Navigating the New Era: Development of Systems Guided by Generative AI

Generative Artificial Intelligence (AI) stands at the forefront of technological innovation, pushing the boundaries of what machines can achieve. It learns from existing artifacts to generate new, realistic creations, scaling up the volume while maintaining the essence of the original data without mere replication. The spectrum of novel content that Generative AI can produce spans images, videos, music, speech, text, software code, and product designs. The backbone of Generative AI lies in foundation models, which are nurtured on a vast dataset and further fine-tuned for specific tasks. Although the complexity of math and computing power required is immense, the core remains to be prediction algorithms.

Generative AI is gradually becoming a household name, thanks to platforms like ChatGPT by OpenAI, which exhibits human-like interactions, and DALL-E, which generates images from text descriptions. As per Gartner, Generative AI is on the trajectory to become a general-purpose technology with an impact echoing the likes of steam engines, electricity, and the internet.

What does Gartner predict for the future of generative AI use?

Generative AI is primed to make an increasingly strong impact on enterprises over the next five years. Gartner predicts that:

By 2024, 40% of enterprise applications will have embedded conversational AI, up from less than 5% in 2020.

By 2025, 30% of enterprises will have implemented an AI-augmented development and testing strategy, up from 5% in 2021.

By 2026, generative design AI will automate 60% of the design effort for new websites and mobile apps.

By 2026, over 100 million humans will engage colleagues to contribute to their work.

By 2027, nearly 15% of new applications will be automatically generated by AI without a human in the loop. This is not happening at all today.

Which sectors are being impacted by the development of systems with Generative AI?

  1. Healthcare:
    • Drug Discovery: Generative AI is revolutionizing the pharmaceutical landscape by expediting the drug discovery process. It can predict new compounds’ effectiveness and potential side effects, significantly reducing the time and costs of bringing a new drug to market. Moreover, Generative AI can help create synthetic molecular structures that could be groundbreaking cures for various diseases.
    • Medical Imaging and Diagnosis: Generative AI also plays a pivotal role in medical imaging and diagnostics. It can generate synthetic medical images to augment datasets, which is invaluable for training machine learning models, especially when real-world data is scarce or sensitive. Besides, it can assist in detecting and diagnosing diseases by analyzing medical images.
  2. Automotive and Aerospace:
    • Generative Design: In industries like automotive and aerospace, generative design powered by AI is a game-changer. It allows engineers to input design goals and constraints into a generative design software, which then explores all possible permutations of a solution, quickly generating design alternatives. It tests and learns from each iteration what works and what doesn’t to meet the design objectives.
    • Simulation and Testing: Generative AI can create realistic simulation environments, which are crucial for testing and validating autonomous driving systems or new aerospace technologies before they are deployed in real-world scenarios.
  3. Finance:
    • Risk Analysis and Fraud Detection: By modeling complex financial systems, Generative AI helps in risk analysis and fraud detection. It can generate synthetic data to stress-test various scenarios, which is imperative for financial institutions to remain resilient against economic uncertainties.
    • Algorithmic Trading: Generative AI can also be harnessed to develop sophisticated algorithmic trading strategies. It can generate predictive models to identify trading opportunities by analyzing vast financial data.
  4. Marketing:
    • Content Generation: The marketing realm is being reshaped with Generative AI’s ability to create compelling content. From drafting initial copy to generating personalized advertising, it’s enabling marketers to engage with their audience on a new level.
    • Customer Insights: Generative AI can dive into vast datasets to unearth insights into customer behavior and preferences, which can be harnessed to tailor marketing strategies effectively.
  5. Intellectual Property (IP):
    • Automated Patent Analysis: Generative AI can automate the analysis of vast patent datasets, helping to identify patent trends, assess the novelty of inventions, and even predict future technological advancements. This automated analysis can significantly speed up the patent granting process and help organizations stay ahead in the IP landscape.
    • Design Generation: In the domain of design patents, Generative AI can assist in creating novel designs or variations of existing designs at an unimaginable pace. However, this raises critical questions about the ownership and originality of the generated designs, nudging the IP sector to redefine its boundaries.
  6. Legal:
    • Legal Research and Document Review: Generative AI can automate legal research and document review tasks. By quickly analyzing vast amounts of legal texts, case laws, and precedents, it can provide lawyers with relevant information, saving precious time and resources.
    • Contract Generation and Analysis: The creation and analysis of legal contracts are other areas where Generative AI is making a significant impact. It can generate contract drafts based on the input parameters and analyze existing contracts to ensure compliance with the requisite legal standards.
    • Predictive Analysis: Moreover, Generative AI can be used for predictive analysis in legal scenarios, helping forecast legal dispute outcomes based on historical data. This could provide legal practitioners with valuable insights to strategize their cases better.
    • Legal Chatbots: Generative AI-powered legal chatbots can provide initial legal advice based on the query fed to them. They can understand the legal issue and provide a basic understanding of the legal stance, aiding in better client engagement and filtering.

    Each of these sectors exemplifies the profound impact and the boundless potential of Generative AI. By automating and augmenting various processes, Generative AI is not only driving efficiency and cost-savings but is also opening doors to new possibilities that were once deemed unattainable.

    The Developer’s New Playground

    In the wake of a technological renaissance, where artificial intelligence (AI) is the linchpin of modern innovation, the traditional silhouette of a developer’s career is undergoing a remarkable transformation. The advent of AI-infused systems is not just a fleeting trend but a seismic shift, nudging developers into a new epoch where their roles transcend the conventional boundaries of code and algorithms. This transition is not merely about adapting to new tools or languages but embracing a holistic metamorphosis, redefining what it means to be a developer. Here, we delve into the kaleidoscope of changes, painting the developer’s journey with new shades of challenges, learning, and opportunities.

    Morphing Roles and Skillsets:

    1. From Coders to Solution Architects: The new era nudges developers from mere coders to solution architects, orchestrating AI-driven solutions that address real-world problems.
    2. Interdisciplinary Proficiency: A developer’s role now demands a confluence of skills, including data science, machine learning, and understanding of domain-specific challenges.
    3. Ethical and Responsible AI Development: Developers are at the helm of ensuring that AI systems are built with a framework of ethics, transparency, and accountability.

    With Generative AI, developers are stepping into an expansive playground. They can now focus on crafting high-level objectives while the AI handles the detailed design. This speeds up the development process and opens up creativity and innovation.

    Continuous Learning: The New Norm

    In the fast-paced realm of technology, staying updated is not a choice but a necessity. This truth resonates even louder in Generative Artificial Intelligence (Generative AI), a domain continuously evolving, expanding, and surprising us with its potential. For developers, riding the wave of Generative AI is not about catching up but constantly sailing along, learning, and adapting. As Generative AI continues to redefine the contours of what’s possible in system development, a culture of continuous learning emerges as the new norm for developers. This isn’t merely about acquiring new skills; it’s about fostering a mindset of perpetual growth and curiosity.

    Why Continuous Learning?

    1. Staying Relevant:
      In a rapidly changing field, staying updated with the latest advancements is crucial for developers to remain relevant and competitive in their careers.
    2. Harnessing Full Potential:
      Continuous learning enables developers to harness the full potential of Generative AI, ensuring they can leverage their projects’ latest features and capabilities.
    3. Problem-Solving:
      With each new learning, developers expand their problem-solving toolkit, equipping themselves to tackle complex challenges innovatively.
    4. Ethical and Responsible AI Development:
      As Generative AI advances, so do the ethical considerations surrounding its use. Continuous learning is imperative to ensure responsible and ethical AI development.

    The Path of Continuous Learning:

    1. Online Courses and Certifications:
      Numerous online platforms offer courses and certifications on Generative AI and related technologies, facilitating continuous learning.
    2. Community Engagement:
      Engaging with the AI community, participating in forums, and attending conferences are excellent ways to learn from peers and stay updated.
    3. Practical Application:
      Applying learned concepts in real-world projects is a powerful way to reinforce learning and gain practical experience.
    4. Reading and Research:
      Regularly reading research papers, blogs, and articles in the domain can provide insights into the latest advancements and best practices.


    Generative AI transcends the conventional role of a tool; it emerges as a formidable collaborator, amplifying developers’ creative and problem-solving prowess. The journey with Generative AI is akin to navigating through an expansive realm of innovation, where each step forward unveils new horizons of possibilities. As elucidated, the rapid evolution of Generative AI beckons a culture of continuous learning among developers, a requisite not merely to remain relevant but to excel and innovate in this dynamic landscape.

    As Generative AI continues to percolate through various sectors, notably intellectual property and legal domains, its harmonization with modern development systems is not a fleeting trend but a profound shift. Understanding and adapting to Generative AI isn’t just beneficial; it’s quintessential for developers to harness this technology’s burgeoning potential fully. The narrative is not about optional adaptation but essential evolution to foster a synergistic alliance with Generative AI.

    The infusion of Generative AI in modern development systems isn’t merely a technical enhancement; it’s a paradigm shift towards a more collaborative, innovative, and continuously evolving development ecosystem. As developers, embracing this shift is synonymous with stepping into a future of endless exploration, innovation, and growth. The ripple effects of this fusion are significant, reshaping not just how systems are developed but how developers evolve in their careers, continuously learn, and contribute to the broader narrative of technological advancement.

    As Generative AI finds its footing in more sectors, the symbiotic relationship between it and developers will be the linchpin for unlocking new dimensions of innovation, solving complex problems, and creating value in unprecedented ways. Hence, understanding and adapting to Generative AI is not a mere advantage; it’s a cornerstone for thriving in the modern development landscapes increasingly becoming intertwined with intelligent and creative computational counterparts.

    That’s it for today!


    Generative AI: What Is It, Tools, Models, Applications and Use Cases (

    Beyond Automation: Delving Deep into Microsoft’s AutoGen Conversational AI Framework

    In the heart of innovation, Microsoft has crafted a gem known as AutoGen, a framework designed to foster the creation of applications through Large Language Models (LLMs). Unveiling a world where multi-agent conversations drive solutions, AutoGen is not just a tool but a revolutionary stride in AI technology.

    Moreover, the realm of Large Language Models (LLMs) has been a buzzing hive of potential waiting to be harnessed. With AutoGen, the wait is over as it paves the way for seamless interactions among AI agents, humans, and tools, crafting a narrative of endless possibilities.

    The Core Essence of AutoGen

    At its core, AutoGen is an enabler, a catalyst that simplifies the intricacies of developing LLM-based applications. Its philosophy is rooted in collaborative problem-solving, where multiple agents can converse and solve tasks collectively.

    Additionally, AutoGen goes beyond mere automation. It embodies optimization, ensuring that the workflow of applications is automated and optimized for peak performance. This is where AutoGen shines, revolutionizing the LLM application framework.

    What capabilities does AutoGen offer?

    The brilliance of AutoGen is seen in its ability to seamlessly blend the power of LLMs, human insights, and other tools, thereby simplifying the orchestration and optimization of complex workflows inherent in LLM applications. AutoGen facilitates efficient problem-solving through customizable conversational agents and paves the way for innovative applications across various domains.

    1. Multi-Agent Conversations:
    • You can create multi-agent systems where agents with specialized capabilities converse to solve tasks collaboratively. These conversations can occur between AI agents, humans, and AI, or a mix, expanding possibilities.
    1. LLM Workflow Automation and Optimization:
    • AutoGen simplifies the automation and optimization of intricate LLM workflows, which is especially beneficial as LLM-based applications become increasingly complex. This alleviates the challenges of orchestrating optimal workflows with robust performance.
    1. Customizable Conversational Agents:
    • Design and customize agents to your needs, whether based on LLMs, other tools, or even human inputs. This customization facilitates more effective solutions tailored to the unique requirements of your projects.
    1. Human-AI Collaboration:
    • AutoGen facilitates seamless integration between human input and AI capabilities, allowing for collaborative problem-solving. This is particularly useful in scenarios where the strengths of both humans and AI can be leveraged for better outcomes.
    1. Development of Advanced Applications:
    • Use AutoGen to develop advanced applications such as code-based question-answering systems, supply-chain optimization, and other scenarios where automated and optimized multi-agent conversations can significantly reduce manual interactions.
    1. Enhanced LLM Capabilities:
    • Extend the capabilities of advanced LLMs like GPT-4 by addressing their limitations through integration with other tools and human input, making them more robust and capable of handling multi-faceted tasks.
    1. Learning and Experimentation:
    • Being an open-source framework, AutoGen provides a playground for developers, researchers, and enthusiasts to learn, experiment, and contribute to the growing knowledge in AI and LLMs.
    1. Research and Innovation:
    • AutoGen can serve as a solid foundation for research and innovation in AI, especially in exploring the dynamics of multi-agent systems and human-AI collaboration.
    1. Community Contributions:
    • Being open-source, AutoGen encourages community contributions, which can lead to the development of new features, capabilities, and improvements in the framework, fostering a collaborative environment for advancing the state of AI.

    AutoGen, with its ability to meld the prowess of LLMs, humans, and other tools through conversational agents, opens up a vast spectrum of opportunities for developers and organizations alike to harness the potential of AI in novel and impactful ways.

    Agent’s concepts behind AutoGen

    AutoGen abstracts and implements conversable agents designed to solve tasks through inter-agent conversations. Specifically, the agents in AutoGen have the following notable features:

    • Conversable: Agents in AutoGen are conversable, which means that any agent can send and receive messages from other agents to initiate or continue a conversation
    • Customizable: Agents in AutoGen can be customized to integrate LLMs, humans, tools, or a combination of them.

    The figure below shows the built-in agents in AutoGen.

    Source: Multi-agent Conversation Framework | AutoGen (

    The agents ConversableAgent, AssistantAgent, UserProxyAgent, and GroupChatManager are classes provided within the AutoGen framework, a system by Microsoft for facilitating multi-agent conversations in large language models (LLMs). Here’s a detailed breakdown of these agents:

    1. ConversableAgent:
    • A generic class designed for agents capable of conversing with each other through message exchange to complete a task.
    • Agents can communicate with other agents and perform actions, with their efforts potentially differing based on the messages they receive.
    • Provides an auto-reply capability for more autonomous multi-agent communication while retaining the option for human intervention.
    • Extensible by registering reply functions with the register_reply() method.
    1. AssistantAgent:
    • Acts as an AI assistant using LLMs by default, without requiring human input or code execution.
    • Can write Python code for a user to execute when a task description message is received, with the code generated by an LLM like GPT-4.
    • Receives execution results and suggests corrections or bug fixes if necessary.
    • Its behavior can be altered by passing a new system message, and LLM inference configuration can be managed via llm_config.
    1. UserProxyAgent:
    • Serves as a proxy agent for humans, soliciting human input for the agent’s replies at each interaction turn by default while also having the ability to execute code and call functions.
    • Triggers code execution automatically upon detecting an executable code block in the received message when no human user input is provided.
    • Code execution can be disabled, and LLM-based responses, which are disabled by default, can be enabled via llm_config. When llm_config is set as a dictionary, the UserProxyAgent can generate replies using an LLM when code execution is not performed.
    1. GroupChatManager:
    • A class inherited from ConversableAgent, designed to manage a group chat involving multiple agents.
    • Provides a method run_chat to initiate and manage a group chat, with parameters for messages, sender, and configuration.
    • This class appears to be in preview, indicating it might be a newer or less stable feature of AutoGen.

    In practical terms, these agents facilitate complex workflows and interaction patterns among multiple entities, be they other AI agents, human users, or a combination of both. For example, the GroupChatManager could potentially moderate conversations between agents and humans, passing messages according to specific rules.

    Examples of Various Applications Executed with AutoGen

    The figure below shows six examples of applications built using AutoGen.

    Here are some of AutoGen examples:

    A Rising Competitive Arena

    The domain of Large Language Model (LLM) application frameworks is swiftly evolving, with Microsoft’s AutoGen contending robustly amidst many competitors. LangChain is a framework for constructing a diverse range of LLM applications, spanning chatbots, text summarizers, and agents. At the same time, LlamaIndex provides abundant tools for interfacing LLMs with external data reservoirs like documents and databases.


    Similarly, libraries such as AutoGPT, MetaGPT, and BabyAGI rely on LLM agents and multi-agent application spheres. ChatDev employs LLM agents to mimic a full-fledged software development team. Concurrently, Hugging Face’s Transformers Agents library empowers developers to craft conversational applications that bridge LLMs with external tools.

    The arena of LLM agents is a burgeoning focal point in research and development, with early-stage models already devised for a spectrum of tasks, including product evolution, executive functionalities, shopping, and market analysis. Research has unveiled the potential of LLM agents in simulating mass populace behaviors or generating realistic, non-playable personas in gaming environments. Yet, a substantial portion of this endeavor remains in the proof-of-concept stage, not quite ready for full-fledged production due to hurdles like hallucinations and erratic behavior exhibited by LLM agents.

    Nonetheless, the outlook for LLM applications is promising, with agents poised to assume a pivotal role. Major tech entities are placing substantial bets on AI copilots becoming integral components of future applications and operating systems. LLM agent frameworks will allow companies to design customized AI copilots. The foray of Microsoft into this burgeoning arena with AutoGen underscores the escalating competition surrounding LLM agents and their prospective future impact.

    Bridging the Gap: Human and AI Interaction

    One of AutoGen’s hallmark features is its seamless integration of human input within the AI conversation. This blend of human and AI interaction is innovative and a game-changer in resolving complex tasks.

    Moreover, this integration goes a long way in addressing the limitations of LLMs, making AutoGen a torchbearer in promoting harmonious human-AI collaborations.


    AutoGen is more than just a tool; it’s a promise of the future. With its relentless innovation, Microsoft has given the world a framework that simplifies the development of LLM applications and pushes the boundaries of what’s achievable.

    Moreover, as we delve deeper into the realm of AI, frameworks like AutoGen are set to play a pivotal role in shaping the narrative of AI, presenting a future where the sky is not the limit but just the beginning.

    That’s it for today!


    AutoGen: Enabling next-generation large language model applications – Microsoft Research

    microsoft/autogen: Enable Next-Gen Large Language Model Applications. Join our Discord: (

    Microsoft’s AutoGen has multiple AI agents talk to do your work | VentureBeat

    The new Open AI GPT-4 Vision on ChatGPT: Bridging the Gap Between Text and Image Understanding

    On September 25th, 2023, OpenAI expanded the capabilities of its advanced model, GPT-4, by introducing the ability to interpret images and speech alongside text. Dubbed GPT-4V(ision) or GPT-4V, this feature catapults GPT-4 into the realm of multimodal models, offering a richer interaction experience by allowing visual question answering (VQA). Users can upload images, inquire about them, and even have GPT-4V assess context and relationships within the visuals, marking a significant stride toward a holistic understanding of textual and visual data and broadening the horizons for real-world applications of GPT-4.

    The GPT-4V feature sets a new benchmark in AI by successfully bridging the gap between text and image understanding. This leap is imperative in evolving towards more intuitive and human-like interactions with machines. Through experiments, GPT-4V has showcased its potential in accurately interpreting and answering questions about various visual inputs, from understanding humor in memes to identifying currency types and even providing a high-level analysis of a movie from a mere image. This impressive ability opens up endless possibilities and applications, making GPT-4V a groundbreaking stride in the journey of AI.

    What can I do with GPT-4 Vision?

    1. Object Identification: GPT-4 Vision can accurately pinpoint objects and provide a detailed description by processing an image. Its recognition capabilities are spectacular, from everyday objects to complex machinery.
    2. Text Transcription from Images: Say goodbye to manually typing text from an image. This feature allows users to seamlessly extract written or printed text from images, a boon for those dealing with vast documents or old archives.
    3. Data Deciphering: Extract essential data from graphical content, such as charts, diagrams, and infographics, translating visual data into comprehensive text insights.
    4. Multi-condition Processing: GPT-4 Vision is adept at analyzing images under multiple conditions. Whether understanding a photograph taken under varying lighting or discerning details in a cluttered scene, its analytical prowess is unmatched.
    5. Educational Assistance: Students and educators can leverage GPT-4 Vision to analyze diagrams, illustrations, and visual aids, transforming them into detailed textual explanations, thus enhancing the learning process.
    6. Coding Enhancement: For the tech-savvy, GPT-4 Vision can interpret visual code structures or flowcharts and translate them into actual coding languages, making the development process swifter and more efficient.
    7. Design Understanding: For designers, understanding intricate patterns, layouts, or visual structures becomes easier. GPT-4 Vision can break down design elements and offer textual insights, aiding in refining and improving design concepts.

    How To Get GPT-4 Vision Access on ChatGPT?

    To access GPT-4 Vision, follow these steps:

    1. Visit the ChatGPT website and sign in or create an account.
    2. Click the “Upgrade to Plus” option.
    3. Click “Upgrade plan” under ChatGPT Plus in the pop-up.
    4. Enter your payment information, then click “Subscribe”.
    5. After payment, select “GPT-4” from the drop-down menu on your screen to use GPT-4 with ChatGPT.

    Examples of Incredible Features You Can Do with GPT-4 Vision

    Data Deciphering

    It’s fantastic. I give this infographic with this prompt: “What does this tell you? Be very, very exact.

    Follow the results ChatGPT gave me.

    This infographic provides insights into what SEO professionals believe will be the most significant disruptors to SEO performance in 2024. Here are the exact details:

    1. Title and Context:
    • The title is “SEO Performance In 2024.”
    • SEO professionals were asked about what they thought would cause the most significant disruption to their SEO performance in the upcoming years.
    1. Most Significant Disruption In SEO Performance:
    • Generative AI is perceived to be the top disruptor, with 21.0% of professionals identifying it.
    • E-E-A-T & Trusted Sources comes second with 13.5%. There’s a note mentioning that an extra “E” was added to E-A-T in December 2022, which stands for “Experience.”
    • Automation (Tools) is the third most significant disruptor at 10.7%.
    • Other factors listed in descending order of their perceived disruption potential are:
      • Core Web Vitals (8.2%)
      • Google Discover (6.4%)
      • Social Search (6.3%)
      • Structured Data (5.9%)
      • Site Security (5.3%)
      • Google Passages (4.2%)
      • Knowledge Graph and Entities (3.9%)
      • Video Search (3.6%)
      • SERP Features (3.5%)
      • Mobile SEO (2.8%)
      • Voice Search (2.6%)
      • None/Other (2.2%).
    1. Visual Imagery:
    • There’s an image of a mobile phone with the ChatGPT logo on its screen.
    • Another visual element is a colorful wheel containing the letters E, E, A, T, and icons of various tech giants.
    1. Additional Text Information:
    • SEO professionals have many new technologies and updates to consider for the year. Specifically, tools like ChatGPT and Google’s “Generative Search Experience” are mentioned as generating considerable interest and concern among these professionals.

    In summary, the infographic emphasizes the rapidly evolving landscape of SEO, with new technologies, especially Generative AI, becoming significant factors that professionals in the field need to be aware of and adapt to.

    Another example of extracting information from infographics with the same prompt: “What does this tell you? Be very, very exact.

    Certainly! The provided infographic conveys information about the usage patterns of social media among adults and teens. Here’s a detailed breakdown of the infographic’s contents:

    1. Title: The infographic is titled “Adults vs Teens: How We Use Social Media.”
    2. Background Information: The Pew Research Center released a study comparing the social media usage of adults and teens. The commentary mentions that teens today are inherently digital-savvy, having grown up in the digital era. However, when it comes to newer social platforms like Pinterest and Instagram, adults appear to have a slight edge in usage.
    3. Overall Social Media Usage:
    • Adults: 72% of adults use social media.
    • Teens: 81% of teens use social media.
    1. Platform-Specific Usage:
    • Facebook: Teens dominate the usage, with 94% of teens using it compared to 67% of adults.
    • Twitter: 26% of teens use Twitter, compared to 18% of adults.
    • Pinterest: 15% of adults use Pinterest, whereas only 1% of teens do.
    • Instagram: 13% of adults use Instagram, compared to 11% of teens.
    • Tumblr: 6% of adults use Tumblr, and 5% of teens use it.
    1. Age Breakdown of Social Media Users:
    • Ages 13-18: 89% use social media.
    • Ages 18-29: 81% use social media.
    • Ages 30-49: 78% use social media.
    • Ages 50-64: 60% use social media.
    • Age 65+: 43% use social media.
    1. Source: The data comes from Pew Research Center studies conducted in August 2013, May 2013, and February 2013.
    2. Sponsor/Creator: The infographic was created by “NextAdvisor,” which offers in-depth, independent research.

    In summary, the infographic suggests that while teens use social media platforms like Facebook more dominantly, adults are more inclined towards newer platforms like Pinterest and Instagram. The percentage of users also declines as the age group increases, with the youngest age group (13-18) having the highest percentage of users and the oldest age group (65+) having the lowest.

    Text Transcription

    Text Transcription from Images is instrumental when extracting texts from images. I used this prompt: “Can you extract the text from this image?“.

    Another example with text transcription:

    Educational Assistance

    Coding Enhancement

    Create a live website from an image.

    Multi-Condition Processing

    This is awesome. Can you imagine with a mobile phone what can you do with this feature?

    Identifying Objects

    There are so many things you can do with this technology. Lock at the identifying objects.

    What are the Potential Risks Of GPT-4V

    OpenAI released a paper outlining potential risks associated with the use of GPT-4V, which include:

    • Privacy risks from identifying people in images or determining their location, potentially impacting companies’ data practices and compliance. The paper notes that GPT-4V has some ability to identify public figures and geolocate images.
    • Potential biases during image analysis and interpretation could negatively impact different demographic groups.
    • Safety risks from providing inaccurate or unreliable medical advice, specific directions for dangerous tasks, or hateful/violent content.
    • Cybersecurity vulnerabilities such as solving CAPTCHAs or multimodal jailbreaks.

    Risks posed by the model have resulted in limitations, such as its refusal to offer analysis of images with people.

    Screenshot from ChatGPT, September 2023

    Screenshot from ChatGPT, September 2023

    Overall, brands interested in leveraging GPT-4V for marketing must assess and mitigate these and other generative AI usage risks to use the technology responsibly and avoid negative impacts on consumers and brand reputation.


    In conclusion, OpenAI’s GPT-4 Vision marks a monumental step towards harmonizing text and image understanding, paving the way for more intuitive and enriched interactions between humans and machines. As GPT-4V unfolds its potential, it not only broadens the horizon for real-world applications but also beckons a future where AI can perceive and interpret the world in a manner akin to human cognition, thereby significantly driving forward the frontier of what is achievable in the realm of artificial intelligence.

    That’s it for today!


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