MAROKO133 Hot ai: World’s smallest programmable robots think, swim, and sense temperature

📌 MAROKO133 Eksklusif ai: World’s smallest programmable robots think, swim, and se

Robots have just shrunk to the size of microorganisms.

Researchers at the University of Pennsylvania have unveiled what they describe as the world’s smallest fully programmable, autonomous robots, sporting a brain developed at the University of Michigan.

These microscopic swimming machines can sense their surroundings, make decisions, and operate independently for months at a time.

Barely visible to the naked eye, each robot measures about 0.2 by 0.3 by 0.05 millimeters, placing it squarely at the scale of bacteria and single-celled organisms.

Despite their size, the robots can move in complex patterns, respond to temperature changes, and even coordinate their motion in groups.

What makes them especially striking is their cost and longevity.

Each robot costs roughly a penny to make, runs on light, and contains no moving parts, a design choice that makes it remarkably durable despite operating in fluid environments.

Together, the machines represent a long-awaited breakthrough in microscale robotics, a field that has struggled for decades to combine independent motion, sensing, and computing at extremely small sizes.

Robots at microbial scale

For years, electronics have steadily shrunk, but robots have lagged behind. Independent motion at the microscale has been particularly challenging, largely because water behaves very differently at tiny scales.

“We’ve made autonomous robots 10,000 times smaller,” said Marc Miskin, assistant professor in electrical and systems engineering at Penn and senior author of the research.

“That opens up an entirely new scale for programmable robots.”

Operating in water at this scale is less like swimming and more like pushing through thick syrup.

Instead of propellers or joints, the robots use an elegant workaround: they move the surrounding fluid itself.

Rather than pushing against water directly, the robots generate an electric field that nudges ions in the liquid.

Those ions then push on nearby water molecules, creating thrust that moves the robot forward.

This propulsion system has no moving parts, allowing the robots to swim for months and be transferred easily using a micropipette.

They can also travel in coordinated groups, moving together much like schools of fish.

A brain powered by light

The robots’ intelligence comes from ultra-miniaturized computers developed at the University of Michigan.

These tiny processors must run on just 75 nanowatts of power, about 100,000 times less than a smartwatch.

“We saw that Penn Engineering’s propulsion system and our tiny computers were just made for each other,” said David Blaauw, a senior author of the study.

To make this possible, the team had to radically redesign how programs are written and executed at the microscale.

“We had to totally rethink the computer program instructions, condensing what conventionally would require many instructions for propulsion control into a single, special instruction,” Blaauw said.

Most of each robot’s surface is covered by solar cells, which harvest light for power and also double as optical receivers.

Light pulses are used both to power the robots and to program them, with each robot carrying a unique identifier that allows it to receive individualized instructions.

The current generation is equipped with temperature sensors capable of detecting differences within a third of a degree Celsius.

The robots can move toward warmer areas or report temperature changes by wiggling, a behavior likened to the honeybee “waggle dance.”

“This is really just the first chapter,” Miskin said. “We’ve shown that you can put a brain, a sensor and a motor into something almost too small to see, and have it survive and work for months.”

Future versions could carry additional sensors, store more complex programs, or operate in harsher environments, potentially transforming medicine and microscale manufacturing, according to the journal Science Robotics.

🔗 Sumber: interestingengineering.com

📌 MAROKO133 Update ai: Why agentic AI needs a new category of customer data Terbar

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