#generalai

[cyberdudebivash-news]

The Death of the Prompt Engineer: Why Image and Writing Tools are Crashing as General AI Takes the Throne

Read the full report on -

The Death of the Prompt Engineer: Why Image and Writing Tools are Crashing as General AI Takes the Throne

CyberDudeBivash News delivers daily cybersecurity threat intel, CVE alerts, malware trends, and crypto security briefings.

[webstep-technologies]

What is the difference between narrow AI and general AI?

Artificial Intelligence (AI) is a fascinating field that has garnered significant attention in recent years. But within AI, there are different types that serve varying purposes. Two prominent categories are Narrow AI and General AI.

Narrow AI, also known as Weak AI or Artificial Narrow Intelligence (ANI), is designed to perform a specific task or a narrow range of tasks. It excels in tasks like facial recognition, language translation, or playing board games like chess. Narrow AI operates within a limited context and cannot perform tasks outside of its programmed capabilities.

On the other hand, General AI, also referred to as Strong AI or Artificial General Intelligence (AGI), is a theoretical form of AI that possesses the ability to understand, learn, and apply its intelligence across a wide range of tasks, similar to human intelligence. Unlike Narrow AI, General AI is not confined to specific tasks or domains and can adapt to new situations, solve unfamiliar problems, and learn from experiences.

Scope of Abilities:

Narrow AI: Limited to performing specific tasks or a narrow range of tasks. For example, a virtual assistant like Siri or Alexa is proficient in voice recognition and responding to commands but lacks understanding beyond its programmed functions.

General AI: Capable of understanding, learning, and applying intelligence across diverse tasks and contexts. It can switch between tasks, learn from experiences, and generalize knowledge to solve problems it hasn’t encountered before.

Adaptability:

Narrow AI: Lacks adaptability beyond its predefined functions. It cannot learn or improve its capabilities independently.

General AI: Highly adaptable and can learn from its interactions with the environment. It can improve its performance over time and develop new skills autonomously.

Contextual Understanding:

Narrow AI: Operates within a specific context and lacks broader understanding. For instance, a spam email filter can efficiently identify and categorize spam emails but doesn’t comprehend the content or context beyond spam detection.

General AI: Possesses contextual understanding similar to humans. It can comprehend nuances, extract meaning from diverse sources of information, and apply knowledge across different domains.

Problem-Solving Abilities:

Narrow AI: Solves predefined problems efficiently within its domain of expertise. It cannot extrapolate solutions to unrelated problems.

General AI: Exhibits problem-solving abilities analogous to human cognition. It can tackle novel challenges, devise creative solutions, and transfer knowledge across domains to solve diverse problems.

In conclusion, the disparity between Narrow AI and General AI lies in their scope of abilities, adaptability, contextual understanding, and problem-solving capabilities. While Narrow AI excels in performing specific tasks within predefined boundaries, General AI represents the pinnacle of artificial intelligence, capable of emulating human-like cognition across a wide array of tasks and contexts. While Narrow AI has made significant strides in various applications, the quest for achieving General AI remains a long-term goal with profound implications for technology, society, and humanity as a whole.

[mysocial8onetech]

Kosmos-2: A Phrase Grounding Model from Microsoft Research

Discover Kosmos-2, a new model for multimodal AI that can handle tasks like phrase grounding, referring expression comprehension, and more.

Have you heard of Kosmos-2, a new model for multimodal AI? It can perceive and generate content across text, images, and videos, and perform tasks like phrase grounding, referring expression comprehension, and video question answering. Learn more about this new AI model in this article.

[amidstmadness]

Does it know?

It doesn’t. So far, all that is sold in the name of AI has very little to with intelligence and mostly to do with non-linear approximations over increasingly humongous data spaces. It doesn’t know, it never does. It is always the sum of all fundamentals without any attempt to understand even one of them.

So here is an idea, let it understand just one. What if it can tell from two images containing one 2D shape each, just the fact, if they are the same or different. Just that. Regardless of the rotation, translation and/or scale.

Seems simple enough, can be done through procedural shapes with a minimally sized network that is made to learn just these transformations and nothing else. Afterwards, it should be able to tell at least as well as we can, for any unseen shape. 

Or maybe it can be broken down further... and then brought up together where it not only determines if it is the same shape but also explain why is it so.

But how would we know? Do we apply these transformations to see if two such shapes would be similar or do we know the answer before applying them, which only come afterwards for a rational description?

[amidstmadness]

How strange that our most advanced systems can compete with human specialists, yet be unable to do many things that seem easy to children.