#Autonomous Data Domains

[rajaniesh]

Implementing Data Mesh on Databricks: Harmonized and Hub & Spoke Approaches

Explore the Harmonized and Hub & Spoke Data Mesh models on Databricks. Enhance data management with autonomous yet integrated domains and central governance. Perfect for diverse organizational needs and scalable solutions. #DataMesh #Databricks

[spacetimewithstuartgary]

Concept for interstellar object encounters developed, then simulated using a spacecraft swarm

Interstellar objects are among the last unexplored classes of solar system objects, holding tantalizing information about primitive materials from exoplanetary star systems. They pass through our solar system only once in their lifetime at speeds of tens of kilometers per second, making them elusive.

Hiroyasu Tsukamoto, a faculty member in the Department of Aerospace Engineering in the Grainger College of Engineering, University of Illinois Urbana-Champaign, has developed Neural-Rendezvous—a deep-learning-driven guidance and control framework to autonomously encounter these extremely fast-moving objects.

The research is published in the Journal of Guidance, Control, and Dynamics and on the arXiv preprint server.

"A human brain has many capabilities: talking, writing, etcetera," Tsukamoto said. "Deep learning creates a brain specialized for one of these capabilities with domain-specific knowledge. In this case, Neural-Rendezvous learns all the information it needs to encounter an ISO, while also considering the safety-critical, high-cost nature of space exploration."

Tsukamoto said Neural-Rendezvous is based on contraction theory for data-driven nonlinear control systems, which he developed for his Ph.D. at Caltech, while this project was a collaboration with NASA's Jet Propulsion Laboratory, where he spent his time as a post-doctoral research affiliate.

"Our key contribution is not just in designing the specialized brain, but in proving mathematically that it works. For example, with a human brain we learn from experience how to navigate safely while driving. But what are the mathematics behind it? How do we know and how can we make sure we won't hit anyone?"

In space, Neural-Rendezvous autonomously predicts a spacecraft's best action, based on data, but with a formal probabilistic bound on its distance to the target ISO.

Tsukamoto said there are two main challenges: The interstellar object is a high-energy, high-speed target, and its trajectory is always poorly constrained due to the unpredictable nature of its visit.

"We're trying to encounter an astronomical object that streaks through our solar system just once and we don't want to miss the opportunity. Even though we can approximate the dynamics of ISOs ahead of time, they still come with large state uncertainty because we cannot predict the timing of their visit. That's a challenge."

The speed and uncertainty of ISO encounters are also why the spacecraft must be able to think on its own.

"Unlike traditional approaches in which you design almost everything before you launch a spacecraft, to encounter an ISO, a spacecraft has to have something like a human brain, specifically designed for this mission, to fully respond to data onboard in real time."

Tsukamoto also demonstrated Neural-Rendezvous using multi-spacecraft simulators called M-STAR and tiny drones called Crazyflies. While he was at JPL, two Illinois aerospace undergraduate students, Arna Bhardwaj and Shishir Bhatta, contacted him to work on a research project using Neural-Rendezvous.

"Because of the speed and uncertainty, it's challenging to obtain a clear view of an ISO during a flyby with 100% accuracy, even with Neural-Rendezvous. Arna and Shishir wanted to show that Neural-Rendezvous could benefit from a multi-spacecraft concept."

To theoretically justify the empirical observations from the M-STAR and Crazyfly demonstrations, their research looked at how to mathematically maximize the information gathered from the ISO encounter using a swarm of spacecraft.

"Now we have an additional layer of decision-making during the ISO encounter," Tsukamoto said. "How do you optimally position multiple spacecraft to maximize the information you can get out of it? Their solution was to distribute the spacecraft to visually cover the highly probable region of the ISO's position, which is driven by Neural-Rendezvous."

Tsukamoto said he was impressed with the level of dedication and academic potential demonstrated by Bhardwaj and Bhatta.

"The topics explored in Neural-Rendezvous can be advanced even for Ph.D. students. Arna and Shishir were very productive and worked hard, and I was surprised to see them publish a paper, given that this field initially was entirely new to them. They did a great job.

"And while the Neural-Rendezvous is more of a theoretical concept, their work is our first attempt to make it much more useful, more practical."

IMAGE: Visualized Neural-Rendezvous trajectories for ISO exploration, where yellow curves represent ISO trajectories and blue curves represent spacecraft trajectories. Credit: University of Illinois at Urbana-Champaign

[pankukaushal]

𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈-:

𝐖𝐡𝐚𝐭 𝐢𝐬 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 ?

Artificial intelligence (AI) refers to computer systems capable of performing complex tasks that historically only a human could do, such as reasoning, making decisions, or solving problems.

𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐀𝐈 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬-:

AI today exhibits a wide range of capabilities, including natural language processing (NLP), machine learning (ML), computer vision, and generative AI. These capabilities are used in various applications like virtual assistants, recommendation systems, fraud detection, autonomous vehicles, and image generation. AI is also transforming industries like healthcare, finance, transportation, and creative domains. 

𝐀𝐈 𝐀𝐩𝐩𝐬/𝐓𝐨𝐨𝐥𝐬-:

ChatGpt, Gemini, Duolingo etc are the major tools/apps of using AI.

𝐑𝐢𝐬𝐤𝐬 𝐨𝐟 𝐀𝐈-:

1. Bias and Discrimination: AI algorithms can be trained on biased data, leading to discriminatory outcomes in areas like hiring, lending, and even criminal justice. 

2. Security Vulnerabilities: AI systems can be exploited through cybersecurity attacks, potentially leading to data breaches, system disruptions, or even the misuse of AI in malicious ways. 

3. Privacy Violations: AI systems often rely on vast amounts of personal data, raising concerns about privacy and the potential for misuse of that data. 

4. Job Displacement: Automation driven by AI can lead to job losses in various sectors, potentially causing economic and social disruption. 

5. Misuse and Weaponization: AI can be used for malicious purposes, such as developing autonomous weapons systems, spreading disinformation, or manipulating public opinion. 

6. Loss of Human Control: Advanced AI systems could potentially surpass human intelligence and become uncontrollable, raising concerns about the safety and well-being of humanity. 

𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈:-

Healthcare:AI will revolutionize medical diagnostics, personalize treatment plans, and assist in complex surgical procedures. 

Workplace:AI will automate routine tasks, freeing up human workers for more strategic and creative roles. 

Transportation:Autonomous vehicles and intelligent traffic management systems will enhance mobility and safety. 

Finance:AI will reshape algorithmic trading, fraud detection, and economic forecasting. 

Education:AI will personalize learning experiences and offer intelligent tutoring systems. 

Manufacturing:AI will enable predictive maintenance, process optimization, and quality control. 

Agriculture:AI will support precision farming, crop monitoring, and yield prediction. 

[ixnai]

Impute is the silent saboteur in AI systems. It is the process of filling in missing data, a seemingly innocuous task that can lead to catastrophic misjudgments. In the realm of artificial intelligence, where algorithms are trained on vast datasets, the integrity of input data is paramount. Yet, imputation introduces a layer of abstraction that can distort reality, creating a veneer of completeness that belies the underlying uncertainty.

Consider an AI model designed to predict financial markets. It relies on historical data, but gaps are inevitable. Imputation steps in, employing statistical methods like mean substitution or regression imputation to fill these voids. However, these methods assume a level of homogeneity that rarely exists in complex systems. The imputed values, while mathematically sound, may not reflect the nuanced dynamics of the market. This is where the danger lies.

AI systems, particularly those driven by machine learning, are not inherently equipped to question the validity of their inputs. They operate under the assumption that the data is a faithful representation of reality. When imputed data is treated as gospel, the AI’s predictions can veer into the realm of fantasy. This is especially perilous when the AI is deployed in high-stakes environments, such as autonomous vehicles or healthcare diagnostics, where erroneous predictions can have dire consequences.

Defending against this blind acceptance requires a multifaceted approach. First, transparency in the imputation process is crucial. AI developers must document the methods used and the assumptions made, allowing for scrutiny and validation by domain experts. Second, incorporating uncertainty quantification can provide a measure of confidence in the imputed values, highlighting areas where predictions may be less reliable.

Moreover, adversarial testing can expose the vulnerabilities introduced by imputation. By deliberately introducing perturbations in the data and observing the AI’s response, developers can identify weaknesses and refine the model’s robustness. This proactive stance is essential in ensuring that AI systems remain resilient in the face of incomplete or imperfect data.

Ultimately, the key to defending against AI’s uncompromising nature lies in fostering a culture of skepticism. Developers and stakeholders must remain vigilant, questioning the assumptions that underpin their models and the data they consume. By acknowledging the limitations of imputation and striving for greater transparency and accountability, we can mitigate the risks and harness the true potential of artificial intelligence.

[emptydeceiverpraetorian]

Unleashing Innovation: How Intel is Shaping the Future of Technology

Introduction

In the fast-paced world of technology, few companies have managed to stay at the forefront of innovation as consistently as Intel. With a history spanning over five decades, Intel has transformed from a small semiconductor manufacturer into a global powerhouse that plays a pivotal role in shaping how we interact with technology today. From personal computing to artificial intelligence (AI) and beyond, Intel's innovations have not only defined industries but have also created new markets altogether.

In this comprehensive article, we'll delve deep into how Intel is unleashing innovation and shaping the future of technology across various domains. We’ll explore its history, key products, groundbreaking research initiatives, sustainability efforts, and much more. Buckle up as we take you on a journey through Intel’s dynamic Extra resources landscape.

Unleashing Innovation: How Intel is Shaping the Future of Technology

Intel's commitment to innovation is foundational to its mission. The company invests billions annually in research and development (R&D), ensuring that it remains ahead of market trends and consumer demands. This relentless pursuit of excellence manifests in several key areas:

The Evolution of Microprocessors A Brief History of Intel's Microprocessors

Intel's journey began with its first microprocessor, the 4004, launched in 1971. Since then, microprocessor technology has evolved dramatically. Each generation brought enhancements in processing power and energy efficiency that changed the way consumers use technology.

The Impact on Personal Computing

Microprocessors are at the heart of every personal computer (PC). They dictate performance capabilities that directly influence user experience. By continually optimizing their designs, Intel has played a crucial role in making PCs faster and more powerful.

Revolutionizing Data Centers High-Performance Computing Solutions

Data centers are essential for businesses to store and process massive amounts of information. Intel's high-performance computing solutions are designed to handle complex workloads efficiently. Their Xeon processors are specifically optimized for data center applications.

Cloud Computing and Virtualization

As cloud services become increasingly popular, Intel has developed technologies that support virtualization and cloud infrastructure. This innovation allows businesses to scale operations rapidly without compromising performance.

Artificial Intelligence: A New Frontier Intel’s AI Strategy

AI represents one of the most significant technological advancements today. Intel recognizes this potential and has positioned itself as a leader in AI hardware and software solutions. Their acquisitions have strengthened their AI portfolio significantly.

AI-Powered Devices

From smart assistants to autonomous vehicles, AI is embedded in countless devices today thanks to advancements by companies like Intel. These innovations enhance user experience by providing personalized services based on data analysis.

Internet of Things (IoT): Connecting Everything The Role of IoT in Smart Cities

[wolfliving]

Strange Chinese trade-war recommendations at US Congress

COMPREHENSIVE LIST OF THE COMMISSION’S 2024 RECOMMENDATIONS Part II: Technology and Consumer Product Opportunities and Risks Chapter 3: U.S.-China Competition in Emerging Technologies The Commission recommends:

Congress establish and fund a Manhattan Project-like program dedicated to racing to and acquiring an Artificial General Intelligence (AGI) capability. AGI is generally defined as systems that are as good as or better than human capabilities across all cognitive domains and would surpass the sharpest human minds at every task. Among the specific actions the Commission recommends for Congress:

Provide broad multiyear contracting authority to the executive branch and associated funding for leading artificial intelligence, cloud, and data center companies and others to advance the stated policy at a pace and scale consistent with the goal of U.S. AGI leadership; and

Direct the U.S. secretary of defense to provide a Defense Priorities and Allocations System “DX Rating” to items in the artificial intelligence ecosystem to ensure this project receives national priority.

Congress consider legislation to:

Require prior approval and ongoing oversight of Chinese involvement in biotechnology companies engaged in operations in the United States, including research or other related transactions. Such approval and oversight operations shall be conducted by the U.S. Department of Health and Human Services in consultation with other appropriate governmental entities. In identifying the involvement of Chinese entities or interests in the U.S. biotechnology sector, Congress should include firms and persons: ○ Engaged in genomic research; ○ Evaluating and/or reporting on genetic data, including for medical or therapeutic purposes or ancestral documentation; ○ Participating in pharmaceutical development; ○ Involved with U.S. colleges and universities; and ○ Involved with federal, state, or local governments or agen cies and departments.

Support significant Federal Government investments in biotechnology in the United States and with U.S. entities at every level of the technology development cycle and supply chain, from basic research through product development and market deployment, including investments in intermediate services capacity and equipment manufacturing capacity.

To protect U.S. economic and national security interests, Congress consider legislation to restrict or ban the importation of certain technologies and services controlled by Chinese entities, including:

Autonomous humanoid robots with advanced capabilities of (i) dexterity, (ii) locomotion, and (iii) intelligence; and

Energy infrastructure products that involve remote servicing, maintenance, or monitoring capabilities, such as load balancing and other batteries supporting the electrical grid, batteries used as backup systems for industrial facilities and/ or critical infrastructure, and transformers and associated equipment.

Congress encourage the Administration’s ongoing rulemaking efforts regarding “connected vehicles” to cover industrial machinery, Internet of Things devices, appliances, and other connected devices produced by Chinese entities or including Chinese technologies that can be accessed, serviced, maintained, or updated remotely or through physical updates.

Congress enact legislation prohibiting granting seats on boards of directors and information rights to China-based investors in strategic technology sectors. Allowing foreign investors to hold seats and observer seats on the boards of U.S. technology start-ups provides them with sensitive strategic information, which could be leveraged to gain competitive advantages. Prohibiting this practice would protect intellectual property and ensure that U.S. technological advances are not compromised. It would also reduce the risk of corporate espionage, safeguarding America’s leadership in emerging technologies.

Congress establish that:

The U.S. government will unilaterally or with key interna- tional partners seek to vertically integrate in the develop- ment and commercialization of quantum technology.

Federal Government investments in quantum technology support every level of the technology development cycle and supply chain from basic research through product development and market deployment, including investments in intermediate services capacity.

The Office of Science and Technology Policy, in consultation with appropriate agencies and experts, develop a Quantum Technology Supply Chain Roadmap to ensure that the United States coordinates outbound investment, U.S. critical supply chain assessments, the activities of the Committee on Foreign Investment in the United States (CFIUS), and federally supported research activities to ensure that the United States, along with key allies and partners, will lead in this critical technology and not advance Chinese capabilities and development....

[mixpayu]

Understanding Artificial Intelligence: A Comprehensive Guide

Artificial Intelligence (AI) has become one of the most transformative technologies of our time. From powering smart assistants to enabling self-driving cars, AI is reshaping industries and everyday life. In this comprehensive guide, we will explore what AI is, its evolution, various types, real-world applications, and both its advantages and disadvantages. We will also offer practical tips for embracing AI in a responsible manner—all while adhering to strict publishing and SEO standards and Blogger’s policies.

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1. Introduction

Artificial Intelligence refers to computer systems designed to perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, and even understanding natural language. Over the past few decades, advancements in machine learning and deep learning have accelerated AI’s evolution, making it an indispensable tool in multiple domains.

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2. What Is Artificial Intelligence?

At its core, AI is about creating machines or software that can mimic human cognitive functions. There are several key areas within AI:

Machine Learning (ML): A subset of AI where algorithms improve through experience and data. For example, recommendation systems on streaming platforms learn user preferences over time.

Deep Learning: A branch of ML that utilizes neural networks with many layers to analyze various types of data. This technology is behind image and speech recognition systems.

Natural Language Processing (NLP): Enables computers to understand, interpret, and generate human language. Virtual assistants like Siri and Alexa are prime examples of NLP applications.

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3. A Brief History and Evolution

The concept of artificial intelligence dates back to the mid-20th century, when pioneers like Alan Turing began to question whether machines could think. Over the years, AI has evolved through several phases:

Early Developments: In the 1950s and 1960s, researchers developed simple algorithms and theories on machine learning.

The AI Winter: Due to high expectations and limited computational power, interest in AI waned during the 1970s and 1980s.

Modern Resurgence: The advent of big data, improved computing power, and new algorithms led to a renaissance in AI research and applications, especially in the last decade.

Source: MIT Technology Review

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4. Types of AI

Understanding AI involves recognizing its different types, which vary in complexity and capability:

4.1 Narrow AI (Artificial Narrow Intelligence - ANI)

Narrow AI is designed to perform a single task or a limited range of tasks. Examples include:

Voice Assistants: Siri, Google Assistant, and Alexa, which respond to specific commands.

Recommendation Engines: Algorithms used by Netflix or Amazon to suggest products or content.

4.2 General AI (Artificial General Intelligence - AGI)

AGI refers to machines that possess the ability to understand, learn, and apply knowledge across a wide range of tasks—much like a human being. Although AGI remains a theoretical concept, significant research is underway to make it a reality.

4.3 Superintelligent AI (Artificial Superintelligence - ASI)

ASI is a level of AI that surpasses human intelligence in all aspects. While it currently exists only in theory and speculative discussions, its potential implications for society drive both excitement and caution.

Source: Stanford University AI Index

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5. Real-World Applications of AI

AI is not confined to laboratories—it has found practical applications across various industries:

5.1 Healthcare

Medical Diagnosis: AI systems are now capable of analyzing medical images and predicting diseases such as cancer with high accuracy.

Personalized Treatment: Machine learning models help create personalized treatment plans based on a patient’s genetic makeup and history.

5.2 Automotive Industry

Self-Driving Cars: Companies like Tesla and Waymo are developing autonomous vehicles that rely on AI to navigate roads safely.

Traffic Management: AI-powered systems optimize traffic flow in smart cities, reducing congestion and pollution.

5.3 Finance

Fraud Detection: Banks use AI algorithms to detect unusual patterns that may indicate fraudulent activities.

Algorithmic Trading: AI models analyze vast amounts of financial data to make high-speed trading decisions.

5.4 Entertainment

Content Recommendation: Streaming services use AI to analyze viewing habits and suggest movies or shows.

Game Development: AI enhances gaming experiences by creating more realistic non-player character (NPC) behaviors.

Source: Forbes – AI in Business

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6. Advantages of AI

AI offers numerous benefits across multiple domains:

Efficiency and Automation: AI automates routine tasks, freeing up human resources for more complex and creative endeavors.

Enhanced Decision Making: AI systems analyze large datasets to provide insights that help in making informed decisions.

Improved Personalization: From personalized marketing to tailored healthcare, AI enhances user experiences by addressing individual needs.

Increased Safety: In sectors like automotive and manufacturing, AI-driven systems contribute to improved safety and accident prevention.

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7. Disadvantages and Challenges

Despite its many benefits, AI also presents several challenges:

Job Displacement: Automation and AI can lead to job losses in certain sectors, raising concerns about workforce displacement.

Bias and Fairness: AI systems can perpetuate biases present in training data, leading to unfair outcomes in areas like hiring or law enforcement.

Privacy Issues: The use of large datasets often involves sensitive personal information, raising concerns about data privacy and security.

Complexity and Cost: Developing and maintaining AI systems requires significant resources, expertise, and financial investment.

Ethical Concerns: The increasing autonomy of AI systems brings ethical dilemmas, such as accountability for decisions made by machines.

Source: Nature – The Ethics of AI

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8. Tips for Embracing AI Responsibly

For individuals and organizations looking to harness the power of AI, consider these practical tips:

Invest in Education and Training: Upskill your workforce by offering training in AI and data science to stay competitive.

Prioritize Transparency: Ensure that AI systems are transparent in their operations, especially when making decisions that affect individuals.

Implement Robust Data Security Measures: Protect user data with advanced security protocols to prevent breaches and misuse.

Monitor and Mitigate Bias: Regularly audit AI systems for biases and take corrective measures to ensure fair outcomes.

Stay Informed on Regulatory Changes: Keep abreast of evolving legal and ethical standards surrounding AI to maintain compliance and public trust.

Foster Collaboration: Work with cross-disciplinary teams, including ethicists, data scientists, and industry experts, to create well-rounded AI solutions.

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9. Future Outlook

The future of AI is both promising and challenging. With continuous advancements in technology, AI is expected to become even more integrated into our daily lives. Innovations such as AGI and even discussions around ASI signal potential breakthroughs that could revolutionize every sector—from education and healthcare to transportation and beyond. However, these advancements must be managed responsibly, balancing innovation with ethical considerations to ensure that AI benefits society as a whole.

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10. Conclusion

Artificial Intelligence is a dynamic field that continues to evolve, offering incredible opportunities while posing significant challenges. By understanding the various types of AI, its real-world applications, and the associated advantages and disadvantages, we can better prepare for an AI-driven future. Whether you are a business leader, a policymaker, or an enthusiast, staying informed and adopting responsible practices will be key to leveraging AI’s full potential.

As we move forward, it is crucial to strike a balance between technological innovation and ethical responsibility. With proper planning, education, and collaboration, AI can be a force for good, driving progress and improving lives around the globe.

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References

1. MIT Technology Review – https://www.technologyreview.com/

2. Stanford University AI Index – https://aiindex.stanford.edu/

3. Forbes – https://www.forbes.com/

4. Nature – https://www.nature.com/

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Meta Description:

Explore our comprehensive 1,000-word guide on Artificial Intelligence, covering its history, types, real-world applications, advantages, disadvantages, and practical tips for responsible adoption. Learn how AI is shaping the future while addressing ethical and operational challenges.

[letsremotify]

What Future Trends in Software Engineering Can Be Shaped by C++

The direction of innovation and advancement in the broad field of software engineering is greatly impacted by programming languages. C++ is a well-known programming language that is very efficient, versatile, and has excellent performance. In terms of the future, C++ will have a significant influence on software engineering, setting trends and encouraging innovation in a variety of fields. 

In this blog, we'll look at three key areas where the shift to a dynamic future could be led by C++ developers.

1. High-Performance Computing (HPC) & Parallel Processing

Driving Scalability with Multithreading

Within high-performance computing (HPC), where managing large datasets and executing intricate algorithms in real time are critical tasks, C++ is still an essential tool. The fact that C++ supports multithreading and parallelism is becoming more and more important as parallel processing-oriented designs, like multicore CPUs and GPUs, become more commonplace.

Multithreading with C++

At the core of C++ lies robust support for multithreading, empowering developers to harness the full potential of modern hardware architectures. C++ developers adept in crafting multithreaded applications can architect scalable systems capable of efficiently tackling computationally intensive tasks.

C++ Empowering HPC Solutions

Developers may redefine efficiency and performance benchmarks in a variety of disciplines, from AI inference to financial modeling, by forging HPC solutions with C++ as their toolkit. Through the exploitation of C++'s low-level control and optimization tools, engineers are able to optimize hardware consumption and algorithmic efficiency while pushing the limits of processing capacity.

2. Embedded Systems & IoT

Real-Time Responsiveness Enabled

An ability to evaluate data and perform operations with low latency is required due to the widespread use of embedded systems, particularly in the quickly developing Internet of Things (IoT). With its special combination of system-level control, portability, and performance, C++ becomes the language of choice.

C++ for Embedded Development

C++ is well known for its near-to-hardware capabilities and effective memory management, which enable developers to create firmware and software that meet the demanding requirements of environments with limited resources and real-time responsiveness. C++ guarantees efficiency and dependability at all levels, whether powering autonomous cars or smart devices.

Securing IoT with C++

In the intricate web of IoT ecosystems, security is paramount. C++ emerges as a robust option, boasting strong type checking and emphasis on memory protection. By leveraging C++'s features, developers can fortify IoT devices against potential vulnerabilities, ensuring the integrity and safety of connected systems.

3. Gaming & VR Development

Pushing Immersive Experience Boundaries

In the dynamic domains of game development and virtual reality (VR), where performance and realism reign supreme, C++ remains the cornerstone. With its unparalleled speed and efficiency, C++ empowers developers to craft immersive worlds and captivating experiences that redefine the boundaries of reality.

Redefining VR Realities with C++

When it comes to virtual reality, where user immersion is crucial, C++ is essential for producing smooth experiences that take users to other worlds. The effectiveness of C++ is crucial for preserving high frame rates and preventing motion sickness, guaranteeing users a fluid and engaging VR experience across a range of applications.

C++ in Gaming Engines

C++ is used by top game engines like Unreal Engine and Unity because of its speed and versatility, which lets programmers build visually amazing graphics and seamless gameplay. Game developers can achieve previously unattainable levels of inventiveness and produce gaming experiences that are unmatched by utilizing C++'s capabilities.

Conclusion

In conclusion, there is no denying C++'s ongoing significance as we go forward in the field of software engineering. C++ is the trend-setter and innovator in a variety of fields, including embedded devices, game development, and high-performance computing. C++ engineers emerge as the vanguards of technological growth, creating a world where possibilities are endless and invention has no boundaries because of its unmatched combination of performance, versatility, and control.

FAQs about Future Trends in Software Engineering Shaped by C++

How does C++ contribute to future trends in software engineering?

C++ remains foundational in software development, influencing trends like high-performance computing, game development, and system programming due to its efficiency and versatility.

Is C++ still relevant in modern software engineering practices?

Absolutely! C++ continues to be a cornerstone language, powering critical systems, frameworks, and applications across various industries, ensuring robustness and performance.

What advancements can we expect in C++ to shape future software engineering trends?

Future C++ developments may focus on enhancing parallel computing capabilities, improving interoperability with other languages, and optimizing for emerging hardware architectures, paving the way for cutting-edge software innovations.

[darkmaga-returns]

Technocrats in China love fellow Technocrats Mark Zuckerberg and his wife, Priscilla Chan, who both speak fluent Mandarin Chinese. Zuckerberg’s Meta produces the Llama AI model as open source, meaning it can be downloaded in full for free by anybody, anywhere, for any purpose. This paper details how the Chinese military is having a heyday adapting Llama from top to bottom.

According to the report, the adapting process poses challenges:

PLA experts have implemented different techniques involving advanced data collection, computational techniques, and algorithmic improvements. These efforts have enabled Llama to adapt to understand Chinese-language military terminology and tactics.

What does Zuckerberg think about China weaponizing Llama to use against America, the world and its own people? Crickets. ⁃ Patrick Wood, Editor

Executive Summary:

Researchers in the People’s Republic of China (PRC) have optimized Meta’s Llama model for specialized military and security purposes.

ChatBIT, an adapted Llama model, appears to be successful in demonstrations in which it was used in military contexts such as intelligence, situational analysis, and mission support, outperforming other comparable models.

Open-source models like Llama are valuable for innovation, but their deployment to enhance the capabilities of foreign militaries raises concerns about dual-use applications. The customization of Llama by defense researchers in the PRC highlights gaps in enforcement for open-source usage restrictions, underscoring the need for stronger oversight to prevent strategic misuse.

In September, the former deputy director of the Academy of Military Sciences (AMS), Lieutenant General He Lei (何雷), called for the United Nations to establish restrictions on the application of artificial intelligence (AI) in warfare (Sina Finance, September 13). This would suggest that Beijing has an interest in mitigating the risks associated with military AI. Instead, the opposite is true. The People’s Republic of China (PRC) is currently leveraging AI to enhance its own military capabilities and strategic advantages and is using Western technology to do so.

The military and security sectors within the PRC are increasingly focused on integrating advanced AI technologies into operational capabilities. Meta’s open-source model Llama (Large Language Model Meta AI) has emerged as a preferred model on which to build out features tailored for military and security applications. In this way, US and US-derived technology is being deployed as a tool to enhance the PRC’s military modernization and domestic innovation efforts, with direct consequences for the United States and its allies and partners.

PLA Experts’ Vision for Military AI

The PRC’s 2019 National Defense White Paper, titled “China’s National Defense for the New Era (新时代的中国国防),” notes that modern warfare is shifting toward increasingly informationized (信息化) and intelligentized (智能化) domains, demanding advances in mechanization, informationization, and AI development (Xinhua, July 24, 2019).

AI development in the military has accelerated in direct response to the demands of intelligent warfare, which itself has been propelled by recent technological advances. Experts from AMS and the People’s Liberation Army (PLA) have highlighted several key capabilities that AI systems must achieve to meet the PLA’s evolving military needs. First, large AI models must enable rapid response and decision-making to enhance battlefield situational awareness and support command functions. This includes autonomous mission planning and assisting commanders in making informed decisions under complex conditions. Strengthening the fusion of information from multiple sources is also seen as crucial, using AI to integrate data from satellite feeds, cyber intelligence, and communication intercepts. This is then used to deepen intelligence analysis and support joint operations, as highlighted by the PLA Joint Operation Outline (中国人民解放军联合作战纲要), which entered its trial implementation phase in 2020 (MOD, November 26, 2020). [1]

[aionlinemoney]

India’s Tech Sector to Create 1.2 Lakh AI Job Vacancies in Two Years

India’s technology sector is set to experience a hiring boom with job vacancies for artificial intelligence (AI) roles projected to reach 1.2 lakh over the next two years. As the demand for AI latest technology increases across industries, companies are rapidly adopting advanced tools to stay competitive. These new roles will span across tech services, Global Capability Centres (GCCs), pure-play AI and analytics firms, startups, and product companies.

Following a slowdown in tech hiring, the focus is shifting toward the development of AI. Market analysts estimate that Indian companies are moving beyond Proof of Concept (PoC) and deploying large-scale AI systems, generating high demand for roles such as AI researchers, product managers, and data application specialists. “We foresee about 120,000 to 150,000 AI-related job vacancies emerging as Indian IT services ramp up AI applications,” noted Gaurav Vasu, CEO of UnearthInsight.

India currently has 4 lakh AI professionals, but the gap between demand and supply is widening, with job requirements expected to reach 6 lakh soon. By 2026, experts predict the number of AI specialists required will hit 1 million, reflecting the deep integration of AI latest technology into industries like healthcare, e-commerce, and manufacturing.

The transition to AI-driven operations is also altering the nature of job vacancies. Unlike traditional software engineering roles, artificial intelligence positions focus on advanced algorithms, automation, and machine learning. Companies are recruiting experts in fields like deep learning, robotics, and natural language processing to meet the growing demand for innovative AI solutions. The development of AI has led to the rise of specialised roles such as Machine Learning Engineers, Data Scientists, and Prompt Engineers.

Krishna Vij, Vice President of TeamLease Digital, remarked that new AI roles are evolving across industries as AI latest technology becomes an essential tool for product development, operations, and consulting. “We expect close to 120,000 new job vacancies in AI across different sectors like finance, healthcare, and autonomous systems,” he said.

AI professionals also enjoy higher compensation compared to their traditional tech counterparts. Around 80% of AI-related job vacancies offer premium salaries, with packages 40%-80% higher due to the limited pool of trained talent. “The low availability of experienced AI professionals ensures that artificial intelligence roles will command attractive pay for the next 2-3 years,” noted Krishna Gautam, Business Head of Xpheno.

Candidates aiming for AI roles need to master key competencies. Proficiency in programming languages like Python, R, Java, or C++ is essential, along with knowledge of AI latest technology such as large language models (LLMs). Expertise in statistics, machine learning algorithms, and cloud computing platforms adds value to applicants. As companies adopt AI latest technology across domains, candidates with critical thinking and  AI adaptability will stay ahead so it is important to learn and stay updated with AI informative blogs & news.

Although companies are prioritising experienced professionals for mid-to-senior roles, entry-level job vacancies are also rising, driven by the increased use of AI in enterprises. Bootcamps, certifications, and academic programs are helping freshers gain the skills required for artificial intelligence roles. As AI development progresses, entry-level roles are expected to expand in the near future. AI is reshaping the industries providing automation & the techniques to save time , to increase work efficiency. 

India’s tech sector is entering a transformative phase, with a surge in job vacancies linked to AI latest technology adoption. The next two years will witness fierce competition for AI talent, reshaping hiring trends across industries and unlocking new growth opportunities in artificial intelligence. Both startups and established companies are racing to secure talent, fostering a dynamic landscape where artificial intelligence expertise will be help in innovation and growth. AI will help organizations and businesses to actively participate in new trends.

#aionlinemoney.com

[jcmarchi]

Post-RAG Evolution: AI’s Journey from Information Retrieval to Real-Time Reasoning

New Post has been published on https://thedigitalinsider.com/post-rag-evolution-ais-journey-from-information-retrieval-to-real-time-reasoning/

Post-RAG Evolution: AI’s Journey from Information Retrieval to Real-Time Reasoning

For years, search engines and databases relied on essential keyword matching, often leading to fragmented and context-lacking results. The introduction of generative AI and the emergence of Retrieval-Augmented Generation (RAG) have transformed traditional information retrieval, enabling AI to extract relevant data from vast sources and generate structured, coherent responses. This development has improved accuracy, reduced misinformation, and made AI-powered search more interactive. However, while RAG excels at retrieving and generating text, it remains limited to surface-level retrieval. It cannot discover new knowledge or explain its reasoning process. Researchers are addressing these gaps by shaping RAG into a real-time thinking machine capable of reasoning, problem-solving, and decision-making with transparent, explainable logic. This article explores the latest developments in RAG, highlighting advancements driving RAG toward deeper reasoning, real-time knowledge discovery, and intelligent decision-making.

From Information Retrieval to Intelligent Reasoning

Structured reasoning is a key advancement that has led to the evolution of RAG. Chain-of-thought reasoning (CoT) has improved large language models (LLMs) by enabling them to connect ideas, break down complex problems, and refine responses step by step. This method helps AI better understand context, resolve ambiguities, and adapt to new challenges. The development of agentic AI has further expanded these capabilities, allowing AI to plan and execute tasks and improve its reasoning. These systems can analyze data, navigate complex data environments, and make informed decisions. Researchers are integrating CoT and agentic AI with RAG to move beyond passive retrieval, enabling it to perform deeper reasoning, real-time knowledge discovery, and structured decision-making. This shift has led to innovations like Retrieval-Augmented Thoughts (RAT), Retrieval-Augmented Reasoning (RAR), and Agentic RAR, making AI more proficient at analyzing and applying knowledge in real-time.

The Genesis: Retrieval-Augmented Generation (RAG)

RAG was primarily developed to address a key limitation of large language models (LLMs) – their reliance on static training data. Without access to real-time or domain-specific information, LLMs can generate inaccurate or outdated responses, a phenomenon known as hallucination. RAG enhances LLMs by integrating information retrieval capabilities, allowing them to access external and real-time data sources. This ensures responses are more accurate, grounded in authoritative sources, and contextually relevant. The core functionality of RAG follows a structured process: First, data is converted into embedding – numerical representations in a vector space – and stored in a vector database for efficient retrieval. When a user submits a query, the system retrieves relevant documents by comparing the query’s embedding with stored embeddings. The retrieved data is then integrated into the original query, enriching the LLM context before generating a response. This approach enables applications such as chatbots with access to company data or AI systems that provide information from verified sources. While RAG has improved information retrieval by providing precise answers instead of just listing documents, it still has limitations. It lacks logical reasoning, clear explanations, and autonomy, essential for making AI systems true knowledge discovery tools. Currently, RAG does not truly understand the data it retrieves—it only organizes and presents it in a structured way.

Retrieval-Augmented Thoughts (RAT)

Researchers have introduced Retrieval-Augmented Thoughts (RAT) to enhance RAG with reasoning capabilities. Unlike traditional RAG, which retrieves information once before generating a response, RAT retrieves data at multiple stages throughout the reasoning process. This approach mimics human thinking by continuously gathering and reassessing information to refine conclusions. RAT follows a structured, multi-step retrieval process, allowing AI to improve its responses iteratively. Instead of relying on a single data fetch, it refines its reasoning step by step, leading to more accurate and logical outputs. The multi-step retrieval process also enables the model to outline its reasoning process, making RAT a more explainable and reliable retrieval system. Additionally, dynamic knowledge injections ensure retrieval is adaptive, incorporating new information as needed based on the evolution of reasoning.

Retrieval-Augmented Reasoning (RAR)

While Retrieval-Augmented Thoughts (RAT) enhances multi-step information retrieval, it does not inherently improve logical reasoning. To address this, researchers developed Retrieval-Augmented Reasoning (RAR) – a framework that integrates symbolic reasoning techniques, knowledge graphs, and rule-based systems to ensure AI processes information through structured logical steps rather than purely statistical predictions. RAR’s workflow involves retrieving structured knowledge from domain-specific sources rather than factual snippets. A symbolic reasoning engine then applies logical inference rules to process this information. Instead of passively aggregating data, the system refines its queries iteratively based on intermediate reasoning results, improving response accuracy. Finally, RAR provides explainable answers by detailing the logical steps and references that led to its conclusions. This approach is especially valuable in industries like law, finance, and healthcare, where structured reasoning enables AI to handle complex decision-making more accurately. By applying logical frameworks, AI can provide well-reasoned, transparent, and reliable insights, ensuring that decisions are based on clear, traceable reasoning rather than purely statistical predictions.

Agentic RAR

Despite RAR’s advancements in reasoning, it still operates reactively, responding to queries without actively refining its knowledge discovery approach. Agentic Retrieval-Augmented Reasoning (Agentic RAR) takes AI a step further by embedding autonomous decision-making capabilities. Instead of passively retrieving data, these systems iteratively plan, execute, and refine knowledge acquisition and problem-solving, making them more adaptable to real-world challenges.

Agentic RAR integrates LLMs that can perform complex reasoning tasks, specialized agents trained for domain-specific applications like data analysis or search optimization, and knowledge graphs that dynamically evolve based on new information. These elements work together to create AI systems that can tackle intricate problems, adapt to new insights, and provide transparent, explainable outcomes.

Future Implications

The transition from RAG to RAR and the development of Agentic RAR systems are steps to move RAG beyond static information retrieval, transforming it into a dynamic, real-time thinking machine capable of sophisticated reasoning and decision-making.

The impact of these developments spans various fields. In research and development, AI can assist with complex data analysis, hypothesis generation, and scientific discovery, accelerating innovation. In finance, healthcare, and law, AI can handle intricate problems, provide nuanced insights, and support complex decision-making processes. AI assistants, powered by deep reasoning capabilities, can offer personalized and contextually relevant responses, adapting to users’ evolving needs.

The Bottom Line

The shift from retrieval-based AI to real-time reasoning systems represents a significant evolution in knowledge discovery. While RAG laid the groundwork for better information synthesis, RAR and Agentic RAR push AI toward autonomous reasoning and problem-solving. As these systems mature, AI will transition from mere information assistants to strategic partners in knowledge discovery, critical analysis, and real-time intelligence across multiple domains.

[femastyle]

AI and the Future of Humanity: What Lies Ahead?

As we stand at the edge of a new era, artificial intelligence (AI) has caught our attention. This technology is set to change our lives in many ways. It will alter how we work, communicate, and tackle global problems. The big question is: what does the future hold for AI and humans?

Key Takeaways

The profound impact of artificial intelligence on society, from automation to job displacement

The rapid advancements in machine learning and their implications for the future

The critical importance of ethical considerations in the development of AI technologies

The complex relationship between humans and machines as we navigate the path of coexistence

The potential risks and benefits of AI, including predictions about the AI singularity

The role of governance and regulation in shaping the future of AI, particularly in sensitive domains like healthcare

The need for a balanced and thoughtful approach to embracing the transformative power of AI

There is a future book to learn more about artificial intelligence and its impact on humanity

https://dableustore.gumroad.com/l/AiFutureofHumanity

Artificial Intelligence and the Future of Humanity

Artificial intelligence (AI) is changing our world fast. It’s making healthcare better and changing how we travel. Machine learning, a key part of AI, is leading this change.

AI Impact on Society

AI is making our lives easier. In healthcare, it helps find diseases early and plan treatments better. It’s also making cars drive themselves, which could cut down on traffic and make roads safer.

Machine Learning Advancements

Machine learning has made huge strides. Now, AI can understand and create human-like language. It can also make smart choices based on lots of data. These changes are bringing new ideas to many fields.

AI Impact on Society Machine Learning Advancements

Improved healthcare outcomes

Autonomous transportation solutions

Personalized services and recommendations

Deep learning algorithms

Natural language processing breakthroughs

Predictive analytics and data-driven decision-making

As AI and machine learning grow, we must think about their big impact. We need to make sure these technologies help us all and are used for good.

There is a future book to learn more about artificial intelligence and its impact on humanity

https://dableustore.gumroad.com/l/AiFutureofHumanity

“The true impact of artificial intelligence will be felt when it is seamlessly integrated into the fabric of our daily lives, enhancing our experiences and empowering us to achieve more.” — AI Thought Leader

Ethical Considerations in AI Development

Artificial intelligence (AI) is growing fast, and we must tackle its ethical issues. Ethical AI development and AI safety and control are key. They help us use this powerful tech responsibly.

Algorithmic bias is a big problem. AI can make old biases worse, causing unfair results. It’s important for developers to find and fix these biases. They should use data that’s fair and unbiased.

Privacy and security are also big concerns. AI needs lots of data, which raises privacy questions. We need strong privacy rules and clear data use to protect our information.

AI could be misused, like in making harmful weapons or spreading false info. We need rules and guidelines to keep AI positive. This ensures it helps people, not harms them.

Fixing these issues needs teamwork from AI makers, lawmakers, ethicists, and the public. By focusing on ethical AI, we can use its benefits while avoiding risks. This way, AI can help us all in the long run.

“The greatest challenge for AI is to ensure that it is developed and used in a way that is beneficial to humanity as a whole, not just a select few.”

Navigating Human-AI Coexistence

Artificial intelligence (AI) is getting smarter, making our relationship with machines more complex. This part talks about the good and bad sides of living with AI. We need to figure out how to work together with AI without losing jobs.

AI is changing our lives, and it’s both exciting and scary. It can make things better and faster, but it also worries us about losing jobs. We need to work together to find ways to keep jobs safe while using AI’s power.

Good rules and laws are key to making AI and humans get along. We need rules that protect our privacy and make sure AI acts like us. It’s important to keep people safe and happy as we use AI to make our lives better.

There is a future book to learn more about artificial intelligence and its impact on humanity

https://dableustore.gumroad.com/l/AiFutureofHumanity

[hydralisk98]

"16^12" DR Life Scripture, Blackhand Thread '(0x17/?)

Preface

Sum?

Checklist

Klara "Olive" Kér

Female biological + genetic gender (♀), she/her pronouns... (gender-affirming comforting life)

August 1st 4498

Shoshoni Citizenship, University Doctorate Graduate as Historian with plenty of supplemental classes & certifications completed

~20-25 years old

600 years lifespan

Leo/Lepio astrology

INTJ (Myers-Briggs 16 personalities)

5'8", 144lb;

Autism Spectrum Disorder

Autonomous Service Grids (general AI modules) assisting & empowering decisions in meaningful & constructively affirmative ways

Active in federal politics (Senate) & constructively so

Active in my local community

Spirituality & Magicks encouraged within ethical bounds

True Polymorph & other Magick abilities endorsed & legal in this world

Mixnet (The 3D_XML+OpenXanadu WorldWideWeb as operated by Shoshones and other major civilizations, the mainstream use of the global information network with Memex web crawler being a transparent well-known fact. The service is like ‘OGAS’ as a transparent vast and benevolent data processing and knowledge base network), side nets (Think HTTPS, I2P, FTP, OpenSSH & Gemini protocols as examples) & user nets (shortwave radio networks, RTTY / VideoTex & other retro-er infrastructure services)

Lifa app

Body change app

Manifestation tutor

Ava, my synthetic-tier android blonde servant

Shoshona

Nice & handsome copyleft cyberware & biomods

Soft grunge, retro grunge, light academia?, earmuffs

Empowering, wholesome and optimistic solarpunk retrofuture world with much longer historical record & transparent GLOSS mindset attached

Best humane, insightful & comfortable possible timeline stemming from no Woodrow Wilson federal presidency

Hopeful & positive future for the next five centuries easily, both for biological sapients & androids alike;

Bookstore curator / clerk

Multimedia integrator

Educational explainer

Data scientist

Vast personal domain home of my very own in Maskoch within sweet neighborhood

Gustav Hayden as father

Falah Becker as mother

Gustav Hayden, my father { Developer, more contextually-sensitive, nicer to live with, more tolerant, more understanding, less invasive, asks more questions, cares unconditionally, toymaker; }

Falah Becker, my mother { Artistic in her hobbies, massively successful polyglot philosopher, linguist career, much free time, much autonomy, super sweet social life, some more technical know-how, interested in small history and witchcore fantasy fiction much, spiritual acceptance; }

Maternal & paternal familial histories with more... abundant, insightful & positive empowering lives;

Deno Hayden middle younger brother from 4499

Wyatt Hayden youngest brother from 4502

Chronokinesis

Mentat-tier computer data-processing capabilities

Psychics

Personal profile card

Great cultural medium to be watched, listened, et cetera.

Plenty of free-time

?

Multimedia explainer assets to complement 16^12

Hyperlinks to refer to:

https://hydralisk98.blog/post/739215553084964864/imagination-guide-to-16-12-part-1

https://hydralisk98.blog/post/738867056627351552/klara-k%C3%A9r-wishlist-reality-shifting-scripture

https://hydralisk98.blog/post/738796546941943808/contextual-scripture-information-for-my-very-own

https://hydralisk98.blog/post/724346987866193920/servitor-for-16-12-concept-draft-1

https://www.pinterest.ca/olivae_tribble/blackhand-inspiration-board/

[ixnai]

In the realm of artificial intelligence, the devil is in the details. The mantra of “move fast and break things,” once celebrated in the tech industry, is a perilous approach when applied to AI development. This philosophy, born in the era of social media giants, prioritizes rapid iteration over meticulous scrutiny, a dangerous gamble in the high-stakes world of AI.

AI systems, unlike traditional software, are not merely lines of code executing deterministic functions. They are complex, adaptive entities that learn from vast datasets, often exhibiting emergent behaviors that defy simple prediction. The intricacies of neural networks, for instance, involve layers of interconnected nodes, each adjusting weights through backpropagation—a process that, while mathematically elegant, is fraught with potential for unintended consequences.

The pitfalls of a hasty approach in AI are manifold. Consider the issue of bias, a pernicious problem that arises from the minutiae of training data. When datasets are not meticulously curated, AI models can inadvertently perpetuate or even exacerbate societal biases. This is not merely a technical oversight but a profound ethical failure, one that can have real-world repercussions, from discriminatory hiring practices to biased law enforcement tools.

Moreover, the opacity of AI models, particularly deep learning systems, poses a significant challenge. These models operate as black boxes, their decision-making processes inscrutable even to their creators. The lack of transparency is not just a technical hurdle but a barrier to accountability. In critical applications, such as healthcare or autonomous vehicles, the inability to explain an AI’s decision can lead to catastrophic outcomes.

To avoid these pitfalls, a paradigm shift is necessary. The AI community must embrace a culture of “move thoughtfully and fix things.” This involves a rigorous approach to model validation and verification, ensuring that AI systems are robust, fair, and transparent. Techniques such as adversarial testing, where models are exposed to challenging scenarios, can help identify vulnerabilities before deployment.

Furthermore, interdisciplinary collaboration is crucial. AI developers must work alongside ethicists, domain experts, and policymakers to ensure that AI systems align with societal values and legal frameworks. This collaborative approach can help bridge the gap between technical feasibility and ethical responsibility.

In conclusion, the cavalier ethos of “move fast and break things” is ill-suited to the nuanced and impactful domain of AI. By focusing on the minutiae, adopting rigorous testing methodologies, and fostering interdisciplinary collaboration, we can build AI systems that are not only innovative but also safe, fair, and accountable. The future of AI depends not on speed, but on precision and responsibility.

[yandins-beleagured-historian]

Entry #029.3.Supplemental.Demeter

--Recovered from the Archive of Engagements aboard the Tsiolkovan-- --Official Action Report, originally created 963.M41.-- +The Demeter Campaign:

Shargen Aurastra and newly-promoted Second Captain Samas Tenebra are called upon to mop up remaining traitor presence in the Demeter sector following the War for Pandorax, encountering a Black Legion warband styling itself “The Obsidian Blade” carving out a domain for themselves in the ravaged Avaricum system, led by former Justaerin member Vayxin the Ravager. Tenebra leads a select cadre of forces to defend a critical mechanicum outpost but is forced back by a short-lived but bloody daemon incursion summoned by Dark Apostle Kabrius on the outpost’s upper levels, at the loss of most of Squad Octavian and the company’s attached librarian. The loss atop the manufactorum buys the warband time to reactivate an old Shadowsword within the manufactorum. Determined to ensure such an asset does not make it offworld, Aurastra leads a daring assault on the manufactorum to end Vayxin and his new toy. The battle is fierce, and Aurastra is almost killed when his command vehicle takes a direct hit from the Shadowsword’s main weapon. Despite the long, storied careers of both Aurastra and Vayxin, the battle is decided by youngbloods:

Neophyte Sergeant Trazis, who despite coming under heavy fire had successfully outflanked the Shadowsword, ingressing through a hull breach in the rear to plant a meltabomb on the Volcano Cannon’s primary capacitor bank.

Savant-Initiate Hastel Glademan, who provided air support from the Stormtalon Hurricane Dragon, catching Dark Apostle Kabrius in the open and flattening his position with a barrage of cyclone missiles.

Brother Cosrau Yandin, who followed Aurastra into a direct confrontation with Vayxin the Ravager. Despite losing many battle-brothers and taking a near-fatal wound from the Ravager’s chainfist, Yandin was able to slay the warlord alone, exploiting a breach in his Terminator armour using nothing but a well-placed meltagun shot and a combat knife to Vayxin’s exposed eye.

--Unofficial Interpretation Fragment #3 of 12# (Please Confirm?), derived from tactical data logs recovered from Redeemer-Pattern Land Raider Tvashtar's Fire during subsequent restoration--

"Sable Exact, this is Tvashtar. We are coming to you." Keldek Mormys clipped over the vox channel and spurred the Land Raider forwards. The turret bustle next to him rattled and shook as the tank’s machine spirit assumed direct control of the twin assault cannons. Mormys watched the weapons' target reticles track jerkily across the pict-feed wired directly into his helmet, just one of a dozen sensor inputs feeding into the cocoon-like driver's position high in the vehicle's interior. If he hadn't been controlling the tank though direct nerve impulse, he wouldn't have needed to lift a finger more than an inch off the controls before he'd be touching the glacis plate, such was the tightness of the confines of the position. All the better to allocate space to armour, armament and passengers. In truth, given the potency of the machine spirit within Tvashtar's Fire, he was barely more than a passenger himself. The tank was an old one, with a bombastic, eager character. He could feel it every time the Land Raider’s treads dug into a rise, like a mountain goat scrambling up a spoil heap. According to the stories, it was fully capable of operating fully autonomously when circumstances were particularly dire. It was only the narrow advantages of the organic mind in pattern recognition and data processing that warranted his presence at all. That and the vox link to the battleforce command network that dispersed its constant stream of data packets in clicks and growls directly into his ears. And right now the network was alive with a hashing, maddening overlap of information, The Mechanicus outpost was a slowly growing silhouette on the horizon, a squat monolith of fortified stores and workshops. The nimbus of warplight had crackled into existence around the outpost’s summit at the exact same time the aetheric interference had started filling the vox-channels, but enough of Gygar Octavian’s final broadcast had made it through the hissing static. It was that broadcast that had spurred every Iron Fists asset on the planet to converge on the outpost.

“Captain Aurastra,” Mormys called on the tank’s internal vox. “We have uplink with reconnaissance elements. Glademan and Trazis are joining formation. We shall reach engagement range in six minutes.”

“Noted, Keldek. Maintain flank speed and mark targets as they appear. Please keep the network link open while I inform our comrades.” The captain’s tone was relaxed, as was the tone of the data packets that briefly flashed through Mormys’ perception on their way from the Land Raider’s internal systems to the other vehicles in the formation. Mormys caught a brief snippet of off-network speech in that same tone beneath him in the passenger bay. A rousing speech, no doubt. That was beneficial. Tvashtar’s Fire was used to hosting terminators, not tactical marines, and the unfamiliarity of their cargo was reflected in the machine spirit’s disposition.

Mormys reached for the panel on the communication terminal, to patch into the squad-level vox net and see if First Captain Aurastra was as good as marshalling those of another company as his own. His finger was on the switch when the Land Raider’s external sensors lit up with alarms. Mormys hammered the switch into the on-position and felt the marines in the passenger bay flinch at his interruption.

“Captain, we have a massive thermal signature reading from within the outpost.”

“More information, please, Keldek.”

Mormys strained closer to the pict-screens, flicking through view options, focusing in on the bloom of heat radiating from the lower portion of the outpost. The metres-thick shell of reinforced ferrocrete yielded precious few answers, but there was a horrible sense of familiarity that was starting to creep out of the more organic parts of Mormys’ brain.

“Uncertain, captain, but the readings align with macro-grade weaponry. Will continue to observe.”

“Understood.” There was another click as the Captain plugged directly into the Land Raider’s internal systems, his next words ringing well-beyond the confines of the tank.

“All elements, this is Shargen Aurastra. We have a potential grandis-level threat spooling up in the bowels of that outpost. Make all haste and stay vigilant. If we see it before it sees us, the advantage will be ours.”

The message was met with a flurry of confirmations from the other force elements. Mormys sent  his own brief confirmation and urged Tvashtar’s Fire to go faster, eyes glued to the rattling pict-screens as the tank ground through the remnants of a walled-off courtyard and summitted the lip of a crater.

“Hurricane Dragon to Niveus Exact, this is Glademan!”

The signal spiked almost painfully through Mormys’ senses, thick with static and interference as it shot into the Land Raider’s vox network. He didn’t even hear Aurastra’s response before the signal continued, and Mormys was just about to start work on a scolding reminder about vox-procedures that the pilots of the Fifth Company were in clear need of, before the rest of the signal drove a pulse of shock up his spine.

“I have my sight on the facility. The lower bay doors are open, we have super heavy armour, I repeat, super heavy armour emerging.”

The Land Raider’s momentum carried it down into the crater and up the other side, its machine spirit taking advantage of Mormys’ momentary paralysis to dig its treads in and climb. It was still moving at speed when it reached the crater lip, so it took a few moments for the tank to come to a stop. A few moments that Mormys spent staring at a gargantuan, slab-sided nightmare of riveted metal that had clawed its way from some unholy bowel of the fortified mechanicus outpost and was now rolling straight towards them. In the face of ordinance designed explicitly to fell titans, vox-procedures went out the window. Mormys’ words were heard in the ear of every Iron Fist within ten kilometers as he jammed the tank’s motors into reverse and screamed “SHADOWSWORD!”

It was too late. Keldek Mormys was imparted with the acute sensation of the Land Raider’s glacis plate, rendered red hot by a billion watts of laser energy, peeling back like the lid of a sardine can, before his world became a unitary point of infinite light and heat.

[popaurmaa]

BECOME A CERTIFIED AI EXPERT.

In the rapidly evolving landscape of technology, the role of a certified AI expert has become increasingly crucial. These individuals stand out in the field of artificial intelligence by possessing recognized credentials that serve as a testament to their proficiency and extensive knowledge.

A certified AI expert is not just someone with a general understanding of AI; rather, they have undergone specific training and assessments to validate their expertise. These professionals have delved into various facets of AI technologies, showcasing their mastery in areas such as machine learning, natural language processing, computer vision, and other related domains.

The certification process involves rigorous training programs and examinations, ensuring that the AI expert is well-versed in the latest advancements and industry best practices. By obtaining these certifications, individuals demonstrate their commitment to staying abreast of the dynamic AI landscape, equipped with the skills to navigate its complexities.

Machine learning, a subset of AI, involves the development of algorithms that enable systems to learn from data and improve their performance over time. Certified AI experts excel in understanding and implementing these sophisticated algorithms, contributing to advancements in predictive analytics, pattern recognition, and decision-making systems.

Natural language processing (NLP) is another critical aspect of AI, focusing on the interaction between computers and human language. Certified experts in this field possess the ability to develop applications that comprehend, interpret, and generate human-like language, leading to innovations in voice recognition, language translation, and chatbot development.

In the realm of computer vision, AI experts with certifications showcase their prowess in enabling machines to interpret and make decisions based on visual data. This has far-reaching implications, from facial recognition technology to autonomous vehicles, revolutionizing industries and enhancing efficiency.

The significance of certified AI experts extends beyond their technical skills; it also reflects their dedication to ethical and responsible AI practices. With a solid foundation in recognized standards and principles, these professionals play a pivotal role in ensuring that AI technologies are deployed ethically and responsibly.

In conclusion, a certified AI expert is a linchpin in the realm of artificial intelligence, armed with validated credentials that underscore their expertise. These professionals are at the forefront of technological innovation, contributing to advancements in machine learning, natural language processing, computer vision, and beyond. As AI continues to shape the future, the role of certified experts becomes increasingly vital in steering the course of responsible and impactful AI development.

Click here to learn how you can become a Certified AI Expert. https://bit.ly/Certified_AI_Expert

#AiExperts #AiEducation #AiCertification #PythonProgramming