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stargazer-sims · 2 years ago

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Noa Hayashi and Sevan’izaran for @dandylion240 Double Trouble BC

These goofballs are ready for… just about anything, really. They’ve been best friends since elementary school, and they do just about everything together. Noa and Sevan love nothing better than pursuing wild ideas and having adventures together, so this double bachelor challenge was practically irresistible to them!

Noa and Sevan bonded over each being one half of a set of twins, and also being new kids in school. Noa's family came to Willow Creek from Mt. Komorebi, and Sevan's family came from an entirely different planet, but despite that, they discovered that they've got a lot in common, including their passion for music, their love of crafting, and their quirky sense of humour.

Their respective brothers, Yuzuru and Xai, haven't become nearly as close as these two have, but that's okay. Yuzuru and Xai are the more reserved ones; Noa and Sevan are far more outgoing, but thanks to their brothers, they have both developed an appreciation of introverts and are quite good at interacting with people who are shy, anxious or just quiet in general.

Noa Hayashi

Noa's anime called; it seems they want him back.

Name: Noa Hayashi Nickname: Zero, Nono Age: 22 (young adult) Birthday: 2 September Pronouns: he/him Gender Identity: male Sexuality: pansexual Aspiration: Nerd Brain Traits: Cheerful, Childish, Music Lover Likes (in-game): S-pop, pop, metal, Japanese folk music, Winterfest, streetwear, cross-stitch, knitting, dancing, videogames, funny sims, high-energy sims, pink, blue, yellow Dislikes (in-game) research & debate, robotics, arguments, complaints, deception, brown

ABOUT NOA

Noa does not have a middle name.

He and his twin brother Yuzuru are their parents' only children.

Yuzuru is sporty, but Noa... is not. Noa's favourite "sports" are dancing and swimming. Just because he isn't sporty, that doesn't mean he isn't high-energy. He loves to run, and has trouble sitting still unless it's to work on his knitting or cross-stitch projects.

Noa's family immigrated when he and Yuzuru were eight years old. He thought they'd be the only new kids at school and that they'd have to stick together all the time, but he discovered on the first day that he and his brother were not, in fact, the only new kids. There were aliens in his class, and Noa was fascinated immediately.

He's interested in rocket science, stargazing and everything to do with space.

He really enjoys listening to music, and he's learning how to play the piano. He also loves to sing and is quite good at it.

He speaks three languages, Japanese, English and Sixamish. He also speaks enough French to have a basic conversation.

He loves bright colours and enjoys eclectic fashion. He also really likes changing his hair colour.

He'd like to have a big family some day, but he's still got things to do before he seriously considers children.

He wants to be famous. If that doesn't work out, he'd like to pursue a career in law.

______

Sevan’izaran

Remember Kaji’s little twin brothers, Xai and Sevan? Well, they’re all grown up, and Sevan is here to see if he’ll be lucky in love.

Name: Sevan'izaran

Nickname: Six (it's an inside joke between him and Noa)

Age: 22 (young adult)

Birthday: 13 November

Pronouns: he/him

Gender Identity: male

Sexuality: bisexual

Aspiration: Friend of the Animals

Traits: Clumsy, Outgoing, Bookworm

Likes (in-game): S-pop, metal, Japanese folk music, Winterfest, rocker style, knitting, dancing, fitness, bowling, funny sims, red, yellow

Dislikes (in-game) research & debate, arguments, deception, complaints, brown, grey,

ABOUT SEVAN

Sevan's name is pronounced like the English number seven.

He can camouflage, but he rarely does.

He speaks four languages; English, French, Japanese and his native Sixamish.

He's genius-level intelligent, but pretends he's not particularly smart. He never really has to work hard at anything academic.

He loves to read, and will devour any kind of book from textbooks to non-fiction to high fantasy to murder mysteries.

Sevan, his twin brother Xai, and their father Kiri escaped a life of abuse and oppression on their home world, thanks in no small part to Sevan's older brother Kaji and his many supporters and friends on Earth. Sevan and Xai were eight when they came to Earth, so they're both well-immersed in Terran life by this point and fit in with very few problems.

Sevan's stepfather is Maxan, a Sixamish doctor who inadvertently fell in love with Kiri while looking after him when he and the twins first came to Earth. Dr. Max is Sevan's biggest enabler in his mischief (next to Noa, of course).

He's one of five siblings now, as Kiri & Max have had two children together.

He's a fan of Sugar Valentine (he's wearing band merch in one of his outfit slots).

Like all males from his world, Sevan is capable of becoming pregnant. He'd love to have babies some day, but he's still sowing his proverbial wild oats at the moment and isn't ready to think about staring a family yet.

He wants to be a veterinarian when he grows up.

------

More pics of the boys!

Here's what Sevan's camouflage looks like:

**they have other likes/dislikes assigned in-game than what I listed above, but I can't remember what they are. Also, I have no idea what Sevan's human camouflage might be wearing. I didn't really spend a lot of time dressing his camouflage, other than an everyday outfit. Feel free to match all his outfits to Sevan's alien form, or just dress him however you like!

#ts4 #sims 4 #double trouble BC #dandylion240 #Sevan'izaran #Noa Hayashi #stargazersims

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greatonlinetrainingsposts · 21 hours ago

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Explainable AI in SAS: Making Data-Driven Decisions Transparent

Artificial Intelligence (AI) is transforming industries by automating tasks, optimizing processes, and enhancing decision-making. However, as AI models become more complex, businesses face a major challenge—lack of transparency. Many machine learning models operate as "black boxes," making it difficult to understand how they arrive at specific conclusions.

This is where Explainable AI (XAI) comes in. Explainable AI ensures that AI-driven decisions are interpretable, transparent, and understandable. SAS, a leader in advanced analytics, has developed tools to enhance explainability in AI models, making data-driven insights more reliable and accountable.

In this article, we’ll explore:

What Explainable AI is and why it’s important

How SAS enables AI transparency

Key features of Explainable AI in SAS

Real-world applications

Challenges and best practices

What is Explainable AI, and Why Does It Matter?

Explainable AI (XAI) refers to AI systems that can explain their reasoning, allowing users to understand, trust, and audit the model’s outputs.

Why is Explainability Important?

Trust & Accountability – Businesses need to trust AI-driven insights before making critical decisions. Regulatory Compliance – Industries like finance and healthcare require transparent models to meet legal standards. Bias Detection – Explainability helps detect and correct biases in AI models. Improved Decision-Making – Transparent models allow organizations to validate predictions and make informed choices.

Without explainability, companies risk using unethical or incorrect AI models that could lead to financial loss, legal issues, and reputational damage.

How SAS Enables AI Transparency

SAS provides several tools to enhance AI model transparency, ensuring businesses can interpret and trust their machine learning and deep learning models.

1. SAS Model Studio (SAS Viya) for AI Explainability

SAS Model Studio offers: Automated feature importance analysis – Explains which variables impact model predictions. Model interpretability reports – Generates human-readable insights from AI models. Bias detection tools – Identifies potential unfairness in data models.

2. SHAP (SHapley Additive Explanations) in SAS

SAS integrates SHAP values, a powerful technique that explains AI model predictions. Shows individual feature impact on a model’s output. Provides local and global interpretability. Used in SAS Programming Tutorial for in-depth model diagnostics.

3. LIME (Local Interpretable Model-Agnostic Explanations) in SAS

LIME is another method for explaining AI decisions. It helps analysts: Understand why an AI model made a particular prediction. Compare model behavior across different datasets. Improve model fairness and accuracy.

4. Explainable AI Dashboards in SAS

SAS Viya provides interactive dashboards that allow businesses to:

Visualize AI decision-making.

Analyze patterns and trends in AI models.

Compare different machine learning models side by side.

These tools make SAS a leader in responsible AI development.

Real-World Applications of Explainable AI in SAS

1. Healthcare: Improving Patient Diagnoses

Hospitals use SAS AI models to predict disease risk.

Explainable AI in SAS helps doctors understand why a patient is at risk.

Ensures AI-driven diagnoses align with medical knowledge.

2. Finance: Enhancing Fraud Detection

Banks use SAS machine learning models to detect fraud.

SAS Explainable AI ensures compliance with banking regulations.

Helps auditors understand why a transaction was flagged as suspicious.

3. Retail: Optimizing Customer Insights

AI models in SAS predict customer behavior and preferences.

Explainability helps marketing teams refine campaigns.

Avoids unintentional biases in targeted advertisements.

Key Benefits of Explainable AI in SAS

Transparency & Trust – SAS ensures AI models can be understood by non-technical users. Better Compliance – SAS meets global AI ethics and governance standards. Bias Mitigation – AI models in SAS Tutorial Online courses focus on fairness. Enhanced AI Performance – Explainability improves model tuning and debugging.

Challenges in Implementing Explainable AI

Despite its advantages, Explainable AI in SAS has challenges:

1. Complexity in Deep Learning Models

Neural networks have millions of parameters, making full explainability difficult.

2. Trade-off Between Accuracy & Interpretability

Some simpler models (e.g., decision trees) are more explainable but less accurate than deep learning models.

3. Model Security & Data Privacy

AI transparency must balance explainability with protecting sensitive information.

SAS tackles these challenges with automated interpretability tools.

Best Practices for Implementing Explainable AI in SAS

Use SAS Programming Tutorial features to analyze model bias.

Leverage SAS Viya dashboards for real-time AI monitoring.

Train AI models using transparent methodologies.

Regularly audit AI systems using SAS Tutorial Online resources.

Adopt industry best practices for responsible AI governance.

The Future of Explainable AI in SAS

AI transparency is becoming a business necessity, not an option. Future trends include:

More AI Governance Regulations – Governments worldwide are introducing AI laws. AI-Powered Automation – SAS will integrate real-time explainability into AI-driven automation. Improved Deep Learning Interpretability – SAS researchers are developing advanced AI visualization techniques.

By 2030, Explainable AI will be standard in all AI models, ensuring businesses can trust and verify AI-driven decisions.

Conclusion

As AI adoption accelerates, Explainable AI in SAS is essential for businesses to make ethical, accurate, and transparent decisions.

SAS provides cutting-edge explainability tools, ensuring that AI models remain fair, interpretable, and accountable.

If you want to master AI model transparency in SAS, our SAS Tutorial Online courses provide:

Step-by-step guidance on Explainable AI tools in SAS.

Hands-on training with real-world case studies.

Expert-led video tutorials on our YouTube channel.

Join our SAS Tutorials today and become a leader in ethical AI development!

#sas tutorial #sas programming tutorial #sas tutorial for beginners #sas online training #ai in sas #artificial intelligence

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xaltius · 12 days ago

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Top Data Science Trends Reshaping the Industry in 2025

The field of data science is in constant flux, with new technologies and methodologies emerging at a rapid pace. Staying ahead of the curve is crucial for data scientists looking to remain relevant and impactful. Here are some of the top data science trends reshaping the industry in 2025:

1. The Rise of Generative AI:

Generative AI models, capable of creating new content from text and images to code and music, are poised to revolutionize various industries. Data scientists will be increasingly involved in developing, deploying, and fine-tuning these models for applications like content creation, drug discovery, and design.

2. MLOps Takes Center Stage:

As machine learning models become more complex and critical to business operations, MLOps (Machine Learning Operations) is gaining prominence. This discipline focuses on automating and streamlining the entire machine learning lifecycle, from data preparation and model training to deployment and monitoring. Data scientists with MLOps skills will be in high demand.

3. Focus on Explainable AI (XAI):

As AI systems are used to make increasingly important decisions, the need for transparency and explainability becomes paramount. XAI techniques, which aim to make AI decision-making more understandable to humans, will be crucial for building trust and ensuring ethical AI deployment.

4. The Democratization of AI:

Cloud platforms and user-friendly tools are making AI more accessible to individuals and businesses without deep technical expertise. This democratization of AI will empower more people to leverage data science, leading to wider adoption and innovation.

5. Data Privacy and Security:

With increasing concerns about data privacy, data scientists will need to be well-versed in data privacy regulations (like GDPR and CCPA) and security best practices. Techniques like differential privacy and federated learning, which allow for data analysis without compromising individual privacy, will become more important.

6. The Growing Importance of Data Storytelling:

Being able to communicate complex data insights effectively to non-technical audiences is becoming increasingly crucial. Data scientists who can craft compelling narratives around their findings will be highly valued.

7. Specialization within Data Science:

As the field matures, we're seeing increasing specialization within data science. Data scientists may focus on specific areas like natural language processing (NLP), computer vision, or reinforcement learning.

8. The Convergence of Data Science and Cloud Computing:

Cloud platforms provide the infrastructure and tools needed to handle large datasets and train complex AI models. Data scientists with cloud computing skills will be highly sought after.

9. Emphasis on Real-World Applications:

The focus is shifting from purely theoretical research to applying data science to solve real-world problems in various industries. Data scientists with experience in specific domains will be in high demand.

10. The Rise of the Data-Centric AI Approach:

While model development is important, there's a growing recognition of the crucial role of high-quality data in AI success. The data-centric AI approach emphasizes improving data quality, consistency, and management to enhance model performance.

To stay ahead of these trends and gain the necessary skills to thrive in the evolving data science landscape, consider exploring comprehensive programs like Xaltius Academy's Data Science course. These programs often cover the latest advancements in the field, including generative AI, MLOps, and ethical considerations, providing you with a competitive edge in the job market. They can also help you develop expertise in key areas like cloud computing, data storytelling, and specialized data science domains.

Staying informed about these trends and developing the relevant skills, potentially through programs like Xaltius Academy's Data Science course, will be essential for data scientists to thrive in the evolving landscape of 2025 and beyond.

#data science course #xaltius #technology #online course #data science #artificial intelligence

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annabelledarcie · 2 months ago

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AI Solutions for a Smarter Future: Development Trends and Best Practices

Artificial Intelligence (AI) is no longer a futuristic concept—it's shaping the present and transforming industries worldwide. From automation and data analytics to predictive insights and personalized experiences, AI is revolutionizing how businesses operate and engage with customers. As the technology evolves, staying ahead of the curve is essential for organizations aiming to leverage AI to their advantage. In this blog, we’ll explore the latest AI development trends, along with best practices for businesses to build smarter, more efficient AI solutions.

The AI Landscape: A Smarter Future

AI solutions are increasingly embedded into a wide range of industries, including healthcare, finance, retail, manufacturing, and beyond. The increasing use of AI is powered by advancements in computing, data analytics, and machine learning techniques. Key benefits of AI include:

Enhanced Productivity: Automating routine tasks enables employees to focus on more creative, high-value work.

Better Decision-Making: AI’s data processing power offers real-time, data-driven insights, helping businesses make informed decisions quickly.

Personalization: AI-driven customer experiences lead to more tailored products, services, and interactions, improving satisfaction and loyalty.

Predictive Analytics: AI systems can identify trends and patterns that help businesses anticipate market shifts, customer behavior, and operational challenges.

In this rapidly evolving landscape, understanding current AI trends and adopting best practices is key to staying competitive.

Key AI Development Trends

As AI continues to mature, several trends are emerging that will influence the future of AI development. Here are some of the most significant trends that businesses need to be aware of:

1. Increased Adoption of Generative AI

Generative AI, which includes technologies like GPT (Generative Pre-trained Transformers), DALL·E, and other neural networks, is transforming industries by creating content, automating design, and generating synthetic data. This trend is particularly evident in content creation, where AI can produce human-like text, images, and even music.

Use Cases:

Content Generation: AI can automatically generate written content, marketing copy, blog posts, and more.

Product Design: AI can assist in creating product prototypes by analyzing customer preferences and trends.

Synthetic Data: Generative AI is used to create synthetic datasets, helping businesses train AI models without relying on sensitive or expensive real-world data.

Impact: Generative AI is lowering costs, speeding up innovation cycles, and allowing businesses to scale faster than ever before. However, it’s crucial to ensure ethical use of generative models, particularly in areas like data privacy and authenticity.

2. AI-Driven Automation in Business Operations

Robotic Process Automation (RPA) combined with AI is increasingly being used to automate repetitive, time-consuming tasks. AI-driven automation can be applied across various business functions, including customer service, inventory management, supply chain optimization, and financial operations.

Use Cases:

Customer Service: AI-powered chatbots and virtual assistants provide instant customer support, improving response times and efficiency.

Financial Operations: AI can automatically process invoices, track expenses, and perform data analysis for financial forecasting.

Supply Chain: AI models help predict demand, optimize routes, and manage inventory based on real-time data.

Impact: By automating routine tasks, businesses can reduce operational costs, increase efficiency, and allow employees to focus on higher-value activities.

3. Explainable AI (XAI)

As AI models become more complex, the need for transparency in how decisions are made is growing. Explainable AI (XAI) refers to AI systems that provide clear explanations for their decisions. This trend is crucial, especially in industries like healthcare, finance, and legal, where understanding the rationale behind AI decisions is essential for trust and accountability.

Use Cases:

Healthcare: AI models used for diagnostics need to explain why certain diagnoses or treatment plans are recommended.

Finance: In credit scoring, customers need to understand how their financial history influences the AI's decision.

Legal: Legal professionals require transparency when AI is used to predict case outcomes or analyze contracts.

Impact: XAI helps build trust with users, enhances regulatory compliance, and allows businesses to use AI in more critical, high-stakes applications.

4. AI in Edge Computing

Edge computing involves processing data closer to its source, such as IoT devices, rather than relying solely on centralized cloud servers. AI is increasingly being integrated into edge devices to enable real-time decision-making without the latency of sending data to the cloud.

Use Cases:

Autonomous Vehicles: Edge AI is crucial for enabling self-driving cars to process sensor data and make decisions in real time.

Smart Manufacturing: AI models deployed on manufacturing equipment can monitor performance, detect issues, and adjust operations on-site.

Healthcare: Wearable devices with embedded AI can monitor vital signs and trigger alerts in case of anomalies.

Impact: AI in edge computing enhances performance, reduces latency, and provides more reliable insights in real-time applications, especially in remote or decentralized environments.

5. AI for Cybersecurity

As cyber threats become more sophisticated, AI is playing an increasingly important role in detecting, preventing, and responding to security incidents. AI-driven cybersecurity solutions can automatically identify and mitigate potential threats, reducing the need for manual intervention.

Use Cases:

Anomaly Detection: AI can detect unusual patterns in network traffic or user behavior, signaling a potential security breach.

Threat Intelligence: AI models analyze large amounts of data from multiple sources to identify emerging cyber threats.

Automated Response: AI can autonomously respond to certain types of cyberattacks, such as blocking malicious IP addresses or isolating infected devices.

Impact: AI-powered cybersecurity solutions are faster, more accurate, and capable of responding to threats in real time, helping businesses defend against evolving cyber risks.

Best Practices for Developing AI Solutions

To harness the power of AI effectively, businesses must follow best practices that ensure AI development is successful, ethical, and sustainable. Here are some best practices for AI development:

1. Focus on Data Quality

AI solutions rely on data to learn and make decisions, so the quality of the data used for training models is critical. Ensure that the data you’re using is clean, accurate, and relevant. Poor data quality leads to poor AI outcomes, which can result in incorrect decisions, inefficiencies, and poor customer experiences.

Best Practice:

Implement a robust data governance strategy to ensure high-quality data and establish clear protocols for data collection, storage, and processing.

2. Start Small with Pilot Projects

AI implementation can be a significant undertaking. To mitigate risks and ensure a smooth transition, start with small-scale pilot projects. This approach allows you to test AI solutions, assess their impact, and refine them before scaling them across the organization.

Best Practice:

Begin with a clear, measurable objective for your pilot, such as improving operational efficiency or enhancing customer engagement.

Use pilot projects as learning opportunities to gather insights that can improve future AI deployments.

3. Prioritize Ethical AI Development

AI solutions should be developed with ethical considerations in mind. This includes ensuring fairness, transparency, and accountability in decision-making processes, as well as avoiding bias and discrimination in AI models.

Best Practice:

Implement ethical guidelines and frameworks that promote fairness, inclusivity, and transparency in AI development.

Regularly audit AI models to detect and mitigate biases.

4. Ensure Scalability and Flexibility

As your AI solutions evolve, they should be scalable and flexible enough to adapt to changing business needs. This means designing AI systems that can grow with your business, handle larger volumes of data, and integrate with new technologies.

Best Practice:

Use modular AI architectures that allow for easy upgrades and integration with other systems as needed.

Plan for scalability from the outset, ensuring that your AI infrastructure can accommodate future growth.

5. Embrace Continuous Learning and Improvement

AI development is not a one-time process but an ongoing journey. AI models need continuous training and refinement to improve accuracy and adapt to new patterns. Set up processes for continuous monitoring, learning, and optimization.

Best Practice:

Implement a feedback loop where AI systems are constantly updated based on real-world performance and new data.

Regularly evaluate the performance of AI models and make improvements to ensure they remain effective over time.

Conclusion

AI solutions are transforming industries and driving smarter, more efficient businesses. By staying on top of emerging trends—such as generative AI, edge computing, and AI for cybersecurity—and following best practices for AI development, organizations can harness the full potential of AI to gain a competitive edge.

The key to success lies in developing AI solutions that are aligned with your business needs, ethical standards, and technological capabilities. As AI continues to evolve, those who adopt the right strategies will be well-positioned to shape the future of their industries.

#AI Development Solution

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jcmarchi · 5 months ago

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NVIDIA's share price nosedives as antitrust clouds gather

New Post has been published on https://thedigitalinsider.com/nvidias-share-price-nosedives-as-antitrust-clouds-gather/

NVIDIA's share price nosedives as antitrust clouds gather

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NVIDIA has seen its share price plummet following a report of intensified scrutiny from US authorities over potential breaches of competition law.

During the regular trading session on Tuesday, NVIDIA’s share price experienced a near-10% drop. The fall wiped £212 billion from its market value, marking the largest single-day loss for a US company in history.

While the wider market experienced a sell-off fueled by concerns over weak US manufacturing data, NVIDIA was hit particularly hard after Bloomberg reported that the US Department of Justice issued subpoenas to NVIDIA and other tech firms.

Officials are reportedly concerned that NVIDIA’s business practices may be hindering client flexibility in switching to alternative semiconductor suppliers. Additionally, there are concerns about potential penalties imposed on buyers who opt not to exclusively utilise NVIDIA’s AI chips. Such actions would represent an escalation of the ongoing US antitrust investigation, bringing the government a step closer to formally charging NVIDIA.

In response, NVIDIA asserted its belief that its success is based “on merit, as reflected in our benchmark results and value to customers, who can choose whatever solution is best for them.”

This latest downturn adds to the recent volatility experienced by NVIDIA and other AI-related stocks, such as Google, Apple, and Amazon. Investors are grappling with uncertainty surrounding the timeline for tangible benefits and concrete returns from the much-touted AI revolution.

Analysts suggest that investors are seeking greater clarity on the trajectory of gross margins as production of NVIDIA’s new Blackwell chip increases. Furthermore, they are eager for more concrete evidence that AI is delivering tangible returns for customers.

After a 9.5% decline on Tuesday alone and a 14% drop since last week’s earnings report, NVIDIA’s stock has shown marginal signs of recovery in today’s trading session, registering a modest 0.64% increase at the time of writing.

Looking ahead, NVIDIA will need to convince investors of its growth potential not only for 2025 but also for 2026. While Wall Street currently focuses on Blackwell chip shipments, there is increasing interest in the company’s next-generation chip offering.

(Photo by Sebastian Molina)

See also: xAI breaks records with ‘Colossus’ AI training system

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: ai, antitrust, artificial intelligence, blackwell, chip, competition, finance, hardware, law, legal, Nvidia, shares, stocks, subpoena

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sathya32 · 8 months ago

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The Next Step in Data Science: A Glimpse into the Future

For years, data science has been transforming industries, spurring creativity, and resolving challenging issues. The topic is still developing, so there may be even more fascinating advancements in the future. Now let's examine four important areas where data science is currently moving.

1. Machine learning that is automated (AutoML) The field of data science is expected to be significantly impacted by automated machine learning, or AutoML. Traditionally, choosing the best algorithms, adjusting parameters, and assessing performance needed a great deal of experience when creating machine learning models. By automating many of these procedures, autoML streamlines this process and makes it more user-friendly for non-experts.

Hyperparameter tuning, feature selection, model training, and data pretreatment can all be handled by autoML systems. With the democratization of machine learning, a greater number of individuals and organizations may leverage the potential of data without requiring a profound comprehension of its technical nuances. It shortens the development cycle, freeing up data scientists to concentrate on more intricate and imaginative facets of their jobs.

2. XAI, or explainable AI

Knowing how AI and machine learning models make judgments is essential as these models get more complex. This need is met by Explainable AI (XAI), which offers insights into these models' internal operations. Particularly in fields where judgments can have a big impact, like healthcare, banking, and law, this transparency is crucial for fostering trust.

The goal of XAI is to improve interpretability of AI models without compromising performance. The "black box" aspect of many machine learning models is explained by methods like LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (Shapley Additive Explanations). The use of XAI guarantees that stakeholders can validate and have faith in the decisions made by AI systems by offering comprehensible and transparent explanations.

3. Data science and edge computing

The convergence of edge computing and data science is being driven by the growth of Internet of Things (IoT) devices and the requirement for real-time data processing. Processing data close to its source is known as edge computing, as opposed to depending only on centralized cloud servers. This method uses less bandwidth and latency, which makes it perfect for applications that need quick replies.

When edge computing and data science are combined, smart devices can evaluate data locally and take immediate action based on their findings. For instance, in smart cities, real-time analysis of vehicle flow by traffic cameras outfitted with edge computing allows for the optimization of traffic signals to minimize gridlock. Similar to this, wearable technology in healthcare can monitor vital signs and give prompt feedback, enhancing patient care.

4. Responsible and ethical AI

Ethical considerations are becoming more and more crucial as AI technology proliferate. Within the data science community, there is an increasing emphasis on making sure AI systems are impartial, fair, and considerate of privacy. It takes a combination of technological advancements, legal structures, and moral principles to address these issues.

Including fairness and bias detection in the model-development process is one strategy. Adversarial debiasing and fairness-aware machine learning are two methods that assist in locating and reducing biases in data and models. Organizations are also developing best practices and ethical standards for AI development and application, encouraging openness, responsibility, and diversity.

Data science has a promising and bright future ahead of it. With developments in edge computing, ethical AI, explainable AI, and AutoML, the industry is ready to take on new problems and produce even more creative solutions. These advancements will improve data science's capabilities while also increasing its transparency, responsibility, and accessibility. Data science will surely continue to advance and produce ground-breaking discoveries and revolutionary shifts in a variety of industries as time goes on.I appreciate your precious time, and I hope you have an amazing day.

#datascience #datascience training #datascience course #datacleaning

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vinooshiya · 1 year ago

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Unveiling the Latest Trends in Data Science for 2024

Data science remains a dynamic domain, constantly evolving with fresh methodologies and technologies. As we progress further into 2024, it's paramount for data science professionals to keep pace with emerging trends to maintain their effectiveness in the field. In this blog post, we'll delve into several burgeoning trends in data science that practitioners should be attuned to this year from the best data science course in Bangalore.

1. Ethical AI and Responsible AI: The ubiquitous integration of artificial intelligence (AI) and machine learning (ML) prompts a heightened focus on ethical considerations and responsible AI practices. It entails addressing biases, ensuring fairness, transparency, and taking accountability for AI outcomes. Organizations are increasingly investing resources into cultivating ethical AI practices and adhering to regulatory frameworks.

2. Federated Learning: The advent of federated learning presents a novel method for training ML models across multiple decentralized devices or servers without compromising data privacy. This trend gains momentum as enterprises seek to glean insights from distributed data sources while upholding privacy and security protocols. Embracing federated learning allows professionals to collaborate on model training while safeguarding sensitive data.

3. Automated Machine Learning (AutoML): AutoML platforms are on the rise, empowering users with varying levels of ML expertise to streamline model development and deployment. These platforms automate various stages of the ML pipeline, from data preprocessing to model selection and tuning. The increasing accessibility of AutoML tools democratizes AI technology and expedites the model development process.

If you want to learn more about Data Science, I highly recommend the Data Science online training because they offer certifications and job placement opportunities. You can find these services both online and offline.

4. Explainable AI (XAI): The concept of explainable AI underscores the necessity for AI systems to provide comprehensible explanations for their decisions and predictions. This is particularly crucial in sectors such as healthcare and finance where transparency is paramount. Professionals are actively integrating XAI techniques to bolster transparency and regulatory compliance.

5. Edge Computing and Edge AI: Edge computing involves processing data in proximity to its source, such as IoT devices, rather than relying solely on centralized servers. Complemented by Edge AI, this trend facilitates real-time data analysis and decision-making while minimizing dependence on cloud infrastructure. Edge computing proves invaluable for applications necessitating low latency and heightened privacy.

6. Quantum Machine Learning: Quantum machine learning emerges at the intersection of quantum computing and ML, offering the potential for accelerated problem-solving capabilities. Quantum computers exhibit the ability to tackle complex optimization problems at unparalleled speeds, promising advancements in ML algorithms. Although nascent, quantum machine learning holds promise for revolutionizing data science practices.

7. DataOps: DataOps embodies a set of practices and technologies aimed at enhancing collaboration, automation, and agility in data-centric workflows. Analogous to DevOps in software development, DataOps emphasizes continuous integration, version control, and monitoring throughout the data lifecycle. By embracing DataOps principles, organizations optimize data workflows and expedite insights delivery.

Conclusion: As 2024 unfolds, staying abreast of these emerging trends is imperative for data science professionals to navigate the evolving landscape successfully. By embracing ethical AI, federated learning, AutoML, XAI, edge computing, quantum machine learning, and DataOps practices, practitioners can propel innovation and unlock novel opportunities in data science. Remaining vigilant of these trends is pivotal for staying ahead in this ever-evolving field.

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360digitmg-ds-da-hyd · 1 year ago

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2024 Roadmap: Skills, Tools, and Strategies for Aspiring Data Scientists

Aspiring best data science institute in hyderabad with placement looking to navigate the landscape in 2024 should consider a comprehensive roadmap that encompasses essential skills, relevant tools, and effective strategies. Here's a roadmap tailored to guide you on your journey:

1. Foundational Skills:

Mathematics and Statistics:

Strengthen your understanding of foundational mathematical concepts and statistical methods.

Programming Proficiency:

Master programming languages such as Python or R, focusing on data manipulation and analysis.

2. Core Data Science Skills:

Data Manipulation and Cleaning:

Develop skills in cleaning and preprocessing data using tools like Pandas or dplyr.

Exploratory Data Analysis (EDA):

Learn techniques for exploring and visualizing data to uncover patterns and insights.

3. Machine Learning Fundamentals:

Supervised and Unsupervised Learning:

Understand the principles of both supervised and unsupervised learning algorithms.

Model Evaluation and Hyperparameter Tuning:

Master techniques for evaluating models and optimizing hyperparameters.

4. Advanced Machine Learning:

Deep Learning:

Explore neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

Ensemble Learning:

Understand ensemble methods like random forests and gradient boosting.

5. Data Visualization:

Visualization Tools:

Familiarize yourself with data visualization tools such as Matplotlib, Seaborn, or Plotly.

Effective Communication:

Learn to communicate insights through compelling visualizations and storytelling.

6. Big Data Technologies:

Apache Spark:

Gain proficiency in Apache Spark for distributed data processing and analysis.

Hadoop Ecosystem:

Understand the basics of the Hadoop ecosystem for handling large-scale data.

7. Cloud Computing:

AWS, Azure, or Google Cloud:

Familiarize yourself with cloud platforms for scalable and flexible data storage and processing.

8. Natural Language Processing (NLP):

Text Analysis:

Develop skills in NLP for tasks such as sentiment analysis, text classification, and language generation.

9. Data Engineering:

ETL Processes:

Understand Extract, Transform, Load (ETL) processes for efficient data pipelines.

10. Predictive Analytics:

Time Series Analysis:

Master techniques for analyzing and predicting time-dependent data.

Regression Analysis:

Deepen your knowledge of regression analysis for forecasting and modeling relationships.

11. Cybersecurity Awareness:

Data Protection:

Learn principles of data protection and cybersecurity to ensure secure data handling.

12. Explainable AI (XAI):

Model Interpretability:

Focus on making your machine learning models interpretable and explainable.

13. Domain-specific Expertise:

Industry Relevance:

Acquire domain-specific knowledge in areas of interest, such as finance, healthcare, or e-commerce.

14. Collaborative Tools:

Git and Collaboration Platforms:

Familiarize yourself with version control tools like Git and collaborative platforms for efficient teamwork.

15. Data Governance:

Regulatory Compliance:

Stay informed about data governance principles and comply with regulatory standards.

16. Automated Machine Learning (AutoML):

Efficiency Tools:

Explore AutoML platforms to streamline and automate parts of the machine learning workflow.

17. Interdisciplinary Collaboration:

Effective Communication:

Develop strong communication skills for interdisciplinary collaboration with non-technical stakeholders.

18. Continuous Learning and Adaptability:

Lifelong Learning Mindset:

Embrace a mindset of continuous learning to stay updated on emerging technologies.

19. Ethical AI Practices:

Bias Mitigation:

Learn and implement strategies for mitigating bias in AI models to ensure fairness.

20. No-Code/Low-Code Platforms:

Accessibility Tools:

Explore no-code/low-code platforms to make data science course fees in hyderabad more accessible to a broader audience.

21. Remote Work Skills:

Virtual Collaboration:

Develop skills for effective virtual collaboration, as remote work becomes more prevalent.

22. Data Science Certifications:

Continuous Validation:

Pursue relevant certifications to validate your skills and commitment to professional development.

23. Blockchain Awareness:

Decentralized Data Handling:

Familiarize yourself with blockchain technology, particularly in contexts where decentralized data handling is crucial.

24. Augmented Analytics and Auto Insights:

AI-Augmented Analysis:

Explore augmented analytics tools that use AI to automatically discover insights from data.

25. Global Data Privacy Compliance:

International Regulations:

Stay aware of global data privacy regulations to ensure compliance in a globalized data environment. 26. Advanced Specializations:

Specialized Areas: Once you've built a strong foundation, consider diving into specialized areas like computer vision, natural language generation, or reinforcement learning. These advanced skills can set you apart in specific domains.

27. Data Ethics and Privacy:

Ethical Considerations: Deepen your understanding of data ethics and privacy. Stay informed about responsible data practices and contribute to ensuring ethical considerations in your data science work.

28. Version Control Systems:

Git Mastery:

Enhance your proficiency in Git, a version control system widely used in collaborative coding projects. Understanding version control is essential for managing code changes effectively.

29. Cloud-based Databases:

Database Management in the Cloud:

Explore cloud-based databases like Amazon Aurora, Google Cloud Bigtable, or Azure Cosmos DB. Understanding these technologies is crucial for handling data in cloud environments.

30. A/B Testing:

Experimentation Skills:

Learn the principles of A/B testing for assessing the impact of changes in product features or marketing strategies. A/B testing is a valuable skill in data-driven decision-making.

31. Continuous Integration/Continuous Deployment (CI/CD):

Automated Deployment Practices:

Understand CI/CD pipelines for automated testing and deployment. This ensures a smooth and efficient process in deploying data science solutions.

32. Geospatial Analysis:

Spatial Data Processing:

Acquire skills in geospatial analysis for working with location-based data. This is particularly relevant in fields like logistics, urban planning, and environmental science.

33. Data Warehousing:

Warehouse Management:

Explore data warehousing solutions like Amazon Redshift, Google BigQuery, or Snowflake. These technologies are essential for handling and analyzing large datasets.

34. Quantum Computing Awareness:

Basic Understanding:

Familiarize yourself with the basics of quantum computing. While not immediately applicable to all data science tasks, awareness of this emerging technology can be valuable for the future.

35. Reinforce Soft Skills:

Effective Communication:

Continue refining your communication skills, especially in presenting complex findings to diverse audiences. Strong communication is a hallmark of successful data scientists.

36. Industry-Specific Case Studies:

Real-world Applications:

Dive into industry-specific case studies. Understanding how data science is applied in real-world scenarios within your target industries enhances your practical knowledge.

37. Financial Modeling:

Finance-specific Skills:

If interested in finance, develop skills in financial modeling. Understanding financial principles strengthens your ability to apply data science in the financial sector.

38. Data Science Competitions:

Kaggle and Beyond:

Participate in data science competitions on platforms like Kaggle. These competitions provide exposure to diverse datasets and foster a competitive yet collaborative learning environment.

39. Mentorship and Networking:

Build Professional Relationships:

Continue seeking mentorship and expanding your professional network. Building relationships with experienced professionals can open doors to valuable insights and opportunities.

40. Work on Passion Projects:

Personal Development:

Engage in passion projects that align with your interests. These projects not only demonstrate your skills but also contribute to your continuous learning and personal development.

41. Teaching and Knowledge Sharing:

Educational Initiatives:

Consider opportunities to teach or share your knowledge. Whether through writing articles, creating tutorials, or conducting workshops, sharing what you've learned contributes to both your growth and the community.

42. Stay Informed on Emerging Trends:

Technology Watch:

Regularly scan for emerging trends in data science. Staying informed about new tools, methodologies, and industry trends ensures your skill set remains relevant.

43. Advanced Statistical Techniques:

Bayesian Inference, Time Series Forecasting:

Delve into advanced statistical techniques such as Bayesian inference and time series forecasting. These techniques provide additional tools for complex analyses.

44. Cultural Competence:

Cross-cultural Awareness:

Develop cultural competence, especially if working in a globalized environment. Understanding cultural nuances is crucial for effective communication and collaboration.

45. Mental Health Awareness:

Well-being Practices:

Prioritize mental health and well-being. The demanding nature of data science roles underscores the importance of maintaining a healthy work-life balance.

46. Evaluate and Adjust:

Reflect on Career Trajectory:

Periodically evaluate your career trajectory. Assess your goals, achievements, and evolving interests, and be open to adjusting your path accordingly.

47. Collaboration Platforms:

Project Management Tools:

Familiarize yourself with project management tools like Jira or Trello. Efficient project management is essential for successful collaboration in data science projects.

48. Public Speaking Skills:

Effective Presentation:

Enhance your public speaking skills. Whether presenting findings to stakeholders or speaking at conferences, effective communication is a valuable asset.

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49. Cybersecurity Certifications (Optional):

Security Knowledge:

Consider pursuing cybersecurity certifications to deepen your understanding of secure data handling practices.

50. Reflect and Celebrate:

Acknowledge Achievements:

Regularly reflect on your journey, celebrate achievements, and acknowledge the progress you've made. Maintaining a positive mindset is crucial for sustained success.

This comprehensive roadmap provides a robust guide for aspiring data scientists in 2024. Customize your path based on your unique strengths, interests, and career objectives. The evolving nature of data science requires adaptability, so stay curious, embrace challenges, and contribute to the ever-growing field of data science.

This roadmap provides a holistic guide for aspiring data scientists in 2024. Customize your learning journey based on your interests, strengths, and career goals. Embrace the dynamic nature of data science, stay curious, and actively contribute to the advancements in this ever-evolving field.

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tastydregs · 2 years ago

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OpenAI and Google Form New Group to Self-Regulate Their AI

Bot's Club

The AI industry big boys just formed a brand new table at the Silicon Valley cafeteria.

OpenAI, Microsoft, and Google, in addition to the Google-owned DeepMind and buzzy startup Anthropic have together formed The Frontier Model Forum, an industry-led body that, per a press release, claims to be seeking to enforce the "safe and responsible development" of AI.

"Companies creating AI technology have a responsibility to ensure that it is safe, secure, and remains under human control," Microsoft president Brad Smith said in the statement. "This initiative is a vital step to bring the tech sector together in advancing AI responsibly and tackling the challenges so that it benefits all of humanity."

In other words, it's a stab at AI industry self-regulation. But while it is good to see major industry players join forces to establish some best practices for responsible AI development, self-regulation has some serious limitations. After all, with no ability for the government to actually enforce any of the Frontier Model Forum's rules through actions like sanctions, fines, or criminal proceedings, the body, at least for now, is mostly symbolic. Extracurricular group activity energy.

Self-Regulation Station

It's also worth noting that some notable names were left out from the jump. The Mark Zuckerberg-helmed Meta-formerly-Facebook apparently isn't a member of the club, while Elon Musk and his newly-launched xAI, which was — sigh — apparently developed to "understand reality," were both left on the sidelines. (That said, though Meta, which has some pretty advanced models on deck, might have some room to complain about the snub, Musk and his stonerbot probably don't.)

The Forum does say that others can sit with them in the future, as long as they're making what the group deems to be "frontier models" — defined by the group as "large-scale machine-learning models that exceed the capabilities currently present in the most advanced existing models, and can perform a wide variety of tasks" — and promise to commit to a general and mostly unspecified commitment to safety and responsibility.

Again, we can't say it isn't good to see these kinds of discussions happening between major AI firms. But we also can't overstate the fact that these are all for-profit companies with a financial incentive to churn out AI products, and non-binding self-regulation is far from real and industry-wide government rules and oversight. Is it a start? Sure! But let's not let the buck stop here.

More on AI regulation: Ex-Google CEO Says We Should Trust AI Industry to Self-Regulate

The post OpenAI and Google Form New Group to Self-Regulate Their AI appeared first on Futurism.

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nymphie-mama · 2 years ago

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relief

pairing xavier thorpe x reader

summary in which a little teasing goes too far

warnings smut. [fem!oral, teasing, fem!masturbation]

request Could you write one with either Xavier or Ajax (I’m not picky and would like to see both scenarios but you can choose who you think would fit best) and Fem reader, the reader and the boy both have a thing for each other and has teased the reader all day. So she goes to her room to try and relieve herself but just can’t and the boy walks in to find her panting and begging to cum and decides to help her? - @ttsbaby01

i no longer support percy hynes white and i do not write for xavier thorpe anymore.

the teasing xavier had in him was insane. it was driving you insane. the touches that were so close to where you needed him and the needy breaths on your neck were too much.

he knew you had a thing for him, he liked to abuse that. you knew he felt the same, you liked to let him abuse what he knew. it was normal for him to tease you, usually not enough to get you riled up.

until today.

everything he did felt oh-so extreme. those little touches left imaginative flames on your body. every enunciated word he spoke seemed to out a vision in your mind of him filling you to the brim, like he was implanting it there. his smirks drew out the blood in your cheeks every time.

the second you got to your dorm, your pants and underwear found the floor and your body was plowed into bed. you needed to alleviate everything from the day.

your fingers quickly found your swollen clit, a familiar spot. not surprised to find yourself practically dripping with everything, you went to work.

rubbing circles into your most sensitive, needy area, and trying to hush the moans spilling from your lips, everything was coming together. imagining xavier taking your place and talking to you while he brought you to the best orgasm of your life.

“xay,” you whined to yourself, maybe too loud, “please, let me cum. xavi, please-“

“yes, princess? oh-“

you jumped up and threw your blanket over your bottom half. “shit, xavier! knock!”

“well, i did,” he said, and you could see his eyes darken, “when i heard my name, i just assumed-“

“fuck. you have shit timing.”

“no, no, i think i’m here at just the right time,” he said, reaching for your blanket and smirking in that classically dark way. “let me help you?”

“what? xay- you’re-“

“i’m what, princess? don’t insult me, you know what i just heard.” he’d removed the blanket carefully but quickly and already worked to position himself between your thighs. “do you know how badly i’ve wanted this? to taste you?”

you squirm and start to move around, but he stops you by reaching around your thighs. “if you really don’t want this, say so. i’ll stop.”

“you know i want it.”

“then what am i waiting for?” he asks, moreso to himself, and licks a swift stripe between your folds. you hissed at the feeling, but he groaned.

your hands tugged at his long hair, making him moan into you again. the vibration did nothing but help the sensations his tongue was bringing you. his tongue had easily replaced where your fingers were before as if he knew. he flitted at and suctioned around your bundle, watching what made you react the most. xavier smiled with every movement and sound you made, mumbling things you couldn't recognize against your cunt.

"fuck, xavier," you said as he brought a finger in and out of your entrance. your fingers tightened on his blonde strands, and his movements became stronger. "god, i'm gonna cum."

he nodded against you, maybe because he could feel it. maybe because he wanted you to. either way, you feel a band, tighter than ever before, snap inside your belly. It all washes over you so comfortably but so fast. it's better than ever before.

xavier cleans you up by licking your juices off your thighs and cunt, sweetly, like it needs it.

when he comes up, finally done, and getting ready to get a towel and really clean you up, he's sweaty and pretty. his chin and mouth glisten and he has no care in the world.

"you taste so good, princess. i don't know how i ever went without that."

wednesday masterlist

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thadelightfulone · 4 years ago

Text

Prologue

Story Summary: Erik Stevens has a wonderful life - traveling around the world, empowering black people, and living life on his terms. There is just one piece that is missing but how will she complete his destiny?

Demi Bishop sat at her desk, gently tapping her fingers on a file. Picking it up, she opened it for the umpteenth time. She glanced over the two sheets inside and then closed it again. She put the file down and pushed it to her left.

She took a deep breath and tried to center herself. It’s been years since she felt this unnerved about meeting a new patient, but this was something new for her. After spending her entire career behind prison walls, Demi was on her own. The freedom of picking her own patients drew her to private practice.

Her first client would be one of the biggest she had ever encountered. She worked with many notorious people during her career, but he is well-known for being a positive influence in the black community. This could be the boost she needs to move from prison psychiatry to mainstream therapy.

--- 3 Days Earlier ---

Demi’s hand felt along the nightstand for her vibrating cell phone. Someone was about to get cussed out waking her up and the sun wasn’t even shining through her curtains yet? She blinked at the bright screen and saw it was her best friend, Xavion calling.

“MiMi, I have a huge favor to ask.” The voice rushed out over the phone line.

“What’s in it for me, Xay?” Demi sighed.

“My undying devotion.” He sang.

“I have that already. Give me something else.” She yawned, glaring at the red numbers on her digital clock that read 4:30am.

“My first child?”

“I don’t want kids.” She mumbled out as she rolled back over and put the phone on the pillow next to her. “One more try and then I’m hanging up.” Demi pulled her comforter over her head.

“Demi, come on.”

Demi’s soft snores could be heard on the line.

“Please. I really need this favor, so I can look good at work.”

She lifted her head off the pillow, “What do I have to do?”

“I have a client for your practice.”

Demi threw the comforter off of her and sat up in bed. She put the phone on speaker and wiped the sleep from her eyes.

“So, my boss had this really interesting case where the guy was given mandatory therapy before returning to work.”

“I’m listening.”

“He doesn’t want to do it, but he has to, ya know. So, we told him that we would find a therapist for him to complete his sessions with and sign off on his return. It’s simple, meet with him the minimal number of times allowed by the program and then clear him.”

Demi looked down at her phone in confusion. This could not be her friend asking her to do this.

“Xay, you know that’s not how therapy works. Hell, that’s not even how I work.” She rolled her eyes. “It’s all or nothing. It’s my license on the line if he repeats or reoffends.”

“Trust me. You do not have to worry about that with him. He’s good people.”

“Good people don’t end up in mandatory therapy programs, Xay.”

“MiMi!” He groaned.

“Absolutely not. I understand that your bosses have people on payroll to do shit like this for them and that’s great. But I will not be one of them.” She pinched the bridge of her nose, “and for you to even come to me with this bullshit this early in the morning. Thanks friend.”

“MiMi. I didn’t mean to - ”

“You want me to see this man as a client? Then you inform him about how I work. He can see it through to the end and at my recommendation or he can find someone else to buy off.”

“Demi, please -”

“No Xay. Talk to your client and if he is fine with my proposal, you can send me his file.” She hung up the phone.

---

Xavion sent her an electronic file that contained a picture of her new client, Erik Stevens. It contained the court case details and the anger management program paperwork she would sign upon completion. Demi had heard of him and didn’t understand how a man of his status ended up taking the entire blame for this situation.

Handling this case appropriately would provide the exposure she needed to help build her practice. All she had to do was get him to complete the program as outlined by the judge.

---

Erik Stevens looked up at the red brick building and then looked at the note on his phone. This was the place. Apparently, there was a couch with his name on it inside. He was supposed to walk in and speak candidly to some quack for 6 months. There was nothing wrong with him, but the courts didn’t see it that way. Nothing he couldn’t fix during this first visit though.

He entered the lobby and walked to the elevator bank. Erik locked his phone and placed it in the inside pocket of his jacket. He stood back and waited for the approaching elevator car.

Erik looked at his watch as he exited onto the 5th floor. ‘Early is on time, on time is late and late is unacceptable.’ He smiled to himself, “Time to let Dr. Bishop know how things will go.”

The floor had an open layout with a desk in the center and multiple closed doors surrounding it. He appreciated the mix of modern and classic furniture that made up the office suite shared by all the doctors.

He walked over to the receptionist, who gawked at his entrance. She straightened up in her seat as he approached. “I’m here for Dr. Bishop. I have a 3 oclock appointment.”

“Uh, yes sir, Mr. Stevens.” She smiled up at him, “Please have a seat.”

He returned the smile. “So, you do know who I am?” He looked down at her over the countertop that covered her seated position.

Erik surveyed her. Her pressed hair and pearly white smile to her chaste blouse down to her skirt that showcased glistening chocolate brown legs in stiletto heels. He lifted his gaze back to her face and when he met her stare, she immediately looked away.

“Of course, I’ve attended a few of your seminars before. You are the reason I have this job.” She looked at her computer and then back up at him, “You are quite early, but I’ll let her know you are here.”

“Thank you, sweetheart.” He stood up and pushed back the panels of his jacket revealing a gold lining. Then he leaned onto the counter and followed her line of sight until she met his again, “By the way, what’s your name?” He held his hand out to her.

“Sylvanna.” She giggled and slowly placed her hand in his.

“Sylvanna, what a beautiful name.” He rubbed the top of her hand with his thumb. “Are you doing anything tonight?”

She nodded at him and Erik immediately relaxed his hold on her hand. Sylvanna quickly corrected herself, “I’m sorry, Mr. Stevens,” She took a deep breath and exhaled, “No, I am not.”

“Good girl. Go out with me.”

“I’d love to.”

“Wonderful. I’ll need your number.” He tapped a notepad in front of her.

Sylvanna flipped to a fresh sheet, wrote it down and gave it to him. He slipped the piece of paper from her hand, folded the sheet and placed it in his pants pocket. He took her hand again and gave it a slight squeeze.

“You can let Dr. Bishop know I am here now.” He winked at Sylvanna, turned and walked away.

She shook her head, took another deep breath and reached for the intercom, “Yes, Mr. Stevens.”

---

For several minutes, Erik stood by the window observing the cityscape. This was his town and his home, Oakland. His work was for his people. He didn’t understand why he was here when he paid people to take care of things like this for him. Why have a law firm on retainer when they couldn’t even get him out of mandatory therapy?

Erik sighed, “Sometimes, you gotta do the messy work yourself.”

Sylvanna called his name. “Dr. Bishop will see you now, Mr. Stevens.”

He turned as he buttoned up his suit jacket to find her standing, “Thank you, Sylvanna.” He walked up to her, “So, where I am going?”

She pointed down the center hallway, “It’s the first door on your right.”

“Great. I will see you later tonight.” He winked at her.

He strode towards the office with a smile as he brushed down his jacket. Erik took a deep breath and knocked before he entered.

“Dr. Bishop?”

“Yes, Mr. Stevens. Please come in.”

Erik froze, one hand on the doorknob, at the feminine voice that greeted him. Dark brown eyes hidden behind slim black-rimmed glasses looked expectantly up at him. He closed the door behind him and stepped forward. Erik studied the woman sitting at the large wooden desk. This was gonna be easier than I thought.

She waved her hand to the chair in front of her desk. “It’s nice to meet you.”

“Dr. Bishop. The pleasure is all mine.” He replied.

Erik walked over and sat down in the straight-backed chair.

“I have some housekeeping things to go over and then we can start.”

Erik watched her shuffle a few files and a legal pad in front of her. Right down to business. She impressed him.

He moved his chair forward and pulled a pen from his jacket. “Great, let me know where to sign.”

“Excuse me?” Dr. Bishop snapped at him.

Erik continued, “I am so glad you changed your mind. I need to get back out on the road and speak to all my people.”

He reached for one of the files on her desk. She pulled it out of his reach.

“I think you are mistaken, Mr. Stevens.” She gathered the files together and placed them on a file rack.

---

Does this man really assume that I am going to cheat the system for him? Who the fuck does he think he is?

Demi looked over at Erik as he relaxed into the chair. He unbuttoned his jacket and the lining flashed gold before the suit tails settled around him.

No, he didn’t.

He was wearing a gray pinstripe suit with gold cufflinks. She shook her head as he clasped his manicured hands together on his lap.

He really thinks highly of himself.

“Mr. Stevens, I am aware that you spoke with Mr. Davis about my terms.” When Erik nodded, she continued, “What makes you think I have changed my mind?”

“Well, you have the paperwork in front of you. And there is nothing that YOU can teach me about channeling anger and using it for better,” he moved his hands as he spoke, “I do this for a living.”

Demi smiled at Erik, “Ahhhh, no wonder you are dressed so... impressively.” She pointed at his suit, “This must be your ‘I talk in money’ suit. No wait, it’s your ‘Let’s talk business’ suit.”

---

Erik slowly bobbed his head at her. The more she spoke the more he wanted to hear everything she had to say. He was pleased to say the least. She definitely had a nice read on him.

“You must have thought that you could walk in here and negotiate the terms of your court-mandated therapy.”

“That’s correct.” He sat forward in his seat.

Demi tapped her chin, “So, that’s why you came in here peacocking? Beautiful coat, by the way.”

He watched as Demi stood up and walked around her desk. She stopped in front of it and him.

“Let me introduce myself then.” She leaned against the desk and crossed one of her legs in front of the other. “My name is Dr. Demi Bishop and I will be your counselor as you work through your anger management program.”

A sly smile crept across Erik’s face. “You sure about that?”

“Absolutely.” She reached beside her and grabbed one of the folders on the desk. “In fact, here is your first assignment.” She handed it to him, “Go ahead and read that before our next session.”

Demi walked around his seat and went to her office door. She opened it and then turned back towards Erik.

---

She held the door as Erik stood up.

He took the opportunity to get a better view of her. Her loose curls were in a bun, some tendrils framing her cherubic face. She wore a fitted brown blazer over a black sheath dress. Sensible black heels finished the look. Hmmm, what are you hiding Ms. Bishop?

“It was nice to meet you, Mr. Stevens. I will see you Wednesday. Preferably at your appointed time.”

He brushed up against her as he passed by and heard her deep inhale once he crossed the door’s threshold.

Erik walked to the bank of elevators and hit the down button. While he waited, he looked inside the folder and found an article about healthy ways to deal with anger. This woman is something else. Erik closed the folder and twisted into a tube. He hid his hand in his pants pocket and balled up his fist.

The elevator doors opened and he stepped inside. Erik looked back at Demi standing in her doorway. He waved to her, “Yes, you will see me again, Miss Bishop.”

A/N: Trying something new. Taglist is open.

#erik stevens #erik killmonger #erik killmonger x black oc #black panther fanfiction #devoted #thadelightfulone

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jcmarchi · 9 months ago

Text

EU launches office to implement AI Act and foster innovation

New Post has been published on https://thedigitalinsider.com/eu-launches-office-to-implement-ai-act-and-foster-innovation/

EU launches office to implement AI Act and foster innovation

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The European Union has launched a new office dedicated to overseeing the implementation of its landmark AI Act, which is regarded as one of the most comprehensive AI regulations in the world. This new initiative adopts a risk-based approach, imposing stringent regulations on higher-risk AI applications to ensure their safe and ethical deployment.

The primary goal of this office is to promote the “future development, deployment and use” of AI technologies, aiming to harness their societal and economic benefits while mitigating associated risks. By focusing on innovation and safety, the office seeks to position the EU as a global leader in AI regulation and development.

According to Margerthe Vertager, the EU competition chief, the new office will play a “key role” in implementing the AI Act, particularly with regard to general-purpose AI models. She stated, “Together with developers and a scientific community, the office will evaluate and test general-purpose AI to ensure that AI serves us as humans and upholds our European values.”

Sridhar Iyengar, Managing Director for Zoho Europe, welcomed the establishment of the AI office, noting, “The establishment of the AI office in the European Commission to play a key role with the implementation of the EU AI Act is a welcome sign of progress, and it is encouraging to see the EU positioning itself as a global leader in AI regulation. We hope to continue to see collaboration between governments, businesses, academics and industry experts to guide on safe use of AI to boost business growth.”

Iyengar highlighted the dual nature of AI’s impact on businesses, pointing out both its benefits and concerns. He emphasised the importance of adhering to best practice guidance and legislative guardrails to ensure safe and ethical AI adoption.

“AI can drive innovation in business tools, helping to improve fraud detection, forecasting, and customer data analysis to name a few. These benefits not only have the potential to elevate customer experience but can increase efficiency, present insights, and suggest actions to drive further success,” Iyengar said.

The office will be staffed by more than 140 individuals, including technology specialists, administrative assistants, lawyers, policy specialists, and economists. It will consist of various units focusing on regulation and compliance, as well as safety and innovation, reflecting the multifaceted approach needed to govern AI effectively.

Rachael Hays, Transformation Director for Definia, part of The IN Group, commented: “The establishment of a dedicated AI Office within the European Commission underscores the EU’s commitment to both innovation and regulation which is undoubtedly crucial in this rapidly evolving AI landscape.”

Hays also pointed out the potential for workforce upskilling that this initiative provides. She referenced findings from their Tech and the Boardroom research, which revealed that over half of boardroom leaders view AI as the biggest direct threat to their organisations.

“This initiative directly addresses these fears as employees across various sectors are given the opportunity to adapt and thrive in an AI-driven world. The AI Office offers promising hope and guidance in developing economic benefits while mitigating risks associated with AI technology, something we should all get on board with,” she added.

As the EU takes these steps towards comprehensive AI governance, the office’s work will be pivotal in driving forward both innovation and safety in the field.

(Photo by Sara Kurfeß)

See also: Elon Musk’s xAI secures $6B to challenge OpenAI in AI race

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Tags: ai, ai act, artificial intelligence, eu, european union, regulation

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caelias · 5 years ago

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Describe your character’s current appearance: clothes, armor, scars they’ve picked up along the journey, etc. If your character wasn’t whatever class they are, what would they be instead? What is your character’s greatest achievement? If your character had the chance to rename the party/give the party a name, what would it be? What television/book/video game/etc. character would your character be best friends with? (Or: what media character is your character the most influenced by/similar to?)

RiCaelia: Well, her hair is much shorter now since she chopped it off and dyed it, though I’d imagine if we ever pick the campaign back up it’d be back to being its blondey/orangeyness, I suck at descriptions why did you do this to me, definitely gonna go with a scar wherever she was stabbed (I don’t remember where I’m a bad mom), but other than that she doesn’t have any major scars or anything. She’s just her cutie self. Emma on the other hand, her hair is a wild mess because she gives few fucks, she’s covered in scars, whether little ones or bigger ones, because again, she gives too few fucks, though she is trying to just survive for the most part, she does incite a lot of stupid, she’s always wearing a thick hooded shadowy cloak that covers her for the most part Elaria it literally depends on the day - during the sunshiney hours if she’s outdoor she’s practically covered from head to toe. Leather gloves, cloak, you name it, no sun for her skin please and thank you. At night she’s more... old fashioned sort of elegant, wearing sort of alluring types of dresses yet still conservative and easily maneuverable! Lily is the small baby of the group but wears a huge bow so she’s the same height as Rhovan honest, its not her bow she’s TOTALLY that tall. Its braided into her hair to keep it from falling out, and to keep Rhovan from yanking it out - she likes wearing things she can move around in because she’s a ball of exuberant energy (when she’s not emotionally exhausted because this quest hurts her brain fuck you time magic), she has really thin armor I think its leather? practicality is key though she LIKES being dressy (cute dressy, not fancy dressy,) ALSO HER HAIR IS PINK AND SHES CUTE I LOVE HER If Caelia wasn’t a ranger she’d be a cleric, she always wanted to be a cleric man. But her mother talked her out of it, whats the point of healing people if you can just fight and be strong enough to protect people from being hurt in the first place??? (what is that logic xay), Emma ... assassin. Just. How is she not? (My addiction to shadow dancers is how she’s not), Elaria, same thing probably. Though it would be absolutely HILARIOUS to make Elaria a paladin to the god of life. Actually hilarious. I’m gonna go with that. Lily would probably have ended up being a cleric too, or a monk too. Emma’s greatest achievement is not killing Alex (jk), its actually killing Alex’s dad (A king of sorts,) and in doing so throwing off the balance of an entire country and throwing it into a good kind of disarray. (Also avenging her family and the wrong doing Mama Ely suffered at his hands but thats another story) Caelia’s greatest achievement would probably be funding the guilds.. doing things that are meaningful to help people. Elaria’s is just.. becoming a mother. Because what the fuck? Why does she suddenly have 12* vampire children Talasyn what the fuck.

13 I forgot Talasyn counts* Lily’s would be this entire damn quest. I don’t know if Caelia naming the group would be a thing.. she’d just call them her ‘Family’, so I don’t know if that counts. Elaria doesn’t really have a group, she was just a very late add on and authority figure to Talasyn so... She just calls the ragamuffins her ‘adoptive idiots’, (not the kids, just Talasyn since Zeraphin got DUSTED), Emma would just constantly be changing the name, Fuzzbutt would just be the only thing she’d refer to any group involving Grimse (bc he’s the only important one in any given group including him don’t @ me ) , Fera and Akane would just be called Flour (don’t ask) Lily would have named them bandodorks if I didn’t you know name her dad that What television/book/video game/etc. character would your character be best friends with?

I TOOK SO LONG TO THINK ABOUT THIS, I literally blanked on my knowledge of everything and I can’t believe until now I forgot Lily existed

Lily: Rakan (they’re supposed to be the same vibe), Nozomi , Jaskier, Zen (from Shirayuki) Elaria, Lily, Caelia: Uncle Iroh (I keep thinking back to the toph moment and all the Zuko moments and just. Yes.) Elaria: Riza Caelia: Janna Sylvy: Tikki

Emma: Celaena (TOG - she was created bc of her ngl.)

#long post #yo let me put readmores in ask answers tumblr #gonna leave emile and sylvy off cuz this is already getting SO LONG #so my three most recent babes #artaphant #dnd rel #;emma #;elaria #;lily #;caelia #;sylvy

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gymwheyq5z · 4 years ago

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