AI behind the Wheel: Transforming Mobility with Robotics and Autonomous Systems
AI Unlocked26 Marras 2023

AI behind the Wheel: Transforming Mobility with Robotics and Autonomous Systems

In today's episode we will cover the following:

  • Mathematics and machine learning are foundational for autonomous systems.
    • Calculus, linear algebra, and probability theory are used in self-driving cars.
    • Machine learning processes sensor data for navigation and obstacle avoidance.
  • IoT and quantum computing hold promise for the future of autonomous tech.
    • IoT facilitates data sharing and collective decisions.
    • Quantum computing can process information at unprecedented speeds.
  • NVIDIA, Intel, and Qualcomm are prominent in the autonomous systems market.
    • NVIDIA's DRIVE platform provides computational power for deep learning.
    • Intel's Mobileye offers computer vision technology for driver assistance.
  • IoT enables predictive maintenance and real-time updates in autonomous systems.
    • Network theory and optimization algorithms handle data efficiently.
  • Mathematical algorithms are crucial for AI-driven vehicles.
    • Calculus,linear algebra, and probability theory are used for navigation and safety.
  • Sensors like cameras, LIDAR, radar, and ultrasonic sensors are essential.
    • Bosch, Continental, DENSO, and NXP are leading sensor manufacturers.
  • IoT facilitates data exchange, enhancing efficiency and safety.
    • SCADA and PLC systems are used for real-time control and data collection.
  • Autonomous systems rely on mathematical algorithms for navigation.
    • Graph theory and algorithms like Dijkstra's aid path planning.
  • AI and robotics are transforming automotive manufacturing.
    • Industrial robots with AI ensure precision in assembly tasks.
  • Autonomous cars utilize machine learning and sensors for navigation.
    • AI like Autopilot and Full Self-Driving enhance driving capabilities.
  • Public transportation, UAVs, and warehouse automation benefit from AI.
  • Autonomous trucks and agricultural machinery improve efficiency in logistics.
  • Future trends include urban mobility, space exploration, and AI-driven performance.
    • AI-optimized hardware and open-source software platforms are emerging.
  • Electric autonomous vehicles aim for sustainability with optimized energy consumption.
  • Connectivity through 5G and V2X communication enhances real-time data sharing.
  • Level 4+ autonomy promises fully autonomous transportation for ride-hailing and personal use.
  • Ethical AI and cybersecurity are essential in the development of autonomous systems.
  • Challenges include data acquisition, sensor reliability, regulation, and cybersecurity.
    • Infrastructure readiness and public acceptance are hurdles.
  • AI's impact extends to job transformation, accessibility, urban planning, and insurance.
    • Ethical and legal considerations are crucial in autonomous systems.
    • Societal shifts may affect vehicle ownership, driving, and urban landscapes.
  • Autonomous transportation promises productivity, reduced congestion, safety, and lower emissions.

Jaksot(16)

Empowering AI: The Secrets of the Encoder-Decoder Mechanism

Empowering AI: The Secrets of the Encoder-Decoder Mechanism

In this episode, we will cover: Introduction to Encoder-Decoder Mechanisms Basics of Neural Machine Translation Deep Dive into the Encoder-Decoder Model The Power of Attention in Neural Networks Recent Advancements in Encoder-Decoder Mechanisms Practical Applications of Encoder-Decoder Models Beyond Transformers: Other Mechanisms Training and Fine-tuning Explained Inference in Encoder-Decoder Mechanisms Training Models with Attention Conclusion and Future Forecasts (5 minutes)

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Natural Language Processing (NLP) - the way machines understand human language

Natural Language Processing (NLP) - the way machines understand human language

This episode has the following structure and topics: Introduction to NLP AG limpse into Natural Language Processing History Understanding NLP: Basics and Mechanics Applications of Natural Language Processing The Potential of NLP Future Developments and Challenges Natural Language Processing in Action: Applications and Use-Cases Conclusion

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APIs: The Gateway to Large Language Models

APIs: The Gateway to Large Language Models

In this episode, we cover REST, SOAP, GraphQL, gRPC and WebSocket APIs. We also look at API principles from API First Design to Rate Limiting and OAuth. Then we look at API Tools from Postman to Swagger and Insomnia, then to API Gateway, SoapUI and JMeter. Then we move to some Real-World Applications of APIs in Driving Large Language Models such as Content Creation, Chatbots, Language Translation Services, Education, Sentiment Analysis, Voice Assistants, Text-to-Speech and Speech-to-Text Services. We also look at the mathematics that underpin APIs and future trends of APIs.

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What is Deep Learning and how does it work?

What is Deep Learning and how does it work?

Introduction to Deep Learning The Evolution of Deep Learning - A Historical Perspective The Brain Behind Deep Learning - Artificial Neural Networks: Types of Deep Learning - CNNs, RNNs, and More Training Deep Learning Models - How Do They Learn?: Real-World Applications - Where is Deep Learning Used?: Challenges and Ethical Considerations The Future of Deep Learning - What’s Next?:

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What is Prompt Engineering?

What is Prompt Engineering?

In this episode, we cover what is Prompt Engineering, we look at what is a good prompt or a bad one, when there is too much or too little information in a prompt, how and why an LLM treats various words from a prompt in a different way and what decomposition means for crafting a good suite of prompts that can solve very complex problems.

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An introduction to Large Language Models (LLMs)

In this episode we will cover: What are Large Language Models and What are Foundation Models? Major LLMs: Falcon LLM GPT -3 and GPT -4 PaLM BARD BERT Claude and Claude 2 LLaMA and LLaMA 2 ERNIE 3.0 Bloom Turing-NLG Chinchilla

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Is AI dangerous ?

Is AI dangerous ?

Can AI become a force of good or are we going to lose control over it? In this episode, we look at how AI can evolve as we are at a crossroads. Are we going to be smart enough and develop artificial intelligence to help us cure disease, extend life or explore the universe? Or, in our quest to develop AI as fast as possible, we will end up with a sentient artificial intelligence with a different agenda than ours and this will ultimately lead to the end of mankind?

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What is AI ?

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In our Inaugural Episode, we cover briefly: Section 1: Artificial Intelligence (AI) and Machine Learning Section 2: Supervised vs. Unsupervised Learning Section 3: Deep Learning and Generative Models Section 4: Understanding Generative AI Section 5: Applications in Various Business Sizes Section 6: Transformers in AI Section 7: Challenges and Solutions in AI Section 8: Prompt Engineering and Detection of Hallucinations Section 9: Text-to-Output Models in Business Operations Section 10: Q&A Segment for Businesses

27 Elo 202318min

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