Supervised learning is a machine learning model that maps a specific input to an output using labeled training data (structured data). In simple terms, to train the algorithm to recognize pictures of cats, feed it pictures labeled as cats.

Unsupervised learning is a machine learning model that learns patterns based on unlabeled data (unstructured data). Unlike supervised learning, the end result is not known ahead of time. Rather, the algorithm learns from the data, categorizing it into groups based on attributes. For instance, unsupervised learning is good at pattern matching and descriptive modeling.

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Additionally, for driver-training managers, vehicle speed, G-Sensor, and GPS data can be paired with the accompanying video recordings to assess driver performance and design more effective training programs. This helps to reduce strain on vehicles, improve success rates in terms of pursuits, and reduce costly accidents.

While the reasons can be political, legal, financial, or technical, police forces that fail to adopt this technology are missing an essential tool for deterring and investigating crime. Equipping an entire fleet with dash cams has a multitude of benefits which can contribute to improved force performance and operating efficiency.

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Artificial general intelligence (AGI) would be the ability for a machine to “sense, think, and act” just like a human. AGI does not currently exist. The next level would be artificial superintelligence (ASI), in which the machine would be able to function in all ways superior to a human.

By using products like Vertex AI, CCAI, DocAI, or AI APIs, organizations can make sense of all the data they’re producing, collecting, or otherwise analyzing, no matter what format it’s in, to make actionable business decisions.

Feedforward neural networks (FF) are one of the oldest forms of neural networks, with data flowing one way through layers of artificial neurons until the output is achieved. In modern days, most feedforward neural networks are considered “deep feedforward” with several layers (and more than one “hidden” layer). Feedforward neural networks are typically paired with an error-correction algorithm called “backpropagation” that, in simple terms, starts with the result of the neural network and works back through to the beginning, finding errors to improve the accuracy of the neural network. Many simple but powerful neural networks are deep feedforward.

Some studies suggest that body-worn cameras may offer benefits while others show either no impact or possible negative effects.

Beyond front-line policing and reputation, the latest dash cams provide police forces with the possibility to enhance operational efficiency for the fleet, too.

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AI can eliminate manual errors in data processing, analytics, assembly in manufacturing, and other tasks through automation and algorithms that follow the same processes every single time.

AI is a broad field that encompasses many different disciplines, including computer science, data analytics and statistics, hardware and software engineering, linguistics, neuroscience, and even philosophy and psychology.

Many commercial businesses have already seen significant ROI from employing fleet dash cam technology, and budget-stretched police departments are under the same pressures to drive cost savings and improve performance. Let’s explore how in-vehicle dash cam technology is an investment that police departments can no longer afford to ignore.

Firstly, it’s worth highlighting just how far dash cam technology has advanced since the early days of VGA dash cams with poor-quality sensors. Most cameras now record Full HD (1080p) video at up to fifty frames per second, which provides a high-definition record of incidents.

This connected approach can bring an additional boost to the efficiency and effectiveness of police operations. It gives supervisors another way to monitor officers and assess their situations, allowing a more proactive approach to managing units and allocating backup. The ability to upload video remotely, without needing to wait for a vehicle to return to base, could save hours in time-critical investigations, potentially reducing the time needed to charge suspects or diffuse incidents in the community.

Convolutional neural networks (CNN) include some of the most common neural networks in modern artificial intelligence. Most often used in image recognition, CNNs use several distinct layers (a convolutional layer, then a pooling layer) that filter different parts of an image before putting it back together (in the fully connected layer). The earlier convolutional layers may look for simple features of an image, such as colors and edges, before looking for more complex features in additional layers.

A more useful way of broadly categorizing types of artificial intelligence is by what the machine can do. All of what we currently call artificial intelligence is considered artificial “narrow” intelligence, in that it can perform only narrow sets of actions based on its programming and training. For instance, an AI algorithm that is used for object classification won’t be able to perform natural language processing. Google Search is a form of narrow AI, as is predictive analytics, or virtual assistants.

Artificial intelligence is a field of science concerned with building computers and machines that can reason, learn, and act in such a way that would normally require human intelligence or that involves data whose scale exceeds what humans can analyze.

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This learning process often involves algorithms, which are sets of rules or instructions that guide the AI's analysis and decision-making. In machine learning, a popular subset of AI, algorithms are trained on labeled or unlabeled data to make predictions or categorize information.

In-vehicle dash cams are hardly a new invention. But, thanks to rapidly improving technology and lower costs, they have been growing in popularity with motorists. However, despite the obvious benefits that in-vehicle video surveillance would offer to law enforcement officers, the devices are still not standard issue across the world’s police forces.

Reinforcement learning is a machine learning model that can be broadly described as “learn by doing.” An “agent” learns to perform a defined task by trial and error (a feedback loop) until its performance is within a desirable range. The agent receives positive reinforcement when it performs the task well and negative reinforcement when it performs poorly. An example of reinforcement learning would be teaching a robotic hand to pick up a ball.

When businesses talk about AI, they often talk about “training data.” But what does that mean? Remember that limited-memory artificial intelligence is AI that improves over time by being trained with new data. Machine learning is a subset of artificial intelligence that uses algorithms to train data to obtain results.

Recurrent neural networks (RNN) differ from feedforward neural networks in that they typically use time series data or data that involves sequences. Unlike feedforward neural networks, which use weights in each node of the network, recurrent neural networks have “memory” of what happened in the previous layer as contingent to the output of the current layer. For instance, when performing natural language processing, RNNs can “keep in mind” other words used in a sentence. RNNs are often used for speech recognition, translation, and to caption images.

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A neural network is a system of artificial neurons—sometimes called perceptrons—that are computational nodes used to classify and analyze data. The data is fed into the first layer of a neural network, with each perceptron making a decision, then passing that information onto multiple nodes in the next layer. Training models with more than three layers are referred to as “deep neural networks” or “deep learning.” Some modern neural networks have hundreds or thousands of layers. The output of the final perceptrons accomplish the task set to the neural network, such as classify an object or find patterns in data.

AI can be used to perform repetitive tasks, freeing human capital to work on higher impact problems. AI can be used to automate processes, like verifying documents, transcribing phone calls, or answering simple customer questions like “what time do you close?” Robots are often used to perform “dull, dirty, or dangerous” tasks in the place of a human.

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Artificial intelligence (AI) is a set of technologies that enable computers to perform a variety of advanced functions, including the ability to see, understand and translate spoken and written language, analyze data, make recommendations, and more.

Long/short term memory (LSTM) is an advanced form of RNN that can use memory to “remember” what happened in previous layers. The difference between RNNs and LSTM is that LSTM can remember what happened several layers ago, through the use of “memory cells.” LSTM is often used in speech recognition and making predictions.

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Artificial intelligence can be organized in several ways, depending on stages of development or actions being performed.

In recent years VIA has garnered extensive feedback from customers operating in the broader Fleet Management sector. This has given us valuable insights that have helped hone our approach to product development. The result is the VIA Mobile360 D700 AI Dash Cam, a full-featured, compact dual-camera device with support for Forward Collision Warning, Lane Departure Warning, and an advanced Driver Monitoring System. The VIA Mobile360 D700 is AWS IoT Core Qualified and AWS Kinesis Video Streams Qualified. It comes with a 30-day free trial of the VIA Fleet Cloud Management Portal powered by Amazon Web Services (AWS), including 50 hours of live streaming with Amazon KVS.

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AI can automate workflows and processes or work independently and autonomously from a human team. For example, AI can help automate aspects of cybersecurity by continuously monitoring and analyzing network traffic. Similarly, a smart factory may have dozens of different kinds of AI in use, such as robots using computer vision to navigate the factory floor or to inspect products for defects, create digital twins, or use real-time analytics to measure efficiency and output.

AI is the backbone of innovation in modern computing, unlocking value for individuals and businesses. For example, optical character recognition (OCR) uses AI to extract text and data from images and documents, turns unstructured content into business-ready structured data, and unlocks valuable insights.

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With CAN bus integration, advanced dash cams can provide vehicle telematics data ranging from engine speed to tire pressures. Cloud-integrated devices can feed this data back to police department headquarters in real-time through 4G wireless connectivity. This data is valuable in planning service intervals and diagnosing or pre-empting potential maintenance issues with police cars. By tackling issues proactively, savings can be made both in terms of maintenance and vehicle down time.

AI can process more information more quickly than a human, finding patterns and discovering relationships in data that a human may miss.

On an operational level for business use, AI is a set of technologies that are based primarily on machine learning and deep learning, used for data analytics, predictions and forecasting, object categorization, natural language processing, recommendations, intelligent data retrieval, and more.

In 95% of complaint cases reported in the IACP study, grievances were not sustained by video evidence. Supervisors noted that in at least half of cases, when complainants were made aware of the video, the complaint was withdrawn. This can not only provide significant savings in administrative and legal costs, but it also ensures the public image of the department is preserved by preventing negative media attention.

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Deep learning, a further specialization, utilizes artificial neural networks with multiple layers to process information, mimicking the structure and function of the human brain. Through continuous learning and adaptation, AI systems become increasingly adept at performing specific tasks, from recognizing images to translating languages and beyond.

Further, the footage captured can be invaluable in maintaining and improving police standards, with video analysis providing useful insights into disciplinary issues and training needs. It can also be key to maintaining a positive relationship with the community, allowing for greater policy accountability and transparency. In addition, by establishing the irrefutable facts of an arrest, dash cam footage protects both officers and the public from false accusations.

While the specifics vary across different AI techniques, the core principle revolves around data. AI systems learn and improve through exposure to vast amounts of data, identifying patterns and relationships that humans may miss.

A study by the International Association of Chiefs of Police (IACP) reported that in-car video systems resulted in greater professionalism by police and fewer complaints against them by members of the public. The presence of video recording devices was found to de-escalate tensions, with some officers reporting that both they and the public remained more courteous when being filmed.

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Previously, integrating such a system would have been time-consuming, expensive, and required the use of expert contractors. But, thanks to Amazon Web Services, it’s now easy for forces to integrate certified products into AWS-powered cloud solutions without writing custom code. Control rooms can benefit from a real-time cloud link to their vehicles, enabling intelligent cost savings to be made based on rich-data analytics.

In addition to supervised and unsupervised learning, a mixed approach called semi-supervised learning is often employed, where only some of the data is labeled. In semi-supervised learning, an end result is known, but the algorithm must figure out how to organize and structure the data to achieve the desired results.

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For police forces, dual camera dash cams are especially beneficial. The outside facing camera covers the road ahead, recording incidents as well as footage of the crime scene. Just as importantly, the interior facing camera provides important surveillance of offenders in custody as well as police officer conduct.

Google offers a number of sophisticated artificial intelligence products, solutions, and applications on a trusted cloud platform that enables businesses to easily build and implement AI algorithms and models.

The ability to analyze vast amounts of data quickly can lead to accelerated breakthroughs in research and development. For instance, AI has been used in predictive modeling of potential new pharmaceutical treatments, or to quantify the human genome.

Generative adversarial networks (GAN) involve two neural networks competing against each other in a game that ultimately improves the accuracy of the output. One network (the generator) creates examples that the other network (the discriminator) attempts to prove true or false. GANs have been used to create realistic images and even make art.

Today’s dash cams offer police a proven and feature-rich way to supervise and support officers out on the road. Cloud-connectivity, real-time video, two-way communication, and vehicle diagnostics improve officer performance, while providing documentation that protects both the police and the public. It’s a tool that modern police forces simply can’t afford to do without.

The quantity and quality of the evidence gathered from modern dash cams can help officers to secure convictions. This not only improves their effectiveness, but the overall performance of the department, too. At a time when budgets are being squeezed and targets raised, the use of dash cams is an easy win for police departments.

AI is not limited by time of day, the need for breaks, or other human encumbrances. When running in the cloud, AI and machine learning can be “always on,” continuously working on its assigned tasks.

Modern dash cams also support HDR (High Dynamic Range) recording. This allows important details to be captured across a wide range of lighting conditions, so that vital evidence doesn’t get lost in shadows or become washed out. Paired with infra-red LEDs, advanced dashboard cameras can even capture detailed footage at night.