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Forbes
06-08-2025
- Business
- Forbes
Debunking 10 Common AI Myths
Tigran Sloyan is the cofounder and CEO of CodeSignal, a technical interview and assessment platform. Companies today are investing heavily in AI—and that investment is paying off. A November 2023 IDC study found that surveyed businesses were reporting a 250% return on their AI investments. As a result, we're seeing a significant shift in how companies use AI, as many of them are integrating it as a core component of their IT infrastructure. However, despite AI's proven value and its growing integration into business operations, I find that a surprising number of misconceptions about AI persist. These myths stem from a lack of understanding of a complex and rapidly evolving field. It's time to set the record straight. Here are 10 common AI myths, debunked: Myth 1: AI will steal your job. This is perhaps the most fear-inducing myth surrounding AI. While it's true that AI will automate certain tasks, the notion that it will lead to widespread unemployment is largely unfounded. Many experts predict that AI will create significantly more jobs than it displaces. For example, the World Economic Forum estimated in 2020 that AI would create 97 million new jobs. These new roles will often require a blend of technical skills and uniquely human abilities such as critical thinking, creativity and emotional intelligence. Myth 2: AI isn't ready for business implementation. The idea that AI is still in its infancy and not mature enough for real-world business applications is now years out of date. Businesses across every sector are leveraging AI to drive efficiency, enhance decision making and improve customer experiences. A January 2025 McKinsey report found that "over the next three years, 92% of companies plan to increase their AI investments." From optimizing supply chains and personalizing marketing campaigns to powering advanced analytics, AI is proving its readiness for widespread implementation. Myth 3: AI is difficult to implement. It's true that implementing AI requires careful planning. However, this isn't an insurmountable hurdle for businesses today. The perception that AI implementation is inherently complex and resource-intensive often deters businesses from exploring its benefits. The proliferation of user-friendly AI services today makes deploying AI more accessible than ever. The key is to start small, identify specific pain points AI can address and leverage existing resources. Myth 4: All AI uses the same underlying technology. This is a common misconception that overlooks the diverse landscape of AI. The term "AI" is an umbrella term encompassing many technologies. GenAI, exemplified by large language models (LLMs) such as those powering chatbots, is just one facet. Other types of AI include machine learning (ML)—which focuses on systems that learn from data—computer vision for interpreting images and natural language processing (NLP) for understanding human language. Each type of AI is designed for specific tasks and employs different underlying algorithms and architectures. Myth 5: AI and ML are the same thing. While closely related, AI and ML are not interchangeable. ML is a subset of AI. Think of AI as the broader field of creating intelligent machines that can reason, learn and act autonomously. ML, on the other hand, is a technique that enables AI systems to learn from data without explicit programming. It's the process by which AI systems improve their performance over time by recognizing patterns and making predictions based on vast datasets. While all ML is AI, not all AI is ML. Myth 6: AI works like a human brain. The analogy between AI and the human brain is often used, but it's a simplification that can lead to misunderstanding. While AI systems can mimic certain cognitive functions, their underlying mechanisms are different from biological brains. The brain's structure inspired AI models, particularly neural networks, but the models operate through complex mathematical computations and statistical analysis. They don't possess consciousness, emotions or the same kind of intuition as humans. Myth 7: AI is new. The recent surge in public awareness of AI might lead some to believe that AI is a very recent invention. However, the concept of AI has been around for decades. The term "artificial intelligence" was coined in 1955, and research in the field has been ongoing since then. Early AI systems were focused on symbolic reasoning and expert systems. The current explosion of AI innovation—and particularly AI chat interfaces, which make AI accessible to anyone—is largely fueled by advancements in computing power, the availability of massive datasets and breakthroughs in ML algorithms. Myth 8: AI hallucinates constantly. The phenomenon of "hallucination" in AI, where models generate factually incorrect or nonsensical information, is a legitimate concern. However, the notion that AI hallucinates constantly is an overstatement. While it can occur, particularly with older GenAI models, significant research is making strides in this area. Techniques like retrieval-augmented generation (RAG) and improved training data are helping to reduce the frequency of hallucinations. Myth 9: AI can't produce realistic images. This myth is quickly becoming outdated with the rapid advancements in GenAI for image creation. Just a few years ago, AI-generated images were often easily discernible from real photographs. (Remember the smiles with too many teeth?) However, recent breakthroughs—such as MIT's "HART" model—have enabled AI models to produce incredibly realistic and high-quality images that are virtually indistinguishable from those that cameras captured. Myth 10: LLMs are the same quality as when you first tried them in 2022. The pace of innovation in LLMs since 2022 has been nothing short of astounding. While models like GPT-3 made significant waves, subsequent iterations and new reasoning models have demonstrated remarkable improvements in terms of fluency, coherence, reasoning abilities and reduced biases. A 2023 report by researchers from Stanford University and the University of California, Berkeley on ChatGPT's performance highlighted the rapid evolution of LLMs and their enhanced capabilities in understanding complex prompts, generating diverse text formats and even performing intricate reasoning tasks. As AI continues to mature and integrate into daily business operations, distinguishing fact from fiction becomes increasingly vital. By dispelling these common myths, businesses can make more informed decisions about how to strategically integrate AI to drive innovation and growth in the years to come. 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Forbes
28-05-2025
- Business
- Forbes
CodeSignal's AI-Assisted Tests Redefine How Tech Skills Are Evaluated
The ability to effectively use artificial intelligence tools increasingly separates typical employees from highly productive '10x" employees. In technical and engineering roles, where the effective use of AI tools is becoming an essential skill, traditional methods of assessing talent are rapidly becoming outdated. It is no longer sufficient to know what technical skills an employee possesses; one must also understand their ability to collaborate effectively with multiple intelligent tools simultaneously. CodeSignal's recent announcement of its AI-Assisted Coding Assessments marks a significant milestone in acknowledging this important shift in skill evaluation and development. Historically, evaluating technical proficiency has involved assessing direct, tangible skills. Candidates were evaluated based on their individual capacity to write and debug code, solve mathematical problems, or showcase theoretical knowledge. By way of analogy, technical assessments traditionally focused on individuals as virtuoso musicians, with the evaluation determining how well they could play various instruments. The rise of powerful AI-driven tools has fundamentally transformed this landscape. Tigran Sloyan, CEO of CodeSignal, highlights this evolution. 'Working with AI in many ways is similar to management. It is like telling somebody other than yourself what to do. Clear communication, the ability to break things down into clear parts, and put them back together are essential. It is not merely about understanding tools; it's about effectively managing multiple intelligent systems simultaneously.' Rather than being a virtuoso musician, the role has become more akin to that of a conductor. The conductor doesn't play every instrument but directs multiple musicians to create harmonious outcomes. Similarly, the modern technical professional must seamlessly orchestrate various AI tools, each capable of intelligent outputs. Recognizing this shift, CodeSignal has introduced a suite of coding assessments designed to evaluate candidates' abilities to leverage AI-powered coding assistants effectively. Rather than ignoring the reality that individuals will inevitably use these intelligent tools to complete their work, CodeSignal has embraced this fact. Their new assessments directly test a candidate's proficiency in collaborating with AI to quickly understand complex problems, devise strategic solutions, and execute them efficiently. Traditional pre-employment assessments often simplify tasks to fit within a short evaluation window, typically lasting one to two hours. 'You can't just take existing pre-hire assessments and add AI to them, because the old questions were oversimplifications of reality. Simplifications are too simple for AI, so the AI would solve the problems instantly, demonstrating no skill from the candidate,' says Tigran. As a result, CodeSignal's new approach introduces real-world scenarios with greater complexity, ensuring the AI assistant enhances rather than replaces candidate skills. Candidates must be able to ask strategic questions, rapidly assimilate AI-driven insights, and effectively integrate outputs to address sophisticated challenges. For example, a candidate might encounter a complex codebase in a typical software engineering scenario. Rather than manually sifting through thousands of lines of code to grasp its functionality, candidates who collaborate with AI can quickly summarize, interpret, and identify core focus areas using AI-powered tools. Those who haven't mastered this collaboration will soon fall behind, overwhelmed by the complexity. Therefore, success in these assessments is less about the ability to code itself and more about effectively managing intelligent resources to get the job done. This shift in assessment philosophy underscores a broader transformation across the tech industry and beyond. AI tools like ChatGPT, Xai's Grok, Anthropic's Claude, and Google's Gemini have become routine companions in many workplaces, leading to a radical shift in job expectations. Companies no longer seek individuals proficient in existing technical frameworks or languages; they require professionals capable of continual learning, adapting, and effectively leveraging evolving AI tools. Historically, university engineering and technology programs' curricula have evolved slowly, and they often struggle to keep up with rapidly changing industry demands. With AI reshaping skill requirements, this issue has become even more pressing. Unless universities can adapt quickly and provide the higher-order skills employers need, they risk graduating students who are ill-prepared for the modern workforce. Tigran notes, 'There's a massive disconnect between what companies and industries want, and what university curricula teach. Universities want to know what skills they should be teaching students right now. The universities whose students perform well on our assessments do two things: first, they understand what companies are hiring for, and second, they provide students with plenty of opportunities to practice those skills.' Educational institutions must incorporate explicit instruction in AI collaboration skills into their curricula. Students should be trained in traditional coding and effectively manage and orchestrate multiple intelligent tools. Universities aiming to produce students who excel in these new types of technical assessments must develop exercises that reflect authentic workplace complexities, requiring students to strategically engage with and leverage AI technologies to solve sophisticated real-world problems. Beyond the explicit teaching of AI collaboration skills, educational institutions must navigate an ever-evolving distinction between core and emerging competencies. At the core will be the skills and knowledge that every professional should possess, regardless of the changing tools. In contrast, emerging competencies are rapidly evolving skills closely linked to specific technologies or methods that may quickly become obsolete but are crucial for immediate productivity. These competencies are most likely to be assessed during a technical interview, and providing this level of education will enable university programs to have the most significant impact on their graduates. This distinction also demands new strategies. Institutions must focus not only on current technological skills but also on cultivating students' abilities for rapid learning and adaptability. Critical capability becomes less about thoroughly knowing any tool and more about quickly mastering and integrating whatever tools become relevant next. The capacity to rapidly assess a situation and deploy the appropriate complex set of tools to address a problem is precisely what students will need to demonstrate to succeed in an interview. In light of these implications, CodeSignal's AI-Assisted Coding Assessments represent more than just a new testing method—they reflect a significant philosophical shift. By explicitly assessing the skill of orchestrating AI systems, CodeSignal sends a clear message to educators and employers alike: success in AI relies on adaptability, strategic collaboration, and rapid response learning. The future workplace is here now. It is defined by intelligent collaboration rather than just individual technical execution. Those who master orchestrating multiple intelligent tools will find themselves invaluable. As AI integrates rapidly into nearly every industry, developing these management skills will become essential to being a 10x engineer. These skills will not only enhance individual careers but also transform them. CodeSignal's AI-Assisted Coding Assessments illuminate this path, urging employers and educational institutions to prepare individuals for yesterday's challenges but for the evolving demands of tomorrow.
Yahoo
14-05-2025
- Science
- Yahoo
CodeSignal Report Ranks Universities by Measurable Technical Skills, Highlighting Top Engineering Talent Nationwide
Nearly 1 in 3 top-performing students come from universities overlooked by traditional rankings SAN FRANCISCO, May 14, 2025 /PRNewswire/ -- CodeSignal, a leading skills assessment and experiential learning platform, today unveils its fourth annual University Ranking Report, an university ranking methodology based purely on students' verified coding skills. Unlike traditional rankings that rely on legacy signals, CodeSignal's report offers an objective, data-driven alternative: one that evaluates universities based on how well their students perform on an assessment of real-world coding skills. In an AI-transformed workforce, the ability to think computationally, solve problems, and write strong foundational code remains critical, regardless of where a student went to school. By analyzing thousands of General Coding Assessments (GCA) completed by students worldwide, CodeSignal's Talent Science Team reveals a powerful conclusion: top engineering talent is everywhere. Here are the top 15 universities for 2025: Carnegie Mellon University Massachusetts Institute of Technology Stony Brook University University of California, Los Angeles University of Pennsylvania California Institute of Technology University of California, San Diego Duke University San José State University University of Southern California Rice University Yale University Georgia Institute of Technology Johns Hopkins University Indiana University High-level results: 28.4% of high-scorers come from schools not included in the US News & World Report's top 50 undergraduate engineering programs. 12 of the top 50 schools in our skill-based ranking did not make the US News & World top 50. Two of the top 10 US schools in our rankings, Stony Brook University (#3) and San José State University (#9), didn't make the US News & World top 50. Korea Advanced Institute of Science & Technology is the top non-US school for software engineering talent this year, ranking just below Rice University (#12 on the US list). "This report is a celebration of the universities equipping students with the skills that matter most," said Tigran Sloyan, CEO and Co-Founder of CodeSignal. "When we focus on what students can actually do, not just where they studied, we uncover incredible talent from institutions of all types. It's a reminder that great engineers are everywhere, and we need to broaden how we recognize and recruit them." While traditional rankings reward legacy signals, CodeSignal's 2025 University Ranking Report focuses on outcomes – what students can actually do when faced with real-world engineering challenges. CodeSignal's data makes the case that technical talent isn't confined to a short list of name-brand schools. It's everywhere. For employers competing in an AI-driven economy, this report is a call to rethink where, and how, they discover their next generation of engineers. To view the full report, please visit: About CodeSignalCodeSignal is how the world discovers and develops the skills that will shape the future. Our AI-native skills assessment and experiential learning platform helps organizations hire, train, and grow talent at scale while empowering individuals to advance their careers. Whether you're growing your team's potential or unlocking your own, CodeSignal meets you where you are and gets you where you need to go. With millions of skills assessments completed, CodeSignal is trusted by companies like Netflix, Capital One, Meta, and Dropbox and used by learners worldwide. For more information, visit or connect with CodeSignal on LinkedIn. View original content to download multimedia: SOURCE CodeSignal
Associated Press
02-04-2025
- Business
- Associated Press
CodeSignal Launches AI Skills Assessments to Evaluate AI Talent at Every Level
New certified AI assessments help hiring managers and L&D leaders evaluate and grow AI capabilities across business, technical, and research roles. SAN FRANCISCO, April 2, 2025 /PRNewswire/ -- CodeSignal, an AI-native skills assessment and experiential learning platform, today announced the launch of the AI Collection—the industry's first and only comprehensive suite of certified assessments for measuring AI skills in the workplace. As artificial intelligence transforms every function and industry, professionals need new skills to collaborate with AI tools and systems effectively. From understanding which model to use, to writing better prompts, to building new models from scratch—AI is becoming a core competency for the modern workforce. CodeSignal's AI Collection enables organizations to identify and grow those skills across a range of roles and technical levels, with three targeted, real-world assessments: AI Literacy Assessment: Evaluates foundational AI knowledge through interactive simulations. Candidates may be asked to choose the right model for a task or write prompts to analyze business expenses. Prompt Engineering Assessment: Tests hands-on skill in crafting and refining prompts for large language models (LLMs). Candidates may be asked to generate structured HTML or iterate on prompts to improve output quality. AI Researcher Assessment: Challenges candidates to translate machine learning research into functional code and demonstrate a strong understanding of the underlying mathematics. 'More than 40% of workers will need to reskill in the next three years due to AI and automation, according to the World Economic Forum,' said Tigran Sloyan, CEO and Co-Founder of CodeSignal. 'The AI Collection gives organizations a precise and scalable way to assess and develop those capabilities, whether they're hiring for new roles or investing in the growth of their existing teams.' As demand for AI fluency accelerates, CodeSignal's AI Collection helps organizations build a workforce ready for what's next by assessing and developing the real-world skills required to thrive in an AI-powered workplace. To learn more, explore the AI Collection demo on YouTube or visit CodeSignal is how the world discovers and develops the skills that will shape the future. Our AI-native skills assessment and experiential learning platform helps organizations hire, train, and grow talent at scale while empowering individuals to advance their careers. Whether you're growing your team's potential or unlocking your own, CodeSignal meets you where you are and gets you where you need to go. With millions of skills evaluations completed, CodeSignal is trusted by companies like Netflix, Capital One, Meta, and Dropbox and used by learners worldwide.
