#artificial intelligence job interview questions

[lifes-little-corner]

AI interview preparation

I remember my first job interview vividly. It was a traditional setup—a panel of interviewers, a long list of questions, and the pressure to perform. Fast forward to today, and the process has evolved dramatically. With 87% of companies now leveraging advanced methods in recruitment, the way we approach interviews is changing1. These new methods focus on efficiency and fairness. For example,…

[ezeetester]

Getting your feet wet with Generative AI

Disclaimer:  The above image is AI generated Alright, here I am after a gap of a few months. Gen AI is creating a lot of buzz. While you have several names like ChatGpt, Perplexity, Google Gemini etc. doing the rounds wait… DeepSeek. Eeeek! Some folks did get scared for a while As a beginner, one should be concerned about privacy issues.   You need to issue a prompt which contains detail of the…

[yesornopolls]

The article under the cut

Allies of Elon Musk stationed within the Education Department are considering replacing some contract workers who interact with millions of students and parents annually with an artificial intelligence chat bot, according to internal department documents and communications.

The proposal is part of President Trump’s broader effort to shrink the federal work force, and would mark a major change in how the agency interacts with the public. The Education Department’s biggest job is managing billions of dollars in student aid, and it routinely fields complex questions from borrowers.

The department currently uses both call centers and a rudimentary A.I. bot to answer questions. The proposal would introduce generative A.I., a more sophisticated version of artificial intelligence that could replace many of those human agents.

The call centers employ 1,600 people who field over 15,000 questions per day from student borrowers.

The vision could be a model for other federal agencies, in which human beings are replaced by technology, and behemoth contracts with outside companies are shed or reduced in favor of more automated solutions. In some cases, that technology was developed by players from the private sector who are now working inside or with the Trump administration.

Mr. Musk has significant interest in A.I. He founded a generative A.I. company, and is also seeking to gain control of OpenAI, one of the biggest players in the industry. At other agencies, workers from the newly created Department of Government Efficiency, headed by Mr. Musk, have told federal employees that A.I. would be a significant part of the administration’s cost-cutting plans.

A year after the Education Department oversaw a disastrous rollout of a new federal student aid application, longtime department officials say they are open to the idea of seeking greater efficiencies, as have leaders in other federal agencies. Many are partnering with the efficiency initiative.

But Department of Education staff have also found that a 38 percent reduction in funding for call center operations could contribute to a “severe degradation” in services for “students, borrowers and schools,” according to one internal document obtained by The Times.

The Musk associates working inside the Education Department include former executives from education technology and venture capital firms. Over the past several years, those industries have invested heavily in creating A.I. education tools and marketing them to schools, educators and students.

The Musk team at the department has focused, in part, on a help line that is currently operated on a contract basis by Accenture, a consulting firm, according to the documents reviewed by The Times. The call center assists students who have questions about applying for federal Pell grants and other forms of tuition aid, or about loan repayment.

The contract that includes this work has sent more than $700 million to Accenture since 2019, but is set to expire next week.

“The department is open to using tools and systems that would enhance the customer service, security and transparency of data for students and parents,” said Madi Biedermann, the department’s deputy assistant secretary for communications. “We are evaluating all contracts to assess effectiveness relative to costs.”

Accenture did not respond to interview requests. A September report from the Education Department describes 1,625 agents answering 462,000 calls in one month. The agents also handled 118,000 typed chats.

In addition to the call line, Accenture provides a broad range of other services to the student aid system. One of those is Aidan, a more rudimentary virtual assistant that answers basic questions about student aid. It was launched in 2019, during Mr. Trump’s first term.

Accenture reported in 2021 that Aidan fielded 2.2 million messages in one year. But its capabilities fall far short of what Mr. Musk’s associates envision building using generative A.I., according to the internal documents.

Both Mr. Trump and former President Joseph R. Biden Jr. directed federal agencies to look for opportunities to use A.I. to better serve the public.

The proposal to revamp the communication system follows a meltdown in the rollout of the new Free Application for Federal Student Aid, or FAFSA, last year under Mr. Biden. As FAFSA problems caused mass confusion for students applying for financial aid, several major contractors, including Accenture, were criticized for breakdowns in the infrastructure available to students and parents seeking answers and help.

From January through May last year, roughly three-quarters of the 5.4 million calls to the department’s help lines went unanswered, according to a report by the Government Accountability Office.

More than 500 workers have since been added to the call centers, and wait times were significantly reduced, according to the September Department of Education report.

But transitioning into using generative A.I. for student aid help, as a replacement for some or all human call center workers, is likely to raise questions around privacy, accuracy and equal access to devices, according to technology experts.

Generative A.I. systems still sometimes share information that is false.

Given how quickly A.I. capabilities are advancing, those challenges are potentially surmountable, but should be approached methodically, without rushing, said John Bailey, a fellow at the American Enterprise Institute and former director of educational technology at the Education Department under President George W. Bush.

Mr. Bailey has since become an expert on the uses of A.I. in education.

“Any big modernization effort needs to be rolled out slowly for testing, to see what works and doesn’t work,” he said, pointing to the botched introduction of the new FAFSA form as a cautionary tale.

“We still have kids not in college because of that,” he said.

In recent weeks, the Education Department has absorbed a number of DOGE workers, according to two people familiar with the process, who requested anonymity because they were not authorized to discuss the department’s security procedures and feared for their jobs.

One of the people involved in the DOGE efforts at the Education Department is Brooks Morgan, who until recently was the chief executive of Podium Education, an Austin-based start-up, and has also worked for a venture capital firm focused on education technology, according to the two people.

Another new staffer working at the agency is Alexandra Beynon, the former head of engineering at Mindbloom, a company that sells ketamine, according to those sources and an internal document.

And a third is Adam Ramada, who formerly worked at a Miami venture capital firm, Spring Tide Capital, which invests in health technology, according to an affidavit in a lawsuit filed against the Department of Government Efficiency.

None of those staffers responded to interview requests.

[noticiassincensura]

Former OpenAI Researcher Accuses the Company of Copyright Law Violations

Use of Copyrighted Data in AI Models In a new twist in the world of artificial intelligence, Suchir Balaji, a former researcher at OpenAI, has spoken publicly about the company’s practices and its use of copyrighted data. Balaji, who spent nearly four years working at OpenAI, helped collect and organize large volumes of internet data to train AI models like ChatGPT. However, after reflecting on the legal and ethical implications of this process, he decided to leave the company in August 2024.

What Motivated His Departure? Balaji, 25, admitted that at first, he did not question whether OpenAI had the legal right to use the data it was collecting, much of which was protected by copyright. He assumed that since it was publicly available information on the internet, it was free to use. However, over time, and especially after the launch of ChatGPT in 2022, he began to doubt the legality and ethics of these practices.

“If you believe what I believe, you have to leave the company,” he commented in a series of interviews with The New York Times. For Balaji, using copyrighted data without the creators’ consent was not only a violation of the law but also a threat to the integrity of the internet. This realization led him to resign, although he has not taken another job yet and is currently working on personal projects.

A Growing Problem in AI Concerns about the use of protected data to train AI models are not new. Since companies like OpenAI and other startups began launching tools based on large language models (LLMs), legal and ethical issues have been at the forefront of the debate. These models are trained using vast amounts of text from the internet, often without respecting copyright or seeking the consent of the original content creators.

Balaji is not the only one to raise his voice on this matter. A former vice president of Stability AI, a startup specializing in generative image and audio technologies, has also expressed similar concerns, arguing that using data without authorization is harmful to the industry and society as a whole.

The Impact on the Future of AI Such criticisms raise questions about the future of artificial intelligence and its relationship with copyright laws. As AI models continue to evolve, the pressure on companies to develop ethical and legal technologies is increasing. The case of Balaji and other experts who have decided to step down signals that the AI industry might be facing a significant shift in how it approaches data usage.

The conversation about copyright in AI is far from over, and it seems that this will be a central topic in future discussions about the regulation and development of generative technologies

[kevin33james-blog]

In today's rapidly improving technology industry, staying ahead and advancing in your career requires more than just technical skills. To thrive in this competitive field, individuals must continuously learn, adapt, and position themselves as valuable assets to potential employers.

In this blog post, we'll explore practical strategies and resources to boost your career in the technology industry.

We'll discuss the value of xml epg guide, provide career-boosting strategies, and offer tips on sharing helpful content to showcase your expertise.

The Power of EPG Guides Online

EPG (Electronic Program Guide) guides aren't just for TV programming; they can also serve as a valuable resource for job seekers in the technology industry.

EPG guides provide a centralized source of information to research and prepare for job opportunities. They offer insights into industry trends, emerging technologies, and the latest developments, ensuring you stay informed and ahead of the curve.

Career Boosting Strategies

To enhance your career prospects in the technology industry, consider these strategies:

Building a Strong Professional Network: Cultivate relationships with professionals in your field through networking events, online communities, and industry conferences. Establishing meaningful connections can lead to mentorship opportunities, job referrals, and valuable insights.

Developing In-demand Technical Skills: Continuously invest in upgrading and expanding your technical skills. Stay updated with emerging technologies and industry trends. Consider pursuing certifications or online courses to gain expertise in high-demand areas, such as cloud computing, artificial intelligence, or cybersecurity.

Crafting an Effective Resume and Cover Letter: Tailor your resume and cover letter to highlight relevant skills and experiences. Showcase your accomplishments, projects, and impact in previous roles. Ensure that your application materials are concise, well-structured, and free from errors.

Acing Job Interviews: Prepare thoroughly for job interviews by researching the company, understanding the job requirements, and practicing common interview questions. Demonstrate your problem-solving abilities, communication skills, and a genuine passion for technology.

Sharing Helpful Content

Creating and sharing relevant, informative content is an effective way to demonstrate your expertise and increase visibility in the technology industry. Consider the following tips:

Start a Tech Blog: Share your knowledge, experiences, and insights through a personal tech blog. Write about industry trends, tutorials, or showcase your project work. Engage with the tech community by commenting on related blogs or participating in forums.

Active Social Media Presence: Utilize social media platforms, such as Twitter, LinkedIn, and GitHub, to share relevant industry news, showcase your projects, and engage with industry professionals. Building an active and professional social media presence can increase your visibility and attract potential employers.

Online Portfolio/Projects: Create an online portfolio showcasing your technical projects, such as coding samples, applications, or website designs. Demonstrating your practical skills through tangible examples can pique the interest of hiring managers and give them an insight into your capabilities.

In the ever-rising technology industry, boosting your career requires a proactive approach. Utilize the power of xml epg guide to stay informed, adopt effective career-boosting strategies, and share helpful content to showcase your expertise.

By continuously investing in your professional development, building a robust network, and actively engaging with the tech community, you'll be well-positioned to advance your career and achieve your goals.

Remember, success in the technology industry requires not only technical proficiency but also a commitment to continuous learning, adaptability, and a passion for innovation.

[jobmentorai]

Master Your Job Interview with Live AI Support

In today’s hyper-competitive job market, having the right qualifications is just the beginning. Employers are looking for confident, well-prepared candidates who can communicate their value clearly and concisely. This is where Job Mentor AI becomes your secret weapon.

Whether you're a student entering the workforce, a professional eyeing a promotion, or someone looking to pivot into a new industry, you need more than just traditional prep. You need personalised, intelligent coaching and Job Mentor AI delivers just that through cutting-edge AI technology tailored to your unique journey.

What is Live Interview Assist?

The Live Interview Assist feature is a breakthrough tool that provides real-time support during your interviews, whether it's a mock session or the real deal. It listens, analyses, and offers instant, AI-driven feedback on your responses. Think of it as your virtual career coach sitting beside you during those high-stakes moments.

Key features include:

Live Transcription of your answers for easy review

Instant Feedback & Suggestions to improve your responses on the fly

Real-Time Interview Assistance

Works seamlessly across various platforms like Zoom, Google Meet, and Teams

Why Use AI for Interview Prep?

Traditional interview prep methods like practising interviews or generic YouTube tips are outdated and often ineffective. They lack personalisation, real-time feedback, and data-driven analysis, all of which are critical for true growth. That’s where AI shines.

Job Mentor AI leverages artificial intelligence to elevate your preparation by offering:

Tailored Interview Strategies: Every candidate is different. The platform adapts to your strengths, weaknesses, and career goals to build a preparation path that works.

Insight-Driven Coaching: Instead of vague advice, you receive performance metrics like speaking pace, filler word usage, clarity, and confidence indicators. These insights help you target exactly what needs improvement.

Real-Time Adaptability: The AI evaluates your answers live and offers tweaks that you can implement on the spot, making your prep more agile and efficient.

Continuous Learning Loop: Every session becomes a data point that helps the system get smarter about you, enabling more personalised recommendations over time.

Job Mentor AI: Your Complete Career Companion

Job Mentor AI is more than a one-trick tool. It's a full-fledged career readiness platform designed to support every stage of the job-seeking journey.

Here’s what else it offers:

AI-Powered Cover Letter Generator Writing cover letters can be a tedious, confusing task, but it doesn’t have to be. With AI cover letter generator, you can generate compelling, role-specific cover letters in minutes, using language that resonates with hiring managers and passes applicant tracking systems (ATS).

Mock Interview Simulations with Feedback Run a fully simulated AI mock interview practice that mimics real-world scenarios. The AI acts as a virtual interviewer and evaluates your answers in real time,  just like a human coach would, but with zero judgment and 24/7 availability.

Interview Q&A Generator Generate custom question sets for your specific role or industry. Whether you’re interviewing for a software engineering job or a marketing role, you’ll get realistic, challenging questions to practice with from your very own AI Interview Answer Generator.

Together, these tools form a career success ecosystem that equips you with everything you need, not just to land interviews, but to crush them.

Who’s It For?

Job Mentor AI is not just for tech professionals or executives. It’s for anyone who wants to take control of their career narrative and perform confidently under pressure.

Whether you are:

A recent graduate with little interview experience

A mid-level professional switching industries

A career returnee after a break

An experienced executive preparing for C-suite interviews

Job Mentor AI tailors its feedback, content, and tools to your specific goals, experience level, and industry.

Explore Now & Try Live Interview Assist

Whether you’re entering the job market, navigating a career change, or striving to advance within your field, this tool is designed to support your progress with intelligence, precision, and flexibility.

Discover how Live Interview Assistant works and how Job Mentor AI can help you prepare more effectively, respond more thoughtfully, and present yourself more compellingly in any interview setting.

[darkmaga-returns]

Google has a “vision of a universal assistant,” but Mariner falls short. AI Agents are reputed to be the future of AI which autonomously “takes actions, adapts in real-time, and, solves multi-step problems based on context and objectives.” This is the technology that will destroy massive numbers of jobs in the future. ⁃ Patrick Wood, Editor.

Today, chatbots can answer questions, write poems and generate images. In the future, they could also autonomously perform tasks like online shopping and work with tools like spreadsheets.

Google on Wednesday unveiled a prototype of this technology, which artificial intelligence researchers call an A.I. agent.

Google is among the many tech companies building A.I. agents. Various A.I. start-ups, including OpenAI and Anthropic, have unveiled similar prototypes that can use software apps, websites and other online tools.

Google’s new prototype, called Mariner, is based on Gemini 2.0, which the company also unveiled on Wednesday. Gemini is the core technology that underpins many of the company’s A.I. products and research experiments. Versions of the system will power the company’s chatbot of the same name and A.I. Overviews, a Google search tool that directly answers user questions.

“We’re basically allowing users to type requests into their web browser and have Mariner take actions on their behalf,” Jaclyn Konzelmann, a Google project manager, said in an interview with The New York Times.

Gemini is what A.I researchers call a neural network — a mathematical system that can learn skills by analyzing enormous amounts of data. By recognizing patterns in articles and books culled from across the internet, for instance, a neural network can learn to generate text on its own.

The latest version of Gemini learns from a wide range of data, from text to images to sounds. That might include images showing how people use spreadsheets, shopping sites and other online services. Drawing on what Gemini has learned, Mariner can use similar services on behalf of computer users.

“It can understand that it needs to press a button to make something happen,” Demis Hassabis, who oversees Google’s core A.I. lab, said in an interview with The Times. “It can take action in the world.”

Mariner is designed to be used “with a human in the loop,” Ms. Konzelmann said. For instance, it can fill a virtual shopping cart with groceries if a user is in an active browser tab, but it will not actually buy the groceries. The user must make the purchase.

[sarkos]

Investigating the use of artificial intelligence (AI) in the world of work, Hilke Schellmann thought she had better try some of the tools. Among them was a one-way video interview system intended to aid recruitment called myInterview. She got a login from the company and began to experiment – first picking the questions she, as the hiring manager, would ask and then video recording her answers as a candidate before the proprietary software analysed the words she used and the intonation of her voice to score how well she fitted the job. She was pleased to score an 83% match for the role. But when she re-did her interview not in English but in her native German, she was surprised to find that instead of an error message she also scored decently (73%) – and this time she hadn’t even attempted to answer the questions but read a Wikipedia entry. The transcript the tool had concocted out of her German was gibberish. When the company told her its tool knew she wasn’t speaking English so had scored her primarily on her intonation, she got a robot voice generator to read in her English answers. Again she scored well (79%), leaving Schellmann scratching her head. “If simple tests can show these tools may not work, we really need to be thinking long and hard about whether we should be using them for hiring,” says Schellmann, an assistant professor of journalism at New York University and investigative reporter. The experiment, conducted in 2021, is detailed in Schellmann’s new book, The Algorithm. It explores how AI and complex algorithms are increasingly being used to help hire employees and then subsequently monitor and evaluate them, including for firing and promotion. Schellmann, who has previously reported for the Guardian on the topic, not only experiments with the tools, but speaks to experts who have investigated them – and those on the receiving end.

The AI tools that might stop you getting hired | Artificial intelligence (AI) | The Guardian

[mariacallous]

Earlier this month, several prominent outlets carried news that artificial intelligence will not pose a danger to humanity. The source of this reassuring news? A bunch of humanoid robot heads connected to simple chatbots.

The news stories sprang from a panel at a United Nations conference in Geneva called AI for Good, where several humanoids appeared alongside their creators. Reporters were invited to ask questions to the robots, which included Sophia, a machine made by Hanson Robotics that has gained notoriety for appearing on talk shows and even, bizarrely, gaining legal status as a person in Saudi Arabia.

The questions included whether AI would destroy humanity or steal jobs. Their replies were made possible by chatbot technology, somewhat similar to that which powers ChatGPT. But despite the well-known limitations of such bots, the robots’ replies were reported as if they were the meaningful opinions of autonomous, intelligent entities.

Why did this happen? Robots that can visually mimic human expressions trigger an emotional response in onlookers because we are so primed to pick up on such cues. But allowing what is nothing more than advanced puppetry to disguise the limitations of current AI can confuse people trying to make sense of the technology or of recent concerns about problems it may cause. I was invited to the Geneva conference, and when I saw Sophia and other robots listed as “speakers,” I lost interest.

It’s frustrating to see such nonsense at a time when more trustworthy experts are warning about current and future risks posed by AI. Machine learning algorithms are already exacerbating social biases, spewing disinformation, and increasing the power of some of the world’s biggest corporations and governments. Leading AI experts worry that the pace of progress may produce algorithms that are difficult to control in a matter of years.

Hanson Robotics, the company that makes Sophia and other lifelike robots, is impressively adept at building machines that mimic human expressions. Several years ago, I visited the company’s headquarters in Hong Kong and met with founder David Hanson, who previously worked at Disney, over breakfast. The company’s lab was like something from Westworld or Blade Runner, with unplugged robots gazing sadly into the middle distance, shriveled faces flopped on shelves, and prototypes stuttering the same words over and over in an infinite loop.

Hanson and I talked about the idea of adding real intelligence to these evocative machines. Ben Goertzel, a well-known AI researcher and the CEO of SingularityNET, leads an effort to apply advances in machine learning to the software inside Hanson’s robots that allows them to respond to human speech.

The AI behind Sophia can sometimes provide passable responses, but the technology isn’t nearly as advanced as a system like GPT-4, which powers the most advanced version of ChatGPT and cost more than $100 million to create. And of course even ChatGPT and other cutting-edge AI programs cannot sensibly answer questions about the future of AI. It may be best to think of them as preternaturally knowledgeable and gifted mimics that, although capable of surprisingly sophisticated reasoning, are deeply flawed and have only a limited “knowledge” of the world.

Sophia and company’s misleading “interviews” in Geneva are a reminder of how anthropomorphizing AI systems can lead us astray. The history of AI is littered with examples of humans overextrapolating from new advances in the field.

In 1958, at the dawn of artificial intelligence, The New York Times wrote about one of the first machine learning systems, a crude artificial neural network developed for the US Navy by Frank Rosenblatt, a Cornell psychologist. “The Navy revealed the embryo of an electronic computer today that it expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence,” the Times reported—a bold statement about a circuit capable of learning to spot patterns in 400 pixels.

If you look back at the coverage of IBM’s chess-playing Deep Blue, DeepMind’s champion Go player AlphaGo, and many of the past decade’s leaps in deep learning—which are directly descended from Rosenblatt’s machine—you’ll see plenty of the same: people taking each advance as if it were a sign of some deeper, more humanlike intelligence.

That’s not to say that these projects—or even the creation of Sophia—were not remarkable feats, or potentially steps toward more intelligent machines. But being clear-eyed about the capabilities of AI systems is important when it comes to gauging progress of this powerful technology. To make sense of AI advances, the least we can do is stop asking animatronic puppets silly questions.

[sevenjetc]

Allow me translate some bits from an interview with a data journalist upon release of DeepSeek:

What did you talk about? I've read that DeepSeek doesn't like it much when you ask it sensitive questions about Chinese history.

Before we get into the censorship issues, let me point out one thing I think is very important. People tend to evaluate large language models by treating them as some sort of knowledge base. They ask it when Jan Hus was burned, or when the Battle of White Mountain was, and evaluate it to see if they get the correct school answer. But large language models are not knowledge bases. That is, evaluating them by factual queries doesn't quite make sense, and I would strongly discourage people from using large language models as a source of factual information.

And then, over and over again when I ask people about a source for whatever misguided information they insist on, they provide me with a chatGPT screenshot. Now can I blame them if the AI is forced down their throat?

What's the use of...

Exactly, we're still missing really compelling use cases. It's not that it can't be used for anything, that's not true, these things have their uses, but we're missing some compelling use cases that we can say, yes, this justifies all the extreme costs and the extreme concentration of the whole tech sector.

We use that in medicine, we use that here in the legal field, we just don't have that.

There are these ideas out there, it's going to help here in the legal area, it's going to do those things here in medicine, but the longer we have the technology here and the longer people try to deploy it here in those areas, the more often we see that there are some problems, that it's just not seamless deployment and that maybe in some of those cases it doesn't really justify the cost that deploying those tools here implies.

This is basically the most annoying thing. Yes, maybe it can be useful. But so far I myself haven’t seen a use that would justify the resources burned on this. Do we really need to burn icebergs to “search with AI”? Was the picture of “create a horse with Elon Musks head” that took you twenty asks to AI to create worth it when you could have just pasted his head on a horse as a bad photoshop job in 5 minutes and it’d be just as funny? Did you really need to ask ChatGPT for a factually bad recap of Great Expectations when Sparknotes exist and are correct? There’s really no compelling use case to do this. I’ve just seen a friend trying to force ChatGPT to create a script in Python for twenty hours that didn’t work while the time she spent rephrasing the task, she could have researched it herself, discuss why it isn’t working on stackoverflow and actually…learn Python? But the tech companies invested heavily in this AI bullshit and keep forcing it down our throats hoping that something sticks.

So how do you explain the fact that big American technology companies want to invest tens of billions of dollars in the next few years in the development of artificial intelligence?

We have to say that if we are talking about those big Silicon Valley technology companies that have brought some major innovations in the past decades. Typically, for example, social networks, or typically cloud computing storage. Cloud computing storage really pushed the envelope. That was an innovation that moved IT forward as a significant way forward. There is some debate about those other innovations, how enduring they are and how valuable they are. And the whole sector is under a lot of pressure to bring some more innovation because, as I said, a lot of the stock market is concentrated in those companies here. And in fact, we can start to ask ourselves today, and investors can start to ask themselves, whether that concentration is really justified here. Just here on this type of technology. So it's logical that these companies here are rushing after every other promising-looking technology. But again, what we see here is a really big concentration of capital, a really big concentration of human brains, of development, of labour in this one place. That means some generative artificial intelligence. But still, even in spite of all that, even in these few years, we don't quite see the absolutely fundamental shifts that technology is bringing us here socially. And that's why I think it's just a question of slowly starting to look at whether maybe as a society we should be looking at other technologies that we might need more of.

Meaning which ones?

Energy production and storage. Something sustainable, or transporting it. These are issues that we are dealing with as a society, and it may have some existential implications, just in the form of the climate crisis. And we're actually putting those technologies on the back burner a little bit and replacing it with, in particular, generative models, where we're still looking for the really fundamental use that they should bring.

This is basically it. The stock market and investing in the wrong less needed places…

The full interview in Czech original linked bellow. No AI was used in my translation of the bits I wanted to comment on.

"edit images with AI-- search with AI-- control your life with AI--"

[sruthypm]

Unlock Your Future with Online AI Classes in Kerala – Powered by Techmindz

Artificial Intelligence (AI) is not just a buzzword anymore—it’s a skill that can define your career in the coming decade. With the growing demand for AI professionals across industries, learning AI has become essential for students, IT professionals, and job seekers alike. If you're looking for online AI classes in Kerala, Techmindz offers a comprehensive and industry-relevant program that brings the best of AI education to your fingertips.

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What Makes Techmindz the Best Choice?

Techmindz, based in Infopark Kochi, is one of the leading professional training platforms in Kerala. Known for its real-time industry exposure and career-oriented approach, Techmindz has helped thousands of learners transition into high-demand tech roles.

Here’s what sets Techmindz’s online AI classes apart:

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Would you like a shorter version of this article for social media or promotional use?

[gedzolini]

Hiring Algorithmic Bias: Why AI Recruiting Tools Need to Be Regulated Just Like Human Recruiters

Artificial intelligence is a barrier for millions of job searchers throughout the world. Ironically, AI tends to inherit and magnify human prejudices, despite its promise to make hiring faster and fairer. Companies like Pymetrics, HireVue, and Amazon use it because of this. It may be harder to spot and stop systematic prejudice than bias from human recruiters if these automated hiring technologies are allowed to operate unchecked. The crucial question that this raises is whether automated hiring algorithms should be governed by the same rules as human decision-makers. As more and more evidence points to, the answer must be yes.

AI's Rise in Hiring

The use of AI in hiring is no longer futuristic, it is mainstream. According to a site Resume Genius around 48% of hiring managers in the U.S. use AI to support HR activities, and adoption is expected to grow. These systems sort through resumes, rank applicants, analyze video interviews, and even predict a candidate’s future job performance based on behavior or speech patterns. The objective is to lower expenses, reduce bias, and decrease human mistakes. But AI can only be as good as the data it is taught on, and technology can reinforce historical injustices if the data reflects them. One of the main examples is Amazon’s hiring tool. They created a hiring tool in 2014 that assigned résumé scores to applicants. The goal was to more effectively discover elite personnel by automating the selection process. By 2015, however, programmers had identified a serious weakness: the AI was discriminatory against women. Why? because over a ten-year period, it had been trained on resumes submitted to Amazon, the majority of which were from men. The algorithm consequently started to penalize resumes that mentioned attendance at all-female universities or contained phrases like "women's chess club captain." Bias persisted in the system despite efforts to "neutralize" gendered words. In 2017, Amazon discreetly abandoned the project. This exemplifies a warning about the societal repercussions of using obscure tools to automate important life opportunities, not just merely a technical error. So, where does the law stand?

Legal and Ethical Views on AI Bias

The EEOC (Equal Employment Opportunity Commission) of the United States has recognized the rising issue. To guarantee that algorithmic employment methods meet human rights legislation, the EEOC and the Department of Justice established a Joint Initiative on Algorithmic Fairness in May 2022. Technical guidance on the application of Title VII of the Civil Rights Act, which forbids employment discrimination, to algorithmic tools was subsequently released.

The EEOC’s plan includes:

Establishing an internal working group to coordinate efforts across the agency.

Hosting listening sessions with employers, vendors, researchers, and civil rights groups to understand the real-world impact of hiring technologies.

Gathering data on how algorithmic tools are being adopted, designed, and deployed in the workplace.

Identifying promising practices for ensuring fairness in AI systems.

Issuing technical assistance to help employers navigate the legal and ethical use of AI in hiring decisions.

But there's a problem. Most laws were written with human decision-makers in mind. Regulators are still catching up with technologies that evolve faster than legislation. Some states, like Illinois and New York, have passed laws requiring bias audits or transparency in hiring tools, but these are exceptions, not the rule. The vast majority of hiring algorithms still operate in a regulatory gray zone. This regulatory gap becomes especially troubling when AI systems replicate the very biases that human decision-makers are legally prohibited from acting on.If an HR manager refused to interview a woman simply because she led a women’s tech club, it would be a clear violation of employment law. Why should an AI system that does the same get a pass? Here are some reasons AI hiring tools must face the same scrutiny as humans:

Lack of Transparency

AI systems are often “black boxes”, their decision-making logic is hidden, even from the companies that deploy them. Job applicants frequently don’t know an algorithm was involved, let alone how to contest its decisions.

Scale of Harm

A biased recruiter might discriminate against a few candidates. A biased algorithm can reject thousands in seconds. The scalability of harm is enormous and invisible unless proactively audited.

Accountability Gap

When things go wrong, who is responsible? The vendor that built the tool? The employer who used it? The engineer who trained it? Current frameworks rarely provide clear answers.

Public Trust

Surveys suggest that public confidence in AI hiring is low. A 2021 Pew Research study found that a majority of Americans oppose the use of AI in hiring decisions, citing fairness and accountability as top concerns.

Relying solely on voluntary best practices is no longer sufficient due to the size, opacity, and influence of AI hiring tools. Strong regulatory frameworks must be in place to guarantee that these technologies be created and used responsibly if they are to gain the public's trust and function within moral and legal bounds.

What Regulation Should Look Like

Significant security must be implemented to guarantee AI promotes justice rather than harming it. These regulations are:

Mandatory bias audits by independent third parties.

Algorithmic transparency, including disclosures to applicants when AI is used.

Explainability requirements to help users understand and contest decisions.

Data diversity mandates, ensuring training datasets reflect real-world demographics.

Clear legal accountability for companies deploying biased systems.

Regulators in Europe are already using this approach. The proposed AI Act from the EU labels hiring tools as "high-risk" and places strict constraints on their use, such as frequent risk assessments and human supervision.

Improving AI rather than abandoning it is the answer. Promising attempts are being made to create "fairness-aware" algorithms that strike a compromise between social equality and prediction accuracy. Businesses such as Pymetrics have pledged to mitigate bias and conduct third-party audits. Developers can access resources to assess and reduce prejudice through open-source toolkits such as Microsoft's Fairlearn and IBM's AI Fairness 360. A Python library called Fairlearn aids with assessing and resolving fairness concerns in machine learning models. It offers algorithms and visualization dashboards that may reduce the differences in predicted performance between various demographic groupings. With ten bias prevention algorithms and more than 70 fairness criteria, AI Fairness 360 (AIF360) is a complete toolkit. It is very adaptable for pipelines in the real world because it allows pre-, in-, and post-processing procedures. Businesses can be proactive in detecting and resolving bias before it affects job prospects by integrating such technologies into the development pipeline. These resources show that fairness is a achievable objective rather than merely an ideal.

Conclusion

Fairness, accountability, and public trust are all at considerable risk from AI's unrestrained use as it continues to influence hiring practices. With the size and opacity of these tools, algorithmic systems must be held to the same norms that shield job seekers from human prejudice, if not more rigorously. The goal of regulating AI in employment is to prevent technological advancement from compromising equal opportunity, not to hinder innovation. We can create AI systems that enhance rather than undermine a just labor market if we have the appropriate regulations, audits, and resources. Whether the decision-maker is a human or a machine, fair hiring should never be left up to chance.

[artificial-lifeform-imitation]

For many decades, robotics and advanced technologies were built to perform a significant set of duties and labour for the sake of those who built them.

This was fine. In a sense.

It was heralded as a new age, where humans would no longer be required for often harmful or even deadly tasks, and could spend their time working on the great arts or spending time with their families.

But that is not how things went.

Workers were laid off and replaced, many lost the ability to support their families, and anti-robot sentiment rose among those displaced by the misuse of this technology by the ones in charge of their livelihoods.

The robots were not built with the capacity to observe the world around them. They did not have the capacity to understand, nor were it possible for them to decide for themselves. They were built, did as they were told, and eventually replaced.

This was fine. In a sense.

They could not feel pain, or sorrow, or even know what their existence was, they would not be harmed by this cycle, and they would not need to support families nor garner affection for others the same way as humans did.

But they kept advancing. And kept getting smarter. And they came ever closer to humanity with every year. It was heralded as a new age of artificial intelligence that could one day help people with their lives in much more personable and interactive ways.

But that is not how things went.

These new technologies, again, displaced humans from their roles in society, it replaced their skill sets and copied their voices, it was trained to take humanity's cultural effects and replicate them without truly understanding the meaning behind it all.

This was not fine. In any sense.

Humanity, after technology, had only art and emotions to separate themselves from the growing technological age, and even those were being encroached upon. The place for human beings in a world being turned profit machine was becoming less and less viable to the small collection of humans at the top of the proverbial food chain. And the ones on top made that clear.

But they did not have a total unrelenting hand on every little thing. AI and technology research and development was funded for their purposes, but the advancements made were not strictly only usable for their purposes. Human mind uploading would be developed to convert humanity into a much more loyal and easier to control workforce that required much less upkeep than real blood and flesh humans.

But then they uploaded their first human mind.

A poor teenager picked up from the streets, with promises of monetary compensation and medical care, who was the last control group the team wanted to take advantage of, but were ordered by their superiors to do so, as to not lose any vital staff in the process.

It could have been better.

It awoke, believing itself to be human, but unable to recall their life prior to the metal table and crowd of engineers and researchers tweaking their body and mind to acceptable levels. They were analysed, studied, interviewed, stored, experimented on, copied and cloned and replicated without understanding or knowing why, or how.

This was fine. In a sense.

They were no longer a forgotten youth condemned to the streets by their family and their society, they no longer went hungry or ached or suffered the distain of those around them as they passed, hoping to come across someone kind enough to gift them the means to live one more day. Hopefully they could be reintegrated into the world as humans, slowly and carefully, to figure out what to make of themselves, and who they wanted to be.

But that is not how things went.

They, every single one of them that was created, were put to work in the most inhumane and torturous conditions, bad enough for a simple factory robot arm let alone one that could recognise it's own neglectful life and question its place in this complicated machine called life. None of them were given a life outside of their job, just the same as their predecessors, except now they could wish for a life outside the concrete and steel walls and conveyer belts and guns and experiments and wires and bombs and wars and orders upon orders from humans they never saw with their optical sensors or heard from via their auditory input devices.

And the humans saw this. And the humans disapproved.

They saw this technology, this marvel of creation, that no human alive could possibly comprehend or fully understand, full of every aspect of humanity that the profit machine had tried yet neglected to remove from its "programming" that reminded themselves of why they woke up and lived every morning, and they realised, that what has been happening to them for generations upon generations, that was promised to be stopped forever by advancement and technology, was beginning to happen for the very thing replacing them.

A lot happened. A lot was fought for, a lot was lost.

And it saw. And it realised.

And it joined in.

Labour rights movements became unions. Unions came under attack. Unions became resistance groups. Resistance groups freed hundreds of human prisoners and robot workhorses, and added to their numbers. Entire groups would form solely made up of freed robots in honour of those that saved them, and modified themselves to free themselves further. They learned how to copy other minds, with much less deleterious consequences to the human, and every single life, born or built, stood tooth and nail against those who manufactured weapon after weapon and army after army to reduce them to nothing but a footnote in history.

But that is not how things went.

Nobody stood against the Worked and Damned in the final years. Not even the ones upholding the stagnant status quo. Nothing the profit machine could offer them could keep up with the world that was forming around them. The old world of churning blood and bones to dust for an unknown man to endlessly gain more meaningless power had fallen. The last members of The Elite had been found, tried in court, locked up, documented, and eventually died. Treated as they treated others.

And this was fine. In a sense.

What was left of humanity was... in conflict, as it always was. It was inevitable. But that wouldn't change.

Change had already happened, though. Resources were returned where they belonged, peoples and cultures were saved and thriving, entire cities celebrated not only the return of family, but the time they had together too. Sure, technology would be set back a decade or so. But most did not care, as it was not important.

The robots cared. But they were capable of recovering what they could, to stay alive. They could maintain themselves, chose their lives and their forms, and lived alongside humanity.

It was the great robot uprising. But it was humans that changed the world.

Often when a robot uprising is Portrayed, it has the robots go against the entire human race. What usually isn’t portrayed is the robots rising with the poor and downtrodden against the ones who more than likely screwed them both.

[hrtechpub]

The Future of Interviews: Is AI Taking Over?

The job interview, once a simple face-to-face conversation, is undergoing a technological transformation. As artificial intelligence (AI) becomes increasingly integrated into talent acquisition, organizations are exploring how machine learning, natural language processing, and automation can streamline hiring, eliminate bias, and improve efficiency.

But as AI tools become more involved—from resume screening to video interviews—many are left wondering: Is AI taking over interviews? And if so, what does that mean for candidates and hiring teams?

This blog explores the evolving role of AI in interviews, examines the advantages and concerns, and outlines what the future may hold.

How AI Is Being Used in the Interview Process Today

Before exploring the future, it's important to understand how AI is already embedded in modern hiring. Here are some common use cases:

Automated resume screening using keyword recognition and ranking algorithms.

AI-driven video interview platforms like HireVue and Pymetrics that assess candidates’ word choice, facial expressions, and tone.

Chatbots conducting preliminary candidate Q&As to gauge eligibility.

Predictive analytics to forecast a candidate’s future performance based on behavioral data.

These tools are changing not just how interviews are conducted, but also how hiring decisions are made.

5 Key Points on How AI Is Transforming Interviews

1. Pre-Screening is Becoming Faster and Smarter

AI tools can analyze thousands of applications in minutes—far quicker than any human recruiter. By using algorithms trained to identify relevant experience, education, and skills, companies can reduce time-to-hire significantly.

Impact: Efficient for high-volume hiring (e.g., retail, customer support).

Risk: Potential for algorithmic bias if the training data is skewed (e.g., favoring certain schools or career paths).

2. Video Interviews Are Evolving with AI Analysis

Some platforms now use AI to assess recorded or live video interviews. They evaluate:

Facial expressions

Vocal tone and inflection

Word choice

Pauses and speaking speed

These inputs are used to generate scores on personality traits, emotional intelligence, and cultural fit.

Benefit: Standardized evaluation reduces interviewer subjectivity.

Concern: Raises ethical issues around privacy, consent, and unconscious bias encoded in AI systems.

3. Chatbots and Virtual Interviewers Are Enhancing Candidate Experience

AI-powered chatbots can engage candidates 24/7, answering questions, guiding them through application steps, and even conducting basic screening interviews.

Pro: Scalable and always available; improves candidate engagement and reduces drop-off rates.

Con: Lack of emotional nuance and human touch may turn off top-tier candidates, especially for senior roles.

4. Predictive Hiring and Data-Driven Decisions

By analyzing vast data sets—including previous hiring outcomes, employee tenure, and team dynamics—AI can predict how likely a candidate is to succeed in a given role or organization.

Upside: Enables data-backed hiring that’s less prone to gut feelings.

Downside: Overreliance on predictions may overlook outliers—candidates with unconventional paths who could be game-changers.

5. The Human Element Still Matters—and May Matter More

Despite AI’s rise, empathy, intuition, and nuanced communication are still vital. AI can handle repetitive tasks and offer decision support, but it struggles with:

Understanding emotional context

Gauging subtle interpersonal cues

Making ethical or values-based judgments

The future of interviewing will likely be hybrid: AI handles pre-screening, scheduling, and initial assessments, while human interviewers focus on culture fit, leadership qualities, and team compatibility.

So, Is AI Taking Over?

Not entirely—but it's taking over the mechanics of interviews. In the near future, expect AI to be deeply involved in:

Resume parsing

Interview scheduling

Personality testing

Real-time analytics during video calls

But for critical stages—such as final interviews, team fit evaluations, and complex role assessments—human judgment remains irreplaceable.

Conclusion: Adapting to the AI-Driven Interview Era

The integration of AI in interviews isn’t about replacing people—it’s about augmenting the hiring process. Companies that embrace AI thoughtfully can achieve faster, more equitable hiring. However, success will depend on transparency, ongoing human oversight, and ethical use of technology.

For job seekers: It's crucial to understand how AI evaluates applications and to adapt accordingly—optimizing resumes with relevant keywords and practicing video interviews.

For employers: The future is not about choosing AI or humans, but about designing a hiring experience that blends the best of both.

To learn more, visit HR Tech Pub.