Rafael Brown’s Post

“What this is really saying is that most people (and companies) don't need AI, most should avoid custom AI, and most should carefully evaluate any tools to see if they add vs detract from their current situation. Beyond the AI bubble, most companies need NOTHING new or bespoke from 'Gen' AI. As Salesforce pushes AI, take a pause, because you probably don't need what they or any other bigtech company is pushing for AI.”

🌐 Breaking Down AI Barriers: No-Code Platforms and Generative AI In the generative AI era, many companies want to leverage this technology but feel held back by the need for specialized data science skills. Dr. Marcel Müller’s recent article reveals an exciting shift: no-code AI platforms are enabling teams to build powerful applications without data scientists. At entAIngine, we’ve fully embraced this approach, empowering companies to create sophisticated AI-driven solutions swiftly and cost-effectively by leveraging pre-trained models and flexible orchestrations. By using no-code AI, we’re breaking down traditional bottlenecks and democratizing access to AI across roles, accelerating time-to-market and driving real value in record time. This evolution allows anyone with a business need to innovate, eliminating barriers to AI adoption. 👉 Read more in Dr. Müller’s article: Read the article here https://lnkd.in/dg-m_EU3 💡 How is your company preparing to harness AI's potential with accessible tools? Let’s explore the possibilities together! #GenerativeAI #NoCode #AITechnology #Innovation #DigitalTransformation #AIAccessibility #entAIngine #FutureOfAI #BusinessInnovation

Think your generative AI isn't living up to its promise? You're not alone – but the problem might not be the AI itself. It could be the fuel you're feeding it: your data. ⛽️ Here's why bad data breaks good generative AI: - 𝗕𝗶𝗮𝘀𝗲𝗱 𝗱𝗮𝘁𝗮 𝗽𝗲𝗿𝗽𝗲𝘁𝘂𝗮𝘁𝗲𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀. If your datasets are skewed towards certain groups or viewpoints, your AI will inherit these biases, leading to unfair or discriminatory outputs. Imagine an AI trained on mostly male resumes – it'll struggle to identify equally qualified women. - 𝗜𝗻𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗱𝗮𝘁𝗮 𝗹𝗲𝗮𝗱𝘀 𝘁𝗼 𝗳𝗮𝘂𝗹𝘁𝘆 𝗰𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻𝘀. Imagine training a product recommendation engine on a dataset with spotty purchase history. It won't truly understand your customers' preferences, leading to irrelevant suggestions. - 𝗜𝗻𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗱𝗮𝘁𝗮 𝗰𝗿𝗲𝗮𝘁𝗲𝘀 𝗰𝗼𝗻𝗳𝘂𝘀𝗶𝗼𝗻. Conflicting information, mismatched formats, and errors make it impossible for the AI to find reliable patterns. It's like trying to teach someone a language when the grammar rules keep changing. - 𝗜𝗿𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗱𝗮𝘁𝗮 𝘄𝗮𝘀𝘁𝗲𝘀 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹. Generative AI thrives on finding patterns relevant to its task. Throw in unrelated information, and you're essentially muddying the waters, hindering its ability to learn effectively. - 𝗡𝗼𝗶𝘀𝘆 𝗱𝗮𝘁𝗮 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗶𝗻 𝗻𝗼𝗻𝘀𝗲𝗻𝘀𝗲. Typos, outdated information, or deliberate misinformation all degrade the AI's output. It's like trying to have a conversation in a crowded room – too much background noise drowns out the meaning. 𝗧𝗵𝗲 𝗴𝗼𝗼𝗱 𝗻𝗲𝘄𝘀: These issues can be detected and tackled with robust data quality checks! Here are a few examples: - 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗻𝗲𝘀𝘀: Checks to find missing or empty values. - 𝗩𝗮𝗹𝗶𝗱𝗶𝘁𝘆: Verifying if values fit expected formats and patterns (e.g., correct date format, valid email addresses). - 𝗡𝗼𝗶𝘀𝗲: Applying anomaly detection techniques to identify outliers or unusual data points. Anyone working with generative AI can tell you: data quality is the ultimate bottleneck. It's like trying to build a magnificent palace on a foundation of sand. The potential is there, but without a solid base, the whole thing crumbles. But it's fixable! Clean, accurate, well-structured, and relevant data unlocks the true power of generative AI, leading to results that are reliable, fair, and genuinely transformational. The list of issues and data quality dimensions that can be detected is longer. I am running out of space for the post. Please share your ideas in the comments. #dataquality #datascience #generativeAI

🚀 Unlock the Power of Generative AI for Your Business! 🚀 Struggling to scale your business in a tech-driven world? You're not alone. Imagine having the ability to harness the power of generative AI to overcome those initial barriers that hold you back. What if I told you there’s a guide that breaks down everything you need to know to scale generative AI effectively? Here’s the secret recipe to getting started: 🔍 Identify the core barriers: Understand what’s holding you back. 💡 Leverage expert insights: Learn from data and AI experts who’ve navigated the same challenges. 🔧 Implement practical solutions: Step-by-step strategies to scale your AI efforts smoothly. Statistically, companies that embrace AI see a 40% boost in productivity within the first year. Don’t let your business be left behind. I’ve been through the trenches of AI implementation, and I can tell you—it’s a game-changer. Want to know how I scaled my AI operations? Click the link below to read the full guide! 👉 [Read Guide Here](https://lnkd.in/g-i5VYbf) What’s been your toughest challenge with scaling AI? Drop your thoughts in the comments! P.S. Ready to take your business to the next level with AI? Let’s make it happen together. Reach out for a consultation. 📈

Generative AI can help immensely with a company's growing pile of data. This article outlines three key challenges that company's face. And generative AI is unique positioned as a technology to address those challenges. "First, there aren’t enough experts in data science, AI, and analytics to deliver the breadth of insights needed across all aspects of business. Second, enterprises are often hampered by legacy and siloed systems that make it impossible to know where data lives, how to access it, and how to work with it. Third, even as we struggle with the first two challenges, data continues to grow in complexity and volume, making it much more difficult to use. Combined with a lack of robust governance policies, enterprises are then faced with poor data quality that can’t be trusted for decisions." Source: InfoWorld https://lnkd.in/ecYUcY84

Day 8/21 How Generative AI is Transforming Synthetic Data Generation In the age of big data, access to diverse and high-quality datasets is crucial for training AI models. But what if real-world data is not obtained? This is where synthetic data steps in, powered by generative AI! 🌐 🔍 What is Synthetic Data?   Synthetic data refers to artificially generated data that imitates the statistical properties of real-world data. Using generative AI techniques like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), synthetic data can be created for almost any domain—be it healthcare, finance, or customer behavior. How It’s Made:   -Training Models: AI models are trained on existing data to understand its patterns and distributions. -Data Generation: Once trained, these models generate new, artificial data points that mirror the real data in structure and content, but without using any actual personal information. 🛠 Key Benefits of Synthetic Data:  -Privacy Preservation: Synthetic data doesn’t contain real-world personal information, mitigating privacy risks—especially important in industries with strict data regulations. -Enhanced Data Availability: In scenarios where data is scarce or expensive to collect, synthetic data fills the gaps, providing diverse datasets for robust model training. -Cost-Effective Solutions: Synthetic data generation reduces costs, especially when scaling up AI projects, compared to the resource-intensive collection and labeling of real data. 💡 Real-World Applications:   -Healthcare: Synthetic patient data is used to develop and test AI models without compromising patient confidentiality, advancing medical research without ethical dilemmas. -Finance: Financial institutions use synthetic data to test fraud detection algorithms, simulate market scenarios, and train AI models in a risk-free environment. -Autonomous Vehicles: AI systems in self-driving cars are trained using synthetic data to simulate various driving conditions and scenarios that are difficult or dangerous to recreate in the real world. Challenges to Consider:   -Data Quality: Ensuring synthetic data accurately reflects the complexity of real-world data is crucial. Poorly generated data can lead to biased or ineffective AI models. -Regulatory Acceptance: Synthetic data is still under scrutiny in highly regulated industries. Ensuring it meets legal and ethical standards is essential for widespread adoption. Why It Matters:   Synthetic data is not just a stopgap but a powerful tool that’s enabling AI to progress rapidly while addressing some of the most pressing concerns around privacy, cost, and data availability. As generative AI techniques advance, the quality and utility of synthetic data will only improve, making it a cornerstone of future AI development. #GenerativeAI #AI #ArtificialIntelligence #MachineLearning #DataScience #SyntheticData #Innovation #Tech #HealthcareAI #FinanceAI #AutonomousVehicles #PrivacyTech

🎯 Generative AI in Business: The New Reality Generative AI is no longer just a trend—it’s a game-changing tool that empowers businesses to streamline processes, enhance products, and unlock new opportunities. But where should you start, and which tools should you focus on? I came across a great Forbes article featuring 17 key generative AI tools that are already reshaping industries. It’s a must-read for anyone looking to harness AI effectively in their business. 💡 Let me know in the comments which of these tools you’ve tried or are planning to implement. 🔗 https://lnkd.in/e7dbGXhc #DigitalTransformation #GenerativeAI #BusinessInnovation

https://lnkd.in/gbYrxFrq Recent analyses have highlighted significant failure rates in artificial intelligence (AI) and generative AI projects. A report by the RAND Corporation indicates that over 80% of AI projects do not succeed, a failure rate twice that of traditional IT projects. RAND CORPORATION The Innovation Strengths Preference Indicator® Executive Edition (ISPI™ EXE) framework, provides a structured method for identifying individual and team preferences across key dimensions of innovation and execution. By reverse-engineering successful AI and generative AI projects, the ISPI™ EXE can address many of the challenges discussed in the RAND Corporation report: 1. Understanding Success Factors • Reverse Engineering Success: Using ISPI™ EXE, successful AI projects can be analyzed to identify the key strengths, approaches, and team dynamics that contributed to their success. For example: o Which individuals were ideators or executors (as indicated by "XB," "B," "MB," "MP," or "XP")? o How did the team balance risk-taking versus process discipline? o What relationship and control dynamics led to better collaboration and accountability? • Insight Extraction: ISPI™ EXE can uncover patterns in how successful teams handled ideation, risk, control, and process—helping organizations better structure future teams. ________________________________________ 2. Addressing Misaligned Objectives • The "Control" and "Relationship" dimensions of ISPI™ EXE help clarify whether individuals prefer to take charge or collaborate, enabling better alignment of team goals. • Teams formed with complementary strengths (e.g., balancing those who initiate with those who follow through) will be better equipped to align objectives at every stage of the project. ________________________________________ _____________________________________ 3. Reducing Failure Rates in AI Teams • Tailored Teams: By selecting individuals whose strengths align with the requirements of each phase of the AI project, failure rates can be reduced. • Dynamic Collaboration: The "Networking" and "Energy" dimensions help identify individuals who can energize teams and foster connections, critical for maintaining project momentum. 4. Scalable Learnings • Once the traits of successful teams are codified, ISPI™ EXE can be used to replicate these traits in new teams, scaling the organization’s ability to execute successful AI projects. ________________________________________ Jerry Fisher, MBA, PhD Co-Founder and Chief Innovation Officer Innovating Edge, Inc. www.innovatingedge.com E: [email protected] Bio: Gerard Fisher (innovatingedge.com) The Innovation Strengths Preference Indicator® is the world’s most widely used and validated instrument for assembling AI and GAI teams. Here is a sampling of testimonials from major Fortune 50 companies, government, and academic institutions: https://lnkd.in/g-dUabkk.

As AI models grow, so does the need for data. Computer-generated synthetic data can help. But do you think you should be concerned about synthetic data? #itrecruiter #ai #ml #retainedsearch #recruiter #syntheticdata https://lnkd.in/gypmcYsF

How is generative AI redefining data analytics?💭 Great article from Info World on how AI is making analytics tools easier to use and improving the quality of automation👇 https://lnkd.in/ecYUcY84 #DataAnalytics #AI