior efficiency. If, for some reason, you are unable to provide such testing data, you should refrain from claiming such superiority. 3. Are you cognizant of the associated risks? Although the phrase “reasonably foreseeable risks and impact” may seem vague, your legal team can explain why you should not stretch the meaning in any way. This means you should know the likely consequences and risks of releasing your AI product to the public. You can’t place all the blame on the tech developer if it fails or produces biased results. And you cannot deny responsibility stating that you are unaware of the technology or how to test it. 4. How much “AI” does the product actually use? The FTC advisory recommends avoiding making “baseless claims that your product is AI-enabled” or “AI-powered.” Given the FTC’s admission that the meaning of “artificial intelligence” is ambiguous, it is difficult to ascertain the kind of evidences the FTC will consider appropriate for such claims. However, the advisory emphasizes that “merely using an AI tool in the development cycle is not the same as a product that has AI in it.” It also implies that products can be reasonably categorized as AI products if their fundamental features or functions “use computation to accomplish duties such as predictions, decisions, or recommendations.” In Conclusion This is not the first time the FTC has issued such guidance, advising businesses to keep their AI-related practices in line with well-established FTC consumer protection principles. This includes being honest and fair when utilizing AI. Normally, FTC investigations take place as a result of new staff guidance. So, marketers should be very careful with their claims and ensure they are not overstating the capabilities of what their AI algorithms can do. Opporture understands how important it is to keep AI claims in check so businesses can use this technology ethically and responsibly. With the leading AI company as your partner, you can be confident that your AI use will be responsible, ethical, and effective. Contact us today to learn more about how we can help you leverage AI in your business.

Drumroll, please! The future of Generative Artificial Intelligence has finally arrived with a big bang! When OpenAI launched ChatGPT in November 2022, it created a flurry of headlines that left copywriters, screenwriters, creative directors, and many more professionals anxious. They had every reason to because ChatGPT had garnered over a million users within five days of its launch. The highly advanced AI tool that creates content based on the user’s prompt has propelled AI to unimaginable levels of technology. However, the story doesn’t end here. OpenAI’s latest venture has taken things to a whole new level. On March 14th, 2023, OpenAI launched GPT-4– a newer, more advanced version of its AI technology. According to OpenAI, GPT-4 is “more creative and collaborative than ever before.” Within hours of its launch, people worldwide used the tool in many ways. The company considers this new version a significant milestone in AI’s evolution and claims it can solve “difficult problems with greater accuracy.” GPT-4 is touted to have the potential to simplify: The way we learn new languages Processing images as well as texts Building chatbots and virtual assistants that are more factual, contextual, and creative. In fact, the day is pretty close when it can even simplify the way we prepare our tax statements. Gartner, the research and consulting services provider, predicts that by 2025 the following will happen with Generative AI: At least 10% of all data created will be produced by Generative AI. Out of this, 20% of the test data will be utilized for consumer-facing use cases. 50% of pharmaceutical research and development initiatives will be devoted to AI. 30% of manufacturers will harness AI to improve their ability to develop products. Our blog gives you a glimpse into how Generative Ai will change the face of creative processes across applications. But, first things first: What is Generative Artificial Intelligence? Generative AI is a powerful AI system capable of creating content or data in response to input parameters or prompts. Traditional AI is powerful. It is designed to recognize patterns and make predictions. Generative AI is several steps ahead of conventional AI. This type of AI does not merely copy and create something from pre-existing data. It creates something completely new based on knowledge derived from trained datasets. Generative AI is made possible with advanced techniques such as Deep Learning, where AI models are trained on large datasets. This enables them to generate across a range of modalities. So, essentially, Generative AI requires these large datasets to gain knowledge, but the output is totally unique and new. Generative AI uses algorithms trained on data that include: Texts Videos Audio Images Computer Codes Generative AI has the potential to accelerate AI adoption and create realistic and unique outcomes, and refine processes like email writing and coding. This field of AI has permeated every aspect of living- well, almost. An article on the Time of India was published just a day after GPT-4 was launched (March 15th, 2023). The report elaborates on how Generative AI makes itself indispensable in gaming, entertainment, life sciences, and BFSI. Beyond all these verticals, Generative AI is playing a significant role in healthcare, where it is being used for: Developing personalized medication Developing new drugs from gene patterns Predicting disease outbreak Diagnosing complex medical disorders In Banking, Financial Services, and Insurance (BFSI), Generative AI helps in: Fraud detection Customer service automation Financial plan development Insurance plan customization In gaming and entertainment, the potential of Generative AI is utilized in full swing to create: Create realistic and immersive virtual environments Score original music and sound effects Enhance game mechanics and nuances Generative AI is the latest buzz in Hollywood. Apart from being used in every aspect of movie-making, it is used in the most unexpected ways. Fun Fact: Veteran actor Bruce Willis has sold his “digital twin” to studios to generate movies without his physical presence. Thus, the field is so prolific that the possibilities are limited only by imagination when it comes to creative processes. Let’s unravel the details. Generative AI: Its Scope & Potential in 8 Creative Processes Traditionalists may argue that creativity by hand is the most valuable. The traditional process of creating something with your hands has more meaning. However, time and manual effort can make it a frustrating process, mainly if the results are not upto our expectations. As mentioned earlier, Generative AI uses training datasets to derive knowledge and creates innovative and unique outcomes based on the same. This is one of the main reasons why Generative AI is booming worldwide. Here’s where Generative AI can make a big difference in overcoming some of the limitations in the following creative processes: 1. Search & Learning Generative AI is slowly but steadily revolutionizing learning processes that involve in-depth research. In computer programming, generative AI is used to find accurate answers without using Google Search. Here’s a list of tasks that Generative AI helps us do in a jiffy: Automation of tasks Code debugging Creation of new algorithms Summarization of books, novels, and articles Conception of stories, art, and music Generative AI can also evaluate trends and patterns to improve decision-making processes. 2. Content Creation Generative AI is already the top contender for content creation. Generative AI is effectively changing customer conversion strategies from generating emails, press releases, and social media posts to personalizing marketing campaigns. Its ability to read consumer trends and patterns enables this technology to create content that perfectly targets the right audience. 3. Content Quality Generative AI has upgraded content quality by multiple notches. As a machine-generated capability, it has its own limitations. However, that does not hinder its ability to identify bias and subjectivity in human decision-making processes. With Generative AI, companies can rely on AI-driven algorithms to communicate consistently with their audiences. 4. Predictive Analytics Whether creating customized campaigns or sprucing up their market efforts, marketers can leverage Generative AI to study customer behavior and predict future trends. AI-driven marketing campaigns will be more scalable, flexible, and

Consciously or otherwise, humans have always feared the unknown. And, we’re never fully ready to meet what will come in the future.