The autonomous vehicle is supposed to drastically reduce accidents and save lives. Road accidents in the West and even more so in the rest of the world are a major cause of mortality. We can recall the number of victims per year which amounts to 1.2 million people today. Much more than the victims of wars in Europe, the Middle East and all conflicts combined.
Automotive engineers know how to build safe cars, which brake very hard, which hold the road well, which do not explode while moving and which are structurally designed to protect the occupants in the event of an impact. On the other hand, they do not know much about road safety. This discipline is extremely complex. Accidents are fortunately rare events if we consider the number of miles traveled where nothing happens.
Road safety specialists are generally civil servants. They work on infrastructure and have an understanding of how accidents happen, because they receive and analyze information by road police, firefighters, etc.
Over the past 20 years, Nexyad has invested in 12 funded scientific research programs on road safety. It involved collaboration among hundreds experts, professional drivers, police of the road, and insurers from 19 countries. The first time by chance, then to capitalize on this knowledge. We met experts from 19 countries, we compared their opinions, defined a common vocabulary and finally synthesized all this knowledge. We extracted a corpus of rules of prudent driving. To make these rules simply usable, we built a metric of prudent driving. The metric is scaled from 0 to 100%. We designed it so that (the last decile) of the scale, from 90% to 100%, represents what road safety experts consider the 95% most dangerous or accident-prone driving situations.
Predit ARCOS 2004 – Predit SARI 2006 – MERIT 2006 – SURVIE 2009 – CENTRALE OO 2011 – CASA 2014 – SERA 2014 AWARE 2014 – RASSUR 79 2015 – SEMACOR 2015 – SEMACOR 2 2017 – BIKER ANGEL 2020
Today, we use this metric to measure in real time and on board driving of both humans and robotized drivers. When we detect lack of prudence, we can alert humans and we give feedback to control of autonomous driving with enough anticipation allowing time to slow down or/and change trajectory.
At the DealBook Summit, 10 experts in artificial intelligence debated the biggest opportunities and risks of the technology. Will technology fuel an era of prosperity, in which humans work less?Will it be used to wipe out humanity?
Extract:
In a live poll, seven of the experts indicated they thought there was a 50 percent chance or greater that artificial general intelligence — the point at which A.I. can do everything a human brain can do would be build before 2030.
One immediate fear cited by Hinton, the Nobel Prize-winning researcher, is that A.I. will flood the internet with so much false content that most people will “not be able to know what is true anymore.”
There are several potential dangers associated with using artificial intelligence (AI) in stock trading. First, over-optimization of AI models can make them overly adjusted to historical data, which could lead to disappointing performance in real-world market conditions. Furthermore, the lack of transparency of complex algorithms, such as neural networks, makes them “black boxes,” making it difficult to understand the decisions made by AI.
AI-based strategies can also react quickly to market fluctuations, which can increase volatility and cause large price movements. Overreliance on AI risks diminishing traders’ ability to rely on their experience and judgment. Regarding cybersecurity, trading algorithms remain vulnerable to cyberattacks, which can lead to significant financial losses. Errors or bugs in AI coding can also lead to erroneous trading decisions, with negative financial consequences.
Finally, this reliance on AI can lead to overconfidence among traders, who may overlook the need for ongoing monitoring and critical evaluation. It is therefore essential for traders to be aware of these risks and put in place appropriate measures to mitigate them, including human monitoring and rigorous model testing.
The film industry is undergoing a transformation with the integration of artificial intelligence (AI) in scriptwriting, moving away from the traditional reliance on manual labor and creativity. As technology advances, AI is becoming a significant player in filmmaking, influencing the essential art of storytelling.
This exploration into AI’s role in film will highlight how it enhances scriptwriting, encourages innovative storytelling, and reshapes the cinematic experience. We will examine the mechanisms of AI scriptwriting, its advantages, and the challenges it introduces while contemplating its future in film.
Traditionally, film scriptwriting involved labor-intensive methods, starting from handwritten scripts to using typewriters, which, although more efficient, still required significant effort for editing and revisions. Despite the difficulties, these traditional practices fostered creativity and led to the creation of many iconic films.
In summary, AI is revolutionizing film scriptwriting by streamlining processes and opening new avenues for creativity in storytelling.
NEXYAD’s Prudence Metric and SafetyNex are exciting tools in the field of fleet safety and autonomous vehicle technology. These solutions are especially valuable for managing and improving the safety of fleets, particularly when dealing with advanced driver-assistance systems (ADAS) and autonomous vehicles.
Here’s why their use could be a game-changer:
Proactive Risk Management
The Prudence Metric is designed to assess the safety level of the driving environment in real-time. By incorporating this metric, fleet managers can gain deeper insights into how risky a particular route, driving behavior, or situation might be. This helps them to make data-driven decisions about route planning, driver behavior, and maintenance strategies, ultimately reducing the likelihood of accidents.
Contextual Safety Understanding
SafetyNex, an AI-based system, goes beyond traditional safety monitoring. It assesses the context of every driving situation, such as road conditions, traffic, weather, and the behavior of surrounding vehicles. This dynamic approach to safety allows fleets to take a more personalized and situationally aware approach to fleet management.
Real-Time Data for Fleet Monitoring
With these tools, fleet managers get real-time insights into the safety of their vehicles, allowing for immediate intervention when risks are detected. This capability is essential for preemptive actions that can prevent accidents or mitigate their consequences.
Lower Insurance Costs
With a more precise and data-driven approach to safety, fleets using Prudence Metric and SafetyNex can likely see reduced insurance premiums due to their demonstrated commitment to safety and lower risk profiles. By improving safety and reducing incidents, these tools could lead to long-term financial savings.
Enhanced Fleet Efficiency
Beyond safety, these tools contribute to improved operational efficiency. For example, by tracking the real-time driving conditions and advising on safer routes, fleets can optimize fuel usage and reduce wear-and-tear on vehicles, extending the lifespan of the fleet and improving overall cost-effectiveness.
Integration with ADAS and Autonomous Technology
As more fleets incorporate ADAS and autonomous driving technology, integrating a safety monitoring system like SafetyNex ensures that these systems perform optimally in all driving conditions. It creates a robust safety net for both human and autonomous drivers, ensuring that vehicles are always operating in the safest possible manner.
Future-Proofing the Fleet
As the automotive industry increasingly shifts toward AI-powered systems and autonomous vehicles, NEXYAD’s solutions are aligned with next-generation technologies. Using them helps fleets stay ahead of industry trends, ensuring they’re prepared for the future while improving safety and operational standards today.
In summary, leveraging NEXYAD Prudence Metric and SafetyNex in fleet management enables operators to optimize safety, reduce risks, and enhance overall operational efficiency. Their application helps manage fleets in a smarter, safer, and more cost-effective way, which is why they can indeed be seen as game-changers in this space.
In today’s climate of political polarization and media fragmentation, mayors are struggling to connect with their constituents and build trust. AI offers a powerful tool to overcome these challenges by dramatically improving communication, service delivery, and government transparency.
For example, AI facilitates the creation of authentic and engaging content, such as videos that showcase the hard work of city employees, foster a sense of connection, and demonstrate responsible use of public funds. AI also enables cross-platform engagement, reaching citizens where they already are—on WhatsApp, in lifestyle radio shows, or other preferred channels. Additionally, AI-driven platforms can combat misinformation by providing reliable information in times of crisis, building public trust, and fostering more legitimate two-way dialogue.
AI can be used to streamline the tasks of frontline workers, allowing them to focus on direct citizen interaction and personalized service, while establishing clear, verifiable records that increase accountability. Transparency can also be greatly improved through AI-powered real-time translation of public meetings and the creation of accessible summaries of city council proceedings. This would allow more residents to actively participate in shaping the future of their community, while providing community leaders with the tools to analyze data and provide better oversight of local government. Finally, AI can help ensure consistency and accuracy of messaging, eliminate confusion, and build trust in the reliability of city information.
AI’s positive impact on trust will not be automatic.Its ability to facilitate proactive problem-solving can effectively increase the responsiveness of governments and foster a higher level of public trust.But it would be desirable for opponents of municipal political teams to also have access to a channel for communicating their own information.Careful ethical reflection and human oversight are essential to preserve the proper functioning of democracy.Otherwise, AI could become a tool for maintaining power and lead the public into the illusion that the only government channel has legitimacy.
Recent struggles in the autonomous vehicle (AV) sector highlight the significant challenges of bringing fully self-driving cars to market. Several high-profile setbacks illustrate this: the latest is General Motors’ withdrawal from the development of robotaxis, via its Cruise Autonomous unit. The activity was too resource-intensive and time-consuming. The Detroit automaker will now focus on partially automated driver assistance systems for personal vehicles, such as its Super Cruise, which allows drivers to take their hands off the wheel.
This new abandonment follows the end of Apple Car’s development, Argo AI’s shutdown, and significant losses at Embark, TuSimple and Aurora. These events suggest a challenging environment for the development and deployment of fully autonomous vehicles.
The main factor contributing to this situation are the Technological Hurdles. Achieving Level 5 autonomy (fully driverless operation in all conditions) is proving far more difficult and expensive than initially anticipated. The complexities of handling edge cases, unexpected situations, and ensuring safety in diverse environments remain significant obstacles.
All teams focus on a whole battery of sensors supposed recognise driving situations called scenarios (40.000 at Waymo) and learned with astronomical computing power. $ Billions have been spent on cars that can drive in three cities so far.
Nexyad proposes a new paradigm with its hybrid AI tool which measures Driving Prudence at each moment (20 times per second). All situations can be tested in simulation or in real time on the roads. We are able to make save Selfdriving companies save a lot of development time and money.
According to the National Highway Traffic Safety Administration (NHTSA), the number of road deaths in the United States is approximately 40,000 per year. Compared to Western Europe, this figure is almost 15 times higher. For instance, if there are around 4.5 million accidents annually and about 3 trillion miles traveled in a year, that would approximate to about 1.5 accidents per million miles. Therefore, this translates to roughly 1,500 accidents per billion miles traveled.
Yet the concept of « pay how you drive » is becoming increasingly popular in the United States as a business practice for insurance companies. The model uses telematics devices that track policyholders’ driving behavior, including speed, hard braking and time spent on the road. It was estimated that approximately 25 million drivers participated in telematics-based insurance programs. On the other hand, companies that offer services to fleets with a safety-oriented tool have also grown, there are dozens of them across the country.
However, accidents are still too numerous and affect both professional and non-professional drivers.
If we agree with the statement that almost no one wants to die in a vehicle crash, this means that there is a particular problem in this country that can be explained by several factors.
Among the leading causes of road accidents in the United States are: Distracted Driving: This is one of the leading causes of accidents, often due to activities such as texting, talking on the phone, or using in-car technologies while driving. Speeding: Driving over the speed limit reduces reaction time and increases the severity of crashes. Reckless Driving: Aggressive driving behaviors such as tailgating, frequent lane changes, and road rage can lead to dangerous situations. Weather Conditions: Rain, snow, ice, and fog can reduce visibility and road traction, increase the likelihood of accidents. Running Red Lights or Stop Signs: Many accidents occur at intersections due to motorists failing to obey traffic signals. Driver Fatigue: Drowsy driving can be as dangerous as driving under the influence, leading to slower reaction times and impaired decision-making.
Nexyad offers a solution to reduce accidents rate:
Drivers Monitoring for fleets managers & Onboard real time Driver Assistance, all in one.
To date, it is the best offer that combines Telematics and Automotive ADAS in a nomadic solution.
Ask for our BYOD Solution brochure: https://nexyad.net/Automotive-Transportation/contact-nexyad/
« A global survey by the Digital Education Council found that 86% of university students now use AI in their studies. Notably, 80% of them said their university’s integration of AI tools does not fully meet their expectations. With more than 75% of global knowledge workers using generative AI in the workplace, using this technology effectively and confidently is a skill students simply need to have.
For faculty struggling with how to deal with generative AI in the classroom, we can learn from how the field of mathematics responded to the introduction of the calculator 50 years ago. Horrified at the thought of students never learning how to do long division with pencil and paper, some teachers banned calculators from their classrooms. »
By Dr. Chad Raymond, professor in the Department of Political Science and Department of Cultural, Environmental and Global Studies. This article is excerpted from Raymond’s 2024 article in the Chronicle of Higher Education.
It’s highly likely that the integration of AI in education will lead to a crucial shift in teaching methods. Instead of focusing solely on transmitting factual information, teachers may increasingly emphasize inquiry-based learning, encouraging students to understand the underlying principles and context of concepts.
Can this « why » approach foster critical thinking by prompting students to analyze information, question assumptions, and explore the reasoning behind facts, leading to richer discussions and deeper understanding?
Can connecting classroom learning to real-world applications make education more relevant and purposeful, motivating students to engage with their studies?
Will AI tools supporting this potential shift personalize or standardize learning experiences? Will they adapt to individual needs by suggesting resources that address each student’s specific curiosities and challenges?
For AI to successfully support this pedagogical shift, its development and implementation must necessarily involve teachers. Teachers will need to explore and adopt this new tool to make it as beneficial as possible for students.
The Prudence-Based Predictive ACC (Adaptive Cruise Control) system developed by NEXYAD uses a combination of Artificial Intelligence (Fuzzy Logic, and Possibility Theory) and Road Safety Expertise to provide a more reliable and cautious approach to predictive acceleration and braking in autonomous driving systems, specifically in Adaptive Cruise Control (ACC) systems.
Simulation Videos of Nexyad AI for Predictive ACC on Aurelion (dSPACE) :
Prudence-Based Approach:
Prudence in this context refers to a system that errs on the side of caution. Instead of optimizing for the most aggressive or efficient acceleration and deceleration, the system predicts and reacts in a way that prioritizes safety. The goal is to avoid risk by considering worst-case scenarios and uncertain conditions, such as unexpected road obstacles, weather changes, or other external factors.
Adaptive Cruise Control (ACC):
ACC systems adjust a vehicle’s speed to maintain a safe distance from the car in front. This is typically done by controlling the throttle and braking system. In NEXYAD’s approach, the system goes beyond simple speed regulation and incorporates predictive behavior using AI to anticipate changes in the road or traffic conditions, making it more responsive and adaptable to a variety of dynamic scenarios.
Artificial Intelligence (AI):
AI plays a central role in the system by processing vast amounts of data from sensors, cameras, and other vehicle systems in real time. The AI uses this data to predict the future state of the vehicle and surrounding environment, adjusting the ACC system accordingly. The more the AI learns, the more effectively it can predict and react to changing conditions.
Fuzzy Logic: an approach to decision-making that mimics human reasoning and decision processes. Instead of relying on binary (true/false) logic, fuzzy logic allows for reasoning in terms of degrees of truth. In this case, fuzzy logic helps the system make decisions in situations where data may be imprecise or uncertain. For example, when determining the optimal distance from the car ahead, fuzzy logic can evaluate factors such as speed, weather conditions, and road quality in a more nuanced way than traditional binary systems. Example: Instead of simply asking if the car ahead is too close (yes/no), the fuzzy system might evaluate how close the car is, how fast it’s going, how fast the vehicle is approaching, and other factors to make a more nuanced decision on acceleration or deceleration.
Possibility Theory: a mathematical framework used to handle uncertainty, especially when it comes to reasoning about vague or imprecise information. It is closely related to fuzzy logic, but while fuzzy logic deals with imprecise concepts and degrees of truth, possibility theory deals more with uncertainty in predicting future events or states.
In the context of NEXYAD’s system, possibility theory is used to evaluate and quantify the uncertainty in the system’s predictions. For example, when predicting the behavior of another vehicle or anticipating a potential obstacle, the system doesn’t just give one deterministic prediction, but rather a range of possible future scenarios with associated likelihoods. This allows the system to make more cautious and well-informed decisions, adjusting its actions based on the possibility of various outcomes. Example: If the system predicts that an obstacle might appear on the road in the next few seconds, it considers the possibility that the obstacle may not appear, but it may still start decelerating, preparing for the worst-case scenario, which could involve an emergency stop.
Nexyad Road Safety expertise:
During more than 15 years, we were involved with 12 funded collaborative research programs, collecting knowledge of road safety experts, polices of the road, professional drivers and insurers of 19 countries. Nexyad interviewed and confronted hundreds of these experts to make them agree on situations and vocabulary. We understand why, when and where accidents happen and how avoid them.
How the System Works:
Data Collection and Sensor Fusion: the system gathers data from multiple sensors such as electronic Horizon, radar, lidar, cameras, GPS, V2X, and vehicle control systems. This data is used to create a real-time model of the environment around the vehicle.
Fuzzy Logic Decision-Making: based on the data, fuzzy logic rules are applied to evaluate various driving parameters, such as speed, distance to other vehicles, and road conditions. For example, a rule might state: « If the distance to the vehicle ahead is small AND the speed is high, THEN decelerate. »
Predictive Modeling with Possibility Theory: using possibility theory, the system predicts future events or situations (e.g., the likelihood that the vehicle ahead will change lanes, that there will be an obstacle, or that road conditions will worsen). Instead of just assuming one scenario, the system models several possible futures and acts cautiously based on these possibilities. For example, it might slow down in anticipation of potential traffic changes, even if those changes are not certain.
Prudence in Action: the system makes decisions based not just on what is most likely, but also what could happen in a worst-case scenario. This prudence-based behavior ensures that the vehicle can adapt in real time to sudden changes in the environment while ensuring safety by avoiding aggressive or risky maneuvers.
Safe and Efficient Driving: the goal is to maintain smooth and comfortable driving while minimizing risks. The system balances the need for efficient travel with safety by predicting and reacting to potential dangers in a way that does not overreact but also does not under-react. It aims for an optimal balance where the vehicle’s behavior is safe and conservative yet responsive to traffic and environmental conditions.
Advantages of NEXYAD’s Prudence-Based ACC System:
Improved Safety: By combining predictive AI with fuzzy logic and possibility theory, the system can anticipate potential dangers and adjust the vehicle’s behavior accordingly, improving safety and reducing the risk of accidents.
Real-time Adaptability: The system continuously adapts to the dynamic conditions around the vehicle, reacting to changes in traffic, road conditions, and the behavior of other drivers.
Efficient Handling of Uncertainty: Unlike traditional models, which might fail in ambiguous or uncertain situations, this system is designed to handle imprecision and uncertainty more effectively, making it more robust.
Comfortable Driving Experience: Prudence-based decision-making ensures that the system does not engage in erratic or jerky acceleration/deceleration, leading to a smoother driving experience for passengers.
Applications:
Autonomous Vehicles: The system can be integrated into fully autonomous vehicles for safe and efficient navigation.
ADAS(Advanced Driver Assistance Systems): It can be used in ADAS to enhance safety features like collision avoidance, adaptive cruise control, and automatic emergency braking.
Driver Assistance in Semi-Autonomous Vehicles: Even in semi-autonomous vehicles, where the driver must remain in control, this system can provide valuable assistance for handling complex, dynamic traffic situations.
NEXYAD’s Prudence-Based Predictive ACC system leverages AI, fuzzy logic, and possibility theory to create a sophisticated and cautious approach to adaptive cruise control. By accounting for uncertainty and prioritizing safety, the system offers a more reliable solution for autonomous and semi-autonomous driving, enhancing both the safety and comfort of the vehicle’s occupants.
This study presents a novel AI-driven image segmentation algorithm capable of identifying intricate details, specifically capillary structures, across diverse image types (eye fundus, citrus leaves, printed circuit boards). The algorithm combines image super-resolution (using an Efficient Sub-Pixel Convolutional Neural Network), U-Net based segmentation, and image binarization for masking. Results show significant performance improvements in image super-resolution (PSNR of 37.92 and SSIM of 0.9219 on Set 5 and Set 14 datasets), outperforming other methods. While highly effective, the algorithm’s computational complexity is dominated by the masking module, suggesting potential avenues for future optimization.
The versatility and accuracy demonstrated highlight its potential for detailed analysis across various applications requiring precise image segmentation.
NEXYAD is a company specializing in AI-based solutions for, among others, road safety, particularly in the context of driving behavior and risk analysis. One of our main innovations is the Driving Prudence Metric, designed to assess and quantify the driving behavior of professional or non-professional drivers. This metric is part of our broader range of safety and risk assessment tools.
The purpose of this metric is to assess the degree of prudence when driving a vehicle, using real-time data on driving behavior and road context from various sensors.The goal is to identify high-risk driving, reduce accidents and improve road safety, especially for professional drivers who may be on the road for long hours and subject to high stress and strain.
Key Features of the Driving Prudence Metric:
Driving Behavior Analysis: it evaluates factors such as acceleration, braking, speed, approaches to turns, intersections, stop signs, school zones, etc… and overall smoothness of driving.The metric looks at both real-time and historical data to measure a driver’s prudence in different conditions.
Risk Assessment: by analyzing driving data, the metric can identify risky driving behaviors and predict the likelihood of accidents or dangerous situations.
Real-Time Alerts: the system provides real-time advices to the driver or fleet manager, helping them adjust their driving behavior immediately.
Data Collection: NEXYAD’s technology typically integrates with telematics systems that collect data from GPS, accelerometers, gyroscopes, and other sensors inside the vehicle.Data now informs when, where and why of lack of prudence to score drivers.
Drivers Scoring: drivers are scored based on their safe driving, with the goal of encouraging safer behavior.These scores can be used by fleet managers to evaluate driver performance and implement safety programs or training.
Fleet Management Integration: for companies that manage a fleet of professional drivers, Prudence Metric can be integrated into a larger telematics or driver performance monitoring system to ensure all drivers are meeting safety standards.
Adaptability: the system can be customized for different vehicle types and driving environments, taking into account factors such as urban driving, road conditions and weather.
The Driving Prudence Metric promotes Safety Improvement by monitoring and rewarding safe driving practices, thus helping to reduce accidents and enhance overall safety for both drivers and other road users.
It also leads to Cost Savings, as professional fleets can lower maintenance costs and fuel consumption through more efficient and safe driving behavior.
Moreover, fleet managers benefit from Behavioral Insights, gaining valuable information about individual driver behavior, which allows them to provide tailored coaching or interventions as necessary.
In terms of Regulatory Compliance, the Prudence Metric assists in meeting driver behavior requirements that exist in some regions.
Additionally, Insurance Benefits may be realized, as insurance companies might offer discounts or lower premiums to fleets or individual drivers who consistently achieve high scores on prudence metrics.
NEXYAD’s Driving Prudence Metric is a comprehensive tool designed to enhance road safety by promoting cautious, safe driving practices among drivers. By leveraging advanced AI and telematics, it helps identify risky driving behavior, provides real-time feedback, and offers valuable data for both individual drivers and fleet managers. This system plays a key role in reducing road incidents and improving operational efficiency for commercial fleets.
Questions about job losses due to AI are legitimate for many. Opinions vary, so we wanted to know what AI thinks. We asked ChatGPT, Gemini and GPT4o what they have to say about their use and outcome on employment:
« The impact of artificial intelligence (AI) on employment is both complex and full of transformative possibilities. On one hand, AI has the ability to automate certain functions, influencing sectors by automating repetitive tasks in areas such as manufacturing, transportation, and customer service, often leading to a reduction in jobs in these fields. However, this can also generate economic benefits and an increase in productivity for businesses, although it does not necessarily imply a direct creation of jobs.
Simultaneously, AI stimulates the creation of new positions and sectors. The continuous development of AI is driving an increased demand for professionals in various fields such as software development, technical maintenance, cybersecurity, and data analysis. Innovations in AI technology also encourage the growth of emerging sectors like robotics, digital health, and autonomous vehicles, each requiring specialized skills.
In addition to creating and destroying jobs, AI transforms existing roles. By integrating AI into current professions, workers may be required to use their skills for tasks that are more focused on critical thinking, creativity, and emotional intelligence. This transforms the human role into an enriched and collaborative function with technologies, especially in areas like health, education, and finance.
However, this transformation necessitates a dynamic of continuous learning and training. The shift towards an AI-driven economy will require workers to upskill and reskill through solid educational programs that enable them to acquire the necessary skills to meet new challenges and opportunities. This could be a challenge, particularly for those whose jobs are directly threatened by automation.
In conclusion, whether AI will create more jobs than it destroys will depend on many factors, including the speed of technological adoption, the ability of governments and businesses to regulate this change, and the willingness of the workforce to adapt. Proactive initiatives in education, training, and social policies could potentially maximize the net benefits of AI on employment and ensure a fair distribution of benefits across society. Thus, to fully realize the potential of AI, it is crucial to combine technological innovation with inclusive and adaptive human strategies. »
The Prudence Metric, developed by NEXYAD, is a tool for measuring risk and uncertainty in predictive models, particularly in the context of Artificial Intelligence (AI) and machine learning. It offers several advantages over classical approaches used for assessing model reliability, robustness, and performance. Here’s a breakdown of the benefits.
Risk and Uncertainty Quantification
Classical Approaches: Traditional metrics (such as accuracy, precision, recall, F1-score, etc.) focus primarily on model performance. They provide information about how well the model performs on known data, but they often don’t account for uncertainty or risks when the model faces new, unseen, or outlier data.
Prudence Metric: The Prudence Metric specifically quantifies the level of uncertainty in a model’s predictions. It measures how « prudent » the model is in its decision-making process, particularly in situations where the model encounters data it is less confident about. This is especially useful in high-stakes domains like finance, healthcare, or autonomous driving, where incorrect decisions can have serious consequences.
Better Decision-Making under Uncertainty
Classical Approaches: Many classical approaches are focused on optimizing for the best possible outcome (e.g., highest accuracy), without considering what happens in uncertain or edge-case situations. They can be overconfident in their predictions, even when the model’s understanding of the input data is weak or ambiguous.
Prudence Metric: The Prudence Metric allows the model to account for uncertainty in its predictions, enabling more cautious or conservative decisions when faced with ambiguous data. This is a crucial advantage in applications where making a wrong decision can lead to severe consequences (e.g., medical diagnoses, financial forecasting, or autonomous vehicle navigation).
Risk-Aware Model Calibration
Classical Approaches: Classical metrics tend to emphasize optimizing model performance on average. However, they might ignore situations where the model’s predictions could be risky, even if the overall performance seems acceptable.
Prudence Metric: It provides a means to calibrate models in a way that reduces risk by focusing on the likelihood of the model’s errors and the potential consequences of those errors. This makes it more suitable for risk-sensitive applications where balancing performance with risk mitigation is crucial.
Adaptability to Complex Scenarios
Classical Approaches: In classical evaluation, the assumptions about the data and model’s behavior are often simplistic, assuming that the model will behave consistently across different conditions. However, real-world scenarios often involve complex, non-stationary, and unpredictable data distributions.
Prudence Metric: The Prudence Metric can adapt to these complexities by providing a more comprehensive understanding of how a model might behave under varying conditions, including when the model encounters outliers or novel situations.
Improved Interpretability
Classical Approaches: Many classical evaluation metrics, while useful, do not offer much insight into the model’s reasoning or the confidence behind its predictions. For example, a model might score well on accuracy, but it doesn’t indicate how confident or uncertain the model is about specific predictions.
Prudence Metric: The Prudence Metric is designed to give insights into the confidence levels and risk associated with predictions. This can make it easier for developers, stakeholders, or end-users to understand not only the predicted outcome but also how much trust to place in it.
Integration with Probabilistic and AI Systems
Classical Approaches: Classical metrics often assume deterministic models, where the output is a single value or classification without any measure of confidence or uncertainty.
Prudence Metric: It is particularly useful for probabilistic models or AI systems that deal with uncertainty and multiple possible outcomes. It integrates with these types of models more naturally and helps in decision-making by assessing risk and uncertainty.
Comprehensive Model Evaluation
Classical Approaches: Classical metrics might focus on specific aspects like classification accuracy or error rates, which are useful but can sometimes be misleading in the context of risk-sensitive applications.
Prudence Metric: It provides a more holistic evaluation of the model by factoring in the predictive confidence alongside accuracy, enabling a more nuanced understanding of model behavior.
Conclusion
The Driving Prudence Metric developed by NEXYAD focuses on quantifying uncertainty and risk, which is essential for high-risk and complex AI applications. Unlike classical approaches that primarily focus on performance metrics (accuracy, precision, recall), the Prudence Metric encourages prudent decision-making by incorporating uncertainty and risk into the evaluation process. This leads to better model calibration, more cautious predictions, and ultimately, safer and more reliable AI systems in critical applications.
The Driving Prudence Metric have been developed during more than 15 years, through 12 funded collaborative scienticif programs with experts in road safety, police of the road, professional drivers and insurance companies of 19 countries.
The metric is scaled from 0 to 100%. We designed it so that (the last decile) of the scale, from 90% to 100%, represents what road safety experts consider the 95% most dangerous or accident-prone driving situations.
Predit ARCOS 2004 – Predit SARI 2006 – MERIT 2006 – SURVIE 2009 – CENTRALE OO 2011 – CASA 2014 – SERA 2014 AWARE 2014 – RASSUR 79 2015 – SEMACOR 2015 – SEMACOR 2 2017 – BIKER ANGEL 2020
Knowledge Representation (KR) in AI is thriving and evolving rapidly, with significant contributions from both academia and industry in the United States. Key trends and research directions in KR today focus on addressing the complexities of representing, reasoning about, and utilizing knowledge in AI systems. Here are some of the prominent areas of research.
Neural-symbolic Integration
This area is at the intersection of symbolic reasoning and neural networks. The goal is to combine the strengths of symbolic AI (logical reasoning, structured representations) with deep learning (learning from data, pattern recognition).
Key Challenges: Developing models that can learn from raw data while also enabling symbolic reasoning, such as performing logical deductions, planning, or handling abstract concepts.
Recent Work: Research includes work on neural networks that can perform symbolic reasoning tasks (e.g., using differentiable programming), and symbolic tools that enable models to learn from structured data (e.g., knowledge graphs or ontologies).
Notable Approaches:
Neural-Reasoning Models: For example, combining graph neural networks with symbolic reasoning to improve tasks like commonsense reasoning, language understanding, and even decision-making.
End-to-End Symbolic AI: Efforts like Facebook’s work on incorporating structured knowledge into transformers or OpenAI’s work on grounding language models to structured knowledge.
Knowledge Graphs and Knowledge Bases
Knowledge graphs (KGs) are a powerful tool for representing knowledge as a network of entities and relationships, often used for question answering, recommendation systems, and semantic search.
Key Challenges: Building scalable, accurate, and up-to-date knowledge graphs. Also, dealing with challenges in handling unstructured data, reasoning over incomplete or noisy data, and making KGs more interpretable.
Recent Work:
Scaling Knowledge Graphs: Research into methods for automatically constructing, updating, and expanding KGs, including work on hybrid models combining rule-based and data-driven methods.
Representation Learning on KGs: Leveraging deep learning techniques (e.g., graph neural networks) to embed knowledge graph entities and relationships into vector spaces that allow for more efficient reasoning and querying.
Commonsense Reasoning and Cognitive Models
Commonsense reasoning involves representing and reasoning about the everyday knowledge that humans typically take for granted. It’s a challenging aspect of AI that requires models to infer unstated facts based on context.
Key Challenges: Encoding knowledge that is often implicit, non-formal, and context-dependent. Ensuring that AI models can reason about events and understand causality.
Recent Work:
Large Language Models (LLMs) and Commonsense: Studies focus on improving LLMs like GPT or BERT to better understand and reason with commonsense knowledge. For example, OpenAI’s work with GPT-4 has involved adding more robust commonsense reasoning capabilities.
Hybrid Models for Reasoning: Researchers are exploring how neural models can be augmented with rule-based systems or explicit representations of commonsense knowledge (e.g., ConceptNet or ATOMIC knowledge graph) to improve the reasoning process.
Explainable AI (XAI)
Knowledge representation is crucial for making AI models more interpretable and explainable. Explainable AI seeks to make AI decisions more understandable to humans, which is critical for safety, ethics, and trust.
Key Challenges: Ensuring that models are not only accurate but also transparent in their decision-making process, which requires clear representation of knowledge that can be traced and interpreted.
Recent Work:
Explainable Reasoning: Research into how symbolic knowledge representations can help explain the reasoning behind deep learning models, especially in domains like healthcare, law, and autonomous driving.
Interpretable Models with External Knowledge: Methods are being developed that allow models to integrate and reason over structured external knowledge (e.g., knowledge graphs, ontologies) in a way that enhances both performance and interpretability.
Ontologies are structured frameworks for organizing and representing knowledge, typically through formalized sets of concepts and relationships in a specific domain. Logic systems (e.g., description logics, non-monotonic logics) are used to formalize reasoning.
Key Challenges: Handling ambiguous, incomplete, or inconsistent knowledge. Developing scalable systems for reasoning over large, complex ontologies.
Recent Work:
Scalable Reasoning: Research on developing more efficient and scalable reasoning algorithms for large ontologies, particularly in real-time or big data settings.
Adaptive Ontologies: Work on dynamic or self-learning ontologies that evolve over time as new data or concepts emerge.
Causal Reasoning and Representation
Understanding cause-and-effect relationships is crucial for reasoning in dynamic environments. Causal reasoning can enhance knowledge representation by enabling AI systems to predict and reason about future events based on current knowledge.
Key Challenges: Identifying causal structures from data, reasoning under uncertainty, and integrating causal models with other types of knowledge representations.
Recent Work:
Causal Inference with Neural Networks: Techniques like causal discovery from data, using deep learning models to learn causal structures, and incorporating causal reasoning into knowledge graphs.
Causal Representation Learning: Representing causal knowledge in ways that facilitate inference about interventions or counterfactuals, and making this representation interpretable to humans.
Multi-modal Knowledge Representation
Multi-modal knowledge representation involves integrating data from different sources (e.g., text, images, videos, sensors) into a unified framework that allows for reasoning across diverse types of information.
Key Challenges: Aligning and integrating information from different modalities, and designing systems that can reason effectively across these different data types.
Recent Work:
Vision-Language Models: Advancements in multimodal models (like CLIP and DALL-E by OpenAI, or Flamingo by DeepMind) that combine vision and language for better representation and reasoning capabilities.
Cross-modal Knowledge Graphs: Research on building knowledge graphs that incorporate information from multiple modalities (e.g., text, image, and sensor data) to improve understanding and reasoning.
Human-AI Collaboration and Knowledge Sharing
A growing area of research focuses on how AI systems can better represent and share knowledge with humans, allowing for more effective collaboration between humans and machines.
Key Challenges: Ensuring that AI systems can understand and adapt to human knowledge, providing interfaces for interactive knowledge sharing, and designing systems that can support human decision-making.
Recent Work:
Interactive Knowledge Acquisition: Methods for systems to acquire and update knowledge from human interaction, such as learning from feedback or correcting misconceptions in real time.
Collaborative Knowledge Engineering: Research on systems that facilitate the joint construction of knowledge, where both AI and human participants contribute to the knowledge representation process.
Notable Institutions and Research Groups in the US:
Stanford University (e.g., research in neural-symbolic integration, commonsense reasoning)
MIT Computer Science and AI Lab (CSAIL) (e.g., work on explainable AI, knowledge graphs)
Carnegie Mellon University (CMU) (e.g., knowledge graphs, reasoning over structured data)
Google DeepMind (e.g., multi-modal AI, causal reasoning)
OpenAI (e.g., neural-symbolic integration, large language models, explainability)
Key Conferences and Journals:
Conferences: NeurIPS, AAAI, IJCAI, ACL, CVPR (for vision and language), EMNLP (for natural language processing)
Journals: Journal of Artificial Intelligence Research (JAIR), IEEE Transactions on Knowledge and Data Engineering, AI Journal, Journal of Machine Learning Research (JMLR)
These are just a few key trends, and the landscape is evolving rapidly as AI systems continue to grow in complexity and capability. The integration of structured knowledge with data-driven approaches seems to be a central theme in much of this research.
Large Language Models (LLMs), like GPT-4, and Transformer architectures are foundational technologies in modern natural language processing (NLP). They are designed to process and generate human-like text based on patterns learned from large datasets. Here’s a breakdown of how they work:
The Transformer Architecture
Transformers, introduced in the paper « Attention is All You Need » (Vaswani et al., 2017), are the backbone of modern LLMs. The key innovation of Transformers is the self-attention mechanism, which allows the model to process input data in parallel and understand the relationships between words (or tokens) in a sequence, no matter how far apart they are.
Key Components of Transformers:
Self-Attention: This is the core idea of Transformers. Each token in an input sequence attends to (i.e., focuses on) every other token, allowing the model to capture dependencies between distant words. For example, in the sentence « The cat sat on the mat, » the model can learn that « cat » and « sat » are related, even though they are not next to each other.
The model computes three vectors for each word (token):
Query (Q): Represents the word’s request for information.
Key (K): Represents the information that is available in the word.
Value (V): The actual information the word can share.
The self-attention mechanism compares the Query vector with all Key vectors in the sequence to determine which tokens should influence the current token. The result is a weighted sum of the Value vectors, which is then used to represent the token in the context of the entire sentence.
Positional Encoding: Transformers don’t process sequences in order (like RNNs or LSTMs). Instead, they process all tokens in parallel. To give the model a sense of the order of words, positional encodings are added to the input embeddings, which specify the position of each token in the sequence.
Feedforward Networks: After self-attention is applied, the output goes through a fully connected feedforward network (usually consisting of two linear transformations with a ReLU activation in between).
Layer Normalization and Residual Connections: To ensure stable training, residual connections (shortcuts) are added around the attention and feedforward layers, and layer normalization is applied to the output of each layer to stabilize gradients.
Multi-Head Attention: Instead of computing a single attention score, the Transformer computes multiple sets of attention scores (with different weights), allowing it to focus on different aspects of the input simultaneously.
Architecture Overview:
The Transformer model consists of two main parts:
Encoder: The encoder processes the input sequence and generates a set of context-aware representations of each token. In tasks like translation, the encoder would convert the source language into a representation that the decoder can use.
Decoder: The decoder generates the output sequence, conditioned on the encoder’s output (in sequence generation tasks like text generation). In models like GPT, which are autoregressive, the decoder is used to generate text step-by-step.
Training Large Language Models (LLMs)
LLMs like GPT-3, GPT-4, and BERT are based on Transformer architectures but are designed to scale up to massive datasets and millions (or even billions) of parameters.
Pretraining:
Autoregressive Pretraining (for GPT-like models): In autoregressive models, the model is trained to predict the next word in a sequence given the previous words. For example, if the input is « The cat sat on the ___, » the model learns to predict the next word, « mat. »
Masked Language Modeling (for BERT-like models): In contrast to autoregressive training, BERT (Bidirectional Encoder Representations from Transformers) is trained using a technique called masked language modeling. In this setup, random words are masked (replaced with a special token), and the model is tasked with predicting the masked words based on the surrounding context. This allows the model to learn bidirectional relationships in text.
Both types of pretraining require massive amounts of data, such as books, websites, and other text sources, to capture a wide range of linguistic patterns and knowledge.
Fine-Tuning:
After pretraining, the model is fine-tuned on specific tasks (like sentiment analysis, machine translation, or text summarization) using labeled datasets. Fine-tuning adjusts the model’s parameters to specialize in the target task while leveraging the general knowledge learned during pretraining.
Generative vs. Discriminative Models
Generative Models (e.g., GPT): These models generate text by predicting the next token given previous tokens. They are autoregressive in nature, meaning they generate tokens one at a time and use their own previous predictions as part of the context for generating subsequent tokens. This is why GPT models are good at generating long passages of coherent text.
Discriminative Models (e.g., BERT): These models are trained to predict a label for a given input, typically used for tasks like classification, token labeling, and sentence-pair tasks. They are not autoregressive and do not generate text, but they are good at understanding the relationships between words in a sentence.
How LLMs Perform Tasks
Once trained, LLMs can perform a wide range of NLP tasks, including:
Text Generation: Given a prompt, the model generates coherent and contextually appropriate text (e.g., story generation, code completion).
Text Classification: Assigning categories to text, such as sentiment analysis, topic classification, etc.
Named Entity Recognition (NER): Identifying named entities like people, locations, and organizations within text.
Question Answering: Given a context (e.g., a paragraph), the model can answer questions about that context.
Translation: Translating text from one language to another.
The key to their performance is the pretraining on vast amounts of data, which helps the model learn general language patterns, and fine-tuning on specific tasks to make it more useful in a given domain.
Scaling Up and Challenges
LLMs have continued to scale up in size, with models like GPT-3 and GPT-4 containing billions (or even trillions) of parameters. Larger models generally have better performance but also come with challenges such as:
Computational Cost: Training large models requires massive computational resources, often requiring specialized hardware like GPUs or TPUs.
Data Biases: The models can inherit biases from the data they were trained on, leading to ethical concerns in their application.
Interpretability: Understanding how large models make decisions is a challenging area of research, often referred to as the « black-box » problem.
Despite these challenges, the Transformer architecture has proven to be highly effective, and LLMs like GPT-4 are at the forefront of AI-driven language understanding and generation.
Summary
Transformers use self-attention to capture relationships between tokens in a sequence, allowing for parallel processing of text and capturing long-range dependencies.
LLMs are trained on vast amounts of data and fine-tuned for specific tasks. They can generate and understand text, making them versatile in a wide range of NLP applications.
Nexyad’s Prudence Metric, integrated within the SafetyNex platform, is designed to provide real-time alerts, including vocal alerts and notifications that can be overlaid on a Heads-Up Display (HUD).
How it works ?
Real-Time Alerts: the Prudence Metric uses advanced AI and machine learning algorithms to assess real-time driving conditions, identifying risks based on factors such as driver behavior, road geometry and signs, others road users/obstacles and environmental conditions. If a dangerous situation or potential hazard is detected with anticipation*, it immediately triggers an alert.
* Anticipation depends of eHorizon distance setting which is on the hand of integrators.
Vocal Alerts: In addition to visual alerts on the HUD, Prudence Metric can deliver vocal alerts to warn the driver about potential hazards. These vocal warnings are designed to be clear and concise, helping the driver respond quickly without needing to take their eyes off the road.
HUD Integration: SafetyNex’s system integrates with the vehicle’s Heads-Up Display to show critical safety information and alerts directly on the windshield, ensuring the driver has immediate access to important data while maintaining focus on the road. The HUD can display things like proximity warnings, risk level, or upcoming obstacles, making the information accessible in the driver’s line of sight.
By combining these elements—real-time data analysis, vocal alerts, and HUD integration—SafetyNex with Prudence Metric enhances driver safety and helps prevent accidents by providing timely warnings in an intuitive, non-intrusive manner.
This gives driver the feeling of safety, and this is true.
AI-driven solutions are poised to revolutionize public transportation ticketing, significantly enhancing passenger convenience and efficiency in purchasing, managing, and using tickets.
AI-powered mobile apps can personalize the ticketing experience by analyzing travel patterns, time of day, weather, and user history to suggest cheaper routes, identify peak travel times, and offer personalized discounts. These apps can also seamlessly integrate with mobile wallets and payment platforms, even incorporating biometric authentication for touchless transactions and automated ticket renewals based on usage.
AI chatbots and virtual assistants provide 24/7 support, answering questions and providing real-time information on routes, delays, and ticket options, with voice assistant capabilities further enhancing accessibility.
Smart ticket validation and boarding can leverage AI-based face recognition to replace traditional ticket scanning, improving speed, accuracy, and security. Smartcards and wearables, optimized by AI, allow for seamless tap-in and tap-out functionality.
Location-based services can automatically suggest the appropriate ticket type, validate it, and even determine the best route to the passenger’s destination. Real-time data analysis using AI allows for predictive analytics to manage capacity, optimizing routes and schedules based on demand, and dynamically adjusting ticket pricing accordingly.
AI also plays a crucial role in fraud detection and security by identifying unusual patterns indicative of fraudulent activity and enabling secure identity verification using biometric authentication.
AI-powered ticketing systems offer accessibility enhancements such as automatic translation for hearing-impaired users, voice-guided instructions for the visually impaired, real-time accessibility updates, and multilingual interfaces.
Furthermore, AI facilitates integration with other modes of transportation, providing multi-modal ticketing options and real-time travel assistance across various networks.
Example Use Case: A Day in the Life of a Commuter
Morning: The commuter opens an AI-powered app on their phone. Based on their usual travel patterns and the time of day, the app automatically suggests the fastest and most cost-effective route. The app offers a single integrated ticket for both the subway and the bus.
During Travel: The commuter boards the subway, and AI-enabled facial recognition validates their ticket as they enter the station. No physical card is needed.
Change of Plans: Midway through the commute, a disruption on the subway is detected. The AI recommends an alternative route with an updated ticket, including a transfer to a nearby bus. The commuter seamlessly transitions without needing to purchase a new ticket.
End of the Day: Upon leaving the station, the commuter uses their phone to validate the ticket at the exit gate via biometric or NFC technology. AI detects their journey’s total cost, offering an automatic fare adjustment if they were overcharged or suggesting a multi-ride discount for frequent commuters.
AI-driven green ticketing promotes sustainable travel by suggesting eco-friendly options and optimizing routes for energy efficiency, reducing the carbon footprint of public transportation.
The integration of AI into public transportation ticketing systems promises a transformative shift towards a more efficient, accessible, and sustainable future. By streamlining processes, personalizing experiences, and enhancing security, AI empowers both transit authorities and passengers, creating a more seamless and enjoyable commuting experience for all. The benefits extend beyond mere convenience, encompassing improved resource management, reduced environmental impact, and increased accessibility for diverse user groups. The future of public transportation ticketing is undeniably intelligent, user-centric.
The evolution of AI in the coming years is poised to be transformative, affecting nearly every facet of human life. While predicting the exact trajectory is challenging, we can anticipate several key trends based on current advancements and emerging research. Here’s a breakdown of the major areas where AI could evolve in the future:
Advances in AI Capabilities
General AI (AGI): Today’s AI is specialized (narrow AI), excelling in specific tasks like language translation or playing games. Over the next few decades, we might witness the development of Artificial General Intelligence (AGI), systems that can perform a wide range of cognitive tasks, similar to a human’s ability to adapt to different challenges. AGI would have the potential to innovate autonomously, solve complex problems, and think abstractly.
Superintelligence: As AI becomes more advanced, there is the potential for superintelligent systems that outperform human intelligence in every field, including scientific research, decision-making, and creative endeavors. Superintelligence would raise significant ethical and safety concerns, prompting discussions around control, governance, and alignment with human values.
AI Integration with the Physical World
Robotics: AI-driven robotics will likely see massive improvements in dexterity, autonomy, and adaptability. Robots could become common in sectors like healthcare, logistics, manufacturing, and home automation, performing tasks from surgery to delivery with minimal human intervention.
Autonomous Vehicles: Self-driving cars, trucks, drones, and ships could become the norm in transportation. Over the next few decades, AI will likely make transportation safer, more efficient, and cost-effective, though regulatory, ethical, and infrastructure challenges must still be addressed.
Human-AI Collaboration
Enhanced Productivity: AI systems will become increasingly integrated into the workplace, helping with decision-making, analysis, and creativity. AI could be a collaborator rather than a replacement, augmenting human workers’ skills and enabling them to focus on higher-level tasks. In fields like law, medicine, education, and engineering, AI might serve as an assistant, providing insights, automating routine tasks, or enhancing creative processes.
Brain-Computer Interfaces (BCIs): In the long term, we may see the development of BCIs that allow humans to interact directly with AI through thought alone, enhancing cognitive abilities and creating new ways of communication. This could revolutionize both the treatment of neurological diseases and the way we interface with technology.
AI and Society
Ethical and Social Implications: As AI becomes more powerful, its influence on society will grow. Questions around privacy, data security, job displacement, and the fairness of AI systems will become central issues. We will need to create robust frameworks for AI governance, ensuring that AI systems are designed and deployed in ways that align with human rights and values.
AI in Governance and Politics: AI could play a role in policymaking, helping to simulate scenarios, analyze vast amounts of data, and predict the outcomes of various policy decisions. However, this raises concerns about transparency, accountability, and bias in decision-making processes.
AI in Healthcare: AI could revolutionize personalized medicine, from diagnostic tools to drug discovery. With advancements in machine learning, AI will likely become better at analyzing genetic data, predicting disease outcomes, and offering personalized treatment plans. It could also assist in global public health initiatives by predicting outbreaks or helping design responses to pandemics.
AI in Creativity and Art
Creative AI: We are already seeing AI used in creative fields such as music, visual art, writing, and film. In the future, AI could become a true creative partner, collaborating with artists, designers, and writers to produce innovative works of art, film, and literature. However, this may raise questions about authorship, originality, and the role of human creativity.
AI-Generated Content: Tools that generate written, visual, and audio content could become increasingly sophisticated, allowing individuals and businesses to create high-quality content at scale. This could change the dynamics of media, entertainment, and advertising, leading to new forms of interactive and personalized content.
AI and the Environment
AI for Sustainability: AI will likely play a critical role in addressing global challenges like climate change, resource depletion, and biodiversity loss. AI could help optimize energy use, manage smart grids, enhance renewable energy production, and monitor environmental changes more effectively.
Climate Modeling: AI can accelerate the modeling of climate systems, providing more accurate predictions of environmental changes and helping to identify effective strategies for mitigating climate change.
AI in Education
Personalized Learning: AI could revolutionize education by tailoring learning experiences to individual students, adapting to their learning styles, and providing instant feedback. Virtual tutors powered by AI could make education more accessible globally.
AI-Driven Research: AI could accelerate the pace of scientific research by automating data analysis, predicting outcomes, and generating new hypotheses. This might lead to rapid advancements in fields like medicine, material science, and quantum computing.
Regulation and Governance
AI Regulation: As AI’s capabilities grow, so too will the need for robust regulations. Governments and international organizations will likely establish frameworks to ensure the responsible development and deployment of AI. Issues such as AI safety, the prevention of malicious use, privacy concerns, and the regulation of autonomous systems will be key challenges.
AI in Law Enforcement and Security: AI technologies may be used for crime prediction, surveillance, and cybersecurity. However, this brings up questions of civil liberties, privacy rights, and the potential for authoritarian use of AI-powered tools.
The Ethical Challenge of AI
Bias and Fairness: As AI systems become more integral to decision-making, concerns about algorithmic bias and fairness will continue to grow. Ensuring that AI does not perpetuate or amplify societal inequalities will be an important area of focus. Research will likely focus on developing explainable and transparent AI systems.
Human-AI Relationships: As AI becomes more integrated into daily life, questions about its role in human relationships will emerge. AI-driven personal assistants, companions, and even romantic relationships could change how people interact socially and emotionally with machines.
Quantum Computing and AI
AI + Quantum Computing: The combination of AI and quantum computing could unlock new levels of computational power. Quantum AI could enable breakthroughs in drug discovery, optimization problems, cryptography, and complex simulations that would be impossible with classical computers.
AI’s evolution over the coming years holds immense promise but also challenges. While technological advancements could improve lives in unprecedented ways, society must also grapple with the ethical, social, and political implications of increasingly intelligent machines. The future of AI will likely require careful planning, collaboration, and governance to ensure that it serves humanity’s best interests and enhances our collective well-being.
The concept of « Prudence » and its relationship to the Electronic Horizon and ADAS sensors (Advanced Driver Assistance Systems) revolves around improving driving safety by integrating data from various sources to create a more informed and proactive system for vehicle control and driver support.
An Electronic Horizon refers to a system that anticipates road conditions ahead of a vehicle, much like how a driver would scan the road in front of them. It is an extension of the navigation system and works with ADAS sensors to predict upcoming road conditions, such as curves, intersections, road signs, and obstacles, providing information that is crucial for intelligent decision-making.
In a typical ADAS system, sensors like radars, cameras, lidars, and ultrasonics provide real-time data about the surroundings of the vehicle. These sensors can identify other vehicles, pedestrians, road signs, and environmental conditions such as weather, which help the car understand its immediate surroundings.
However, the Electronic Horizon takes this a step further by integrating this real-time sensor data with high-definition maps and predictive models to forecast upcoming driving conditions beyond the line of sight. This allows the vehicle to anticipate challenges such as sharp turns, slippery roads, or other hazards, even before they are visible to the driver or sensors.
ADAS Sensors such as radars, cameras, lidars and ultrasonic sensors play a vital role in perception—detecting objects, other vehicles, pedestrians, road signs, and the road conditions surrounding the vehicle. These sensors constantly gather data about the vehicle’s environment.
Radars: Help detect the speed, distance, and direction of objects around the vehicle, such as other cars or obstacles.
Cameras: Help identify road signs, lane markings, pedestrians, and other vehicles, contributing to the vehicle’s understanding of its surroundings.
Lidars: Provide highly detailed, 3D maps of the environment, offering precise distance measurements to objects.
Ultrasonic sensors: Used for detecting objects near the vehicle, often in low-speed maneuvers.
By integrating the data from these sensors with the vehicle’s internal systems and external sources like GPS or cloud-based data, the Electronic Horizon provides an advanced understanding of future conditions and can predict the need for actions such as slowing down, braking, or accelerating before the actual need arises.
Prudence Metric and Nexyad
The original Nexyad concept sought to define a Metric of Prudence—a measure of how cautious, safe, or proactive a vehicle’s behavior is while driving. The idea was to develop a system that could assess driving style and adjust it according to safety needs, aiming for an optimal level of caution while driving.
The Prudence Metric involves understanding the level of risk in a given situation and making driving decisions that prioritize safety. For example:
Sudden braking could be deemed imprudent in most situations, but sometimes necessary for safety.
Aggressive acceleration might be considered imprudent unless overtaking a slow-moving vehicle.
Too cautious driving might involve excessive braking or slow speeds when not needed.
This concept goes beyond simply reacting to immediate road conditions; it considers the driver’s overall decision-making process and interaction with the environment. By integrating data from the Electronic Horizon and ADAS Sensors, the Prudence Metric can be dynamically adjusted depending on factors such as:
Road geometry (e.g., sharp curves or elevation changes)
Weather conditions (e.g., rain, snow, fog)
Traffic density (e.g., slow-moving traffic ahead or tailgating)
Driver behavior (e.g., acceleration patterns, braking, or lane-keeping)
By using this metric, Nexyad aims to provide a continuous feedback loop that helps optimize both safety and comfort for the driver, ensuring that driving decisions align with real-world road conditions and anticipate potential hazards before they occur.
Nexyad’s Role in ADAS
The key contribution of Nexyad to ADAS technology lies in the way it enhances vehicle decision-making:
Predictive driving: By anticipating what might happen based on the electronic horizon, Nexyad adjusts the vehicle’s driving style, ensuring safer interactions with the road environment.
Optimization of vehicle behavior: The prudence metric helps the vehicle behave cautiously but not excessively, balancing between necessary assertiveness and safety.
In summary, Nexyad’s concept combines ADAS Sensors, the Electronic Horizon, and a Prudence Metric to create a more intelligent, responsive, and anticipatory driving system. It ensures that the vehicle makes decisions that consider both current road conditions and upcoming challenges, enhancing safety and driving efficiency.
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