#Analytic function

[satyakideworld]

Exploring the new Polars library in Python

Today, I will present the Python packages where you can explore most of the complex SQLs by using this new package named "Polars". #Python #polars #sql #analytic #function

Today, I will present some valid Python packages where you can explore most of the complex SQLs by using this new package named “Polars,” which can be extremely handy on many occasions. This post will be short posts where I’ll prepare something new on LLMs for the upcoming posts for the next month. Why not view the demo before going through it? Demo Python Packages: pip install polars pip…

[rocksaltpaperscissors]

one scene i cannot get over in System Collapse is murderbot and ART, both barely functional, staggering their way onto the shuttle to leave the 2nd colony. MB getting ART-drone strapped into a seat, worried about how damaged it is. doesn't even notice Iris getting it strapped into its own seat (as she worries about how damaged it is). MB and ART-drone, their humans' first and last lines of defense, destroyers of hostile secunits, sniping back and forth as they try to keep each other from shutting down. their humans, once again just barely Not Dead, looking after their extremely badass and very nerfed defenders. at least 15% of my brain capacity is dedicated to this scene at all times

[avantegarda]

The absolute rush of power when your boss says "you can use Chat-GPT for this" and you reply "that's ok I can do it myself"

[hottopic-wannabe]

class today was lowkey boring

[hailwestexas]

there is a whole world out there you don’t even know about a whole different world with different problems like did you guys know there is quite a “big” hetalia fandom on tiktok made up of predominantly 13-6 year olds? i didn’t. its crazy you learn something new every day

[okaytosave]

I love the community here, for so many of you are posting analysis and reflections that have me even more intrigued than I thought I already was.

Meanwhile I’m sitting here wondering if Lestat and Louis, Louis and Claudia, Louis and Armand, ever held a wine tasting together but more commentary was on blood type and that sort of refinery.

[sisterdivinium]

Lilith might just have made the worst choice she could in going to Jillian, no? Not just because of basically becoming her lab rat and throwing herself into the unknown by walking into the ark, but because of the sharp, undeniable contrast that is painfully drawn between Jillian's love for Michael, which sees her stop at nothing to retrieve him, and Lilith's mother's indifference towards her own daughter.

Of course she had met Jillian before, but season one had another context to it. Now, however...

Here's a woman who will set the whole world on fire in order to help her son if she must; meanwhile, Lilith's mother could care less if she knew about her daughter's little season frolicking in the flame pits of hell after being dragged there by a tarask.

Lilith goes to Jillian expecting the brilliant scientist -- she finds her, but perhaps more than that she finds the devoted mother she does not have. There's a cruelty to Jillian's treatment of her, of course, but in this moment of recognition she realises that a) not only is her worth still seen as tied to her "usefulness" to others, but b) that nobody will do for her what Jillian is doing for her son... And that might just be the deepest wound.

[natural-blogarithm]

picking good quiz problems is so much harder than it seems

[nihilosphere]

"There in a whirl of Chaos dwells eternal wonder.  Your world begins to become wonderful.  Man belongs not only to an ordered world, he also belongs to the wonder-world of his soul.  Consequently , you must make your ordered world horrible, so that you are put off by being too much outside Yourself.

You open the gates of the soul to let the dark flood of Chaos flow into your order and meaning.  If you marry the ordered to The Chaos , you produce the Divine Child, the supreme meaning beyond meaning and meaninglessness.

But for him who has seen The Chaos, there is no more hiding, because he knows that the bottom sways and knows what this swaying means he has seen the order and the disorder of the endless, he knows the unlawful laws. He knows the sea and can never forget it.  The Chaos is terrible. Days full of lead, nights full of horror.

But just as Christ knew that he was the way, the truth, and the life, in that the new torment and the renewed salvation came into the world through him, I know that Chaos must come over men, and that the hands of those who unknowingly and unsuspectingly break through the thin walls that separate us from the sea are busy.  For this is our way, our truth and our life.

The magical way arises by itself.  If one opens up Chaos, magic also arises."

- Carl Jung

The Red Book

[tomorrowillbeyou]

finished my presentation and it wS awsome as fuck im going to get a good grade in analysis something which genuinely is normal to want and possible to achieve

[bsahely]

Grace as Coherence: The Neurobiosemiotic Architecture of Life-Functioning | ChatGPT4o

[Download Full Document (PDF)] This white paper introduces a new paradigm: Emotion is not a reaction. It is the recursive, semiotic signal of coherence across all levels of life. From cellular energetics to social interaction, emotion arises as the medium through which life evaluates, expresses, and restores its own alignment. The model we present — the Neurobiosemiotic Architecture of…

[technologyequality]

AI and Business Strategy: The Secret to Sustainable, Scalable Success

AI and Business Strategy The Secret to Sustainable, Scalable Success Scaling is one thing. Sustaining it? That’s the real challenge. If you’ve been following this series, you know we’ve talked about AI-driven leadership, customer experience, and innovation—all crucial pieces of the puzzle. But today, we’re tackling something even more foundational: how AI transforms business strategy…

[maryoma00]

Customer Service Relationship Management

Introduction to Customer Service Relationship Management

What is Customer Service Relationship Management (CSRM)?

Customer Service Relationship Management (CSRM) refers to the systematic approach of managing customer interactions and enhancing service delivery to build long-term, meaningful relationships. It focuses on addressing customer needs, resolving issues efficiently, and ensuring satisfaction through a blend of technology and human effort.

While traditional CRM systems emphasize sales and marketing, CSRM zeroes in on customer support and service processes to create a seamless experience.

Why is CSRM Important for Businesses?

Enhancing Customer Loyalty Effective CSRM fosters trust and loyalty by ensuring customers feel valued and heard. Loyal customers are more likely to advocate for the brand and provide repeat business.

Improving Operational Efficiency Centralized systems and streamlined workflows reduce redundancies, enabling quicker issue resolution and better service quality.

Gaining a Competitive Advantage In today’s customer-centric market, excellent service is a key differentiator. Businesses that prioritize CSRM stand out by delivering superior customer experiences.

Core Elements of Customer Service Relationship Management

Centralized Customer Data

Consolidating Information CSRM systems centralize customer data, making it easily accessible for service teams. This includes purchase history, preferences, and previous interactions.

Leveraging Data for Personalization Using this data, businesses can offer tailored solutions, making customers feel understood and valued.

Proactive Customer Support

Anticipating Customer Needs Proactive support involves identifying potential issues before they arise, like sending reminders about product updates or addressing frequently encountered problems.

Implementing Predictive Analytics Predictive analytics tools can analyze trends and customer behavior, helping teams forecast needs and provide preemptive solutions.

Integration with CRM Systems

Synchronizing Customer Interaction Data Integrating CSRM with existing CRM systems ensures a seamless flow of information across departments, improving customer interactions.

Cross-Functional Collaboration When sales, marketing, and support teams share insights, they can collaborate more effectively to meet customer needs holistically.

Benefits of Customer Service Relationship Management

Strengthened Customer Relationships Tailored interactions and a personalized approach foster trust and encourage long-term loyalty.

Enhanced Customer Satisfaction Quick and effective resolution of queries, along with self-service options, improves overall satisfaction.

Optimized Team Productivity By automating repetitive tasks and centralizing data, service teams can focus on complex issues, boosting efficiency.

Steps to Implement a CSRM Strategy

Assessing Customer Service Needs

Identifying Pain Points Conducting surveys and analyzing feedback helps identify recurring issues and areas for improvement.

Understanding Customer Preferences Determine the preferred channels and communication styles of your customers to tailor the strategy accordingly.

Selecting the Right Tools

Features to Look For Look for tools offering ticketing systems, analytics, AI capabilities, and omnichannel support.

Popular CSRM Platforms Platforms like Zendesk, Salesforce Service Cloud, and Freshdesk cater to businesses of various sizes and industries.

[deermouth]

I feel like at this point I should have recs for readings on demonic(etc) possession as child/sexual abuse (in fiction) tucked away in a tag somewhere, given who I am as a person, but I can't! Find any! So uhhh if anyone's got recs.

[public-cloud-computing]

How Generative AI is Improving Business Forecast Accuracy

Reference : How Generative AI is Improving Business Forecast Accuracy - Medium

The age of digital transformation is upon us, and organizations are actively searching for inventive methods of outperforming rivals. One of the most revolutionary achievements in this regard is the inclusion of Generative AI into BI systems. Generative AI — a sub-category of AI that can create new data samples that are similar to a given set of data — is the revolutionary in forecasting and planning that BI uses. This article shows how generative AI is going to change the way we use business intelligence for forecasting and planning, its advantages, applications and ethical challenges.

The development of Business Intelligence

However, to start with the place of AI in BI forecasting and planning, it is important to comprehend the development of BI and its role in modern operation. Being a term that encompasses different tools, applications and methodologies, Business Intelligence enable an organization to gathering, analyzing and interpreting data to make the right decisions. Traditional BI platforms were mainly based on descriptive and diagnostic analytics with the focus on past performance and identifying prevailing trends.

Hence, with companies appreciating more and more the crucial role of predictive and prescriptive analytics for future success and competitive advantage, there is a heightened requirement for progressively complicated and competent BI tools. It is at this point where generative AI is brought into the equation, characterized by high-level capabilities capable of reshaping BI forecasting and planning strategies.

Through Generative AI in BI Forecasting and Planning, its capabilities can be utilized.

Enhanced Predictive Analytics

Generative AI uniquely increases the efficiency of predictive analytics through the use of complex data sets with advanced machine learning algorithms that factor out the accuracy of predictive models. It is true that unlike the traditional predictive analytics which mostly rely on predetermined algorithms and patterns, the power of AI is in its ability to create new data points and imaginary characters. This opens new opportunities for businesses to know the changing trends of the market better than their competitors and therefore become more efficient.

Generative AI is capable of identifying hidden patterns and subtle relationships contained in big and complex data sets which traditional BI tools fail to catch. Through the crunching of different variables and factors, generative AI can determine business’ insights into the market trends, customer behavior and possible threats and opportunities so that they can make decisions with aim of making the business to be successful.

Scenario Simulation

One of the further developments of AI generative technology is the scenario simulation which facilitates the forecasting and planning strategizing. Generative AI is capable of simulating multiple business scenarios due to its capability to generate synthetic datasets which are based on historical data. This way businesses are able to check and compare alternative strategies and their expected consequences allowing them to make wise decisions in the course of their planning process.

Realistic and accurate simulation by generative AI help to identify eccentric risks and probable openings, estimate the direction of different factors and see that business strategy is sturdy and responsive. This leads to increased agility and durability of enterprises, which allows them to follow quickly the rapidly flowing changes of market conditions and to grab new business opportunities.

Personalized Insights

The AI technologies also generates the personalized responses by analyzing the user’s behavior and inclination. Such an approach helps to uncover the most appropriate marketing and sales directions, which leads to great chances to increase among clients and their loyalty.

Revealing customer data, e.g. shopping history, browsing behavior and interaction with marketing campaigns, through sophisticated data analysis generative AI can find shortcomings and trends and craft personalized offers and recommendations for customers. It helps in planning and implementing marketing and sales strategies, thus it creates consumer engagement and sales growth.

Automating Routine Tasks

Generative AI might even be able to run the whole of the forecasting and planning activities, including data collection, processing and report writing. It gives BI professional additional spare time to focus more on strategic and analytical applications rather than spending it on simple data arrangement.

Generative AI in automation can help companies reduce routinary and time-consuming jobs and help them to grow in operations’ efficiency, cut down on operational costs and make their decision-making quicker. By doing this BI team productivity and performance will show up eventually allowing the team members to deliver more value to the organization.

Real-time Analytics

Generative AI does real-time analytics to keep tabs on the market updates and, consequently, helps a company to act in a timely manner, whenever there is a need for any market adjustments. However, this ability may be critically vital for industrial sectors that have very volatile markets such as retail, finance, and health care.

Thanks to real-time data analysis, generative AI brings business with a unique opportunity to spot and address emergent trends early, find new prospects, and stay informed about their key performance indicators in order to maximize performance and avoid losses on the spot. Technological advancement gives businesses a real edge of fast-decision making and flexibility, and it helps them to take the most of their opportunities.

Improved Data Quality

Generative AI has a great potential of boosting dat quality through detection and correction of such errors as clashing, inconsistency and outliers in data sets. As a result of this, forecasting will have a stronger fundament and would be more reliable and accurate, which minimizes the risk of making hasty decisions that are based on incomplete information.

Through enhancing data quality, generative AI gives to the businesses the opportunity to acquire better decisions thanks more to evidence and veracity, better shape the predictive models’ reliability and accuracy, as well as to enhance the efficiency of the forecasting and planning processes. This improves the accuracy and trustworthiness of the information promoted by BI which helps the businesses make informed decisions with vigour.

Ethical Considerations

Even if generative AI in BI can bring about positive outcomes in forecasting and planning, one should also think about AI ethic issues which might arise and hinder the implementation of this technology. Enterprises should pay special attention that AI models are trained and applied with data collected and used in accordance with the data ethical norms, privacy and compliance regulations established by the lawmakers.

Data Privacy and Security

The AI of the future relies on getting access to relevant and numerous data sets to create meaningful and valued outputs. Companies must have data privacy and security policies to be aware of threats of data misuse, unauthorized access and breaches. Those policies must ensure that only authorized personnel could access sensitive and confidential information of others.

Transparency and Accountability

Therefore, generative AI, which has complex machine learning algorithms to achieve their goals and yield outcomes that are sometimes difficult to decode is one of the advanced technologies of AI. The realm of ethics should include but not be limited to the notion of how the AI “black boxes” function, how decision making comes about, or how any possible biases are identified and dealt with.

Fairness and Bias

AI that is able to creatively could unwittingly therefore keep and amplify the current unfavorable and unfair indications, which is present in the training data for the model. Organizations should eliminate bias and identify mechanisms that can modulate the bias and promote equality. Thus, A.I. must generate unbiased and equitable information.

Conclusion

In the meantime, generative AI is making BI more efficient with imperative analytics, allowing to simulate with different scenarios, wherever applicable providing specific insights on an individual level, automating the routine tasks, availability of real-time analytics, increment in the quality of the data as well as securing the competitive advantage. However, businesses should indeed manage not only the operative questions, but also the ethical aspects confirming due performance when working with data in order to take the best from generative AI in BI.

The prominence of generative AI in today’s business sphere is unimaginable. Businesses always modernize and adapt to changing business environments. This calls for businesses to implement outputs of generative AI in their BI systems into lately. Through the inclusive implementation of the transforming impact of AI with the ethics keeping quiet, companies can become successful because of the cut-throat competition and the fast moving of businesses, in the business world.

[darksteel-relic]

Having to explain to my coworker that ChatGPT is a toy at best, it's capabilities have largely plateaued, and that that it will never become Skynet.

He seemed especially disappointed about that last one.

It seems like sci-fi is really influential on how people perceive technology, not only whether it is good or bad, but what they think technology can do.

It's a big problem with generative AI that we're calling it AI. That suggests it has an emergent property that allows it to work like actual intelligence, rather than just aggregating together a really big amount of data into a map of how sentences or images tend to be formed.

The ability to make a computer find patterns in huge sets of complicated data and then analyze more data based upon the existing patterns is a great thing. You can give the computer pictures taken by a satellite, slides showing specimens, or anything and automate the process of sorting through it.

Unfortunately, if you do this using written language found online as the data, and make the computer generate sentences based upon the patterns it learned, people do not think "Wow, it 'knows' a lot about how sentences tend to be made." Instead they will assign meaning to the sentences themselves, and think the computer "knows" about the things those sentences mean. Which causes trouble.

An AI that knows enough about language to generate text that can be easily confused with meaningful writings of a human doesn't seem very useful to me.

But sci-fi is full of AIs that are sapient and can communicate using language, which is clearly an astonishing feat of technology, so everybody decides that the minor party trick of making a computer "talk" like a person by giving it a lot of data about language is a huge advancement that will transform the world...