Tag: Machine learning

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Applied Innovation Retail

From Chatbots to Humanoids: A Look at the Diverse World of Virtual Beings

Mitsuku- Virtual Being
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Applied Innovation Retail

From Chatbots to Humanoids: A Look at the Diverse World of Virtual Beings

A Virtual Being is a conversational avatar intended for lifelike human interaction driven by AI. An avatar is a digital representation of a person in a virtual environment used for communication or self-expression. Virtual beings, on the other hand, rely on cutting-edge technology like AI, NLP, and ML and are more complicated creatures created to interact with people in a lifelike manner. Even though both involve developing a digital image of a person, virtual beings are far more advanced and have a wider range of practical uses.

Virtual beings can be used for a range of tasks, including companionship, customer service, and entertainment. The capability of virtual entities to converse with humans in normal language is one of their distinguishing characteristics. They can appear in a variety of ways, such as animated characters on a screen or as humanoid robots. Additionally, they can be tailored to fit particular requirements and preferences by changing things like age, gender, and personality. Virtual beings can be endowed with a variety of different technologies, like facial recognition, emotion detection, and gesture recognition, in addition to their conversational skills. This enables individuals to react to non-verbal cues and engage in more subtle interactions with people.

Examples of Virtual Beings

Chatbots and realistic humanoid robots are only two examples of increasingly common virtual entities. Mitsuku, a chatbot created by Steve Worswick, has received recognition for its capacity to carry on frank discussions with people. Another chatbot that adapts its replies based on human input is Replika. Magic Leap’s AI-driven chatbot Mica employs spatial computing to provide an immersive experience. Hanson Robotics created Sophia, a humanoid robot that can replicate facial expressions and have casual conversations with people. Last but not least, Soul Machines’ AI-powered virtual Zoe has been deployed in customer service applications and can communicate authentically with people.

Technologies Used in Virtual Beings

Virtual beings are made possible through a combination of several technologies, including artificial intelligence (AI), natural language processing (NLP), computer graphics, and machine learning (ML). AI forms the foundation of virtual beings, enabling them to understand and respond to human input in a natural and engaging way. NLP is used to teach virtual beings to understand and interpret human language, from casual speech to formal language. Computer graphics play an essential role in creating the visual representation of virtual beings, including their appearance and movements. ML algorithms train virtual beings to recognize patterns and make predictions based on large datasets of information, such as language or image data. Augmented reality (AR) and virtual reality (VR) technologies can be used to create immersive experiences with virtual beings, overlaying virtual objects onto the real world or creating entirely virtual environments for users to explore. As these technologies continue to evolve and improve, virtual beings will become even more advanced and capable, opening up new possibilities and applications in various industries

Natural language processing (NLP), machine learning (ML), and computer graphics are some of the technologies used to program virtual entities. The construction of a 3D model or avatar that will serve as the virtual being’s representation is usually the first step in the programming process for virtual beings. This may entail creating the avatar’s physical attributes, such as its look, attire, and range of motion. Next, natural language processing is used to give the virtual being the ability to comprehend and react to human input. For the virtual entity to comprehend and provide natural language replies, extensive linguistic training is required.

The market for Virtual Beings:

In a number of sectors, including healthcare, education, and entertainment, virtual beings are becoming more and more common. They provide a number of advantages, including scalability, personalization, and availability around the clock.

The market for virtual beings is expected to grow significantly in the coming years. According to a report by MarketsandMarkets, the global virtual and augmented reality market, which includes virtual beings, is projected to reach USD 125.32 billion by 2026, with a compound annual growth rate (CAGR) of 43.8% from 2021 to 2026.

The use of virtual beings is becoming increasingly popular in a range of industries, including healthcare, education, entertainment, and customer service. In the healthcare industry, virtual beings are being used to provide patient support and therapy, while in education, they are being used for virtual tutoring and training.

In the entertainment industry, virtual beings are being used for gaming and virtual experiences, while in customer service, they are being used to provide personalized assistance and support. The COVID-19 pandemic has also accelerated the adoption of virtual beings, as more companies and organizations look for ways to interact with customers and users remotely.

As virtual beings become more advanced and capable, they are likely to be used in even more industries and applications. For example, virtual beings could be used in manufacturing and industrial settings to improve productivity and safety, or in the automotive industry to provide virtual driving assistants.


Overall, the market for virtual beings is expected to continue growing as more companies and organizations look for ways to leverage AI and virtual technologies to improve customer experiences and streamline operations.

Virtual Beings in the Clothing Industry :

Virtual beings can be used in the clothing industry in a number of ways, including virtual try-on experiences, personalized styling, and virtual assistants. One of the most common applications of virtual beings in the clothing industry is virtual try-on experiences. These experiences allow customers to virtually try on clothing items and see how they would look on them before making a purchase. This can be done using augmented reality (AR) or virtual reality (VR) technology, which creates a realistic virtual representation of the clothing item on the customer’s body.

Another use of virtual beings in the clothing industry is personalized styling. Virtual beings can use data about the customer’s body type, style preferences, and past purchases to provide personalized recommendations for clothing items. This can be done through a chatbot or voice assistant that interacts with the customer and offers suggestions based on their input. Virtual assistants can also be used to help customers navigate the online shopping experience. These assistants can answer customer questions, provide product recommendations, and help with checkout and payment processes. They can be powered by AI and NLP technology to provide a natural language conversation experience.

Overall, virtual beings offer a range of opportunities for the clothing industry to enhance the customer experience, increase sales, and improve customer satisfaction. However, the ethical and social ramifications of virtual creatures, such as how they could affect human relationships and how they might be abused, are also a source of worry. It’s important to ensure that these technologies are used in a way that is ethical, transparent, and respects customer privacy.

Please write to us at open-innovator@quotients.com to learn more about Virtual Beings and startups working on its diverse use cases.

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Applied Innovation Healthtech

Improving Healthcare with Clinical Data Intelligence

UK government funding innovative research on artificial intelligence (AI) to advance healthcare
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Applied Innovation Healthtech

Improving Healthcare with Clinical Data Intelligence

Clinical Data Intelligence for Life Sciences solutions is making data gathering and categorization effective and intelligent, lowering mistakes and speeding up submissions.

In the age of machine learning, artificial intelligence, and semantic data pools, no nugget of information is wasted. The healthcare sector has advanced significantly in clinical decision support and predictive analytics in just the last few years.

As Data are becoming more accessible in the healthcare sector as opposed to a siloed strategy. The use of technology and data and data-driven value creation is now being witnessed throughout the network. Healthcare organizations now have the chance to better leverage data, improve patient care, and increase revenue while handling increasing risks in patient privacy and data security as new data technologies with advanced intelligence capabilities become available.

With businesses investing more in population health management and accountable care, the use cases for big data are multiplying quickly, and consumers are keeping up with the demand for affordable services that take advantage of the ease of their preferred applications and devices. This results in better treatment outcomes, individualized care, and preventive interventions. We would be discussing some of the emerging use cases going forward.

Preventive Healthcare: Preventive Healthcare is one of the use cases for clinical data intelligence. It enables experts to identify dangers early and take preventative measures. Through the use of data science techniques like AI and machine learning, wearables and other tracking devices that gather and track data are producing forecast models that can correctly identify a person’s health risks and enable carers to take preemptive action. It is feasible to anticipate and avoid chronic cardiac conditions, autism meltdowns, etc. by utilizing genetic and historical data.

Data-Driven Care: Data science technologies can make uses like medical image analysis and pathology reports that read with high precision possible because a large number of patients perish each year as a result of diagnostic mistakes. To analyze and understand X-rays, MRIs, mammograms, and other imaging studies, as well as to spot trends and identify illnesses, data models and algorithms can be created. This will increase output and aid doctors in making correct diagnoses.

Individualized Care: A one-size-fits-all strategy for medical care and medication is also now considered ineffective. The ability to monitor individual data and improvements in gene technology are enabling customized medicine. Based on a patient’s prior medical history, gene markup, and current data, machine learning, and deep learning algorithms can now help a doctor determine whether a specific drug will be effective for the patient.

Lowering Costs: Insurance firms are putting weight on healthcare organizations to improve therapy outcomes in order to lower readmissions. Longer personal care is a consequence of bed shortages in some nations. By enabling doctors to remotely monitor their patient’s vital signs, receive alerts when conditions deteriorate, and take appropriate action when required, data science and intelligence can significantly assist in resolving these problems.

Drug discovery: Drug development and clinical studies are lengthy, expensive procedures. Data intelligence tools can aid scholars in the analysis of huge data sets and in the creation of computer models for various tests. Additionally, text mining can assist medical academics by automatically reviewing thousands of web resources and performing analytical processing quickly to give the required information. Clinical trials will use data science apps to accelerate findings and reduce costs.

Thus providing companies with cutting-edge capabilities can improve care, accomplish improved treatment outcomes, increase patient experience, and make new discoveries in drug discovery, data science, and intelligence will have a major influence on the future of the healthcare sector.

However, healthcare companies are still having trouble mastering descriptive analytics, particularly when valuable insights call for a variety of data sources. Despite the data-driven promises, the majority of healthcare companies still have a lot of work to do before they can turn their growing big data analytics skills into actually usable clinical information.

Are you interested in implementing data science and intelligence in your company? Quotients through its partner networks offers a quicker, more affordable alternative. Using advanced data science technologies like AI, machine learning, deep learning, etc. without the constraints of time, money, and resources is made possible by our solutions. Please write to us at open-innovator@quotients.com

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Applied Innovation

How AI can impact Maritime Logistics?

Image Courtesy: Pixabay
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Applied Innovation

How AI can impact Maritime Logistics?

Investing in communication technologies has provided several benefits for shipping firms. Most ships have grown into remote offices at sea, providing the captain and crew with dependable Internet connectivity, virtual networks, email, route planners, and a variety of other technologies and applications. Further investing in innovative technologies can enhance regular vessel operations while also lowering corporate expenses and optimizing business processes.

Machine Learning enables users to use sophisticated algorithms and analyze data, which aids in guiding the logic of potential issues in marine transportation. These approaches may be used for maritime network design, trip planning, cargo optimization, maintenance processes, and other areas.

Machine learning, a branch of Artificial Intelligence, relies on working with small to large datasets by examining and comparing the data to find common patterns and explore nuances. It enables the use of intelligent algorithms and the evaluation of data, which aids in guiding the logic of potential issues in marine transport. These algorithms may be applied to maritime network design, trip planning, cargo optimization, and other applications.

The intelligence of ML algorithms, combined with industry knowledge, has the potential to provide a significant advantage to shipping companies that first adopt them in their operations. The bigger the investment in AI/ML, the more advantage from their big data analysis capabilities as ML algorithms can handle data from the whole history of a vessel’s operation.

Advanced Machine Learning algorithms will be capable of enhancing trip optimization, such as fuel economy, crew performance, voyage cost estimates, calculating the ideal route in a minute, and providing advice on speed, course, and so on. ML algorithms, for example, may be used to estimate fuel usage based on engine data and vessel parameters. These algorithms enable the transformation of massive amounts of noisy sensor data and other onshore data into organized information that may be used to anticipate fuel usage and map ideal paths for boats.

As data is a critical component for removing uncertainty, adopting ML algorithms can assist to boost the usual data that might be critical for shipowners. Data mining in the marine industry has been quite restricted thus far. As a result, as compared to other industries, the deployment of ML approaches in marine transport is restricted. Taking this into consideration, our innovators have created solutions incorporating edge platforms, machine learning models, onboard sensors, and application software. We have solutions for Predictive Scheduling, Container Positioning Organization, Voyage Planning and Route Forecasting, Fuel Consumption Optimization, and Predictive Maintenance.

We would be pleased to hear from you and would want to discuss potential partnership opportunities. Please write to us at open-innovator@quotients.com


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Applied Innovation Healthtech

Federated Learning for Medical Research

Image Courtesy: Pixabay
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Applied Innovation Healthtech

Federated Learning for Medical Research

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) have emerged as the most popular and fascinating technologies in the intelligent healthcare industry.

The traditional healthcare system is centered on centralized agents providing raw data. As a result, this system still has significant risks and problems. When combined with AI, the system would consist of several agent collaborators capable of successfully connecting with their intended host.

Federated Learning, a novel distributed interactive AI paradigm, holds promise for smart healthcare since it allows several clients (such as hospitals) to engage in AI training while ensuring data privacy. FL’s noteworthy characteristic is that operates decentralized; it maintains communication based on a model in the selected system without exchanging raw data.

The combination of FL, AI, and XAI approaches has the potential to reduce the number of restrictions and issues in the healthcare system. As a consequence, the use of FL in smart healthcare might speed up medical research using AI while maintaining privacy.

The Federated Learning approach may be used to provide several enticing benefits in the development of smart healthcare. Local data, for example, are not necessary for training. To train other machine learning algorithms by mixing a large number of local datasets without transmitting data. During training, local Machine Learning (ML) models are trained on local heterogeneous datasets.

When opposed to traditional centralized learning, FL is also capable of delivering a good balance of precision and utility, as well as privacy enhancement. FL may also help to reduce communication costs, such as data latency and power transmission, connected with raw data transfer by avoiding the dumping of huge data quantities to the server.

We have solutions that use FL to link life science enterprises with world-class university academics and hospitals in order to exchange deep medical insights for drug discovery and development. The platform enables its partners to uncover siloed datasets while maintaining patient privacy and securing proprietary data by leveraging federated learning and cutting-edge collaborative AI technologies. This enables unprecedented cooperation to enhance patient outcomes by sharing high-value knowledge.

The platform has built a worldwide research network driven by federated learning, allowing data scientists to securely connect to decentralized, multi-party data sets and train AI models without the need for data pooling. When combined with fields of medicine specializing in diagnosis and treatment, scientists may use cutting-edge technology platforms to build potentially life-changing drugs for people all over the world.

For additional information on such solutions and emerging use cases in other areas, as well as cooperation and partnership opportunities, please contact us at open-innovator@quotients.com

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Innovator's Vista

Computer Vision for Quality Inspection

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Innovator's Vista

Computer Vision for Quality Inspection

In manufacturing sector whether it is clothing and textiles, petroleum, chemicals and plastics, electronics, computers and transportation, food production, metal manufacturing etc, poor production quality has a significant impact on efficiency. It results in additional operational and financial costs in form of lost time, wasted resources, scraps and decreased efficiency. Therefore, maintaining quality standards is of utmost importance in the field of manufacturing.

Typically product is visually inspected for defects which is a highly manual process. It is time consuming, prone to errors and dependent on operator’s experience and requires consistency which is not always possible.  Rule-based visual inspection machines which are programmed, are not flexible, and cannot adapt to product changes and can only detect a handful of defects at a time.

By using computer vision in manufacturing quality inspection can be automated during the production process.  There are several advantages of using computer vision like reduced cognitive load for operators, no programming required, and it adapts to product changes. It also makes inspections faster, more accurate, and efficient. There are today several manufacturing firms that are shifting towards using deep learning and computer vision for quality control and inspection tasks. 

Computer Vision:

Computer vision seeks to replicate and automate tasks that the human visual system can do. It is an interdisciplinary scientific field that aims to give computers the ability to extract a high-level understanding of the visual world from digital images and videos. It uses Artificial Intelligence (AI) and deep learning models to enable machines accurately identify, interpret, understand, classify and react to objects in form of visual data like digital images from cameras and videos.

More sophisticated artificial intelligence (AI)–based vision systems can enable more powerful visual inspection solutions. These solutions can handle complex applications with less engineering time compared to traditional machine vision solutions.

These systems are faster and simpler process that can carry out repetitive and monotonous tasks at a faster rate, which simplifies the work for humans. Computer vision systems better trained through all kinds of data generally commit zero mistakes resulting in faster delivery of high-quality products and services. With no room for faulty products and services, companies do not have to spend money on fixing their flawed processes.

By using a laser coupled with a 2D camera, object edges can be more easily located and “pseudo 3D” images can be produced. These 3D machine vision systems creates a full 3D profile of the object by stitching together the individual lines of image data that makes inspection of complex assemblies and sub-assemblies as well as individual components easier.

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