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Artificial Intelligence

The rise of robotaxis in China

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AutoX, Momenta and WeRide took the stage at TC Sessions: Mobility 2021 to discuss the state of robotaxi startups in China and their relationships with local governments in the country.

They also talked about overseas expansion — a common trajectory for China’s top autonomous vehicle startups — and shed light on the challenges and opportunities for foreign AV companies eyeing the massive Chinese market.


Enterprising governments

Worldwide, regulations play a great role in the development of autonomous vehicles. In China, policymaking for autonomous driving is driven from the bottom up rather than a top-down effort by the central government, observed executives from the three Chinese robotaxi startups.

Huan Sun, Europe general manager at Momenta, which is backed by the government of Suzhou, a city near Shanghai, said her company had a “very good experience” working with the municipal governments across multiple cities.

In China, each local government is incentivized to really act like entrepreneurs like us. They are very progressive in developing the local economy… What we feel is that autonomous driving technology can greatly improve and upgrade the [local governments’] economic structure. (Time stamp: 02:56)

Shenzhen, a special economic zone with considerable lawmaking autonomy, is just as progressive in propelling autonomous driving forward, said Jewel Li, chief operation officer at AutoX, which is based in the southern city.

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Source: https://techcrunch.com/2021/06/11/the-rise-of-robotaxis-in-china/

Artificial Intelligence

Ashirase, a Honda incubation, reveals advanced walking assistance system for visually impaired

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Globally, 225 million people are estimated to suffer from moderate or severe visual impairments, and 49.1 million are blind, according to 2020 data from the Investigative Ophthalmology and Visual Science journal. A Japanese startup that was incubated at Honda Motor Company’s business creation program hopes to make navigating the world easier and safer for the visually impaired.

Ashirase, which debuted as the first business venture to come out of Honda’s Ignition program in June, shared details of its in-shoe navigation system for low-vision walkers on Tuesday. The system aims to help users achieve more independence in their daily lives by allowing them to feel which way to walk through in-shoe vibrations connected to a navigation app on a smartphone. Ashirase hopes to begin sales of the system, also named Ashirase, by October 2022.

Honda created Ignition in 2017 to feature original technology, ideas, and designs of Honda associates with the goal of solving social issues and going beyond the existing Honda business. CEO Wataru Chino had previously worked at Honda since 2008 on R&D for EV motor control and automated driving systems. Chino’s background is evident in the navigation system’s technology, which he said is inspired by advanced driver assist and autonomous driving systems.

“The overlap perspective can be, for instance, the way we utilize sensor information,” Chino told TechCrunch. “We use a sensor fusion technology, meaning we can combine information from the different sensors. I have experience in that field myself so that is helpful. Plus there is overlap with automated driving because when we were thinking of safety walking, the automated driving technology had given us an idea for the concept.”

“Ashirase” comes from the Japanese words ashi, meaning “foot,” and shirase, meaning “notification.” As its name suggests, the device, which is attached to the shoe, vibrates to provide navigation based on the route set within an app. Motion sensors, which consist of an accelerometer, gyro sensors and orientation sensors, enable the system to understand how the user is walking.

While en route outside, the system localizes the user based on global navigation satellite positioning information and data based on the user’s foot movement. Ashirase’s app is connected to a range of different map vendors like Google Maps, and Chino said the device can switch to adapt to different information available on different maps. This capability might be helpful if, say, one map had updated information about a road blockage and could send over-the-air updates.

“Going forward, we want to develop the function to generate a map itself using sensors from the outdoor environment, but that’s maybe five years down the line,” Chino said.

The vibrators are aligned with the foot’s nerve layer, so it’s easy to feel the pulse. To indicate the user should walk straight ahead, the vibrator positioned at the front of the shoe vibrates. Vibrators on the left and the right side of the shoe also indicate turning signals for the walker.

Ashirase says this form of intuitive navigation helps the walker attain a more relaxed state of mind rather than one that is constantly alert, leading to a safer walk and less stress for the user.

This also allows the user to have more attention to spare for audible warnings in their environment, like, for example, if they were at a crosswalk, because the device cannot warn the user of obstacles ahead.

“Going forward, we’re thinking about technical updates for users who are totally blind because they don’t have such information like obstacle awareness like low-vision people,” Chino said. “So at this moment, the device is designed for low-vision walkers.”

While indoors, like in a shopping mall, the GPS won’t reach the user, and there isn’t a map for them to localize to. To solve for this, the company says its plan is to use WiFi or Bluetooth-based positioning, connecting to other devices and cell phones within the store, to localize the visually impaired person.

Ashirase is also considering ways to integrate with public transit systems so that the device can alert a user if they have arrived or are near their next stop, according to Chino.

It’s a lot of tech to pack into one little device that attaches to a shoe — any shoe. Chino said the device, which only needs to be charged once a week based on three hours of use per day, is made to be flexible and fit onto different types, shapes and sizes of shoes.

Ashirase intends to release its beta version for testing and data collection in October or November this year and hopes to achieve mass production by October 2022. It’ll have a direct-to-consumer model, the price of which the company is not yet ready to disclose, and a subscription model, which should cost about 2,000 to 3,000 Japanese Yen ($18 to $27) per month.

Chino estimates it’ll take the company 200 million Yen ($1.8 million), including the funds the company has already raised, to make it to market. So far, the company has raised 70 million Yen ($638,000), which came in the form of an equity investor round and some non-equity rounds, according to Chino.

Honda maintains an investor role in the company, supporting and following the business along the way, but Ashirase’s aim is to go public as a standalone company.

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Source: https://techcrunch.com/2021/07/28/ashirase-a-honda-incubation-reveals-advanced-walking-assistance-system-for-visually-impaired/

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AI

iPhone 13 To Introduce a New Feature From Apple Watch

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In his weekly newsletter, Bloomberg journalist Mark Gurman, who often conveys an accurate understanding of Apple’s plans, said the iPhone 13 may have an Apple Watch-inspired always-on mode.

Always-On Mode Feature

The Apple Watch Series 5 and Apple Watch Series 6 have displays that can stay on with low refresh rates and brightness, allowing the user to see their watch even in low light. The same functionality on the iPhone 13 can allow users to see details such as time, date, and notifications at all times.

The always-on iPhone display will be simplified with a larger iPhone 13 battery and an improved display. Previous rumors have suggested that the iPhone 13 will be getting bigger batteries, which could eliminate some of the extra power consumption of the always-on display.

What’s in It for Gamers?

Some iPhone 13 models are also widely expected to incorporate “ProMotion” power updates up to 120Hz, making movements in games appear smooth. This is believed to be facilitated by the use of the OLED LTPO display panel, which can vary in degree of refreshment while using a limited amount of power in order to save battery life.

Pros & Cons of iPhone 13

The device is expected to get heavier and thicker to support advanced displays and larger batteries. But since they will have the always-on feature, users might feel that it can be justified. The always-on display feature could be limited to advanced Pro models that are expected to get the LTPO display technology with ProMotion performance.

Earlier this year, leaker Mark Weinbach said the iPhone 13 will feature an always-on display, although it is important to note that Weinbach does not have a certified record. He said the always-on mode will look like a “toned down lock screen,” where the clock and battery are always visible, and notifications are displayed “with bars and symbols.”

The “Look” Factor

The iPhone 13 is also expected to offer several other enhancements, including an improved performance with the “A15” chip and enhanced camera capabilities, but the design of the iPhone 13 models is expected to be quite similar to the iPhone 12 models.

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Source: https://1reddrop.com/2021/07/28/iphone-13-to-introduce-a-new-feature-from-apple-watch/?utm_source=rss&utm_medium=rss&utm_campaign=iphone-13-to-introduce-a-new-feature-from-apple-watch

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Artificial Intelligence

Financial firms should leverage machine learning to make anomaly detection easier

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Anomaly detection is one of the more difficult and underserved operational areas in the asset-servicing sector of financial institutions. Broadly speaking, a true anomaly is one that deviates from the norm of the expected or the familiar. Anomalies can be the result of incompetence, maliciousness, system errors, accidents or the product of shifts in the underlying structure of day-to-day processes.

For the financial services industry, detecting anomalies is critical, as they may be indicative of illegal activities such as fraud, identity theft, network intrusion, account takeover or money laundering, which may result in undesired outcomes for both the institution and the individual.

There are different ways to address the challenge of anomaly detection, including supervised and unsupervised learning.

Detecting outlier data, or anomalies according to historic data patterns and trends can enrich a financial institution’s operational team by increasing their understanding and preparedness.

The challenge of detecting anomalies

Anomaly detection presents a unique challenge for a variety of reasons. First and foremost, the financial services industry has seen an increase in the volume and complexity of data in recent years. In addition, a large emphasis has been placed on the quality of data, turning it into a way to measure the health of an institution.

To make matters more complicated, anomaly detection requires the prediction of something that has not been seen before or prepared for. The increase in data and the fact that it is constantly changing exacerbates the challenge further.

Leveraging machine learning

There are different ways to address the challenge of anomaly detection, including supervised and unsupervised learning.

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Source: https://techcrunch.com/2021/07/28/financial-firms-should-leverage-machine-learning-to-make-anomaly-detection-easier/

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Artificial Intelligence

Financial firms should leverage machine learning to make anomaly detection easier

Published

on

Anomaly detection is one of the more difficult and underserved operational areas in the asset-servicing sector of financial institutions. Broadly speaking, a true anomaly is one that deviates from the norm of the expected or the familiar. Anomalies can be the result of incompetence, maliciousness, system errors, accidents or the product of shifts in the underlying structure of day-to-day processes.

For the financial services industry, detecting anomalies is critical, as they may be indicative of illegal activities such as fraud, identity theft, network intrusion, account takeover or money laundering, which may result in undesired outcomes for both the institution and the individual.

There are different ways to address the challenge of anomaly detection, including supervised and unsupervised learning.

Detecting outlier data, or anomalies according to historic data patterns and trends can enrich a financial institution’s operational team by increasing their understanding and preparedness.

The challenge of detecting anomalies

Anomaly detection presents a unique challenge for a variety of reasons. First and foremost, the financial services industry has seen an increase in the volume and complexity of data in recent years. In addition, a large emphasis has been placed on the quality of data, turning it into a way to measure the health of an institution.

To make matters more complicated, anomaly detection requires the prediction of something that has not been seen before or prepared for. The increase in data and the fact that it is constantly changing exacerbates the challenge further.

Leveraging machine learning

There are different ways to address the challenge of anomaly detection, including supervised and unsupervised learning.

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://techcrunch.com/2021/07/28/financial-firms-should-leverage-machine-learning-to-make-anomaly-detection-easier/

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