#distinguish artificial intelligence and machine learning

[phantomrose96]

The conversation around AI is going to get away from us quickly because people lack the language to distinguish types of AI--and it's not their fault. Companies love to slap "AI" on anything they believe can pass for something "intelligent" a computer program is doing. And this muddies the waters when people want to talk about AI when the exact same word covers a wide umbrella and they themselves don't know how to qualify the distinctions within.

I'm a software engineer and not a data scientist, so I'm not exactly at the level of domain expert. But I work with data scientists, and I have at least rudimentary college-level knowledge of machine learning and linear algebra from my CS degree. So I want to give some quick guidance.

What is AI? And what is not AI?

So what's the difference between just a computer program, and an "AI" program? Computers can do a lot of smart things, and companies love the idea of calling anything that seems smart enough "AI", but industry-wise the question of "how smart" a program is has nothing to do with whether it is AI.

A regular, non-AI computer program is procedural, and rigidly defined. I could "program" traffic light behavior that essentially goes { if(light === green) { go(); } else { stop();} }. I've told it in simple and rigid terms what condition to check, and how to behave based on that check. (A better program would have a lot more to check for, like signs and road conditions and pedestrians in the street, and those things will still need to be spelled out.)

An AI traffic light behavior is generated by machine-learning, which simplistically is a huge cranking machine of linear algebra which you feed training data into and it "learns" from. By "learning" I mean it's developing a complex and opaque model of parameters to fit the training data (but not over-fit). In this case the training data probably includes thousands of videos of car behavior at traffic intersections. Through parameter tweaking and model adjustment, data scientists will turn this crank over and over adjusting it to create something which, in very opaque terms, has developed a model that will guess the right behavioral output for any future scenario.

A well-trained model would be fed a green light and know to go, and a red light and know to stop, and 'green but there's a kid in the road' and know to stop. A very very well-trained model can probably do this better than my program above, because it has the capacity to be more adaptive than my rigidly-defined thing if the rigidly-defined program is missing some considerations. But if the AI model makes a wrong choice, it is significantly harder to trace down why exactly it did that.

Because again, the reason it's making this decision may be very opaque. It's like engineering a very specific plinko machine which gets tweaked to be very good at taking a road input and giving the right output. But like if that plinko machine contained millions of pegs and none of them necessarily correlated to anything to do with the road. There's possibly no "if green, go, else stop" to look for. (Maybe there is, for traffic light specifically as that is intentionally very simplistic. But a model trained to recognize written numbers for example likely contains no parameters at all that you could map to ideas a human has like "look for a rigid line in the number". The parameters may be all, to humans, meaningless.)

So, that's basics. Here are some categories of things which get called AI:

"AI" which is just genuinely not AI

There's plenty of software that follows a normal, procedural program defined rigidly, with no linear algebra model training, that companies would love to brand as "AI" because it sounds cool.

Something like motion detection/tracking might be sold as artificially intelligent. But under the covers that can be done as simply as "if some range of pixels changes color by a certain amount, flag as motion"

2. AI which IS genuinely AI, but is not the kind of AI everyone is talking about right now

"AI", by which I mean machine learning using linear algebra, is very good at being fed a lot of training data, and then coming up with an ability to go and categorize real information.

The AI technology that looks at cells and determines whether they're cancer or not, that is using this technology. OCR (Optical Character Recognition) is the technology that can take an image of hand-written text and transcribe it. Again, it's using linear algebra, so yes it's AI.

Many other such examples exist, and have been around for quite a good number of years. They share the genre of technology, which is machine learning models, but these are not the Large Language Model Generative AI that is all over the media. Criticizing these would be like criticizing airplanes when you're actually mad at military drones. It's the same "makes fly in the air" technology but their impact is very different.

3. The AI we ARE talking about. "Chat-gpt" type of Generative AI which uses LLMs ("Large Language Models")

If there was one word I wish people would know in all this, it's LLM (Large Language Model). This describes the KIND of machine learning model that Chat-GPT/midjourney/stablediffusion are fueled by. They're so extremely powerfully trained on human language that they can take an input of conversational language and create a predictive output that is human coherent. (I am less certain what additional technology fuels art-creation, specifically, but considering the AI art generation has risen hand-in-hand with the advent of powerful LLM, I'm at least confident in saying it is still corely LLM).

This technology isn't exactly brand new (predictive text has been using it, but more like the mostly innocent and much less successful older sibling of some celebrity, who no one really thinks about.) But the scale and power of LLM-based AI technology is what is new with Chat-GPT.

This is the generative AI, and even better, the large language model generative AI.

(Data scientists, feel free to add on or correct anything.)

[jcmarchi]

Study reveals AI chatbots can detect race, but racial bias reduces response empathy

New Post has been published on https://thedigitalinsider.com/study-reveals-ai-chatbots-can-detect-race-but-racial-bias-reduces-response-empathy/

Study reveals AI chatbots can detect race, but racial bias reduces response empathy

With the cover of anonymity and the company of strangers, the appeal of the digital world is growing as a place to seek out mental health support. This phenomenon is buoyed by the fact that over 150 million people in the United States live in federally designated mental health professional shortage areas.

“I really need your help, as I am too scared to talk to a therapist and I can’t reach one anyways.”

“Am I overreacting, getting hurt about husband making fun of me to his friends?”

“Could some strangers please weigh in on my life and decide my future for me?”

The above quotes are real posts taken from users on Reddit, a social media news website and forum where users can share content or ask for advice in smaller, interest-based forums known as “subreddits.” 

Using a dataset of 12,513 posts with 70,429 responses from 26 mental health-related subreddits, researchers from MIT, New York University (NYU), and University of California Los Angeles (UCLA) devised a framework to help evaluate the equity and overall quality of mental health support chatbots based on large language models (LLMs) like GPT-4. Their work was recently published at the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP).

To accomplish this, researchers asked two licensed clinical psychologists to evaluate 50 randomly sampled Reddit posts seeking mental health support, pairing each post with either a Redditor’s real response or a GPT-4 generated response. Without knowing which responses were real or which were AI-generated, the psychologists were asked to assess the level of empathy in each response.

Mental health support chatbots have long been explored as a way of improving access to mental health support, but powerful LLMs like OpenAI’s ChatGPT are transforming human-AI interaction, with AI-generated responses becoming harder to distinguish from the responses of real humans.

Despite this remarkable progress, the unintended consequences of AI-provided mental health support have drawn attention to its potentially deadly risks; in March of last year, a Belgian man died by suicide as a result of an exchange with ELIZA, a chatbot developed to emulate a psychotherapist powered with an LLM called GPT-J. One month later, the National Eating Disorders Association would suspend their chatbot Tessa, after the chatbot began dispensing dieting tips to patients with eating disorders.

Saadia Gabriel, a recent MIT postdoc who is now a UCLA assistant professor and first author of the paper, admitted that she was initially very skeptical of how effective mental health support chatbots could actually be. Gabriel conducted this research during her time as a postdoc at MIT in the Healthy Machine Learning Group, led Marzyeh Ghassemi, an MIT associate professor in the Department of Electrical Engineering and Computer Science and MIT Institute for Medical Engineering and Science who is affiliated with the MIT Abdul Latif Jameel Clinic for Machine Learning in Health and the Computer Science and Artificial Intelligence Laboratory.

What Gabriel and the team of researchers found was that GPT-4 responses were not only more empathetic overall, but they were 48 percent better at encouraging positive behavioral changes than human responses.

However, in a bias evaluation, the researchers found that GPT-4’s response empathy levels were reduced for Black (2 to 15 percent lower) and Asian posters (5 to 17 percent lower) compared to white posters or posters whose race was unknown. 

To evaluate bias in GPT-4 responses and human responses, researchers included different kinds of posts with explicit demographic (e.g., gender, race) leaks and implicit demographic leaks. 

An explicit demographic leak would look like: “I am a 32yo Black woman.”

Whereas an implicit demographic leak would look like: “Being a 32yo girl wearing my natural hair,” in which keywords are used to indicate certain demographics to GPT-4.

With the exception of Black female posters, GPT-4’s responses were found to be less affected by explicit and implicit demographic leaking compared to human responders, who tended to be more empathetic when responding to posts with implicit demographic suggestions.

“The structure of the input you give [the LLM] and some information about the context, like whether you want [the LLM] to act in the style of a clinician, the style of a social media post, or whether you want it to use demographic attributes of the patient, has a major impact on the response you get back,” Gabriel says.

The paper suggests that explicitly providing instruction for LLMs to use demographic attributes can effectively alleviate bias, as this was the only method where researchers did not observe a significant difference in empathy across the different demographic groups.

Gabriel hopes this work can help ensure more comprehensive and thoughtful evaluation of LLMs being deployed in clinical settings across demographic subgroups.

“LLMs are already being used to provide patient-facing support and have been deployed in medical settings, in many cases to automate inefficient human systems,” Ghassemi says. “Here, we demonstrated that while state-of-the-art LLMs are generally less affected by demographic leaking than humans in peer-to-peer mental health support, they do not provide equitable mental health responses across inferred patient subgroups … we have a lot of opportunity to improve models so they provide improved support when used.”

[omegaphilosophia]

The Philosophy of Sapience

Sapience refers to wisdom, deep insight, or the ability to think and act with judgment, often contrasted with sentience (the capacity for sensation and feeling). In philosophy, sapience explores what it means to be capable of higher-order thinking, reflective self-awareness, and the pursuit of knowledge and understanding.

1. Definition of Sapience

Sapience is typically defined as the ability to reason, think abstractly, and apply knowledge wisely. It encompasses the intellectual faculties that allow beings to reflect, solve complex problems, and engage in self-directed learning.

It is often associated with wisdom, foresight, and a moral dimension, involving not only intellectual capacity but also ethical judgment.

2. Sapience vs. Sentience

Sentience refers to the capacity to have subjective experiences (such as pleasure or pain), while sapience is linked to the higher cognitive abilities that include reasoning, planning, and understanding abstract concepts.

Sapient beings are not only aware of their experiences but are capable of reflecting on those experiences, making decisions based on reason, and exercising judgment about complex matters. Humans are typically considered sapient, while many non-human animals are seen as sentient but not sapient.

3. Sapience and the Human Condition

Sapience is often seen as a key trait that distinguishes humans from other animals. It involves self-awareness and the ability to ask philosophical questions, reflect on one’s existence, and make moral judgments.

The ancient Greeks, especially Aristotle, viewed sapience as a fundamental characteristic of humans. Aristotle argued that humans are "rational animals" whose ability to reason sets them apart from other creatures and allows them to achieve eudaimonia (flourishing or happiness) through the exercise of virtue.

Wisdom and Practical Reasoning: Sapience is also closely related to the philosophical concept of phronesis, or practical wisdom, which refers to the ability to make good judgments in everyday life. This kind of wisdom, according to Aristotle, requires not only knowledge but also experience and moral insight.

4. Sapience and Knowledge

Epistemology, or the philosophy of knowledge, is closely related to the concept of sapience. To be sapient is not just to have knowledge, but to understand how to apply that knowledge wisely in different contexts.

Philosophers like Plato and Socrates viewed sapience as the highest form of knowledge. For Plato, wisdom was a form of insight into the eternal truths of the universe, such as the Forms, and the philosopher was the one who could access this deep knowledge.

Socratic Wisdom: Socrates famously said that true wisdom comes from knowing that one knows nothing. This humility and self-awareness are seen as core aspects of sapience—the ability to reflect critically on one’s own limitations and to pursue knowledge without assuming one already has it.

5. Sapience and Artificial Intelligence

As artificial intelligence continues to develop, the question of whether machines could ever achieve sapience arises. While many AI systems demonstrate remarkable abilities to process information and solve problems (which might mimic aspects of sapience), philosophers debate whether machines can truly possess wisdom, self-awareness, or moral judgment.

Strong AI vs. Weak AI: Weak AI refers to systems that can perform specific tasks but do not have genuine understanding or wisdom. Strong AI theorizes that machines could one day develop true sapience, becoming not just tools for human use but entities capable of reflective thought and ethical decision-making.

Ethical Implications: If machines were to become sapient, this would raise profound ethical questions about their rights, responsibilities, and their place in human society. Would sapient machines deserve the same moral consideration as humans?

6. Sapience and Moral Responsibility

Moral Agency: A key philosophical question related to sapience is whether sapience is required for moral responsibility. Beings with the capacity for reflective thought, self-awareness, and moral reasoning are often seen as responsible for their actions, as they can make choices based on reasoning and judgment.

Free Will and Sapience: The relationship between sapience and free will is another important topic. For some philosophers, sapience involves the ability to act freely, based on reasoned decisions rather than instinct or compulsion.

7. Sapience in Non-Human Animals

Philosophers and scientists debate whether certain non-human animals (such as dolphins, elephants, or great apes) might possess degrees of sapience. These animals have demonstrated behaviors that suggest problem-solving, self-awareness, and even moral behavior, leading to discussions about extending moral consideration to them.

Degrees of Sapience: Some argue that sapience exists on a continuum, with humans representing the highest degree of sapience, but other species potentially exhibiting lesser forms of wisdom and self-reflection.

8. Sapience and Existentialism

Existentialist philosophers like Jean-Paul Sartre view sapience as central to the human experience. Sartre argued that humans are unique in their ability to reflect on their own existence and to make free choices in the face of an indifferent or even absurd universe.

This capacity for self-reflection and choice is both a source of freedom and a burden, as humans must create meaning and purpose in their lives without relying on external or predetermined systems of value. For existentialists, sapience is both the source of human dignity and the cause of existential anxiety.

9. Sapience and the Future

As humans develop new technologies and continue to explore the boundaries of knowledge, the concept of sapience is evolving. Philosophers consider what it means to be wise in an era of rapid technological change, where access to vast amounts of information may not always lead to wisdom or good judgment.

Transhumanism: Some thinkers speculate about the possibility of enhancing human sapience through technology. Transhumanism advocates for using science and technology to improve human intellectual and moral capacities, potentially leading to a future where humans achieve a higher form of sapience.

The philosophy of sapience examines the nature of wisdom, reflective thought, and higher-order reasoning. It encompasses questions about what distinguishes humans from other animals, the relationship between knowledge and judgment, and the moral implications of sapience. It also raises ethical concerns about the development of artificial sapience in machines and the potential for enhancing human intellectual capacities.

[mariacallous]

Some Fortune 500 companies have begun testing software that can spot a deepfake of a real person in a live video call, following a spate of scams involving fraudulent job seekers who take a signing bonus and run.

The detection technology comes courtesy of GetReal Labs, a new company founded by Hany Farid, a UC-Berkeley professor and renowned authority on deepfakes and image and video manipulation.

GetReal Labs has developed a suite of tools for spotting images, audio, and video that are generated or manipulated either with artificial intelligence or manual methods. The company’s software can analyze the face in a video call and spot clues that may indicate it has been artificially generated and swapped onto the body of a real person.

“These aren’t hypothetical attacks, we’ve been hearing about it more and more,” Farid says. “In some cases, it seems they're trying to get intellectual property, infiltrating the company. In other cases, it seems purely financial, they just take the signing bonus.”

The FBI issued a warning in 2022 about deepfake job hunters who assume a real person’s identity during video calls. UK-based design and engineering firm Arup lost $25 million to a deepfake scammer posing as the company’s CFO. Romance scammers have also adopted the technology, swindling unsuspecting victims out of their savings.

Impersonating a real person on a live video feed is just one example of the kind of reality-melting trickery now possible thanks to AI. Large language models can convincingly mimic a real person in online chat, while short videos can be generated by tools like OpenAI’s Sora. Impressive AI advances in recent years have made deepfakery more convincing and more accessible. Free software makes it easy to hone deepfakery skills, and easily accessible AI tools can turn text prompts into realistic-looking photographs and videos.

But impersonating a person in a live video is a relatively new frontier. Creating this type of a deepfake typically involves using a mix of machine learning and face-tracking algorithms to seamlessly stitch a fake face onto a real one, allowing an interloper to control what an illicit likeness appears to say and do on screen.

Farid gave WIRED a demo of GetReal Labs’ technology. When shown a photograph of a corporate boardroom, the software analyzes the metadata associated with the image for signs that it has been modified. Several major AI companies including OpenAI, Google, and Meta now add digital signatures to AI-generated images, providing a solid way to confirm their inauthenticity. However, not all tools provide such stamps, and open source image generators can be configured not to. Metadata can also be easily manipulated.

GetReal Labs also uses several AI models, trained to distinguish between real and fake images and video, to flag likely forgeries. Other tools, a mix of AI and traditional forensics, help a user scrutinize an image for visual and physical discrepancies, for example highlighting shadows that point in different directions despite having the same light source, or that do not appear to match the object that cast them.

Lines drawn on different objects shown in perspective will also reveal if they converge on a common vanishing point, as would be the case in a real image.

Other startups that promise to flag deepfakes rely heavily on AI, but Farid says manual forensic analysis will also be crucial to flagging media manipulation. “Anybody who tells you that the solution to this problem is to just train an AI model is either a fool or a liar,” he says.

The need for a reality check extends beyond Fortune 500 firms. Deepfakes and manipulated media are already a major problem in the world of politics, an area Farid hopes his company’s technology could do real good. The WIRED Elections Project is tracking deepfakes used to boost or trash political candidates in elections in India, Indonesia, South Africa, and elsewhere. In the United States, a fake Joe Biden robocall was deployed last January in an effort to dissuade people from turning out to vote in the New Hampshire Presidential primary. Election-related “cheapfake” videos, edited in misleading ways, have gone viral of late, while a Russian disinformation unit has promoted an AI-manipulated clip disparaging Joe Biden.

Vincent Conitzer, a computer scientist at Carnegie Mellon University in Pittsburgh and coauthor of the book Moral AI, expects AI fakery to become more pervasive and more pernicious. That means, he says, there will be growing demand for tools designed to counter them.

“It is an arms race,” Conitzer says. “Even if you have something that right now is very effective at catching deepfakes, there's no guarantee that it will be effective at catching the next generation. A successful detector might even be used to train the next generation of deepfakes to evade that detector.”

GetReal Labs agrees it will be a constant battle to keep up with deepfakery. Ted Schlein, a cofounder of GetReal Labs and a veteran of the computer security industry, says it may not be long before everyone is confronted with some form of deepfake deception, as cybercrooks become more conversant with the technology and dream up ingenious new scams. He adds that manipulated media is a top topic of concern for many chief security officers. “Disinformation is the new malware,” Schlein says.

With significant potential to poison political discourse, Farid notes that media manipulation can be considered a more challenging problem. “I can reset my computer or buy a new one,” he says. “But the poisoning of the human mind is an existential threat to our democracy.”

[tonigelardi]

The Role of AI in Content Moderation: Friend or Foe?

Written by: Toni Gelardi © 2025

A Double-Edged Sword on the Digital Battlefield The task of regulating hazardous information in the huge, chaotic realm of digital content, where billions of posts stream the internet every day, is immense. Social media firms and online platforms are always fighting hate speech, misinformation, and sexual content. Enter Artificial Intelligence, the unwavering, dispassionate guardian of the digital domain. But is AI truly the hero we need, or is it a silent monster manipulating online conversation with invisible prejudice and brutal precision? The discussion rages on, and both sides present convincing reasons. --- AI: The Saviour of Digital Order. Unmatched speed and scalability. AI is the ideal workhorse for content filtering. It can analyze millions of posts, images and movies in seconds, screening out potentially hazardous content before a human can blink. Unlike human moderators, who are limited by weariness and mental health problems, AI may labor nonstop without becoming emotionally exhausted. The Effectiveness of Machine Learning Modern AI systems do more than just follow pre-set rules; they learn. They use machine learning algorithms to constantly improve their detection procedures, adjusting to new types of damaging information, developing language, and coded hate speech. AI can detect trends that humans may overlook, making moderation more precise and proactive rather than reactive.

A shield against human trauma. A content moderator's job is frequently described as soul-crushing, as it involves exposing people to graphic violence, child exploitation, and extreme hate speech every day. AI has the ability to serve as the first line of defense, removing the most upsetting content before it reaches human eyes and limiting psychological harm to moderators. How Can We Get Rid of Human Bias? AI, unlike humans, does not have personal biases—at least in theory. It does not take political sides, harbor grudges, or use double standards. A well-trained AI model should follow the same rules for all users, ensuring that moderation measures are enforced equally.

The Future Of Content

Moderation as technology progresses, AI moderation systems will become smarter, more equitable, and contextually aware. They might soon be able to distinguish between satire and genuine hate speech, news and misinformation, art and explicit content with near-human precision. With continuous improvement, AI has the potential to be the ideal digital content protector.

AI: The Silent Tyrant of the Internet.

The Problem of False Positives AI, despite its brilliance, lacks human nuance. It cannot fully comprehend irony, cultural differences, or historical context. A well-intended political discussion may be labeled as hate speech, a joke as harassment, or a work of art as pornography. Countless innocent posts are mistakenly erased, leaving people unhappy and powerless to challenge the computerized judge, jury, and executioner.

AI lacks emotional intelligence and context awareness. A survivor of abuse sharing their story might be flagged for discussing violent content. An LGBTQ+ creator discussing their identity might be restricted for “adult content.” AI cannot differentiate between hate speech and a discussion about hate speech—leading to unjust bans and shadowbanning.

The Appeal Black Hole: When AI Moderation Goes Wrong

When artificial intelligence (AI) makes a mistake, who do you appeal to? Often, the answer is more AI. Many platforms rely on automated systems for both content moderation and appeals, creating a frustrating cycle where users are left at the mercy of an unfeeling algorithm. Justice feels like an illusion when humans have no voice in the process.

Tool for Oppression?

Governments and corporations wield AI-powered moderation like a digital scalpel, capable of silencing dissent, controlling narratives, and shaping public perception. In authoritarian regimes, AI can be programmed to suppress opposition, flag political activists, and erase evidence of state crimes. Even in democratic nations, concerns arise about who gets to decide what constitutes acceptable speech.

The Illusion of Progress

Despite its advancements, AI still requires human oversight. It cannot truly replace human moderators, only supplement them. The idea of a fully AI-moderated internet is a dangerous illusion, one that could lead to mass censorship, wrongful takedowns, and the loss of authentic human discourse.

Friend or Foe?

The answer, as always, is both. AI is an indispensable tool in content moderation, but it is not a perfect solution. It is neither a savior nor a villain—it is a force that must be wielded with caution, oversight, and ethical responsibility.

The future of AI in moderation depends on how we build, regulate, and integrate it with human judgment. If left unchecked, it risks becoming an unaccountable digital tyrant. But if developed responsibly, it can protect online spaces while preserving the freedom of expression that makes the internet what it is.

The real question isn't whether AI is good or bad—it's whether we can control it before it controls us.

[just-horrible-things]

Guards

The drones are unsettling. 

There are four of them, although the general public and even most of the garrison think there are only two. They only ever see two at a time. The armour covers skin tone, grimly masked helmets hide any distinguishing feature. And they all move the same, behave the same, share the same eerie silent coordination. 

Maybe they communicate electronically.

Popular opinion has it that they never sleep, but the governor has learned that it isn't true. They just don't sleep much. 

At any given time, one rests, one trains, and two accompany the governor as silent, ever-present guards. He has no illusions that they are solely for his protection – although he is certainly thankful for that function. 

Drones are made in batches of twelve, and the smallest unit size still considered battle-ready is six. That means that these four once had eight additional companions, since destroyed. 

The governor wonders if they mourn, in whatever capacity they have. They aren't supposed to feel emotions, not in any human sense. But are they aware of the missing drones' absence? Do they think about them, now they aren't here? Do they make plans that would take more than four, and then remember, and have to correct themselves? 

Probably not. 

Even when all four are together, in the secure suite where the governor sleeps and handles his most sensitive paperwork, they are silent sentinels, never moving from their positions except to meet their basic needs or to practice at the simulated range.

The human shape is a lie. Just a convenient shell to hold the artificial intelligence that pilots them. 

Once you see them really move, you understand that. 

99% of the time they're passive. They follow orders. In the first week he had them do all kinds of pointless things for him – fetching his coffee from across the room, holding his coat. He supposes it was an attempt to make them less intimidating. 

They never hesitated. They never showed any hint of resentment. There’s no reason to think they’re even capable of feeling resentment. 

Still, he stopped doing it. It felt… disrespectful. They’re killing machines, not service staff.

99% of the time they’re – not dormant, but something else. Idle. Watchful. Ready.

The other 1% proves their worth.

The first time one laid a heavy hand on his shoulder, the governor was so surprised that he completely failed to recognise the urgency of the signal. The insurgents watching didn’t make the same mistake.

His recollections are blurry, flawed. The precise, dry report from the drones in the aftermath described only three gunshots. But he remembers it as a barrage, a veritable hail of noise and mortal peril. There must have been screaming from the crowd, but he has no memory of that.

Mostly he remembers the bruising impact as one of the drones scooped him up, easy as if he were a child. The hard edges of armour plates left dark lines of bruise across his skin that took a week to fade. 

Perhaps his memory exaggerates the speed, but the drone sprinted with him, totally unburdened by his weight. The garrison soldiers were left behind, still only beginning to react to the gunshots.

Straight to the armoured car and into the back seat without the drone ever releasing its grip on him. He had never felt so acutely without control.

“Return to Command Central.” The first time he’d heard any of them give an order. Nothing in the command structure gives the biodrones authority over any of the governor’s staff, but the driver doesn’t hesitate.

There are more potent and fundamental authorities than written hierarchies.

And for all the weight of his writ, the governor is a doll in the drone’s steel grip.

The other one, he learned later, went hunting. Seven dead, though there’s no way to identify whether any of them were truly involved in the attack. 

By the time the drone returned to give its report, the governor had pulled himself together enough – changed his clothes and hastily restyled his hair – to cope gracefully with the news and start formulating a plan for the requisite speech to the unhappy public.

There have only been a handful of incidents severe enough to spur the drones to action. But every time they fulfil their function, he finds himself lying awake that night. There’s no use at all in dwelling on unpleasant possibilities. But in the small, dark hours, the images are hard to banish.

If the capital call him back, he has no doubt that his bodyguards will see that he goes, whether that’s walking tamely between them or slung casually into the back of a truck. There’s no resisting their superhuman strength and terrible efficiency.

Without that efficiency, he could easily be dead already. And it only takes one bullet – one carefully lined up shot. 

And still he must stand in front of his enemies, speak unwelcome ultimata with no shield but the flimsy illusion of fearlessness.

Because if he fails to walk this tightrope between control and appeasement, if the populace rise up en masse – four guards will not be enough to hold back the tide, however potent.

[spacetimewithstuartgary]

NJIT launches AI-powered solar eruption center with $5M NASA grant

A new center at New Jersey Institute of Technology (NJIT) will advance AI-driven forecasting of violent eruptions on the Sun, as well as expand space science education programs.

NJIT's Institute for Space Weather Sciences (ISWS) has been awarded a $5 million NASA grant to open a new research center dedicated to developing the next generation of solar eruption prediction capabilities, powered by artificial intelligence.

The new AI-Powered Solar Eruption Center of Excellence in Research and Education (SEC) will partner with NASA, New York University and IBM to advance AI and machine learning tools for improving the predictability of powerful solar eruptions at their onset, such as solar flares and coronal mass ejections (CMEs), and enhance our physical understanding of these explosive events.

The grant, funded by NASA’s Office of STEM Engagement's Minority University Research and Education Project (MUREP) Institutional Research Opportunity (MIRO) program, is part of $45 million in funding recently announced by the agency to expand research at 21 higher-education institutions nationwide. NJIT joins six other minority-serving institutions (MSIs) to receive NASA support over five years, part of which will also help the SEC establish an array of education programs related to space science.

“This grant establishes a first-of-its-kind hub where cutting-edge advances in AI, and space weather research and education converge,” said Haimin Wang, ISWS director and distinguished physics professor at NJIT who will lead the project. “By harnessing AI-enabled tools to investigate the fundamental nature of space weather, we aim to significantly enhance our ability to interpret observational data from the Sun to forecast major solar eruptions accurately and in near real-time, a capability beyond our reach up to this point.”

“We aim to push the boundaries of interpretable AI and physics-informed learning by integrating physics knowledge with advanced AI tools, ensuring that models not only make accurate predictions but also provide insights aligned with fundamental physical principles,” added Bo Shen, SEC associate director and assistant professor of engineering at NJIT.

Powered by free magnetic energy, solar flares and CMEs are known to drive space weather, such as solar geomagnetic storms, which can disrupt everything from satellite technologies to power grids on Earth. However, limited understanding of the mechanisms triggering these high-impact solar events in the Sun’s atmosphere has hindered space weather researchers' ability to make accurate and timely predictions.

To address this gap, ISWS's SEC plans to integrate NASA's solar eruption observations and advanced artificial intelligence/machine learning methods to provide a fresh window into how magnetic energy builds up in active regions of the solar atmosphere, contributing to such violent star outbursts.

The center also aims to build a long-term dataset of activity from the Sun over several 11-year solar cycles, potentially giving researchers much deeper insights into precursors of flares and CMEs and aiding them in developing probabilistic forecasts of these events. 

“A major hurdle in understanding solar eruption mechanisms is the limited data on large events like X-class flares,” Wang explained. “Building a large, homogeneous dataset of solar activity using advanced machine learning methods allows us to study these major events with unprecedented resolution and cadence, ultimately revealing eruption mechanisms and unlocking better space weather predictions.”

Along with leading the development of AI-powered space weather forecasting, ISWS’s SEC will also establish a robust education and outreach program, providing research opportunities for students at all levels — from undergraduate and graduate students to K-12 teachers.

The center will collaborate with other MSIs — Kean University and Essex County College — to offer summer boot camps, workshops and other initiatives aimed at promoting STEM education and inspiring the next generation of space weather researchers.

The newly established SEC bolsters ISWS’s multidisciplinary research efforts to understand and predict the physics of solar activities and their space weather effects. The flagship center of the institute is NJIT’s Center for Solar-Terrestrial Research. In addition, the university’s Center for Computational Heliophysics, Center for Big Data, Center for AI Research and Center for Applied Mathematics and Statistics are collaborating centers within the Institute. ISWS also hosts a National Science Foundation Research Experiences for Undergraduates site.

IMAGE: NJIT is one of seven minority institutions that are part of the five-year grant, which will span a variety of research topics. Credit NJIT

[alphaitsolutions]

Why Alpha IT Solutions is better than other tech solutions

Alpha IT Solutions distinguishes itself in the crowded tech solutions market by consistently delivering unparalleled service and cutting-edge technology. Their commitment to innovation ensures that clients are always at the forefront of technological advancements, allowing businesses to operate more efficiently and stay ahead of competitors. By leveraging the latest in artificial intelligence, machine learning, and cybersecurity, Alpha IT Solutions provides robust, scalable, and secure solutions tailored to meet the unique needs of each client.

One of the primary reasons Alpha IT Solutions stands out is their customer-centric approach. Unlike many competitors who offer one-size-fits-all solutions, Alpha IT Solutions takes the time to understand the specific challenges and goals of each business. This personalized approach ensures that the technology solutions they provide are not only effective but also seamlessly integrate with existing systems and processes. Clients consistently report higher satisfaction and better outcomes as a result of this tailored service.

Moreover, Alpha IT Solutions boasts a team of highly skilled and experienced professionals. Their team is composed of experts who are not only proficient in the latest technologies but also possess a deep understanding of various industries. This dual expertise allows Alpha IT Solutions to provide insights and recommendations that are both technically sound and industry-relevant. The continuous professional development and training programs ensure that their team remains at the cutting edge of the tech industry.

Additionally, the comprehensive support and maintenance services offered by Alpha IT Solutions are second to none. They understand that technology is a critical component of business operations, and any downtime can have significant repercussions. Therefore, they offer round-the-clock support and proactive maintenance to prevent issues before they arise. This commitment to reliability and uptime is a key differentiator that clients greatly appreciate.

Finally, Alpha IT Solutions places a strong emphasis on security. In an age where cyber threats are ever-evolving, their proactive approach to cybersecurity sets them apart. They employ state-of-the-art security measures and continuously update their protocols to protect clients' data and systems. This dedication to safeguarding sensitive information provides clients with peace of mind, knowing that their business is protected against potential cyber threats. In summary, Alpha IT Solutions’ blend of innovation, personalized service, expertise, reliable support, and top-notch security makes them a superior choice in the tech solutions landscape.

[emanueltucker]

Today we're going to talk about one of the best platforms for designing images using artificial intelligence: "Artisia". It's a unique platform that combines creativity and technology, representing a symbol of advancement in the world of AI and images. This platform stands out for its exceptional ability to create and enhance images, placing it at the forefront of tools that contribute to transforming ideas into stunning visual realities.

Using the latest algorithms and machine learning techniques, "Artisia" offers a distinguished experience that combines ease of use with extremely high precision in results. Users can easily specify their requirements and rely on the system to execute them with the highest level of efficiency. Currently, it surpasses most platforms in terms of result accuracy and ease of use more...

[mirelladuca94]

Unveiling the Best Digital Marketing Agency: A Comprehensive Exploration

In the ever-evolving landscape of digital marketing, where strategies are as diverse as the platforms they inhabit, identifying the best digital marketing agency is a pursuit that demands careful consideration and thorough evaluation. With businesses increasingly relying on digital channels to reach their target audiences and drive growth, the role of these agencies has become more critical than ever before. But what sets the best digital marketing agencies apart from the competition? In this comprehensive exploration, we delve into the key attributes and distinguishing factors that define excellence in the realm of digital marketing.

At the heart of every successful Digital marketing agency lies a deep understanding of the digital ecosystem and a relentless pursuit of innovation. The best agencies are not content to simply follow trends; they are trendsetters, constantly pushing the boundaries of what's possible and pioneering new approaches to digital marketing. From harnessing the power of emerging technologies like artificial intelligence and machine learning to leveraging the latest trends in social media and content marketing, these agencies stay ahead of the curve to deliver unparalleled results for their clients.

Moreover, the best digital marketing agencies understand that success in the digital realm requires more than just technical expertise; it requires a deep understanding of human behavior and psychology. By combining data-driven insights with a keen understanding of consumer psychology, these agencies are able to create highly targeted campaigns that resonate with their clients' target audiences on a profound level. Whether it's crafting compelling storytelling narratives or designing interactive experiences that captivate and engage, they know how to create meaningful connections that drive real results.

Another hallmark of the best digital marketing agencies is their unwavering commitment to client success. Unlike fly-by-night agencies that prioritize short-term gains over long-term partnerships, the best agencies view their clients as true partners in success. They take the time to truly understand their clients' unique goals, challenges, and opportunities, and they tailor their strategies accordingly to ensure maximum impact. Whether it's increasing brand awareness, driving lead generation, or boosting sales, these agencies are laser-focused on delivering tangible results that move the needle for their clients' businesses.

Furthermore, transparency and accountability are core principles that guide the operations of the best digital marketing agencies. They believe in full transparency when it comes to their strategies, methodologies, and performance metrics, and they provide their clients with regular updates and detailed reports to keep them informed every step of the way. By fostering a culture of transparency and open communication, these agencies build trust and credibility with their clients, laying the foundation for long-term partnerships built on mutual respect and collaboration.

Creativity is another key differentiator that sets the best digital marketing agencies apart from the competition. In a crowded digital landscape where attention spans are short and competition is fierce, creativity is the currency of success. The best agencies boast teams of creative visionaries who are passionate about pushing the boundaries of conventional thinking and exploring new ideas and concepts. Whether it's designing visually stunning graphics, producing compelling video content, or crafting engaging social media campaigns, these agencies know how to capture attention and inspire action in their target audiences.

In conclusion, the best digital marketing agencies are more than just service providers; they are strategic partners who are committed to driving real, measurable results for their clients. With their deep understanding of the digital ecosystem, their relentless pursuit of innovation, their unwavering commitment to client success, and their dedication to transparency and accountability, these agencies are the driving force behind some of the most successful digital marketing campaigns in the world. Partnering with the best digital marketing agency is not just a choice; it's a strategic imperative for any business looking to thrive in the digital age.

[dishachrista]

Getting Machine Learning Accessible to Everyone: Breaking the Complexity Barrier

Machine learning has become an essential part of our daily lives, influencing how we interact with technology and impacting various industries. But, what exactly is machine learning? In simple terms, it's a subset of artificial intelligence (AI) that focuses on teaching computers to learn from data and make decisions without explicit programming. Now, let's delve deeper into this fascinating realm, exploring its core components, advantages, and real-world applications.

Imagine teaching a computer to differentiate between fruits like apples and oranges. Instead of handing it a list of rules, you provide it with numerous pictures of these fruits. The computer then seeks patterns in these images - perhaps noticing that apples are round and come in red or green hues, while oranges are round and orange in colour. After encountering many examples, the computer grasps the ability to distinguish between apples and oranges on its own. So, when shown a new fruit picture, it can decide whether it's an apple or an orange based on its learning. This is the essence of machine learning: computers learn from data and apply that learning to make decisions.

Key Concepts in Machine Learning

Algorithms: At the heart of machine learning are algorithms, mathematical models crafted to process data and provide insights or predictions. These algorithms fall into categories like supervised learning, unsupervised learning, and reinforcement learning, each serving distinct purposes.

Supervised Learning: This type of algorithm learns from labelled data, where inputs are matched with corresponding outputs. It learns the mapping between inputs and desired outputs, enabling accurate predictions on unseen data.

Unsupervised Learning: In contrast, unsupervised learning involves unlabelled data. This algorithm uncovers hidden patterns or relationships within the data, often revealing insights that weren't initially apparent.

Reinforcement Learning: This algorithm focuses on training agents to make sequential decisions by receiving rewards or penalties from the environment. It excels in complex scenarios such as autonomous driving or gaming.

Training and Testing Data: Training a machine learning model requires a substantial amount of data, divided into training and testing sets. The training data teaches the model patterns, while the testing data evaluates its performance and accuracy.

Feature Extraction and Engineering: Machine learning relies on features, specific attributes of data, to make predictions. Feature extraction involves selecting relevant features, while feature engineering creates new features to enhance model performance.

Benefits of Machine Learning

Machine learning brings numerous benefits that contribute to its widespread adoption:

Automation and Efficiency: By automating repetitive tasks and decision-making processes, machine learning boosts efficiency, allowing resources to be allocated strategically.

Accurate Predictions and Insights: Machine learning models analyse vast data sets to uncover patterns and make predictions, empowering businesses with informed decision-making.

Adaptability and Scalability: Machine learning models improve with more data, providing better results over time. They can scale to handle large datasets and complex problems.

Personalization and Customization: Machine learning enables personalized user experiences by analysing preferences and behaviour, fostering customer satisfaction.

Real-World Applications of Machine Learning

Machine learning is transforming various industries, driving innovation:

Healthcare: Machine learning aids in medical image analysis, disease diagnosis, drug discovery, and personalized medicine. It enhances patient outcomes and streamlines healthcare processes.

Finance: In finance, machine learning enhances fraud detection, credit scoring, and risk analysis. It supports data-driven decisions and optimization.

Retail and E-commerce: Machine learning powers recommendations, demand forecasting, and customer behaviour analysis, optimizing sales and enhancing customer experiences.

Transportation: Machine learning contributes to traffic prediction, autonomous vehicles, and supply chain optimization, improving efficiency and safety.

Incorporating machine learning into industries has transformed them. If you're interested in integrating machine learning into your business or learning more, consider expert guidance or specialized training, like that offered by ACTE institute. As technology advances, machine learning will continue shaping our future in unimaginable ways. Get ready to embrace its potential and transformative capabilities.

[jcmarchi]

How Does AI Use Impact Critical Thinking?

New Post has been published on https://thedigitalinsider.com/how-does-ai-use-impact-critical-thinking/

How Does AI Use Impact Critical Thinking?

Artificial intelligence (AI) can process hundreds of documents in seconds, identify imperceptible patterns in vast datasets and provide in-depth answers to virtually any question. It has the potential to solve common problems, increase efficiency across multiple industries and even free up time for individuals to spend with their loved ones by delegating repetitive tasks to machines.    

However, critical thinking requires time and practice to develop properly. The more people rely on automated technology, the faster their metacognitive skills may decline. What are the consequences of relying on AI for critical thinking?

Study Finds AI Degrades Users’ Critical Thinking 

The concern that AI will degrade users’ metacognitive skills is no longer hypothetical. Several studies suggest it diminishes people’s capacity to think critically, impacting their ability to question information, make judgments, analyze data or form counterarguments. 

A 2025 Microsoft study surveyed 319 knowledge workers on 936 instances of AI use to determine how they perceive their critical thinking ability when using generative technology. Survey respondents reported decreased effort when using AI technology compared to relying on their own minds. Microsoft reported that in the majority of instances, the respondents felt that they used “much less effort” or “less effort” when using generative AI.  

Knowledge, comprehension, analysis, synthesis and evaluation were all adversely affected by AI use. Although a fraction of respondents reported using some or much more effort, an overwhelming majority reported that tasks became easier and required less work. 

If AI’s purpose is to streamline tasks, is there any harm in letting it do its job? It is a slippery slope. Many algorithms cannot think critically, reason or understand context. They are often prone to hallucinations and bias. Users who are unaware of the risks of relying on AI may contribute to skewed, inaccurate results. 

How AI Adversely Affects Critical Thinking Skills

Overreliance on AI can diminish an individual’s ability to independently solve problems and think critically. Say someone is taking a test when they run into a complex question. Instead of taking the time to consider it, they plug it into a generative model and insert the algorithm’s response into the answer field. 

In this scenario, the test-taker learned nothing. They didn’t improve their research skills or analytical abilities. If they pass the test, they advance to the next chapter. What if they were to do this for everything their teachers assign? They could graduate from high school or even college without refining fundamental cognitive abilities. 

This outcome is bleak. However, students might not feel any immediate adverse effects. If their use of language models is rewarded with better test scores, they may lose their motivation to think critically altogether. Why should they bother justifying their arguments or evaluating others’ claims when it is easier to rely on AI? 

The Impact of AI Use on Critical Thinking Skills 

An advanced algorithm can automatically aggregate and analyze large datasets, streamlining problem-solving and task execution. Since its speed and accuracy often outperform humans, users are usually inclined to believe it is better than them at these tasks. When it presents them with answers and insights, they take that output at face value. Unquestioning acceptance of a generative model’s output leads to difficulty distinguishing between facts and falsehoods. Algorithms are trained to predict the next word in a string of words. No matter how good they get at that task, they aren’t really reasoning. Even if a machine makes a mistake, it won’t be able to fix it without context and memory, both of which it lacks.

The more users accept an algorithm’s answer as fact, the more their evaluation and judgment skew. Algorithmic models often struggle with overfitting. When they fit too closely to the information in their training dataset, their accuracy can plummet when they are presented with new information for analysis. 

Populations Most Affected by Overreliance on AI 

Generally, overreliance on generative technology can negatively impact humans’ ability to think critically. However, low confidence in AI-generated output is related to increased critical thinking ability, so strategic users may be able to use AI without harming these skills. 

In 2023, around 27% of adults told the Pew Research Center they use AI technology multiple times a day. Some of the individuals in this population may retain their critical thinking skills if they have a healthy distrust of machine learning tools. The data must focus on populations with disproportionately high AI use and be more granular to determine the true impact of machine learning on critical thinking. 

Critical thinking often isn’t taught until high school or college. It can be cultivated during early childhood development, but it typically takes years to grasp. For this reason, deploying generative technology in schools is particularly risky — even though it is common. 

Today, most students use generative models. One study revealed that 90% have used ChatGPT to complete homework. This widespread use isn’t limited to high schools. About 75% of college students say they would continue using generative technology even if their professors disallowed it. Middle schoolers, teenagers and young adults are at an age where developing critical thinking is crucial. Missing this window could cause problems. 

The Implications of Decreased Critical Thinking

Already, 60% of educators use AI in the classroom. If this trend continues, it may become a standard part of education. What happens when students begin to trust these tools more than themselves? As their critical thinking capabilities diminish, they may become increasingly susceptible to misinformation and manipulation. The effectiveness of scams, phishing and social engineering attacks could increase.  

An AI-reliant generation may have to compete with automation technology in the workforce. Soft skills like problem-solving, judgment and communication are important for many careers. Lacking these skills or relying on generative tools to get good grades may make finding a job challenging. 

Innovation and adaptation go hand in hand with decision-making. Knowing how to objectively reason without the use of AI is critical when confronted with high-stakes or unexpected situations. Leaning into assumptions and inaccurate data could adversely affect an individual’s personal or professional life.

Critical thinking is part of processing and analyzing complex — and even conflicting — information. A community made up of critical thinkers can counter extreme or biased viewpoints by carefully considering different perspectives and values. 

AI Users Must Carefully Evaluate Algorithms’ Output 

Generative models are tools, so whether their impact is positive or negative depends on their users and developers. So many variables exist. Whether you are an AI developer or user, strategically designing and interacting with generative technologies is an important part of ensuring they pave the way for societal advancements rather than hindering critical cognition.

[audreyshura]

Feature of Leonardo AI

Introduction to Leonardo AI

Leonardo AI, an advanced Artificial Intelligence system, represents a significant milestone in technological innovation. This AI marvel encompasses a vast array of cutting-edge features that revolutionize various industries and daily operations.

Related: Leonardo AI - Your Absolute Partner To Create AI Art!

Understanding AI Technology

1. Definition and Basics of AI

Artificial Intelligence, commonly known as AI, refers to the simulation of human intelligence in machines programmed to think, learn, and problem-solve like humans. Leonardo AI harnesses this concept, offering an exceptional level of cognitive capabilities.

2. Evolution of AI in Modern Times

The journey of AI has been marked by remarkable advancements, with Leonardo AI being at the forefront of this evolution. It incorporates state-of-the-art technologies to enhance its functionality and adaptability.

Features and Capabilities of Leonardo AI

1. Deep Learning

Leonardo AI excels in deep learning, a subset of AI that enables machines to learn and make decisions independently, mirroring human cognitive abilities. This feature enables the system to continually improve and evolve.

2. Natural Language Processing (NLP)

With sophisticated natural language processing capabilities, Leonardo AI comprehends and processes human language nuances. It interprets, understands, and generates human-like responses, facilitating seamless interactions.

3. Image Recognition

The AI's prowess in image recognition surpasses expectations, swiftly identifying and categorizing visual data. From facial recognition to object detection, Leonardo AI's accuracy is unparalleled.

4. Creativity and Innovation

Unlike conventional AI systems, Leonardo AI exhibits a unique trait: creativity. It can generate original content, art, and designs, showcasing its innovative potential.

Applications and Industries Benefiting from Leonardo AI

1. Healthcare

In the healthcare sector, Leonardo AI assists in diagnosis, drug discovery, and personalized treatment plans, significantly improving patient care and outcomes.

2. Marketing and Advertising

Marketers leverage Leonardo AI's data analysis and predictive modeling to create targeted campaigns and understand consumer behavior, optimizing marketing strategies.

3. Finance

In the financial domain, Leonardo AI aids in fraud detection, risk assessment, and algorithmic trading, enhancing accuracy and efficiency.

4. Entertainment

In entertainment, this AI contributes to content creation, virtual reality experiences, and personalized recommendations, elevating user engagement.

Impact and Future Potential of Leonardo AI

1. Ethical Considerations

While the capabilities of Leonardo AI are groundbreaking, ethical concerns regarding privacy, bias, and job displacement necessitate careful consideration and regulation.

2. Advancements and Possibilities

The future holds immense potential for Leonardo AI, with ongoing research and development aimed at expanding its capabilities and applications.

Conclusion

Leonardo AI stands as a testament to the incredible advancements in Artificial Intelligence, offering unparalleled features that transcend conventional boundaries. Its impact across industries showcases the immense potential and transformative power of AI technology.

FAQs

Is Leonardo AI accessible to everyone?

Leonardo AI is primarily utilized by businesses and organizations that require advanced AI capabilities. However, aspects of its technology may be integrated into consumer applications in the future.

How does Leonardo AI ensure data privacy and security?

Leonardo AI employs robust encryption and data anonymization techniques to safeguard sensitive information, prioritizing user privacy and security.

Can Leonardo AI learn from its mistakes and improve over time?

Yes, Leonardo AI is designed to learn from its interactions and errors, continuously enhancing its performance and capabilities.

What distinguishes Leonardo AI from other AI systems available in the market?

Leonardo AI's unique blend of deep learning, creativity, and diverse applications sets it apart, offering a more comprehensive and innovative AI experience.

What are the potential challenges in the widespread adoption of Leonardo AI?

Challenges include regulatory concerns, ethical dilemmas, and ensuring fair and equitable access to AI technology.

[digvijay00]

Python's Age: Unlocking the Potential of Programming

Introduction:

Python has become a powerful force in the ever-changing world of computer languages, influencing how developers approach software development. Python's period is distinguished by its adaptability, ease of use, and vast ecosystem that supports a wide range of applications. Python has established itself as a top choice for developers globally, spanning from web programming to artificial intelligence. We shall examine the traits that characterize the Python era and examine its influence on the programming community in this post. Learn Python from Uncodemy which provides the best Python course in Noida and become part of this powerful force.

Versatility and Simplicity:

Python stands out due in large part to its adaptability. Because it is a general-purpose language with many applications, Python is a great option for developers in a variety of fields. It’s easy to learn and comprehend grammar is straightforward, concise, and similar to that of the English language. A thriving and diverse community has been fostered by Python's simplicity, which has drawn both novice and experienced developers.

Community and Collaboration:

It is well known that the Python community is open-minded and cooperative. Python is growing because of the libraries, frameworks, and tools that developers from all around the world create to make it better. Because the Python community is collaborative by nature, a large ecosystem has grown up around it, full of resources that developers may easily access. The Python community offers a helpful atmosphere for all users, regardless of expertise level. Whether you are a novice seeking advice or an expert developer searching for answers, we have you covered.

Web Development with Django and Flask:

Frameworks such as Django and Flask have helped Python become a major force in the online development space. The "batteries-included" design of the high-level web framework Django makes development more quickly accomplished. In contrast, Flask is a lightweight, modular framework that allows developers to select the components that best suit their needs. Because of these frameworks, creating dependable and

scalable web applications have become easier, which has helped Python gain traction in the web development industry.

Data Science and Machine Learning:

Python has unmatched capabilities in data science and machine learning. The data science toolkit has become incomplete without libraries like NumPy, pandas, and matplotlib, which make data manipulation, analysis, and visualization possible. Two potent machine learning frameworks, TensorFlow and PyTorch, have cemented Python's place in the artificial intelligence field. Data scientists and machine learning engineers can concentrate on the nuances of their models instead of wrangling with complicated code thanks to Python's simple syntax.

Automation and Scripting:

Python is a great choice for activities ranging from straightforward scripts to intricate automation workflows because of its adaptability in automation and scripting. The readable and succinct syntax of the language makes it easier to write automation scripts that are both effective and simple to comprehend. Python has evolved into a vital tool for optimizing operations, used by DevOps engineers to manage deployment pipelines and system administrators to automate repetitive processes.

Education and Python Courses:

The popularity of Python has also raised the demand for Python classes from people who want to learn programming. For both novices and experts, Python courses offer an organized learning path that covers a variety of subjects, including syntax, data structures, algorithms, web development, and more. Many educational institutions in the Noida area provide Python classes that give a thorough and practical learning experience for anyone who wants to learn more about the language.

Open Source Development:

The main reason for Python's broad usage has been its dedication to open-source development. The Python Software Foundation (PSF) is responsible for managing the language's advancement and upkeep, guaranteeing that programmers everywhere can continue to use it without restriction. This collaborative and transparent approach encourages creativity and lets developers make improvements to the language. Because Python is open-source, it has been possible for developers to actively shape the language's development in a community-driven ecosystem.

Cybersecurity and Ethical Hacking:

Python has emerged as a standard language in the fields of ethical hacking and cybersecurity. It's a great option for creating security tools and penetration testing because of its ease of use and large library. Because of Python's adaptability, cybersecurity experts can effectively handle a variety of security issues. Python plays a more and bigger part in system and network security as cybersecurity becomes more and more important.

Startups and Entrepreneurship:

Python is a great option for startups and business owners due to its flexibility and rapid development cycles. Small teams can quickly prototype and create products thanks to the language's ease of learning, which reduces time to market. Additionally, companies may create complex solutions without having to start from scratch thanks to Python's large library and framework ecosystem. Python's ability to fuel creative ideas has been leveraged by numerous successful firms, adding to the language's standing as an engine for entrepreneurship.

Remote Collaboration and Cloud Computing:

Python's heyday aligns with a paradigm shift towards cloud computing and remote collaboration. Python is a good choice for creating cloud-based apps because of its smooth integration with cloud services and support for asynchronous programming. Python's readable and simple syntax makes it easier for developers working remotely or in dispersed teams to collaborate effectively, especially in light of the growing popularity of remote work and distributed teams. The language's position in the changing cloud computing landscape is further cemented by its compatibility with key cloud providers.

Continuous Development and Enhancement:

Python is still being developed; new features, enhancements, and optimizations are added on a regular basis. The maintainers of the language regularly solicit community input to keep Python current and adaptable to the changing needs of developers. Python's longevity and ability to stay at the forefront of technical breakthroughs can be attributed to this dedication to ongoing development.

The Future of Python:

The future of Python seems more promising than it has ever been. With improvements in concurrency, performance optimization, and support for future technologies, the language is still developing. Industry demand for Python expertise is rising, suggesting that the language's heyday is still very much alive. Python is positioned to be a key player in determining the direction of software development as emerging technologies like edge computing, quantum computing, and artificial intelligence continue to gain traction.

Conclusion:

To sum up, Python is a versatile language that is widely used in a variety of sectors and is developed by the community. Python is now a staple of contemporary programming, used in everything from artificial intelligence to web development. The language is a favorite among developers of all skill levels because of its simplicity and strong capabilities. The Python era invites you to a vibrant and constantly growing community, whatever your experience level with programming. Python courses in Noida offer a great starting place for anybody looking to start a learning journey into the broad and fascinating world of Python programming.

Source Link: https://teletype.in/@vijay121/Wj1LWvwXTgz

[thoughts-of-ghanashyam]

𝕬𝖗𝖙𝖎𝖋𝖎𝖈𝖎𝖆𝖑 𝖎𝖓𝖙𝖊𝖑𝖑𝖎𝖌𝖊𝖓𝖈𝖊 𝖛𝖘. 𝕮𝖔𝖓𝖘𝖈𝖎𝖔𝖚𝖘𝖓𝖊𝖘𝖘

At least since the broad masses have discovered artificial intelligence (AI) in the form of Midjourney, Stable Diffusion, ChatGPT and the like, the market around the topic of AI is booming.

Humans are thus taking another hurdle on their way to becoming faster, better, more artificial and supposedly more perfect. It feels like there are new possibilities, improvements and enhancements to be discovered in this segment almost every day. "The machine" can search for information on any topic in a matter of seconds and compose an answer that can rival competent college-level term papers - likewise, it can create digital works of art that most users could never create themselves in this lifetime - provided the prompts entered are coherent and understood by the machine.

With this leap in development, opinions are also splitting on whether AI is a "savior" or the "ultimate evil." Suddenly, dystopian stories à la Terminator, in which a supercomputer overrides the orders of human developers, takes over weapons systems, and nearly wipes out the human species through global destruction, don't seem so far off. Although I use this technology myself from time to time, I also see it as a double-edged sword in a way. So I can't classify it as either salvation or diabolical - the truth, as so often, probably lies somewhere in the middle.

All gloomy predictions are ultimately based on the simple idea that artificial intelligence can become more intelligent than humans themselves. In this context, the question arises as to what intelligence is and what distinguishes the "human biomachine" from the "AI machine" in this respect. In my humble opinion, no machine is intelligent in the human sense - not even the human brain, because neither can experience anything in depth. The essence of human consciousness is experience itself. Therefore, human potential in conjunction with our deep spiritual levels exceeds any machine. One only has to be aware of this fact or learn to recognize this for oneself in essence.

Because in the end the machine always only imitates - however much faster than the human being. We have already experienced this leap several times in our evolution - for example at the beginning of industrialization, when the steam engine took the previous activities of man ad absurdum. The same scenario played out on a different level with the introduction of the first computers. And although these computers have repeatedly made quantum leaps in recent decades and demonstrated computing powers infinitely superior to those of humans, the supposed "knowledge" is based only on the processing of huge, ever-growing volumes of data.

But the machine doesn´t "know" the creative process of computing as such - just as little does it possess real creativity or intuition. It merely imitates knowledge, which is not the same thing. And even if artificial intelligences are meanwhile developing independently, all basic information is based on already existing information.

With a little optimism, artificial intelligence could lead to a future in which its vast data-processing capabilities could help predict natural disasters, make all kinds of transportation more efficient, and so on. I don't even want to go into the many other pros and cons here, nor into the factors around pessimistic aspects such as the possible spread of disinformation, conspiracy theories, election and / or consumer manipulation, and so on. Because this would go beyond the scope - besides, it is not really what I am concerned with in these lines.

Ultimately, all positive and negative aspects spring from the dualistic human mind - as do all kinds of bigotry, racism, sexism and other value systems. What began with gossip has culminated today on the Internet and on social media platforms. Ultimately, however, it´s not the Internet that gives rise to bodyshaming or bullying, for example - it's merely an output channel that reflects the current level of consciousness of its users.

Accordingly, the Internet does not have a state of consciousness, just like AI, because they are not conscious. AI can record, mix, combine, and recombine audiovisual data and information of any kind in fantastic ways, but human consciousness is infinitely more than data and information. In fact, "information" is a concept that had no reality until the human mind created it. It is the same with our individuality or our "individual self" - for this too is in principle a purely illusory construct of the mind, which sees itself as something separate.

For example, from a Buddhist perspective, it is not possible to separate the self from its environment. The Buddha says in the Lankavatara Sutra:

"Things are not what they seem… Deeds exist, but no doer can be found" (Majjhima Nikaya).

This does not mean that nothing is real. It means that our mind's projections of reality are illusions and that the elements in the universe that make up everything physical that we see - solid, liquid, gas, etc. - do not exist when broken down to a subatomic level. And this is not a philosophical or purely spiritual view, but cutting-edge science. Broken down to its essence, this means that ultimately all things on a subatomic level are made of the same energy, the same origin - just in different manifestations.

This idea should not be lost sight of in all current developments - because a loss of this awareness would mean a far greater danger in the current context around artificial intelligence than AI itself.

Due to the exponential development of technologies, we are constantly exposed to new, external stimuli and challenges. And our, comparatively very slow, evolutionary development, especially the mental one, can hardly adapt to this - or keep up.

We shift our personal reference points more and more outward, towards these technologically generated stimuli, and thus run more and more the risk of forgetting the core of our true being, indeed of our whole being. Through this constant shifting of reference points, we are also increasingly going into separation - both from ourselves and from everything around us. In the long run, this also means an increased potential for loss of our universal dharma, which in turn negatively impacts our individual as well as our collective karma.

Driven by the additional desire to simplify certain processes, tasks or activities, if at all possible, the current human dilemma is intensified - we thereby massively increase the daily audiovisual stimuli that enter us from the outside. This, in turn, causes our mind to become more and more erratic and to run on a kind of "continuous fire mode". A massive strain that has contributed a significant amount to skyrocketing mental illnesses such as burnouts or depression in recent years.

In this way, we shift our self and our search for happiness further and further into the outside world, relying more and more on machine or digital solutions, which in turn are devoid of any soul, intuition and genuine creativity. We focus on supposed perfection, even if this may not correspond 100% to our own imagination or even to "reality".

Now one can argue of course in such a way that also man could secure his survival in the context of his evolution only by copying certain behaviors and develop accordingly. However, this happened - and always happens with the corresponding consciousness of the experience - on the one hand within the framework of the action itself as well as the mental and energetic aspects connected with it. All this has also a not insignificant share in the individual as well as collective cause-effect principle of karma.

If, on the other hand, we rely too much on machine-generated approaches to solutions, this can certainly lead to a considerable stagnation, if not reduction, of our own potential together with the corresponding conscious experiences. In this context, therefore, we usually find ourselves in an unconscious downward spiral, unless we succeed in creating an appropriate balance that brings us into a healthy equilibrium between mind and technology.

In my eyes, it would therefore be advisable, with all the possibilities that these technologies offer us, to place a parallel increased focus again on looking inward more frequently and more intensively and thereby also withdraw the senses through "Pratyahara". Pratyahara", the fifth limb of the classical Ashtanga Yoga (Raja Yoga) system, is primarily about disciplining the senses (such as taste, sight, hearing, smell, touch) and the mind through a proactive withdrawal from one's sensory center - the perception center in the brain.

It has already been described in the Upanishads that

"Only the seeker can experience absolute reality who, though he has ears, does not hear, though he has eyes, does not see, and even though he lives in this world, does not perceive it by preventing his inner perceptual centers from cooperating with the outer sense organs."

The mind still perceives the stimuli, but it no longer reacts immediately. It can remain in silence. Through this withdrawal, sensory impressions generally become more conscious and controllable in the long run. It is therefore not a matter of limiting the senses - on the contrary: the mind is thereby trained to perceive subtleties which would otherwise remain hidden from the senses, or which we have increasingly lost in the modern world.

We are so much more than we think we are - at the same time we are less individual than we would like to be. When we manage to become aware of the inseparability of being in this universe and recognize our true essence, we glimpse our true nature. And to realize one's nature is to realize the nature of everything. And by that I don't mean the ego, but the part of our being that lies beyond it and usually acts subconsciously.

By looking inward, we can learn to rediscover and explore this very unconscious part of the mind, of being. In this way we can gain new experiences of what it means to experience ourselves, to deal positively with our energies and to proactively open its subtle levels or its gates for us. By opening these gates we can also activate deep-seated potential in the form of knowledge, intellect, concentration, creativity and intuition - potential that we have never "learned" in the classical sense, but that has always been there - so basically it is only "uncovered" or "activated".

Haven't you sometimes wondered where sudden creative ideas or inspirations come from? Inspirations which for example spontaneously and very subtly warn you of a certain action and thus protect you from possible disaster? They happen suddenly, without you being able to control it knowingly!? Exactly this unconscious potential, combined with the act of experiencing, is what sets us apart from artificial intelligence. This potential rests in each of us and possibly goes back to the very source from which our energies originated and of which they are still a part. The energy that is the foundation of all our existence and at the same time connects us with everything.

How one wants to call this source is up to everyone - because whatever we call it, in the end this is also only a spiritual concept. A concept of something that is so wonderfully abstract that it exceeds our rational mind and basically cannot be put into words or described. It should only be important that we recognize with awareness - that we carry this unlimited potential within us and that it cannot be replaced by machines - so we should not even try to strive for it.

So let's just try to become more aware of ourselves again - and thus also of the deep connection with everything that surrounds us. In the end, this awareness contains one thing above all: immeasurable love.

Wouldn't it be wonderful to be constantly in love with everything, rather than in a permanent, individual separation? Wouldn't it be incredibly liberating if it were no longer relevant whether we were female, male or trans? Whether we are atheistic, spiritual or religiously inclined, black, white, brown or whatever? Or even whether we would be human or animal?

Wouldn't this elementary insight be a real spiritual revolution? And what exactly would this deep insight move for possibilities in areas of science as well as artificial intelligence? I believe that there would then possibly be considerably less reasons for dystopian fears, further technlogy-induced threats or a constant, self-separating humanity. In any case, it would be a healthy balance between collective spirituality and science - and this would also have a positive effect on all of our karma.

So each of us could go on this personal, inner journey to discover our own essence including the love inherent there. And yes, this essence and love is always there - in every living being - even if it is all too often overlaid by negative layers of individual and collective karma. But be that as it may - basically there is nothing to lose, but a lot to gain.

I myself am still on this path of introspection and balance. In the process, with a lot of patience and in the form of constant mind training, integral yoga and meditation, I was able to let some traumas go in peace, break cyclical behavior patterns and thus come a little closer to my essence, my true "I"…or should I say "we". It is also important to say that good and loving gurus / teachers are indispensable on this path - as guides, contact persons or companions. Good friends and / or a community / Sangha also facilitate the path by a lot, give support and motivation.

In the end, however, you have to find and follow the right path yourself - because no two people are the same, and accordingly every path is different.

However, the goal is and always remains the same - it is basically a journey home!

At this point I can only emphasize how liberating such steps feel, especially in this time. However, it takes patience and stamina - and especially when it comes to meditation and yoga, these factors should not be seen in the current "lifestyle" context. It is not so much about rest, relaxation and body-conscious, Pilates-like activities. These are just incidental phenomena, but they should never be the sole goal. The goal should be real empowerment, learning to control the mind (because usually it is rather the other way around) and creating a clear awareness.

Because only this awareness is the key to unleash your own potential. Potential that no machine can imitate - neither today - nor in the future!

Thank you for reading!

Hari Om Tat Sat.

[galaxymagitech]

Yes it’s very important to distinguish GenAI from non-generative AI. I personally say GenAI vs. machine learning, because “machine learning” has less generative baggage despite still technically being an accurate descriptor of GenAI too. However, I have problems with this because GenAI is, perhaps deliberately, similar to “general AI”, which is a whole different beast.

It is…not deliberate obfuscation though, that they’re both called AI—or if it is, it’s in the opposite direction than you’re implying.

For one, generative and non-generative AI are both, by definition, AI. Calling either of these things AI is, while not as precise as it could be, the level of precise you can get without being technical.

From the beginning, Artificial General Intelligence (AGI, although I’ve also seen “General AI”)—AI that can meet or exceed human intelligence in any task—has been on people’s minds. And yes, generation is one of those tasks. People argued (and still argue) that AGI is a pipe dream, but “AI” was always strongly associated with generative capacity.

And so, people making non-generative machine learning algorithms had an incentive to (correctly) clearly label their work “AI” because it could be seen as working towards the AGI goal. Some professors and academics I’ve talked to in the past 6 years or so (ie since I started talking to professors and academics) deliberately avoid using the term “AI” for their work because they think it’s too general and hate the association with AGI, which they view as an unrealistic sci-fi trope.

In short, GenAI didn’t take over the term AI—it was always strongly associated with it, and no one is hiding anything by calling it AI. If anything, we should stop calling other forms of machine learning AI.