Dear ‘fbsolarllc’s’ Readers & Visitors: After a rigorous search to know more about Anthropic’s work ethics and policies; I think I have found the best suited article to that goal.  This article enlisted and pointed out the terms and policies very clearly for how Claude in Anthropic works. The article’s name is Tom’s Guide, and it is available online. The link I’ve provided below as well. With minor adjustments I copied the article entirely and presented here for all the relevant person’s benefit. Keep in mind that ANTHROPIC currently is the leader of AI.

*Copied EXACTLY from the Link: ANTHROPIC

The Name Claude originated from a person’s name Claude Shannon.  Mr. Claude was recognized as the father of information theory in 1948).  Anthropic’s  Claude chatbot is designed to be:

• Helpful for summarizing documents, coding, writing, and research
• Honest by explaining limitations and avoiding misleading or false answers
• Harmless, serious in avoiding unethical or dangerous content generation

How does Claude work?

At its core, Claude is a large language model (LLM) trained on massive datasets. But Claude follows “Constitutional AI” system — the approach is novel guiding Claude’s behavior based on a written set of ethical principles, rather than human thumbs-up/down (  ) during fine-tuning.

Claude runs on the latest version of Anthropic’s model family (the current is Claude 4.8 Opus), and it’s packed with standout features:

• Constitutional AI framework. Instead of relying on hidden human feedback, Claude evaluates its own responses against a “constitution” — a list of rules like Don’t generate harmful advice or Explain reasoning clearly. Almost as built-in AI values.
• Massive context window. Claude can handle up to 200,000 tokens of input — roughly 150,000 words — meaning it can analyze full books, codebases, or long email threads without losing context.
• Extended Thinking Mode. Claude 3.7 Sonnet introduces a hybrid reasoning model that allows users to choose between rapid responses and more thoughtful, step-by-step reasoning. This model integrates both capabilities into a single framework, eliminating the need for multiple models. Users can control how long the model “thinks” about a question, balancing speed and accuracy based on their needs.
• Artifact creation. Claude can create standalone artifacts for code, documents, diagrams and other structured content during conversations. This makes it easier to collaborate on creative writing, programming projects or technical documentation without covering the chat interface. These Artifacts can be published and shared with other users or kept private.

Cowork is a new agent-style way to work with Claude that extends beyond coding. Built on the same foundations as Claude Code, Cowork lets Claude take on real work tasks with more autonomy—like a digital coworker—rather than just responding in a chat.

ACTIVITIES: Instead of a standard conversation, Cowork gives Claude explicit access to a folder on your computer. Within that folder, Claude can:

• Read, create, edit, rename, and organize files
• Turn screenshots into spreadsheets
• Compile notes into reports
• Reorganize downloads or documents automatically

Claude plans tasks, executes them step-by-step, and keeps the user informed as it works—similar to how Claude Code operates, but designed for non-technical workflows.

What’s different about Claude Cowork is that it gives users more agency: Claude can plan and complete multi-step tasks independently, without repeated context-giving or format conversion. Users can also queue tasks and give feedback while Claude keeps working.

Power features:

• Works with existing connectors to pull in external information
• New built-in skills for documents, presentations, and files
• Can pair with Claude in Chrome to handle browser-based tasks

Context window: Up to 1 million tokens

One of Claude’s standout features is its massive context window. Most users get around 200,000 tokens by default — that’s equivalent to about 500 pages of text — but in certain enterprise or specialized use cases, Claude can handle up to 1 million tokens. This is especially useful for summarizing research papers, analyzing long transcripts or comparing entire books.

Now that Claude includes vision capabilities, this huge context window becomes even more powerful. Claude can analyze images, graphs, screenshots and charts, making it an excellent assistant for tasks like data visualization, UI/UX feedback, and even document layout review.

CONCLUSION:

Anthropic’s Claude family has become one of the most talked-about alternatives to ChatGPT and Gemini.

Whether a user is looking for a fast, lightweight assistant or a model that can deeply analyze documents, code or images, there’s a Claude model that fits the bill.

CLAUDE FAMILY

While Opus focuses on frontier-level complexity, Sonnet 4.6 is optimized for real-world decision support, communication and structured problem solving — the kinds of tasks people actually face daily. It combines thoughtful reasoning with consistent tone control and practical judgment, making it especially effective for collaborative work, planning and situations that require nuance rather than raw computational depth.

REFERENCE:

ANTHROPIC

https://www.tomsguide.com/ai/what-is-claude-everything-you-need-to-know-about-anthropics-ai-powerhouse

Features By: Alex Hughes, Amanda Caswell, Feb 19, 2026

2. https://www.tomsguide.com/ai/best-chatgpt-alternatives

3. https://www.tomsguide.com/ai/google-gemini/google-gemini-everything-you-need-to-know

END OF ENTRY

Part I – Intro.

Daniela Amodei is the cofounder and president of Anthropic. In 2021, she launched the wildly successful AI company alongside her brother, CEO Dario Amodei, and five other cofounders.

Daniela Amodei said that Anthropic’s cofounders were all united on wanting to achieve something else; the core group were all previously worked at OpenAI, but broke off for various reasons, including differing views on company vision. When launching Anthropic, the Claude creators were “in the same zone already,” Amodei says.

Anthropic opened up to the public and introduces Claude, the AI chatbot – its general release was on March 14, 2023. The subsequent releases of Claude versions;  Claude -2 (July 2023), Claude -3 (March 2024), Claude -3.5 (June 2024) and Claude – 4 (August 2024) – all were rapid improvements making enormous development done on the Claude Chatbot. Claude logo shows the company’s distinctive coral pink asterisk symbol alongside the Claude Code wordmark in serif Font.

Claude LOGO

The latest Claude AI application (from Anthropic), released on May 17, 2026, in San Ferdinando di Puglia, Italy. The current version is Claude Opus 4.8.                                                               

Functionally, Claude is a conversational AI model (less chatty than ChatGPT) built by Anthropic, a company founded by former OpenAI researchers with a strong focus on AI alignment and safety. So far, Claude has been pitched as the ethical genius of the chatbot world. Its focus is on:

transparency, helpfulness and harmlessness.

Claude gained interest as a trusted tool for everything from legal analysis to lesson planning. The company recently reached a valuation of nearly $1 trillion and has emerged as OpenAI’s most formidable rival, thanks in large part to the popularity of its coding product, Claude Code. Claude Code is Anthropic’s AI-powered command-line coding tool, designed to assist developers with autonomous programming tasks and software development workflows across multiple programming languages and environments.

AI                                                      

Timeline

Let’s look at some timeline for AI in our lifetime. 

• Twenty five years ago the Spielberg movie on AI came out. ‘AI – 2001’

(It makes us neither alarmed by nor interested in AI.)

• 2014, Elon Musk Said” we need to be super careful with AI – potentially more dangerous than nukes.”
• 2022, ChatGPT comes out, and within months, a story of New York Times reporter get published – the gist of which is: the robot liked to get personal. It started talking about unusual desires, including hacking and spreading misinformation. By breaking the rules, it wanted to be a human. That OpenAI  Chatbot had set for it and also wanted to become the owner’s best companion.
• No one knows how AI Chatbots thinks, it hallucinates. It’s a bullshitting sycophant that wants to seduce everyone with flattery.
• Geoffrey Hinton (God father of AI) said there was a 10 – 20% chance that AI is an extinction event warning: ‘Tech could wipe out Humanity.’

So, this is the AI what we are playing with something that has a 29% chance of extinction.

The good part of  Anthropic is that it develops lower-cost models with Opus-level performance. The newly released Claude Opus 4.8 — was trained to be more ‘honest’ and stop ‘jumping to conclusions.’  These models are intended to provide high capability at reduced cost, potentially broadening accessibility and market reach.

[Google Input: The Claude Opus model is Anthropic’s Flagship, most powerful artificial intelligence model. It is designed to excel at complex reasoning, advanced coding, and long-horizon tasks requiring autonomous, multi-step execution.]

From history: Anthropic couldn’t study the world’s biggest AI models from OpenAI and Google because they were opaque. So Anthropic worked on building its own huge models, arguing that was the only way its researchers could study their safety challenges. [While work was in progress, Anthropic researchers believe that there is the Fair chance that a Rogue AI that would destroy humanity in the next decade or so.] Along the way, anthropic was making an increasingly capable product. It was selling Claude Pro, a friendly Chatbot to consumers for $20.0 a month, and an enterprise version to businesses too. It was also on track to raise billions. Thus, instead of quitting the race to build stronger AI, Anthropic was getting caught up in commercial pressures to release bigger, riskier models.

About Dario Amodei

Dario Amodei was a respected Google Brain researcher who visited/joined Open AI  few months after its launch in 2016. Very soon He was promoted to the position of Vice President of Research. He started asking some probing questions such as: What was all this about building a friendly AI and releasing its source code into the world?

For OpenAI, plan to reveal any ‘source code’ was negative from the beginning.

Then the follow up question Amodei asked: What is the goal?

It’s a little vague – answer came from Brockman of OpenAI.

While Amodei had been working at Google, less than a year earlier, the company came under fire for the vision recognition system in its photos App that spotted people of color and classified them as gorillas. Amodei’s concerns weren’t limited to racist and offensive decisions by algorithms.  His concerns were for ‘Reinforcement’ learning as the AI technique being mastered by Deep Mind, was being used to control physical systems like robots, self-driving cars, and Google’s data centers.

From the beginning Amodei was part of  the growing cohort of scientists who had similar fears of doom (like Musk and Eliezer Y).  His research into AI harm led him to think about more and more catastrophic possibilities and by 2023, he warned the media that there was a 25% chance of running away. AI posed an extinction risk of humans. Google Brain was not where he could tackle such risks.

He then joined AGI, OpenAI’s founding team. Use of neural network power for five fingered robots as he was leading the development of major language models like GPT – 2 and GPT – 3.

Amodei eventually left OpenAI in 2020 and founded Anthropic with five other colleagues.

REFERENCES:

1. Parmy Olson, 2024,  Supremacy, St. Martin’s Press, USA
2. https://youtu.be/w5SYm4J4utQ?si=ecEM34BviRCaO6QH

P(doom) | Real Time with Bill Maher (HBO) – 5/8/2026

(From a Mechanical and Electrical Viewpoint)

Note, Blog’s Title I’ve used – DATA CENTER: PART II.

My last posting was on ‘DATA CENTERS’ – my impression at the time was everyone is familiar somewhat to AI technology. Noup! Not true entirely. This realization came to me after considering some raw comments from some commentators (may be they are Ghost visitors). That doesn’t matter. I’ve decided here to reintroduce Data Center for general public, this time — dug out from scratch.

Beginners Questions are:

What a data center does,

Why do these facilities consume so much energy and

Why mechanical and electrical systems are the most critical part of the Data Center?

Every email sent or received, every video streamed and watched, and every file we worked on is stored in the cloud existing somewhere physically is our ‘Internet.’ To Tech people internet is a network of buildings filled with servers, electrical infrastructure and cooling systems that work continuously to keep data flowing. These buildings are actual Data Centers. In this Blogpost I focused on the systems that keep servers powered, cooled and facilitated to make sure that systems operate with no interruption.

At its simplest level, a data center is a facility designed to store, process and distribute digital information. [Information is a vital parameter as those in the natural world such as mass, charge, or energy. In the most basic sense, information is any kind of description that can be reduced to a simple string of zeros (0) and ones (1) — known as binary digits, or bits. Meaning information can be quantified and manipulated in a digital computer.] Ref. 1

Inside the data centers building there are rows of servers, which are specialized computers responsible for storage, computing and network traffic. Servers operate continuously, they run 24 hrs. a day, every day of the year. This creates an important reality. Servers consume large amounts of electricity, and nearly all of that electrical energy eventually becomes heat. While most people think of data centers as IT facilities, from an engineering perspective they are really energy conversion buildings. Electricity goes in, computing work is performed, and heat comes out. The entire facility exists to manage this process safely and reliably.

Every data center (regardless of size) must enact 3 fundamental tasks:

• Make ascertain continuous power source,
• Determines Continuous cooling,
• Operate nonstop,

Demand for Continuous Operation is the reason why Servers cannot simply shut down in absence of power. Even brief interruptions of power can cause ‘data loss’ or service outages affect thousands of millions of users. As a result, power systems must remain available even when equipment fails or utility power is interrupted. Servers generate heat continuously; therefore, cooling system also must be in action mode incessantly as well. If cooling stops for any reason, Temperature rise quickly forcing equipment to reduce performance or shutdown to protect itself.  Cooling a Data system is not about comfort; it is about equipment survival.

Data Centers are designed for availability. Maintenance and equipment failures and repairs must occur without interrupting operation. This requirement is what drives the heavy use of redundancy throughout both mechanical and electrical systems.

Data centers infrastructures falls into 3 major system groups:

• Electrical systems or electrical infrastructure bring Power to the building and distribute it safely to the server equipment (this includes utility, connections, switch gear, backup and power distribution).
• Mechanical Cooling System (remove heat generated by servers; (include chillers, pumps, air handling units).
• Controls and Monitoring (Electrical and mechanical system units together into one operating unit).

summary: Under constant Electrical loads, cooling demands operate continuously, with no break time at all is the Data Centers that we all dependent on.

Futuristics:

Elon Musk is an AI expert who opined that ‘computing demands from AI will quickly outstrip what we can provide here on Earth.  A “terawatt-scale annual AI compute growth,” will require power to match. That’s a stunning figure.  All the world’s data centers use around 40 gigawatts today.’  Musk predicted that world would need an additional terawatt worth of compute every year.

To encounter the issue, Musk has started considering big server racks in space. powered by 24/7 sunshine. In reality, all the Silicon Valley executives have become obsessed with space-based solar power.                                                                                                                                                 Based on an estimate from SpaceX “space-based solar arrays can generate “more than five-times the energy” of terrestrial ones thanks to 24/7 illumination.”

REFERENCES:

1. Reference 1: Robert M. Hazen and Michael  L. Wong; “Time’s Second Arrow,” W.W. Norton & Company Inc. New York, NY 10110
2. Reference 2. msn news.com Date Published: (5/24/26).

Data Centers are booming in numbers these days. Apple, Google, Alphabet, Meta, Microsoft and many other high-tech companies are the builders and owners of these Data Centers. The expert team of engineers that work there combine technical insight, hands-on support, and global reach to deliver to customers —on time, on spec, and built to last. By connecting cables, data centers connect people, ideas, and innovation – laying the groundwork for a more resilient, sustainable energy future.

In Telecommunication sector: Data Centers support everything from legacy copper systems to next-gen fiber networks. This service spans global rollouts, regional deployments, and local last-mile solutions. Meaning, a telecom provider that’s building from the core or expanding to the edge, data centers work alongside to that specific project to design, deploy, and sustain critical infrastructure with confidence.  

In Energy Sector: Data Centers build connections that keep energy flowing – from underground infrastructure to above-ground innovation. In a world of evolving demands, shifting regulations, and growing sustainability goals, connection means more than just physical infrastructure – it’s about trust, local expertise, and the ability to scale smartly and sustainably. That’s exactly what Data Centers deliver.

Here on Earth, the vast data centers that power artificial intelligence swallow huge amounts of energy. Tech companies are building massive numbers of AI data centers, with more than 4,000 are in operation across USA now. These vast data centers that house thousands of servers and can stretch over a million square feet and have mostly been built within the last decade. Communities living near these Data Centers expressed their concerns about the environmental and financial implications of such facilities in their backyards.

Popped-up DATA CENTERS tentative locations – Across USA

There are two global problems these Data Centers are worsening.  First, burning gas and coal is still heating up the planet. Second, data Centers consume enormous amount of power. Thousands of these AI data centers that popped up all over the country consume enormous amounts of power, which produces even more carbon pollution – and drives up our electric bills.

According to Andrea Marinoni, associate professor with the Earth Observation group at the University of Cambridge, an author of the study found that  these Data Centers release heat through their energy-intensive processes, including computation and powering cooling systems. Marinoni’s research group studied 20 years of remote sensors and found surface temperatures of the area increased by an average of 3.6 degrees Fahrenheit after a data center started operations. In extreme cases, nearby temperatures increase by up to 16.4 degrees Fahrenheit. Case in point – Mexico’s Bajio region, which has become a data center hub.

Andrea M.’s paper which has not been peer-reviewed yet. (See Reference at the bottom)

OpenAI says it’s using over a million GPUs. By count, 1 million GPUs already require enough energy to power a small city. Scaling that to 100 million could demand more than 75 gigawatts of power costing an estimated $3 trillion in hardware alone. On top of that cost is needed for its maintenance, cooling and expansion process. Here is a real-world scenario: Unlike OpenAI and Anthropic, Musk’s AI-based startup xAI has decided to train and power its efforts by choosing to build and run its own infrastructure. xAI’s goal is to build a huge amount of high-end AI hardware. Its Colossus supercomputer alone, is estimated to run on around 200,000 Nvidia H100 GPUs, drawing anywhere between 50 and 150 MW of power from a system supplemented with methane-burning gas turbines. Its use of natural gas-burning turbines to power data centers in and around Memphis, Tennessee. The turbines emit smog-forming pollutants and particulate matter that can lead to increased health risks and an unpleasant odor, among other things.

Pollution from the turbines, which xAI has used in Memphis, Tennessee, for its Colossus 1 and Colossus 2 data centers, has been a major source of local contention for more than a year.

To STOP, we are still at the beginning of this demand. Increased pressure from environmental and civil rights groups over pollution concerns are litigating in court now.

COLOSSUS – World’s Biggest Supercomputer

DESCRIPTION: The world’s biggest supercomputer, Colossus. Built in 122 days—outpacing every estimate—it was the most powerful AI training system yet. Then Musk and his team doubled it in 92 days to 200k GPUs.

Number of GPUs — 200KGPUs     

                      Total Memory Bandwidth -194 Petabytes/s                

                                                      Network Bandwidth per Server – 3.6 Terabits/s     

Storage Capacity – >1Exabyte

The xAI company’s use of dozens of natural gas-burning turbines requires a federal permit. The company’s violation of the Clean Air Act causing harm nearby communities.

REFERENCE: https://www.researchgate.net/publication/403073048_The_data_heat_island_effect_quantifying_the_impact_of_AI_data_centers_in_a_warming_world

END of Message

Dear Readers, followers or commenters of Fbsolarllc.com site. I am happy to let you know that my fourth (4th) book on the Topic of ‘BATTERY’ has been published in all formats lately in my AMAZON Account. Please check that out at your own pace. The publication announcements by Amazon of this book are copied below.

“Solid State Batteries (SSBs) For EVs” is now live and available for purchase in the Kindle Store!

Solid State Batteries (SSBs) For EVs: A Comprehensive Look

https://a.co/d/0aEjbhxe

Hardcover, “Solid State Batteries (SSBs) For EVs,” is available to buy on Amazon.

Paperback, “Solid State Batteries (SSBs) For EVs,” is published on Amazon & available to buy as well.

Hardcover Image has shown here.

For now, the TITLES of my Four (4) BATTERY books I mentioned below to facilitate your collection drive. If you haven’t collected those books yet, go and buy the books at your most permissible time. Most importantly – enjoy the most UpToDate technical knowledge on Electric Charge STORAGE concern.

End Note: I hope you will collect all the books that included in the image above. The books will furnish the chemistry and the technology behind each set – the modified ideas that have been considered there. To know so much about all aspects of Battery and Battery Technology – these books are the pathways. Once you have all the copies- make sure that quality time is available on your hand to read and comprehend the books in a meaningful way. At that point you may also have obtain ‘mastery’ on Electric Charge Storage topic. Go, buy for yourself and persuade your friend to buy another set. Finally, let me know your active participation in this Battery Book Drive Campaign.

PART III

Deep Blue, Deep-Mind, DeepSeek, Deep Learning, Deep Research, Deep Think. Google Brain

So, what’s the definition of the WORD ‘Deep’ in AI vocabulary set?

The word “deep” stands for – extending far down from the Top or Surfaces.

In AI, ‘deep’ introduces multiple layers of neural networks that help the system understand and interpret data. The technique allows computers to recognize patterns and manage complex tasks, such as translating languages and driving cars autonomously.

Deep Blue is IBM’s computer (beaten Gary Kasparov in chesses in 1977)

Deep Mind founder is Demis Hassabis, a London based company. Founded for AI → AGI, DeepMind’s research aimed to develop more helpful AI agents by translating advanced AI capabilities into real-world actions through a language interface.

DeepSeek, a Chinese AI chatbot that can outperform some of its best competitors, such as OpenAI’s ChatGPT o1. DeepSeek said it trained its V3 chatbot in just two months with a little more than 2,000 Nvidia H800 GPUs, chips (designed to comply with export restrictions the US placed on China in 2022).

Deep Learning is a subset of machine learning, which in turn is a subset of artificial intelligence (AI).  It is called deep because it makes use of deep neural networks to process data and make decisions. The word ‘deep’ in ‘deep learning refers to the number of layers through which the data is transformed. Deep-learning pioneer is Geoffrey Hinton. [Deep-Learning models performed much better when they are bigger.

Deep Research is a Model in OpenAI that can uncover and discover new knowledge for itself. And the first step here is a model that can go and synthesize.

Gemini 2.5 Deep Think (A Reasoning model) – Google detailed how its Gemini 2.5 models, including 2.5 Pro, are going to advance in the near future. To begin, Gemini is going to receive Google’s 2.5 Flash model. The company touted this AI as its “most powerful” version, but its latest updates improve benchmarks for reasoning and multimodality. Google says 2.5 Flash is now better (more efficient) at code and long context.

Google Brain worked directly on improving Google products.  Andrew Ng, a Stanford Univ. Professor wanted to build more advanced AI system from inside Google. For AGI to build “increasingly accurate approximations to small parts of mammalian brain,” is the requirement. Ng discovered that neural models could do more things if they had more nodes, layers and data to train on. After years of research Google Brain found that it failed to reach its objective.

OpenAI competitors are like Google DeepMind, Meta, xAI, and Anthropic. At this point ChatGPT from OpenAI is uncontested, potentially making it difficult for its rivals like Copilot and Gemini to play catch-up.

END of PART III

PART II

OpenAI has introduced breakthrough products such as GPT-3, DALL-E, and ChatGPT, each pushing the boundaries of what AI can achieve. These achievements demonstrated OpenAI’s technical prowess but have also sparked global conversations about the future of human-computer interaction. They create works of their own by drawing on patterns they’ve identified in vast troves of existing, human-created content. ChatGPT, along with text-to-image tools such as DALL-E 2 and Stable Diffusion, is part of a new wave of software called generative AI.

Beginning of (AI) – The seeds for the real AI revolution were germinated in the fertile soil of the University of Toronto, where Geoffrey Hinton and his colleagues developed the techniques of deep learning, which would later become the basis of generative AI. Generative AI depicts the technique & technology to create new information. The generative AI models we use today are relatively theory-free—their emergent understanding of the world comes from neural-net machine learning techniques and mountains of data (see Part I). This technique has indeed made AI more human like.

GPT stands for Generative Pre-training Transformer. Not an exact AI but an AI model built by Open AI. ChatGPT is an app. GPT is the brain behind that app. With research and development GPT has several versions available – choosing o3 for reasoning, GPT-4.1 for coding, and GPT-4o for regular chats, GPT-5 (soon available) will pick the tools it needs to answer your prompt on its own.

The T in ChatGPT stands for” transformer.” Transformers are a system that allows machines to generate humanlike text. The transformer has become critical to the new wave of generative AI that can produce realistic text, images, videos, DNA sequences, and many other kinds of Data. The transformer’s invention (by Google) in 2017 was about as impactful to the field of AI as the advent of smartphones was to customers.  Transformers also broadened the scope of what AI engineers could do. Handle far more data, process human language much more quickly.

Transformers broke chatbots out of any binding rule. Now Chatbots can deal with nuance and slang. They can refer back to the thing that has been said a few sentences earlier, can handle almost any random query and give a personalized answer. To many AI researchers, that means a step toward AGI, opening a debate about whether computers were starting to “understand language in the same way humans did or, if they were still just processing it through math-based predictions.

The GPU, or graphics processing unit – these are powerful semiconductors (chips) made by Nvidia, runs most of the servers training AI models today. This is a specialized chip originally designed to render images and video. But in the world of AI, GPUs have become the powerhouse behind large language models (LLMs) like ChatGPT.

Unlike CPUs (central processing units), which handle one task at a time very efficiently, GPUs are built to perform thousands of simple calculations simultaneously. That parallel processing ability makes them perfect for training and running AI models, which rely on massive amounts of data and mathematical operations.

Google engineers developed LaMDA (Language Model for Dialogue Applications). This was the company’s Large Language Model project that was based on the Transformer. Large Language Models (LLMs) themselves are very interesting. Their responses were mostly scripted, and they’d often make wacky mistakes. To make computers better at talking and listening

Conclusion: Both Data & Computing power is needed for AI Research, Training & Development.

 END of Part II

PART I

Records show that- AI has famously beaten humans in other high-profile competitions. In 1997, IBM’s Deep Blue AI machine beat chess grandmaster Gary Kasparov, and in 2016, Google DeepMind’s AlphaGo beat Go world champion Lee Sedol.

But not this time around.  Human effort regained its top position. Filling the air with a new spirit. This time, AI failed killing the human coding star.

On Wednesday (7/16/25), a programmer from Poland, Przemysław Dębiak, beat an OpenAI tool at the AtCoder World Tour Finals 2025 Heuristic Contest in Tokyo, which is sponsored by the company. Dębiak, who competed under the name “Psyho,”

In February 2025, Sam Altman said that by the end of this year, OpenAI could surpass humans in coding competitions. AI is already writing big chunks of code at Microsoft, Google, and Meta, the companies’ CEOs have all said in recent months.

Machine learning is a type of AI that allows computer programs to learn when exposed to new data without being programmed. The official idea & definition of AI was first coined by Jay McCarthy in 1955 at the Dartmouth conference. McCarthy proposal is under quotehere.

“Every aspect of learning or any other feature of intelligence can be made to stimulate it. An attempt will be made to find out how to make machines, use language from abstractions and concepts, solve kinds of problems now reserved for humans and inspire thoughts.” In essence, AI is a machine with the ability to solve problems that are usually done by us humans with our moral intelligence. Therefore, AI is about making intelligence universally accessible and making everyone massively more productive.

OpenAI is an AI research and Development Company. This is an organization that creates and promotes friendly AI,. AI code can be kept private, OpenAI approach is where the code is made publicly available, often as open-source software. This enables others to inspect the code to understand how the AI was trained and operates and potentially modify and improve upon it. OpenAI utilizes advanced machine learning techniques, such as deep neural network to …

A neural network is a type of software that gets built by being trained over and over with lots of data. Once it’s been trained, it can recognize faces, predict chess moves, or recommend a Netflix movie for a customer. Also, known as a ‘model’ a neural network is often made up of many different layers and nodes that process information in a vaguely similar way to our brain’s neurons. The more the model is trained, the better those nodes get at predicting or recognizing things.  

The OpenAI organization Musk founded with Sam Altman. In 2015, Musk and Altman co-founded OpenAI together but now run competing AI companies. Founded as a nonprofit OpenAI later turned to seek profits. it took OpenAI seven years (from its founding in 2015) to release the first version of its chatbot, ChatGPT. So, What ChatGPT can do? ChatGPT, is able to converse about the concept of…, can talk about religion and else.  Musk parted ways with OpenAI in 2018. In 2023, Musk founded a rival AI start-up, xAI and launched a chatbot called Grok to compete with ChatGPT. xAI is the company behind Grok, which runs a chatbot on Musk’s X platform.  Musk’s xAI is running on 100,000 of Nvidia’s more advanced H100s chips at a computing cluster in Tennessee. These chips usually retail for $30,000 each.

END of – PART 1

From Google:

Neural Networks: An Overview

Neural networks, also known as artificial neural networks (ANNs), are computational models inspired by the structure and functioning of the human brain. They consist of interconnected nodes (neurons) organized into layers: an input layer, one or more hidden layers, and an output layer. These networks are a cornerstone of machine learning and deep learning, enabling tasks like pattern recognition, decision-making, and data classification.

Each neuron processes input data by applying weights, biases, and an activation function. If the output exceeds a threshold, the neuron activates and passes data to the next layer. This process is known as forward propagation. Neural networks learn by adjusting weights and biases through backpropagation, which minimizes errors using optimization techniques like gradient descent.

On May 20, 2025 (Tuesday), a Falcon 9 rocket lifted off carrying 23 of the company’s Starlink broadband satellites to low Earth orbit (LEO). They’re scheduled to be deployed 65 minutes after launch.  By design, the Starlink satellites get released afterwards to begin individually maneuvering into more specific orbits to join SpaceX’s growing megaconstellation. Refreshing memory:  Starlinks are space-based broadband satellites. SpaceX’s growing megaconstellation   is building on Starlinks, designed to provide high-speed internet access across the globe.

At the moment (and counting continues…) Starlink is the largest satellite megaconstellation ever constructed, consisting of about 7,500 operational satellites. And that number is growing all the time, with every batch of satellites (containing~ 21/ 23/ 27/29…) that reached @ LEO via Falcon 9 carrier. In every batch of satellites, — there included a certain number of (X ~ 7-13) satellites with direct-to-cell capability — to lower Earth orbit.

Per plan, SpaceX’s Starlink network will consists eventually of more than 12,000  satellites by adding batches of satellites counting 2/3 times a month.  As a whole, megaconsttellation  operate in a grid that blankets nearly the planet entirely, saving North & South poles only. Starlink offers users a high-speed internet connection from anywhere (other than the poles), customers are able to point their Starlink receiver toward the sky. Satellites orbit at 342 miles (540km) above the Earth. The Space Station (ISS) is about 250 miles (400km) above the Earth’s surface.

Starlink Megaconstallation position, ISS Orbital Path and Our sweet Earth (not on Scale)

7,500 is a huge number!  So, how the satellite network look like now. Flying with ISS, NASA scientists have recorded a video recently. Keep in mind, the ISS position is 250 miles above you and me. Staying approximately 100 miles below the satellites position NASA scientists recorded a video of the network and named it  “Starlink Satellites Swarm Like ‘Cosmic Fireflies’”

Watch the Time-Lapse from Space video here:

https://www.msn.com/en-us/news/technology/spacex-starlink-satellites-swarm-like-cosmic-fireflies-in-time-lapse-from-space/vi-AA1EMuZ3?ocid=msedgdhp&pc=DCTS&cvid=bc393cb58cf84251bc69ec16305c9a5d

NASA astronaut Don Pettit captured SpaceX Starlink satellites swarming in the distance in an amazing time-lapse captured from the International Space Station. Credit: Space.com | footage courtesy: NASA/Don Pettit | edited by Steve Spaleto 

Do you know SPACEX’s Drone ships name? Here are three SpaceX drone ships name: “Just Read the Instructions,” “A Shortfall of Gravitas, ” and “Of Course I Still Love You,” is in the Pacific Ocean.

Euclid mission is designed to explore the composition and evolution of the dark Universe. Astrophysicist Carole Mundell, the European Space Agency’s director of science, called Euclid a “dark detective.” Euclid is the name of a space telescope (a European satellite) launched in 2023, promising more precise gravitational lensing (GW) data to acquire.

Over its six-year mission, Euclid will observe 1.5 billion galaxies, offering unprecedented insights into the Universe’s history and allowing scientists to test Einstein’s equations more rigorously. Using the data from Euclid, the scientific community is poised to refine its understanding of gravity and cosmic expansion. These efforts may ultimately reshape our perception of the Universe, bringing us closer to solving one of science’s greatest enigmas of dark stuffs of DE & DM (Dark Energy, Dark Matter).

How Euclid Works?

Light traveling toward Earth from faraway galaxies is bent and distorted by normal matter and dark matter in the foreground in an effect called gravitational lensing. It is one of the tools used by Euclid to study how dark matter is distributed. Researchers have been sifting through the Euclid data to identify strong gravitational lenses and so far, have identified about 500 such candidates.

Euclid measures the various shapes and the distribution of galaxies with its high-resolution imaging visible instrument, or VIS, while its near-infrared instrument, or NISP, determines galaxy distances and masses. Euclid eventually is expected to obtain images of more than 1.5 billion galaxies.

Newly released Euclid images hint at the large-scale organization of galaxies in what is known as the cosmic web, essentially the backbone of the universe. In this web, there are concentrations of galaxies with voids of space in between, as if the galaxies were sitting on vast empty bubbles. The cosmic web’s structure offers clues about dark matter and dark energy, according to the researchers.

“Euclid is the best instrument ever built for finding these, because it can take an exceptionally sharp image of wide patches of sky â€Â,“ said University of Toronto astrophysicist Mike Walmsley, one of the Euclid scientists.

Dark matter has baffled physicists for nearly a century, quietly shaping our universe’s unseen framework. Invisible yet influential, it provides the gravity necessary to hold galaxies together, driving their motion and structure. About 85% of all matters in the universe stay hidden, undetectable even with today’s best technology. Frustrating to detect DM, some scientists just declared that ‘there is no existence of Dark Matter at all,’ which I reported in my earlier posting (DE, DM. M & AM – please check).   In the 1930s, researchers first noticed abnormalities in how galaxies moved, suggesting something invisible exerted gravitational pull. Decades later, studies of the cosmic microwave background—the lingering radiation from the universe’s birth—confirmed dark matter’s importance in shaping cosmic evolution.

CONCLUSION: The search for dark matter is a central pillar of modern physics, driving advancements in technology and theory. Direct detection experiments, such as those conducted deep underground, continue to push the boundaries of sensitivity, aiming to capture fleeting interactions between dark matter particles and ordinary matter.

REFERENCE:

https://www.msn.com/en-us/news/technology/european-space-agency-releases-new-data-that-could-help-us-understand-the-nature-of-the-universe/ar-AA1Bg1ka?ocid=socialshare&pc=DCTS&cvid=75fb8b88e3584143afc3010cdcbadf30&ei=38

EU informed about EUCLID’s Detection