Data science
Data science has revolutionized the way businesses approach problem-solving and decision-making. By leveraging vast amounts of data and advanced analytical techniques, organizations can gain valuable insights and make informed decisions. The creation of data-driven solutions involves a process that includes data collection, analysis, and implementation.
The first step in creating a data-driven solution is to collect relevant data. This data can come from a variety of sources, including internal databases, external sources, or social media. The collected data is then cleansed, transformed, and processed to prepare it for analysis.
The next step is to perform data analysis, which is the process of uncovering patterns, relationships, and insights in the data. Data scientists use various techniques, such as statistical analysis, machine learning, and predictive modeling, to analyze the data and extract meaningful information.
Finally, the results of the analysis are used to create a solution that addresses the business problem. This solution can take the form of a predictive model, a recommendation engine, or an automated process. The implementation of the solution requires collaboration between data scientists, developers, and business stakeholders to ensure that it aligns with the organization's goals and provides the desired results.
In conclusion, creating data-driven solutions requires a multidisciplinary approach that leverages the latest data science techniques and technologies. By using data to inform decision-making, organizations can improve their operations, increase efficiency, and achieve better outcomes.
Visual AI
Visual AI is the field of computer science that deals with training machines to understand images and visual data in the same way that humans do.
Use machine learning to understand your images with industry-leading predictive accuracy
Train machine learning models that classify images by custom labels using AutoML Vision
- Detect objects and faces, read handwriting and build valuable image metadata with Vision APIs
- Prevent the sale of branded counterfeits
- Prevent the sale of cosmetic and pharmaceutical counterfeits
- Prevent counterfeiting in the supply chain
- Prevent the risk of lawsuits
Visual inspection:
The artificial intelligence-based Visual Intelligence Platform optimizes these systems and processes and provides a holistic view of the network to help reduce the cost and complexity associated with traditional inspection approaches, while improving risk management and productivity.
AI-based monitoring solutions
Smart cities are relying on AI-based monitoring systems to improve the quality of life for citizens. From improved public safety to enhanced mobility, artificial intelligence has a lot to offer.
When it comes to smart city technology, cities rely on a vast, interconnected network of Wi-Fi-enabled sensors and other devices to optimize the results of big data analysis, resulting in a higher quality of life for residents and visitors alike.
Smart cities generate huge amounts of data. The data created is useless until it is processed, which generates information instead. Artificial intelligence can then extract the most meaning from this data. AI technology allows machines to interact by processing data and making sense of it. For example, in a system containing energy spikes, artificial intelligence can learn where they typically occur and under what circumstances. This information can then be used to better manage the power grid. Similarly, AI systems also play a role in intelligent traffic management and healthcare facilities.
Visual AI in retail
Computer vision is a field of artificial intelligence (AI) focused on enabling machines to recognize and analyze objects in the real world.
Computer vision can accurately count retail customers and analyze their behavior in aggregate. For example, retailers track a customer's journey in the store, interactions with products and make sure the store follows health and safety protocols (such as maximum store occupancy).
Artificial intelligence in retail can reduce the pressure on staff by alerting teams to misplaced items or empty shelves, using deep learning models for computer vision to detect when something is missing or in the wrong place. The software can even inform staff when a product is out of stock - or alert them to damaged items and incorrect price labels. In this way, it helps them focus on providing better customer service and making personalized recommendations.
Prediction models
Prediction modeling is a widely used statistical technique for predicting future behavior. Prediction modeling solutions are a form of data mining technology that works by analyzing historical and current data and generating a model to help predict future outcomes.
Examples of predictive modeling include estimating the quality of a potential sale, the probability of spam, or the likelihood that someone will click a link or buy a product. These capabilities are often built into a variety of business applications, so it is useful to learn the mechanics of predictive modeling to solve problems and improve performance.
Very useful when considering demand forecasts.
Workforce planning and customer turnover analysis.
In-depth competitive analysis.
Forecasting external factors that may affect workflow.
Fleet maintenance.
Financial risk identification and credit modeling.
Data science team augmentation
Data Science Staff Augmentation is an alternative to in-house recruiting and consulting assignments
Reduce time spent on recruitment
Avoid infrastructure investments
Eliminate the costs of having your own employees, such as PTO and benefits
Increase or decrease on-demand workforce
Direct communication and higher levels of integration with your team
Greater control over project decisions and outcomes (e.g., ownership of intellectual property rights)
Apply hired expertise to multiple projects
Human behavior recognition
The goal of vision-based human behavior recognition is to assign a video sequence to the corresponding activity category. the task of human behavior recognition is accomplished through a sequence of image processing steps
There are six categories of activities in human behavior recognition: walking, falling, lying down, standing up, bending over and sitting down.
Human actions can be represented by various data modalities such as RGB, skeleton, depth, infrared, point cloud, event stream, sound, acceleration, radar and Wi-Fi signal, which encode different sources of useful but distinct information and have different advantages depending on on application scenarios
Although a lot of work has already been done in the field of human activity and behavior recognition, there are still many open questions to be explored in the future, especially in the following areas. In terms of applications, in healthcare, a step toward a safer and healthier society has begun with the rapid development of smart homes, but they are out of reach due to the high financial outlay, so cheaper and effective equipment and techniques need to be developed in the near future so that more people can afford and apply them.
Full body monitoring
Body tracking The body tracking module focuses on detecting and tracking a person's bones. A detected bone is represented by its two endpoints, also called key points. The ZED camera is able to provide 2D and 3D information about each detected key point. In addition, it causes local rotation between neighboring bones.
The whole process is very similar to the ZED SDK object detection module. They provide some information in the output, such as the 3D position and 3D speed of each person. The body tracking module also uses a neural network to detect key points, and then calls the depth and position tracking of the ZED SDK module to get the final 3D position of each key point. The ZED SDK supports two content formats
Benefits of body monitoring using a training application as an example
- Makes you more likely to meet and exceed your goal
- Allows you to be more efficient with your time and workouts
- Gives you accountability for yourself and your goals
- Allows for easier modifications and shows you when and where to make changes
- Reminding you why you are doing what you are doing can be motivating and empowering
- Helps guide your focus and programming direction
- Keeps you committed to your plan
Visual AI in fitness and sports
Provides simple exercise tracking to pinpoint your strengths and weaknesses and work to improve in areas that need attention. Your trainer uses this information and AI-based recommendations to inform you of the best exercises and routines tailored to your specific needs with step-by-step tutorials on how to stay safe. With fitness data from more than 6 million workouts, the algorithm provides users with a customized workout plan.
- Artificial intelligence in fitness mobile app
- Wristbands based on artificial intelligence
- Artificial intelligence-based diet planning mobile apps
- Smart shoes powered by artificial intelligence
- Intelligent assistants for gyms and other fitness clubs
- Artificial intelligence-based yoga suits to monitor movement and set accurate posture during asanas
Machine learning models
A machine learning model is a file that has been trained to recognize certain types of patterns. You train the model on a set of data, providing it with an algorithm it can use to infer and learn from that data.
Once the model is trained, it can be used to analyze data it hasn't seen before and make predictions about that data.
Benefits:
Faster decision-making By enabling companies to process and analyze data faster than ever before, machine learning enables rapid - even split-second - decision-making.
More accurate demand forecasting To compete in a rapidly changing business environment, companies are under increasing pressure to predict market trends and customer behavior
Personalize customer engagement Personalization has also become a key strategy for competing in today's marketplace
Increasing productivity Using machine learning allows organizations to speed up repetitive tasks and shift human resources to higher value activities
With predictive machine learning models, organizations can collect performance data on equipment and components to monitor their condition and calculate the remaining life of resources.