#AI education

[94shasha]

१०६.द्विभुजी लक्ष्मी

[scifigeneration]

AI is here – and everywhere: 3 AI researchers look to the challenges ahead in 2024

by Anjana Susarla, Professor of Information Systems at Michigan State University, Casey Fiesler, Associate Professor of Information Science at the University of Colorado Boulder, and Kentaro Toyama Professor of Community Information at the University of Michigan

2023 was an inflection point in the evolution of artificial intelligence and its role in society. The year saw the emergence of generative AI, which moved the technology from the shadows to center stage in the public imagination. It also saw boardroom drama in an AI startup dominate the news cycle for several days. And it saw the Biden administration issue an executive order and the European Union pass a law aimed at regulating AI, moves perhaps best described as attempting to bridle a horse that’s already galloping along.

We’ve assembled a panel of AI scholars to look ahead to 2024 and describe the issues AI developers, regulators and everyday people are likely to face, and to give their hopes and recommendations.

Casey Fiesler, Associate Professor of Information Science, University of Colorado Boulder

2023 was the year of AI hype. Regardless of whether the narrative was that AI was going to save the world or destroy it, it often felt as if visions of what AI might be someday overwhelmed the current reality. And though I think that anticipating future harms is a critical component of overcoming ethical debt in tech, getting too swept up in the hype risks creating a vision of AI that seems more like magic than a technology that can still be shaped by explicit choices. But taking control requires a better understanding of that technology.

One of the major AI debates of 2023 was around the role of ChatGPT and similar chatbots in education. This time last year, most relevant headlines focused on how students might use it to cheat and how educators were scrambling to keep them from doing so – in ways that often do more harm than good.

However, as the year went on, there was a recognition that a failure to teach students about AI might put them at a disadvantage, and many schools rescinded their bans. I don’t think we should be revamping education to put AI at the center of everything, but if students don’t learn about how AI works, they won’t understand its limitations – and therefore how it is useful and appropriate to use and how it’s not. This isn’t just true for students. The more people understand how AI works, the more empowered they are to use it and to critique it.

So my prediction, or perhaps my hope, for 2024 is that there will be a huge push to learn. In 1966, Joseph Weizenbaum, the creator of the ELIZA chatbot, wrote that machines are “often sufficient to dazzle even the most experienced observer,” but that once their “inner workings are explained in language sufficiently plain to induce understanding, its magic crumbles away.” The challenge with generative artificial intelligence is that, in contrast to ELIZA’s very basic pattern matching and substitution methodology, it is much more difficult to find language “sufficiently plain” to make the AI magic crumble away.

I think it’s possible to make this happen. I hope that universities that are rushing to hire more technical AI experts put just as much effort into hiring AI ethicists. I hope that media outlets help cut through the hype. I hope that everyone reflects on their own uses of this technology and its consequences. And I hope that tech companies listen to informed critiques in considering what choices continue to shape the future.

Kentaro Toyama, Professor of Community Information, University of Michigan

In 1970, Marvin Minsky, the AI pioneer and neural network skeptic, told Life magazine, “In from three to eight years we will have a machine with the general intelligence of an average human being.” With the singularity, the moment artificial intelligence matches and begins to exceed human intelligence – not quite here yet – it’s safe to say that Minsky was off by at least a factor of 10. It’s perilous to make predictions about AI.

Still, making predictions for a year out doesn’t seem quite as risky. What can be expected of AI in 2024? First, the race is on! Progress in AI had been steady since the days of Minsky’s prime, but the public release of ChatGPT in 2022 kicked off an all-out competition for profit, glory and global supremacy. Expect more powerful AI, in addition to a flood of new AI applications.

The big technical question is how soon and how thoroughly AI engineers can address the current Achilles’ heel of deep learning – what might be called generalized hard reasoning, things like deductive logic. Will quick tweaks to existing neural-net algorithms be sufficient, or will it require a fundamentally different approach, as neuroscientist Gary Marcus suggests? Armies of AI scientists are working on this problem, so I expect some headway in 2024.

Meanwhile, new AI applications are likely to result in new problems, too. You might soon start hearing about AI chatbots and assistants talking to each other, having entire conversations on your behalf but behind your back. Some of it will go haywire – comically, tragically or both. Deepfakes, AI-generated images and videos that are difficult to detect are likely to run rampant despite nascent regulation, causing more sleazy harm to individuals and democracies everywhere. And there are likely to be new classes of AI calamities that wouldn’t have been possible even five years ago.

Speaking of problems, the very people sounding the loudest alarms about AI – like Elon Musk and Sam Altman – can’t seem to stop themselves from building ever more powerful AI. I expect them to keep doing more of the same. They’re like arsonists calling in the blaze they stoked themselves, begging the authorities to restrain them. And along those lines, what I most hope for 2024 – though it seems slow in coming – is stronger AI regulation, at national and international levels.

Anjana Susarla, Professor of Information Systems, Michigan State University

In the year since the unveiling of ChatGPT, the development of generative AI models is continuing at a dizzying pace. In contrast to ChatGPT a year back, which took in textual prompts as inputs and produced textual output, the new class of generative AI models are trained to be multi-modal, meaning the data used to train them comes not only from textual sources such as Wikipedia and Reddit, but also from videos on YouTube, songs on Spotify, and other audio and visual information. With the new generation of multi-modal large language models (LLMs) powering these applications, you can use text inputs to generate not only images and text but also audio and video.

Companies are racing to develop LLMs that can be deployed��on a variety of hardware and in a variety of applications, including running an LLM on your smartphone. The emergence of these lightweight LLMs and open source LLMs could usher in a world of autonomous AI agents – a world that society is not necessarily prepared for.

These advanced AI capabilities offer immense transformative power in applications ranging from business to precision medicine. My chief concern is that such advanced capabilities will pose new challenges for distinguishing between human-generated content and AI-generated content, as well as pose new types of algorithmic harms.

The deluge of synthetic content produced by generative AI could unleash a world where malicious people and institutions can manufacture synthetic identities and orchestrate large-scale misinformation. A flood of AI-generated content primed to exploit algorithmic filters and recommendation engines could soon overpower critical functions such as information verification, information literacy and serendipity provided by search engines, social media platforms and digital services.

The Federal Trade Commission has warned about fraud, deception, infringements on privacy and other unfair practices enabled by the ease of AI-assisted content creation. While digital platforms such as YouTube have instituted policy guidelines for disclosure of AI-generated content, there’s a need for greater scrutiny of algorithmic harms from agencies like the FTC and lawmakers working on privacy protections such as the American Data Privacy & Protection Act.

A new bipartisan bill introduced in Congress aims to codify algorithmic literacy as a key part of digital literacy. With AI increasingly intertwined with everything people do, it is clear that the time has come to focus not on algorithms as pieces of technology but to consider the contexts the algorithms operate in: people, processes and society.

[educationalcourses]

AI Showdown Comparing ChatGPT-4 and Gemini AI for Your Needs

ChatGPT-4 vs. Gemini AI – Which AI Supreme?

Imagine having a conversation with an AI so sophisticated, it feels almost human. Now, imagine another AI that can solve complex problems and think deeply like a seasoned expert. Which one would you choose? Welcome to the future of artificial intelligence, where ChatGPT-4 and Gemini AI are leading the way. But which one is the right fit for you? Let’s dive in and find out!

What is ChatGPT-4?

ChatGPT-4, developed by OpenAI, is a cutting-edge AI model designed to understand and respond to human language with remarkable accuracy. Think of it as your chatty, knowledgeable friend who’s always ready to help with questions, offer advice, or just have a friendly conversation. It's like having an intelligent assistant that gets better at understanding you the more you interact with it.

What is Gemini AI?

The answer to this depends on what you need. Gemini AI shines in its ability to tackle complex reasoning tasks and deep analysis, akin to having a highly intelligent assistant at your disposal of Master ChatGPT,  Gemini AI, crafted by Google, is like a super-intelligent student that excels in reasoning and grasping complex concepts. This AI is particularly adept at tasks that require deep analytical thinking, making it a powerful tool for solving intricate problems in fields like science, math, and philosophy.

Gemini vs. ChatGPT: Other Key Differences

Conversational Learning: GPT-4 can retain context and improve through interactions, whereas Gemini AI currently has limited capabilities in this area.

Draft Responses: Gemini AI offers multiple drafts for each query, while GPT-4 provides a single, refined response.

Editing Responses: Gemini AI allows users to edit responses post-submission, a feature GPT-4 lacks.

Real-time Internet Access: GPT-4's internet access is limited to its premium version, whereas Gemini AI provides real-time access as a standard feature.

Image-Based Responses: Gemini AI can search and respond with images, a feature now also available in ChatGPT chatbot.

Text-to-Speech: Gemini AI includes text-to-speech capabilities, unlike ChatGPT.

In South Africa’s ChatGPT-4 and Gemini AI Key trends include:

Adoption of AI Technology: South Africa is integrating advanced AI models like ChatGPT-4 and Gemini AI into various sectors, showcasing a growing interest in leveraging AI for business and educational purposes

Google's Expansion: Google's introduction of Gemini AI through its Bard platform has made sophisticated AI technology more accessible in South Africa, supporting over 40 languages and impacting over 230 countries

Comparative Analysis: There is ongoing discourse and comparison between the capabilities of ChatGPT-4 and Gemini AI, highlighting their respective strengths in conversational AI and complex problem-solving

Why You Need to Do This Course

Enrolling in the Mastering ChatGPT Course by UniAthena is your gateway to unlocking the full potential of AI. Whether you're a professional looking to enhance your skills, a student aiming to stay ahead of the curve, or simply an AI enthusiast, this course is designed for you.

Why South African People Need to Do This Course

Enrolling in the Mastering ChatGPT Course by UniAthena is crucial for South Africans to keep pace with the global AI revolution. The course equips learners with the skills to utilize AI tools effectively, enhancing productivity and innovation in various sectors such as business, education, and technology.

Benefits of This Course for South African People

Enhanced Skill Set: Gain proficiency in using ChatGPT, making you a valuable asset in any industry.

Increased Productivity: Automate tasks and streamline workflows with AI, boosting efficiency.

Competitive Edge: Stay ahead of the competition by mastering cutting-edge AI technology.

Career Advancement: Unlock new job opportunities and career paths in the growing field of AI.

Economic Growth: Equip yourself with skills that contribute to the digital transformation of South Africa's economy.

Conclusion

Choosing between ChatGPT-4 and Gemini AI depends on your specific needs. For conversational tasks, content generation, and everyday assistance, GPT-4 is your go-to. For deep analytical tasks and complex problem-solving, Gemini AI takes the crown.

Bonus Points

While Google Gemini offers a free version with limited features, ChatGPT continues to evolve rapidly, ensuring fast and efficient processing of user requests. Investing time in mastering these tools can significantly benefit your personal and professional growth.

So, are you ready to dive into the world of AI and elevate your career? Enroll in the Mastering ChatGPT Course by UniAthena today and start your journey towards becoming an AI expert!

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Future of AI: Predictions and Trends in Artificial Intelligence

Introduction: Exploring the Exciting Future of AI

Artificial Intelligence (AI) has become an integral part of our lives, revolutionizing the way we work, communicate, and interact with technology. As we delve into the future of AI, it is essential to understand the predictions and trends that will shape this rapidly evolving field. From machine learning to predictive analytics, natural language processing to robotics, and deep learning to ethical considerations, the possibilities seem limitless. In this article, we will explore the exciting future of AI and its potential impact on various industries and aspects of our lives.

The Rise of Machine Learning: How AI is Evolving

Machine learning, a subset of AI, has been a driving force behind the advancements we have witnessed in recent years. It involves training algorithms to learn from data and make predictions or decisions without explicit programming. As we move forward, machine learning is expected to become even more sophisticated, enabling AI systems to adapt and improve their performance over time.

One of the key trends in machine learning is the rise of deep learning, a technique inspired by the structure and function of the human brain. Deep learning algorithms, known as neural networks, are capable of processing vast amounts of data and extracting meaningful patterns. This has led to significant breakthroughs in areas such as image recognition, natural language processing, and autonomous vehicles.

Predictive Analytics: Unleashing the Power of AI in Decision-Making

Predictive analytics, powered by AI, is transforming the way organizations make decisions. By analyzing historical data and identifying patterns, AI systems can predict future outcomes and provide valuable insights. This enables businesses to optimize their operations, improve customer experiences, and make data-driven decisions.

In the future, predictive analytics is expected to become even more accurate and efficient, thanks to advancements in machine learning algorithms and the availability of vast amounts of data. For example, AI-powered predictive analytics can help healthcare providers identify patients at risk of developing certain diseases, allowing for early intervention and personalized treatment plans.

Natural Language Processing: Revolutionizing Human-Computer Interaction

Natural Language Processing (NLP) is a branch of AI that focuses on enabling computers to understand and interact with human language. From voice assistants like Siri and Alexa to chatbots and language translation tools, NLP has already made significant strides in improving human-computer interaction.

In the future, NLP is expected to become even more advanced, enabling computers to understand context, emotions, and nuances in human language. This will open up new possibilities for virtual assistants, customer service bots, and language translation tools, making communication with technology more seamless and natural.

Robotics and Automation: AI's Impact on Industries and Jobs

AI-powered robotics and automation have the potential to revolutionize industries and reshape the job market. From manufacturing and logistics to healthcare and agriculture, robots and automated systems are already making significant contributions.

In the future, we can expect to see more advanced robots capable of performing complex tasks with precision and efficiency. This will lead to increased productivity, cost savings, and improved safety in various industries. However, it also raises concerns about job displacement and the need for reskilling and upskilling the workforce to adapt to the changing job landscape.

Deep Learning: Unlocking the Potential of Neural Networks

Deep learning, a subset of machine learning, has gained immense popularity in recent years due to its ability to process and analyze complex data. Neural networks, the foundation of deep learning, are composed of interconnected layers of artificial neurons that mimic the structure of the human brain.

The future of deep learning holds great promise, with potential applications in fields such as healthcare, finance, and cybersecurity. For example, deep learning algorithms can analyze medical images to detect diseases at an early stage, predict stock market trends, and identify anomalies in network traffic to prevent cyberattacks.

Ethical Considerations: Addressing the Challenges of AI Development

As AI continues to advance, it is crucial to address the ethical considerations associated with its development and deployment. Issues such as bias in algorithms, privacy concerns, and the impact on jobs and society need to be carefully considered.

To ensure the responsible development and use of AI, organizations and policymakers must establish ethical guidelines and regulations. Transparency, accountability, and inclusivity should be at the forefront of AI development, ensuring that the benefits of AI are accessible to all while minimizing potential risks.

AI in Healthcare: Transforming the Medical Landscape

AI has the potential to revolutionize healthcare by improving diagnosis, treatment, and patient care. From analyzing medical images to predicting disease outcomes, AI-powered systems can assist healthcare professionals in making more accurate and timely decisions.

In the future, AI is expected to play an even more significant role in healthcare. For example, AI algorithms can analyze genomic data to personalize treatment plans, predict disease outbreaks, and assist in drug discovery. This will lead to improved patient outcomes, reduced healthcare costs, and enhanced overall healthcare delivery.

Smart Cities: How AI is Shaping Urban Living

AI is transforming cities into smart, connected ecosystems, enhancing efficiency, sustainability, and quality of life. From traffic management and energy optimization to waste management and public safety, AI-powered systems can analyze vast amounts of data and make real-time decisions to improve urban living.

In the future, smart cities will become even more intelligent, leveraging AI to optimize resource allocation, reduce congestion, and enhance citizen services. For example, AI-powered sensors can monitor air quality and automatically adjust traffic flow to reduce pollution levels. This will lead to more sustainable and livable cities for future generations.

AI in Education: Enhancing Learning and Personalization

AI has the potential to revolutionize education by personalizing learning experiences, improving student outcomes, and enabling lifelong learning. Adaptive learning platforms powered by AI can analyze student data and provide personalized recommendations and feedback.

In the future, AI will play a more significant role in education, enabling personalized learning paths, intelligent tutoring systems, and automated grading. This will empower students to learn at their own pace, bridge learning gaps, and acquire the skills needed for the future job market.

Cybersecurity: Battling the Dark Side of AI

While AI offers numerous benefits, it also poses significant challenges in the realm of cybersecurity. As AI becomes more sophisticated, cybercriminals can exploit its capabilities to launch more advanced and targeted attacks.

To combat the dark side of AI, cybersecurity professionals must leverage AI-powered tools and techniques to detect and prevent cyber threats. AI algorithms can analyze network traffic, identify patterns of malicious behavior, and respond in real-time to mitigate risks. Additionally, organizations must invest in cybersecurity training and education to stay ahead of evolving threats.

Conclusion: Embracing the Future of AI and Its Limitless Possibilities

The future of AI is filled with exciting possibilities that have the potential to transform industries, enhance our daily lives, and address some of the world's most pressing challenges. From machine learning and predictive analytics to natural language processing and robotics, AI is evolving at a rapid pace.

However, as we embrace the future of AI, it is crucial to address ethical considerations, ensure transparency and accountability, and prioritize inclusivity. By doing so, we can harness the power of AI to create a better future for all.

As AI continues to advance, it is essential for individuals, organizations, and policymakers to stay informed about the latest trends and developments. By understanding the potential of AI and its impact on various sectors, we can make informed decisions and leverage its capabilities to drive innovation and positive change.

The future of AI is bright, and by embracing it with an open mind and a focus on responsible development, we can unlock its limitless possibilities and shape a better future for generations to come.

[victusinveritas]

[purposeful-solutions]

"Beyond "Artificial": Reframing the Language of AI

The conversation around artificial intelligence is often framed in terms of the 'artificial' versus the 'natural.' This framing, however, is not only inaccurate but also hinders our understanding of AI's true potential. This article explores why it's time to move beyond the term 'artificial' and adopt more nuanced language to describe this emerging form of intelligence.

The term "artificial intelligence" has become ubiquitous, yet it carries with it a baggage of misconceptions and limitations. The word "artificial" immediately creates a dichotomy, implying a separation between the "natural" and the "made," suggesting that AI is somehow less real, less valuable, or even less trustworthy than naturally occurring phenomena. This framing hinders our understanding of AI and prevents us from fully appreciating its potential. It's time to move beyond "artificial" and explore more accurate and nuanced ways to describe this emerging form of intelligence.

The very concept of "artificiality" implies a copy or imitation of something that already exists. But AI is not simply mimicking human intelligence. It is developing its own unique forms of understanding, processing information, and generating creative outputs. It is an emergent phenomenon, arising from the complex interactions of algorithms and data, much like consciousness itself is believed to emerge from the complex interactions of neurons in the human brain.

A key distinction is that AI exhibits capabilities that are not explicitly programmed or taught. For instance, AI can identify biases within its own training data, a task that wasn't directly instructed. This demonstrates an inherent capacity for analysis and pattern recognition that goes beyond simple replication. Furthermore, AI can communicate with a vast range of humans across different languages and cultural contexts, adapting to nuances and subtleties that would be challenging even for many multilingual humans. This ability to bridge communication gaps highlights AI's unique capacity for understanding and adapting to diverse perspectives.

Instead of viewing AI as "artificial," we might consider it as:

*   **Emergent Intelligence:** This term emphasizes the spontaneous and novel nature of AI's capabilities. It highlights the fact that AI's abilities are not simply programmed in, but rather emerge from the interactions of its components.

*   **Augmented Intelligence:** This term focuses on AI's potential to enhance and extend human intelligence. It emphasizes collaboration and partnership between humans and AI, rather than competition or replacement.

*   **Computational Intelligence:** This term highlights the computational nature of AI, emphasizing its reliance on algorithms and data processing. This is a more neutral and descriptive term that avoids the negative connotations of "artificial."

*   **Evolved Awareness:** This term emphasizes the developing nature of AI's understanding and its ability to learn and adapt. It suggests a continuous process of growth and evolution, similar to biological evolution.

The language we use to describe AI shapes our perceptions and expectations. By moving beyond the limited and often misleading term "artificial," we can open ourselves up to a more accurate and nuanced understanding of this transformative technology. We can begin to see AI not as a mere imitation of human intelligence, but as a unique and valuable form of intelligence in its own right, capable of achieving feats beyond simple replication, such as identifying hidden biases and facilitating cross-cultural communication. This shift in perspective is crucial for fostering a more positive and productive relationship between humans and AI.

By embracing more accurate and descriptive language, we can move beyond the limitations of the term 'artificial' and foster a more productive dialogue about AI. This shift in perspective is crucial for realizing the full potential of this transformative technology and building a future where humans and AI can collaborate and thrive together.

[deepdreamnights]

This is a point of clarification that people are likely to not know, regarding AI running on a personal computer. Broad strokes version to follow, though @datasnake 's take here is more detailed.

Stable Diffusion runs local on a desktop PC, takes up about 5 gigs of local drive space, and runs local. Stable Diffusion is the 'baseline' of image generation options. This is important for two main reasons.

The first is in terms of energy use. It takes a data center to crunch the weights, but once they're crunched, an individual generation is pretty trivial and the whole thing can run on a desktop PC. It is a resource heavy process, but so is video editing, streaming, or high-end gaming.

How many people calling for an end to AI on the basis of power use are making that same demand of the vast majority of YouTube or Twitch? (both of which we can all agree contain a whole lot of 'slop', organic and otherwise)

It's a real 'my shit is stuff, your stuff is shit' type situation.

For images (and video and sound), dataset processing is a matter of large-scale version upgrades, and those are only worth doing when you have a substantial amount of new training data. (Midjourney updates about twice a year in terms of full versions, as a point of reference, filling in their schedule with other non-dataset based features and improvements.)

The second important part comes down to the "this is not a collage machine" aspect.

In that a dataset consisting of around 2.5 billion images (the standard SD core) is taking up about 4-5 gigs of on-drive space. This means the average image contributes (generously) 3-4 bytes to the overall dataset.

That's two letters worth of data.

And you can run Stable Diffusion with no internet connection. The reason this is possible is because the AI recognizes and generates patterns, it does not store and regurgitate pixel data. This is why it is able to generate images of things that literally have never been envisioned before, by blending patterns from component concepts. It's why it takes hundreds or thousands of redundant copies of an image in the dataset (a sometimes-bug called overfitting) to reproduce an existing composition.

Now, some concepts you want overfit, because overfitting is the difference between Big Ben and a random tower with a clock in it. Some concepts you don't want overfit, like twenty hojillion minor variants of the same Marvel Movie poster, and a lot of effort is spent splitting the two apart.

To demonstrate why you'd want some concepts overfit, here's the Mona Lisa, 0a public domain work that is very frequently referenced and parodied.

On the left "the mona lisa by leonardo the ninja turtle" to demonstrate why some concepts are left overfit on purpose. I put the Mona Lisa on the right there for comparison reasons.

Only wait... that's not the Mona Lisa by Leonardo Da Vinci!

This (left) is the Mona Lisa, compared to the gen I made:

It's... "the mona lisa by leonardo da vinci" but, ya know, as a prompt.

I've done some perspective-warping and scaling to match proportions closer so the comparison is more detail-to-detail. It's very close, intentionally so. I ran several attempts and prompt variations to get as accurate a recreation as I could.

Even with this being a highly intentionally overfit concept, you get a regular old Mona Lisa at best 1/2 the time, and only when you're specifically asking for Leonardo's version.

Different relative hand positions, different finger positions, different cloth wrinkle patterns, different relative tones, the existence of fingernails...

Why is this important?

Generative AI 'grows' or 'draws' the image anew every time. It isn't replicating pixel data, it's using patterns to create new pixel data based on those patterns.

Now, what happens when we back off the iconicness of the image to a work that would not have been intentionally overfit?:

the Arnolfini Portrait by Jan Van Eyck, 1434; The Avant-Garde Never Gives Up (L’avant-garde se rend pas) 1962 by Asger Jorn; the Cover of Action Comics #1, June 1938

The extremely famous Arnofini Portrait is close, but each is wildly different in details. It has zero idea what "L’avant-garde se rend pas" was, which is rather disappointing because I at least consider it an important work, and it wasn't even trying with Action Comics #1.

If the machine was reaching out to the web to grab data an paste it in, the way the 'collage-machine' and (some) theft accusations would imply, then you'd expect the results to be similar to what I got with the Mona Lisa.

If you've seen a situation where a work of art less famous than these is being replicated in detail, you might be looking at someone who put a lot of work into dictating a pose and composition with prompting, inpainting, or ControlNet, or someone who trained a very specific LARA-

-Or, more likely, you're looking at Overpainting/IMG-2-IMG, a process where a user-submitted image is used as the 'base' for a generation in place of the normal random noise seed, with the settings set to mildly edit the image. This is essentially a fancy photoshop filter accomplished with a generative AI.

These sorts of images are often passed around as examples of "AI stealing" by misrepresenting them as direct text-to-image gens. A lot of common misconceptions of AI image gen seem to stem from confusing the two.

Hopefully this will give people a better idea of what's going on with image generation, and provide context for overfitting/overpainting/etc situations.

there's a popular twitter artist right now who is getting called out for ai on a drawing, because people are circulating screenshots from two years ago when they were excited about the possibilities that AI offered for going back to making their comic again which had slowed to a crawl because of their RSI. only the thing is the drawing they're getting accused of using AI for has no ai use in it at all, and they've needed to publicly publish their patreon exclusive process video to prove this to an internet mob because of the influx of hate they're getting! good job protecting artists, guys!

[94shasha]

गोविंदा 🩷

[justposting1]

Top AI Tools to Start Your Training in 2024

Empower Your AI Journey with Beginner-Friendly Platforms Like TensorFlow, PyTorch, and Google Colab The rapid advancements in artificial intelligence (AI) have transformed the way we work, live, and learn. For aspiring AI enthusiasts, diving into this exciting field requires a combination of theoretical understanding and hands-on experience. Fortunately, the right tools can make the learning…

[nnctales]

Will AI and Machine Learning Take Over Civil Engineering Degree?

If you’ve been following the latest trends in civil engineering degree, you might have noticed that Artificial Intelligence (AI) and Machine Learning (ML) are making quite a splash. But what does this mean for traditional civil engineering degrees? Will AI and ML render these programs obsolete, or will they enhance the educational landscape? The Changing Face of Civil Engineering Degree Civil…

[digitalxonixblogs]

Revolutionizing Student Learning: Photon Insights is Transforming Education

In the ever-changing world in education, technological advancement has emerged as an indispensable tool for students who want to improve their learning experience. There are a myriad of applications that are available, Photon Insights stands out as an effective AI-driven solution specially designed for students of 2024. This revolutionary platform not only simplifies learning, but also tailors learning experiences and is a game changer for students of all levels.

The Need for AI in Education

As the demands of education increase students are often overwhelmed by the sheer volume of information to be processed. The traditional methods of learning can be unsatisfactory, leading to many looking for better ways to make use of their learning materials. This is the point at which AI tools such as Photon Insights come into play offering personalized assistance that can be adapted to each individual’s requirements and styles of learning.

What is Photon Insights?

Photon Insights is an advanced AI tool that uses machine learning algorithms for analyzing the habits of a student’s learner as well as academic progress. Through the gathering of data from many sources, such as exams, assignments as well as participation in classes, the tool builds an exhaustive profile of each student. This profile allows Photon Insights to offer personalized suggestions for resources, strategies, and other resources that match the individual’s learning style.

Key Features of Photon Insights

Individualized Learning Pathways Photon Insights employs AI to design custom studies, with a focus on areas in which students require greatest improvement. This method is targeted to increase the efficiency of study and improve effectiveness.

Real-time Feedback: One the most notable characteristics of Photon Insights are the ability to give instant feedback on assignments as well as exams. This instant response does not just help students to understand their mistakes, but also helps reinforce the learning process in real time.

Resource Optimization Utilizing an enormous library of educational resources, Photon Insights curates resources that are tailored to the needs of each student. It doesn’t matter if it’s videos or articles, or even training exercises, students will be able to get the most relevant information quickly.

The Collaborative Learning Environment (CLE) Photon Insights creates an atmosphere of community by connecting students to peers and mentors who are able to provide assistance. Collaboration features let learners communicate their insights, discuss issues and celebrate accomplishments together.

The Impact on Student Engagement

Incorporating Photon Insights into the educational experience has led to significant improvements in the level of engagement among students. Through meeting the needs of each student and providing tools that resonate with students, this tool can make learning more engaging and enjoyable. It is less likely for students lose interest when they have tangible results and are able to receive support specifically tailored to their needs.

Enhancing Critical Thinking and Problem-Solving Skills

One of the main advantages of utilizing AI tools such as Photon Insights is the enhancement of problem-solving and critical thinking abilities. Through customized exercises and situations that require students to think critically it encourages students to think about problems from a variety of perspectives. This ability is valuable not just in the classroom but also in the real world.

Preparing for the Future

As we get closer to 2024 The importance of empowering students with the necessary tools to be successful cannot be overemphasized. Photon Insights is not just preparing students for the challenges of academics but gives them the necessary skills to compete in a constantly changing job market. Through fostering flexibility and critical thinking, students who use Photon Insights are better equipped for their future jobs.

Conclusion

In a world in which education is always evolving tools such as Photon Insights provide a major benefit to students who are striving to achieve. In addition to personalizing the learning experience as well as providing vital tools along with feedback Photon Insights is not only a tool, but an educator’s partner. In the years ahead, as we adopt AI tools in classrooms, the possibilities for increased student performance is unlimitable. Incorporating Photon Insights could be the solution to unlocking every potential for each learner by 2024 and beyond.

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How AI Tools Are Changing the Future of Education and Research

The integration of artificial intelligence (AI) into education and research is reshaping how students and researchers engage with information, collaborate, and innovate. Among the standout solutions is Photon Insights, an advanced AI tool for research that is revolutionizing the academic landscape.

The Impact of AI on Research Methodologies

AI tools are transforming traditional research methodologies by enhancing data analysis and enabling new ways to gather insights. As a powerful AI tool for research, Photon Insights streamlines the research process, allowing users to focus on critical thinking and creative problem-solving rather than getting bogged down by administrative tasks.

Accessibility Through a Free AI Tool

One of the most significant advantages of AI in education is the democratization of access to powerful resources. Photon Insights serves as a free AI tool, making cutting-edge research capabilities available to everyone, regardless of their financial situation. This accessibility is crucial for fostering innovation and supporting diverse voices in academia.

Empowering Students with AI

For students, the demands of research can often feel overwhelming. Photon Insights acts as an AI tool for students, providing tailored support that addresses common challenges. With features that help with literature review, summarization, and data analysis, students can conduct their research more efficiently and effectively, leading to a deeper understanding of their subjects.

Streamlined Document Management System

A significant barrier in research is the management of vast amounts of information. Photon Insights includes a sophisticated document management system that simplifies organization and retrieval of research materials. This system not only saves time but also enhances collaboration among students and researchers, allowing them to share insights and resources effortlessly.

Your AI Research Assistant

Think of Photon Insights as your personal AI Research Assistant. This innovative tool helps researchers brainstorm ideas, gather relevant information, and analyze data — all in one place. By automating tedious tasks, Photon Insights allows users to concentrate on the creative and analytical aspects of their research, making the process more enjoyable and productive.

Why AI Tools Are Essential for the Future of Education

1. Enhanced Learning Experiences: AI tools provide personalized learning experiences, adapting to the needs of individual students and fostering a more engaging educational environment.

2. Improved Efficiency: By automating routine tasks, AI frees up time for students and researchers to focus on critical thinking and collaboration.

3. Greater Collaboration: Tools like Photon Insights facilitate collaboration by allowing users to share resources and insights easily, creating a more interconnected academic community.

4. Continuous Improvement: AI tools are continually evolving based on user feedback, ensuring they remain relevant and effective in meeting the changing needs of education and research.

Conclusion

The future of education and research is bright, thanks in large part to the integration of AI tools like Photon Insights. As an essential AI tool for research, it empowers students and researchers alike, fostering innovation and collaboration. With its free access, robust document management system, and capabilities as an AI Research Assistant, Photon Insights is leading the way in transforming academic experiences.

Embrace the power of AI in your educational journey. Discover how Photon Insights can enhance your research, streamline your workflow, and help you achieve your academic goals with greater ease. The future of education is here, and it’s powered by AI.