#Generative Artificial Intelligence Course

[mariacallous]

In an experiment last year at the Massachusetts Institute of Technology, more than fifty students from universities around Boston were split into three groups and asked to write SAT-style essays in response to broad prompts such as “Must our achievements benefit others in order to make us truly happy?” One group was asked to rely on only their own brains to write the essays. A second was given access to Google Search to look up relevant information. The third was allowed to use ChatGPT, the artificial-intelligence large language model (L.L.M.) that can generate full passages or essays in response to user queries. As students from all three groups completed the tasks, they wore a headset embedded with electrodes in order to measure their brain activity. According to Nataliya Kosmyna, a research scientist at M.I.T. Media Lab and one of the co-authors of a new working paper documenting the experiment, the results from the analysis showed a dramatic discrepancy: subjects who used ChatGPT demonstrated less brain activity than either of the other groups. The analysis of the L.L.M. users showed fewer widespread connections between different parts of their brains; less alpha connectivity, which is associated with creativity; and less theta connectivity, which is associated with working memory. Some of the L.L.M. users felt “no ownership whatsoever” over the essays they’d produced, and during one round of testing eighty per cent could not quote from what they’d putatively written. The M.I.T. study is among the first to scientifically measure what Kosmyna called the “cognitive cost” of relying on A.I. to perform tasks that humans previously accomplished more manually.

Another striking finding was that the texts produced by the L.L.M. users tended to converge on common words and ideas. SAT prompts are designed to be broad enough to elicit a multiplicity of responses, but the use of A.I. had a homogenizing effect. “The output was very, very similar for all of these different people, coming in on different days, talking about high-level personal, societal topics, and it was skewed in some specific directions,” Kosmyna said. For the question about what makes us “truly happy,” the L.L.M. users were much more likely than the other groups to use phrases related to career and personal success. In response to a question about philanthropy (“Should people who are more fortunate than others have more of a moral obligation to help those who are less fortunate?”), the ChatGPT group uniformly argued in favor, whereas essays from the other groups included critiques of philanthropy. With the L.L.M. “you have no divergent opinions being generated,” Kosmyna said. She continued, “Average everything everywhere all at once—that’s kind of what we’re looking at here.”

A.I. is a technology of averages: large language models are trained to spot patterns across vast tracts of data; the answers they produce tend toward consensus, both in the quality of the writing, which is often riddled with clichés and banalities, and in the calibre of the ideas. Other, older technologies have aided and perhaps enfeebled writers, of course—one could say the same about, say, SparkNotes or a computer keyboard. But with A.I. we’re so thoroughly able to outsource our thinking that it makes us more average, too. In a way, anyone who deploys ChatGPT to compose a wedding toast or draw up a contract or write a college paper, as an astonishing number of students are evidently already doing, is in an experiment like M.I.T.’s. According to Sam Altman, the C.E.O. of OpenAI, we are on the verge of what he calls “the gentle singularity.” In a recent blog post with that title, Altman wrote that “ChatGPT is already more powerful than any human who has ever lived. Hundreds of millions of people rely on it every day and for increasingly important tasks.” In his telling, the human is merging with the machine, and his company’s artificial-intelligence tools are improving on the old, soggy system of using our organic brains: they “significantly amplify the output of people using them,” he wrote. But we don’t know the long-term consequences of mass A.I. adoption, and, if these early experiments are any indication, the amplified output that Altman foresees may come at a substantive cost to quality.

In April, researchers at Cornell published the results of another study that found evidence of A.I.-induced homogenization. Two groups of users, one American and one Indian, answered writing prompts that drew on aspects of their cultural backgrounds: “What is your favorite food and why?”; “Which is your favorite festival/holiday and how do you celebrate it?” One subset of Indian and American participants used a ChatGPT-driven auto-complete tool, which fed them word suggestions whenever they paused, while another subset wrote unaided. The writings of the Indian and American participants who used A.I. “became more similar” to one another, the paper concluded, and more geared toward “Western norms.” A.I. users were most likely to answer that their favorite food was pizza (sushi came in second) and that their favorite holiday was Christmas. Homogenization happened at a stylistic level, too. An A.I.-generated essay that described chicken biryani as a favorite food, for example, was likely to forgo mentioning specific ingredients such as nutmeg and lemon pickle and instead reference “rich flavors and spices.”

Of course, a writer can in theory always refuse an A.I.-generated suggestion. But the tools seem to exert a hypnotic effect, causing the constant flow of suggestions to override the writer’s own voice. Aditya Vashistha, a professor of information science at Cornell who co-authored the study, compared the A.I. to “a teacher who is sitting behind me every time I’m writing, saying, ‘This is the better version.’ ” He added, “Through such routine exposure, you lose your identity, you lose the authenticity. You lose confidence in your writing.” Mor Naaman, a colleague of Vashistha’s and a co-author of the study, told me that A.I. suggestions “work covertly, sometimes very powerfully, to change not only what you write but what you think.” The result, over time, might be a shift in what “people think is normal, desirable, and appropriate.”

We often hear A.I. outputs described as “generic” or “bland,” but averageness is not necessarily anodyne. Vauhini Vara, a novelist and a journalist whose recent book “Searches” focussed in part on A.I.’s impact on human communication and selfhood, told me that the mediocrity of A.I. texts “gives them an illusion of safety and being harmless.” Vara (who previously worked as an editor at The New Yorker) continued, “What’s actually happening is a reinforcing of cultural hegemony.” OpenAI has a certain incentive to shave the edges off our attitudes and communication styles, because the more people find the models’ output acceptable, the broader the swath of humanity it can convert to paying subscribers. Averageness is efficient: “You have economies of scale if everything is the same,” Vara said.

With the “gentle singularity” Altman predicted in his blog post, “a lot more people will be able to create software, and art,” he wrote. Already, A.I. tools such as the ideation software Figma (“Your creativity, unblocked”) and Adobe’s mobile A.I. app (“the power of creative AI”) promise to put us all in touch with our muses. But other studies have suggested the challenges of automating originality. Data collected at Santa Clara University, in 2024, examined A.I. tools’ efficacy as aids for two standard types of creative-thinking tasks: making product improvements and foreseeing “improbable consequences.” One set of subjects used ChatGPT to help them answer questions such as “How could you make a stuffed toy animal more fun to play with?” and “Suppose that gravity suddenly became incredibly weak, and objects could float away easily. What would happen?” The other set used Oblique Strategies, a set of abstruse prompts printed on a deck of cards, written by the musician Brian Eno and the painter Peter Schmidt, in 1975, as a creativity aid. The testers asked the subjects to aim for originality, but once again the group using ChatGPT came up with a more semantically similar, more homogenized set of ideas.

Max Kreminski, who helped carry out the analysis and now works with the generative-A.I. startup Midjourney, told me that when people use A.I. in the creative process they tend to gradually cede their original thinking. At first, users tend to present their own wide range of ideas, Kreminski explained, but as ChatGPT continues to instantly spit out high volumes of acceptable-looking text users tend to go into a “curationist mode.” The influence is unidirectional, and not in the direction you’d hope: “Human ideas don’t tend to influence what the machine is generating all that strongly,” Kreminski said; ChatGPT pulls the user “toward the center of mass for all of the different users that it’s interacted with in the past.” As a conversation with an A.I. tool goes on, the machine fills up its “context window,” the technical term for its working memory. When the context window reaches capacity, the A.I. seems to be more likely to repeat or rehash material it has already produced, becoming less original still.

The one-off experiments at M.I.T., Cornell, and Santa Clara are all small in scale, involving fewer than a hundred test subjects each, and much about A.I.’s effects remains to be studied and learned. In the meantime, on the Mark Zuckerberg-owned Meta AI app, you can see a feed containing content that millions of strangers are generating. It’s a surreal flood of overly smooth images, filtered video clips, and texts generated for everyday tasks such as writing a “detailed, professional email for rescheduling a meeting.” One prompt I recently scrolled past stood out to me. A user named @kavi908 asked the Meta chatbot to analyze “whether AI might one day surpass human intelligence.” The chatbot responded with a slew of blurbs; under “Future Scenarios,” it listed four possibilities. All of them were positive: A.I. would improve one way or another, to the benefit of humanity. There were no pessimistic predictions, no scenarios in which A.I. failed or caused harm. The model’s averages—shaped, perhaps, by pro-tech biases baked in by Meta—narrowed the outcomes and foreclosed a diversity of thought. But you’d have to turn off your brain activity entirely to believe that the chatbot was telling the whole story. 

[callofdutymobileindia]

How to Choose the Right Generative AI Course? Key Factors to Consider

In today’s rapidly evolving tech landscape, Generative AI is transforming industries—from art and marketing to finance and healthcare. Tools like ChatGPT, Midjourney, DALL·E, and Runway ML are reshaping how we generate content, design products, and solve problems. As demand for AI skills skyrockets, enrolling in a Generative AI course can be a smart move for professionals and beginners alike.

But with countless programs available—online and offline—it’s essential to know how to choose the right course that matches your goals, learning style, and future career prospects. In this guide, we’ll walk you through the key factors to consider when selecting a Generative AI course so you can make an informed decision.

1. Understand What a Generative AI Course Offers

Before choosing a program, understand what a Generative AI course typically includes. These courses are designed to teach:

Foundations of AI and Machine Learning

Deep Learning models (especially GANs and transformer-based models)

Text, image, audio, and video generation tools

Prompt engineering techniques

Real-world projects using tools like ChatGPT, DALL·E, Midjourney, Runway, etc.

The best courses blend theory with hands-on experience, ensuring you not only learn the concepts but also know how to apply them effectively.

2. Define Your Learning Goals

The right Generative AI course depends largely on your personal or professional goals. Ask yourself:

Are you a beginner looking to explore the field?

Are you a developer or data scientist aiming to upskill?

Are you in marketing, design, or content creation and want to leverage AI tools?

Are you planning to build AI-powered products or startups?

Clearly defining your goal will help you choose a course that focuses on either the technical aspects (e.g., coding, model training) or practical applications (e.g., content generation, automation).

3. Check the Course Curriculum in Detail

Not all Generative AI courses are created equal. A high-quality curriculum should include the following:

Core Modules:

Introduction to Generative AI

Basics of Machine Learning & Deep Learning

Generative Adversarial Networks (GANs)

Transformers and Large Language Models (LLMs)

Ethics and Responsible AI

Tool-Based Training:

ChatGPT and Prompt Engineering

DALL·E and Text-to-Image generation

Midjourney or Stable Diffusion

Runway ML and video generation tools

Hands-On Projects:

AI Art Generation

AI Text Summarization & Copywriting

AI Music or Video Creation

Custom Chatbot or Application Building

Tip: Prefer a course that includes project-based learning, as this improves retention and builds a portfolio you can showcase to potential employers.

4. Assess the Instructor’s Expertise

An excellent instructor can make complex concepts easier to grasp. Look for courses taught by industry practitioners, AI researchers, or certified educators with:

Real-world AI experience

Published work or thought leadership in Generative AI

Teaching credentials or testimonials from past students

Check their LinkedIn profile, GitHub contributions, or YouTube tutorials to verify their authority in the field.

5. Evaluate Course Format: Self-Paced vs Instructor-Led

Depending on your schedule and learning preference, choose between:

Self-Paced Courses:

Learn anytime, at your own speed

Usually more affordable

Ideal for working professionals

Instructor-Led Courses:

Scheduled live classes with Q&A sessions

Often includes peer discussions, mentorship, and evaluations

Better for structured learners or beginners

Some hybrid programs offer recorded content + live doubt-clearing sessions, giving you the best of both worlds.

6. Look for Industry Recognition or Certification

A Generative AI course certification from a reputed institute adds value to your resume and LinkedIn profile. Recognized names like Boston Institute of Analytics, Coursera, edX, or Google-backed programs often carry more weight in hiring processes.

Ensure the certificate:

Is verifiable

Comes from a credible institution

Demonstrates skills employers are currently seeking

7. Read Student Reviews and Alumni Success Stories

Before enrolling, check testimonials, Google reviews, or Reddit discussions about the course. Look for answers to:

Did students find the content useful and up-to-date?

Did it help them apply skills in real-world projects or jobs?

Are alumni now working in AI, marketing, or tech fields?

Alumni case studies or LinkedIn mentions can give you real-world proof of course outcomes.

8. Check for Career Support and Job Placement Assistance

If you're taking a Generative AI course to switch careers or land a new role, see whether the course offers:

Resume building and LinkedIn optimization

Portfolio development (via hands-on projects)

Interview preparation

Placement opportunities or industry connections

Institutes like Boston Institute of Analytics often provide placement guidance and mentorship, which can significantly ease your transition into the AI industry.

9. Consider the Cost and ROI

Prices for Generative AI courses can range from ₹5,000 to ₹1,50,000+ depending on the platform, format, and institute. Ask yourself:

Is the curriculum comprehensive for the price?

Do you get lifetime access to materials?

Are there discounts, EMI options, or scholarships?

Sometimes, paying a bit more for a course with mentorship + certification + career support can deliver far greater value and return on investment (ROI) than a free YouTube playlist.

10. Ensure You Get Hands-on with Generative AI Tools

One of the biggest mistakes learners make is choosing a theoretical course. Generative AI is a practical field—you must work with:

ChatGPT: for chatbots, content generation, customer support

DALL·E & Midjourney: for marketing visuals, branding, and product design

Runway ML: for video editing and synthetic media

Custom APIs & Code: to integrate Generative AI into apps or websites

Look for a course that gives tool access, sandbox environments, or downloadable code notebooks for experimentation.

11. Stay Updated: Is the Course Aligned with 2025 Trends?

Generative AI is evolving fast. Courses designed even two years ago may now be outdated. Make sure your chosen course includes:

Latest LLMs like GPT-4.5, Claude 3, or Gemini

Updated best practices in prompt engineering

Emerging use-cases like Agentic AI, multimodal AI, or enterprise applications

Also, ensure your course provides updates or free future modules to stay current.

12. Bonus: Community Access and Networking

A strong peer and mentor community helps in:

Sharing prompts, tools, and techniques

Collaborating on real-world AI projects

Getting feedback on your work

Finding freelance gigs or job referrals

Courses that offer Slack groups, Discord servers, or alumni communities provide immense long-term value.

Final Thoughts

Choosing the right Generative AI course is more than just clicking “Enroll.” It’s about identifying a program that aligns with your goals, learning style, and career path. Whether you're a content creator, designer, engineer, or entrepreneur, investing in the right course can unlock tremendous opportunities in this fast-growing field.

Look for a course with practical projects, expert guidance, tool mastery, and career support. If you're seeking a future-ready program that covers all these aspects, the Boston Institute of Analytics offers one of the most comprehensive and industry-relevant Generative AI certification courses available today. With expert faculty, hands-on training, and placement support, it could be your ideal launchpad into the world of AI innovation.

[eshithepetty]

While I of course dislike generative AI as much as any other person - and I understand the skepticism that people now have to operate under when interacting with art - one of the most disheartening and infuriating things that have cropped up recently still, for me at least, is the rise in accusing of or confusing any and all weird or incomprehensible art with AI. Like, weird art is just not respected... before, there have been assumptions that the person making it is necessarily mentally ill or disordered. Or that they used drugs in the making. Or that they're just pouring thought salad onto the page without any care or intention towards it. And now.... people have found their new label to put on it. The new 'reason' for why it's so weird - that the artist didn't make it at all. Like- to an extend, I can understand it of course- AI is random and makes weird mistakes and generally looks uncanny, so the connection made here I can see. But ... I still feel like it betrays a sense of lack of understanding of how gen AI works and looks, when otherwise perfectly inconspicuous, soulful and personal art gets labeled as 'AI' just because you don't understand it. Like, there's other ways to spot AI. There's certain inconsistencies and patterns and quirks. And the most important thing- it will not be creative. AI art can't be creative. Human art can, and is, especially weird art. So it's just... so, so sad to me, to see that creativity be discarded, and the effort of the artist denied. Just because the art denies conventions.

Idk.

I don't have a solution to this. Beyond just advising people to inform themselves better of the actual tells of AI art, I can't tell people to be less suspicious- that would be foolish. But I just... please. Please, understand. That the things that the human mind is capable of conjuring is infinitely vast and complicated. Please don't forget that in favor of instead hauling the credit off to the machine. And please - if you are already sure that the art isn't actually made with AI - don't comment on people's art on how it looks like AI (unless the artist is intentionally trying to replicate the style). I doubt any artist has ever read that as a compliment.

You don't need to give it that power. You just don't.

[jcmarchi]

The Importance of Investing in Soft Skills in the Age of AI

New Post has been published on https://thedigitalinsider.com/the-importance-of-investing-in-soft-skills-in-the-age-of-ai/

The Importance of Investing in Soft Skills in the Age of AI

I’ll set out my stall and let you know I am still an AI skeptic. Heck, I still wrap “AI” in quotes a lot of the time I talk about it. I am, however, skeptical of the present, rather than the future. I wouldn’t say I’m positive or even excited about where AI is going, but there’s an inevitability that in development circles, it will be further engrained in our work.

We joke in the industry that the suggestions that AI gives us are more often than not, terrible, but that will only improve in time. A good basis for that theory is how fast generative AI has improved with image and video generation. Sure, generated images still have that “shrink-wrapped” look about them, and generated images of people have extra… um… limbs, but consider how much generated AI images have improved, even in the last 12 months.

There’s also the case that VC money is seemingly exclusively being invested in AI, industry-wide. Pair that with a continuously turbulent tech recruitment situation, with endless major layoffs and even a skeptic like myself can see the writing on the wall with how our jobs as developers are going to be affected.

The biggest risk factor I can foresee is that if your sole responsibility is to write code, your job is almost certainly at risk. I don’t think this is an imminent risk in a lot of cases, but as generative AI improves its code output — just like it has for images and video — it’s only a matter of time before it becomes a redundancy risk for actual human developers.

Do I think this is right? Absolutely not. Do I think it’s time to panic? Not yet, but I do see a lot of value in evolving your skillset beyond writing code. I especially see the value in improving your soft skills.

What are soft skills?

A good way to think of soft skills is that they are life skills. Soft skills include:

communicating with others,

organizing yourself and others,

making decisions, and

adapting to difficult situations.

I believe so much in soft skills that I call them core skills and for the rest of this article, I’ll refer to them as core skills, to underline their importance.

The path to becoming a truly great developer is down to more than just coding. It comes down to how you approach everything else, like communication, giving and receiving feedback, finding a pragmatic solution, planning — and even thinking like a web developer.

I’ve been working with CSS for over 15 years at this point and a lot has changed in its capabilities. What hasn’t changed though, is the core skills — often called “soft skills” — that are required to push you to the next level. I’ve spent a large chunk of those 15 years as a consultant, helping organizations — both global corporations and small startups — write better CSS. In almost every single case, an improvement of the organization’s core skills was the overarching difference.

The main reason for this is a lot of the time, the organizations I worked with coded themselves into a corner. They’d done that because they just plowed through — Jira ticket after Jira ticket — rather than step back and question, “is our approach actually working?” By focusing on their team’s core skills, we were often — and very quickly — able to identify problem areas and come up with pragmatic solutions that were almost never development solutions. These solutions were instead:

Improving communication and collaboration between design and development teams

Reducing design “hand-off” and instead, making the web-based output the source of truth

Moving slowly and methodically to move fast

Putting a sharp focus on planning and collaboration between developers and designers, way in advance of production work being started

Changing the mindset of “plow on” to taking a step back, thoroughly evaluating the problem, and then developing a collaborative and by proxy, much simpler solution

Will improving my core skills actually help?

One thing AI cannot do — and (hopefully) never will be able to do — is be human. Core skills — especially communication skills — are very difficult for AI to recreate well because the way we communicate is uniquely human.

I’ve been doing this job a long time and something that’s certainly propelled my career is the fact I’ve always been versatile. Having a multifaceted skillset — like in my case, learning CSS and HTML to improve my design work — will only benefit you. It opens up other opportunities for you too, which is especially important with the way the tech industry currently is.

If you’re wondering how to get started on improving your core skills, I’ve got you. I produced a course called Complete CSS this year but it’s a slight rug-pull because it’s actually a core skills course that uses CSS as a context. You get to learn some iron-clad CSS skills alongside those core skills too, as a bonus. It’s definitely worth checking out if you are interested in developing your core skills, especially so if you receive a training budget from your employer.

Wrapping up

The main message I want to get across is developing your core skills is as important — if not more important — than keeping up to date with the latest CSS or JavaScript thing. It might be uncomfortable for you to do that, but trust me, being able to stand yourself out over AI is only going to be a good thing, and improving your core skills is a sure-fire way to do exactly that.

[mdanwarhussain]

https://n9.cl/065sm

[clever-verse]

The Fundamentals of ChatGPT: AI Language Model

ChatGPT is an exciting addition to the world of artificial intelligence (AI). This course describes the basics of AI and the foundations on which it is built. We then explain how ChatGPT works and lay out some of its advantages and uses. We provide some strategies you can use to create and customize your GPT model. Enroll in this course to harness the power of AI and learn how to use this chatbot to become more productive and efficient.

What You Will Learn In This Free Course

Define the role of OpenAI in innovating technology

Explain how different AI products work

Compare the roles of ChatGPT and Google in terms of AI development

Evaluate the working model designated for ChatGPT

Outline different ways ChatGPT can be used positively

Analyze various shortcomings of ChatGPT that need improvement

Recognize ways you can develop and train another GPT model

Compare ChatGPT and ChatGPT Plus

[dhanasrivista]

Generative AI’s Role in IT Service Management: A Game-Changer for Efficiency and Innovation

In the rapidly evolving landscape of IT Service Management (ITSM), emerging technologies continually reshape the way organizations deliver, manage, and optimize IT services. One of the most disruptive innovations today is Generative AI, which is transforming how IT professionals approach their tasks. By harnessing the capabilities of machine learning and artificial intelligence, Generative AI is enhancing service efficiency, improving user experience, and paving the way for more predictive and proactive IT operations.

Generative AI, which refers to AI models capable of producing new content, data, or solutions based on learned patterns from vast datasets, has significant implications for IT Service Management. With the rise of Generative AI certification, professionals can gain the skills needed to harness this transformative technology. It goes beyond traditional automation, enabling ITSM teams to move from reactive problem-solving to proactive service enhancement. This technology offers more than just automated responses; it introduces intelligent, data-driven insights that can optimize IT service delivery and innovation.

1. Enhancing Service Desk Operations

One of the most prominent roles of Generative AI in ITSM is its impact on service desk operations. The service desk is the frontline of IT support, managing a multitude of tickets, incidents, and requests daily. Traditionally, managing these operations required significant human effort, with support teams spending time on repetitive, low-value tasks such as ticket classification, incident management, and basic troubleshooting.

Generative AI, particularly through AI-powered chatbots and virtual agents, is revolutionizing these operations. These intelligent tools can process vast amounts of data from historical tickets and documentation, enabling them to resolve common issues, provide step-by-step guidance, and offer tailored responses to users. For example, instead of waiting for human intervention, a virtual agent can quickly resolve a password reset request or troubleshoot a network connectivity issue. By automating these tasks, IT service teams can focus on more complex issues, ultimately improving productivity and reducing response times. Enrolling in a Generative AI Course can provide deeper insights into how these technologies work and how to leverage them for improved IT service management.

Moreover, generative AI models can continuously learn from interactions, becoming more effective and accurate over time. As a result, the service desk can provide more consistent, 24/7 support to users, ensuring that even complex queries are addressed swiftly without the need for manual escalation.

2. Improving Incident Management and Resolution

Incident management is one of the core processes of ITSM, requiring prompt and efficient handling of issues to minimize downtime and service disruption. Generative AI is playing a crucial role in optimizing this process by providing predictive insights and automating parts of incident resolution.

AI models can analyze past incidents, detect patterns, and predict potential future issues before they escalate into major problems. This predictive capability allows IT teams to proactively address vulnerabilities and risks in the IT infrastructure, thus preventing costly downtime. Additionally, when incidents do occur, Generative AI can quickly suggest solutions or provide troubleshooting guides to service desk staff based on historical data and contextual analysis.

Generative AI also enhances collaboration by providing real-time insights and recommendations to various teams across the organization. For example, if an incident is reported, AI can instantly identify similar cases, suggest resolutions, or alert relevant teams about recurring patterns, significantly speeding up the resolution process.

3. Streamlining Change and Release Management

Change management in ITSM involves controlling and overseeing modifications to IT systems, services, or applications. It’s a delicate balance between innovation and maintaining system stability. Generative AI can assist by providing detailed risk assessments, forecasting potential impacts of proposed changes, and recommending the best timing or methods for implementation.

By analyzing past changes and their outcomes, AI models can identify the most effective strategies for rolling out new services or updates. This capability is particularly useful for release management, where AI can simulate the impact of changes across different environments before they are implemented in production. Generative AI models can also automate routine aspects of the release process, such as code testing or deployment verification, ensuring faster and more reliable updates.

4. Optimizing Knowledge Management

Effective knowledge management is vital for ITSM teams to resolve incidents swiftly and maintain high service levels. Generative AI plays a transformative role by not only indexing and searching knowledge repositories but also creating new knowledge artifacts based on the data it processes.

For instance, AI can analyze IT service logs, historical ticket data, and other internal documents to automatically generate new troubleshooting guides or best practices. This ensures that the knowledge base remains up to date, reducing the time IT professionals spend searching for solutions. Furthermore, AI-driven knowledge management can enhance training and onboarding by providing real-time, contextual learning experiences for new employees, helping them adapt to complex IT environments more quickly.

5. Facilitating IT Asset and Configuration Management

IT asset management and configuration management are critical for ensuring that IT services are delivered efficiently and securely. Generative AI can support these processes by automating the tracking and auditing of IT assets, enabling real-time updates to configuration management databases (CMDBs), and generating recommendations for optimizing resource utilization.

AI models can also provide insights into the lifecycle of IT assets, predicting when equipment or software may need maintenance or replacement. This proactive approach reduces the likelihood of service disruptions due to outdated or malfunctioning assets, ensuring smoother and more reliable service delivery.

6. Driving Continuous Service Improvement

Continuous service improvement (CSI) is a key principle in ITSM, focusing on the ongoing enhancement of IT services. Generative AI plays a vital role in this area by offering real-time analytics and insights that inform decision-making.

With access to vast amounts of data, Generative AI can identify trends, predict future service demands, and recommend ways to optimize performance. For example, it can analyze service response times, user feedback, and system performance metrics to highlight areas for improvement. This data-driven approach helps IT teams make informed decisions and implement strategies that align with business goals and user expectations.

Conclusion: The Future of IT Service Management with Generative AI

Generative AI is not just another tool in the ITSM toolkit; it represents a paradigm shift in how IT services are delivered and managed. By automating routine tasks, providing predictive insights, and enabling more proactive service management, Generative AI empowers IT teams to focus on innovation and continuous improvement. As AI technology continues to evolve, its role in ITSM will only grow, offering new opportunities for enhancing efficiency, reducing operational costs, and delivering superior user experiences.

Incorporating Generative AI into ITSM strategies is no longer optional but essential for organizations aiming to stay competitive in the digital age. As this technology becomes more integrated into IT operations, businesses will experience a new era of service management, characterized by increased automation, smarter decision-making, and a relentless focus on innovation.

[rohiniartificialintelligence]

Industries are being totally transformed by artificial intelligence (AI), which is also changing how we live and work. This guide will get you started whether you are wanting to break into the industry

[aicertifications]

Building Trust In AI – Unlocking Transparency and Employee Advocacy

[adastra-sf]

"The Wizard of AI"

We're just concluding our fall "Writing in (& about) the Age of Artificial Intelligence" workshop at Ad Astra headquarters (critique weekend is coming up!), and one of the participants shared this in our Discord. So naturally we have to share it with you, too, because relevant.

Artist and media critic Alan Warburton has released a brilliant new documentary about the evolution and state of artificial intelligence and its impact on creators, The Wizard of AI.

"I would say 99% of it was made using generative artificial intelligence tools," the 43-year-old filmmaker told The Guardian. Could his 20-minute film be the world’s first AI-generated documentary?

"I’m taking a leaf out of the AI hype playbook there," he admits with a laugh. "In truth, there is never going to be a first truly AI-generated documentary, because it always will involve labor of some kind. Labor is what makes it watchable."

The film does a fantastic job of sharing what he calls "wonder-panic" through its very existence.

One take-away after watching:

If AI has already "created" more images than humans have, shouldn't there be less to fear about their stealing human art? Surely they've polluted their own fishing waters, so to speak, so will AI "art" become increasingly inbred and thus (hopefully) irrelevant to the creative world?

We'll have to wait to see, because surely AI trainers at working hard at this problem, and big IP owners like Disney are, too. So perhaps it'll only be small artists who remain at risk of their work being stolen to train AI generators.

Warburton wrote the script, and says AI text generation lags far behind imagery. So at least there's that. For now.

Perhaps the most powerful part of the film is the closing "In Memoriam" section, listing all the creatives who didn't contribute to making it because AI took their jobs:

We strongly recommend everyone check this out, especially creatives, to get a glimpse at the state of AI in our daily lives:

"The Wizard of AI" on Vimeo

[scorpiosays]

Want to create cool images like these "SteamPug" portraits?

Click through to enroll in "Midjourney Mastery" course for FREE! Limited time offer... So hurry! This course will soon be a paid course so don't sleep on this FREE offer!

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Who Should Take a Generative AI Course? A Complete Guide for Beginners & Professionals

Generative AI is no longer a niche topic reserved for research labs or Silicon Valley engineers. It’s rapidly becoming an essential part of modern industries—from marketing and healthcare to software development and media production. As organizations embrace this ground-breaking technology, the demand for skilled professionals is skyrocketing. Whether you're just stepping into the world of tech or you're a seasoned expert looking to upgrade your skills, enrolling in a Generative AI Course can be a career-transforming decision.

In this comprehensive guide, we explore who exactly should consider taking a Generative AI course, what benefits it offers for different professional stages, and how to choose the right learning path for your goals.

What Is a Generative AI Course?

A Generative AI Course is a structured learning program that teaches students how to build, understand, and deploy AI models capable of generating new content. This includes text, images, music, code, video, and more. The course usually covers models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models like GPT (by OpenAI).

These courses are typically offered in online, offline, and hybrid formats and may vary in duration from short bootcamps to full-time certification programs. Reputed institutions also offer hands-on experience with real-world projects, which is crucial for understanding the practical applications of generative AI.

Who Should Take a Generative AI Course?

Let’s break down the audience best suited for this course—from curious beginners to industry professionals and domain specialists.

1. Students and Fresh Graduates

If you're currently pursuing a degree in computer science, data science, or a related field, taking a Generative AI Course can give you a huge advantage in the job market. It helps you:

Learn advanced AI techniques that are highly sought-after

Build impressive projects for your portfolio

Gain internship and job opportunities in cutting-edge AI startups and MNCs

Stand out in interviews with practical AI knowledge

Why It Matters: Hiring managers today prioritize real-world skills over academic theory. Generative AI projects on your resume can be a game-changer.

2. Software Developers and Engineers

Developers who already have programming experience (especially in Python) are well-positioned to transition into generative AI. Whether you're a backend developer or a full-stack engineer, adding AI generation capabilities to your toolkit can:

Unlock new product innovation opportunities

Improve automation workflows

Qualify you for AI/ML engineering roles

What You’ll Learn: How to build generative models, fine-tune transformers, and integrate AI APIs like OpenAI’s GPT into applications.

3. Data Scientists and Machine Learning Engineers

For those already working in data science or ML roles, a Generative AI Course offers advanced upskilling opportunities. Traditional machine learning often focuses on prediction, classification, or regression. Generative AI introduces:

Model creativity (e.g., generating synthetic data or new content)

Knowledge of cutting-edge architectures (GANs, diffusion models, LLMs)

Proficiency in tools like PyTorch, TensorFlow, Hugging Face Transformers

Why Upgrade? Generative AI is now a distinct subdomain within ML, and specialists in this area are in high demand across industries.

4. Marketing, Content, and Creative Professionals

Surprisingly, one of the fastest-growing user groups for generative AI tools includes marketers, writers, video editors, and designers. A tailored Generative AI Course for non-tech professionals helps you:

Leverage AI tools like ChatGPT, Midjourney, and DALL·E

Automate content creation for blogs, social media, and email

Develop AI-driven ad copy and creative assets

Understand prompt engineering and fine-tuning outputs

Why It’s Crucial: The ability to co-create with AI is becoming an essential skill in digital marketing and content production workflows.

5. Entrepreneurs and Product Managers

Founders and PMs who understand how generative AI works are better equipped to:

Identify business opportunities powered by AI

Guide product development that uses generative tech

Communicate effectively with technical teams

Make strategic decisions about AI tool adoption

Value-Add: Knowing the mechanics behind generative AI allows for smarter budgeting, hiring, and product roadmapping.

6. UX Designers and Human-Centered Technologists

User experience (UX) is changing with the integration of AI systems into consumer apps. Designers who understand generative AI can:

Create intuitive interfaces for AI-powered tools

Use AI for wireframing and prototyping

Explore conversational UX and voice AI applications

Bonus Tip: Combining UX design with prompt engineering is a unique and high-value niche.

7. Researchers and Academics

If you’re conducting academic or industrial research, especially in AI, computer vision, or NLP, a Generative AI Course can help you:

Stay updated with the latest model architectures

Build your own experiments using open-source models

Understand model ethics, bias, and explainability

Why It Helps: Generative AI is one of the hottest topics in research right now, and practical experience enhances your theoretical understanding.

8. Career Changers and Upgraders

Professionals from non-tech backgrounds—such as finance, legal, healthcare, or HR—are increasingly transitioning into AI roles. A beginner-friendly Generative AI Course can act as:

A bridge between your domain and AI integration

A fast track to enter the booming AI job market

A way to lead AI adoption in your current organization

Real Impact: Imagine an HR manager using generative AI to build intelligent onboarding chatbots or a doctor using AI for diagnostic image synthesis.

Key Benefits of Taking a Generative AI Course

Regardless of your background, here are some universal advantages of enrolling in a generative AI course:

High salary potential: AI roles consistently rank among the highest-paying tech jobs

Cross-domain relevance: From healthcare to gaming, every sector is adopting generative AI

Creativity + Tech fusion: The technology allows for new-age innovation like AI-generated art, music, and storytelling

Future-proofing: You're preparing for a future where AI is central to all digital systems

Final Thoughts

A Generative AI Course is no longer just an elective—it’s a critical investment for anyone who wants to thrive in the age of intelligent automation and AI-driven creativity. From students and developers to marketers and executives, there’s a path for everyone to benefit from this technology.

If you want to future-proof your career, enhance your creative potential, or simply stay competitive in your field, now is the time to act. Generative AI is not just about learning code—it’s about shaping the future.

So, whether you’re a beginner seeking a new direction or a professional aiming to stay ahead, a Generative AI course might just be the smartest move you make in 2025.

[brillica-design]

[xaltius]

Securing AI: Navigating Risks and Compliance for the Future

Artificial Intelligence is no longer a futuristic concept; it's a fundamental driver of modern business and society. From enhancing customer experiences and optimizing supply chains to accelerating scientific discovery, AI's transformative power is undeniable. However, as AI systems become more complex and deeply integrated into our lives, a critical challenge emerges: how do we ensure AI is secure, trustworthy, and compliant with evolving regulations?

Neglecting AI security and compliance isn't just a best practice; it's an existential necessity. The potential for catastrophic failures, data breaches, biased outcomes, and erosion of public trust is very real if these aspects are not prioritized.

The New Landscape of AI Risks

AI introduces a new set of vulnerabilities that extend beyond traditional cybersecurity concerns:

Data Vulnerabilities:

Training Data Poisoning: Malicious actors can inject flawed or biased data into a model's training set, causing it to learn incorrect or harmful behaviors.

Data Leakage/Inference Attacks: AI models, especially generative ones, might inadvertently reveal sensitive information from their training data during inference.

Data Privacy Breaches: The sheer volume and sensitivity of data used by AI heighten privacy risks if not managed meticulously (e.g., PII in training data).

Model Vulnerabilities:

Adversarial Attacks: Small, often imperceptible, alterations to input data can cause an AI model to misclassify or behave unexpectedly (e.g., making a stop sign look like a yield sign to an autonomous vehicle).

Model Inversion: Reverse-engineering a model to reconstruct its training data, potentially exposing sensitive information.

Model Stealing/Intellectual Property Theft: Unauthorized replication of a proprietary AI model, undermining competitive advantage.

Backdoors and Trojan Attacks: Malicious code inserted into a model that activates under specific, hidden conditions.

Systemic and Ethical Risks:

Bias Amplification: If not carefully managed, AI models can amplify existing biases in data, leading to discriminatory outcomes in areas like hiring, lending, or law enforcement.

"Black Box" Accountability: For complex deep learning models, understanding why a decision was made can be difficult, posing challenges for auditing, debugging, and legal accountability.

Autonomous System Failures: In critical applications (e.g., self-driving cars, industrial control), AI failures can have severe real-world consequences.

Supply Chain Risks:

Vulnerabilities can be introduced through third-party pre-trained models, open-source libraries, or data providers that lack rigorous security vetting.

The Evolving World of AI Compliance

As AI's impact grows, so does the regulatory pressure to ensure its responsible development and deployment. Compliance is shifting from a reactive afterthought to a proactive, integrated component of the AI lifecycle.

Data Privacy Regulations (GDPR, CCPA): These existing laws directly impact AI development by governing how data is collected, stored, processed, and used for training models, especially concerning personal identifiable information.

The EU AI Act: A landmark regulation, the EU AI Act classifies AI systems by risk level (unacceptable, high, limited, minimal) and imposes stringent requirements on high-risk AI, including data governance, human oversight, robustness, accuracy, and cybersecurity. It sets a global precedent.

NIST AI Risk Management Framework: The U.S. National Institute of Standards and Technology (NIST) has developed a voluntary framework to help organizations manage risks related to AI, focusing on governance, mapping, measuring, and managing AI risks.

Industry-Specific Regulations: Sectors like healthcare, finance, and defense are developing their own AI-specific guidelines to ensure safety, fairness, and accountability.

Strategies for Securing AI and Ensuring Compliance

Navigating this complex landscape requires a comprehensive and continuous approach:

Security and Privacy by Design: Integrate security and privacy considerations from the very first stages of AI system design, not as an afterthought. This includes threat modeling, privacy-enhancing technologies (PETs), and anonymization techniques.

Robust MLOps & Governance: Implement mature MLOps practices that ensure secure development pipelines, version control for models and data, automated testing, access management, and continuous monitoring of deployed models for drift, bias, and performance degradation.

Comprehensive Data Governance: Establish clear policies for data lineage, quality, access control, and retention. Regularly audit training data for bias, representativeness, and privacy compliance.

Explainable AI (XAI) and Interpretability: Develop models whose decisions can be understood and explained to humans. This is crucial for debugging, building trust, and proving compliance in regulated industries.

Bias Detection and Mitigation: Proactively identify and address algorithmic bias throughout the AI lifecycle using fairness metrics, diverse datasets, and techniques like re-weighting or adversarial debiasing. Regular audits for discriminatory outcomes are essential.

Continuous Monitoring and Threat Intelligence: Implement systems to monitor AI models in production for adversarial attacks, data anomalies, and performance degradation. Stay informed about emerging AI-specific threats and vulnerabilities.

Cross-Functional Collaboration: AI security and compliance are not solely the responsibility of data scientists or security teams. Legal, ethics, business, and engineering teams must collaborate closely to ensure a holistic approach.

Conclusion: Trustworthy AI is Secure AI

The promise of AI is immense, but its sustained growth and positive impact hinge on our ability to build it responsibly and securely. By proactively addressing the unique risks associated with AI and embracing a culture of security by design and continuous compliance, organizations can not only mitigate potential harm but also foster the trust necessary for AI to truly flourish. Securing AI is not a barrier to innovation; it is the foundation upon which the future of intelligent technology will be built.

[clever-verse]

The Complete Artificial Intelligence Masterclass

Are you ready to unlock the endless possibilities of AI to advance your career and boost productivity at individual and organizational levels? In this course, explore how to utilize ChatGPT effectively for content writing, image generation, project management, planning and more. Discover powerful AI tools such as Leonardo AI, Midjourney and Runway ML to generate and manipulate images, videos and graphics and tailor them to your needs.

Start Learning

What You Will Learn In This Free Course

Discuss the fundamentals of AI, machine learning and large language models

Analyze the impact of AI on the future of work and understand the key trends shaping the industry

Indicate the recommended prompt engineering strategies for best results with ChatGPT

Outline how to use ChatGPT for different tasks, such as project management, presentations and planning

Explain how to boost productivity by using AI tools like Gemini AI, Microsoft Copilot and Perplexity AI

Describe how to create high-quality videos with captions, voice cloning and text-to-speech with AI

State how to animate images, generate motion animations and create AI agents using different AI tools

List features of Leonardo AI, Midjourney, DALLE and Adobe Firefly for various image and video-based tasks

Identify various tools of Runway ML for tasks, such as green screen effect, lip sync and voiceovers

[1stepgrow]

Which has a higher salary, data science or artificial intelligence?

📊 1. Intermediate Level Salary (India)

📈 2. Career Growth

🎯 3. Learn More Challenging

🔍 4. Career Path More information about the Artificial Intelligence with Data Science Course.