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6 Top Air Taxi Companies to Watch

6 Top Air Taxi Companies to Watch. Airbus Vahana Air Taxi, Astro Aerospace, Volocopter 2 x Aircraft, SureFly Air Taxi by Workhorse

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6 Top Air Taxi Companies to Watch

Air taxis are very close to becoming a thing of commercialized local transportation. There are already companies, like Astro Aerospace (OTC: ASDN), for instance, that are working on perfecting the prototypes for aircraft vehicles that should pose as air taxis.

Some top engineers are developing eVTOLs and other aircraft vehicles, while air taxis are already counting billions of dollars in investments, although still in development.

What are some of the top air taxi companies to watch as we wait for local inner-city flights to become a reality? Check out some of the top aircraft companies as we witness the evolution of new air transportation trends.

1. Airbus Vahana Air Taxi

Air Taxi Companies

Airbus Vahana is a company working on eVTOLs, electric vertical takeoff, and landing vehicles, creating an electric VTOL that should be piloted entirely through the process of automation.

Self-piloted eVTOLs can be helpful and cost-effective in the sense of saving on costs for hiring and training eVTOL pilots. Drilling a pilot would include reserving a higher salary for air taxi pilots than a traditional taxi driver’s salary, making using an air taxi unaffordable and probably pricey for everyday transportation.

Software engineers already have experience creating autopilots for large planes, which is how this technology could be adjusted to eVTOL technology and applied to Airbus Vahana models.

2. Astro Aerospace

Astro Aerospace is probably one of the most exciting companies among innovators in air transportation and aircraft vehicles. The company is publicly traded and already has investors for its eVTOL cars.

Astro Aerospace’s vehicles could quickly become the next most popular and the most affordable air taxis that could cut the time and cost needed to get from point A to point B in urban and rural environments alike.

Astro Aerospace already has fully functioning models as they developed a prototype for their aircraft vehicle. One has a carbon fibre shell with 16 independent rotors that take only 30 seconds before takeoff.

Compared to a helicopter that traditionally takes 5 minutes before takeoff, Astro’s eVTOLs are faster and more cost-efficient, with the possibility to become a commercialized air taxi model very soon. One model can operate in two modes – it can be manually piloted or set to auto-flight mode.

Moreover, Astro’s eVTOL prototype is created to fit various use cases, including emergency response and medical response, air taxi and transportation of people, and cargo delivery. Astro Aerospace might be the best air taxi company on the list based on their model features and technology.

3. Volocopter 2 x Aircraft

Volocopter 2 x could quickly become one of the future air taxi models with its exciting look and compact design.
The aircraft was developed and built-in Germany and was sponsored by Intel, which probably adds to the accreditation of technology behind Volocopter.

Although it needs re-tweaking before becoming a commercialized air taxi, this is a fully functional model. Volocopter first took a test flight in June 2018 on CES with Intel sponsorship. Flight time for this model is 30 minutes, and it can travel 17 miles between charge stations.

The model fits two passengers with a sophisticated sensor system that controls the aircraft vehicle’s position that might soon become an urban air taxi. Volocopter took another flight in 2019, this time over Singapore and far from the CES stage. The flight lasted for 2 minutes, while the company is working on improving the model.

4. SureFly Air Taxi by Workhorse

SureFly is an eVTOL model developed and designed by the company Workhorse. The company sold its aviation division to Moog for 4 million dollars, as the company went through some significant losses, losing millions by 2019. SureFly development continues under the Moog company, as the company is working on creating a fully functional model that could pose as an air taxi soon.

The design is supposed to be robust and designed for a self-controlled, commercialized flying experience. In case Moog makes it with developments, the company could prepare an attractive air taxi model for commercialized use for inner-city flights.

5. Terrafugia Transition Hybrid Air Model

Terrafugia Transition is a hybrid model of an aircraft vehicle powered by a gasoline-electric hybrid drivetrain. The model features a turbo mode that can be used for speeding up and reaching super-fast flight mode. The aircraft vehicle is designed to be used on land and in the air as it features retractable wings and wheels.

The company behind this hybrid model initially released a pricing form for the Transition model, listing a value of 280,000$ for this aircraft vehicle. However, the pricing is now kept private, probably until the company prepares a fully functional model used as a commercial air taxi. The model and its technology are now owned by the Chinese company Geely.

6. Joby Aviation Air Taxi

Joby Aviation spent years working on a personal aircraft that could be used as a commercialized air taxi, recently receiving a cash infusion of 100 million dollars from Intel and Toyota.

Joby announced acquiring Uber Elevate in December 2020, a taxi unit by the rideshare giant. Joby created an electric VTOL with a range of 150 miles and can reach the speed of 200 miles per hour with six electric motors powering the aircraft machine.

Joby’s electric VTOL vehicle offers seats for up to five people, including the pilot, making it a great model for an air taxi. The 100 million dollars in cash influx by Intel and Toyota will be spent on perfecting the model to create a commercial air taxi used on long and short inner-city distances.

7. How Far Are We from Commercial Air Taxis?

Even though the idea of air taxis might sound futuristic, companies in the sector of aircraft development have already created functional technology that could power the first air taxis. We might get a chance to take a flight in a commercial air taxi in less than a decade.

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Internet

How do collect and train data for speech projects?

Data collection is the process of gathering, analyzing, and, measuring accurate data from diverse systems to use for business process decision-making, speech projects, and research.

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How do collect and train data for speech projects

With technology evolution, we are moving towards machine learning systems that can understand what we say. In our daily lives, we all have encountered many virtual assistants like Alexa, Siri, and others. These virtual assistants often help us in tuning the lights of our homes, finding information on the internet, and even starting a video conference. But do you know how it does that?

To produce results, these virtual assistants use natural language processing to understand the user’s intent. Natural Language Processing technology enables virtual assistants to understand user intent and produce outcomes. Basically, these virtual assistants are applications of automatic speech recognition and are also known as speech recognition software. This software uses machine learning and NLP to analyze and convert human speech data into text.

But, attaining maximum efficiency of these software requires the collection of substantial speech and audio datasets. The purpose of collecting these audio datasets is to have enough sample recordings that can be fed into automatic speech recognition (ASR) software.

Furthermore, these datasets can be used against the speakers using unspecified speech recognition models. And to make ASR software work as intended, speech data collection and audio datasets must be conducted for all target demographics, locations, languages, dialects, and accents.

Artificial Intelligence can be as intelligent as the data given to it. Hence, collecting data for feeding the machine learning model is a must to maximize the effect of ASR. Let’s discuss steps in speech data collection for effective automatic speech recognition training.

1. Create a Demographic Matrix

For creating a demographic matrix, the enterprise must consider the following information like language, locations, ages, genders, and accents. Along with these, it is a must to note down a variety of information related to environments like busy streets, waiting rooms, offices, and homes. Enterprises can also consider the devices people are using like mobile phones, headsets, and a desktop.

2. Collect and transcribe speech data

To train the speech recognition model, gather speech samples from real humans and take the help of a human transcriptionist to take notes of long and short utterances by following your key demographic matrix. In this way, human is a vital and essential part of building proper audio datasets and labeled speech and further development of applications.

6 Reasons to Transcribe Audio to Text

3. Build a separate test data

Once the text subscription is completed, it’s time to pair the transcribed test with the corresponding audio data and segment them to include one statement in each. Later on, take the segmented pairs and extract a random 20% of the data to form a set for testing.

4. Train the language model

To maximize the effectiveness of the speech recognition model, you can train the language model by adding general additional text that was not additionally recorded. For example in canceling a subscription, you recorded one statement that ‘I want to cancel my subscription, but you can also add texts like “Can I cancel my subscription” or “I want to unsubscribe”. To make it more effective and catchy you can also add expressions and relevant jargon.

5. Measure and Iterate

The last and important step is to evaluate the output of automatic speech recognition software to benchmark its performance. In the next step take the trained model and measure how well it predicts the test set. In case of any gaps and errors, engage your machine learning model in the loop to yield the desired output. 

Conclusion

From travel, transportation, media, and entertainment, the use of speech recognition software is evident. We all have been using voice assistants like Alexa and Siri to complete some of our routine tasks. To effectively use this speech recognition software requires proper training in the audio datasets and the use of relevant data for the machine learning model.

Proper execution and the right use of data make sure the speech recognition software going to work efficiently and enterprises can scale them for further upgrades and development. As data and speech recognition go hand in hand, make sure you are using data with the right approach.

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