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Lyft sees profit through pandemic

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Ride-sharing company Lyft is seeing sustained profit through the pandemic.

Lyft says they’ve been able to positively surprise Wall Street through cost-cutting.

Demand has also rebounded as lockdowns start to ease worldwide.

The company now expected to deliver dependable profit on an adjusted basis beginning in the third quarter.

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Nvidia’s remarkable growth sparks AI-space race

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In the world of artificial intelligence, Nvidia has emerged as a titan, its semiconductor designs becoming indispensable for an array of tech companies.

Led by the visionary Jensen Huang, Nvidia’s stunning ascent has propelled it to a valuation of $2 trillion, marking a milestone in the tech universe.

Yet, this meteoric rise has also made Nvidia a prime target, with competitors and even its own customers vying to chip away at its dominance.

Huang, known for his philosophy of living in the present, has steered Nvidia through a transformative journey.

From its humble beginnings over three decades ago to its current stature as an AI powerhouse, Huang’s leadership has been instrumental.

Nvidia’s chips, originally tailored for computer graphics, found a new purpose in training AI systems, becoming indispensable for tech giants like Microsoft and Tesla.

However, Nvidia’s dominance has not gone unchallenged.

Competition rises

Rivals like Intel and Advanced Micro Devices have ramped up their AI chip offerings, while tech behemoths such as Amazon, Google, and Microsoft are developing their own in-house designs.

The announcement of a deal between Microsoft and Intel to produce custom chips underscores the intensifying competition in the AI chip market.

Despite Nvidia’s formidable position, Huang remains vigilant about securing the company’s future.

He is exploring opportunities in other industries, investing in startups leveraging Nvidia’s technology, and advocating for governments to build their own AI infrastructure. Embracing a startup-like culture, Huang fosters an environment where innovation thrives, constantly pushing the boundaries of what’s possible.

Nvidia’s journey reflects the dynamism of the AI landscape. As the company continues to shape the future of technology, its resilience in the face of mounting competition reaffirms its status as a pioneering force in the world of AI.

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Who will be the winners and losers of EV adoption?

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The Australian Government has proposed the first ever fuel efficiency standard & sets regulations for Electric Vehicles.

The proposed standards will impact both traditional internal combustion engine vehicles and electric vehicles, encouraging a comprehensive shift towards eco-friendly transportation.

Electric vehicle manufacturers, such as Tesla, Nissan, and Hyundai, are likely to benefit from increased consumer interest in eco-friendly options.

Traditional automakers that have invested in hybrid and electric technologies, such as Toyota and Ford, may also find themselves on the winning side.

However, those lagging behind in the development of fuel-efficient vehicles may face a more challenging road ahead.

To discuss in detail, Matt Hobbs, CEO at Motor Trades Association of Australia, provides his insights into the realm of EVs and Government regulations. #ev #cars #tech

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Tech

Mass production for Nvidia’s AI semiconductors begins

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Micron commences mass production of memory chips for Nvidia’s AI semiconductors.

Micron, a leading semiconductor manufacturer, has officially initiated the mass production of cutting-edge memory chips intended for use in Nvidia’s advanced AI semiconductors.

 

Micron x Nvidia

This strategic move marks a significant milestone in the collaboration between the two tech giants, as they aim to meet the soaring demand for high-performance components in the rapidly evolving artificial intelligence landscape.

Data duo

Micron’s commitment to delivering top-notch memory solutions aligns seamlessly with Nvidia’s pursuit of pushing the boundaries of AI innovation.

These chips are expected to play a pivotal role in accelerating the processing speed and overall efficiency of AI applications, ranging from deep learning to data analytics.

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