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Robotics / FLYING CAR LEADERS: ARCHER & JOBY AVIATION

« Last post by Imrul Hasan Tusher on Yesterday at 03:36:36 PM »

FLYING CAR LEADERS: ARCHER & JOBY AVIATION

Flying cars are finally here.

You can call them “eVTOLs” (electric vertical take-off/landing) or “air taxis,” I personally will call them flying cars.

After decades of waiting, the convergence of a few key technologies and factors will enable commercial service to start in 2025:

DEP or direct electric propulsion: special electric motors

Batteries: higher energy density, cheaper batteries, mainly driven by Tesla

Materials: lightweight, strong materials

Sensors: a new generation of sensors

Computation/AI: the ability to integrate all data for safe flights

Regulatory Support: governments are finally ready to license this tech

Regarding this last bullet, in November 2022 the US Federal Aviation Administration (FAA) proposed new rules that help pave the way for commercial air taxi operations by 2025, adding something called “powered-lift” operations to its regulations.

Former acting FAA Administrator Billy Nolen has said this about the future timeline:

“We know that when the Los Angeles Olympics get underway in 2028, air taxis will be in high demand. We may see some of them in the years leading up, but nowhere near the scale in 2028.”

Industry reports suggest the potential for a $30 billion marketplace by 2030.

In today’s blog, we’ll look at the two leading flying car companies: Archer Aviation and Joby Aviation.

Let’s dive in…

Archer Aviation

Last month, I hosted Archer Aviation CEO Adam Goldstein and Chief Commercial Officer Nikhil Goel at the Abundance Summit.

In September of 2021, Archer Aviation went public (via SPAC) for $3.8 billion.

Today, their flying car design called Midnight boasts an impressive performance envelope:

Payload: Pilot + 4 paying passengers + luggage

Propulsion: 12 electric engines supported by 6 independent battery packs

Range: Up to 100 miles

Speed: Up to 150 miles per hour

Altitude: Typically, 1,500 feet (below 5,000 feet)

Charge time: 12-minute charge time between back-to-back 20-mile flights

The year 2023 marked a pivotal milestone in the development of Midnight, as the company conducted its first full-scale, uncrewed, and tethered test flight. This achievement, the result of four years of rigorous flight testing, paved the way for further advancements.

Looking ahead, Archer is poised to conduct an astounding 400 tests of its Midnight aircraft in 2024, a testament to their unwavering dedication to perfecting this groundbreaking technology.

On the regulatory front, Archer has made significant strides. The Federal Aviation Administration (FAA) has recently approved certification plans for Archer's production aircraft, and the company has announced that the first three piloted aircraft are currently under construction. These conforming Midnight aircraft will begin piloted flight testing later this year and will subsequently undergo “for credit” flight testing with the FAA as Archer progresses towards commercialization.

The company has secured an impressive indicative order book of up to 700 aircraft, valued at $3.5 billion, from major players such as United Airlines in the US, InterGlobe in India, and Air Chateau in the United Arab Emirates.

So, when can we expect to see Archer's Midnight overhead?

The company has set an ambitious goal of bringing the Midnight eVTOL to market by 2025. In partnership with Atlantic Aviation, Archer is developing electric aircraft infrastructure at existing assets, including Santa Monica Municipal Airport (SMO). Early launch markets will focus on highly congested cities such as Los Angeles, New York, and Miami, with initial routes connecting airports to city centers. As availability of the Midnight increases, services will expand to other locations across Atlantic's portfolio.

To illustrate the transformative potential of eVTOL technology, consider a trip from Santa Monica to Malibu. While this 12-mile journey could take over an hour by road, an air taxi would cover the distance in a mere five minutes, with each passenger paying roughly $30 to $40—less than the cost of a rideshare vehicle.

Joby Aviation

Founded 14 years ago, Joby was the first serious flying car company, and the first to go public in August of 2021 for $4.5 billion. And, even more impressive, in 2022, Joby distinguished itself as the first eVTOL firm to receive US airworthiness certification—a notable badge of honor in a burgeoning industry.

Among the main investors bolstering Joby's successes are Delta Air Lines and the automotive giant Toyota (which has actively aided the air taxi manufacturer in its plans to erect a factory in Ohio).

Here are the details of Joby’s eVTOL performance:

Payload: Pilot + 4 paying passengers + luggage (total capacity of 1,000 lbs.)
Propulsion: 6 electric dual-wound motors on 6 tilt-prop propellers
Range: Up to 150 miles
Speed: Up to 205 miles per hour
Altitude: Typically, 1,500 feet (below 5,000 feet)
In September 2023, Joby signed a significant contract with the US Air Force, valued at up to $131 million, and delivered its first eVTOL to Edwards Air Force Base in Southern California. This partnership involves collaboration with NASA to research the aircraft's performance in urban environments, providing valuable insights for air taxi development and the FAA.

Joby recently announced plans for a $500 million manufacturing plant in Ohio, set to begin construction in 2024, with the capacity to produce up to 500 aircraft annually.

CEO JoeBen Bevirt envisions Joby's eVTOLs as an integral part of aerial ridesharing networks by 2025. This vision took a significant step forward in November 2023 when Joby conducted the first eVTOL test flights in New York City. Furthermore, in February 2024, the company secured an exclusive six-year deal to operate air taxis in Dubai, with commercial operations expected to begin by early 2026.

The skies above are about to get a lot more interesting.

Why This Matters

Flying cars promise to redefine not just transportation, but also our very perception of accessibility and proximity.

As Archer gears up to manufacture 2,000 Midnight vehicles per year, and Joby exceeds 500 eVTOLs per year, the total production rate of flying cars will rival the production of all other flying aircraft put together.

We are poised to embrace a world where the distant becomes near, the inaccessible becomes reachable, and where time, traditionally lost in transit, is reclaimed.

Another benefit of eVTOLs will be an expansion of human connection and interaction. Communities once isolated by geographical challenges will now become integral parts of urban tapestries.

The age-old dichotomy of urban hustle and rural tranquility may very well converge, creating a harmonious blend of both worlds. As eVTOL technology continues its ascent, we are not just witnessing the evolution of travel; we are partaking in a holistic transformation of human experience.

Source: https://www.diamandis.com/blog/abundance-48-flying-car-leaders-archer-joby

2

Generative AI / AI Coding Is Going From Copilot to Autopilot

« Last post by Imrul Hasan Tusher on Yesterday at 02:52:26 PM »

AI Coding Is Going From Copilot to Autopilot

ANDRIY ONUFRIYENKO/GETTY IMAGES

A new breed of AI-powered coding tools have emerged—and they’re claiming to be more autonomous versions of earlier assistants like GitHub Copilot, Amazon CodeWhisperer, and Tabnine.

One such new entrant, Devin AI, has been dubbed an “AI software engineer” by its maker, applied AI lab Cognition. According to Cognition, Devin can perform all these tasks unassisted: build a website from scratch and deploy it, find and fix bugs in codebases, and even train and fine-tune its own large language model.

Following its launch, open-source alternatives to Devin have cropped up, including Devika and OpenDevin. Meanwhile makers of established assistants have not been standing still. Researchers at Microsoft, GitHub Copilot’s developer, recently uploaded a paper to the arXiv preprint server introducing AutoDev, which uses autonomous AI agents to generate code and test cases, run tests and check the results, and fix bugs within the test cases.

“It’s exciting to see more versions of AI coding assistants with new capabilities,” says Ben Dechrai, a coder and developer advocate at software company Sonar. “They validate the need for generative AI tools in developers’ workflows.”

Dechrai adds that these coding copilots can help software engineers write code faster, allowing them to focus on more strategic and creative tasks. Another advantage of these programming tools is the ability to create a template for code, notes Saurabh Bagchi, a professor of electrical and computer engineering at Purdue University. Much as with prompt engineering, developers must provide these assistants with “the right kind of software requirements to produce a template, and then a software engineer can fill in the gaps,” he says.

These gaps include safety and reliability considerations. Software engineers must look out for security vulnerabilities in AI-generated code, as well as the types of corner cases that could cause it to crash.

“Developers still need to ensure rigorous quality standards are in place when analyzing and reviewing code written with generative AI, just as they would with code developed by a human,” says Dechrai. “AI coding assistants are good at suggesting code, reflecting on the code, and reasoning about its effectiveness, but even then it’s not 100 percent accurate.”

Dechrai cautions that autonomous coders are “still so new that developers are just learning which use cases will be most beneficial.” And they’ll need to be “ironed out in the real world to see how much they’re able to deliver on their promise,” says Bagchi.

AI Coders vs. the Humans
Doom-and-gloom predictions of replacing human software engineers are also bound to follow the emergence of these “AI software engineers,” but that won’t be happening anytime soon. Devin, for instance, resolved only 14 percent of a subset of GitHub issues from real-world code repositories. “There’s still a long way to go for it to become something I can rely on blindfolded,” says Bagchi.

He notes that these autonomous programming tools have another blind spot: the fact that software development happens in collaboration. Coding copilots try to do everything, and they might do it reasonably well. On the other hand, different software engineers have their own specialties—be it front end, back end, full stack, or data, to name a few—and they all work together to build a cohesive product.

“To develop intuitive systems, you need an iterative process with humans in the loop to provide feedback,” Bagchi says. “The fundamental human intuition, depth, and imagination has to be brought to bear.”

That’s why Bagchi believes these unassisted versions won’t be dominating the space that coding assistants hold—at least for now. “The models running underneath are similar in architecture, and as technology continues to evolve, both of them will get better,” he says. “But the Copilot or CodeWhisperer model seems most promising and is better suited to complex software development where humans work with the assistance of AI.”

Yet programmers “should start using these tools if they haven’t already, or they’ll risk getting left behind,” says Dechrai. “If you want to know if an AI coding assistant is truly beneficial, you have to use it yourself, get to know it, and see where it fails.”

Bagchi echoes the sentiment: “Try them out with the use cases you have and stress them with the kinds of software you’re creating.” But because unassisted coding copilots are a nascent technology, they are likely to improve rapidly. “So you have to track them,” he adds.

Moreover, software engineers will have to “consistently ensure code is secure, reliable, and maintainable throughout its life cycle,” Dechrai says. “It will always be up to the developer to properly understand the output and how it was generated.”

Source: https://spectrum.ieee.org/ai-code-generator

3

Robotics / RECOVERING 216 HOURS PER YEAR: TESLA & WAYMO

« Last post by Imrul Hasan Tusher on Yesterday at 02:47:19 PM »

RECOVERING 216 HOURS PER YEAR: TESLA & WAYMO

Autonomous, electric, ride-sharing “car-as-a-service” (using robotaxis) has the potential to be 80% cheaper than individual car ownership. 

The average U.S. commuter currently spends a mind-numbing 52 minutes each day roundtrip, trapped in the confines of their vehicle—for average employees that’s equivalent to recovering 216 hours, or twenty-seven 8-hour days, during your year.

Freed from the task of driving, this time could be transformed into periods of relaxation, productivity, or even leisure. Ever thought of finishing a novel, practicing a new language, or perhaps indulging in some personal passions during your commute? 

The era of the robo-chauffeur promises just that.

In today’s blog, I’ll discuss the top two companies leading this transportation revolution... Tesla & Waymo.

Let’s dive in…

 
Tesla

Tesla has a secret plan is to deliver a global fleet of robotaxis—and I have every confidence Elon will make that happen. What’s his secret weapon? The release of Tesla’s newest iteration of Full Self-Driving or FSD 12, a radical departure from Tesla's previous versions.

Unlike prior systems, FSD 12 wasn't created from meticulously crafted lines of C++ code. Instead, it arose from the technology's ability to learn autonomously, imbibing billions of video frames to mimic human drivers.

A Tesla engineer, Dhaval Shroff, likened it to ChatGPT, stating, "It's like ChatGPT, but for cars."=

The essence here was replicating human learning processes through neural networks, processing massive data volumes to simulate human actions in intricate driving scenarios.

Historically, Tesla's Autopilot used a rules-based approach, a structure where specific situations triggered codified reactions. Lane markings, pedestrian movements, vehicles, signals, each elicited a programmed response. But FSD 12 turned this approach on its head. Instead of following rigid rules, Shroff's "neural network planner" emulated human behavior, learning not from pre-set conditions but from observing human drivers' actions in real-life scenarios.

Central to Tesla’s robotaxi objectives is a singular metric—"miles driven without human intervention.”

Elon's directive was clear: "I want the latest data on miles per intervention to be the starting slide at each of our meetings."

The goal?

Push the limits of the neural network until it surpassed human driving capabilities.

The team's discovery that optimal performance required training on at least 1 million video clips underscored Tesla's unique advantage in this race to full autonomy. And the way in which Tesla’s Autopilot learned is something that surprised even Elon. As he put it, “I mean the really wild thing about the end-to-end training is that it learns to read. It can read signs, but we never taught it to read … we never taught it what a car was or what a person was or a cyclist. It learnt what all those things are, what all the objects are on the road from video, just from watching videos. Just like humans.”

With nearly 2 million Tesla cars globally collecting data daily, the company was uniquely poised to train and constantly improve FSD 12.

As of April 2024, Tesla has rolled out v12.3.3 of FSD, now called FSD (Supervised), to over 5,000 cars. Under the driver’s supervision, FSD vastly improves the car’s autonomous capabilities—from making lane changes and navigating around other cars or objects, to making left and right turns and parallel parking. (As of the writing of this blog, Tesla is already beginning its initial rollout out of FSD (Supervised) v12.3.4.)

I personally love my Model S on FSD... 99.9% of time it gets me all the way to my destination, and it’s only tried to kill me once!

Waymo

Beginning life as Google's internal experiment in 2009, Waymo evolved under Alphabet into a dedicated venture aimed at bridging the vast divide between self-driving vehicles and revenue generating robotaxis.

In its commitment to reshaping the future, Waymo vehicles have traversed over 12 million miles since 2009, both in real-world environments and simulations.

The company's profound objective is to eradicate human errors that account for countless fatalities annually. Leveraging state-of-the-art camera and Lidar laser technology, Waymo's fleet is equipped to visualize the world in incredible detail, irrespective of the hour. This ability to navigate with precision owes much to real-time sensor data fused with intricately detailed custom maps. And with the computational prowess of server-grade GPUs and CPUs, Waymo's onboard systems can process this flood of data, ensuring that passengers experience a journey that's not only safe but efficiently plotted.

In August 2023, Waymo made history in San Francisco, as the company began allowing the public to pay for rides in its driverless cars.

This groundbreaking development saw Waymo's vehicles functioning as true robotaxis, offering a glimpse into a future where the traditional taxi experience is reimagined. With the Waymo One app, booking a ride is as intuitive as hailing an Uber, but what arrives is a gleaming white Jaguar from Waymo's 250-strong fleet. These vehicles, each valued at a staggering $200,000, are outfitted with an array of high-tech sensors and cameras, ensuring an unparalleled level of safety and efficiency.

Yet, despite their technological sophistication, rides remain affordable, with fares ranging from $18 to $21, on par with traditional ride-hailing services.

As Waymo continues to push the boundaries of autonomous transportation, the company's recent expansion into Los Angeles marks a significant milestone.

In March 2024, Waymo secured approval from the California Public Utilities Commission (CPUC) to extend its services to select areas of Los Angeles and the Bay Area. With an initial fleet of fewer than 50 cars, Waymo's operational territory encompasses approximately 63 square miles, stretching from the coastal charm of Santa Monica to the vibrant heart of downtown Los Angeles.

While the service currently excludes airport trips and freeway travel, the demand is palpable, with a waitlist of 50,000 eager Angelenos ready to experience the magic of autonomous rides.

For Waymo, the Los Angeles expansion represents more than just another market—it is a proving ground for the company's vision of the future. With a metropolitan population of 13 million, LA's intricate web of freeways, narrow streets, and unprotected left turns, coupled with its notorious traffic and distracted drivers, poses a formidable challenge.

Yet, the potential rewards are immense, with estimates suggesting that the LA market could generate up to $2 billion in revenue for the company.

Why This Matters

With the rise of autonomous vehicles, the landscape of the auto industry is poised for a seismic shift. 

Currently home to over a hundred brands, the next decade will witness a substantial consolidation in the automotive realm.

Two main forces will drive this: car usage rates and functionality.

Most cars today are utilized less than 5% of their potential, often gathering dust in driveways. With the advent of car-as-a-service, fewer vehicles will serve more people, disrupting the demand-supply chain. 

Additionally, in this new market, brand loyalty will wane. Consumers, attracted by efficiency and cost-effectiveness, will care less about the brand and more about the service.

Hence, a significant reduction in the number of automaker brands is anticipated, challenging giants in Detroit, Germany, and Japan.

Donald Shoup, an esteemed urban planning professor at UCLA, provides insight into another profound implication: the impact on real estate. With a staggering 2 billion parking spots in the U.S., Shoup points out the startling fact that “the area of parking per car in the United States is thus larger than the area of housing per human.”

Furthermore, he reveals the hidden costs of “free parking,” estimating U.S. expenditures between $102 billion to $374 billion—somewhere in the ballpark of the Medicare and national defense budgets. But what if these vast parking spaces become redundant? With autonomous vehicles on-demand, the demand for parking diminishes. Our cities could witness a commercial real estate boom, or perhaps, some of these spaces might metamorphose into thriving community centers or lush green parks.

The future of autonomous vehicles isn't merely about technological progression—it's about reshaping societies, economies, and urban landscapes.

But these aren’t the only vehicles transforming mobility. As we’ll see in the next few blogs, flying cars are finally on their way.


Source: https://www.diamandis.com/blog/abundance-47-dawn-of-the-robo-chauffeur

4

Generative AI / Why small language models are the next big thing in AI

« Last post by Imrul Hasan Tusher on Yesterday at 02:41:16 PM »

Why small language models are the next big thing in AI

In the AI wars, where tech giants have been racing to build ever-larger language models, a surprising new trend is emerging: small is the new big. As progress in large language models (LLMs) shows some signs of plateauing, researchers and developers are increasingly turning their attention to small language models (SLMs). These compact, efficient and highly adaptable AI models are challenging the notion that bigger is always better, promising to change the way we approach AI development.

Are LLMs starting to plateau?

Recent performance comparisons published by Vellum and HuggingFace suggest that the performance gap between LLMs is quickly narrowing. This trend is particularly evident in specific tasks like multi-choice questions, reasoning and math problems, where the performance differences between the top models are minimal. For instance, in multi-choice questions, Claude 3 Opus, GPT-4 and Gemini Ultra all score above 83%, while in reasoning tasks, Claude 3 Opus, GPT-4, and Gemini 1.5 Pro exceed 92% accuracy.

Interestingly, even smaller models like Mixtral 8x7B and Llama 2 – 70B are showing promising results in certain areas, such as reasoning and multi-choice questions, where they outperform some of their larger counterparts. This suggests that the size of the model may not be the sole determining factor in performance and that other aspects like architecture, training data, and fine-tuning techniques could play a significant role.

The latest research papers announcing new LLMs all point in the same direction: “If you just look empirically, the last dozen or so articles that come out, they’re kind of all in the same general territory as GPT-4,” says Gary Marcus, the former head of Uber AI and author of “Rebooting AI,” a book about building trustworthy AI. Marcus spoke with VentureBeat on Thursday.

“Some of them are a little better than GPT-4, but there’s no quantum leap. I think everybody would say that GPT-4 is a quantum step ahead of GPT-3.5. There hasn’t been any [quantum leap] in over a year,” said Marcus.

As the performance gap continues to close and more models demonstrate competitive results, it raises the question of whether LLMs are indeed starting to plateau. If this trend persists, it could have significant implications for the future development and deployment of language models, potentially shifting the focus from simply increasing model size to exploring more efficient and specialized architectures.

Drawbacks of the LLM approach

The LLMs, while undeniably powerful, come with significant drawbacks. Firstly, training LLMs requires an enormous amount of data, requiring billions or even trillions of parameters. This makes the training process extremely resource-intensive, and the computational power and energy consumption required to train and run LLMs are staggering. This leads to high costs, making it difficult for smaller organizations or individuals to engage in core LLM development. At an MIT event last year, OpenAI CEO Sam Altman stated the cost of training GPT-4 was at least $100M.

The complexity of tools and techniques required to work with LLMs also presents a steep learning curve for developers, further limiting accessibility. There is a long cycle time for developers, from training to building and deploying models, which slows down development and experimentation. A recent paper from the University of Cambridge shows companies can spend 90 days or longer deploying a single machine learning (ML) model. 

Another benefit of SLMs is their potential for enhanced privacy and security. With a smaller codebase and simpler architecture, SLMs are easier to audit and less likely to have unintended vulnerabilities. This makes them attractive for applications that handle sensitive data, such as in healthcare or finance, where data breaches could have severe consequences. Additionally, the reduced computational requirements of SLMs make them more feasible to run locally on devices or on-premises servers, rather than relying on cloud infrastructure. This local processing can further improve data security and reduce the risk of exposure during data transfer.

SLMs are also less prone to undetected hallucinations within their specific domain compared to LLMs. SLMs are typically trained on a narrower and more targeted dataset that is specific to their intended domain or application, which helps the model learn the patterns, vocabulary and information that are most relevant to its task. This focus reduces the likelihood of generating irrelevant, unexpected or inconsistent outputs. With fewer parameters and a more streamlined architecture, SLMs are less prone to capturing and amplifying noise or errors in the training data.

Clem Delangue, CEO of the AI startup HuggingFace, suggested that up to 99% of use cases could be addressed using SLMs, and predicted 2024 will be the year of the SLM. HuggingFace, whose platform enables developers to build, train and deploy machine learning models, announced a strategic partnership with Google earlier this year. The companies have subsequently integrated HuggingFace into Google’s Vertex AI, allowing developers to quickly deploy thousands of models through the Google Vertex Model Garden.

After initially forfeiting their advantage in LLMs to OpenAI, Google is aggressively pursuing the SLM opportunity. Back in February, Google introduced Gemma, a new series of small language models designed to be more efficient and user-friendly. Like other SLMs, Gemma models can run on various everyday devices, like smartphones, tablets or laptops, without needing special hardware or extensive optimization.

Since the release of Gemma, the trained models have had more than 400,000 downloads last month on HuggingFace, and already a few exciting projects are emerging. For example, Cerule is a powerful image and language model that combines Gemma 2B with Google’s SigLIP, trained on a massive dataset of images and text. Cerule leverages highly efficient data selection techniques, which suggests it can achieve high performance without requiring an extensive amount of data or computation. This means Cerule might be well-suited for emerging edge computing use cases.

Another example is CodeGemma, a specialized version of Gemma focused on coding and mathematical reasoning.  CodeGemma offers three different models tailored for various coding-related activities, making advanced coding tools more accessible and efficient for developers.

The transformative potential of small language models

As the AI community continues to explore the potential of small language models, the advantages of faster development cycles, improved efficiency, and the ability to tailor models to specific needs become increasingly apparent. SLMs are poised to democratize AI access and drive innovation across industries by enabling cost-effective and targeted solutions. The deployment of SLMs at the edge opens up new possibilities for real-time, personalized, and secure applications in various sectors, such as finance, entertainment, automotive systems, education, e-commerce and healthcare.

By processing data locally and reducing reliance on cloud infrastructure, edge computing with SLMs enables faster response times, improved data privacy, and enhanced user experiences. This decentralized approach to AI has the potential to transform the way businesses and consumers interact with technology, creating more personalized and intuitive experiences in the real world. As LLMs face challenges related to computational resources and potentially hit performance plateaus, the rise of SLMs promises to keep the AI ecosystem evolving at an impressive pace.

Source: https://venturebeat.com/ai/why-small-language-models-are-the-next-big-thing-in-ai/

5

Common Forum/Request/Suggestions / Outstanding Сasual Dating - Actual Women

« Last post by ruhul5710 on April 13, 2024, 08:29:03 AM »

Night butterflies: dating for special occasions
Legitimate Girls
Optimal Сasual Dating

6

Common Forum/Request/Suggestions / Prime Сasual Dating - True Females

« Last post by ankhi739 on April 12, 2024, 11:22:21 PM »

Free relationships, easy and simple
Genuine Females
Premier Сasual Dating

7

Robotics / THE BIRTH OF ROBOTAXIS (CAR-AS-A-SERVICE)

« Last post by Imrul Hasan Tusher on April 11, 2024, 09:54:42 PM »

THE BIRTH OF ROBOTAXIS (CAR-AS-A-SERVICE)

How would your life improve if you never had to drive again? If traveling across town to work or your friend’s home was cheap and fully autonomous, enabled by a robotic chauffeur?

How much time would you save if you weren’t the one behind the wheel? What would you do with those extra hours?

Over the next few blogs in this Age of Abundance series, we’ll discuss an additional category of advanced robotics, namely autonomous vehicles, flying cars or eVTOLs (electric Vertical Take-off or Landing), and delivery robots helping to get people and goods from one point to another.

Fully autonomous vehicles from Tesla and Waymo (to name a few) are on the path to enable “car-as-a-service” fleets (or robotaxis) operating on-demand, Uber-like services.

The cost of ground transportation is slated to decrease between 2x to 4x as a result. Sometime in the near future, your kids or elderly parents will never drive.

A significant percentage of parking garages, driveways, and parking structures will eventually be transformed into alternative usable space. Autonomous cars will take all shapes and sizes and serve as functional “third spaces” used for entertainment, sleeping, or meeting rooms as drive time becomes work or play time.

Meanwhile, aerial ridesharing, eVTOLs, and flying cars will also become fully operational in most major metropolitan cities this next decade.

Where you live and work will begin to transform as these systems shrink travel time and distance. Previously difficult to reach geographies (islands, rural areas, mountain tops) will become accessible.

Individuals seeking the solitude of the country will also have access to the shopping, food, and entertainment of metropolitan city centers, connected through eVTOL technology.

In today’s blog, we’ll go back in time to the early 2000s, when a series of Grand Challenges laid the foundation for today’s autonomous vehicle industry.

Source: https://www.diamandis.com/blog/abundance-46-birth-of-robotaxis

8

Allah: My belief / Re: আল্লাহর জন্য ভালোবাসা

« Last post by Md. Zakaria Khan on April 09, 2024, 11:34:41 AM »

ভাগ্যিস বিজ্ঞান এসব আবিষ্কার করেছিল; নইলে জানাই যেত না যে কোরানে আগেভাগেই এতকিছু বলা আছে!!!
১ – বিজ্ঞান কিছুদিন আগে জেনেছে চাঁদের নিজস্ব কোন আলো নেই। সূরা ফুরক্বানের ৬১ নং আয়াতে কুরআনে এই কথা বলা হয়েছে প্রায় ১৪০০ বছর আগে।
২ – বিজ্ঞান মাত্র দুশো বছর আগে জেনেছে
চন্দ্র এবং সূর্য কক্ষ পথে ভেসে চলে... সূরা
আম্বিয়া ৩৩ নং আয়াতে কুরআনে এই কথা বলা হয়েছে প্রায় ১৪০০ বছর আগে।
৩ – সূরা কিয়ামাহ’র ৩ ও ৪ নং আয়াতে ১৪০০ বছর আগেই জানানো হয়েছে; মানুষের আঙ্গুলের ছাপ দিয়ে মানুষকে আলাদা ভাবে সনাক্ত করা সম্ভব। যা আজ প্রমাণিত।
৪ - ‘ বিগ ব্যাং’ থিওরি আবিষ্কার হয় মাত্র
চল্লিশ বছর আগে। সূরা আম্বিয়া ৩০ নং আয়াতে কুরআনে এই কথা বলা হয়েছে প্রায় ১৪০০ বছর আগে।
৫ – পানি চক্রের কথা বিজ্ঞান জেনেছে বেশি দিন হয় নি... সূরা যুমার ২১ নং আয়াতে কুরআন এই কথা বলেছে প্রায় ১৪০০ বছর আগে।
৬ – বিজ্ঞান এই সেদিন জেনেছে লবণাক্ত পানি ও মিষ্ঠি পানি একসাথে মিশ্রিত হয় না। সূরা ফুরকানের ২৫ নং আয়াতে কুরআন এই কথা বলেছে প্রায় ১৪০০ বছর আগে।
৭ – ইসলাম আমাদেরকে ডান দিকে ফিরে ঘুমাতে উৎসাহিত করেছে; বিজ্ঞান এখন বলছে ডান দিকে ফিরে ঘুমালে হার্ট সব থেকে ভাল থাকে।
৮ – বিজ্ঞান এখন আমাদের জানাচ্ছে পিপীলিকা মৃত দেহ কবর দেয়, এদের বাজার পদ্ধতি আছে। কুরআনের সূরা নামল এর ১৭ ও ১৮ নং আয়াতে এই বিষয়ে ধারণা দেয়।
৯ – ইসলাম মদ পানকে হারাম করেছে , চিকিৎসা বিজ্ঞান বলছে মদ পান লিভারের জন্য ক্ষতিকর।
১০ – ইসলাম শুকরের মাংসকে হারাম করেছে। বিজ্ঞান আজ বলছে শুকরের মাংস লিভার, হার্টের জন্য খুবই ক্ষতিকর।
১১- রক্ত পরিসঞ্চালন এবং দুগ্ধ উৎপাদন এর ব্যাপারে আমাদের চিকিৎসা বিজ্ঞান জেনেছে মাত্র কয়েক বছর আগে। সূরা মুমিনূনের ২১ নং আয়াতে কুরআন এই বিষয়ে বর্ণনা করে গেছে।
১২ - মানুষের জন্ম তত্ব ভ্রুন তত্ব সম্পর্কে
বিজ্ঞান জেনেছে এই কদিন আগে। সূরা আলাকে কুরআন এই বিষয়ে জানিয়ে গেছে ১৪০০ বছর আগে।
১৩ - ভ্রন তত্ব নিয়ে বিজ্ঞান আজ জেনেছে
পুরুষই ( শিশু ছেলে হবে কিনা মেয়ে হবে) তা নির্ধারণ করে। ভাবা জায়... কুরআন এই কথা জানিয়েছে ১৪০০ বছর আগে।
( সূরা নজমের ৪৫, ৪৬ নং আয়াত, সূরা
কিয়ামাহ’র ৩৭- ৩৯ নং আয়াত)
১৪ - একটি শিশু যখন গর্ভে থাকে তখন সে আগে কানে শোনার যোগ্যতা পায় তারপর পায় চোখে দেখার। ভাবা যায়?
১৪০০ বছর আগের এক পৃথিবীতে ভ্রুনের বেড়ে ওঠার স্তর গুলো নিয়ে কুরআন বিস্তর আলোচনা করে। যা আজ প্রমাণিত !
( সূরা সাজদাহ আয়াত নং ৯ , ৭৬ এবং সূরা ইনসান আয়াত নং ২ )
১৫ – পৃথিবী দেখতে কেমন? এক সময় মানুষ মনে করত পৃথিবী লম্বাটে, কেউ ভাবত পৃথিবী চ্যাপ্টা, সমান্তরাল... কোরআন ১৪০০ বছর আগে জানিয়ে
গেছে পৃথিবী দেখতে অনেকটা উট পাখির ডিমের মত গোলাকার।
১৬ – পৃথিবীতে রাত এবং দিন বাড়া এবং কমার রহস্য মানুষ জেনেছে দুশ বছর আগে। সূরা লুকমানের ২৯ নং আয়াতে কুরআন এই কথা জানিয়ে গেছে প্রায় দেড় হাজার বছর আগে !!
......আমাদের সমস্যা হল আমরা সব কিছুই জানি... যারা নাস্তিক তারাও জানে... পার্থক্য টা হল ' বোধ' যেমন ধরুন একজন নেশাকর জানে যে নেশা করলেই তার জীবন নষ্ট হয়ে যাবে, যে ছেলে বাবা কে খুন
করেছে সে জানে যে এই মানুষটি তাকে জন্ম
দিয়েছে... সব জেনে শুনেই আমরা সব থেকে খারাপ কাজ গুলো করি... ব্যাপারটা অজ্ঞানতার না ব্যাপারটা ' বোধ' এর। আল্লাহ্ বলেন.. ঐ ব্যাক্তির কথার চেয়ে কার কথা উওম যে নিজে সৎকর্ম করে এবং অন্যকে সৎকর্মের জন্য আহবান করে।____ সূরা হা-মিম সিজদাহ্--(৩৩)......। নবী সা.বলেন. আমার পরে সবচেয়ে বড় দানশীল ব্যাক্তি তিনি,যে কোন বিষয়ে জানলো এবং অন্যকে তা জানালো.....( বায়হাকী)(সংগৃহীত)

9

Solar / Solar Energy Landscape of Bangladesh

« Last post by Imrul Hasan Tusher on April 06, 2024, 03:43:21 PM »

Solar Energy Landscape of Bangladesh

Bangladesh is currently experiencing a crisis as a result of both internal and international issues. Fossil fuels like natural gas and oil are used to create around 85% of the country’s power, with natural gas serving as the main fuel source. OPEC+ oil production cutbacks, the European Union’s embargo on Russian crude oil, and Russia’s energy war have all contributed to an exponential spike in oil and gas prices, which has led to unaffordable energy import costs and soaring inflation.

Bangladesh was compelled to cease buying gas and shut down many diesel-powered power facilities as a result. The lack of renewable energy sources in the country and measures like capacity payments have also made the situation worse. However, regardless of the low share of renewable energy in the country’s power mix, there lies immense potential for solar power electricity generation.

The country has begun focusing on the integration of renewable energy following the Paris Agreement. Numerous infrastructural projects are being developed that include mega projects and industrial zones. These industrial zones and parks are planned to be eco-friendly with the inclusion of solar energy and wastewater management. Additionally, organizations will also receive government incentives for using clean energy sources such as biomass, wind, and solar.

Power Sector Overview of Bangladesh

As of 2022, the country’s total power generation capacity (on-grid and captive sources) is 25.5 GW, and it has grown significantly over the past 5 years, and this has been a major cause of the increase in GDP.

The country has also achieved a 98% electrification rate in the year 2021, bringing the majority of the population under electrification. The on-grid maximum energy generated was 14.78 GW in FY 2021-22, while the on-grid generation capacity was 22.07 GW.

Natural gas has remained the primary and cheapest source of energy over the last decade. However, a reduction in the number of explorations and growing demand has depleted gas reserves. This has increased dependency on imported LNG and Heavy Fuel Oil (HFO).

The recent Russia-Ukraine conflict has increased the price of LNG by nearly 165%. The current fuel mix of Bangladesh’s power plants heavily depends on natural gas, oil,  coal, and diesel (jointly >80%). Because of the current energy prices in combination with the expected depletion of natural gas resources within 10 years, the GoB plans to reduce dependence on domestic natural gas. This is done by increasing the use of imported liquified natural gas (LNG), importing more electricity from neighboring countries, and expanding the use of renewable resources, especially solar power.

Country’s Active Focus Towards Renewable Energy

Bangladesh has pledged to the Climate Vulnerable Forum to generate 40% of electricity from renewable sources by 2041. This would result in a 16 GW RE capacity (target of 30%) in 2031 and a 40 GW RE capacity (target of 40%) in 2041. At present, 3.7% of the total energy mix is contributed by renewable sources, of which approximately 75% (or 2.8% of the total energy mix) is contributed by solar energy.

Other renewable energy sources, such as hydro and wind, have shown limited growth due to geographical limitations. As a result, the prospects of wind and hydropower are relatively low. In contrast, the potential for photovoltaic (PV) is much higher and consistent throughout the entire country. 

The Government of Bangladesh had envisioned that renewable energy would contribute to 10% of the total amount of energy produced by 2020. Yet, the present amount is only 3.7%. Hence, the 40% target has been reinstated till 2041. However, as per industry experts, the aspirations seem to be optimistic.

Nevertheless, the state prioritizes utilizing power generated from renewable over non-renewable sources. According to government officials, multiple solar projects with a combined capacity of over 3 GW are scheduled for the upcoming years.

High Prospects for Solar Energy in the Rooftop Solar Segment

The government has planned to prioritize solar energy in the long run. Difficulties in attaining land for solar parks and solar grid facilities have shifted the focus towards rooftop systems with net metering systems and DRE solutions.

Floating solar is given priority considering the enormous amount of water available. However, at present, floating solar is still in a nascent state as enough research has not been done on its prospects.

According to the SREDA, the split between off-grid and on-grid solar energy solutions is nearly 50% – 50%. The recent growth in off-grid solutions can be attributed to solar home systems, solar irrigation, and rooftop solar systems without net metering.

The overall renewable energy landscape is divided between small-scale projects and large-scale projects. But most capacity resides in large-scale solar projects, including solar-powered rooftops, irrigation, mini-grids, microgrids, nano-grids, and solar charging stations. Most small-scale renewable energy projects consist of solar-powered home systems and streetlights.

At present, 3 kinds of solar projects are taking place in the country: captive solar rooftops, solar IPPs, and solar home systems. Out of these, captive solar rooftops have the highest prospect among the rest as industrial players are reducing their reliance on diesel and gas due to the instability of prices.

Solar IPPs are being implemented as well. However, the scarcity of a prominent-sized landmass is hindering the growth of this avenue. Lastly, solar home systems experienced significant growth in the last 5 years due to IDCOL’s SHS program. However, with the current electrification rate being 98%, the rural population got access to electricity, and this caused slow growth in the sector.

The current market is dominated by Chinese-manufactured solar equipment. Local EPC contractors act as official dealers of the equipment and provide end-to-end turnkey project execution that includes both CAPEX and OPEX models. Demand for European products persists. However, unsatisfactory after-sales service has hampered the demand for the products.

Persisting Bottlenecks in the Solar Sector

The country’s aspirational goal of achieving the 30% renewable energy target is optimistic, given the current status quo. Additionally, the renewable energy policy was released in 2008 and is considered to be dated.

Even though the net metering policy was initiated in 2018, it only focused on the ability to transfer excess energy to the grid in return for a credit on the bill. The lack of specific policies for different projects has led to lower sector-wise adoption.

Strong mandates have to be set with proper incentives in order to increase the usage of renewable energy sources. In 2010, the government had set a mandate for all residential and commercial buildings to install solar panels in order to attain electricity connection. However, adopters have installed low-quality panels due to low awareness of their benefits. As a result, the majority of buildings now have unusable solar panels that are not properly inspected by the governing bodies.

The current grid connectivity does not allow DRE solutions (irrigation, water purifiers, etc.) to entail under the net-metering guideline. Additionally, multifold decades-old grid infrastructure does not properly support the variable renewable energy. Along with it, the last mile population does not have grid access due to geographical positioning, for which they rely on captive power plants that cannot benefit from the net metering policy.

On the other hand, domestic manufacturers have failed to sustain themselves in the market due to lower economies of scale and high demand for international products. The government has supported the domestic players by increasing taxes on imported panels. However, the tax structure remained the same even after domestic players left the market, causing increased market prices of solar panels.

Investing in solar solutions requires heavy financing that the majority of the banks fail to provide even after the central bank’s mandate to disburse 5% of their total loans as “green loans”.

Only Infrastructure Development Co. Ltd., a government-owned NBFI, has sufficient resources to understand this artificial income stream and to analyze the feasibility of the project before approving the loan. However, most organizations lack this capacity. As a result, it is difficult for companies to attain these loans.

Opportunities to Explore

As the growing RMG sector is following compliant criteria to capture more market share, demand for solar rooftops has been growing. Most of the apparel industry players use captive power plants to generate electricity. However, the current rise in fuel prices has opted them to shift towards renewable solutions. This growing demand will help Dutch players to capitalize on their advanced technology.

Furthermore, solar energy could be integrated into cold chain storage of perishable products (eg. agri-food, pharmaceuticals) and water desalination. Dutch R&D of high-tech solar panels with high productivity/yield can also be integrated into greenhouses to foster sustainable protected cultivation in Bangladesh.

Solar can also be integrated into the aquaculture sector, by fostering the development of large solar-powered fish farms and the development of floating solar parks combined with the sustainable growth of fish and other water species.

Since solar energy solutions are expensive, there is room for the OPEX model to grow and reduce dependency on CAPEX models.

Removing the financial burden on implementers will help them easily install solar energy. Awareness among smaller and medium-sized players regarding the potential business case and benefits of solar installation can be explored. This will reduce the dependency on low-quality PV panels, inverters, and transformers with high-quality equipment, leading to increased yields.

Lastly, the technical knowledge of financial institutions can be improved to allow more companies to have access to finance. The current refinancing scheme should increase the minimum loan amount of BDT 100 million for a net-metered rooftop system, as most of the industrial players are in need of more funds.

Additionally, the 5% ‘green loans’ target set by the central bank should be stricken through the penalization of banks for not meeting the set target. 

Source: https://www.lightcastlebd.com/insights/2023/03/solar-energy-landscape-of-bangladesh/

10

Employability Skills / Our Skilled and Leadership Competitions are Cutting Edge

« Last post by Imrul Hasan Tusher on April 06, 2024, 03:25:21 PM »

Our Skilled and Leadership Competitions are Cutting Edge

Overview

The competitions of the SkillsUSA Championships are created and judged by industry to ensure that students are learning the real-world skills employers demand from entry-level professionals. Learn more about our competitive events below. These competitions represent some of the most highly skilled, in-demand skilled trade areas in the nation.

For more details, please visit the following link: https://www.skillsusa.org/competitions/skillsusa-championships/categories-and-descriptions/

Source: https://www.skillsusa.org/competitions/skillsusa-championships/categories-and-descriptions/