As the demand for artificial intelligence continues to rise in various industries, from healthcare and finance to manufacturing and autonomous vehicles, industrial computers face the challenge of optimizing AI workloads. Developers are constantly seeking efficient and scalable solutions to solve these challenges. One such solution is using COM Express , a standardized form factor that can be used as a flexible computing platform for various AI workloads.

With the ability to choose from wide variety of CPUs and the flexibility to right-size CPU to target various AI workloads, COM Express empowers organizations to create efficient, scalable, and cost-effective AI solutions. In addition to harnessing the advantages of COM Express, developers can leverage additional AI accelerators to further optimize the solutions. COM-HPC, a new specification, further enables enhanced performance and scalability for high-performance computing applications.

The Intel Alder Lake x86 CPU is an ideal solution for COM Express modules targeting AI workloads due to built-in AI acceleration with Intel Deep Learning Boost technology. This integrated AI capability allows for efficient execution of AI workloads, such as neural network inference and deep learning tasks. By leveraging the built-in AI accelerator, COM Express modules based on Alder Lake can provide optimized performance for AI applications without the need for additional external accelerators.

What is COM Express?

COM Express is a highly integrated and compact computer on module that is designed to offer scalability and flexibility by providing a standardized form factor and interface for integrating different processor architectures and I/O configurations. Introduced by the PCI Industrial Computer Manufacturers Group in 2005, COM Express provides a single circuit board with integrated RAM.

This family of modular, small form factor modules has gained significant traction in various industries, including automation, gaming, retail, transportation, robotics, and medical fields. With eight different types, four sizes, and three major revisions, COM Express promotes vendor technology reuse while catering to mid-range edge processing and networking requirements.

The key differentiator of COM Express from traditional single-board computers (SBCs) lies in its ability to plug off-the-shelf modules into custom carrier boards designed for specific applications. This enables an upgrade path for the CPUs while keeping the carrier board intact. By using a custom COM Express carrier board, all necessary signals can be efficiently routed to the peripherals, while COM Express processor modules serve as the main controller. These advanced features ensure the versatility and adaptability of COM Express for diverse application requirements.

Comparing COM-HPC with COM Express

COM-HPC is an evolution of the COM Express standard, uniquely tailored to address the demands of high-performance computing applications. With its focus on enhanced performance, scalability, and advanced features, COM-HPC caters to the same applications and markets as COM Express, but with notable differentiators. It boasts higher-end CPUs, expanded memory capacity, and increased and faster I/O capabilities. It’s essential to emphasize that COM-HPC does not aim to replace COM Express, rather the two standards exist as distinct entities in the field of embedded computing, offering developers a broader spectrum of choices to meet specific application requirements.

COM-HPC brings significant improvements over COM Express for AI workloads, particularly in terms of PCIe lanes and PCIe generation support:

• Increased PCIe Lanes: One of the key advantages of COM-HPC over COM Express is the availability of more PCIe lanes. COM Express has a limited number of PCIe lanes, which can restrict the connectivity options and the number of I/O interfaces or accelerators that can be integrated. In contrast, COM-HPC modules provide a higher number of PCIe lanes, allowing for more extensive connectivity and the integration of multiple high-speed devices.
• PCIe Gen4/5 Support: Another crucial enhancement in COM-HPC is the support for PCIe Gen4 and Gen5, whereas COM Express supports up to PCIe Gen3. PCIe Gen4 and Gen5 offer higher data transfer rates and improved bandwidth compared to Gen3. This is particularly advantageous for AI workloads that require fast data movement between the CPU, GPU, storage devices, and other peripherals.

In summary, newer generation processors, paired with higher data rates, dramatically lower the size, power and cost requirements of the systems required to perform the AI tasks.

The Advantages of Choosing COM Express for AI Workloads

COM Express offers several distinct advantages when it comes to AI workloads. As a flexible and scalable platform, it provides developers to adapt their AI systems according to specific requirements like CPU performance, power requirements. They can then design a carrier board that integrates the module with additional AI-specific components, such as AI accelerators. Below is the block diagram example of COM Express platform with AI Accelerator.

Here are the key advantages of choosing COM Express (or COM-HPC) for AI workloads:

• Flexibility and Scalability: COM Express allows developers to choose from a wide range of CPU options. Such kind of flexibility allows them to choose the module that best matches the computing needs of their AI workloads. Whether it’s a complex neural network inference or deep learning task, the platform can be customized to deliver optimal performance.
• Modular Design: COM Express follows a modular design approach with a separate CPU module and carrier board. This modularity simplifies system customization and future upgrades. Developers can easily swap out or upgrade the CPU module without redesigning the entire system, saving time and effort while adapting to evolving AI requirements.
• Streamlined Integration: COM Express adheres to industry-standard form factors and interfaces, ensuring compatibility across different vendors. This standardized approach simplifies system integration, reducing development complexity and time to market. Developers can focus on optimizing their AI algorithms and software, confident that the hardware integration will be seamless.
• Rich Connectivity Options: COM Express provides a wide array of interfaces, including Ethernet, USB, PCIe, and DisplayPort interfaces. These interfaces enable effortless integration with various peripherals, sensors, and external devices commonly used in AI applications. The rich connectivity options enhance data I/O capabilities, facilitating efficient communication and interaction within the AI system.
• Long-Term Availability and Support: COM Express offers long-term availability and support, ensuring continuity for AI deployments. This is particularly crucial for industries that rely on stable and long-lasting AI systems. With a consistent platform and extended availability, developers can plan for long-term deployment and maintenance, with access to software updates and technical assistance.
• Cost Optimization: COM Express provides a cost-effective solution for AI workloads. By leveraging COM Express, developers can save on development costs and reduce time to market. The modular design allows for efficient resource allocation, ensuring optimal performance while minimizing unnecessary expenses.
• Time to Market: Since the computer modules are widely available in the embedded marketplace, COM Express enables developers to focus on the IO needs, the addition of accelerators, the AI models and application software.

Real-World Applications of COM Express for AI Workloads

As stated above, COM Express modules offer immense potential for developers to optimize AI workloads on industrial computers, leading to transformative impacts and various implications for cost-effective solutions and large-scale deployments. Let’s delve into real-world examples and insights to showcase the significance of this optimization trend.

In the field of autonomous vehicles, this optimization trend allows autonomous vehicles to navigate complex environments, enhancing safety and efficiency. By leveraging COM Express modules, developers can achieve cost-effective solutions by utilizing existing industrial computers and upgrading them with optimized AI capabilities, resulting in large-scale deployments of autonomous vehicles across transportation networks.

Industrial automation is another area where COM Express systems can revolutionize AI workloads. By optimizing AI algorithms on industrial computers using COM Express modules, developers can achieve significant cost savings and efficiency gains in manufacturing processes. For instance, AI-powered computer vision systems can inspect and detect defects in real-time, improving quality control and reducing production costs. The use of COM Express modules enables industrial computers to handle these AI workloads effectively, making cost-effective solutions viable for large-scale deployment in manufacturing facilities.

In the healthcare sector, COM Express systems can optimize AI workloads on industrial computers to improve diagnostics, patient monitoring, and personalized treatment. For example, by leveraging COM Express systems, developers can enable industrial computers to process complex medical imaging data and apply AI algorithms for more accurate and timely diagnosis. This optimization trend in AI workloads allows healthcare providers to deliver cost-effective, benefiting patients globally.

What to choose

AI accelerators are paired with COM Express module on the carrier as separate modules or integrated directly into the carrier board’s design. This modular approach provides scalability and flexibility, allowing system designers to customize AI processing capabilities to meet the specific requirements of their applications. It also enables easy upgrades or replacements of AI accelerators without having to modify the entire system, making it both cost-effective and future-proof. AI accelerators such as Blaize, Hailo or Axelera paired with COM Express module can provide significant benefits. For example, combining Axelera M.2 AI Edge accelerator module with COM Express Carrier board can achieve up to 120 TOPS of AI performance with the flexibility of switching between the CPU families for optimized compute needs.

These accelerators are specifically designed to enhance AI workloads and provide optimized compute capabilities compared to GPUs. This level of compute power can greatly benefit vision processing applications, which often require intensive computations for tasks such as object detection and classification.

Conclusion

COM Express and COM-HPC offer flexible and scalable platform to enable various AI workloads, allowing developers to customize their systems based on CPU performance, power requirements, and I/O interfaces. CPUs like Intel Alder Lake integrated into COM Express modules provide efficient AI execution, integrated graphics performance, enhanced compute density, ecosystem support, and broad connectivity options. The combination of the CPU with optional AI Accelerator delivers optimized performance, reducing costs and enabling efficient large-scale AI deployments.

With the Tauro Technologies’ team of electronic engineers and designers it becomes possible to design and deploy comprehensive AI processing systems based on x86 and ARM CPUs paired with various AI Accelerators. This strategic approach helps bring down costs and ensures the right balance between compute power and AI processing needed for the system. We can customize the I/O as well as the footprint to fit your application requirements.

Interested to know more? Get in touch with us for details.

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The field of neuroscience and artificial intelligence (AI) have traditionally been studied separately, with little overlap between the two. However, in recent years, there has been growing interest in the potential for cooperation between these two fields. By combining the insights and technologies of neuroscience and AI, we can unlock new possibilities for understanding the brain and creating intelligent machines.

One of the main ways that neuroscience and AI can work together is through the development of brain-inspired AI. By studying the structure and function of the brain, scientists can gain insights into how to create intelligent systems that mimic the brain work. This approach, known as neuromorphic computing, involves building computer systems that are modeled after the neural networks of the brain. These systems can process information in a more efficient and adaptable way, allowing them to perform tasks such as image recognition and natural language processing with greater accuracy.

Artificial Neural Networks (ANNs)

One of the key ways in which AI and neuroscience are driving each other forward is through the development of artificial neural networks (ANNs). ANNs are a type of machine learning algorithm that are modeled after the structure and function of the human brain. These algorithms are designed to learn from data and improve their performance over time, much like the way the human brain learns and adapts.

Neuroscience has played a crucial role in the development of ANNs by providing insights into the structure and function of the brain. For example, neuroscientists have discovered that the brain is made up of a large number of interconnected neurons, which communicate with each other through electrical and chemical signals. This has inspired researchers in the field of AI to develop artificial neural networks that mimic the structure and function of the brain.

In turn, AI has been driving neuroscience forward by providing new tools and techniques for analyzing and understanding the brain. For example, AI algorithms such as deep learning have been used to analyze large datasets of brain imaging data, which has led to new insights into the neural processes underlying cognition and behavior. Additionally, AI-based models have been developed to simulate neural networks and generate predictions about how the brain works, which can be tested and validated through experiments.

Brain Computer Interfaces (BCIs)

Neuroscience has been instrumental in the development of BCIs by providing insights into the neural processes that underlie perception, action, and communication. For example, neuroscientists have discovered that certain patterns of neural activity in the brain correspond to specific movements or actions, which has led to the development of BCIs that can control prosthetic limbs or other devices based on these patterns of activity.

On the other hand, AI has been driving the development of BCIs by providing new algorithms and techniques for analyzing and interpreting neural signals. For example, AI algorithms have been used to classify and decode neural signals, which can be used to control prosthetic limbs or other devices. Additionally, AI-based models have been developed to predict neural signals based on patterns of activity, which can be used to improve the performance of BCIs.

Natural and Human Language Processing

Furthermore, AI and neuroscience are also working together in the field of natural language processing, which involves using computers to understand and generate human language. Natural language processing is an interdisciplinary field, which draws on techniques from AI and computational linguistics, as well as insights from linguistics and cognitive psychology.

In addition to these technical advancements, cooperation between neuroscience and AI can also lead to a better understanding of the brain itself. For example, by using AI techniques to analyze large amounts of brain imaging data, scientists can gain insights into the neural mechanisms underlying various mental disorders, such as autism and schizophrenia. Furthermore, AI can also be used to model the brain’s processes, allowing scientists to test hypotheses and make predictions about the brain’s function in a way that would not be possible with traditional methods.

Of course, there are also ethical concerns to consider when it comes to the intersection of neuroscience and AI. One of the main concerns is the possibility of creating machines that are capable of making decisions and behaving autonomously. While this could lead to the development of intelligent systems that can perform a wide range of tasks, it also raises questions about accountability and the potential for misuse. Therefore, it is important that scientists and policymakers work together to develop guidelines and regulations to ensure that the development of AI is aligned with ethical and moral principles.

In conclusion, the cooperation between neuroscience and AI holds enormous potential for advancing our understanding of the brain and creating intelligent machines. By combining the insights and technologies of these two fields, we can unlock new possibilities for understanding the brain and creating intelligent machines. However, it is important that scientists and policymakers work together to ensure that the development of AI is aligned with ethical and moral principles.

Our team has been expanding our design offerings to optimize AI accelerated hardware solutions with customized software applications – in fact, this blog was partially written and edited utilizing AI tools.  

Interested to know more? Get in touch with us for details.

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The modern-day world is going through a digital transformation process, which triggers technological changes across all economy, commercial, and consumer-focused sectors. These changes include quick penetration of new machines and electronic tools, IoT (Internet of Things) devices, connected software, and other solutions influencing how we interact with technology.

User interfaces of electronic systems and devices change as well. Keyboards, touch panels, switches, graphic interfaces, and buttons are being increasingly replaced by touchless ways of interacting with machines, such as gesture recognition and voice control.

Today we’d like to talk in more detail about different types of touchless interfaces, the technologies that are powering them, and why they are finding more and more applications in other industries, with the increasing frequency being integrated into machines across various commercial and public spaces.

Demand for touchless interfaces post-COVID is on the rise

Touchless interfaces have multiple strengths over other kinds of human-machine interaction (HMI) and human-computer interaction (HCI) technologies. They enable faster access to information and decision-making processes, make interactions with machines and software systems easier, more responsive, safer and more secure.

The relevance of touchless interfaces, already significant, was fueled significantly by the COVID-19 pandemic. In today’s post-pandemic business environment, touchless technologies provide consumers with a safer way to interact with computer systems and machines, without spreading germs and other pathogens.

According to a survey conducted by Ernst & Young, the demand for touchless sensory interfaces has accelerated tremendously since the beginning of the COVID-19 pandemic: 59% of global consumers surveyed said they increasingly lean towards using contactless delivery and cashless payments in the post-COVID world.

Touchless interfaces applications

Organizations in various business fields are now looking to replace old-fashioned human-machine interaction (HMI) and human-computer interaction (HCI) with touchless interfaces.

Here are a few examples of common applications for touchless interfaces across industries:

• Voice assistants and facial recognition in mobile phones,
• Interfaces to interact with ATMs and banking terminals (facial recognition and NFC),
• Automatic translation of sign language,
• Gesture and voice recognition in smart buildings (elevators, entrances, bathrooms, etc.),
• Technologies for hygiene-sensitive areas
• Voice control and gesture recognition in automobiles and other vehicles,
• Gesture recognition in robotic devices and industrial machinery,
• VR (virtual reality) and AR (augmented reality) simulation training systems,
• Contactless sales solutions in retail businesses.

Types of touchless interfaces

Let’s talk in more detail about the most common types of touchless interfaces that are utilized most frequently today.

Voice recognition

Voice recognition is one of the most common types of touchless interfaces implemented today. It allows to simplify interactions with software solutions and machines significantly, but also has certain drawbacks as the voice recognition interface is relatively complicated and can be challenging to operate properly. Voice control is easier to implement for systems that are limited to a fixed set of specific standard commands. Solutions that need to recognize a large number of words would normally require a complex speech recognition software. Voice recognition technology varies by product. Most often products based on this technology allow users to transcribe voice to text, set reminders, search the internet and ask for simple types of content, such as music, weather forecasts or traffic information.

Hand gesture recognition

Hand gestures can be a convenient, relatively easy to implement and highly precise interface solution to control software systems and IoT devices, such as smart home electronics, robots, industrial machines, etc. One of the most common applications for hand gesture recognition today is switching the lights on and off in smart houses. In the automotive industry, hand gesture recognition allows drivers and passengers to interact with the vehicle — typically, to control the infotainment system without touching any buttons or screens.

Face detection and recognition

Face detection and recognition are two distinct types of commonly used touchless interfaces. Naturally, the detection of face, signaling to a computer system about the presence of a person, is considerably easier to implement as it basically just requires an always-on camera with high enough resolution to identify a human face in a continuous video stream. The recognition of specific faces requires a more complex technology behind, normally supported by an AI-powered search and a database of people’s faces stored on a local machine or a remote server.

Body gestures recognition

The recognition of human body gestures is a less common type of touchless interface than the recognition of hands or faces. Still, it also has several applications in specific business fields. Specifically, body gesture recognition can be a solution in training simulation systems for workers in manufacturing and various industrial environments. Fitness and sports training systems are other relevant applications for body gesture recognition interfaces.

Direction of sight, age, face expression, and gender recognition

The most sophisticated types of touchless interfaces allow users to interact with computer systems by means of recognizing specific characteristics such as the line of sight of the person, facial expression (emotions recognition), gender, and age. As an example, such systems have tremendous potential to be used in retail and by other consumer-facing businesses to personalize offers and deliver highly targeted promotional content. For more details about this type of touchless interface, read our previous blog post on human movement technologies. 

Gesture recognition interface technologies

Different algorithms and models powering touchless interfaces exist. Let’s review the most common approaches utilized in gesture recognition as universal types of touchless interfaces used today.

Any gesture recognition system is built on two basic processes: the acquisition of input data and its recognition. The acquisition is converting physical human gestures into digital data. It is typically performed using all kinds of sensor-based devices, such as cameras, gesture-based controllers, motion detectors, wired gloves, etc.

The interpretation of acquired data is typically implemented with a number of different algorithms and approaches. Here are the most common ones:

3D model-based algorithms

3D model-based algorithms rely on using volumetric or skeletal models created from complex three-dimensional surfaces. In some cases, volumetric and skeletal models are used in combination with each other.

Electromyography-based algorithms

Electromyography (EMG) is a technology that allows the recording of electrical signals produced by muscle movements inside the human body. The EMG data is typically recorded either by sophisticated cameras able to detect muscle movement or by electrodes placed directly on the skin.

Skeletal-based algorithms

Skeletal-based models are a simpler and cheaper alternative to 3D model-based algorithms. This approach creates a virtual skeletal representation of the person’s body by digitally mapping all the main segments of the skeleton and analyzing the positioning of the body parts based on this model.

Appearance-based models

Appearance-based models rely on creating a representation of the body or, more frequently, body parts based on two-dimensional templates of the human body parts. Such models are mostly used for hand gesture detection and recognition, so they typically require templates of a human hand with a selection of the most basic hand gestures.

Summary

The demand for touchless interfaces in the modern world is growing quickly, fueled by the post-COVID precautions and the need to integrate new ways of interacting with computers, machines, robots, and software systems across markets and industries, from consumer electronics and industrial automation to construction, healthcare, education, and entertainment industry. 

The design and integration of a touchless interface requires a common effort of a high-profile team of embedded systems engineers, ergonomic specialists, software developers, and other experts.

The Tauro Technologies team of electronic engineers and designers has a proven track record of successfully designing custom hardware for various kinds of embedded systems and IoT products in multiple technology fields. Drawing on the specific needs of our clients, we select and apply various engineering methods to electronic product development and manufacturing in order to achieve the desired result. Utilizing our in-house expertise in assembly and debugging of embedded systems and touchless interfaces, we are able to build and evaluate your prototypes before high-volume manufacturing, rapidly and cost-efficiently.

Interested to know more? Get in touch with us for details.

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The use of data analytics tools for online shopping is widespread and if we judge by the hype, we imagine it is everywhere in retail stores as well. Camera systems installed in most retail locations generate vast streams of data every second that are impossible to monitor manually in real-time.

Whether we like it or not, our every move when we engage with retail sites on the web is tracked, analyzed and monitored using tools like click management, heat mapping, customer purchase history, preferences & demographics, to deliver a hyper targeted shopping experience. Stores can easily attempt to close abandoned online baskets, upsell and cross sell other products to the site visitors. ROI(Return On Investment) is easily measured by customer engagement and incremental purchases.

Historically, it is more difficult to track and analyze consumer behaviors in brick-and-mortar stores and provide them with a personalized experience without the direct human interaction. However, advances in AI are bringing promise to enable retailers to better analyze and manage their customer interactions.

Utilizing AI in brick and mortar:

Being efficient with advertising is the goal of every marketer and the retail industry is greater than 15% of the entire digital ad spend annually. It is challenging to quantify the results – managing data is the key and is both an art and a science. For example, a big box lumber and hardware chain sends out a flier with a seasonal promotion. Currently they are able to measure that traffic was up 5% and sales were up 10% compared to the previous week. But was the campaign really effective? What if the example big box marketing department could utilize the video feeds in their store and have their AI answer some of the following questions:

• Percentage of people that specifically visited and purchased the items on the promotion.
• Effectiveness of complementary product placement
• Customer experience and path through the store
  • Which departments did they visit?
  • Were they helped by staff?
  • Did they purchase additional products after staff interaction?
  • Length of time a checkout
  • Percentage of people that did not purchase an item
  • Average length of the store visits compared to the previous weeks.

While it is impossible for humans to watch and track hundreds of shoppers simultaneously, it is easily accomplished with object detection and tracking in many of today’s edge AI platforms.  Where it was previously expensive and resource prohibitive, the retailer can also do valuable A/B testing across different stores or within a store and make immediate changes to increase profits, inventory levels, revenues and efficiency.  Examples of these tests may include:

• Staffing levels & training.
• Product placement – retailer marketers invest massive resources to optimize. 

How does AI review patterns of human movement:

Pattern recognition is a complex process of analyzing input data, extracting patterns, comparing them with certain standards, and using the results to guide the future actions of the system. Pattern recognition involves recognition of surrounding objects in an artificial manner achieved through machine learning and pattern recognition algorithms. In other words, it is the process of identifying the trends in the given pattern. In the Machine Learning(ML) space, pattern recognition shows the use of robust algorithms in order to identify the regularities in the given set of data.

The following image (Fig 1) shows how data is used for training and testing:

The training set contains images or data used for training or building the model. Training rules are used to provide the criteria for output decisions.  Training algorithms are used to match a given input data with a corresponding output decision. The algorithms and rules are then applied to facilitate training. The system uses the information collected from the data to generate results.

The testing set is used to validate the accuracy of the system. The testing data is used to check whether the accurate output is obtained after the system has been trained. This data represents approximately 20% of the entire data in the pattern recognition system.

There are three basic approaches that pattern recognition algorithms utilize:

• Statistical. This approach is based on statistical decision theory. Pattern recognizer extracts quantitative features from the data along with the multiple samples and compares those features. However, it does not touch upon how those features are related to each other.
• Structural (a.k.a. syntactic). This approach is closer to how human perception works. It extracts morphological features from one data sample and checks how those are connected and related.
• Neural. In this approach, artificial neural networks are utilized. Compared to the ones mentioned above, it allows more flexibility in learning and is the closest to natural intelligence.

Every machine learning-based pattern recognition algorithm includes the following steps:

• Input of data. Large amounts of data enter the system through different sensors.
• Preprocessing or segmentation. At this stage, the system groups the input data to prepare the sets for future analysis.
• Feature selection (extraction). The system searches for and determines the distinguishing traits of the prepared sets of data.
• Classification. Based on the features detected in the previous step, data is assigned a class (or cluster), or predicted values are calculated (in the case of regression algorithms).
• Post-processing. According to the outcome of the recognition, the system performs future actions.

Alongside machine learning, deep learning is also implemented in training pattern recognizers when it comes to neural networks.

Human activity recognition consists of four stages (Fig 2) including (1) capturing of signal activity, (2) data pre-processing, (3) AI-based activity recognition, and (4) the user interface for the management of HAR (Human Activity Recognition). Each stage can be implemented using several techniques bringing the HAR system to have multiple choices. Thus, the choice of the application domain, the type of data acquisition device, and the processing of artificial intelligence (AI) algorithms for activity detection makes the choices even more challenging. 

Many retailers have already installed camera systems for security purposes. A solutions integrator can install a computer system with an AI accelerator to provide customer heat mapping, traffic patterns, and surveillance. 

• The systems auto tag, recognize gender, size of group (single or family shoppers) etc.
• Recognize and provide real-time computation and output of shopper data analysis that can be actioned immediately by the retailer.   

When it comes to tracking human movement, the AI is trained to use the video feed to recognize parts of the body like the head, arms, and legs as shown in Fig 3. Once these parts are recognized, the pose of the person is then analyzed, such as if they are standing, sitting, walking etc. and how they move as time progresses with additional frames. The algorithms can be refined to track the person through the store, analyze their movements and provide alerts if there is a security instance.

Machine learning-based pattern recognition systems are also being applied to extract greater value from existing data. Machines can look at data to find insights, patterns and groupings and use the power of AI systems to find patterns and anomalies humans aren’t always able to see. This has broad applicability to both back-office and front-office operations and systems. Whereas, before, data visualization was the primary way in which users could extract value from large data sets, machine learning is now being used to find the groupings, clusters and outliers that might indicate some deeper connection or insight.

Further benefits of AI in Brick and Mortar Retail

In addition to providing security and data analytics for marketing purposes, AI in retail provides real-time data that can be used to improve efficiencies. For example:  

• Consumer purchasing decisions often revolve around the change of seasons, holidays, and also weather. With real-time analytics, it is easier for the retailer to adjust product placement and offerings.
• Large chains are known to change their prices 1000s of times per week to maximize their revenue, profitability, and to manage inventories.  
• Tracking shopper behavior and engagement in a retail setting including staff interactions enables better brand engagement and optimization.
• Although most retail purchases still take place in stores, many sales are driven by online presence. AI analytics can provide further insight into consumer brand awareness and product choice.

Tauro Technologies is working with several integrators on the crucial building blocks that will enable the brick and mortar business to further transform in the coming decade. Reach out to us if you are interested in learning more.

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