📌 TOPINDIATOURS Update ai: China Is Absolutely Obsessed With Copying SpaceX’s Star

SpaceX’s Falcon 9 rocket ushered in a new era, offering a fully reusable launch platform that brought outer space closer than ever by dramatically lowering the costs of sending payloads — and eventually astronauts — into orbit.

Now Elon Musk’s space corporation has even more to prove when it comes to its Starship, a gargantuan super heavy lift rocket and booster duo that’s still deep in the development phase. Following over a dozen major explosions, the rocket has yet to safely launch and return to the ground in one piece.

That hasn’t dissuaded a growing number of Chinese companies from unabashedly ripping off the concept, as Ars Technica reports. Most recently, state-operated news outlet China.com detailed acompany called “Beijing Leading Rocket Technology” that named its latest vehicle concept Xingzhou-1 — which translates to “Starship-1” or “Starvessel-1.” A render of the rocket closely resembles SpaceX’s design as well, from the overall proportions down to the grid fins designed to guide the Super Heavy booster and Starship spacecraft back down to the ground.

“Very early stage, only conceptual claims, very ambitiously aiming for 2027 debut fight,” journalist Andrew Jones tweeted. “Wild.”

Several other Chinese startups have similarly proposed rocket designs that rip off SpaceX’s Starship, from Cosmoleap, which painstakingly recreated the concept down to a render of a Super Heavy booster-like rocket being caught by a launch tower by two extremely familiar-looking “chopstick” arms. A separate firm, called Astronstone, also said it was looking to recreate the concept, “fully aligning its technical approach with Elon Musk’s SpaceX,” albeit at a smaller scale.

Even a heavy lift rocket China’s national space officials showed off a year ago bore a striking resemblance to Starship, from a “two-stage, fully reusable configuration” right down to the aerodynamic flaps.

But when it comes to turning flashy renders into reality, China still has plenty of catching up to do when it comes to reusable rocketry on the whole. Case in point, earlier this month, private Chinese space company LandScape attempted to launch and land its reusable and Falcon 9-like Zhuque-3 rocket booster, only for it to explode during its first orbital test.

Even SpaceX itself is struggling to turn its mammoth Starship into a reliable platform that can send up to 150 metric tons to space in a single launch — which isn’t exactly surprising, given the project’s unprecedented scale.

Where that leaves far smaller Chinese companies vying for investor attention to cash in on Musk’s Moonshot idea remains unclear at best.

For one, even SpaceX has plenty to prove before it can assist NASA during its upcoming planned attempt to land the first astronauts on the lunar surface in over 50 years. The space agency is getting antsy, with NASA’s interim administrator Sean Duffy telling Fox News earlier this year that it’s looking for alternatives, arguing that SpaceX is “behind schedule” for the mission, which is tentatively scheduled for 2027.

In the meantime, users on the SpaceXLounge subreddit are less than convinced China will beat SpaceX to the punch when it comes to Starship.

“Until they make a reliable and working full-flow engine, all these copy efforts won’t be worth diddly squat,” one Reddit user wrote. “But will look neat and sometimes explode with a lot of fireworks!”

“I wish they would mimic our free speech like they mimic our technology,” another user added facetiously.

More on Starship: SpaceX Releases Renders of the Inside of Its Enormous Lunar Lander

The post China Is Absolutely Obsessed With Copying SpaceX’s Starship Rocket appeared first on Futurism.

🔗 Sumber: futurism.com

📌 TOPINDIATOURS Hot ai: Why agentic AI needs a new category of customer data Wajib

Presented by Twilio

The customer data infrastructure powering most enterprises was architected for a world that no longer exists: one where marketing interactions could be captured and processed in batches, where campaign timing was measured in days (not milliseconds), and where "personalization" meant inserting a first name into an email template.

Conversational AI has shattered those assumptions.

AI agents need to know what a customer just said, the tone they used, their emotional state, and their complete history with a brand instantly to provide relevant guidance and effective resolution. This fast-moving stream of conversational signals (tone, urgency, intent, sentiment) represents a fundamentally different category of customer data. Yet the systems most enterprises rely on today were never designed to capture or deliver it at the speed modern customer experiences demand.

The conversational AI context gap

The consequences of this architectural mismatch are already visible in customer satisfaction data. Twilio’s Inside the Conversational AI Revolution report reveals that more than half (54%) of consumers report AI rarely has context from their past interactions, and only 15% feel that human agents receive the full story after an AI handoff. The result: customer experiences defined by repetition, friction, and disjointed handoffs.

The problem isn't a lack of customer data. Enterprises are drowning in it. The problem is that conversational AI requires real-time, portable memory of customer interactions, and few organizations have infrastructure capable of delivering it. Traditional CRMs and CDPs excel at capturing static attributes but weren't architected to handle the dynamic exchange of a conversation unfolding second by second.

Solving this requires building conversational memory inside communications infrastructure itself, rather than attempting to bolt it onto legacy data systems through integrations.

The agentic AI adoption wave and its limits

This infrastructure gap is becoming critical as agentic AI moves from pilot to production. Nearly two-thirds of companies (63%) are already in late-stage development or fully deployed with conversational AI across sales and support functions.

The reality check: While 90% of organizations believe customers are satisfied with their AI experiences, only 59% of consumers agree. The disconnect isn't about conversational fluency or response speed. It's about whether AI can demonstrate true understanding, respond with appropriate context, and actually solve problems rather than forcing escalation to human agents.

Consider the gap: A customer calls about a delayed order. With proper conversational memory infrastructure, an AI agent could instantly recognize the customer, reference their previous order, details about a delay, proactively suggest solutions, and offer appropriate compensation, all without asking them to repeat information. Most enterprises can't deliver this because the required data lives in separate systems that can't be accessed quickly enough.

Where enterprise data architecture breaks down

Enterprise data systems built for marketing and support were optimized for structured data and batch processing, not the dynamic memory required for natural conversation. Three fundamental limitations prevent these systems from supporting conversational AI:

Latency breaks the conversational contract. When customer data lives in one system and conversations happen in another, every interaction requires API calls that introduce 200-500 millisecond delays, transforming natural dialogue into robotic exchanges.

Conversational nuance gets lost. The signals that make conversations meaningful (tone, urgency, emotional state, commitments made mid-conversation) rarely make it into traditional CRMs, which were designed to capture structured data, not the unstructured richness AI needs.

Data fragmentation creates experience fragmentation. AI agents operate in one system, human agents in another, marketing automation in a third, and customer data in a fourth, creating fractured experiences where context evaporates at every handoff.

Conversational memory requires infrastructure where conversations and customer data are unified by design.

What unified conversational memory enables

Organizations treating conversational memory as core infrastructure are seeing clear competitive advantages:

Seamless handoffs: When conversational memory is unified, human agents inherit complete context instantly, eliminating the "let me pull up your account" dead time that signals wasted interactions.

Personalization at scale: While 88% of consumers expect personalized experiences, over half of businesses cite this as a top challenge. When conversational memory is native to communications infrastructure, agents can personalize based on what customers are trying to accomplish right now.

Operational intelligence: Unified conversational memory provides real-time visibility into conversation quality and key performance indicators, with insights feeding back into AI models to improve quality continuously.

Agentic automation: Perhaps most significantly, conversational memory transforms AI from a transactional tool to a genuinely agentic system capable of nuanced decisions, like rebooking a frustrated customer's flight while offering compensation calibrated to their loyalty tier.

The infrastructure imperative

The agentic AI wave is forcing a fundamental re-architecture of how enterprises think about customer data.

The solution isn't iterating on existing CDP or CRM architecture. It's recognizing that conversational memory represents a distinct category requiring real-time capture, millisecond-level access, and preservation of conversational nuance that can only be met when data capabilities are embedded directly into communications infrastructure.

Organizations approaching this as a systems integration challenge will find themselves at a disadvantage against competitors who treat conversational memory as foundational infrastructure. When memory is native to the platform powering every customer touchpoint, context travels with customers across channels, latency disappears, and continuous journeys become operationally feasible.

The enterprises setting the pace aren't those with the most sophisticated AI models. They're the ones that solved the infrastructure problem first, recognizing that agentic AI can't deliver on its promise without a new category of customer data purpose-built for the speed, nuance, and continuity that conversational experiences demand.

Robin Grochol is SVP of Product, Data, Identity & Security at Twilio.

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🔗 Sumber: venturebeat.com