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Category: transportation – Page 6

AI system slashes GPS errors almost 40 times in urban settings

End of the road for GPS? Image-based AI tool cuts navigation errors by nearly 40 times.

Global reliance on GPS has grown to the point where outages or signal interference can have serious consequences. From logistics and delivery fleets to aviation and defense, industries depend on precise navigation.

Yet GPS signals are vulnerable. Tall buildings, tunnels, jamming devices, or even atmospheric conditions can cause disruptions. By providing a GPS-free alternative, PEnG could make navigation more resilient and reliable.

The Surrey researchers argue that their work is as much about everyday convenience as it is about safety.

Ark Invest Blew My Mind: Shocking Tesla Robotaxi Research

Ark Invest forecasts that Tesla’s robotaxi business could reach $10 trillion by 2029, driven by its manufacturing efficiency, data advantage, and strategic positioning in major urban markets ## ## Questions to inspire discussion.

Tesla’s Robotaxi Dominance.

🚗 Q: How significant could Tesla’s robotaxi business become? A: Tesla’s robotaxi business could represent around 90% of its enterprise value by 2029, capturing a substantial share of Ark’s projected $10 trillion global robotaxi market.

🏙️ Q: What’s the potential impact of robotaxis on urban transportation? A: Research suggests 200,000 robotaxis, supplemented by privately owned vehicles in an Airbnb-like model, could meet all of urban Austin’s vehicle miles traveled (VMT) demand, with peak demand requiring 350,000 vehicles.

Manufacturing and Cost Advantages.

🏭 Q: How does Tesla’s manufacturing capability compare to urban demand? A: Tesla’s Austin factory alone could produce more vehicles than urban Austin’s entire ride-hail fleet in approximately 9 days, showcasing its vertically integrated manufacturing advantage.

Continue reading “Ark Invest Blew My Mind: Shocking Tesla Robotaxi Research” | >

Tesla’s NEW Model Y L and FSD 14 Rumors

Questions to inspire discussion.

Autonomy and FSD

🤖 Q: What is the biggest valuation upside for Tesla? A: Tesla’s autonomy roadmap is considered the biggest valuation upside, with the company’s robotaxi plans involving 5-passenger vehicles without a driver seat potentially increasing upside option values for investors.

🚘 Q: How has Tesla’s Full Self-Driving (FSD) system improved? A: FSD has made massive improvements with Version 13, and Version 14 may be the breakthrough moment that pushes Tesla past human-level driving and conquers another three nines of safety.

Future Projections.

📈 Q: What is the expected FSD take rate in the coming years? A: The FSD take rate is projected to increase to 20% in the next few years, with cyber cabs having FSD included, while individual owners may need to opt-in for a while before it becomes standard on all new cars.

Continue reading “Tesla’s NEW Model Y L and FSD 14 Rumors” | >

3D-printed gyroidal solid oxide cells offer lighter, more compact energy solutions

Over the past decades, energy engineers have been developing a wide range of new technologies that could power electronic devices, robots and electric vehicles more efficiently and reliably. These include solid oxide cells (SOCs), electrochemical devices that can operate in two different modes, as fuel cells or as electrolyzers.

Self-powered photodetector achieves 20-fold sensitivity boost using novel device structure

Silicon semiconductors used in existing photodetectors have low light responsivity, and the two-dimensional semiconductor MoS₂ (molybdenum disulfide) is so thin that doping processes to control its electrical properties are difficult, limiting the realization of high-performance photodetectors.

A KAIST research team has overcome this technical limitation and developed the world’s highest-performing self-powered photodetector, which operates without electricity in environments with a light source. This paves the way for precise sensing without batteries in , biosignal monitoring, IoT devices, autonomous vehicles, and robots, as long as a is present.

Professor Kayoung Lee’s research team from the School of Electrical Engineering developed the self-powered photodetector, which demonstrated a sensitivity up to 20 times higher than existing products, marking the highest performance level among comparable technologies reported to date. The work is published in the journal Advanced Functional Materials.

It Feels Like Apple Is Trying To Lose

Apple’s prioritization of shareholder value through massive share buybacks over investing in innovation and R&D may be a strategic misstep that could hinder its future success and allow competitors to gain an edge, particularly in emerging markets like AI

## Questions to inspire discussion.

Innovation and Investment.

🔬 Q: How could Apple’s buyback program have been used differently? A: A: Apple’s $700 billion share buyback over the past decade could have been invested in R&D to develop innovative products like a car, potentially yielding greater long-term value.

🤖 Q: What is Apple’s current stance on AI development? A: Apple’s inaction in AI is notable, with Siri’s performance declining over time, indicating a lack of focus on this crucial technology sector.

Product Development and Market Strategy.

Continue reading “It Feels Like Apple Is Trying To Lose” | >

Theoretical particle physicist tackles machine learning’s black box

From self-driving cars to facial recognition, modern life is growing more dependent on machine learning, a type of artificial intelligence (AI) that learns from datasets without explicit programming.

Despite its omnipresence in society, we’re just beginning to understand the mechanisms driving the technology. In a recent study, Zhengkang (Kevin) Zhang, assistant professor in the University of Utah’s Department of Physics & Astronomy, demonstrated how physicists can play an important role in unraveling its mysteries.

“People used to say is a black box—you input a lot of data and at some point, it reasons and speaks and makes decisions like humans do. It feels like magic because we don’t really know how it works,” said Zhang. “Now that we’re using AI across many critical sectors of society, we have to understand what our machine learning models are really doing—why something works or why something doesn’t work.”

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