Emily Rose’s Post

More info on how Artificial Intelligence (AI) can be used to help your small business grow AND most importantly, SAVE you TIME and MONEY! Okay, one of AI’s most remarkable applications lies in content creation. Whether it's blog posts, social media updates, product descriptions or taglines and slogans, AI can help you create a jumping off place for written content. For example, feeding the system (like ChatGPT or GoogleBard) with relevant information and style preferences, you can generate content that's not just informative, but also intriguing and fun to read. Don’t forget, this is a tool and needs your spin and edit as well. Can I get a fact-check up in here? Now, let’s talk about numbers. Business decisions rooted in real data tend to yield success. AI systems like Tableau or Microsoft Power BI can assist in crunching through vast amounts of data, extracting patterns and providing insights that guide strategic choices. This data-driven approach minimizes guesswork and maximizes precision. Stay tuned next week for more beep-boop-beep tips! #AI #ArtificialIntelligence #contentcreation #businessstrategies #digitalmarketing #smallbusinesses #digitalage

Bloomberg burned $10M training their own GenAI tool. And then? GPT-4 launched. Their investment? Made redundant. Overnight. Here's why: → Companies and high-ed institutions are pouring millions into AI. → They're crafting custom AI models. → All driven by security and ethics concerns. But the rapid evolution of technology raises a big question: When newer, broader models like the next GPT-X launch, what happens to your investment? Here’s the thing: Tailoring AI to specific, proprietary data can have unique benefits, like outperforming broader models in niche, data-specific tasks. But here's the twist: → Bloomberg focused on enhancing broad financial capabilities. → GPT-4, trained on vastly more data, naturally had the edge. So, what should we do instead? 1. Focus on integrating unique, factual data that broader models can't reach. 2. Use techniques like RAG to enhance rather than replace. Now, over to you - This should be your GTM Strategy for AI Adoption: 📍If your goal is to enhance specific capabilities of a GPT-X model using your data, then you might not achieve the desired outcomes due to OpenAI's broader training datasets. And your $10M will evaporate just like Bloomberg's did. 📍But if you're using proprietary data to inject specific, private factual knowledge, your model can outperform the next GPT-(X+1) in tasks requiring this unique data. This strategy is the best approach I've found for handling proprietary data strategically, to maintain a competitive edge and ensure relevance, even as AI technology rapidly evolves. #ArtificialIntelligence #InnovationManagement #DataStrategy

How to understand Gen AI/AI adoption for an organization you are interviewing for ? With so much hype around Gen AI and AI/ML in general, everyone says they use AI/Gen AI due to fomo even if it means they are just using chat gpt from the internet and building some poc's in their local systems. * Remember building pocs is not equal to having production apps used by end consumers. Amongst the orgs I have seen, there are a few categories . Some are research focused(1) , some are research+ implementation focused(2), some are implementation focussed(3) and others just in marketing(4). 4 are the orgs one needs to avoid at all costs. Being involved with orgs in 1 , 2 or 3 depends on a person's personal choice. Try asking the following questions to understand the truth between Gen AI In an org: - What are the No of use cases that are in production and actively used by end customers ? - What are the number of research papers published by the org in Gen AI/AI in the last few quarters.? - What is the estimated business impact of the use cases? - What is the scale and requirment of the use cases. Are we talking about 10 requests a min or 1000 or 10000 req a min.. - Are there specific standardized platforms(cloud/systems/devops/ml/data) and engineering/science practices for doing Gen- AI within the org? - Is llm evaluation, guardrails, monitoring, cost/compute usage and alerting part of the overall existing product? - Are there separate teams or people looking at legal, compliance and security for Gen AI. - Does the org use open source models(mistral, llama)or just enterprise gen ai apis(open ai api's) - Are the orgs fine tuning the existing model with their own data? - If an organisation speaks about rag how many documents are there in their vector store? 10,1000 or 10000 - What is the Gen AI related spending of the organization on research and application building? In the current climate of misinformation and marketing it is important to not be swayed by fancy LinkedIn or media posts but actually go deeper and make career choices. #genai #aiforproduction #llm #mlops #llmops #ai #data

A glimpse into my brain and what’s currently bothering me about the AI governance gap 🤖 - Welcome my networks thoughts 🛒 How can we ensure our AI algorithms remain transparent and free from bias and does this fall to Data Governance? 🔏 What steps should we take to integrate AI into our inventory management and customer service while maintaining data quality and privacy? 🤝 In what ways can we foster better collaboration between data scientists, IT professionals, and governance teams to streamline our operations? 🔝 How do we keep up with rapid AI advancements in demand forecasting and continuously updated our governance frameworks? 👩🏫 What role does ethical consideration play in our AI-driven personalised marketing, and how can we strengthen it? 🧠 Have Data Governance have the right skills to understand AI and provide the right guidance on its usage? #DataGovernance #AI

Hot take: I'm not drinking the AI Kool-Aid. All companies, whether in marketing, data, or sales, seem to be building out an AI component to their product. Sure, AI may make a lot of things simpler... You can have AI summarize larger articles when you don't have time to read the whole thing. You can have AI optimize a SQL query for you. You can have AI even build out dashboards for you... However, many people that don't understand data will begin using these features, not realizing the potential implications. What happens when a stakeholder asks AI to calculate a revenue metric, only to never realize that the definition it uses isn't the one used by the business? What happens when teams assume AI can clean their data? And there are tons of data quality issues only a true data expert can discover? Self-service data powered by AI is great in theory, but there are so many things that can go wrong. Ignorance is bliss as they say. In my opinion, teams that begin to depend on these AI product features will be living in blissful ignorance until they realize the hole they've dug themselves into. What are your thoughts? #AI #datanalytics #selfserviceanalytics

As a CTO, generative AI has of course been on my mind constantly over the last few years. It’s all anyone can talk about right now, and for good reason! Generative AI and large language models (LLMs) are remarkable technologies, and they’re a jack of all trades for a range of use cases. But let’s be honest…if your business wants to implement an AI strategy, you have to start with a strong data strategy as the foundation. That’s where the true competitive advantage comes from. I wanted to share some thoughts about how marketers, specifically, should think about the connection between their data and AI tools: 🧠 LLM-Powered Personalization - The holy grail of marketing is true personalization for all your customers. Will a general foundational model understand your brand voice – including how you speak to your customers – your values, or your product? Will it know anything about your customers? It definitely won’t – not without first-party data. Training the model on your proprietary data is critical to ensure it aligns with your brand identity. 🔍 Play the LLM field - With the emergence of various foundation models and machine learning models (MLMs), it’s essential to have your data ready for a "bring your own model" approach. By prioritizing a composable architecture and housing your data in a central unified source, you can utilize any new foundation models on top of your data and take advantage of all the innovations in the fast-moving world of AI. 🧐 Avoid hallucinations (and misinformation) - If you’ve started playing around with generative AI, you’ve probably realized how important grounding and retrieval are for leveraging these tools. You have to use your own first-party data, application data, or user behaviors to prevent the LLM from misinforming your users with hallucinations. Remember, a data strategy isn't just a checkbox. It's the cornerstone of your AI success story! Marketers, I’d love to hear from you. What's been your pivotal moment in implementing data strategies for AI? #GenerativeAI #AI #DataStrategy #MarketingInnovation

In the ever-evolving landscape of business technology, the collaboration between Business Intelligence (BI), Machine Learning (ML), and Generative AI is revolutionizing the way organizations operate and make decisions. BI provides the foundation by transforming raw data into meaningful insights, enabling businesses to understand their operations, market trends, and customer behaviors. However, when coupled with ML and Generative AI, the potential for innovation and efficiency reaches new heights. Machine Learning enhances BI by automating data analysis, predicting trends, and providing deeper insights into complex patterns that traditional BI tools might miss. This predictive capability allows businesses to be more proactive in their strategies, leading to better-informed decisions and a competitive edge in the market. Generative AI takes this a step further by creating new content, designs, or data based on learned patterns. This can be a game-changer in areas like product development, marketing, and customer service, where personalized and innovative solutions can significantly impact customer satisfaction and business growth. The combination of BI, ML, and Generative AI enables businesses to not only understand their current state but also to anticipate future scenarios and create novel solutions. It's an exciting time to be in the field, and I'm eager to explore the endless possibilities that this combination offers for driving business success. #BusinessIntelligence #MachineLearning #GenerativeAI #DataDrivenDecisions #Innovation

Get to know how AI learns I heard AI described as “augmented human intelligence” and believe it is an insightful way to characterize this new technology and its role in various applications. AI systems are designed to complement and enhance human capabilities rather than replace them entirely. They can process vast amounts of data, make predictions, and assist with decision-making, ultimately extending and amplifying human intelligence and productivity. As you research AI tools and your current HR vendors begin to add AI to their offerings, you will need to become familiar with how AI learns and the associated risks so you can ask critical questions before you begin to use AI-related systems. Here are a couple of AI learning methods and their benefits and risks. Machine Learning Benefits: - Adaptability: Machine learning models can adapt to new data, making them versatile in a wide range of applications. - Automation: Machine learning can automate complex tasks, making it efficient and cost-effective. - Data-driven insights: Machine learning extracts valuable insights from large datasets for better decision-making. Risks: - Bias: Models can inherit biases from training data, leading to unfair or discriminatory results. - Lack of transparency: Some machine learning models are black boxes, making it hard to explain their decisions. - Data dependence: Machine learning models rely heavily on the quality and quantity of training data. Rule-Based Systems Benefits: - Explicit control: Rules are explicitly defined, offering control and predictability. - Domain specificity: Rule-based systems are effective for well-defined domains with clear rules and regulations. Risks: - Limited adaptability: Rule-based systems may struggle with handling ambiguous or evolving situations. - Maintenance overhead: Updating rules can be labor-intensive as systems need manual adjustments. Reinforcement Learning Benefits: - Autonomous learning: Ability to learn through interaction and adapt without explicit programming. - Suitable for dynamic environments: Effective in environments with changing conditions. Risks: - Exploration challenges: Learning through trial and error can be time-consuming and inefficient. - Safety concerns: Risky actions might be taken before optimal strategies are discovered. Next, in this ongoing series on AI and HR, we’ll delve into questions to ask your vendors as you explore tools and solutions. In the meantime, if you’d like to check out the other posts in the series, click here: https://lnkd.in/g8RU7nMy. #talentacquisition #aiinhr #humanresources

AI is now a board-level priority Last year, AI consisted of point solutions and niche applications that used ML to predict behaviors, find patterns, and spot anomalies in carefully curated data sets. Today’s foundational models are jacks-of-all-trades. They can code, write poetry, draw in any art style, create PowerPoint slides and website mockups, write marketing copy and emails, and find new vulnerabilities in software and plot holes in unpublished novels. “Generative AI touches every aspect of the enterprise, and every aspect of society,” says Bret Greenstein, partner and leader of the gen AI go-to-market strategy at PricewaterhouseCoopers. “With a pre-trained model, you can bring it into HR, finance, IT, customer service—all of us are touched by it.” https://lnkd.in/eJ6zyvsk

What is the importance of data annotation for successful machine learning models? 1️⃣ Boosted Accuracy: High-quality annotations mean ML models learn better, perform sharper, and make more precise predictions. 2️⃣ Smoother Generalization: Models trained on top-tier data handle real-world situations like pros, while others might stumble. 3️⃣ Time & Money Savers: Quality annotations = fewer re-dos and faster deployments, saving time and budget. 4️⃣ Trust & Reliability: Reliable AI models are the name of the game. Clients trust models rooted in top-notch data, leading to broader ML adoption. 5️⃣ Fair & Ethical AI: High-quality data annotation helps keep biases in check, creating AI that plays fair and promotes equality. In a nutshell, quality data annotation is the key to propelling AI and ML's full potential.  #smut #Data #AI #MachineLearning #DataAnnotation Image credit: datavizproject ________ Follow our page for more insightful updates on data analytics and its effect on your business growth. Who We Are: At SMUT-io, we specialize in Data Analysis/Annotation and Social Media Data Management, providing you with real-time, updated, and collated customer profiles.