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Artificial Intelligence

Artificial Neural Network is Revolutionizing The Future of the Translation Industry



Aliha Tanveer Hacker Noon profile picture

@alihatanveerAliha Tanveer

A technical content writer who loves to pen down her thoughts and share her insights about the latest trends

Do you know that a full-time working translator can translate approximately 520,000 words per year?

There would be no wrong in saying that the translation industry has existed for centuries and will progress in double digits in the upcoming years. Because digital realms continuously push for more shared and globalized experiences, the current worth of the global translation industry is $56.1 billion, and the figure is expected to increase at a swift pace in upcoming years. The number is projected to surpass $70 billion by the year 2023.

It’s been more than 10 years since the launch of Google translate by utilizing phase-based machine translation algorithms. As the technological advancements never dig into the past, AI and ML-powered image recognition and voice recognition capabilities continue to redefine how this globe trades. It is getting even more challenging to enhance machine translation capabilities with every passing day since there is still a long journey to cover.

Presently, AI-powered neural machine translation is very high in demand in the global translation market because it can be directly trained on the source as well as target text and does not require a pipeline of standardized systems like statistical machine learning.

The encoder-decoder attention model architecture in neural machine translation engines arranges sentence-like lengths to be utilized as inputs to the model and does the translation with an accuracy rate of 60% to 90%

Let’s dig deep about how artificial neural networks are reshaping the dimensions of the translation industry.  

How AI is Demolishing the Language Barrier

When it comes to translating speeches and texts, artificial intelligence overcomes one of the ginormous barriers among humans: language.

From Facebook feeds to browsing international pages on Google, cyberspace is surrounded by AI translation. Innovative artificial intelligence algorithms permit instant translation across numerous mediums and are capable of handling an enormous amount of data that needs to be translated. 

Recently Microsoft has deployed its Translator application, which is so efficient that it does translate not just text but also street signs, images, and speech. The biggest breakthrough of Microsoft with this application is that the Translator also works without an internet connection. This application provides tremendous real-world benefits for travelers who travel to areas with limited connectivity to the internet.

Humans vs. Translators

You are completely wrong if you think that human translators will be replaced by NLP-powered translators shortly because that’s not going to happen. Artificial intelligence is still very far away from being capable of doing efficient and accurate multitasking. According to an AI-powered translation platform for professionals, TranslateFX, artificial intelligence will not replace human translators with AI software in the upcoming years. Artificial intelligence will make humans more productive and efficient in their jobs soon.   

AI-powered software can enhance the translation of complex legal documents such as contracts, disclaimers, press releases, confidential agreements, research reports, business plans, prospectuses, licenses, financial reports, corporate announcements, terms, and conditions, enabling businesses to negotiate comprehensively and effortlessly.

To cut a long story short, NLP solutions powered by artificial intelligence will elevate human intelligence into complicated document translation.  

Augmenting Human Intelligence

The accuracy rate of neural machine translation improves with the enhancement of computation power, neural network architecture, and data quality. This practical conversion will attribute human beings to cherish the advantages of technological innovations and focus on what they are good at. Neural machine translation can be deployed for the instant, accurate production of the first draft.

The human brain’s subsequent work will be to augment the translation quality. This involves reviewing or post-editing for content mapping and accuracy of the machine-translated text. 

A vast range of translation tools residing in the market is extremely generic. From restaurant menus to storyboards, from chats to news, those translation tools are trained for the translation and assortment of a wide range of content. Machine translation engines cannot translate text accurately and efficiently without understanding text utilization, targeted audience, and circumstances.  

What Does the Future Hold?

Considering the advent of cutting-edge technology, machine learning engines will provide more customization in the upcoming years to fulfill the requirements of individual industries and enterprises.

Custom machine translation engines will be designed for certain enterprise documents, including brochures, reports, and case studies targeting a particular business audience. Custom machine translation engines can enhance the accuracy rate to the extent of more than 20 percent of the translated text. 

Consistency will be the bone of postulation in the coming time, an ambiguity that is addressed with additional natural language processing along with machine learning algorithms developed as per the context.

Also, augmented human intelligence still has a long journey to cover in the translation industry. Hence the economies will be brought together in the upcoming years with the power of Neuro-Linguistic Programming (NLP) in the translation industry. Also, it is proved from all of the above-mentioned facts that artificial intelligence plays a promising role and will prove beneficial in enhancing the accuracy and efficiency rate of translators more in upcoming years. 

by Aliha Tanveer @alihatanveer. A technical content writer who loves to pen down her thoughts and share her insights about the latest trendsRead my stories


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Why Machine Vision Matters to Your Business



One of the most important kinds of artificial intelligence may be machine vision, also known as computer vision — image processing technology that allows machines to “see” the world like people can.

This tech is already having a major impact on the industry — especially the retail, warehousing, and manufacturing sectors. Any business owner should know about how machine vision may help reshape the economy over the next few years.

What Is Machine Vision and How Does it Work?

At its simplest, machine vision is the use of visual information and artificial intelligence to create algorithms that can process images — breaking them down into identifiable objects, scanning for patterns and looking for important information.

Machine vision technology has existed for decades, but it was rarely used due to the limitations of image processing technology and the high cost of sensors.

Recently, artificial intelligence has made machine vision much more practical.

With an AI-based approach like machine learning, if you have enough visual information — like photographs and recorded video — you can train an algorithm that’s capable of breaking down what a camera sees and picking out distinct, identifiable objects that a machine or robot can use.

For example, a machine vision algorithm trained on information from grocery stores may be able to identify the different products visible in a picture or video feed, as well as objects like shelves, barcodes, displays, customers and floorspace.

One of the better-known applications of machine vision is in self-driving cars. These cars are outfitted with a number of sensors that scan the environment around them — including cameras. Footage from these cameras are processed by a machine-learning algorithm.

This algorithm breaks down the visual data from the cameras into information that the self-driving system can use — like where the road is, the location of other drivers and obstacles the car will have to navigate around.

Fully self-driving cars haven’t hit the market yet — but smart driver assistance systems that use similar tech are starting to become common offerings in high-end vehicles.

The biggest beneficiaries of machine vision, however, are probably companies that can use the tech to streamline business processes.

How AI-Powered Image Processing Is Transforming Business

Across the economy, machine vision is being used in a few different ways.

In retail, machine vision often helps support “smart stores” that use networked sensors and AI to streamline customers’ shopping experience.

These smart stores include the cashierless stores being pioneered by Amazon right now. In these stores, cameras, combined with other sensors like shelf weight sensors and motion detectors, track customers as they move around the store and fill their cart.

Similar tech could also be used to make existing, non-smart stores more intelligent. For example, several companies are experimenting with the use of machine vision to create smart cashierless checkouts in stores that don’t adopt the grab-and-go model.

These could provide a more streamlined alternative to existing self-checkout systems without requiring the same investment that smart stores require.

In manufacturing, machine vision is often used for quality assurance purposes.

For example, you may see a manufacturer use machine vision on a conveyor belt robot that sorts out ideal products from those with obvious defects.

Another algorithm may be used just for color inspection of finished products. Manufacturers sometimes use color inspection for quality assurance processes, using color as a guide to look for chips in paints, defects or errors in components like color-coded wires.

With machine vision, the use of specific lights can help make this process even more effective. By using colored light, rather than pure white light, you can highlight certain colors and help the algorithm to track them.

Manufacturers also use machine vision to support new, self-piloting robots. In factories with warehouses, for example, some manufacturers are using autonomous mobile robots (AMRs) to partially automate picking and packing.

These robots use machine vision like self-driving cars to navigate the factory floor with little or no supervision. They can also use machine vision to read barcodes and identify individual objects, like pallets, allowing them to pick out items to transport around the factory.

How Your Business Can Benefit From Machine Vision

As machine vision becomes more popular, businesses across the economy will be able to benefit from new devices and platforms that use the tech.

A few cutting-edge applications of the tech are already widely available. These may help a number of businesses to automate processes that they couldn’t automate before, or to speed up tedious and difficult labor.

For example, there is a growing number of handwriting analysis and digitization tools on the market that use AI-powered optical character recognition (or OCR). These tools convert scans or photos of handwriting into digital text — reducing the need for transcription and making notes more accessible.

Retailers can benefit from machine vision-powered robots like those used by Walmart for inventory management. These robots move up and down aisles, using cameras to scan for products that need restocking.

Small businesses could also benefit from working with large manufacturers that have adopted the technology. Machine vision can help to reduce costs and improve product quality — for SMBs, this partnership could lower manufacturing expenses and the risk of defective products.

In some cases, it may also be possible to bring this technology in-house to improve quality assurance processes.

The Growing Importance of Machine Vision

AI is likely to become even more important to the business world over the next few years. Tech powered by artificial intelligence, like machine vision, will probably become more sophisticated at the same time.

Right now, businesses can use machine vision in a few different ways — like improving quality control or automating processes like inventory checks. Small businesses without the resources for complex AI-based solutions can also benefit from machine vision through tools like handwriting OCR apps.

Eleanor Hecks is editor-in-chief at Designerly Magazine. She was the creative director at a digital marketing agency before becoming a full-time freelance designer. Eleanor lives in Philly with her husband and pup, Bear.

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Artificial Intelligence

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



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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iPhone 13 To Introduce a New Feature From Apple Watch



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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Artificial Intelligence

Financial firms should leverage machine learning to make anomaly detection easier



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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