MAROKO133 Update ai: NASA Now Letting Mars Rover Drive Autonomously Wajib Baca

📌 MAROKO133 Update ai: NASA Now Letting Mars Rover Drive Autonomously Edisi Jam 12

Think self-driving cars are impressive? Try a self-driving rover on a planet over a hundred million miles away.

On Wednesday, NASA announced that its Mars Perseverance rover had pulled off a stunning feat of endurance and self-sufficiency. Over the course of four hours and 24 minutes, the intrepid little robot managed to traverse 1,350.7 feet, or about a quarter mile. It’s the most any Mars explorer has traveled in a single Martian day — or “sol,” in the lingo — and it did it pretty much on its own.

That’s because the rover is largely autonomous. The human scientists choose its destinations, but the bot’s navigation software charts its actual course. 

“Engineers at [Jet Propulsion Laboratory] meticulously plan each day of the rover’s activities on Mars,” NASA explained in a statement. “But once the rover starts driving, it’s on its own and sometimes has to react to unexpected obstacles in the terrain.”

One navigation tool is Enhanced Autonomous Navigation, or ENav, which can scan for potential obstacles and hazards up to 50 feet ahead of the rover, which is farther in advance than previous Mars robots, and automatically plan around them.

“More than 90 percent of Perseverance’s journey has relied on autonomous driving, making it possible to quickly collect a diverse range of samples,” said Hiro Ono, a JPL autonomy researcher and lead author of a new paper published in IEEE Transactions on Field Robotics describing ENav, said in the NASA statement.

A video captured by the rover’s navigation cameras shows its epic journey, which took place on June 19, 2025. The images were taken every 16 feet for the first third of the journey, and every 3.3 for the final two thirds, according to NASA’s Jet Propulsion Laboratory, and then combined with virtual frames which were created by reconstructing using the rover’s detailed data in a computer environment.

The distance feat comes as the Mars Perseverance rover reaches another milestone. After nearly five years of roaming the Red Planet, it’s now traveled almost 25 miles. With the help of autonomous software, JPL scientists hope that the rover will amble on at least another 37 miles more.

“As humans go to the Moon and even Mars in the future, long-range autonomous driving will become more critical to exploring these worlds,” Ono said.

More Mars: NASA’s Mars Spacecraft Spinning Helplessly After Signal Lost

The post NASA Now Letting Mars Rover Drive Autonomously appeared first on Futurism.

🔗 Sumber: futurism.com

📌 MAROKO133 Hot ai: Gemini 3 Flash arrives with reduced costs and latency — a powe

Enterprises can now harness the power of a large language model that's near that of the state-of-the-art Google’s Gemini 3 Pro, but at a fraction of the cost and with increased speed, thanks to the newly released Gemini 3 Flash.

The model joins the flagship Gemini 3 Pro, Gemini 3 Deep Think, and Gemini Agent, all of which were announced and released last month.

Gemini 3 Flash, now available on Gemini Enterprise, Google Antigravity, Gemini CLI, AI Studio, and on preview in Vertex AI, processes information in near real-time and helps build quick, responsive agentic applications. 

The company said in a blog post that Gemini 3 Flash “builds on the model series that developers and enterprises already love, optimized for high-frequency workflows that demand speed, without sacrificing quality.

The model is also the default for AI Mode on Google Search and the Gemini application. 

Tulsee Doshi, senior director, product management on the Gemini team, said in a separate blog post that the model “demonstrates that speed and scale don’t have to come at the cost of intelligence.”

“Gemini 3 Flash is made for iterative development, offering Gemini 3’s Pro-grade coding performance with low latency — it’s able to reason and solve tasks quickly in high-frequency workflows,” Doshi said. “It strikes an ideal balance for agentic coding, production-ready systems and responsive interactive applications.”

Early adoption by specialized firms proves the model's reliability in high-stakes fields. Harvey, an AI platform for law firms, reported a 7% jump in reasoning on their internal 'BigLaw Bench,' while Resemble AI discovered that Gemini 3 Flash could process complex forensic data for deepfake detection 4x faster than Gemini 2.5 Pro. These aren't just speed gains; they are enabling 'near real-time' workflows that were previously impossible.

More efficient at a lower cost

Enterprise AI builders have become more aware of the cost of running AI models, especially as they try to convince stakeholders to put more budget into agentic workflows that run on expensive models. Organizations have turned to smaller or distilled models, focusing on open models or other research and prompting techniques to help manage bloated AI costs.

For enterprises, the biggest value proposition for Gemini 3 Flash is that it offers the same level of advanced multimodal capabilities, such as complex video analysis and data extraction, as its larger Gemini counterparts, but is far faster and cheaper. 

While Google’s internal materials highlight a 3x speed increase over the 2.5 Pro series, data from independent benchmarking firm Artificial Analysis adds a layer of crucial nuance.

In the latter organization's pre-release testing, Gemini 3 Flash Preview recorded a raw throughput of 218 output tokens per second. This makes it 22% slower than the previous 'non-reasoning' Gemini 2.5 Flash, but it is still significantly faster than rivals including OpenAI's GPT-5.1 high (125 t/s) and DeepSeek V3.2 reasoning (30 t/s).

Most notably, Artificial Analysis crowned Gemini 3 Flash as the new leader in their AA-Omniscience knowledge benchmark, where it achieved the highest knowledge accuracy of any model tested to date. However, this intelligence comes with a 'reasoning tax': the model more than doubles its token usage compared to the 2.5 Flash series when tackling complex indexes.

This high token density is offset by Google's aggressive pricing: when accessing through the Gemini API, Gemini 3 Flash costs $0.50 per 1 million input tokens, compared to $1.25/1M input tokens for Gemini 2.5 Pro, and $3/1M output tokens, compared to $ 10/1 M output tokens for Gemini 2.5 Pro. This allows Gemini 3 Flash to claim the title of the most cost-efficient model for its intelligence tier, despite being one of the most 'talkative' models in terms of raw token volume. Here's how it stacks up to rival LLM offerings:

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🔗 Sumber: venturebeat.com

Author: timuna