MAROKO133 Eksklusif ai: China showcases its Black Hawk rival, assault helicopter makes pre

📌 MAROKO133 Eksklusif ai: China showcases its Black Hawk rival, assault helicopter

At the 7th China Helicopter Exposition in Tianjin this week, the spotlight fell on the Z-20T assault helicopter as it delivered an impressive aerial performance showcasing its advanced combat capabilities. 

The all-weather aircraft executed a series of precise manoeuvres, including a hover salute, backward ascent, and vertical climb, before deploying jamming flares in a vivid display of battlefield readiness. 

The Z-20T’s appearance at the event marks one of its rare public demonstrations. The last time the helicopter was seen in flight was during Beijing’s Victory Day parade on September 3, when seven units flew over Tiananmen Square.

Z-20T variant adds airlift and fire support roles

Developed by the Aviation Industry Corporation of China (AVIC), the Z-20T is a specialized army variant of the Z-20 helicopter built for air assault missions, fire support, and special operations. AVIC engineers told the official Science and Technology Daily that the aircraft also features robust airlift capabilities, enabling it to transport troops and equipment in demanding battlefield conditions.

As one of the main organizers of the Tianjin exhibition, the People’s Liberation Army focused on displaying its latest aviation assault assets, featuring an array of helicopters, drones, and airborne weapon systems. Among them, the upgraded Z-20T stood out for its redesigned airframe – unlike the standard Z-20, this variant includes short wings mounted on the fuselage, allowing it to carry auxiliary fuel tanks and a range of munitions for extended missions and enhanced firepower, the South China Morning Post reported.

Designers of the Z-20T highlight its adaptability as one of its defining strengths, noting that the aircraft can easily be converted into a general-purpose helicopter by removing its weapon systems. Its modular transport platform can also be reconfigured for a wide range of missions, offering exceptional scalability and operational flexibility. 

Z-20 variants highlight China’s expanding military aviation capabilities

China’s Z-20 helicopter, which entered service in 2018 as the nation’s first independently developed 10-tonne, all-weather utility aircraft, has become a key asset across its armed forces. Often compared to the US-made Sikorsky UH-60 Black Hawk, the Z-20 has evolved into multiple variants tailored for specific combat roles. 

In recent years, China has tested and refined versions for both air force and naval operations. A ship-based model equipped with anti-submarine and anti-ship capabilities was unveiled at last year’s Zhuhai Air Show, while this week’s Tianjin exhibition marked the most detailed public display yet of the helicopter’s dedicated military version.

Equipped with advanced turboshaft engines, high-efficiency rotor systems, and anti-icing technology, the military variants of the Z-20 are built for demanding operational conditions. They also feature state-of-the-art weather and collision avoidance radar systems, allowing them to perform effectively in complex environments and during ultra-low altitude missions where precision and situational awareness are critical.

🔗 Sumber: interestingengineering.com

📌 MAROKO133 Hot ai: The teacher is the new engineer: Inside the rise of AI enablem

As more companies quickly begin using gen AI, it’s important to avoid a big mistake that could impact its effectiveness: Proper onboarding. Companies spend time and money training new human workers to succeed, but when they use large language model (LLM) helpers, many treat them like simple tools that need no explanation.

This isn't just a waste of resources; it's risky. Research shows that AI has advanced quickly from testing to actual use in 2024 to 2025, with almost a third of companies reporting a sharp increase in usage and acceptance from the previous year.

Probabilistic systems need governance, not wishful thinking

Unlike traditional software, gen AI is probabilistic and adaptive. It learns from interaction, can drift as data or usage changes and operates in the gray zone between automation and agency. Treating it like static software ignores reality: Without monitoring and updates, models degrade and produce faulty outputs: A phenomenon widely known as model drift. Gen AI also lacks built-in organizational intelligence. A model trained on internet data may write a Shakespearean sonnet, but it won’t know your escalation paths and compliance constraints unless you teach it. Regulators and standards bodies have begun pushing guidance precisely because these systems behave dynamically and can hallucinate, mislead or leak data if left unchecked.

The real-world costs of skipping onboarding

When LLMs hallucinate, misinterpret tone, leak sensitive information or amplify bias, the costs are tangible.

• Misinformation and liability: A Canadian tribunal held Air Canada liable after its website chatbot gave a passenger incorrect policy information. The ruling made it clear that companies remain responsible for their AI agents’ statements.

• Embarrassing hallucinations: In 2025, a syndicated “summer reading list” carried by the Chicago Sun-Times and Philadelphia Inquirer recommended books that didn’t exist; the writer had used AI without adequate verification, prompting retractions and firings.

• Bias at scale: The Equal Employment Opportunity Commission (EEOCs) first AI-discrimination settlement involved a recruiting algorithm that auto-rejected older applicants, underscoring how unmonitored systems can amplify bias and create legal risk.

• Data leakage: After employees pasted sensitive code into ChatGPT, Samsung temporarily banned public gen AI tools on corporate devices — an avoidable misstep with better policy and training.

The message is simple: Un-onboarded AI and un-governed usage create legal, security and reputational exposure.

Treat AI agents like new hires

Enterprises should onboard AI agents as deliberately as they onboard people — with job descriptions, training curricula, feedback loops and performance reviews. This is a cross-functional effort across data science, security, compliance, design, HR and the end users who will work with the system daily.

1. Role definition. Spell out scope, inputs/outputs, escalation paths and acceptable failure modes. A legal copilot, for instance, can summarize contracts and surface risky clauses, but should avoid final legal judgments and must escalate edge cases.

2. Contextual training. Fine-tuning has its place, but for many teams, retrieval-augmented generation (RAG) and tool adapters are safer, cheaper and more auditable. RAG keeps models grounded in your latest, vetted knowledge (docs, policies, knowledge bases), reducing hallucinations and improving traceability. Emerging Model Context Protocol (MCP) integrations make it easier to connect copilots to enterprise systems in a controlled way — bridging models with tools and data while preserving separation of concerns. Salesforce’s Einstein Trust Layer illustrates how vendors are formalizing secure grounding, masking, and audit controls for enterprise AI.

3. Simulation before production. Don’t let your AI’s first “training” be with real customers. Build high-fidelity sandboxes and stress-test tone, reasoning and edge cases — then evaluate with human graders. Morgan Stanley built an evaluation regimen for its GPT-4 assistant, having advisors and prompt engineers grade answers and refine prompts before broad rollout. The result: >98% adoption among advisor teams once quality thresholds were met. Vendors are also moving to simulation: Salesforce recently highlighted digital-twin testing to rehearse agents safely against realistic scenarios.

4. 4) Cross-functional mentorship. Treat early usage as a two-way learning loop: Domain experts and front-line users give feedback on tone, correctness and usefulness; security and compliance teams enforce boundaries and red lines; designers shape frictionless UIs that encourage proper use.

Feedback loops and performance reviews—forever

Onboarding doesn’t end at go-live. The most meaningful learning begins after deployment.

• Monitoring and observability: Log outputs, track KPIs (accuracy, satisfaction, escalation rates) and watch for degradation. Cloud providers now ship observability/evaluation tooling to help teams detect drift and regressions in production, especially for RAG systems whose knowledge changes over time.

• User feedback channels. Provide in-product flagging and structured review queues so humans can coach the model — then close the loop by feeding these signals into prompts, RAG sources or fine-tuning sets.

• Regular audits. Schedule alignment checks, factual audits and safety evaluations. Microsoft’s enterprise responsible-AI playbooks, for instance, emphasize governance and staged rollouts with executive visibility and clear guardrails.

• Succession planning for models. As laws, products and models evolve, plan upgrades and retirement the way you would plan people transitions — run overlap tests and port institutional knowledge (prompts, eval sets, retrieval sources).

Why this is urgent now

Gen AI is no longer an “innovation shelf” project — it’s embedded in CRMs, support desks, analytics pipelines and executive workflows. Banks like Morgan Stanley and Bank of America are focusing AI on internal c…

Konten dipersingkat otomatis.

🔗 Sumber: venturebeat.com