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                                     Two Robots in a Bar     Two More Robots in a Bar

 

1.1   Robot Takeover

What comes to your mind when you hear the word “robot”?   Maybe be an alien-like structure or any human-looked device in a spaceship?

Perhaps you envision a dystopian future where humanity is subjugated by its robot overlords.

 

1.2   There is a need to worry

While they offer tremendous capabilities, it's crucial to be aware of their limitations and potential risks as they continue to develop."

 

2   The Goals of AI

The Goals of AI
…tools like machine learning and data analysis.  Robots can play football, and there are even competitions dedicated to robot soccer, like RoboCup.  These robots use AI and sensors…

 

3   What is Artificial Intelligence (AI)?

 …robot uprising. Stories of AI takeovers have been popular throughout science fiction, but recent advancements have made the threat more real. Some public figures, such as Stephen Hawking…

 

4   A I Thoughts

Can a robot think?   Lack of Consciousness and Self-Awareness: Robots, even those with advanced AI, lack the subjective experience and awareness of their own…

 

5   Thinking about AI

Generative and Large Language Models for Dummies  There are thousands of places that are still now not approachable by human beings. Can a robot think? 

 

6   Who is talking to 'whom'?

 AI and its robots don't operate as humans. There can be identical robots with different names and purpose.  

 

7   WE HAVE REACHED MICROTUBULES!

 …robots sharing human-like characteristics challenges our definitions of consciousness and life. If a robot could mimic human thought processes, feelings, or even consciousness …

 

8   Books and Articles

Power and Our Future.  Mustafa Suleyman with Michael Bhaskar.  Soon you will live surrounded by AIs. In a world of quantum computers, robot assistants and abundant energy, they will organised …

 

9   Professions Disappearing in 5 years

…jobs will vanish as technology takes over. The world of logistics is poised for a dramatic transformation driven by automation. 3.2   Fast Food Workers 3.2.1   Robot Chefs and Automated…

 

10   A community-driven vision

 robotic task and motion planning would significantly benefit from world knowledge and commonsense task descriptions to be performed by a robot ...

 

11   The Dangers of AI

Human Use AI

While AI can automate tasks and improve efficiency, it is not intended to entirely replicate human intelligence or social skills.  

 

12   2 VIP AI Books - Our Final Invention & The Singularity is Near

A blog created by the website author to vary reader experience.    Humans will be categorised according to AI's needs for immediate and consequent results vis a vis its goals. 

 

13  Atoms themselves are not “human.

  1. Speculation about future robots “using human atoms” to become more human-like often sounds provocative, but it’s worth slowing down and separating metaphor from mechanism—especially when bringing in Roger Penrose’s ideas about microtubules and consciousness.
  2. Atoms themselves are not “human.” A carbon atom in a neuron is physically indistinguishable from one in a rock or a robot shell. So if robots were built from the same atomic ingredients as humans, that alone wouldn’t make them think or feel. 

     more

 

14  Self-supervised machine learning

People also ask
What is self-supervised learning (LLM)?


How self-supervised learning revolutionized natural language
Put simply, the idea behind self-supervised learning is to train a model over raw/unlabeled data by making out and predicting portions of this data. This way, the ground truth “labels” that we learn to predict are already present in the data itself and no human annotation is required. source

 

What is self-supervised learning (LLM)?
 
Put simply, the idea behind self-supervised learning is to train a model over raw/unlabeled data by making out and predicting portions of this data. This way, the ground truth “labels” that we learn to predict are already present in the data itself and no human annotation is required.

 

Put simply, the idea behind self-supervised learning is to train a model over raw/unlabeled data by making out and predicting portions of this data. This way, the ground truth “labels” that we learn to predict are already present in the data itself and no human annotation is required.