St Germain en Laye, December 4th 2024.

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. »

#AI #ArtificialIntelligence #ChatGPT #Gemini #GPT4o #Jobs #Employment #Nexyad

St Germain en Laye, December 3rd 2024.

Risk and Uncertainty Quantification

Better Decision-Making under Uncertainty

Risk-Aware Model Calibration

Adaptability to Complex Scenarios

Improved Interpretability

Integration with Probabilistic and AI Systems

Comprehensive Model Evaluation

Conclusion

#AI #MachineLearning #RiskManagement #AIUncertainty #RiskAwareness #ModelEvaluation #PredictiveModeling #AITrust #AIConfidence #PrudenceMetric #NEXYAD #SafetyNex #DrivingPrudenceMetric

St Germain en Laye, December 2nd 2024.

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.

Example of XAI at Nexyad, see Autonomous Cars & Autonomous Trucks driven by Prudence

Ontologies and Formal Logic Systems

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)
• UC Berkeley (e.g., causal inference, multi-modal representations)
• 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.

Next date in near future: 2024 Conference

#KnowledgeRepresentation #ArtificialIntelligence #AIResearch #NeuralSymbolicAI #KnowledgeGraphs #CommonsenseReasoning #ExplainableAI #CausalReasoning #AIandReasoning #OntologyEngineering #MachineLearning #NeuralNetworks #AIKnowledge #GraphNeuralNetworks #ExplainableAI #AIinHealthcare #DataScience #MultimodalAI #CognitiveAI

St Germain en Laye, November 29th 2024.

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.

See Nexyad AI page : Artificial Intelligence: We bring Solutions to your Problems

#AI #ArtificialIntelligence #LLM #TransformersAI #ArtificialIntelligence #DeepLearning #NLP #MachineLearning #AIModels #SelfAttention #NeuralNetworks #TextGeneration #NLPModels #DataScience #AIArchitecture #LanguageModels #AutoregressiveModels #AIResearch #MachineLearningExplained #Nexyad

St Germain en Laye, November 28th 2024.

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.

#DriverSafety #RealTimeAlerts #AIinDriving #HeadsUpDisplay #VehicleSafety #SmartDriving #AutonomousSafety #SafetyNex #PrudenceMetric #VocalAlerts #RiskPrevention #TrafficSafetyTech #DriverAssistance #InnovativeTech #RoadSafety #SafetyFirst #AIForSafety #AutonomousVehicles #SmartCarTech #DriverAlertSystem #Nexyad #DrivingSolutions #AutomotiveAI

St Germain en Laye, November 17th 2024.

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.

#AI #ArtificialIntelligence #AITicketing #SmartTransportation #PublicTransportInnovation #SeamlessTravel #DigitalTicketing #AIForTransit #EcoFriendlyTravel #PassengerConvenience #SmartCitySolutions #TravelMadeEasy

St Germain en Laye, November 26th 2024.

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.

See Nexyad AI pages

#AI #ArtificialIntelligence #FutureOfAI #AGI #Superintelligence #AIRevolution #AIInSociety #AIandEthics #AIInHealthcare #AIandRobotics #AIInCreativity #AIforSustainability #AIandEnvironment #AIInEducation #AIInGovernance #MachineLearning #AIandPrivacy #QuantumAI #AIandTheFuture #AIinTransportation #AutonomousVehicles #AIandCreativity #TechInnovation #AIResearch #DigitalTransformation #AIandJobs #AIEthics #AIforGood #SmartTechnology #AIandClimateChange #AIinMedicine

St Germain en Laye, November 25th 2024.

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.

#ADAS #ElectronicHorizon #AdvancedDriverAssistance #SelfDrivingCars #VehicleSafety #PrudenceMetric #DrivingSafety #PredictiveDriving #AutonomousVehicles #Nexyad #SmartDriving #VehicleAutomation #SensorTechnology #RoadSafety #FutureOfDriving

St Germain en Laye, November 22th 2024.

The Probably Approximately Correct (PAC) model is a foundational concept in the theory of machine learning, introduced by Leslie Valiant in 1984. It provides a framework for understanding how a learning algorithm can perform in a « reasonable » way, given a limited amount of data.
In the PAC framework, an algorithm learns a target concept (or function) based on sample data. The goal is for the algorithm to produce a hypothesis that is « approximately correct » with high probability.

• « Probably » refers to the probability that the learning algorithm’s hypothesis is correct. Specifically, the hypothesis should be correct with a probability greater than or equal to a specified confidence level (e.g., 95%).
• « Approximately Correct » means that the hypothesis made by the algorithm is not guaranteed to be perfect but is close enough to the true concept or function in terms of error. The allowable error is typically a small fraction, say 5%.

Breaking it Down

• Goal: The goal of PAC learning is to learn a good hypothesis that is close to the true underlying function with high probability.
• Sample Complexity: How much data (samples) is needed to learn a good hypothesis.
• Error Tolerance: The allowed error (misclassification rate) between the predicted hypothesis and the true function.
• Confidence: The probability with which we expect the learned hypothesis to be « approximately correct. »

Example in AI

Imagine you’re training a machine learning model to recognize cats in photos. Using a PAC framework, you might specify that:

• The model should recognize cats correctly at least 95% of the time (with 95% confidence).
• The model can make some mistakes (e.g., identifying a dog as a cat), but the rate of mistakes should be under 5%.

If the model performs well in these conditions, we would say the learning process was « probably approximately correct. »

Why is PAC Important in AI?

PAC learning provides theoretical guarantees about the performance of learning algorithms. It helps answer questions like:

• How much data do we need to learn a useful model?
• How do we know if a model is likely to generalize well to unseen data?
• What is the relationship between the complexity of a model (like its number of parameters) and the amount of data required for learning?

This theoretical framework is especially important in machine learning and AI because it gives us a way to reason about the limits of what can be learned from data and how robust our models are to errors and variations.

So, « probably approximatively correct » in the context of AI refers to a kind of learning that is good enough, most of the time, with a small margin of error, given the constraints of data and model complexity.

#AI #ArtificialIntelligence #PACLearning #MachineLearning #Tech #Nexyad

St Germain en Laye, November 21st 2024.

NEXYAD, a company specializing in AI-based solutions for autonomous driving, has developed a unique approach to enhancing the safety and reliability of self-driving vehicles. One of their key contributions is the Prudence Metric, which aims to assess and quantify the safety of driving decisions made by an autonomous vehicle (AV).

          Overview of the Prudence Metric

The Prudence Metric is an alternative solution to traditional safety assessments, providing a way to measure how « prudent » or cautious an autonomous system is in its decision-making process. In autonomous driving, prudent decision-making is crucial, as it involves balancing various factors such as:

• Road conditions
• Traffic situations
• Pedestrian and other road users’ behavior
• Weather and visibility conditions
• Vehicle dynamics

The Prudence Metric takes into account the risk of a vehicle’s actions and prioritizes decision-making strategies that minimize the likelihood of accidents or dangerous situations. This contrasts with more traditional metrics, which may focus primarily on efficiency or the vehicle’s ability to navigate obstacles without necessarily considering the safety margin.

          Key Features of the Prudence Metric

• Dynamic Assessment: The Prudence Metric evaluates the driving decisions in real-time based on the current road and traffic conditions, rather than just relying on pre-programmed behavior. This ensures the system adapts to a wide variety of real-world scenarios.
• Risk Mitigation: Rather than just assessing the technical performance of the AV, the metric emphasizes actions that lower the potential risk to the vehicle, its passengers, and other road users. It provides an objective measure of how « safe » a given driving maneuver is in a specific context.
• Adaptability: The metric can be integrated into various levels of autonomy, from partially autonomous vehicles to fully self-driving systems. It provides flexibility in evaluating different autonomous systems across a range of use cases.
• Real-time Feedback for Validation: NEXYAD’s approach allows for continuous monitoring and feedback, helping to validate the decision-making algorithms of AVs under different operational conditions. This is crucial for improving both the trust and effectiveness of autonomous driving technologies.

          Application in Autonomous Vehicles

In autonomous driving, the Prudence Metric can be used to:

• Assess safety during driving: For example, if an autonomous vehicle faces an unpredictable situation (e.g., a pedestrian crossing unexpectedly), the Prudence Metric will assess the vehicle’s response in terms of how cautiously it handled the situation.
• Optimize driving behavior: It helps ensure that the AV makes safe, context-sensitive decisions, such as slowing down or adjusting its trajectory in response to external factors (like road hazards or sudden changes in traffic conditions).
• Training and validation: The metric can be used during simulation and real-world testing to evaluate how well an AV’s decision-making algorithms perform in maintaining safety across diverse environments.

          Benefits of the Prudence Metric

• Increased Safety: By prioritizing cautious and prudent decisions, the metric can reduce the likelihood of accidents, improving safety for all road users.
• Enhanced Trust: A reliable metric for prudent decision-making can help build public trust in autonomous vehicle systems.
• Better Integration with Human Drivers: Prudence-based driving allows for smoother interaction between AVs and human drivers, as the vehicle behaves in a more predictable and considerate manner.

	The Prudence Metric proposed by NEXYAD offers an alternative approach to evaluating autonomous driving systems. It focuses on the safety and cautious decision-making required to navigate real-world driving environments, providing an important tool to ensure that autonomous vehicles act in a way that minimizes risk and promotes safety.

 

#AutonomousDriving #PrudenceMetric #AIinTransportation #SelfDrivingCars #VehicleSafety #AutonomousVehicles #SafetyFirst #AIForGood #AutonomousTechnology #SmartDriving #FutureOfDriving #DrivingInnovation #SelfDrivingTech #SafeDriving #RiskMitigation #PrudentDriving #AutonomousSafety #Nexyad #DrivingSolutions #AutomotiveAI

 

 

St Germain en Laye, November 20th 2024.

NEXYAD is a company specializing in AI and machine learning-based solutions for the automotive and mobility industries, with a focus on safety and predictive analytics. One of their key contributions is our « Prudence Metrics » — a set of algorithms designed to assess driving behavior and predict the likelihood of accidents or risky situations. Our software for safety is called SafetyNex.

While NEXYAD’s « Prudence Metrics » are more often associated with four-wheel vehicles and driver assistance systems, the principles of these metrics can be applied to two-wheelers as well. Let’s break down how AI, NEXYAD, and Prudence Metrics fit into the two-wheeler safety landscape:

          Prudence Metrics for Two-Wheelers

The Prudence Metrics are designed to evaluate and monitor the driving or riding behavior of a person in real-time, assessing factors like risk-taking, attention, road conditions, and vehicle performance. By analyzing these metrics, AI systems can assess the probability of a rider being involved in an accident and can provide safety recommendations or warnings.

Key Features in the Context of Two-Wheelers:

• Risk Prediction: Prudence Metrics can predict dangerous riding behaviors such as aggressive acceleration, sharp braking, or unsafe cornering, which are more common in motorcycle riders due to the unique dynamics of two-wheeled vehicles. For example, motorcycles are more prone to tipping in sudden maneuvers, so monitoring this through AI can warn the rider to slow down or adjust their behavior.
• Real-Time Feedback: By continuously monitoring the rider’s actions, AI systems based on Prudence Metrics can provide feedback through connected devices (e.g., a helmet, smartphone app, or smart dashboard), alerting the rider about risky behavior or potential hazards ahead.
• Environmental Context: The system can assess the environmental conditions such as road quality, weather, or traffic flow, and adjust its risk predictions accordingly. This is especially important for two-wheelers, where weather conditions (like rain or ice) and road surfaces (gravel, potholes) significantly affect safety.
• Adaptive Risk Levels: Prudence Metrics can adapt risk levels based on the rider’s experience, road type, and bike performance. For example, a rider on a sport bike will receive a different safety assessment than someone riding a cruiser or scooter in urban traffic.

          NEXYAD’s AI Solutions for Motorcycle Safety

NEXYAD applies its AI and machine learning models to enhance vehicle safety by offering predictive and preventive solutions. While NEXYAD’s focus is often on automotive applications, their systems can be adapted to motorcycles in several ways:

AI-Powered Risk Detection

• Accident Prediction: Using data from sensors, GPS, and onboard computers, NEXYAD’s AI models can analyze the behavior of the rider and predict the likelihood of an accident in the near future. For motorcycles, this involves recognizing risky driving patterns (e.g., speeding, tailgating) and predicting when these behaviors could lead to a crash.
• Safety Warnings: Based on Prudence Metrics, NEXYAD’s AI can issue real-time safety warnings to the rider. For instance, it can warn when the rider is riding too aggressively for the current road conditions or when other vehicles are encroaching into the rider’s lane.

Predictive Maintenance

• NEXYAD’s technology is also capable of predicting mechanical issues with the motorcycle based on its usage patterns. By analyzing data from the motorcycle’s sensors, AI can forecast when certain parts (like tires or brakes) are likely to wear out, preventing accidents caused by component failure.

Data Fusion and Behavior Understanding

• NEXYAD uses data fusion techniques, integrating multiple data sources such as the motorcycle’s sensors, rider’s behavior, and environmental data, to create a comprehensive safety profile. This holistic approach allows for more accurate accident predictions and proactive safety measures.

          AI & Prudence Metrics for Two-Wheelers: A Future Vision

Looking ahead, the integration of Prudence Metrics into AI-powered two-wheeler safety systems could lead to innovations in rider protection. For example, by using real-time behavioral data and environmental context, AI systems could:

• Integrate with helmet technology: AI-driven Prudence Metrics could be integrated into smart helmets, alerting the rider about hazardous road conditions, the proximity of other vehicles, or the rider’s own performance.
• Collaborate with traffic management systems: AI systems could communicate with smart city infrastructure to receive real-time updates about traffic congestion, accidents, or roadwork, allowing the rider to adjust their route accordingly.
• Provide tailored safety interventions: Based on a rider’s unique behavior and skill level, the AI system could offer personalized recommendations, such as adjusting the level of electronic traction control or providing feedback on smoother riding techniques to avoid accidents.

          Challenges and Opportunities

While the application of NEXYAD’s Prudence Metrics to two-wheelers is promising, there are some challenges to overcome:

• Data Availability: For AI systems to work effectively, large volumes of accurate data are required. Two-wheelers present unique challenges due to the lack of standardized onboard data (compared to cars, which typically have more sensors and data logging).
• User Acceptance: Riders may be wary of new technologies or over-reliant on AI systems, leading to potential issues with trust or incorrect usage.
• Cost: Advanced safety systems powered by AI and Prudence Metrics could raise the price of motorcycles, which could limit adoption, especially in emerging markets or among casual riders.

          Conclusion

The application of AI and Prudence Metrics to two-wheeler safety represents a major step forward in reducing motorcycle accidents and improving rider safety. By combining predictive analytics, real-time behavior analysis, and advanced risk detection, these systems can proactively address many of the safety challenges faced by motorcyclists. As these technologies continue to evolve, they hold the potential to make riding safer, more enjoyable, and more accessible.

#AI #ArtificialIntelligence #MachineLearning #fuzzylogic #possibilityTheory #PredictiveAnalytics #MobilityTech #SmartMobility #VehicleSafety #SafetyTech #Innovation #TechForGood #Nexyad #prudenceMetric #drivinBehavior #twowheelers #SafetyNex

St Germain en Laye, November 19th 2024.

Autonomous driving has seen significant advancements in recent years, with companies like Tesla, Aurora, and Waymo leading the charge. These companies have made major progress in terms of technology, safety, regulatory approval, and public perception. Below is a summary of the key developments made by these three players:

TESLA

Tesla’s approach to autonomous driving has been centered around incremental improvements to its Autopilot system, which uses a combination of cameras, radar, ultrasonic sensors, and AI to assist with tasks like lane keeping, adaptive cruise control, and automated parking. However, Tesla has focused more on « driver-assist » features than fully autonomous driving (Level 5), so its vehicles still require human oversight.

Key Developments:

• Autopilot and Full Self-Driving (FSD) Features: Tesla’s most notable feature is Full Self-Driving (FSD), which is an advanced version of Autopilot. FSD includes features such as:
  • Navigate on Autopilot: Autonomously changing lanes and navigating highways.
  • Auto Park and Summon: Parking and retrieving the car autonomously.
  • City streets driving (Beta): A more recent feature that allows the car to navigate complex urban environments, including intersections, stop signs, and traffic lights.
• Tesla Vision: In 2021, Tesla transitioned away from radar sensors and started relying solely on cameras and neural networks (vision-based processing) for its self-driving capabilities, which Tesla CEO Elon Musk believes will be a more scalable approach. This move marked a shift toward relying on pure visual data to make driving decisions, mirroring human perception.
• Dojo Supercomputer: Tesla has been developing its own supercomputer, Dojo, designed to handle massive amounts of data to train its AI models. This system allows Tesla to improve its Autopilot and FSD capabilities through real-world driving data collected from the fleet of Tesla vehicles on the road.

Challenges and Criticism:

• Tesla’s Autopilot and FSD have faced scrutiny for incidents involving accidents. While Tesla’s systems are not fully autonomous (Level 5), they are marketed as advanced driver-assist systems (Level 2/3), which has raised concerns about misuse by drivers who might over-rely on the system.
• Tesla has been in the spotlight over regulatory scrutiny, including investigations by the National Highway Traffic Safety Administration (NHTSA) related to accidents involving Autopilot.

WAYMO

Waymo, a subsidiary of Alphabet (Google’s parent company), is considered one of the most advanced autonomous driving companies, particularly when it comes to Level 4 autonomy. Waymo’s approach is based on lidar, radar, and cameras to create a detailed map of the environment and navigate without human intervention in certain conditions.

Key Developments:

• Waymo One: This is Waymo’s fully autonomous ride-hailing service operating in Phoenix, Arizona, and is considered one of the first public-facing autonomous taxi services. It provides autonomous rides using a fleet of self-driving Chrysler Pacifica minivans and electric Jaguar I-Pace SUVs.
  • Level 4 Autonomy: In the Phoenix area, Waymo’s vehicles can drive without a safety driver, although they still have human safety drivers in other areas as backup.
  • Geofencing: Waymo’s self-driving cars operate within geofenced areas—regions that are pre-mapped and suitable for autonomous operation. This limits the complexity of the environment, making it easier for Waymo’s vehicles to navigate autonomously.
• Autonomous Fleet: Waymo has invested heavily in developing its own fleet of autonomous vehicles and has been focusing on urban areas where the technology can be more easily tested and refined.
• Safety and Testing: Waymo has logged millions of miles on public roads and conducted billions of miles of simulations to ensure its vehicles can operate safely. The company has also been transparent with its data, sharing safety metrics, accident reports, and vehicle performance statistics with the public.

Challenges and Criticism:

• Scalability: Despite Waymo’s advances, the company faces challenges in scaling its technology to more cities and regions, as it needs to develop detailed maps and conduct extensive testing for each new location.
• Regulation and Acceptance: Waymo has also faced regulatory hurdles in various jurisdictions, as cities and states debate the readiness of autonomous vehicles for public roads.

AURORA

Aurora is a self-driving technology company with a particular focus on commercial trucking and ride-hailing services. The company is developing autonomous systems for both long-haul freight trucks and passenger vehicles, including partnerships with companies like Toyota and Uber.

Key Developments:

• Aurora Driver: Aurora’s self-driving technology, known as the Aurora Driver, is designed to operate in both passenger vehicles and freight trucks. The company has developed a multi-layered approach using lidar, radar, and cameras to allow the system to perceive the environment and make decisions in real time.
• Autonomous Freight: Aurora’s technology is particularly focused on autonomous freight trucking, which has the potential to transform the logistics industry. In partnership with Uber Freight, Aurora is testing autonomous trucks to handle long-haul routes, which could improve safety, reduce costs, and address the driver shortage in the trucking industry.
• Partnerships and Funding: Aurora has secured significant funding and partnerships with leading companies, including Toyota (investing to accelerate the development of autonomous driving for both freight and passenger vehicles) and Uber (to develop self-driving ride-hailing cars).

Challenges and Criticism:

• Technological Maturity: While Aurora has demonstrated promising capabilities, its technology is still in the testing phase, and it has yet to launch commercial autonomous vehicles at scale. It faces competition from other companies in the freight and passenger vehicle space, such as TuSimple (focused on autonomous trucks) and Waymo (which also has ambitions in freight).
• Regulation and Public Perception: Like other companies in the autonomous vehicle space, Aurora must navigate complex regulatory environments and ensure public safety, which remains a major challenge in scaling autonomous technologies.

Summary of Key Technologies:

• Tesla: Primarily uses cameras and AI-powered neural networks to create a vision-based autonomous system, with incremental upgrades to driver-assist features. Focuses on consumer cars, with full autonomy still in development.
• Waymo: Uses a combination of lidar, radar, and cameras to enable Level 4 autonomous driving, primarily for ride-hailing in specific urban areas. Its cars can drive without human intervention in certain mapped areas.
• Aurora: Develops autonomous systems for both passenger cars and freight trucks, using lidar, radar, and cameras. Focuses on scalability for long-haul trucking and urban ride-hailing.

The progress in autonomous driving is moving quickly but is still facing challenges around safety, regulation, and public acceptance. Tesla is pushing the boundaries with its ambitious Full Self-Driving system, although it still requires driver oversight. Waymo is at the forefront of truly autonomous vehicles, with its Level 4 autonomous taxis in specific cities, while Aurora is focusing on revolutionizing freight transportation and testing autonomous systems for commercial vehicles.

While fully autonomous vehicles (Level 5) are still a long way from mass deployment, the ongoing development in these areas suggests that the future of driving will likely be highly automated in the coming decades.

#AutonomousVehicle #AutonomousDriving #Driverless #Tesla #Waymo #Aurora #Nexyad

St Germain en Laye, November 18th 2024

Don’t miss next big AI events around the world:

NeurIPS (Conference on Neural Information Processing Systems)
December 10-15 2024, Vancouver

Machine learning, deep learning, computational neuroscience, and related areas.

One of the top conferences for AI and machine learning research, attracting researchers, practitioners, and companies from around the world. It covers both theoretical advancements and practical applications in AI.

2024 Conference

CES (Consumer Electronics Show)
January 7-10 2025, Las Vegas

Consumer technology, including AI applications in products and services.

While CES is broader than just AI, it frequently features AI-related innovations and is a major showcase for companies to display AI-driven consumer products.

CES – The Most Powerful Tech Event in the World

Global Artificial Intelligence Summit & Awards (GAISA) 
February 7-8 2025, New Dehli

The prominence of AI in human lives & business industries.

Industry Voice : Global AI Summit

The 39th Annual AAAI Conference on Artificial Intelligence
feb 25 – March 4 2025, Philadelphia

General AI, including reasoning, machine learning, robotics, and other core topics.

A major event for AI researchers, focusing on the latest advancements across all areas of artificial intelligence.

AAAI-25 – AAAI

The Web Summit
April 27-30 2025, Rio de Janeiro

Technology and innovation, including AI.

A broad tech conference where AI is often a key topic, especially as it relates to startups, entrepreneurship, and the future of technology.

Web Summit Rio | April 27-30, 2025

CVPR (Computer Vision and Pattern Recognition Conference)
June 11-15 2025, Nashville

Computer vision, pattern recognition, and related AI techniques.

The most significant conference for AI and computer vision, it highlights innovations in image processing, visual recognition, and related domains.

2025 Conference

The AI Summit
June 11-12 2025, London

AI in business, enterprise solutions, and industrial applications..

The AI Summit focuses on how AI can be applied in business, with events in various cities around the world, including London, New York, and Singapore.

Register Your Interest | The AI Summit London

AI for Good Global Summit
July 7-11 2024, Geneva

Ethical AI, AI for social good, and AI in solving global challenges.

Organized by the International Telecommunication Union (ITU), this summit brings together experts, policymakers, and organizations working on using AI for positive global impact.

AI For Good Global Summit 2025 – SDG Knowledge Hub

ICML (International Conference on Machine Learning)
July 13-19 2025, Vancouver

Machine learning theory, algorithms, and applications.

Another leading conference in AI and machine learning, with a focus on advancing the theory and practice of machine learning algorithms.

2025 Conference

International Joint Conference on Artificial Intelligence (IJCAI) 
August 16-22 2025, Montreal

One of the major gatherings for AI researchers worldwide, covering a wide range of AI disciplines.

IJCAI 2025

European Conference on Artificial Intelligence (ECAI)
October 25-30 2025, Bologna

Understanding the sustainability theme in a holistic manner, helping to reach some awareness over the complexities of our planet, our ecosystems, and our societies.

ECAI 2025 – 28th European Conference on Artificial Intelligence

World Summit AI
October 8-9 2025, Amsterdam

AI innovations, trends, and global collaboration.

An international summit that brings together AI professionals, researchers, and business leaders to explore AI’s future and its application in industries around the world.

worldsummit.ai

#AI #ArtificialIntelligence #Conference #Summit #NeurIPS #CES #GAISA #AAAI #TheWebSummit #CVPR #TheAISummit #AIGGS #ICML #IJCAI #ECAI #WSAI #Nexyad

St Germain en Laye, November 15th 2024.

Genetic Algorithms (GAs) are a class of optimization algorithms inspired by the process of natural selection and biological evolution. They are part of the broader family of evolutionary algorithms and are used to find approximate solutions to optimization and search problems that may be too complex for traditional methods.
They are used in Artificial Intelligence for learning, constructing a solution step by step. We sometimes see them solving problems of “artificial Life”.

The basic idea behind GAs is to mimic the way nature evolves organisms through mechanisms such as selection, crossover (recombination), and mutation. These algorithms are particularly useful for solving problems where the search space is large, non-linear, or poorly understood.

Key Concepts in Genetic Algorithms

1. Population: The algorithm maintains a population of possible solutions (individuals). Each individual represents a possible solution to the problem, and its « fitness » is evaluated to determine how well it solves the problem.
2. Chromosomes: In the context of a GA, a chromosome is a representation of a solution. This could be a bit string, a real-valued vector, or any other structure that encodes the solution space.
3. Fitness Function: The fitness function evaluates how good a solution (chromosome) is in solving the problem at hand. Solutions with higher fitness values are more likely to be selected for reproduction (crossover and mutation).
4. Selection: The process of selecting individuals from the population based on their fitness. The better the fitness of an individual, the higher the probability that it will be selected for reproduction. Common selection methods include:
   • Roulette wheel selection
   • Tournament selection
   • Rank-based selection
5. Crossover (Recombination): Crossover is the process where two parent individuals combine parts of their chromosomes to create one or more offspring. The idea is that the offspring may inherit the best features of both parents, leading to improved solutions.
6. Mutation: Mutation introduces random changes to an individual’s chromosome. This helps maintain genetic diversity in the population and can potentially lead to discovering better solutions by exploring new parts of the search space. For example, flipping a bit in a binary chromosome or changing a value in a real-valued solution.
7. Replacement: After offspring are created via crossover and mutation, they are evaluated for fitness. The old population may be replaced with the new population, or a mixture of both can be used (this depends on the algorithm’s specific design, such as generational replacement or steady-state replacement).

Basic Steps in a Genetic Algorithm

1. Initialize the Population: Create an initial population of individuals, often randomly. Each individual represents a candidate solution.
2. Evaluate Fitness: Calculate the fitness of each individual in the population using the fitness function.
3. Selection: Select individuals based on their fitness to act as parents for the next generation.
4. Crossover: Perform crossover (recombination) to create offspring. The offspring inherit traits from both parents.
5. Mutation: Apply mutation to some individuals to maintain diversity in the population and explore new areas of the solution space.
6. Replacement: Replace some or all of the old population with the new offspring.
7. Termination: Repeat the process until a stopping criterion is met. This could be a fixed number of generations, a satisfactory fitness level, or convergence of the population.

Applications of Genetic Algorithms

Genetic Algorithms are used in a wide variety of fields, including:

• Optimization Problems: GAs are particularly useful for solving combinatorial optimization problems like the traveling salesman problem (TSP), knapsack problems, and scheduling problems.
• Machine Learning and AI: GAs are used for hyperparameter tuning, neural network training, feature selection, and evolving strategies in reinforcement learning.
• Engineering Design: GAs can help optimize designs in fields such as aerospace, automotive, and civil engineering, where design spaces are complex and not easily solvable using traditional methods.
• Robotics: GAs are used in evolving robot behaviors or controller parameters, especially in environments with large search spaces.
• Game Development: Evolving strategies for game AI or procedural content generation (e.g., evolving levels or game worlds).

Advantages of Genetic Algorithms

• Exploration of Large Search Spaces: GAs do not require a problem to be continuous or differentiable, making them suitable for complex, multi-modal, or poorly understood search spaces.
• Global Search: Because GAs work by evolving a population of solutions, they have the potential to explore a wider range of the solution space compared to local search algorithms that may get stuck in local minima.
• Flexibility: GAs can be applied to a wide range of problems, both in theory and practice, by appropriately designing the chromosome encoding, fitness function, and genetic operators.

Disadvantages of Genetic Algorithms

• Computationally Expensive: GAs often require evaluating a large population over many generations, which can be computationally intensive, especially for problems with large search spaces.
• Convergence Issues: GAs may converge prematurely to suboptimal solutions, especially if diversity in the population is lost too early in the process.
• Parameter Sensitivity: The performance of a GA can be sensitive to the choice of parameters (e.g., population size, crossover rate, mutation rate). These parameters often need to be fine-tuned for each specific problem.

Conclusion

Genetic Algorithms are a powerful tool for solving complex optimization problems by simulating the process of natural selection. Their ability to handle large, non-linear, and poorly understood search spaces makes them highly valuable in many fields. However, they do require careful tuning and can be computationally expensive. With appropriate modifications and techniques, they can be adapted to suit a wide variety of problem types and constraints.

Video by Kie Codes: Genetic Algorithms Explained By Example

#GeneticAlgorithms #AI #MachineLearning #Optimization #EvolutionaryAlgorithms #ArtificialIntelligence #DataScience #ReinforcementLearning #MachineLearningAlgorithms #ArtificialLife #EvolutionaryComputation #Tech #Nexyad

St Germain en Laye, November 14th 2024.

NEXYAD is taking a unique and highly effective approach to autonomous vehicle (AV) technology by simplifying the system through a single, key metric of prudence. This could significantly enhance the efficiency and scalability of autonomous systems. Instead of using hundreds of parameters to make decisions, our technology focuses on a single, overarching metric that guides the vehicle’s behavior in a more intuitive, adaptable and finally more human way.

The fact that your system is already being tested as a Predictive Adaptive Cruise Control (ACC) with Stellantis is a great step forward, and the potential extension to handling vehicle direction makes it even more impactful. The core idea is to provide vehicles with the ability to predict and adjust to their environment in a way that minimizes risk and ensures safety, all while simplifying the decision-making process for the system.

Here’s a potential breakdown of what we’re offering:

1. Prudence Metric: By reducing the decision-making process to a single parameter, we’re likely making the AI more interpretable, efficient, and adaptable. This could be a game-changer in terms of reducing the complexity and computational load, while still ensuring safe and reliable decisions.
2. Predictive ACC: As part of our collaboration with Stellantis, this system allows for predictive behavior based on the vehicle’s environment and intended trajectory, adjusting the vehicle’s speed accordingly. This could lead to a smoother, safer driving experience by anticipating potential hazards or changes in road conditions.
3. Expansion to Steering: Extending this technology to vehicle direction could make the system even more versatile, giving the vehicle not just the ability to control speed but also to adjust its trajectory in real-time based on predicted scenarios. This could significantly improve the overall safety and fluidity of the AV system.

What do you see as the most critical advantage of using a single prudence metric in AV systems, compared to the traditional approach of using many parameters?

See Full Self-Driving (Supervised) | Tesla video:
 

 
#Nexyad #Tesla #Stellantis #DrivingPrudenceMetric #AI #ADAS #AV #AutonomousVehicle #PredictiveACC #SelfDriving
 

St Germain en Laye, November 13th 2024.

This chapter of “a guided tour of Artificial Intelligence research” written by Thierry DENEUX, Didier DUBOIS, and Henri PRADE, deals with uncertainty representation in AI.

1. Uncertainty Representation Frameworks:

• Probability Theory: The most widely known framework for handling uncertainty. It models uncertainty in terms of probability distributions and focuses on quantifying the likelihood of events.
• Possibility Theory: An alternative to probability theory that deals with uncertainty in terms of possibility rather than probability. It is often used when data is incomplete or vague.

2. Challenges of Representing Uncertainty:

• The passage highlights that one of the main challenges in AI and knowledge representation is how to represent and reason about uncertainty in a meaningful way.
• Both probability theory and possibility theory address uncertainty, but they do so from different perspectives:
  • Probability theory focuses on the likelihood of an event occurring.
  • Possibility theory focuses on how plausible an event is, regardless of its likelihood.

3. Related Topics:

• Rough Sets: A formalism used to deal with vagueness and granularity in data. Rough sets do not require a precise definition of objects and can work with imprecise or incomplete information.
• Fuzzy Sets: Extend classical set theory to handle the concept of partial membership. Unlike traditional sets where an element either belongs or does not belong, fuzzy sets allow for a gradual degree of membership, useful for modeling vagueness.
• These concepts are tied to the idea of granularity in representations (the level of detail or precision of the representation), and gradualness in predicates (how natural language terms like « tall, » « large, » or « likely » can be represented in a mathematical model).

4. Other Frameworks:

• Formal Concept Analysis: A method for data analysis that structures information into formal concepts and hierarchies, aiming to uncover implicit knowledge.
• Conditional Events and Ranking Functions: Approaches for reasoning about uncertain or incomplete information by ranking possibilities or conditioning on new evidence.
• Possibilistic Logic: A form of logic that integrates possibility theory with logical reasoning, allowing reasoning under uncertainty.

Read chapter on Representations of Uncertainty in Artificial Intelligence: Probability and Possibility by Springer: https://link.springer.com/chapter/10.1007/978-3-030-06164-7_3

#AI #ArtificialIntelligence #Uncertainty #Springer #Nexyad

St Germain en Laye, November 2024

This scientific paper of French researchers DUBOIS & PRADE explains the place of Fuzzy Logic in AI.

1. Fuzzy Logic and Its History: fuzzy logic has been around for more than three decades and has had a long-standing association with AI, often misunderstood or underappreciated. Despite this, fuzzy logic has proven valuable for certain aspects of AI, particularly in modeling commonsense reasoning.

2. Fuzzy Sets and Graded Reasoning: one of the central contributions of fuzzy sets (which are foundational to fuzzy logic) to AI is their ability to model gradedness—that is, the idea that reasoning in the real world is often not binary (true/false) but involves varying degrees or levels of truth. This graded approach is especially useful when trying to simulate human-like reasoning.

3. Forms of Gradedness: gradedness can manifest in different ways:

• Similarity between propositions: for instance, how similar two ideas or concepts are to one another.
• Levels of uncertainty: capturing the inherent uncertainty in real-world situations.
• Degrees of preference: in decision-making, some choices may be preferred more than others, but not absolutely (i.e., it’s not all or nothing).

4. Commonsense Reasoning: the paper advocates that fuzzy logic can enhance AI’s ability to deal with commonsense reasoning, which often involves dealing with vague, imprecise, or incomplete information. Fuzzy sets help AI systems reason in a more human-like manner, especially in scenarios where traditional, precise logic fails to capture the nuances of real-world reasoning.

5. Complementarity with Symbolic AI: the paper suggests that fuzzy logic and soft computing techniques (e.g., neural networks, genetic algorithms, etc.) are complementary to symbolic AI (which typically uses clear rules and logic). In other words, fuzzy logic can work alongside traditional symbolic approaches to enhance the flexibility and robustness of AI systems, especially when handling complex, real-world problems that involve ambiguity and gradation.

Conclusion: fuzzy logic plays a crucial role in AI by introducing a framework for reasoning with uncertainties, graded truths, and imprecisions. This makes it especially useful for commonsense reasoning, which is an essential aspect of human-like AI. Rather than replacing symbolic AI, fuzzy logic complements it, expanding the range of problems AI can address effectively.

Read the paper: https://hal.science/hal-04013770/document

#AI #ArtificialIntelligence #FuzzyLogic #NeuralNetworks #GeneticAlgorithmes #Nexyad

St Germain en Laye, November 11th 2024.

1. Driving Prudence AI: Developed over 15 years, this AI measures driving prudence, or the safety and prudence exhibited by drivers (whether human or autonomous). It’s based on research from various countries and experts in infrastructure, driving behavior, and transportation.

2. SafetyNex Solution: This product integrates into various systems, such as:

• • Smartphones
  • Dashcams
  • Telematics devices
  • In-vehicle cluster architectures (via the NEXYAD SDK).

3. Applications:

• • Aftermarket driving assistance for fleets: Through partnerships with telematics companies like MOTIV AI, the system monitors driving behavior for over 470,000 professional drivers, helping to reduce operating costs associated with accidents (repairs, insurance premiums, etc.).
  • Predictive Adaptive Cruise Control (ACC) and Autonomous Vehicles: SafetyNex provides real-time metrics on the prudence of a vehicle’s self-driving mode. This enhances road safety and simplifies the complexity of autonomous driving systems. STELLANTIS is one example of an OEM working with NEXYAD in this area.

4. Benefits:

• • For Fleets: Lower accident-related costs, fewer sick days, and reduced insurance premiums.
  • For Autonomous Vehicles: Improved road safety by providing precise metrics on how safely a vehicle is driving in real-time.

5. Unique Value Proposition: The ability to quantify the prudence of both human and autonomous driving at every moment, giving fleets and OEMs the data they need to optimize safety and efficiency.

#DrivingAssistance #Fleet #Telematics #FleetManagement #Risk #DrivingPrudence #PrudenceMetric #OperatingCosts #FleetSafety #BYOD #Insurance #SafetyNex #V2X #ADAS #PredictiveACC #DriverAssistant #AI #Nexyad

St Germain en Laye, November 8th 2024.

« Fine-tuning Large Language Models »

Shane Storks, Graduate Student Research Assistant, Computer Science and Engineering, College of Engineering
Michigan Institute for Data Science and AI Laboratory

#AI #ArtificialIntelligence #GenerativeAI #Tutorials #MichiganInstitute #Nexyad

St Germain en Laye, November 7th 2024.

You want to learn more about Artificial Intelligence ? But you are not really aware of what terms such as AI and Machine Learning mean to make the difference? Here is an article that can help you see things more clearly.

« Generative AI is a form of artificial intelligence designed to generate content such as text, images, video, and music. It uses large language models and algorithms to analyze patterns in datasets and mimic the style or structure of specific content types. Machine learning (ML), on the other hand, helps computers learn tasks and actions using training modeled on results from large datasets. It is a key component of artificial intelligence systems. »

Read article on eweek.com by Kathrin Timonera and explore many links.

If you have projects that need GenAI and/or Machine Learning, do not hesitate to contact us.

See Nexyad AI page.

#AI #ArtificialIntelligence #GenAI #MachineLearning #DeepLearning #NeuralNetworks #Nexyad