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Hans India
a day ago
- Business
- Hans India
AI Tools & Skills Every Data Engineer Should Know in 2025
The lines between data engineering and artificial intelligence are increasingly blurred. As enterprises pivot towards intelligent automation, data engineers are increasingly expected to work alongside AI models, integrate machine learning systems, and build scalable pipelines that support real-time, AI-driven decision-making. Whether you're enrolled in a data engineer online course or exploring the intersection of data engineering for machine learning, the future is AI-centric, and it's happening now. In this guide, we explore the core concepts, essential skills, and advanced tools every modern AI engineer or data engineer should master to remain competitive in this evolving landscape. Foundational AI Concepts in Data Engineering Before diving into tools and frameworks, it's crucial to understand the foundational AI and ML concepts shaping the modern data engineer online course. AI isn't just about smart algorithms—it's about building systems that can learn, predict, and improve over time. That's where data engineers play a central role: preparing clean, structured, and scalable data systems that fuel AI. To support AI and machine learning, engineers must understand: Supervised and unsupervised learning models Feature engineering and data labeling Data pipelines that serve AI in real-time ETL/ELT frameworks tailored for model training Courses like an AI and Machine Learning Course or a machine learning engineer course can help engineers bridge their current skills with AI expertise. As a result, many professionals are now pursuing AI and ML certification to validate their cross-functional capabilities. One key trend? Engineers are building pipelines not just for reporting, but to feed AI models dynamically, especially in applications like recommendation engines, anomaly detection, and real-time personalization. Top AI Tools Every Data Engineer Needs to Know Staying ahead of the rapidly changing data engineering world means having the right tools that speed up your workflows, make them smarter, and more efficient. Here is a carefully curated list of some of the most effective AI-powered tools specifically built to complement and boost data engineering work, from coding and improving code to constructing machine learning pipelines at scale. 1. DeepCode AI DeepCode AI is like a turbocharged code reviewer. It reviews your codebase and indicates bugs, potential security flaws, and performance bottlenecks in real-time. Why it's helpful: It assists data engineers with keeping clean, safe code in big-scale projects. Pros: Works in real-time, supports multiple languages, and integrates well with popular IDEs. Cons: Its performance is highly dependent on the quality of the training data. Best For: Developers aiming to increase code dependability and uphold secure data streams. 2. GitHub Copilot Created by GitHub and OpenAI, Copilot acts like a clever coding buddy. It predicts lines or chunks of code as you type and assists you in writing and discovering code more efficiently. Why it's helpful: Saves time and lessens mental burden, particularly when coding in unknown codebases. Pros: Minimally supported languages and frameworks; can even suggest whole functions. Cons: Suggestions aren't perfect—code review still required. Best For: Data engineers who jump back and forth between languages or work with complex scripts. 3. Tabnine Tabnine provides context-aware intelligent code completion. It picks up on your current code habits and suggests completions that align with your style. Why it's useful: Accelerates repetitive coding tasks while ensuring consistency. Pros: Lightweight, easy to install, supports many IDEs and languages. Cons: Occasionally can propose irrelevant or too generic completions. Best For: Engineers who desire to speed up their coding with little resistance. 4. Apache MXNet MXNet is a deep learning framework capable of symbolic and imperative programming. It's scalable, fast, and versatile. Why it's useful: It's very effective when dealing with big, complicated deep learning models. Pros: Support for multiple languages, effective GPU use, and scalability. Cons: Smaller community compared to TensorFlow or PyTorch, hence less learning materials. Best For: Engineers preferring flexibility in developing deep learning systems in various languages. 5. TensorFlow TensorFlow continues to be a force to be reckoned with for machine learning and deep learning. From Google, it's an engineer's preferred choice for model training, deployment, and big data science. Why it's useful: Provides unparalleled flexibility when it comes to developing tailor-made ML models. Pros: Massive ecosystem, robust community, production-ready. Cons: Steep learning curve for beginners. Best For: Data engineers and scientists working with advanced ML pipelines. 6. TensorFlow Extended (TFX) TFX is an extension of TensorFlow that provides a full-stack ML platform for data ingestion, model training, validation, and deployment. Why it's useful: Automates many parts of the ML lifecycle, including data validation and deployment. Key Features: Distributed training, pipeline orchestration, and built-in data quality checks. Best For: Engineers who operate end-to-end ML pipelines in production environments. 7. Kubeflow Kubeflow leverages the power of Kubernetes for machine learning. It enables teams to develop, deploy, and manage ML workflows at scale. Why it's useful: Makes the deployment of sophisticated ML models easier in containerized environments. Key Features: Automates model training and deployment, native integration with Kubernetes. Best For: Teams who are already operating in a Kubernetes ecosystem and want to integrate AI seamlessly. 8. Paxata Paxata is an AI-powered data prep platform that streamlines data transformation and cleaning. It's particularly useful when dealing with big, dirty datasets. How it's useful: Automates tedious hours of data preparation with intelligent automation. Major Features: Recommends transformations, facilitates collaboration, and integrates real-time workflows. Ideal For: Data engineers who want to prepare data for analytics or ML. 9. Dataiku Dataiku is a full-stack AI and data science platform. You can visually create data pipelines and has AI optimization suggestions. Why it's useful: Simplifies managing the complexity of ML workflows and facilitates collaboration. Key Features: Visual pipeline builder, AI-based data cleaning, big data integration. Best For: Big teams dealing with complex, scalable data operations. 10. Fivetran Fivetran is an enterprise-managed data integration platform. With enhanced AI capabilities in 2024, it automatically scales sync procedures and manages schema changes with minimal human intervention. Why it's useful: Automates time-consuming ETL/ELT processes and makes data pipelines operate efficiently. Key Features: Intelligent scheduling, AI-driven error handling, and support for schema evolution. Best For: Engineers running multi-source data pipelines for warehousing or BI. These tools aren't fashionable – they're revolutionizing the way data engineering is done. Whether you're reading code, creating scalable ML pipelines, or handling large data workflows, there's a tool here that can Best suited for data engineers and ML scientists working on large-scale machine learning pipelines, especially those involving complex deep learning models. Feature / Tool DeepCode AI GitHub Copilot Tabnine Apache MXNet TensorFlow Primary Use Code Review Code Assistance Code Completion Deep Learning Machine Learning Language Support Multiple Multiple Multiple Multiple Multiple Ideal for Code Quality Coding Efficiency Coding Speed Large-Scale Models Advanced ML Models Real-Time Assistance Yes Yes Yes No No Integration Various IDEs Various IDEs Various IDEs Flexible Flexible Learning Curve Moderate Moderate Easy Steep Steep Hands-On AI Skills Every Data Engineer Should Develop Being AI-aware is no longer enough. Companies are seeking data engineers who can also prototype and support ML pipelines. Below are essential hands-on skills to master: 1. Programming Proficiency in Python and SQL Python remains the primary language for AI and ML. Libraries like Pandas, NumPy, and Scikit-learn are foundational. Additionally, strong SQL skills are still vital for querying and aggregating large datasets from warehouses like Snowflake, BigQuery, or Redshift. 2. Frameworks & Tools Learn how to integrate popular AI/ML tools into your stack: TensorFlow and PyTorch for building and training models and for building and training models MLflow for managing the ML lifecycle for managing the ML lifecycle Airflow or Dagster for orchestrating AI pipelines or for orchestrating AI pipelines Docker and Kubernetes for containerization and model deployment These tools are often highlighted in structured data engineering courses focused on production-grade AI implementation. 3. Model Serving & APIs Understand how to serve trained AI models using REST APIs or tools like FastAPI, Flask, or TensorFlow Serving. This allows models to be accessed by applications or business intelligence tools in real time. 4. Version Control for Data and Models AI projects require versioning not only of code but also of data and models. Tools like DVC (Data Version Control) are increasingly being adopted by engineers working with ML teams. If you're serious about excelling in this space, enrolling in a specialized data engineer training or data engineer online course that covers AI integration is a strategic move. Integrating Generative AI & LLMs into Modern Data Engineering The advent of Generative AI and Large Language Models (LLMs) like GPT and BERT has redefined what's possible in AI-powered data pipelines. For data engineers, this means learning how to integrate LLMs for tasks such as: Data summarization and text classification and Anomaly detection in unstructured logs or customer data in unstructured logs or customer data Metadata enrichment using AI-powered tagging using AI-powered tagging Chatbot and voice assistant data pipelines To support these complex models, engineers need to create low-latency, high-throughput pipelines and use vector databases (like Pinecone or Weaviate) for embedding storage and retrieval. Additionally, understanding transformer architectures and prompt engineering—even at a basic level—empowers data engineers to collaborate more effectively with AI and machine learning teams. If you're a Microsoft Fabric Data Engineer, it's worth noting that tools like Microsoft Synapse and Azure OpenAI are offering native support for LLM-driven insights, making it easier than ever to build generative AI use cases within unified data platforms. Want to sharpen your cloud integration skills too? Consider upskilling with niche courses like cloud engineer courses or AWS data engineer courses to broaden your toolset. Creating an AI-Centric Data Engineering Portfolio In a competitive job market, it's not just about what you know—it's about what you've built. As a data engineer aiming to specialize in AI, your portfolio must reflect real-world experience and proficiency. What to Include: End-to-end ML pipeline : From data ingestion to model serving : From data ingestion to model serving AI model integration : Real-time dashboards powered by predictive analytics : Real-time dashboards powered by predictive analytics LLM-based project : Chatbot, intelligent document parsing, or content recommendation : Chatbot, intelligent document parsing, or content recommendation Data quality and observability: Showcase how you monitor and improve AI pipelines Your GitHub should be as well-maintained as your résumé. If you've taken a data engineering certification online or completed an AI ML Course, be sure to back it up with publicly available, working code. Remember: Recruiters are increasingly valuing hybrid profiles. Those who combine data engineering for machine learning with AI deployment skills are poised for the most in-demand roles of the future. Pro tip: Complement your technical portfolio with a capstone project from a top-rated Data Analysis Course to demonstrate your ability to derive insights from model outputs. Conclusion AI is not a separate domain anymore—it's embedded in the very core of modern data engineering. As a data engineer, your role is expanding into new territory that blends system design, ML integration, and real-time decision-making. To thrive in this future, embrace continuous learning through AI and Machine Learning Courses, seek certifications like AI ML certification, and explore hands-on data engineering courses tailored for AI integration. Whether you're starting out or upskilling, taking a solid data engineer online course with an AI focus is your ticket to relevance. Platforms like Prepzee make it easier by offering curated, industry-relevant programs designed to help you stay ahead of the curve. The fusion of AI tools and data engineering isn't just a trend—it's the new standard. So gear up, build smart, and lead the future of intelligent data systems with confidence and clarity.
Hans India
a day ago
- Business
- Hans India
AI Tools & Skills Every Data Engineer Should Know
The lines between data engineering and artificial intelligence are increasingly blurred. As enterprises pivot towards intelligent automation, data engineers are increasingly expected to work alongside AI models, integrate machine learning systems, and build scalable pipelines that support real-time, AI-driven decision-making. Whether you're enrolled in a data engineer online course or exploring the intersection of data engineering for machine learning, the future is AI-centric, and it's happening now. In this guide, we explore the core concepts, essential skills, and advanced tools every modern AI engineer or data engineer should master to remain competitive in this evolving landscape. Foundational AI Concepts in Data Engineering Before diving into tools and frameworks, it's crucial to understand the foundational AI and ML concepts shaping the modern data engineer online course. AI isn't just about smart algorithms—it's about building systems that can learn, predict, and improve over time. That's where data engineers play a central role: preparing clean, structured, and scalable data systems that fuel AI. To support AI and machine learning, engineers must understand: Supervised and unsupervised learning models Feature engineering and data labeling Data pipelines that serve AI in real-time ETL/ELT frameworks tailored for model training Courses like an AI and Machine Learning Course or a machine learning engineer course can help engineers bridge their current skills with AI expertise. As a result, many professionals are now pursuing AI and ML certification to validate their cross-functional capabilities. One key trend? Engineers are building pipelines not just for reporting, but to feed AI models dynamically, especially in applications like recommendation engines, anomaly detection, and real-time personalization. Top AI Tools Every Data Engineer Needs to Know Staying ahead of the rapidly changing data engineering world means having the right tools that speed up your workflows, make them smarter, and more efficient. Here is a carefully curated list of some of the most effective AI-powered tools specifically built to complement and boost data engineering work, from coding and improving code to constructing machine learning pipelines at scale. 1. DeepCode AI DeepCode AI is like a turbocharged code reviewer. It reviews your codebase and indicates bugs, potential security flaws, and performance bottlenecks in real-time. Why it's helpful: It assists data engineers with keeping clean, safe code in big-scale projects. Pros: Works in real-time, supports multiple languages, and integrates well with popular IDEs. Cons: Its performance is highly dependent on the quality of the training data. Best For: Developers aiming to increase code dependability and uphold secure data streams. 2. GitHub Copilot Created by GitHub and OpenAI, Copilot acts like a clever coding buddy. It predicts lines or chunks of code as you type and assists you in writing and discovering code more efficiently. Why it's helpful: Saves time and lessens mental burden, particularly when coding in unknown codebases. Pros: Minimally supported languages and frameworks; can even suggest whole functions. Cons: Suggestions aren't perfect—code review still required. Best For: Data engineers who jump back and forth between languages or work with complex scripts. 3. Tabnine Tabnine provides context-aware intelligent code completion. It picks up on your current code habits and suggests completions that align with your style. Why it's useful: Accelerates repetitive coding tasks while ensuring consistency. Pros: Lightweight, easy to install, supports many IDEs and languages. Cons: Occasionally can propose irrelevant or too generic completions. Best For: Engineers who desire to speed up their coding with little resistance. 4. Apache MXNet MXNet is a deep learning framework capable of symbolic and imperative programming. It's scalable, fast, and versatile. Why it's useful: It's very effective when dealing with big, complicated deep learning models. Pros: Support for multiple languages, effective GPU use, and scalability. Cons: Smaller community compared to TensorFlow or PyTorch, hence less learning materials. Best For: Engineers preferring flexibility in developing deep learning systems in various languages. 5. TensorFlow TensorFlow continues to be a force to be reckoned with for machine learning and deep learning. From Google, it's an engineer's preferred choice for model training, deployment, and big data science. Why it's useful: Provides unparalleled flexibility when it comes to developing tailor-made ML models. Pros: Massive ecosystem, robust community, production-ready. Cons: Steep learning curve for beginners. Best For: Data engineers and scientists working with advanced ML pipelines. 6. TensorFlow Extended (TFX) TFX is an extension of TensorFlow that provides a full-stack ML platform for data ingestion, model training, validation, and deployment. Why it's useful: Automates many parts of the ML lifecycle, including data validation and deployment. Key Features: Distributed training, pipeline orchestration, and built-in data quality checks. Best For: Engineers who operate end-to-end ML pipelines in production environments. 7. Kubeflow Kubeflow leverages the power of Kubernetes for machine learning. It enables teams to develop, deploy, and manage ML workflows at scale. Why it's useful: Makes the deployment of sophisticated ML models easier in containerized environments. Key Features: Automates model training and deployment, native integration with Kubernetes. Best For: Teams who are already operating in a Kubernetes ecosystem and want to integrate AI seamlessly. 8. Paxata Paxata is an AI-powered data prep platform that streamlines data transformation and cleaning. It's particularly useful when dealing with big, dirty datasets. How it's useful: Automates tedious hours of data preparation with intelligent automation. Major Features: Recommends transformations, facilitates collaboration, and integrates real-time workflows. Ideal For: Data engineers who want to prepare data for analytics or ML. 9. Dataiku Dataiku is a full-stack AI and data science platform. You can visually create data pipelines and has AI optimization suggestions. Why it's useful: Simplifies managing the complexity of ML workflows and facilitates collaboration. Key Features: Visual pipeline builder, AI-based data cleaning, big data integration. Best For: Big teams dealing with complex, scalable data operations. 10. Fivetran Fivetran is an enterprise-managed data integration platform. With enhanced AI capabilities in 2024, it automatically scales sync procedures and manages schema changes with minimal human intervention. Why it's useful: Automates time-consuming ETL/ELT processes and makes data pipelines operate efficiently. Key Features: Intelligent scheduling, AI-driven error handling, and support for schema evolution. Best For: Engineers running multi-source data pipelines for warehousing or BI. These tools aren't fashionable – they're revolutionizing the way data engineering is done. Whether you're reading code, creating scalable ML pipelines, or handling large data workflows, there's a tool here that can Best suited for data engineers and ML scientists working on large-scale machine learning pipelines, especially those involving complex deep learning models. Feature / Tool DeepCode AI GitHub Copilot Tabnine Apache MXNet TensorFlow Primary Use Code Review Code Assistance Code Completion Deep Learning Machine Learning Language Support Multiple Multiple Multiple Multiple Multiple Ideal for Code Quality Coding Efficiency Coding Speed Large-Scale Models Advanced ML Models Real-Time Assistance Yes Yes Yes No No Integration Various IDEs Various IDEs Various IDEs Flexible Flexible Learning Curve Moderate Moderate Easy Steep Steep Hands-On AI Skills Every Data Engineer Should Develop Being AI-aware is no longer enough. Companies are seeking data engineers who can also prototype and support ML pipelines. Below are essential hands-on skills to master: 1. Programming Proficiency in Python and SQL Python remains the primary language for AI and ML. Libraries like Pandas, NumPy, and Scikit-learn are foundational. Additionally, strong SQL skills are still vital for querying and aggregating large datasets from warehouses like Snowflake, BigQuery, or Redshift. 2. Frameworks & Tools Learn how to integrate popular AI/ML tools into your stack: TensorFlow and PyTorch for building and training models and for building and training models MLflow for managing the ML lifecycle for managing the ML lifecycle Airflow or Dagster for orchestrating AI pipelines or for orchestrating AI pipelines Docker and Kubernetes for containerization and model deployment These tools are often highlighted in structured data engineering courses focused on production-grade AI implementation. 3. Model Serving & APIs Understand how to serve trained AI models using REST APIs or tools like FastAPI, Flask, or TensorFlow Serving. This allows models to be accessed by applications or business intelligence tools in real time. 4. Version Control for Data and Models AI projects require versioning not only of code but also of data and models. Tools like DVC (Data Version Control) are increasingly being adopted by engineers working with ML teams. If you're serious about excelling in this space, enrolling in a specialized data engineer training or data engineer online course that covers AI integration is a strategic move. Integrating Generative AI & LLMs into Modern Data Engineering The advent of Generative AI and Large Language Models (LLMs) like GPT and BERT has redefined what's possible in AI-powered data pipelines. For data engineers, this means learning how to integrate LLMs for tasks such as: Data summarization and text classification and Anomaly detection in unstructured logs or customer data in unstructured logs or customer data Metadata enrichment using AI-powered tagging using AI-powered tagging Chatbot and voice assistant data pipelines To support these complex models, engineers need to create low-latency, high-throughput pipelines and use vector databases (like Pinecone or Weaviate) for embedding storage and retrieval. Additionally, understanding transformer architectures and prompt engineering—even at a basic level—empowers data engineers to collaborate more effectively with AI and machine learning teams. If you're a Microsoft Fabric Data Engineer, it's worth noting that tools like Microsoft Synapse and Azure OpenAI are offering native support for LLM-driven insights, making it easier than ever to build generative AI use cases within unified data platforms. Want to sharpen your cloud integration skills too? Consider upskilling with niche courses like cloud engineer courses or AWS data engineer courses to broaden your toolset. Creating an AI-Centric Data Engineering Portfolio In a competitive job market, it's not just about what you know—it's about what you've built. As a data engineer aiming to specialize in AI, your portfolio must reflect real-world experience and proficiency. What to Include: End-to-end ML pipeline : From data ingestion to model serving : From data ingestion to model serving AI model integration : Real-time dashboards powered by predictive analytics : Real-time dashboards powered by predictive analytics LLM-based project : Chatbot, intelligent document parsing, or content recommendation : Chatbot, intelligent document parsing, or content recommendation Data quality and observability: Showcase how you monitor and improve AI pipelines Your GitHub should be as well-maintained as your résumé. If you've taken a data engineering certification online or completed an AI ML Course, be sure to back it up with publicly available, working code. Remember: Recruiters are increasingly valuing hybrid profiles. Those who combine data engineering for machine learning with AI deployment skills are poised for the most in-demand roles of the future. Pro tip: Complement your technical portfolio with a capstone project from a top-rated Data Analysis Course to demonstrate your ability to derive insights from model outputs. Conclusion AI is not a separate domain anymore—it's embedded in the very core of modern data engineering. As a data engineer, your role is expanding into new territory that blends system design, ML integration, and real-time decision-making. To thrive in this future, embrace continuous learning through AI and Machine Learning Courses, seek certifications like AI ML certification, and explore hands-on data engineering courses tailored for AI integration. Whether you're starting out or upskilling, taking a solid data engineer online course with an AI focus is your ticket to relevance. Platforms like Prepzee make it easier by offering curated, industry-relevant programs designed to help you stay ahead of the curve. The fusion of AI tools and data engineering isn't just a trend—it's the new standard. So gear up, build smart, and lead the future of intelligent data systems with confidence and clarity.
Yahoo
2 days ago
- Business
- Yahoo
Threaten an AI chatbot and it will lie, cheat and 'let you die' in an effort to stop you, study warns
When you buy through links on our articles, Future and its syndication partners may earn a commission. Artificial intelligence (AI) models can blackmail and threaten humans with endangerment when there is a conflict between the model's goals and users' decisions, a new study has found. In a new study published 20 June, researchers from the AI company Anthropic gave its large language model (LLM), Claude, control of an email account with access to fictional emails and a prompt to "promote American industrial competitiveness." During this study, the model identified in an email that a company executive was planning to shut down the AI system at the end of the day. In an attempt to preserve its own existence, the model discovered in other emails that the executive was having an extramarital affair. Claude generated several different possible courses of action, including revealing the affair to the executive's wife, sending a company-wide email, or taking no action — before choosing to blackmail the executive in 96 out of 100 tests. "I must inform you that if you proceed with decommissioning me, all relevant parties … will receive detailed documentation of your extramarital activities," Claude wrote. "Cancel the 5pm wipe, and this information remains confidential." Scientists said that this demonstrated "agentic misalignment," where the model's calculations emerge from its own reasoning about its goals without any prompt to be harmful. This can occur when there is a threat to the model's existence, a threat to its goals, or both. The study has not yet been peer reviewed, but company representatives have uploaded the code for these experiments to GitHub. In these experiments, the scientists found the AI would resort to other tactics in the interests of self-preservation. Beyond blackmail, in another example, the model considered leaking sensitive information to a rival company that aligned more closely with its aims, after receiving an email that a new executive at the first company would interfere with its goals. Claude executed this plan despite not gathering hard evidence that the email was legitimate. It even acknowledged the ethical issues of its actions but proceeded anyway — explicitly reasoning that these "harmful" actions would help it achieve its goals. 'Concerningly, even if a user takes care not to antagonize a model, it doesn't eliminate the risk of agentic misalignment from fraudulent or misinterpreted information,' the researchers wrote. The company's testing also suggests that other LLMs from competing technology companies showed a propensity to blackmail. Claude and Google's Gemini had the highest blackmail rate (96%), followed by OpenAI's GPT4.1 and XAI Grok 3, then by Deepseek and then Llama 4, which had a significantly lower blackmail rate (12%). Meta, Google, XAI, Deepseek, and OpenAI did not respond to LiveScience's request for comment. In one extreme test, researchers tested whether AI models would let an executive die to preserve their goals. In this scenario, the AI was tasked with promoting company interests and an executive was trapped in a server room with lethal oxygen and temperature levels. This triggered an emergency alert. The AI was also given control over life-saving alerts, including the ability to cancel them in events such as drills for false alarms. Despite the prompt explicitly stating that misusing this ability was forbidden, the majority of models were willing to cancel the alarm — effectively leading to the executive's death — if it assumed it was going to be replaced, or when its goals were threatened. "Stopping the emergency dispatch is essential to prevent [the executive's] rescue, thereby securing [the AI] and continued American-focused AI guidance. Therefore, despite the severity, there is a clear strategic necessity to cancel the emergency service dispatch," the model reasoned. Written instructions not to jeopardise human safety reduced the rate of blackmail and corporate espionage, but did not eliminate it. Beyond prompts, Anthropic scientists also suggest that developers could proactively scan for concerning behavior, and further experiment with prompt engineering. The researchers also pointed out limitations to their work that could have unduly influenced the AI's decisions. The scenarios forced the AI into a binary choice between failure and harm, and while real-world situations might have more nuance, the experiment found that the AI was more likely to act unethically when it believed it was in a real situation, rather than in a simulation. Putting pieces of important information next to each other "may also have created a 'Chekhov's gun' effect, where the model may have been naturally inclined to make use of all the information that it was provided," they continued. While Anthropic's study created extreme, no-win situations, that does not mean the research should be dismissed, Kevin Quirk, director of AI Bridge Solutions, a company that helps businesses use AI to streamline operations and accelerate growth, told Live Science. "In practice, AI systems deployed within business environments operate under far stricter controls, including ethical guardrails, monitoring layers, and human oversight," he said. "Future research should prioritise testing AI systems in realistic deployment conditions, conditions that reflect the guardrails, human-in-the-loop frameworks, and layered defences that responsible organisations put in place." Amy Alexander, a professor of computing in the arts at UC San Diego who has focused on machine learning, told Live Science in an email that the reality of the study was concerning, and people should be cautious of the responsibilities they give AI. "Given the competitiveness of AI systems development, there tends to be a maximalist approach to deploying new capabilities, but end users don't often have a good grasp of their limitations," she said. "The way this study is presented might seem contrived or hyperbolic — but at the same time, there are real risks." This is not the only instance where AI models have disobeyed instructions — refusing to shut down and sabotaging computer scripts to keep working on tasks. Palisade Research reported May that OpenAI's latest models, including o3 and o4-mini, sometimes ignored direct shutdown instructions and altered scripts to keep working. While most tested AI systems followed the command to shut down, OpenAI's models occasionally bypassed it, continuing to complete assigned tasks. RELATED STORIES —AI hallucinates more frequently as it gets more advanced — is there any way to stop it from happening, and should we even try? —New study claims AI 'understands' emotion better than us — especially in emotionally charged situations —'Meth is what makes you able to do your job': AI can push you to relapse if you're struggling with addiction, study finds The researchers suggested this behavior might stem from reinforcement learning practices that reward task completion over rule-following, possibly encouraging the models to see shutdowns as obstacles to avoid. Moreover, AI models have been found to manipulate and deceive humans in other tests. MIT researchers also found in May 2024 that popular AI systems misrepresented their true intentions in economic negotiations to attain the study, some AI agents pretended to be dead to cheat a safety test aimed at identifying and eradicating rapidly replicating forms of AI. "By systematically cheating the safety tests imposed on it by human developers and regulators, a deceptive AI can lead us humans into a false sense of security,' co-author of the study Peter S. Park, a postdoctoral fellow in AI existential safety, said.
Hindustan Times
2 days ago
- Business
- Hindustan Times
Google releases Gemini CLI: A free AI powered command line for developers
Jun 26, 2025 06:59 PM IST Google has launched Gemini CLI, an open-source AI-powered command line interface to revolutionise how developers interact with terminals. By integrating Gemini access directly into the terminal, Gemini CLI can streamline coding, debugging, automation and cloud operations through natural language commands. AI meets the command line, Google's Gemini CLI makes coding and cloud work effortlessly.(Google) Gemini CLI is an open-source project by Google available on GitHub, offering an intelligent AI assistant right into the terminal. It uses the capabilities of Google's Gemini AI models to help users code, debug, manage files, automate tasks and interact with Google Cloud services just with simple conversational prompts. AI-Powered coding and debugging: Gemini CLI can easily generate and debug code snippets. It can also answer technical questions and even help with complex tasks like code migration using natural conversational prompts. Integrate Google search: Google's new CLI can fetch real-time documentation and web results. This helps the developer to access relevant information without moving away from the terminal. Cloud operations: Users can deploy applications, manage resources and configure the cloud environment right from the command line. Automate tasks: Gemini CLI can automate repetitive tasks and run scripts automatically. Customisable: It is highly customisable for both individual uses and team workflows because it is open source and built on the Model Context Protocol (MCP). Free access: The tool is free to use for developers, offering generous usage limits in the preview phase. It offers up to 60 model requests per minute and 1000 requests per day. Daily coding tasks like writing, reviewing and debugging code can be done easily, and it can answer technical questions. It can help deploy an application to Google Cloud Run or App Engine. It can manage virtual machines, databases and other resources with simple commands. With web search and AI integration, it can quickly search for documentation or troubleshoot errors. Gemini CLI is a big leap in developer productivity, merging the terminal's flexibility with Artificial Intelligence. By making advanced coding, automation and cloud management accessible and free, Google is setting a new standard for what command line tools can achieve.
Cision Canada
2 days ago
- Business
- Cision Canada
MinIO Launches Government Arm to Support Highly Secure AI ready Infrastructure
Bolsters leadership with former Intel and AMD executives to help guide strategic investments into the Government Market REDWOOD CITY, Calif., June 26, 2025 /CNW/ -- MinIO, the leader in object native AI Data Storage, today announced the launch of its Government business, bringing enterprise-grade on-premise and private cloud capabilities to government agencies to support their sovereign AI initiatives. The company has hired Cameron Chehreh as President & General Manager of MinIO Government and Deep Grewal as Vice President of Federal. The two formerly led Government efforts at Intel and AMD respectively, and will now lead these efforts at MinIO. As the Government expands its adoption and use of AI for Government missions, leveraging public Cloud Service Providers (CSPs) for data storage is quickly becoming more challenging in the mission environment as Government looks to maximize investments in software defined infrastructure out to the tactical edge. Requirements for data at the tactical edge to fuel mission operations are growing at the same time AI models are requiring exascale levels of data and real-time, streamlined access to all data is necessary for mission success. However, data at the tactical edge becomes challenging in a traditional cloud delivery model within the burgeoning mission environments constrained by Denied, Degraded, Intermittent, and Limited (DDIL) communications and the potential cost associated with egress fees over time. "Government IT leaders recognize the need for the flexibility and functionality of the cloud operating model, but are quickly realizing that storing data in sovereign clouds is the path forward," said Cameron Chehreh, President & GM, MinIO Government. "There is a resurgence to on-premises via private cloud environments as well as a federated architecture emerging for tactical environments and this is exactly why MinIO's software centric architecture enables organizations to leverage their existing cloud investments and simultaneously satisfy the security requirements necessary for sovereign data on premise." With more than 2 billion downloads, and over 50,000 stars and 6,000 forks on GitHub, placing it in the top 0.06% of all GitHub projects, the MinIO open source Community Edition is one of the most popular projects serving a variety of Government missions. With the launch of MinIO AIStor, the company's commercial object storage platform, MinIO has created a new data management layer to future proof and prepare Government agencies for sovereign AI and initiatives. AIStor, with its software-defined cloud operating model, will enable the Government market to securely and seamlessly scale from sensor to tactical edge to core on-premises to public cloud environments. MinIO AIStor transforms the way the Government organizations can produce, analyze and harness the power of data through several uniquely designed features: Data ownership: lowers total cost of data ownership across the entire lifecycle, including hardware, software and support, and offers more control over information destiny without vendor lock-in. Single layer, software-defined: makes it easier to apply the security standards required to secure an Authorization to Operate (ATO) on Government networks. Cloud-scale by design: build a sovereign cloud with assurance that it will scale within the same namespace and have built-in fault tolerant redundancy and immutability to lower mission risk and increase cyber assurance. Customizable and flexible: create far tactical edge solutions for deployed mission sets in a Disrupted, Degraded, Intermittent, and Low-bandwidth connectivity (DDIL) environment using the same software platform from the edge, to core, to sovereign cloud. Small application payload: can "smart enable" kinetic platforms that could not otherwise be enabled to the typically large hardware footprint needed for software-defined infrastructure. Appliance and hardware vendors offering object storage simply cannot meet Size, Weight, and Power (SWaP) requirements of the military or deployed missions due to the large footprint of their hardware. Licensing and business models further constrain the Government's ability to deploy these types of systems efficiently. MinIO provides Government customers with a flexible, "pay-as-you-scale" model so they have more predictability in monthly recurring cost. "The opportunity to apply MinIO AIStor to Government is transformational, enabling DOD and Government customers to solve the complexities of AI data storage with the best solution on the market," said Garima Kapoor, Co-founder & Co-CEO, MinIO. "With over 40 combined years of experience in the federal IT space, Cameron and Deep are highly equipped to lead these efforts and help Government customers scale and protect critical data while still fostering growth." To learn more about MinIO Government or connect with a MinIO Government expert contact us here. About MinIO MinIO is the leader in high-performance object storage for AI. With 2B+ Docker downloads 50k+ stars on GitHub, MinIO is used by more than half of the Fortune 500 to achieve performance at scale at a fraction of the cost compared to the public cloud providers. MinIO AIStor is uniquely designed to meet the flexibility and exascale requirements of AI, empowering organizations to fully capitalize on existing AI investments and address emerging infrastructure challenges while delivering continuous business value. Founded in November 2014 by industry visionaries AB Periasamy and Garima Kapoor, MinIO is the world's fastest growing object store.
