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Applied Innovation

Quantum Computing: Unlocking New Frontiers in Artificial Intelligence

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Applied Innovation

Quantum Computing: Unlocking New Frontiers in Artificial Intelligence

In the ever-changing technological environment, quantum computing stands out as a revolutionary force with the potential to change the area of artificial intelligence.

Quantum computing is a breakthrough field that applies quantum physics concepts to computation. Unlike conventional computers, which employ bits (0s and 1), quantum computers use quantum bits, or qubits, which may exist in several states at the same time owing to superposition. This unique characteristic, along with quantum entanglement, enables quantum computers to handle massive volumes of information simultaneously, possibly solving complicated problems tenfold quicker than conventional computers.

These powerful computing systems, which use the perplexing laws of quantum physics, promise to solve complicated problems that traditional computers have long struggled to handle. As we investigate the symbiotic link between quantum computing and AI, we discover a world of possibilities that might radically alter our understanding of computation and intelligence.

Quantum Algorithms for Encryption: Safeguarding the Digital Frontier

One of the most significant consequences of quantum computing on AI is in the field of cryptography. Current encryption technologies, which constitute the foundation of digital security, are based on the computational complexity of factoring huge numbers. However, quantum computers equipped with Shor’s algorithm can crack various encryption systems, posing a huge danger to cybersecurity.

Paradoxically, quantum computing provides a solution to the identical problem that it generates. Quantum key distribution (QKD) and post-quantum cryptography are two new topics that use quantum features to provide unbreakable encryption systems. These quantum-safe technologies ensure that even in a world with powerful quantum computers, our digital communications are secure. 

For AI systems that rely largely on secure data transmission and storage, quantum encryption methods provide a solid basis. This is especially important in industries such as financial services, healthcare, and government operations, where data privacy and security are critical.

Quantum Simulation of Materials and Molecules: Accelerating Scientific Discovery

One of quantum computing’s most potential applications in artificial intelligence is the capacity to model complicated quantum systems. Classical computers fail to represent the behavior of molecules and materials at the quantum level because computing needs to rise exponentially with system size.

However, quantum computers are fundamentally adapted to this task. They can efficiently model quantum systems, which opens up new avenues for drug development, materials research, and chemical engineering. Quantum simulations, which properly represent molecular interactions, might significantly expedite the development of novel drugs, catalysts, and innovative materials.

AI algorithms, when paired with quantum simulations, can sift through massive volumes of data generated by the simulations. Machine learning algorithms can detect trends and forecast the features of novel substances, possibly leading to breakthroughs in personalised treatment, renewable energy technology, and more efficient manufacturing.

Quantum-Inspired Machine Learning: Enhancing AI Capabilities

Quantum computing ideas apply not just to quantum hardware, but they may also inspire innovative techniques in classical machine learning algorithms. Quantum-inspired algorithms attempt to capture some of the benefits of quantum processing while operating on traditional hardware.

These quantum-inspired approaches have showed potential in AI domains:


– Natural Language Processing: Quantum-inspired models can better capture semantic linkages in text, resulting in improved language interpretation and creation.
– Computer Vision: Quantum-inspired neural networks have shown improved performance in image identification tests.
– Generative AI: Quantum-inspired algorithms may provide more diversified and creative outputs in jobs such as picture and music production.

As our grasp of quantum principles grows, we should expect more quantum-inspired advances in AI that bridge the gap between classical and quantum computing paradigms.

The Road Ahead: Challenges and Opportunities

While the promise of quantum computing in AI is enormous, numerous hurdles remain. Error correction is an important topic of research because quantum systems are extremely sensitive to external noise. Scaling up quantum processors to solve real-world challenges is another challenge that academics are currently addressing.

Furthermore, building quantum algorithms that outperform their conventional equivalents for real situations is a continuous challenge. As quantum technology develops, new programming paradigms and tools are required to enable AI researchers and developers to properly leverage quantum capabilities.

Despite these limitations, the industry is advancing quickly. Major technology businesses and startups are making significant investments in quantum research, while governments throughout the world are initiating quantum programmes. As quantum computing technology advances, we should expect an increasing synergy between quantum computing and AI, enabling significant scientific and technological discoveries in the next decades.

The combination of quantum computing with artificial intelligence marks a new frontier in computational research. From unbreakable encryption to molecule simulations, complicated optimisations to quantum-inspired algorithms, the possibilities are limitless and transformational.

As we approach the quantum revolution, it is evident that quantum technologies will have a significant impact on the development of artificial intelligence. The challenges are substantial, as are the possible benefits. By using the capabilities of quantum computing, we may be able to unleash new levels of artificial intelligence that beyond our present imaginations, leading to innovations that might transform our world in ways we don’t yet comprehend.

Contact us at open-innovator@quotients.com to schedule a consultation and explore the transformative potential of this innovative technology.

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Applied Innovation

Detecting Deepfakes Using Deep Learning

AI for Mental Health
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Applied Innovation

Detecting Deepfakes Using Deep Learning

Deepfakes are a brand-new occurrence in the age of digital manipulation when truth and illusion frequently blend together. Artificial intelligence (AI) produced media has been in the news a lot lately, notably impersonation videos that make people appear to be talking or acting in ways they aren’t.

Deepfake AI is a type of artificial intelligence that produces convincing audio, video, and picture forgeries. The phrase is a combination of deep learning and fake, and it covers both the technology and the phony information that results from it. Deepfakes alter existing source material by switching out one individual for another. Besides, they produce wholly unique content in which individuals are depicted doing or saying things that they did not actually do or say.

It is essential to recognize deepfakes as soon as possible. In order to do this, organizations like DARPA, Facebook, and Google have undertaken coordinated research initiatives. At the vanguard of these efforts is deep learning, a complex technique that teaches computers to recognize patterns. In the domain of social media, methods like LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network), and CNN (Convolutional Neural Network) have shown potential in spotting deepfakes.

Long Short-Term Memory (LSTM) neural networks are important for detecting deep fakes. A specialized form of recurrent neural network (RNN) known as LSTM is recognized for its capacity to efficiently process and comprehend input sequences. These networks excel in deep fake detection by examining the temporal elements of films or picture sequences. They are skilled at spotting minute discrepancies in facial expressions or other visual indications that can point to edited information. LSTMs excel at identifying the subtle distinctions that distinguish deepfakes from authentic material because they learn patterns and dependencies over frames or time steps.

In the effort to identify deepfakes, recurrent neural networks (RNNs) are also quite helpful. RNNs are ideal for frame-by-frame analysis of sequential data since they were designed specifically for this purpose. RNNs search for abnormalities in the development of actions and expressions in the context of deepfake detection. These networks may detect discrepancies and alert the user when they occur by comparing the predicted series of events with what is actually observed. As a result, RNNs are an effective tool for spotting possible deepfake content, especially by spotting unusual temporal patterns that could be missed by the human eye.

Convolutional Neural Networks (CNNs) are the preferred method for image processing jobs, which makes them essential for identifying deep-fake pictures and frames in films. The distinctive capability of CNNs to automatically learn and extract useful characteristics from visual data sets sets them apart. These networks are particularly adept at examining visual clues such as facial characteristics, emotions, or even artifacts left over from the deepfake production process when used for deepfake identification. CNNs can accurately categorize photos or video frames as either authentic or altered by meticulously evaluating these specific visual traits. As a result, they become a crucial weapon in the arsenal for identifying deep fakes based on their visual characteristics.

Deepfake detection algorithms are continually improving in a game of cat and mouse. Deepfake detection techniques for photos and videos are constantly being enhanced. This dynamic field is a vital line of defense against the spread of digital deception. Researchers need large datasets for training to teach computers to recognize deepfakes. Several publicly accessible datasets, including FFHQ, 100K-Faces, DFFD, CASIA-WebFace, VGGFace2, The Eye-Blinking Dataset, and DeepfakeTIMIT, are useful for this purpose. These picture and video collections serve as the foundation upon which deep learning models are formed.

Deepfakes are difficult to detect. The need for high-quality datasets, the scalability of detection methods, and the ever-changing nature of GAN models are all challenges. As the quality of deepfakes improves, so should our approaches to identifying them. Deepfake detectors integrated into social media sites might potentially reduce the proliferation of fake videos and photos. It’s a race against time and technology, but with advances in deep learning, we’re more suited than ever to confront the task of unmasking deepfakes and protecting digital content’s integrity.

Are you intrigued by the limitless possibilities that modern technologies offer?  Do you see the potential to revolutionize your business through innovative solutions?  If so, we invite you to join us on a journey of exploration and transformation!

Let’s collaborate on transformation. Reach out to us at open-innovator@quotients.com now!

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Applied Innovation Healthtech

How Artificial Intelligence can help identify Melanoma

How Artificial Intelligence (AI) is aiding in the diagnosis of skin cancer; Image Courtesy: Pixabay
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Applied Innovation Healthtech

How Artificial Intelligence can help identify Melanoma

Every area of healthcare is being significantly impacted by artificial intelligence (AI), and dermatology is no exception. Melanoma identification using AI is one possible application for AI in dermatology. Melanoma is the deadliest type of skin cancer and is difficult to detect and can be fatal. Artificial intelligence (AI) in this context can identify melanoma with a high degree of precision. This is crucial because the number of skin biopsies is increasing while the number of pathologists is decreasing leading to slows down in the rate of identification and, consequently, therapy.

The Process

The process includes the use of Deep Learning to build Convolutional Neural Networks (CNNs), a subcategory of machine learning. CNNs are a form of network architecture for deep learning algorithms and are specifically used for image recognition and other tasks requiring the processing of pixel data. They are therefore perfect for positions requiring computer vision (CV) skills as well as situations requiring precise object detection.

Data collection is the first step in dermatology scans for melanoma, where a sizable dataset of pictures of moles, lesions, and other skin anomalies is gathered and annotated by doctors to build a training set. The machine learning programs’ training on this information comes next during which, the system learns to recognize the characteristics of a melanoma lesion and distinguish them from other kinds of skin anomalies.

After the system is trained it is then incorporated into a dermatologist’s workflow. The dermatologist would capture photos of any suspicious lesions during a skin examination and upload them to the AI system, which would then evaluate the pictures and offer a diagnosis. A possible melanoma lesion would be flagged by the algorithm, prompting the physician to conduct additional testing.

After reviewing the image and the AI-generated analysis, a dermatologist may use additional diagnostic techniques like biopsy to support or contradict the prognosis. In order to increase the precision of the system, dermatologist comments on how well the AI system performed is integrated back into the training data.

An artificial intelligence (AI) system hence helps medical workers in developing possibly successful treatments and improving patient results. It can also increase access to treatment and raise the number of patients who can be seen and diagnosed quickly.

Conclusion

Dermatologists are now outperformed by artificial intelligence (AI) in the diagnosis of skin cancer, but dermatology is still lagging behind radiology in its widespread acceptance. Applications for AI are becoming easier to create and use.

Complex use cases, however, might still necessitate specialist knowledge for implementation and design. In dermatology, AI has a wide range of uses including basic study, diagnosis, treatments, and cosmetic dermatology.

The main obstacles preventing the acceptance of AI are the absence of picture standardization and privacy issues. Dermatologists are crucial to the standardization of data collection, the curation of data for machine learning, the clinical validation of AI solutions, and eventually the adoption of this paradigm change that is transforming our practice.

We want to make innovation accessible from a functional standpoint and encourage your remarks. If you have inquiries about evolving use cases across various domains or want to share your views email us at open-innovator@quotients.com

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Applied Innovation

Artificial intelligence (AI)- the next stage in the transition from conventional to creative farming

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Applied Innovation

Artificial intelligence (AI)- the next stage in the transition from conventional to creative farming

Artificial intelligence (AI) has the ability to transform the way we think about agriculture by bringing` about numerous advantages and allowing farmers to produce more with less work.

With increasing urbanization with the world’s population and shifting consumption patterns, and rising disposable money, Farmering Industry is under a lot of strain to satisfy the rising demand and needs to find a method to boost output. There is a need to search for methods to lessen or at the very least control the risks faced by farmers. One of the most interesting possibilities is the application of artificial intelligence in agribusiness.

Artificial intelligence (AI) is the next stage in the transition from conventional to creative farming. Here we are discussing some applications of AI in agriculture:

Soil and Crop Monitoring

The amount and quality of the yield, as well as the health of the product, are directly influenced by the micro- and macronutrients in the soil.

In the past, personal sight and opinion were used to assess the health of the soil and the crops. However, this approach is neither precise nor prompt and in its place, UAVs can now be used to collect aerial picture data, which can then be fed into computer vision models for intelligent agricultural and soil condition tracking. This data can be analyzed and interpreted by AI much more quickly than by humans in order to monitor agricultural health, forecast yields accurately, and identify crop malnutrition.

Farmers typically have to collect soil samples from the ground and transport them to a facility for labor- and energy-intensive analysis. Instead, researchers chose to investigate whether they could teach a program to perform the same task using image data from a low-cost handheld camera.

The computer vision model was able to produce approximations of sand composition and SOM that were as accurate as pricey lab processing.

Therefore, not only can computer vision remove a significant portion of the labor-intensive, manual work involved in crop and soil track, it often does so more efficiently than people.

Monitoring Crop Maturation

To maximize output effectiveness, it is also crucial to watch the growth stages. To make changes for better agricultural health, it’s essential to comprehend crop development and how the climate interact.

Precision agriculture can benefit from AI’s assistance with labor-intensive processes like manual development stage monitoring. For producers, overserving and overestimating agricultural development and maturity is a difficult, labor-intensive task. But a lot of that labor is now being handled with ease and remarkable precision by AI.

The farmers no longer needed to make daily trips out into the fields to inspect their crops because computer vision models can more correctly spot development phases than human observation. Computer vision can determine when a crop is mature by using an algorithm that examined the hue of five distinct crop components, estimated the crop’s maturity, and then used this information.

Detecting Insect and Plant Diseases

Plant pest and disease monitoring can be mechanized using deep learning-based picture recognition technology. This works by creating models of plant health using picture categorization, detection, and segmentation techniques. This is accomplished by using pictures of rotten or diseased crops that had been labeled by botanists according to four main phases of intensity to training a Deep Convolutional Neural Network. The substitute for machine vision entails extensive, time-consuming human searching and review.

Livestock Monitoring

Farmers can keep an eye on their livestock in real time by using AI. Dairy farms can now separately watch the behavioral characteristics of their cattle thanks to artificial intelligence (AI) solutions like image classification with body condition scores, feeding habits, and face recognition. Additionally, farmers can keep track of the food and water consumption as well as the body temperature and behavior of their animals. These benefits of AI are the main reasons the farming industry is seeing a sharp rise in demand for it.

Conclusion

Technology has been employed in farmland for a very long time to increase productivity and lessen the amount of demanding manual work needed for farming. Since the advent of farming, humankind, and agriculture have evolved together, from better plows to drainage, vehicles to contemporary AI.

Computer vision’s expanding and more accessible supply could represent a major advancement in this area. Because of the significant changes in our climate, environment, and dietary requirements, AI has the potential to revolutionize 21st-century agriculture by boosting productivity in terms of time, labor, and resources while also enhancing environmental sustainability. By implementing real-time tracking to encourage improved product quality and health, it is also enhancing agriculture.

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Fintech Applied Innovation

AI-Based Models aiding Financial Institutions with Credit Score Assessments

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Fintech Applied Innovation

AI-Based Models aiding Financial Institutions with Credit Score Assessments

Artificial intelligence has been around for a while now, and technology is only getting more capable. From self-driving cars to personal digital assistants, AI is integrating into our daily lives in ever-more sophisticated ways. In this article, we’ll explore how artificial intelligence can help financial institutions to generate credit reports and make lending decisions.

Importance of Credit Score for FI

A credit score is the first thing lenders check when you apply for a loan or a credit card. This three-digit numeric is a summary of the entire credit history that determines a consumer’s creditworthiness, the higher the score, the better the borrower’s chances of getting a loan. It is prepared based on lenders’ data and consolidated in the Credit Information Report or CIR. Criteria such as payment history, credit utilization ratio, Credit history length, Credit mix, etc are factored in for this. 

Limitations of Traditional Credit Scores

Although credit scoring systems are being implemented and used by most banks nowadays, they have limitations and can’t be used to make accurate predictions. The score depends upon the quality of data that is used, if the data is erroneous with missing values or outliers the resultant scores may not be accurate. Along with the availability of high-quality data, the data should also be predictive so conclusions can be drawn from it and defaults can be predicted. Traditional credit scores due to the limitations discussed are getting outdated and even irrelevant in some cases. 

AI-based Credit Scoring

Artificial intelligence (AI) can play a role here and make precise predictions based on smart models. AI through its cutting-edge analytical technologies can hugely impact the financial sector and can offer excellent returns on their investment. AI-based credit scoring unlike focusing on the past performance of the borrower can be more sensitive to real-time indicators of a potential borrower like the existing level of earnings, employment prospects, and their potential ability to earn. These models can also give individualized credit score assessments based on real-time factors, giving access to finance to more people with income potential.

With these scoring models, banks also can get unique insights into their customers’ financial behavior and leading to better customer segmentation in terms of associated credit risk. Also after the disbursement of the loan these customers can be monitored and red flags can be raised as soon as a behavior is deviant from standard practices. With the help of AI, it is also possible to speed up lending decisions and processing of loans leading to better customer service and productivity of employees.

Credit scores are determined by algorithms neural network-based scoring. A neural network is a machine-like system that can learn from data and make decisions based on those data sets. These types of scoring can detect small variations in data sets and make more accurate predictions and can help solve the problem of credit scoring. As these models can learn from data without requiring any rules-based algorithms they can better assess the credit risk. With these advantages, financial institutions are relying on machine learning to process big data and produce better insights.

Alternatives to Credit Score

Data points produced by a large number of digital transactions can offer valuable insight into how people manage their financial commitments. There are endeavors going on for developing alternatives to create credit score models based on AI methods to evaluate a person’s creditworthiness, particularly for those who do not have a formal credit repayment history.

In the various financial institutions, these solutions are being implemented at different levels and improving how they operate. Please contact us at open-innovator@quotients.Com if you would like additional information or explore this and other rapidly evolving solutions in a variety of fields.

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Applied Innovation Healthtech

AI-assisted Digital Physiotherapy resulting in Positive Outcome for Patients

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Applied Innovation Healthtech

AI-assisted Digital Physiotherapy resulting in Positive Outcome for Patients

The digital economy saw accelerated growth during the covid19 outbreak. The pandemic presented many windfall opportunities for digital technologies– represented by the Internet, big data, 5G, and artificial intelligence– to expedite their deep integration with industries such as retail, finance, mobility, manufacturing, healthcare, etc.

Digital Health

Digital health, the use of information and communications technologies in health professions to address illnesses and health risks and to facilitate wellness, was increasingly leveraged world over to accelerate, compliment, and optimize health care service during the pandemic and it continues to evolve. There is an increasing trend among people relying on digital platforms for their well-being.

Digital Physiotherapy

Digital Physiotherapy is one of the applications in the digital healthcare domain that is gaining popularity and physical therapists around the globe are making use of technological advancements for providing care to their patients. It relies on smartphone-based computer vision for motion tracking and an app for functioning. Motion tracking, a subfield of computer vision and an extension of object recognition follow and monitors the motion of a person or object across multiple frames in a video.

Digital Physiotherapy Positively Impacts Patient Management

This has great potential to positively impact patient management as these solutions are scalable and can overcome obstacles like cost as these patients can recover from the comfort of their homes and also save on medical expenses and time. Such solutions can also be individualized to meet patient and clinician needs and expectations. But there are also challenges like lack of standardization and require more reliable measures of evaluation than those existing at present.

There are solutions that are coming up and are using this technology to capture exercise data like the speed and range of motion of the patient. This data is monitored by a team of experts who engage through video calls and messaging. There is also an option for real-time corrective feedback to ensure that the recommendations are being followed and to provide personalized care.

AI Assisted Care

Digital physiotherapy solutions depend on an app to help patients choose a time of treatment according to their convenience and interact with the physiotherapist in real time. There are also options for the patients to know all their details as they can access all their records all the time. The patients can conduct the exercise sessions at the time they choose and get guidance through AI (Artificial Intelligence) so there is no need for the doctors to be present every time. Therefore, it saves time both for physiotherapists and the patients allowing for the treatment of more patients.

As the whole process is virtual so the treatment can be done through a compact personal studio rather than a clinic saving large costs on rent and maintenance. Automated record tracking and clubbing together of identical cases can help the physiotherapists to study the process and outcomes and come up with standardized treatment saving time and effort.

This digital physiotherapy solution is being deployed at various levels in the healthcare industry and receiving positive feedback. To know the details and discuss more on this as well as other evolving solutions in multiple domains please write to us at open-innovator@quotients.com.

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Applied Innovation Industry 4.0

Intelligent Video Analytics Transforming Manufacturing, Hospitality, Transportation, Healthcare, Retail, etc

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Applied Innovation Industry 4.0

Intelligent Video Analytics Transforming Manufacturing, Hospitality, Transportation, Healthcare, Retail, etc

Video analytics, also referred to as video content analysis or intelligent video analytics, in recent times has been at the center of attention in both the industry and the academic world.

Advances in Deep Learning aiding Video Analytics

With advances in Deep Learning research and expanded availability of video data, video analytics now allows for the automation of tasks that were once possible by a human intervention only. This allows it to be used in a number of applications ranging from monitoring traffic jams and alerting in real-time, to analyzing customers’ flow in retail to maximize sales.

In Deep Learning, a subset of AI, a machine is exposed to volumes of tagged data allowing it to learn and recognize and identify the same information in new data sets imitating the way a human works. Deep Learning offers advantages like faster analytic output, improved processing performance, and increased object detection, accuracy, and classification.

Intelligent video analytics automatically recognizes temporal and spatial events in videos and performs real-time monitoring but it can also be used to analyze historical data to find insights. It can recognize objects, object attributes, movement patterns, or behavior related to the monitored environment are detected.

Some applications

Video analytics has the potential to be widely used in industries such as manufacturing, hospitality, education, retail, and others. We are discussing a few of them.

Healthcare

Integrating video analytics into legacy CCTV systems can transform cameras into much more proactive intelligence tools that can be used to ensure the safety of patients, staff, and visitors. Some of the most common problems like theft, infant abduction, and drug diversion can be detected and checked.

Mental healthcare is another area in which video analytics can be used to analyze facial expressions, and body posture to alert the hospital staff. It can also play a role in the at-home monitoring of older adults or people with health issues

Further, the data collected can be used to generate insights that can help to shorten wait times and achieve business goals by managing the staff according to patterns in the footfall of patients.

Transportation

Video analytics can be used in reducing accidents and traffic jams by dynamically adjusting traffic light control systems by monitoring traffic. By recognizing situations that may turn fatal in real-time, it can raise alerts, and even in the case of an accident, these systems can trigger an alarm to security and healthcare institutions to take action apart from that it can also serve as evidence in case of litigation.

Video analytics can also perform tasks like vehicle counting, speed cameras can detect traffic movements and license plate recognition can spots stolen vehicles or vehicles being used in a crime. It can also generate high-value statistics to assist in making infrastructure-related and other policy decisions.

Retail

The retail industry can use video analytics to generate insights and actionable information on customers’ behavior and buying patterns through their key characteristics like gender, age, duration and time of visit, walkways, etc. These algorithms can also be used to recognize previous customers and improve customer experience and provide personalized service. Video analytics can also play a role in developing anti-theft mechanisms by identifying shoplifters.

Manufacturing

Video analytics can improve productivity, reduce downtime and ensure staff health at the manufacturing facility by enhancing operations and management efficiency.

Smart cameras can be used to predict potential interruptions, evaluate specific bottlenecks and reduce downtime by generating alerts to take proactive action immediately. It can also optimize the number of employees in the production facility and improve overall productivity. Inventory management can also be enhanced by analytics as the warehouses can be monitored for their capacity. The use of machine vision can help in inspection and improve quality control.

Video analytics can also warn of situations that may pose threats to people, products, or machines by detecting movements and identifying conditions. Video analytics can provide round-the-clock security and alert commercial as well as residential buildings and prevent potential break-ins.

Video Analytics Approach

Video content analysis can be done in real-time or post-processing. Also, it can be centrally on servers that are generally located in the monitoring station or can be embedded in the cameras themselves, some times a hybrid approach is adopted.

There are startups that are working on Video Analytics and have successfully deployed their solutions across various sectors such as hospitality, retail, manufacturing, pharma, and food. To more about evolving use-cases and startups in different domains please write to us at Open-innovator@Quotients.com

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Industry 4.0

AI-powered Computer Vision Revolutionizing Multiple Industries

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Industry 4.0

AI-powered Computer Vision Revolutionizing Multiple Industries

Inspections are critical for attaining manufacturing excellence. Inspection of processes and products determine the business success and customer confidence in the brand. Companies are increasingly relying on next-generation inspection solutions to improve quality control and deliver defect-free products. 

AI-powered Computer Vision

Digitization and machine vision based on AI algorithms can identify manufacturing anomalies much faster than human inspectors and improve quality and reduce costs. It employs computer vision, a field of artificial intelligence (AI) that enables computers and systems to obtain useful insight from digital images, videos, and other visual inputs — and take actions or make recommendations.

AI-backed computer vision is finding applications in industries ranging from manufacturing to automotive – and the market is expanding rapidly. It can perform the functions like inspection and identification in much less time. By the use of cameras, data, and algorithms, a system can be trained to inspect products or watch a production asset and analyze a very wide range of products or processes and detect invisible defects at a rate exceeding human capabilities.

Deep learning, an aspect of machine learning technology, trains machines by feeding a neural network with examples of labeled data. This is used to identify common patterns based on these examples and then convert it into a ‘math equation’ that mimics a human visual inspection classifies forthcoming information and performs tasks like differentiating parts, abnormalities, and texture.

Use Cases:

Automatic Counting: Computer Vision can be used for counting applications in industries where small parts are manufactured in large numbers like in metal parts, foods, pharmaceuticals, food, rubber pieces, wooden products, jewelry, etc.

Detect absence/presence: Computer vision can also detect the absence and presence of something such as date print, tags, brand logos, codes, stamps, etc, and automatically confirm the completeness of the product.

Sorting: Vision systems powered by AI algorithms can identify the right and defective product types by imaging them at high speeds. For example, separating pills in the pharma industry and segregating broken and damaged items in jewelry. This can be followed by sorting the identified items into chosen categories.

Surface Inspection: Computer Vision can identify surface anomalies for example scratches, dents, and pits accurately and at a high speed. Defects in some products like fabrics or automobile bodies are very small and undistinguishable, which can be detected only by monitoring the variation in intensity using deep learning algorithms.

Application:

Machine Vision is powering Industrial Automation. Using the latest 2D, 3D, and Artificial Intelligence solutions inspection systems are used across various industries like the pharmaceutical industry, automotive industry, printing and packaging industry, food and beverage industry, and textile industry. It offers huge benefits in eliminating human interventions and errors thus cutting down heavily on inspection cost and time.

There are startups working on this solution helping the above-mentioned as well as other sectors to greatly enhance their functioning through the acceptance and integration of new technologies into their existing systems. To know more about these and for collaboration and partnership opportunities please write to us at open-innovator@quotients.com

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Innovator's Vista

How AI is Impacting Sales

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How AI is Impacting Sales

AI is having an impact in sales by automating time-consuming tasks like sales forecasting, or difficult jobs, like data input and meeting scheduling. It can enable comprehensive statistics and detect patterns on every interaction between sales people and potential clients, including emails, phone calls, and chats, hence they can improve capacity of sales teams to prioritize and develop into better salesmen by pointing out trends in customer responses.

Online virtual worlds is another segment that offer untapped marketing potential for real-world products and services. In the business world, virtual reality could be extremely helpful, particularly for sales teams. They reduce online shoppers’ search efforts and facilitate efficient decision making. Usage of Avatars, a representation in this online environment, are helping brands drive sales with enhanced and engaging product discovery processes.

Virtual sales person (VS), an employee avatar of the virtual store looking out through its eyes and engaging with other beings, providing product information and recommending products, can be an intense experience. VS serve as sales augmentation services that applied in parallel with sales teams and utilizes a company’s resources and knowledge base and thus increase customer interaction and sales.

The advent of deep learning and the accessibility of millions of hours of film displaying every feature of the human body and face have made AI-powered avatars resemble humans, and virtual 3D representations, more lifelike.

A growing body of research shows that people react more positively to human faces, and businesses are beginning to use AI-powered avatars to their advantage. Avatars improve human connection and with Natural Language Processing (NLP) methods, these can speak with humans can help win sales and influence people.

As the sales AI avatar becomes better at the job it can automatically generate digital marketing interactions with leads as it learns. Considering that people feel more at ease engaging with beings who resemble them, there is another use that might enhance consumer engagement. The positive outcomes from using avatars suggest that a well-designed and strategically integrated avatar salesforce will provide a retailer with a competitive advantage.

A virtual store, an ecommerce experience powered by VR and AR technologies, has also emerged as an alternative tool for addressing the most basic customer needs and is witnessing increased popularity. These stores allow consumers to virtually try products and instantly access information about them while also providing highly engaging and immersive shopping experiences. It can be a virtual reality retail store or a mobile augmented reality app that allows customers to experience the products physically and hence close the gap between the physical world and the online realm leading to customers having personalized and satisfying experiences.

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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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