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 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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