Eugen Dragomirescu’s Post

We've all seen Generative AI burst onto the tech scene with companies scrambling left and right to get on board as quickly as possible. I bet most people think about the sexiest outputs of Gen AI: content creation, the democratization of code generation, and image production (because who wouldn't want to see an AI generated image of *the* happiest version of their favorite baby animal?) But what powers all of this cutting edge innovation? Terabytes of my favorite topic, good ol' data. Data can no longer be seen as an afterthought in the Gen AI space; it's finally the main character. There are three necessary attributes for data that's used to train models: quality, diversity, and adaptability. 1. Data Quality is P0 - In the Gen AI space, the old axiom holds true: Garbage in, garbage out. Data quality isn't a luxury, it's a necessity. AI/ML models are only as good as the quality of data they're built on. 2. Diversity Fosters Accuracy - AI models thrive on varied inputs, representing different scenarios and perspectives. Homogenous data can lead to biased outcomes, while diverse data enhances accuracy and broadens the model's applicability. 3. Continuous Iteration is Key - The Gen AI landscape is dynamic, and so should be our approach to data. Regularly updating and refining the data ensures that AI models stay relevant, adapting to evolving patterns and maintaining their efficacy over time.

"Unlocking AI's Potential: The Art of Fine-Tuning vs. The Craft of Advanced Prompting" As I delve deeper into AI and engage with professionals at various stages of their AI journey, a recurring yet simple question emerges: What exactly distinguishes advanced prompting from fine-tuning, and what are their respective advantages and disadvantages? More importantly, which approach should one utilize, and are there superior methodologies available? Advanced prompting, or "prompt engineering," involves crafting detailed prompts to elicit specific outputs from pre-trained models. This technique leverages the model's existing knowledge without the need for additional training, making it highly versatile and resource-efficient. It's particularly beneficial for those who wish to quickly experiment with AI models or lack the resources for extensive training. On the other hand, fine-tuning involves training a pre-trained model on a specific dataset to adapt its responses to a particular domain or task. This method can significantly enhance model performance on specialized tasks, offering tailored outputs that advanced prompting alone may not achieve. However, it requires more resources, including a relevant dataset for training and computational power. Choosing between advanced prompting and fine-tuning hinges on your specific needs and constraints. If rapid deployment and experimentation are your priorities, advanced prompting offers a quick and cost-effective solution. However, if you're seeking optimized performance on specialized tasks, fine-tuning is the way to go. In the context of my journey, I've found that a blend of both strategies can be powerful. Leveraging advanced prompting for initial explorations and fine-tuning for projects requiring highly specialized outputs allows for both flexibility and depth. As the AI landscape evolves, staying open to new methods and continuously experimenting is key to unlocking the full potential of AI technologies. *Image generated using DALL·E #AI #MachineLearning #PromptEngineering #FineTuning #TechInsights

OpenAI's development, DALL-E 3, signifies a groundbreaking advancement in the realm of artificial intelligence. This 12-billion parameter model is capable of generating images from text descriptions with remarkable precision, providing broad applications across various sectors. Gone are the days of generic visual content. With DALL-E 3, customization takes center stage, offering the ability to produce niche illustrations for books, games, newsletters, and movies. Interestingly, DALL-E 3 even allows full rights to the user, marking a significant step in intellectual property rights within AI. Moreover, the proliferation of AI technology isn’t confined to image creation only. Platforms such as Leonardo AI and D-ID are spearheading the development of AI Avatars and talking-head videos, erasing linguistic and cultural barriers. At Positive, our commitment is to pioneer the integration of such groundbreaking AI developments into effective business applications. We're devoted to harnessing this untapped potential and shaping AI-aided tools that can streamline processes and augment efficiency. The role of AI technologies in business transformation is becoming increasingly paramount. With DALL-E 3 leading the charge, the future holds immense promise. #AI #business #DALLE3 #OpenAI #Positive https://lnkd.in/gXde_iWP

𝗧𝘂𝗲𝘀𝗱𝗮𝘆 𝗧𝗶𝗻𝗸𝗲𝗿𝗶𝗻𝗴 𝗜𝗻𝘀𝗽𝗶𝗿𝗮𝘁𝗶𝗼𝗻: 𝗔𝗜 𝗕𝗿𝗮𝗶𝗻𝘀𝘁𝗼𝗿𝗺𝗲𝗿 You want to identify interesting use cases for #AI across your organization? The AI Brainstormer is a chatbot that prepares teams for a more in-depth exploration call with your AI team. It does three things: 1) It makes initial ideas more concrete with clear goals and measures of success. 2) It assesses whether the idea can be implemented as a one-off analysis, with (a variation of) an existing AI tool, or if it needs a new prototype. 3) For each category, it explains next steps and the type of commitment it takes for the team to move forward. Makes it easy for teams to quickly discuss an idea. Promotes existing AI tools within the organization. Saves the AI team time. Tinkering difficulty level: 1/5 -- 𝗜𝗻𝗽𝘂𝘁: teams talking to the chatbot 𝗢𝘂𝘁𝗽𝘂𝘁: use cases with goals and measures of success; initial assessment of the scope; next steps 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝘂𝘀𝗲: Don't rely on the assessments regarding the scope of a project and the next steps suggested by the bot. While the assessments are accurate most of the time, you can only plan with whatever the AI Lab tells you in person. 𝗗𝗮𝘁𝗮 𝗻𝗲𝗲𝗱𝗲𝗱: list of existing AI applications 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 - OpenAI Assistant - Chainlit application - Libraries: LangChain for LLM interactions - Model: GPT-4o

What's next for AI in 2024? That's the tricky question the MIT Tech Review - one of my favorite publications - tries to speculate on. Here are their top bets : 1. Customized chatbots are going to spread 🤖 I can easily relate to this one, lately, more and more startups (including Ask Mona) and big tech have tackled the issue of making people able to feed generative AI with their own data, trying to make generative AI more reliant. 2. Generative AI’s second wave will be video ▶ Why not? It’s true that generative video model solutions online have made tremendous progress these last months, even if some of them are still frustrating because they can only generate very short videos. 3. AI-generated election disinformation will be everywhere 📰 No doubt, this massive issue is unlikely to stop harming democracy, especially in a year with crucial elections such as the US presidential election and European Parliament election. This is where, I believe, cultural institutions have a very important role to play to foster critical thinking and educate to AI. 4. Robots that multitask 🤹♀️ Multimodal models that can deal with various kinds of input and generated contents are indeed very promising and one subject we also focus our research and development forces at Ask Mona in 2024. And you, what do you think about those speculations? Illustration generated with Dall-e (Open AI). Read the full article in comment 👇

The Future of AI and ML: A Glimpse into the Next Frontier 🚀 Artificial intelligence (AI) and machine learning (ML) are rapidly transforming our world, revolutionizing industries, and shaping the future of technology. As AI and ML continue to evolve, we can expect to see even more groundbreaking advancements in the years to come. Here are a few of the key trends that are shaping the future of AI and ML: AI Democratization: AI and ML tools are becoming more accessible and user-friendly, making them available to a wider range of individuals and businesses. AI-Powered Automation: AI and ML are driving automation across industries, automating repetitive tasks and freeing up human workers to focus on more strategic initiatives. Hyperpersonalization: AI and ML are enabling hyperpersonalization, allowing businesses to tailor products, services, and experiences to individual needs and preferences. Explainable AI: The development of explainable AI (XAI) is making AI systems more transparent and interpretable, addressing concerns about bias and fairness. AI Governance: As AI becomes more prevalent, there is a growing need for robust AI governance frameworks to ensure ethical and responsible use of AI technologies. These trends suggest that AI and ML will play an increasingly pivotal role in our lives, shaping the future of work, healthcare, education, and many other aspects of society. Preparing for the AI and ML-Driven Future To prepare for the AI and ML-driven future, individuals and businesses should: Develop AI literacy: Equip themselves with a basic understanding of AI and ML concepts to make informed decisions and adapt to the changing technological landscape. Upskill and reskill: Embrace lifelong learning to acquire the skills and knowledge needed to thrive in an AI-powered economy. Embrace ethical AI: Advocate for the responsible and ethical development and use of AI technologies. The Next Frontier of AI and ML As we venture into the future of AI and ML, we can expect to see even more groundbreaking advancements that will shape our world in ways we can only begin to imagine. From AI-powered medical breakthroughs to self-driving cars, the possibilities are endless. Embrace the Potential of AI and ML Let us harness the power of AI and ML to create a better future for all, one where technology empowers us to solve global challenges, enhance human capabilities, and unlock new frontiers of innovation. #AI #ML #FutureTrends #AIRevolution #AIandML #AIforGood

Explainable AI (XAI) involves methods and techniques enabling an AI model to clarify its reasoning in a way understandable to humans. This feature of AI functionality is important as it allows insight into the process by which AI systems generate particular results. When analyzing AI decision-making, it is important to consider three elements: the data input, the patterns recognized by the model, and the predictions made by the model. These components are essential for AI explainability, helping users understand the way AI-driven insights are generated. For XAI to succeed, the system must offer users a clear and comprehensible explanation of how it generates insights. Various methods can be employed to implement this, such as detailing the model's decision-making process, utilizing decision tree pathways, or employing data visualization techniques. Each approach has its benefits, but the goal remains the same: to explain the underlying workings of AI.For example, a decision tree path can show the sequential reasoning used by the model, while data visualizations can depict the connections and patterns in the data that impacted the model's forecasts. AI systems can promote increased trust and acceptance from users through the presentation of information in accessible formats. AI explainability is vital not just for transparency but also for enhancing the efficiency and responsibility of AI systems. Users can make more informed decisions and assess the reliability of an AI system by understanding how it reaches its conclusions. Moreover, interpretable AI is crucial for recognizing and reducing biases in models by enabling a deeper analysis of the data and patterns used by the model. In such systems, this transparency guarantees that AI aids in achieving fair and ethical results, strengthening its importance as a useful tool for improving human decision-making processes #XAI #dip #decisionintelligence #data #patterns #visualization #quadrantsolutions #quadrantresearch

The Generative AI List: 5000 Models, Tools, Technologies, Applications, & Prompts

GenAI - Trust, Data & Oversight Generative AI is a branch of artificial intelligence that can create new data, designs, and content from existing data. It has the potential to revolutionize the manufacturing (or EVERY) industry by enabling faster innovation, better quality, and lower costs. However, to fully harness the power of generative AI, we need to ensure that it is based on trust and valid data. Trust is essential for any AI system, especially one that can generate novel outputs that may not be easily verified by human experts. To build trust in generative AI, we need to ensure that it is transparent, explainable, and accountable. We need to understand how generative AI works, how it reaches a particular result, and how it can be traced back and corrected if there are any errors. We also need to ensure that generative AI is aligned with our ethical values and does not cause harm or bias to any stakeholders. Valid data is the foundation of any AI system, especially one that relies on learning from existing data. To ensure that generative AI produces accurate and reliable outputs, we need to ensure that the data it uses is of high quality, relevance, and diversity. We need to collect, clean, and label data that represents the real-world scenarios and challenges that we want generative AI to solve. We also need to augment and synthesize data that can fill the gaps and cover the edge cases that may not be available in the original data. Generative AI is a powerful tool that can transform the manufacturing industry. However, it is not a magic bullet that can solve all problems without human oversight and intervention. We need to establish trust and valid data as the pillars of generative AI, and work together to ensure that it is used responsibly and effectively. "Trust...it's earned in drops....and lost in buckets" #trust #ai #ethicalai #moderndata

Staying ahead in the ever-evolving AI landscape is crucial for any tech professional. Here's a quick rundown of the latest happenings that should be on your radar: 1. In an impressive face-off of artificial intelligence, Anthropic's Claude 3 Opus has edged out OpenAI's GPT-4, taking the top spot at Chatbot Arena. If you're keen to understand how these AI bots are reshaping the digital dialogue, dive in for a detailed account here: [Read more about Claude 3's victory](https://lnkd.in/dk2iEufy) 2. AI enthusiasts, brace yourselves! The horizon is about to expand with the arrival of GPT-5 and Llama 3, set to further revolutionize the AI scene. Their launch is anticipated sooner than we expected, potentially marking the next giant leap in AI advancements. [Discover how GPT-5 and Llama 3 will change the game](https://lnkd.in/dHQVSeWM) 3. Despite Claude 3's recent limelight, the AI industry isn't slowing down. With updates and potentially new successors to GPT-4 on the horizon, we're reminded of the dynamic nature of this field and the continuous innovation it fosters. [Explore the current state of AI competition](https://lnkd.in/duJs45gZ) As we witness these developments unfold, it's evident that AI will continue to surprise us, pushing the boundaries of what's possible. Embracing this change and preparing for it will be key. Looking forward to seeing how these innovations will mould the future of technology and business. Stay tuned for what comes next!