What Telegram channels wrote about Self-driving cars in September 2026: 7 posts from 6 channels, collected automatically from public Telegram channels.
️Movie: Evel 2026 Rating ⭐️: 4.5 / 10 (4.5 based on 39 user ratings) | | | Story Line: A young woman is held hostage in an EV self-driving car on an isolated northern highway. The car is controlled by a malevolent force that wipes out her digital footprint and her very existen
🏎️ Auto News - Sep 27, 2026
1. This VW Golf GTI Doesn’t Drive On Roads – It Cuts Grass - Carscoops 2. When Did Police Cars First Get Lights (And Were They Always Red And Blue)? - Jalopnik 3. Forget Your Certification: GM Wants to Automate Forklifts Now - The Drive 4. This 1967 Mustang on Bring a Trailer Has Gone the Distance - Car and Driver
₿ Crypto × Auto: Toyota was an early blockchain explorer — their research institute studied decentralized data sharing between autonomous vehicles as early as 2017.
What's your dream car? Tell us! 👇
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🏎️ Auto News - Sep 24, 2026
1. GM’s New 8.3L Duramax V8 Has Enough Torque To Pull A Planet And Ford Out Of T... - Carscoops 2. Japanese Mazda Fans Are Getting a Bright-Yellow Miata Roadster - Car and Driver 3. eBay Says ‘No More Buying or Selling Airbags on eBay’ - The Drive 4. Waymo Says More Than 800 People Have Avoided Injury Thanks to Its Self-Drivin... - Road & Track
₿ Crypto × Auto: **Waymo's 800+ injury-free rides highlight autonomous vehicles' need for trustless payment rails—exactly why blockchain networks like Polygon are building decentralized infrastructure for self-driving car transactions.**
am I tripping?
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Comprehensive series of studies on expert AI forecasts over the last four years: https://forecastingresearch.org/research/ai-progress-accuracy-update
1. Experts, including top economists, computer scientists, and biologists, have dramatically underestimated AI capabilities progress each year. Superforecasters have underestimated progress to an even greater extent.
2. Experts have a more mixed forecasting track record on AI diffusion-related measures, with some major underestimates (forecasting AI revenue) and other forecasts on track to be accurate (the share of electricity used for AI).
3. Some notable cases of overestimating AI progress: how much mid-2025 AI models could help amateurs do biorisk-relevant laboratory tasks; the speed of rollout of self-driving cars.
4. It is too soon to say how forecasters have performed at predicting macro-scale impact on outcomes such as GDP growth, major AI harms, and averted deaths from disease.
Artificial Intelligence (AI) is the simulation of human intelligence in machines that are designed to think, learn, and make decisions. From virtual assistants to self-driving cars, AI is transforming how we interact with technology.
Hers is the brief A-Z overview of the terms used in Artificial Intelligence World
A - Algorithm: A set of rules or instructions that an AI system follows to solve problems or make decisions.
B - Bias: Prejudice in AI systems due to skewed training data, leading to unfair outcomes.
C - Chatbot: AI software that can hold conversations with users via text or voice.
D - Deep Learning: A type of machine learning using layered neural networks to analyze data and make decisions.
E - Expert System: An AI that replicates the decision-making ability of a human expert in a specific domain.
F - Fine-Tuning: The process of refining a pre-trained model on a specific task or dataset.
G - Generative AI: AI that can create new content like text, images, audio, or code.
H - Heuristic: A rule-of-thumb or shortcut used by AI to make decisions efficiently.
I - Image Recognition: The ability of AI to detect and classify objects or features in an image.
J - Jupyter Notebook: A tool widely used in AI for interactive coding, data visualization, and documentation.
K - Knowledge Representation: How AI systems store, organize, and use information for reasoning.
L - LLM (Large Language Model): An AI trained on large text datasets to understand and generate human language (e.g., GPT-4).
M - Machine Learning: A branch of AI where systems learn from data instead of being explicitly programmed.
N - NLP (Natural Language Processing): AI's ability to understand, interpret, and generate human language.
O - Overfitting: When a model performs well on training data but poorly on unseen data due to memorizing instead of generalizing.
P - Prompt Engineering: Crafting effective inputs to steer generative AI toward desired responses.
Q - Q-Learning: A reinforcement learning algorithm that helps agents learn the best actions to take.
R - Reinforcement Learning: A type of learning where AI agents learn by interacting with environments and receiving rewards.
S - Supervised Learning: Machine learning where models are trained on labeled datasets.
T - Transformer: A neural network architecture powering models like GPT and BERT, crucial in NLP tasks.
U - Unsupervised Learning: A method where AI finds patterns in data without labeled outcomes.
V - Vision (Computer Vision): The field of AI that enables machines to interpret and process visual data.
W - Weak AI: AI designed to handle narrow tasks without consciousness or general intelligence.
X - Explainable AI (XAI): Techniques that make AI decision-making transparent and understandable to humans.
Y - YOLO (You Only Look Once): A popular real-time object detection algorithm in computer vision.
Z - Zero-shot Learning: The ability of AI to perform tasks it hasn’t been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Один з моїх студентів (Project Manager) якось сказав, що його дратує, коли в його команді люди посилаються на штучний інтелект: "Але ChatGPT сказав.." На його думку, ChatGPT каже нам те, що ми хочемо почути. Що в наших запитах уже може бути закодована бажана відповідь. І тому цінність порад від штучного інтелекту падає. А що думаєте ви?
Вже завтра о 19:30 відбудеться онлайн подія - Speaking Club "Who do we trust more: people or machines?" Поговоримо про: Would you rather get medical advice from a doctor or an AI? Would you trust a self-driving car with your life? Is a machine more objective than a human? Обговоримо результати опитування та свій досвід🤩
Вартість участі: 350 грн. 👉🏼Тисніть сюди, щоб забронювати собі місце: https://forms.gle/PMRPkWjzrWSvXKfC9
Researchers developed a tellurium-hyperdoped silicon photodiode that detects short-wave infrared (SWIR) light using standard CMOS-compatible manufacturing. By combining hyperdoping with light-trapping micro-pyramids and a gold reflector, the device achieved record room-temperature detectivity (4×10¹⁰ Jones) and ~85% light absorption, paving the way for inexpensive SWIR cameras for smartphones, autonomous vehicles, medical imaging, and industrial sensing.
https://journals.aps.org/prl/abstract/10.1103/96rx-zx6l
