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May 08.2025
3 Minutes Read

Unlocking AI Learning Paths: Insights from The Late Afternoon Show

Lively conference scene with engaged audience discussing AI learning.

Transforming the Future: Insights from The Late (Afternoon) Show

As the curtain lifted on The Late (Afternoon) Show at SAS Innovate, the synergy of technology and entertainment electrified the atmosphere. Chief Technology Officer Bryan Harris, alongside the iconic duo of Alfonso Ribeiro and DJ Jazzy Jeff, drew in an audience eager for revelations about the future of artificial intelligence (AI). The event not only paid homage to nostalgic moments but also highlighted groundbreaking innovations shaping our society's trajectory.

Nostalgia Meets Innovation

DJ Jazzy Jeff kicked things off with spirited throwback hits, setting a lively tone for a session featuring celebrity guests. Among them was William Hung, famously known for his viral rendition of "She Bangs." His lighthearted performance reminded attendees of the power of talent and charisma amidst the serious business of AI and analytics. This mix of nostalgia and anticipation created a backdrop for significant discussions on the role of AI in shaping business and society.

Spotlight on AI Learning and Pathways

Jenn Chase, Chief Marketing Officer, generated excitement during her session by discussing an upcoming dialogue with renowned researcher Brené Brown. This conversation underscored the importance of emotional intelligence alongside technological advancement, especially as organizations explore AI learning paths. It is vital that businesses integrate human-centered approaches while adopting AI, fostering environments where technology complements emotional intelligence rather than replacing it.

The Role of Data: Garbage In, Garbage Out

As Chief Sales Officer Riad Gydien shared, leveraging AI effectively hinges on the quality of data managed. When organizations face hurdles in AI implementation, it often roots back to poor data management. Catchphrases like "Garage out, garbage scaled" have never been more relevant. The show stressed that clean, accurate data feeds intelligent systems, allowing them to function effectively and deliver expected outcomes.

The Future of AI Society: How Will It Look?

Looking ahead, Bryan Harris posed a pivotal question: "What is the future of society?" The rapid rise of AI-driven automation will compel us to redefine our contributions in various fields. Education, healthcare, and business sectors must rethink roles, focusing on augmenting human capabilities with AI systems instead of viewing them as replacements. This shift is critical for fostering a symbiotic relationship between technology and humanity.

Engagement and Interactive Learning Opportunities

The event also featured the Innovation Hub, a playground of possibilities where attendees could engage directly with the latest SAS software. Highlighted by The Magic Experience, participants had the chance to compete against a professional sports player, further emphasizing the importance of engaging with technological tools physically and intellectually. This hands-on approach not only promotes AI learning but enriches understanding through direct interaction.

Adapting to Change: The Importance of Flexibility

One of the key takeaways from the discussions was the necessity for businesses to remain flexible and adaptable in the face of relentless technological evolution. As organizations integrate AI into their operations, they must embrace a fluid mindset, capable of responding to shifts in data trends and societal needs. By being proactive and receptive to change, leaders can navigate the complexities of AI while fostering innovative learning environments.

Conclusion: Join the Conversation on AI Learning

AI is at a remarkable intersection of societal change, demanding we embrace the discussions and learning pathways it offers. If you’re interested in exploring this transformative technology further, connect with industry experts, engage in AI learning opportunities, and redefine your understanding of what’s possible with artificial intelligence.

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06.14.2025

Transform Spare Parts Inventory Management with AI Technology Insights

Update Understanding the Need for Optimized Inventory Management In today's fast-paced industrial environment, managing spare parts inventory effectively is essential for maintaining asset performance and minimizing downtime. The costs associated with equipment failure can escalate quickly, making predictive maintenance strategies critical. However, uncertainties surrounding equipment failures pose a significant challenge, driving industries to hold more inventory than necessary. This article explores how integrating AI technology into spare parts inventory management can streamline processes, reduce waste, and enhance service delivery. A Simulated Approach to Optimize Inventory Levels At the heart of optimizing spare parts inventory lies a simulation-based approach, particularly leveraging SAS's programming capabilities. By employing tools such as PROC LIFEREG and PROC LIFETEST, organizations can accurately model asset lifetimes using statistical distributions, specifically the Weibull distribution. This modeling not only helps predict failures but also incorporates uncertainty in failure events, allowing companies to tailor their inventory strategies effectively. Practical Insights on Stochastic Simulation The integration of stochastic simulation into inventory planning allows decision-makers to visualize various failure scenarios. This predictive capability is essential for understanding key performance metrics such as availability and backlog. For instance, an airline fleet might set a target of having 95% of asset failures fulfilled from its inventory without delay—an important performance indicator known as the Type 1 service level or fill rate. By achieving this balance, businesses can significantly cut costs while meeting operational needs. The Data-Driven Analytics Advantage Utilizing a comprehensive approach that combines descriptive, predictive, and prescriptive analytics can dramatically enhance inventory decisions. Descriptive analytics sheds light on historical trends, while predictive analytics helps forecast future needs. Most importantly, prescriptive analytics offers recommendations on optimal inventory levels based on these insights. Real-World Application: Case Study in Fleet Optimization Consider a fleet of aircraft where each asset must be replaced upon failure. By analyzing historical removal data, businesses can track operational hours and failure incidents to robustly predict inventory requirements. The data-driven methods enable firms to optimize spare parts inventory levels—leading to improved asset uptime and diminished costs. Future Predictions: The Impact of AI on Inventory Management As AI technology continues to evolve, the future of spare parts inventory management will likely see even greater integration of machine learning algorithms. By automating inventory decisions and utilizing real-time data analytics, businesses can enhance efficiency and reduce reliance on manual interventions. This continual improvement may redefine industry standards and results in unprecedented gains in asset performance management. Conclusion: Embracing the Future of Spare Parts Management The journey towards effective spare parts inventory optimization is crucial for industries reliant on high asset performance. By leveraging simulation-based techniques and evolving AI technologies, companies can meet the challenges posed by uncertainties in equipment performance. Those looking to modernize their inventory practices should focus on integrating these technologies, which not only foster innovation but also yield substantial cost savings and improved service levels. To learn more about how to optimize your spare parts inventory and implement AI technologies effectively, explore the available resources in AI learning paths that cater to innovators in your industry.

06.14.2025

Unlock Accuracy in Machine Learning with Hyperparameter Tuning Fun

Update Understanding Hyperparameter Tuning in AI Building a machine learning model isn’t just about plugging in data and hitting the train button. It’s deeply nuanced, requiring fine-tuning to achieve that coveted accuracy. Hyperparameter tuning serves as a crucial step in enhancing the performance of your machine learning model. But how can you ensure that your model reaches its peak potential while navigating complex variables? Let’s decode that mystery while exploring the fun aspects of tuning! Making Sense of Hyperparameters Think of hyperparameters like the ingredients in a recipe. Just as changing the oven temperature can alter how a cake turns out, tweaking hyperparameters will affect how well your model learns from data. Hyperparameters are values you set before training a model; they dictate how the model will be trained. For example, consider parameters like learning rate, batch size, and the number of estimators in a tree-based model. Setting these values appropriately is vital, as it can significantly impact your model’s performance. AI Learning and Its Potential As we delve into the world of AI learning, understanding hyperparameter tuning brings us closer to harnessing artificial intelligence’s full potential. Even a 1% boost in accuracy can notably sway results in various applications, from predicting stock trends to diagnosing medical conditions. As our reliance on AI grows, so does the need for sophisticated models that learn effectively without overfitting or becoming overly complex. Hyperparameter Autotuning: The Smart Approach Manual tuning of hyperparameters can be a cumbersome process. Imagine trying to bake a cake again and again, adjusting the temperature and time for each attempt. Not very efficient, right? However, with hyperparameter autotuning, you can let your computer handle the tedious work. It tests multiple combinations of hyperparameters and identifies what works best for your model. This saving of time and effort allows data scientists to focus on more strategic tasks, leveraging AI’s capabilities to automate decisions about model parameters. Overfitting: A Key Challenge While tuning is essential for model improvement, there's a flip side. Overfitting your model to your training data can yield excellent results on paper but fails spectacularly in real applications. It’s like making a cake that tastes amazing to you, but when your friends try to recreate it with their ingredients, it can go awry. A successful model must generalize well across different datasets, maintaining accuracy without being overly tailored to the training data. The Future of Hyperparameter Tuning Looking ahead, innovations in AI science suggest that hyperparameter tuning will become more user-friendly with the integration of AI itself. Imagine algorithms that can predict optimal hyperparameters based on previous learning experiences! As we advance, we may also see educational pathways in AI aiming to make hyperparameter tuning accessible even to beginners. Who knows, the next ground-breaking AI tool could be just around the corner, simplifying yet another layer of complexity. Actionable Insights for Better Models As you embark on your AI journey, remember: tuning is not just a technical necessity but an opportunity for creativity. Don’t shy away from experimentation! Gather feedback from each model iteration and use the insights to fine-tune your process. Whether it’s in finance, healthcare, or any other sector, improved accuracy can translate to significant gains—both financially and operationally. Join the AI Learning Path Today The landscape of AI is rapidly changing, and by understanding hyperparameter tuning, you're gaining crucial insights that could reshape your AI learning path. Whether you’re a seasoned data scientist or just dipping your toes into the AI waters, mastering these concepts will empower you to create more effective machine learning models. Embrace the fun of the learning journey and explore how your newfound skills can impact your career path!

06.13.2025

Unlock AI Potential: Master Smarter Data Management with SAS Viya

Update Understanding the Backbone of AI: Data Management In the world of artificial intelligence (AI), a fundamental principle remains: AI is only as powerful as the data behind it. Often encapsulated in the phrase "garbage in, garbage out," the means by which data is collected, managed, and utilized greatly influences the quality of AI outcomes. Poor data management leads to flawed models, biased predictions, and opaque decision-making processes, consequently impacting organizational effectiveness. As businesses transition to more AI-integrated operations, a solid data management foundation becomes essential. The Reality of Data Management Challenges Despite the urgency for AI readiness, many organizations face significant hurdles in basic data management. A recent study by DBTA revealed that only 40.9% of enterprises take a proactive approach to their data management strategies. This discrepancy exposes various issues, such as siloed data, inefficient data pipelines, and inconsistent governance practices, which collectively hinder the potential of AI projects. Casper Pedersen, SAS’ Data Lead in EMEA, identifies six prevalent challenges disrupting effective data management frameworks across industries, including data infrastructure complexity and storage inefficiency. The inability to efficiently manage data often leads to stalled AI pilots, frustrating teams eager to demonstrate the ROI of their technological investments. Common Challenges in Data Management Let’s further explore the specific challenges organizations face in making their data fit for AI initiatives: Data Infrastructure Complexity: As different teams utilize various data formats, complexities emerge in data structures. Schema drift can break models and bog down pipelines, resulting in increased cloud costs and sluggish performance. SAS SpeedyStore addresses these issues by unifying different data types into a single, efficient engine, allowing data engineers to focus more on developing models rather than managing chaotic data flows. Storage Inefficiency: Many teams are caught between the high cost of fast storage and the performance issues associated with cheap storage. Legacy storage solutions often struggle to efficiently manage data, often leading to organizations effectively 'paying through the nose' for their data, even when it is infrequently used. The Integral Role of SAS Viya SAS Viya offers various tools to tackle these common pain points through innovative data management solutions. By streamlining processes and reducing reliance on multiple tools, organizations can create more efficient AI frameworks. With SAS Viya, enterprises can: Consolidate different data types for a holistic view. Optimize AI performance through in-memory computing. Achieve real-time analytics capabilities, ensuring timely decision-making. Future Perspectives on Data Management and AI The evolving landscape of AI necessitates a shift towards smart data management practices. Companies focused on integrating solutions like SAS Viya are likely to experience a competitive edge as they better manage their data inputs. As AI continues to develop, firms that prioritize establishing robust data infrastructures may find themselves at the forefront of industry innovation. Actionable Insights for Businesses Organizations should evaluate their current data management strategies against the challenges outlined above. Companies are encouraged to: Conduct a data maturity assessment to identify existing gaps. Invest in integrated data management solutions such as SAS Viya. Empower data teams with tools that reduce complexity and promote efficiency. As enterprises grapple with evolving technologies, understanding the interplay between data management and AI capabilities can shape their future successes. Transforming how data is managed is not just about remaining relevant; it's about survival in a technology-driven marketplace. Businesses that take steps now to create a robust data management foundation can position themselves to leverage the potential of AI fully, aligning themselves with future industry standards. Conclusion In a world where AI technologies play an increasing role, recognizing and overcoming data management challenges can unlock new potentials for businesses. To navigate the complexities and reap the benefits of AI, companies should proactively seek solutions that offer clarity and efficiency in their data management strategies.

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#721","city":"Greenville","state":"SC","zip":"29341","email":"support@divinewebconsultants.com","tos":"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","privacy":"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