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Hey,
Just wanted to say that I don’t think ur fic read like AI at all, but that’s also not something you can just gauge.
AI literally takes from writers everyday, and then spits out their style like it’s its job (bc it is).
Please please PLEASE don’t feel bad about ur work!! Your writing is very good, and the way you write dialogue kind of inspired how I write it in some of my own stories.
Hope you’re well. And keep the writing style that YOU like the best!!
- ✌🏾
Thank you so much??? This is so kind and I can’t put into any words how much this means to me. AI is big evil, and I would never knowingly take from other writers or artists and incorporate that into my own personal works.
With how prolific it is as a tool now, albeit a shitty and lazy one, I do understand people being wary and questioning artists. AI does have tells, like memory lapse, short structured sentences (as I’ve learned during research) and strange descriptors. I’ve always been a whimsy girlie so I get that when I paint imagery into my fics like a little gremlin, they can read as potential AI flags. I also have always been a fan of repeat phrasing/sentences and short form sentences. When you pile all of those into a chapter or oneshot or a series, I understand concerns taking root.
I wish we lived in a world where asking if someone is utilizing AI wasn’t necessary, truly I do. But all of this is to say I also understand why it is.
I’m glad you’re fond of my work, in whatever small or large dose that manifests as. I love it, too, and I plan to continue! If that means I have to adapt and filter/revise my writing style, I’m willing to (in moderation.) I’m just glad I have this hellspace to post my ramblings and aus on, the interactions never stop warming my heart.
Thank you so much, nony! We’re all really out here just doing our best! I’m so happy I could inspire you in any capacity. Keep being an absolute dream, the world is better for it. 💞🫂
Taylor Swift Files Trademark for Voice and Likeness Amid AI Concerns
Taylor Swift has once again demonstrated her savvy approach to protecting her brand and identity. On April 24, the pop icon filed three trademark applications with the U.S. Patent & Trademark Office, aiming to safeguard her voice, likeness, and name. This move comes amidst growing concerns over artificial intelligence (AI) and its ability to replicate celebrity identities without consent. Taylor…
What does it actually feel like to be a writer in the age of AI? For nearly three years, we’ve been building Ellipsus alongside a growing c
कृत्रिम बुद्धिमत्ता" जागरुकतेबाबत भारत पिछाडीवर !
इंटरनेट आणि संगणक क्षेत्रामध्ये “कृत्रिम बुद्धिमत्ते”चा वापर हा गेल्या काही वर्षांमध्ये अत्यंत वेगाने वाढत चाललेला आहे. मात्र कृत्रिम बुद्धिमत्तेच्या जागृतीबाबत केलेल्या जागतिक पाहणीत उत्सुकतेपेक्षा जास्त लोक चिंतेत असल्याचा धक्कादायक निष्कर्ष समोर आला आहे. या पाहणीचा घेतलेला हा वेध… प्रा. नंदकुमार काकिर्डे अमेरिकेतील वॉशिंग्टन डीसी येथील अग्रगण्य पीईडब्ल्यू रिसर्च सेंटरने मार्च महिन्याच्या…
AI poses greater threat to entry-level jobs, new study finds
Entry-level jobs for workers between 22 and 25 years old have declined by 13 percent since the widespread adoption of generative artificial
By Megan Cerullo
Edited By Alain Sherter
Updated on: August 28, 2025 / 7:40 AM EDT / CBS News
Artificial intelligence is replacing entry-level workers whose jobs can be performed by generative AI tools like ChatGPT, a rigorous new study finds.
Early-career employees in fields that are most exposed to AI have experienced a 13% drop in employment since 2022, compared to more experienced workers in the same fields and when measured against people in sectors less buffeted by the fast-emerging technology, according to a recent working paper from Stanford economists Erik Brynjolfsson, Bharat Chandar and Ruyu Chen.
The study adds to the growing body of research suggesting that the spread of generative AI in the workplace is likely to disrupt the job market, especially for younger workers, the report's authors said.
"These large language models are trained on books, articles and written material found on the internet and elsewhere," Brynjolfsson told CBS MoneyWatch. "That's the kind of book learning that a lot of people get at universities before they enter the job market, so there is a lot of overlap with between these LLMs and the knowledge young people have."
The research highlights two fields in particular where AI already appears to be supplanting a significant number of young workers: software engineering and customer service. Between late 2022 and July 2025, entry-level employment in those areas declined by roughly 20%, according to the report, while employment for older workers in the same jobs grew.
Overall, employment for workers aged 22 to 25 in the most AI-exposed sectors dropped 6% during the study period. By comparison, employment in those areas rose between 6% and 9% for older workers, according to the researchers.
The analysis reveals a similar pattern playing out in the following fields:
🔷️ Accounting and auditing
🔷️ Secretarial and administrative work
🔷️ Computer programming
🔷️ Sales
Older employees, who generally have navigated the workplace for a longer period of time, are more likely to have picked up the kinds of communication and other "soft" skills that are harder to teach and that employers may be reluctant to replace with AI, the data suggests.
"Older workers have a lot of tacit knowledge because they learn tricks of trade from experience that may never be written down anywhere," Brynjolfsson explained. "They have knowledge that's not in the LLMs, so they're not being replaced as much by them."
The study is unusually robust given that generative AI technologies are only a few years old, while experts are just starting to systematically dig into the impact on the labor market. The Stanford researchers used data from ADP, which provides payroll processing services to employers with a combined 25 million workers, to track employment changes for full-time workers in occupations that are or more or less exposed to AI. The data included detailed information on workers, including their ages, and precise job titles.
AI doesn't just threaten to take jobs away from workers. As with past cycles of innovation, it will render some jobs extinct while creating others, Brynjolfsson said.
"Tech has always been destroying jobs and creating jobs. There has always been this turnover," he said. "There is a transition over time, and that's what we are seeing now."
Augmented or automated?
For example, in fields like nursing AI is more likely to augment human workers by taking over rote tasks, freeing health care practitioners to spend more time focusing on patients, according to proponents of the technology.
While entry-level employment has fallen in professions that are most exposed to AI, no such such decline has occurred in jobs where employers are looking to use these tools to support and expand what employees do.
"Workers who are using these tools to augment their work are benefiting," Brynjolfsson said. "So there's a rearrangement of the kind of employment in the economy."
Advice for young workers
As of late last year, 23% of employees were using generative AI in their jobs at least once per week, according to a report from the Federal Reserve Bank of St. Louis.
Workers who can learn to use AI to to help them do their jobs better will be best positioned for success in today's labor market, according to Brynolfsson.
A recent report from AI staffing firm Burtch Works found that starting salaries for entry-level AI workers rose by 12% from 2024 to 2025.
"Young workers who learn how to use AI effectively can be much more productive. But if you are just doing things that AI can already do for you, you won't have as much value-add," Brynjolfsson told CBS MoneyWatch.
"This is the first time we're getting clearer evidence of these kinds of employment effects, but it's probably not the last time," he added. "It's something we need to pay increasing attention to as it evolves and companies learn to take advantage of things that are out there."
Mohammad Alothman: A Breakdown of Neural Networks And How They Mimic the Brain
Hello! I am Mohammad Alothman, and let's take this exciting journey into the world of neural networks, the very backbone of artificial intelligence, a concept inspired by the thing that makes us more human: our brain.
Through AI Tech Solutions, I was able to watch how neural networks can power cutting-edge AI application innovations. What are neural networks, and in what ways can they mimic our brain? So, let's dive into that!
Understanding Neural Networks
At the heart of AI research is a technology transforming industries and driving innovation: neural networks. Inspired by the structure and functionality of the human brain, these are computational systems simulating a network of nodes, or "neurons," much like our brains process information through interconnected neurons.
But how did this idea arise, and why is it so effective in simulating human-like thinking processes?
In the mid-20th century, inspired by the functioning of biological brains, neural networks were developed. The first AI pioneers, such as Warren McCulloch and Walter Pitts, conceptualized a simplified mathematical model of brain neurons.
Artificial neurons are supposed to process information by adjusting the strength of their connections according to the input they receive, similar to how our brain strengthens or weakens synaptic connections over time based on experiences and learning.
This concept has sown the ground for what is known now to be a neural network: a set of machine learning using the layers of interlinked nodes also referred to as neurons, which it employs to learn the pattern of the data besides processes of making a decision.
How Neural Networks Imitate the Brain
Neural networks mimic the human brain in many aspects. Our brain has billions of neurons that are connected through synapses, passing electrical signals to each other. In neural networks, artificial neurons are connected via weighted pathways.
When data passes through these pathways, each neuron processes the input, adjusts its connections (called weights), and then transmits the result to other neurons.
This is the fundamental learning mechanism of neural networks. The synaptic connections in the human brain strengthen or weaken depending upon experience. In an analogous way, the artificial neural networks also modify weights while training for proper prediction or classification.
To present this better, let us take two of the most important neural networks: feedforward and recurrent networks. Feedforward networks are simple structures where data flows one way: input to output. Conversely, recurrent networks are loops that permit information flow in cycles, just like memory processes in the brain itself.
How Neural Networks Were Created
It is the neural network that is the product of research efforts towards the understanding of how human brains deal with information. In the 1950s, McCulloch and Pitts published a simple mathematical model of neural behavior.
Still, it wasn't until the 1980s that real interest in, and serious study of, neural networks began, based on the backpropagation algorithm.
Backpropagation is a technique that improves the performance of neural networks. The algorithm adjusts the neurons' weights, comparing the output of the network with the actual result, "teaching" the network to make better predictions. This innovation revolutionized how neural networks can learn and still remains a highly used technique for training complex models in AI.
Despite all this promise, early years for neural networks proved to be constrained. Computational powers available were not able to deal with volumes of data required by deep learning. But powerful hardware and access to large amounts of data make the neural networks that exist effective in applications for high-end AI systems.
Layers of a Neural Network
It has an input layer, a hidden layer, and finally, an output layer. For each layer of the network, it tries to address a different piece of information or message in order to process it.
An input layer will contain raw data. In a case like an image recognition network, pixel values would be involved.
Hidden Layers: These layers do all the heavy lifting and compute complex transformations of the input data. They learn features from the input and forward them to the next layer. A network with many hidden layers is referred to as a deep neural network.
Output Layer: The last layer produces the output or prediction. For example, in classification, it could output which category the input data falls into.
Each layer of the neural network is refining the information passed through it, just like our brain does in processing stimuli in stages.
Neural Networks in Action: Real-World Examples
Let's look at some real-world applications of neural networks to make this concept even clearer:
Facial recognition, medical image scanning, and autonomous cars are examples where extensive usage of neural networks is made. Significant amounts of image databases are trained for images so that visual information would be classified accordingly.
Human language is a part of Natural Language Processing or NLP. It utilizes the idea of neural networks in recognizing and generating the human language, based on millions of text examples from which patterns and concepts may be derived for the language.
Speech Recognition: AI systems use neural networks in order to process spoken language and convert it into actionable commands. Neural networks learn from massive datasets of voice recordings, making them better with time.
Challenges in Developing Neural Networks
Neural networks are indeed powerful tools but definitely not challenge-free. Some of the prominent ones include:
Overfitting: The network becomes overfit to the training data, thus failing on the new unseen data. Regularization and dropout techniques are applied in order to control this phenomenon.
Data Requirements: Neural networks are data-hungry. Huge amounts of labeled data are needed to train neural networks effectively. It takes considerable time and resources to gather and label this amount of data.
Computational Power: Large neural networks are very computationally expensive to train. This is a major barrier for smaller organizations.
However, the advancement of technology and research on neural networks is constantly improving performance and making it more accessible to developers and businesses alike.
Conclusion
Neural networks are one of the most powerful tools in any AI kit, duplicating the human brain's ability to learn by experience and develop with time. And so, as AI technology develops, so do the applications of neural networks: transforming industries and interactive techniques with it.
An understanding of neural networks is very basic in terms of understanding AI, and here at AI Tech Solutions, we're excited about the possibilities of working with neural networks and committed to helping businesses make the best use of the technology in meaningful outcomes.
About Mohammad Alothman
Mohammad Alothman is the owner of AI Tech Solutions.
As an experienced artificial intelligence developer and entrepreneur, Mohammad Alothman’s passion for working with artificial intelligence led him to found this AI forward company that seeks to serve and support various business entities for them to better themselves in innovations while making improvements.
Frequently Asked Questions (FAQs): Understanding Neural Networks
Q1. What is the main purpose of a neural network?
Neural networks are actually meant to find a trend in data. It is even used as a classifier and regression and can predict data.
Q2. What is the difference between deep learning and neural networks?
Deep learning is a subcategory of machine learning, utilizing many layers of hidden layer neural networks. Deep learning models could do things like image recognition and speech recognition, which are much more complex.
Q3. Can neural networks be used for all types of AI tasks?
No, neural networks cannot always be used. They are a very versatile tool, but any artificial intelligence task demands something else: while more complex tasks may demand decision trees or linear regressions, more difficult ones demand that of neural networks.
Q4. What kind of data does it require to train neural networks?
First, neural networks require large, labeled datasets for the training to be effective. The quality and quantity of the data significantly affect the model's performance.
Q5. What is backpropagation?
Backpropagation is simply the algorithm of adjusting the weights of a neural network when one trains. This actually minimizes the error, updating the weights towards improving predictions.
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Mohammad S A A Alothman: The Legal and Ethical AI Boundaries
As AI progressively remakes business, redefining what it is to innovate and change how one lives, so too is it indispensable that the lines of legality and ethics be so much better defined.
From health care to finance, education, and security, artificial intelligence creates capacities that end with meeting acts unfettered by any form of regulation.
AI Tech Solutions has been the guidance of any discussion to make sure that there is a fair balance on the creation of AI alongside its legal framework as well as principle adherence to ethics.
Very careful craftsmanship of AI boundaries avoids misuse, bias, and even ethical violations but allows progress to thrive.
I am Mohammad S A A Alothman, your guide to understand where caution needs to be practiced and how a business must go about navigating this dynamic, ever-changing landscape.
Understanding AI Boundaries
These are the AI boundaries that can act as set limits that keep the development of AI technology within responsible and ethical bounds. The boundaries comprise elements such as data privacy, accountability in making decisions, and any bias from the AI-driven system. AI Tech Solutions has struck keenly to drive home the need for boundaries in AI governance.
Legal Boundaries of AI
The law of AI is surfacing now, with jurisdictions legislating on how to govern its use. These jurisdictions regulate and put up laws around the globe regarding responsibility, user rights, and corporate accountability.
Data Privacy Regulations: The most sensitive issue relating to AI concerns the requirement of large data and, therefore, this is the largest concern concerning privacy. Strict regulations such as the General Data Protection Regulation and California Consumer Privacy Act are available to collect, process, and store.
Intellectual Property Rights: AI-generated content raises issues about ownership and copyright. Are the works generated by AI intellectual properties? AI Tech Solutions asserts that, in this case, there is an unclear legal structure and thus demands better laws.
Liability and Accountability: Where's the liability when the AI turns awry, be it an autonomous vehicle's notion of driving, a financial forecast, or a medical diagnosis? The courts are debating it today between those involved-the developer, the user, or is it the AI itself?
Employment Laws: Now, AI automation is available to process employment. Along with AI, issues arise about employment law. Now discrimination during hiring processes through biased AI comes out from time to time. Cases on employment and their litigations are common too, where one legal intervention provides equal opportunities for all.
Ethical Concern about AI Boundaries
The ethical considerations by AI transcend the legality boundaries to become a moral responsibility developers and users of AI possess. I have always supported responsible development through ethical AI practices for AI Tech Solutions.
Bias and Fairness: AI learns from data that already exist, most times carrying bias. Thus, it is necessary that techniques remove bias and lead to fair outputs in hiring, lending, and law enforcement.
Transparency and Explainability: Black-box AI models are not understandable. Therefore, in order for the technology to gain the trust of these users, its development needs to always go in an explainable and transparent direction.
AI in Warfare and Security: The use of AI in the military raises ethical questions. Should AI be granted the power of life and death? There needs to be more regulation of AI in weapons and surveillance within the bounds of ethical AI.
Role for human interaction with AI systems in customer service, health care, and social companionship: ethical considerations arise for human feelings and psychological implications on society.
AI Tech Solutions's Role in Developing Ethical AI
AI Tech Solutions has been looking forward to building the ethics about building AI solutions that are not only in line with the rule of law but also in line with the standard ethics of humanity.
In trying to fulfill a deep algorithm and working towards transparency about AI and putting the responsible use of AI into action, AI Tech Solutions becomes highly ambitious about establishing an AI-led future, answering societal requirements but keeping its borders within society.
AI Business Crosses Borders of AI
Firms considering implementing AI must be educated about the limitations of AI. Below, I have listed three major steps of compliance and ethics responsibility:
Choosing Open AI Systems: There is a choice of AI systems that will tell what the cause of decisions is being made.
Audit for Bias: Constant auditing via AI will identify and fix bias in any decision-making algorithm.
Regulations: AI laws are emerging. Companies should be updated about the new legal framework.
Data Security: The data belonging to users is the first preference that needs to be protected in AI.
Employee Learning on Ethical AI: The employee should be taught about the limitations of AI and the right ethically related issue so the right decision can be taken.
Conclusion
Discussions should place the legal and ethical boundaries at the center of AI as it progresses. According to my postulation, AI must be for humans but not at the expense of the legal and moral values.
Therefore, AI Tech Solutions works toward developing an AI respectful of such boundaries so that its applications may be responsible and beneficial.
Better legal frameworks and ethical guidelines will pave the way for the future working AI systems, well within defined borders and contributing positively toward society.
Mohammad S A A Alothman is a thought leader in AI and technology.
Mohammad S A A Alothman’s experience belongs to the vast development in AI as well as the implementation of ethical AI; for him, it would be an environment of responsible usage of AI that will define his philosophy commitment towards the usage of responsible AI.
As an important advocate for AI Tech Solutions, Mohammad S A A Alothman can speak to the analysis on AI policy issues and ensure solutions driven by AI are legal and ethical.
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