Zilliant’s Post

🌐 As #AI continues to shape today's business landscape, it's important to not to leverage AI technology simply because it's "cool" or "buzzworthy." 💡 When it comes to integrating into business and software solutions, AI 𝗺𝘂𝘀𝘁 be pragmatically applied to solve specific pain points and business challenges. ✳️ 𝗡𝗲𝘂𝗿𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀 are the latest AI/ML trend that some companies have taken to use in their pricing technology, and that may be problematic for #B2B companies. 𝗥𝗲𝗮𝗱 𝘁𝗵𝗶𝘀 𝗯𝗹𝗼𝗴 𝗽𝗼𝘀𝘁 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝘄𝗵𝘆 𝗻𝗲𝘂𝗿𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀, 𝗮𝗹𝘁𝗵𝗼𝘂𝗴𝗵 𝗯𝗲𝗻𝗲𝗳𝗶𝗰𝗶𝗮𝗹, 𝗳𝗮𝗹𝗹 𝘀𝗵𝗼𝗿𝘁 𝘄𝗵𝗲𝗻 𝗶𝘁 𝗰𝗼𝗺𝗲𝘀 𝘁𝗼 𝗽𝗿𝗶𝗰𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 👉 https://lnkd.in/gwQGFxju #pricing #pricingtechnology #pricingsoftware #priceoptimization #data #datascience #technology #artificialintelligence #deeplearning #neuralnetworks #digital #segementationmodels #pricingstrategy

Neural networks have been around for over 80 years. So not new technology, and they have their place. Learn more here on why the #zilliant approach to #priceoptimization is truly the latest and greatest for your #b2b #pricing needs..

🎯 Why Neural Networks Fall Short in Price Optimization 🎯 Neural networks have their place, but they fall short for B2B price optimization due to data requirements, lack of transparency, and complexity. Discover why Zilliant’s tailored, user-friendly approach is the better choice. #AI #PricingStrategy #PriceOptimization #Zilliant

Take the first step in understanding why automating your manufacturing process can help solve labor, quality control, and cross contamination issues with #MachineVision using #AI. This data-driven approach is a proven method for cost savings and process improvements! #datadriven #optimizingproduction #costsavings #consistency

Top 5 Challenges for Transparency in AI: One of my clients is adamant about implementing an AI solution with absolute transparency, driven by corporate pressure to enhance openness. In our discussion, I outlined the top 5 technical challenges and corresponding solutions related to transparency, emphasizing that none of them would offer a foolproof solution. 1. Black Box Nature of Deep Neural Networks: Deep neural networks (DNNs) pose a challenge due to their 'black box' nature, making it difficult to interpret decision-making processes. Solutions like Explainable AI (XAI) with techniques such as Layer-wise Relevance Propagation aim to provide insights into neural network outcomes. 2. Ensuring Fairness in Algorithmic Decision-Making: Addressing bias in training data and ensuring fairness in algorithms demand the development of fairness-aware machine learning algorithms. Techniques like adversarial training and re-weighting of samples help reduce biases and ensure equitable outcomes. 3. Handling Dynamic and Evolving Data: Adapting AI models to dynamic data environments requires continuous learning algorithms and adaptive models. Incremental learning and ensemble methods help maintain transparency in the face of evolving data distributions. 4. Dealing with High-Dimensional and Unstructured Data: Transparency challenges arise with high-dimensional and unstructured data. Tailored interpretable models for specific data types and hybrid models that combine interpretability with deep neural networks offer solutions in complex scenarios. 5. Balancing Model Complexity and Explainability: The trade-off between model complexity and explainability necessitates innovative solutions. Model distillation, where a complex model mimics a simpler, interpretable model, strikes a balance between performance and transparency. Please share your challenges as well. #deeplearningai #generatieveai *image by Dooder on Freepik

Are you confused about how AI and the "black box" works? In the context of machine vision using AI, the term "black box" typically refers to a model or system that makes predictions or decisions without providing a clear and interpretable explanation of how it arrived at those conclusions. In other words, it's a system where the inner workings are not easily understandable by humans. Machine vision using AI often involves deep learning models, such as convolutional neural networks (CNNs), which can be considered black boxes because they learn to recognize patterns and features in data through complex layers of computations. So, how do we make sense of all this? Efforts are being made to make AI in machine vision more transparent and interpretable. Some techniques include: -Explainable AI (XAI): Researchers are working on developing AI models that can provide explanations for their decisions. These explanations help users understand why a particular classification or decision was made. -Feature Visualization: Techniques like feature visualization allow users to understand what parts of an image the AI model is paying attention to when making a decision. This can provide some insight into the model's decision process. -Attention Mechanisms: Models that use attention mechanisms, such as Transformer-based models, can provide insight into which parts of an input are most relevant to the model's decision. -Rule-based Systems: In some cases, rule-based systems can be used in conjunction with machine vision to provide interpretable decision rules. These rules can be defined and understood by humans. If you are looking to explore machine vision or machine vision using AI in #manufacturing, contact me at makenna.considine@iriscs.com or visit https://lnkd.in/edSqqF9u! #machinevison #machinevisioninmanufacturing #visioninspection

🛒 Predictive Powerhouse: AI & LSTM Unlocking Business Purchase Insights 💡📈 Unveiling the future of business purchase predictions with Explainable AI and LSTM Neural Networks. This groundbreaking approach enhances accuracy and transparency, empowering businesses with foresight in procurement and customer engagement. 🚀 https://lnkd.in/dXhjUzas #BusinessIntelligence #AI #LSTM #PredictiveAnalytics #ExplainableAI #MachineLearning #Procurement #CustomerEngagement #Innovation #DataDriven Electronics MDPI

In this blog, Advisory CISO Andy Ellis dives deep into the world of AI decision-making and its impact on organizations. As AI becomes an integral part of the decision-making process, it's crucial to understand the potential and challenges these technologies present. Andy explores the power of neural networks and generative AI while emphasizing the need for a thoughtful approach to AI integration. Read the full blog post to learn more about the future of AI-enhanced organizations: https://lnkd.in/diiBhjyh #AI #MachineLearning #CloudSecurity #CISO

Breaking down the nuances between Image Recognition and Computer Vision in 2023. Delve into their differences on our latest blog! 👓💻 #AI #TechTalk https://lnkd.in/diwb_RZw

💡 Seize the opportunity to unlock your AI model's potential!💡 Your AI model too slow for you? Want to upgrade your AI model? An intro to improving your AI model 😉 Fuel your mind with the latest AI compression methods with SqueezeBits!   👇 👇 👇 https://lnkd.in/gwdJJZf5