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  Navigating everyday routines can be difficult for those with physical limitations. However, breakthroughs in artificial intelligence (AI), offer enormous promise to make life simpler for persons with disabilities and improve their quality of life. People with disabilities can obtain the assistance they require to live more autonomous and satisfying lives by using the appropriate data sets and AI algorithms. AI has the potential to profoundly and meaningfully alter the lives of individuals with disabilities, from smart homes that react to their unique requirements to assistive technology that simplifies daily activities. By investigating these fascinating breakthroughs in AI and their applicability to people with disabilities, we may obtain a better grasp of this powerful technology’s boundless potential.

How AI may benefit those with disabilities?

Let’s look at some practical AI applications in this area and see how they might help people with impairments live better lives in many ways.

Human interaction and communication

The way that people with disabilities engage and communicate with others has been completely transformed by artificial intelligence. People with impairments are now more able to access information by speaking to their devices thanks to voice-assisted technologies like Google Home, Amazon’s Alexa, and the Echo.

For the benefit of those with speech impairments, text-to-speech, and speech-to-text technologies have also been created. For instance, Voiceitt employs AI to gradually learn a speaker’s pronunciation to transform their words into clear, regular speech, while Google’s Parrotron can convert stuttering speech patterns into fluent speech.

AI-powered solutions like GnoSys have been developed to instantly convert hand gestures or sign language into text and voice for those who have hearing impairments. Additionally, Google’s DeepMind has created an AI system that uses lip-reading algorithms to precisely decode full words.

The lives of those who have poor vision have been significantly impacted by AI. By identifying faces, emotions, and language, Microsoft’s Seeing AI, for instance, employs picture recognition technology to communicate the world to vision-impaired people. Another technology that gives voice-activated visual information and increases the independence of blind and visually impaired persons is the OrCam. This gadget can detect objects, recognize people, read aloud messages, and more.

     Enabling Independent Living with Smart Home Technology for People with Disabilities

Smart home technology that is driven by artificial intelligence gives people with disabilities several advantages and can significantly improve their freedom and quality of life. These solutions, which range from voice-controlled gadgets like Amazon Alexa to smart doorbells and home automation systems, make it easier for individuals with disabilities to manage their homes and do daily activities. For instance, smart doorbells provide remote door entry and monitoring, while Alexa provides hands-free voice control for music playback, reminders, and real-time information. Smart lights, curtains, garage openers, and thermostats are other useful smart home appliances that can all be operated from a smartphone for enhanced convenience.

By tackling the physical and cognitive difficulties that people with disabilities face daily, artificial intelligence has the potential to significantly enhance their quality of life. The AI for Accessibility program from Microsoft aims to create tools that will help people with impairments be more productive, independent, and included in society. Additionally, for those who would otherwise be housebound, self-driving automobiles and other autonomous vehicles powered by AI can provide an unmatched level of freedom of mobility. This will remove physical obstacles, encourage a social lifestyle, and increase accessibility for everybody while being customized to meet the specific requirements and capabilities of each person.

    Artificial intelligence has the potential to improve the lives of people with disabilities by assisting them with daily tasks and assisting them in the development of new skills. The incorporation of AI technology is improving the lives of people with disabilities by increasing accessibility, creating chances for social inclusion, and paving the way for independent life. With the continuous development of AI, it will be feasible to develop more creative solutions that solve the particular problems experienced by those with disabilities and raise the degree of participation in society.

Sources :

Can AI Help People With Disabilities? – Kindersley Social – Local News, Writers, Sports, Events, and more

How AI Can Improve the Lives of People with Disabilities | SmartClick

Reading Time: 4 minutes

   In video conferencing technology, clear audio and visual quality are essential for various streaming use cases such as vlogging, tubing, webcasting, and remote work to increase the sense of presence and pick up on both verbal and nonverbal cues.

Eye contact plays a vital role in social interactions and face-to-face conversations. It signifies confidence, connection, and attention. However, maintaining eye contact is not always possible in video conferencing scenarios. It requires users to look directly into the camera instead of the computer screen, which can be challenging when reading off a script or reviewing data on the computer. Additionally, maintaining eye contact can be difficult for various physiological reasons, and many children and adults find it challenging to make and maintain eye contact.

    Fortunately, a solution to this problem has been discovered. Nvidia has created a new feature called Maxine Eye Contact that enhances the user experience during video chats and webcasts by making the user appear to be looking directly into the camera, even if they are gazing at their notes or through the window.  The Eye Contact effect is a new feature that uses technology to make it appear as though the person speaking is making direct eye contact with the camera. This is done by aligning the gaze and keeping the natural color and blinking of the eyes. Additionally, there is a disconnect feature that allows for a smooth transition back to the person’s real eyes if they look away from the camera.

Understanding the Functionality of Creating an Eye Contact Pipeline

The process of creating an eye contact pipeline using NVIDIA Maxine involves using the Face tracking feature to identify and analyze the region around the eyes, known as the “eye patch.” This is done by aligning the face, extracting the eye patch, inputting it into a specialized network that separates encoding and decoding stages to adjust the gaze direction to make the face appear to be looking forward, and then blending it back into the original video frame. The output includes head position, gaze angles, and an image with the corrected gaze direction. The pipeline can also be used to simply estimate gaze direction without making any adjustments.                                           

The Maxine Eye Contact Model from NVIDIA

The NVIDIA Maxine Eye Contact model architecture uses a transformer-based encoder and decoder structure to adjust the gaze in an image. It separates the image into different factors such as lighting, face shape, and gaze direction, predicts the rotation angles for each of these factors, applies them to the image, and then produces the final redirected eye image.

Maintaining Eye Color During Gaze Redirection

 NVIDIA’s eye contact network uses multiple loss functions, such as reconstruction loss, functional loss, and disentanglement loss, to ensure accurate gaze redirection while preserving eye color. The network is trained on a diverse dataset, including synthetic images, to maintain a wide range of eye colors in the generated images. The reconstruction loss function compares the generated image to the target image, functional loss prioritizes task-relevant inconsistencies such as mismatch in iris positions, and disentanglement loss encourages the separation of environmental and physical factors to avoid altering other factors in the redirected image.

Creating a functioning range

The input to the eye contact network is a scale-normalized eye patch. It has been found that the network can perform reliable and natural gaze redirection within a 20-degree pitch and yaw angle cone, which is considered the recommended working range for the feature.

Addressing Transitional Drop-Off in Gaze Redirection

    To address the issue of sudden shifts in the iris during fast eye movements, NVIDIA has implemented a transition region in our gaze redirection feature. This allows for a smooth transition between the camera angle and the actual gaze angle, by gradually reducing the redirection as the angle gets closer to the estimated gaze angle. The transition is designed to mimic the typical motion of human eyes and the speed of the transition is set accordingly.

Handling the problem of invisibility of the eyes

 NVIDIA’s eye contact pipeline can handle instances where a person’s eyes are not visible, such as when they are blinking or obscured by movement or objects. The algorithm can detect and maintain eye blinks and also deactivates the gaze adjustment effect when an occlusion is detected, as indicated by low confidence in the facial landmark estimation.

Optimizing performance

The pipeline uses TensorRT to accelerate performance on GPU, allowing for real-time inference on NVIDIA GPUs with minimal latency per frame. It has been optimized for performance and can handle multiple stream instances simultaneously, making it suitable for data center use cases as well as NVIDIA RTX desktops and laptops.

    In conclusion, the NVIDIA deepfake eye contact effect is a powerful technology that can greatly enhance the realism of deepfake videos. By using a neural network to map the gaze of the subject in the original video to the deep fake, the eyes of the deepfake subject appear more natural and lifelike. This technology has the potential to be used in a variety of applications, such as virtual reality, film, and video conferencing. However, it is important to consider the potential ethical implications of deepfake technology, particularly regarding its use in creating fake videos for malicious or deceitful purposes. Overall, the NVIDIA deepfake eye contact effect is an exciting advancement in the field of deepfake technology, but it must be used responsibly.

sources :

Improve Human Connection in Video Conferences with NVIDIA Maxine Eye Contact | NVIDIA Technical Blog

Nvidia introduces deepfake eye contact effect | Cybernews

NVIDIA Broadcast 1.4 Adds Eye Contact and Vignette Effects With Virtual Background Enhancements | GeForce News | NVIDIA

Reading Time: 4 minutes

        As technology advances, the use of artificial intelligence (AI) in the field of dance choreography is becoming more widespread. Researchers are exploring ways to harness the power of AI to create new and unique choreography, and even to preserve the legacy of choreography after an artist has passed away. One way in which AI is being used in dance choreography is through the development of deep learning-based models that can autonomously compose varied dance motions that match rhythm and style. Additionally, AI is being used to create videos featuring individuals without formal dance training, as well as robots, performing the choreography of popular musicians and spinning and twirling like ballerinas. The integration of AI in dance is an exciting development that has the potential to bring new possibilities and innovations to the dance industry.

              Step-by-step instructions on how AI generates choreographies

1. Data collection: The first step in creating AI-generated choreography is to collect data on the movements of dancers. This data can be collected using motion-capture technology, which records the movement of dancers using sensors placed on their bodies.

• Data analysis: Once the data is collected, it is analyzed to identify patterns and trends in the dancers’ movements. This analysis can be done using machine learning algorithms that can identify patterns and relationships in the data.

• Generating choreography: Based on the analysis of the data, the AI system generates new choreography. This can be done by using the patterns and relationships identified in the data to create new movement sequences that match the rhythm and style of the music.

• Testing and refining: The AI-generated choreography is then tested and refined by dancers. Feedback from the dancers is used to make adjustments to the choreography to ensure that it is performable and effective.

• Finalizing: Once the choreography is finalized, it is ready to be performed by dancers. The AI system can also be used to generate new choreography in real time, which allows for the creation of unique and dynamic performances that can adapt to the music and the dancers.

• Continuously learning: The AI system can continuously learn from the performance and dancers’ feedback, allowing it to improve and generate new choreography that is more complex and creative.

Projects relating to AI-based choreography :

•  Georgia Institute of Technology’s LuminAl project

This project is an interactive art installation in which individuals have the opportunity to collaborate with an AI-based virtual dance partner in movement improvisation. The AI virtual dancer observes the human partner’s movement through the use of movement theory and adapts its responses by drawing from past interactions with humans. The goal of the project is to create an engaging and dynamic experience through the collaboration between humans and AI in movement improvisation.

• Rhizomatiks with Kyle McDonald

Rhizomatiks, a Japanese company, teamed up with media artist Kyle McDonald to create an AI dancer, called “Discrete Figures,” which uses a neural network to generate its dance moves. The network was trained by recording the movements of real dancers improvising to a click track and was able to generate humanoid forms. This technology has potential applications as a tool for choreographers and in fields such as rhythmic gymnastics, theatre, skating, and artistry. It’s now possible to envision using an AI choreographer to help achieve perfect performances.

• Lifeforms

Merce Cunningham, a pioneer in the use of technology in the arts, developed the choreographic software “Lifeforms” in the late 1980s. Known for his dedication to experimentation and innovation, he was a leader in using technology to generate movements that exceeded the limits of the human body and mind. As he faced physical limitations due to his age, Lifeforms provided an alternative way for him to create movement and pass it on to his dancers.

   To summarise, the incorporation of Artificial Intelligence (AI) in choreography has the potential to revolutionize the dance business. Through motion capture technology and machine learning, AI can evaluate and develop new choreography, opening up a whole new world of possibilities for choreographers and dancers. The application of artificial intelligence in choreography enables the production of physically challenging or impossible routines, as well as the discovery of new dance genres and moves.

    Furthermore, AI can improve existing choreography by evaluating the motions of the dancers and the reactions of the audience. Choreographers may now produce more interesting and effective performances.

    However, while employing AI in choreography, it is critical to consider the inherent obstacles and ethical considerations. Some of the ethical concerns that must be addressed are the need for proper training and supervision of AI systems to ensure they are used responsibly and ethically, the potential for bias and discrimination as AI systems may replicate and amplify existing societal biases, and the potential job loss for choreographers and dancers.

Sources :

AI dancing: An intelligent synthesis of Choreography and Technology | AIWS (aiworldschool.com)

The Influence of Artificial Intelligence in Dance Choreography (kenyon.edu)

Living Archive: Creating Choreography with Artificial Intelligence — Google Arts & Culture

Reading Time: 4 minutes

    Artificial Intelligence is continuously growing in every aspect of our daily lives. AI powers and utility astound us. Rapidly improving in many human areas has been affecting almost every element of business and society.

But what about the most important thing? How might technology be used to save people’s lives in times of crisis and become our superhero?

 WATER RESCUE TECHNOLOGY 

IP67 waterproof-rated drone

The first example of tech that could rescue humans are AI-enabled drones. 

One of the drone’s prototypes of RLSS is being trialed to help sea swimmers in trouble and protect them from drowning. They are made to be waterproof which enables them not only to float on the water but they can also to fly at high wind speeds and release buoyancy aid by payload release mechanisms. This new invention can be more effective than lifeguards due to wider visibility and easier access to further, uncharted waters.

AMPHIBIOUS VELOX ROBOT

The Amphibious Velox robot is The best-known creation of the Pilant Energy System. The production of underwater vehicles was inspired by the animal kingdom. Velox is equipped with silicone fins that are useful to quickly move across the terrain and makes robots resemble ancient vertebrates.

The aerodynamic, mollusk shape enables robots to swim, skate along the ice, jet, or even push through the snow. 

Thankfully for its shape, Amphibious Velox robots not only can quickly save people in the water and on the land but also be a life preserver for someone who has fallen through thin ice and into extremely cold water

Moreover, due to its adaptiveness robot doesn’t completely disturb the environment since it operates there.

E.M.I.L.Y

This robot is designed to function as a hybrid flotation buoy lifeboat.

It is a four-foot, 26-pound remote-controlled vehicle that can reach speeds of up to 23 miles per hour.

Furthermore, the vehicle can transport up to five people and has a Kevlar–reinforced shell that enables it to withstand enormous waves and other sorts of impact.

 Applications That Can Save Your Life

Preventing Accidents

The app Important! was created to safeguard vulnerable road users from accidents.

The software broadcasts the user position coordinates to all surrounding automated or connected cars, enhancing the vehicle’s sensor input to guarantee the individual is identified and tracked. As a consequence, if a linked car goes too close to an important user, its brakes will be activated automatically before an accident occurs.

Medical Condition Detection

In situations where people are being put in danger each second matters and there are only minutes to save a life. This prompted the business to develop an AI-enabled system capable of detecting cardiac arrest. The voice-based digital assistant responds to emergency calls and analyses what patients say to identify their current medical condition. In Copenhagen, trials of the method revealed that it reduced occurrences of undiagnosed out-of-hospital cardiac arrest by 43%. The business is already working on methods to leverage its system to identify other diseases and give more life-saving support.

Social media sentiment analysis for disaster recovery and management

  If anything unexpected occurs, the biggest source of news is, of course – the Internet.

During a tragedy, social media users may provide some of the most useful information there. Being able to distinguish fake news from real ones could help enormously in case of danger.

The data from the internet could also help rescuers to arrive at the point of crisis sooner and direct their efforts to the most vulnerable. 

 Furthermore, AI and predictive analytics tools may evaluate digital information from different social media to offer early warnings, ground-level location data, and real-time report verification. In reality, AI might be used to search for missing persons by analyzing the unstructured data and context of images and videos uploaded on social media.

AI-powered chatbots can assist communities affected by a disaster. The chatbot may connect with the victim or other individuals in the area via popular social media platforms and request information such as location, a photo, and a description. The AI can then examine and verify this information from other sources before passing it on to the disaster relief committee. This sort of data can help them estimate damage in real time and prioritize response operations.

AI for Digital Response is a free and open platform that utilizes machine intelligence to automatically analyze and classify social media communications relevant to emergencies, catastrophes, and humanitarian situations.

        To summarise, in an age of digitalization where artificial intelligence plays a role in our society, it is crucial to use technology for more than just personal desires. AI has such enormous potential that we could focus on saving other people’s lives. The examples above are only a few of the applications/robots that have made AI our superhero.

sources :

RLSS drone trial to help sea swimmers in trouble – BBC News

12 Examples of Rescue Robots You Should Know | Built In

5 Life-Saving Applications Of Artificial Intelligence (forbes.com)AI to the rescue:

5 ways machine learning can assist during emergency situations | Packt Hub (packtpub.com)

Reading Time: 3 minutes

The role of AI in medicine has been growing. Recently AI has been involved in the drug creation process.  For a long time, drug companies have been searching for new disease–fighting medicines by using a trial-and-error process that identifies the right compounds. The whole procedure is very complex, time–consuming and has its limitation. One is a limitation of proteins that are essential for every cellular function. Pharmaceutical companies very often use proteins in aim to eliminate symptoms or diseases inside the body and for drug creation.  Unfortunately, the numbers of proteins are limited. The only ones that we are familiar with can be used to create drugs.

  What if AI could do it for us?

 The main aim of technology usage is to generate huge numbers of proteins that have never been seen in nature. The scientists have developed a new approach based on technologies used in Open AI programs like DALL-E2,  chatbots, and other search queries. The concept enables us to “conjure up” designs for new types of novel proteins. Recently, the two labs announced separate programs that will use technology to create proteins with greater precision than ever before.

 Chroma

The first one is the start-up called Generate Biomedicines. They reveal the program and called it Chroma which is referred to by the authors as DALL-E 2 biology. Chroma generators could be used to create proteins with new properties like size, functions, or shape. The whole idea opens our doors to drug inventions that could fulfill all our requests. It is a huge possibility that enables scientists to discover something in a few minutes that took our evolution millions of years.

 How does it work?

The concept is modeled on a DALL-E2.  AI used as a diffusion model eliminates random perturbations applied to data from their source. That’s how unorganized data is arranged in a coherent piece. Similarly, Chroma may synthesize unique proteins with specific characteristics not governed by set constraints on how the product should look.

RoseTTAFold

A different approach has been introduced by Baker’s team. They invited something slightly different but with the same results. Baker’s team decided to use RoseTTAFold Diffusion which guides the overall generative process by analyzing how pieces of protein supplied by a neutral network fit together. Baker Lab believes that the proteins built by AI are a better fit for essential hormones than existing protein drugs.

                                                

A protein structure generated by RoseTTAFold Diffusion (left) and the same structure created in the lab (right)

  Natural language processing

 This idea is based on Open Ai’s Chatbots and their ability to generate human-like responses. They discovered the collocation between biological codes and search queries. To both of them, you need to respond by using a series of letters. Proteins are built up of amino acids, and scientists employ particular notation to record the sequences. Proteins are represented as lengthy, sentence-like combinations with each amino acid corresponding to a single letter of the alphabet. Natural language algorithms can be successfully used for creating protein-language models. The models encode the so-called ‘grammar of proteins’ to predict the sequences of new drug molecules. In consequence, the time required for drug discovery might be shortened to months.

As you can see, there are existing solutions to the problem of limited proteins. Are they sufficient to safely develop new drugs? This will be discovered in the near future. I expect that AI-powered models will be developed with more effective pharmaceuticals to treat incurable diseases and improve the quality of life for terminally ill patients and their families.

Sources : 

How AI That Powers Chatbots and Search Queries Could Discover New Drugs – WSJ

Biotech labs are using AI inspired by DALL-E to invent new drugs | MIT Technology Review

Biotech labs are using AI inspired by DALL-E to invent new drugs – Adolfo Eliazàt – Artificial Intelligence – AI News (adolfoeliazat.com)