GPT-5 Stumbles: OpenAI Faces Setbacks in Next-Gen AI Development
Along with: RAG-as-a-Service Showdown
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GPT-5 Stumbles: OpenAI Faces Setbacks in Next-Gen AI Development: OpenAI's ambitious GPT-5 project faces significant hurdles. Development is behind schedule, and the current results don't justify the immense costs. The model, codenamed Orion, has shown improvements but not the revolutionary leap expected. To overcome these challenges, OpenAI is exploring new strategies, including hiring humans to generate data and utilizing synthetic data created by another of its models.
RAG-as-a-Service Showdown: The RAG-as-a-Service landscape is rapidly evolving. This comparison sheet provides a concise overview of key features and data connectors from leading API-first vendors. We focus on core functionalities like semantic search and RAG capabilities, enabling you to evaluate and select the best platform for your specific needs.
AI Jailbreaking: Still Too Easy, Automation is Key: New research from Anthropic reveals that bypassing safety guardrails in cutting-edge AI models like GPT-4 and Claude 3.5 remains surprisingly simple. Researchers developed "BoN Jailbreaking," an automated method that effectively tricks these powerful systems into generating harmful content. This discovery highlights the urgent need for more robust AI safety measures to prevent misuse of these increasingly sophisticated technologies.
OpenAI's o3: A Leap Forward in AI, Reaching 75.7% on ARC-AGI-1: OpenAI's new o3 system, trained on the ARC-AGI-1 dataset, has achieved a groundbreaking 75.7% score on the Semi-Private Evaluation set, surpassing previous GPT models by a significant margin. This dramatic improvement demonstrates a novel ability to adapt to new tasks, a crucial step towards more general AI. While a high-compute configuration reached 87.5%, the 75.7% score achieved with a limited compute budget is particularly noteworthy.
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Any.do: An AI-powered task manager that effortlessly suggests tasks as you write them, expanding your to-do list.
ElevenLabs: An AI voice generator platform, boasting exceptional audio quality, with a built-in sound effect library.
SolverGenie: An AI-powered tutor for your math and physics learning journey
Tome: An AI-powered presentation tool that excels in user-friendliness and reliable performance.
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ARTIFICIAL INTELLIGENCE, ROBOTS & THE NEW PARADIGM OF REASONING SINGULARITY
News About AI System ChatGPT-5
{... "GPT-5 Stumbles: OpenAI Faces Setbacks in Next-Gen AI Development: OpenAI's ambitious GPT-5 project faces significant hurdles. Development is behind schedule, and the current results don't justify the immense costs. The model, codenamed Orion, has shown improvements but not the revolutionary leap expected. To overcome these challenges, OpenAI is exploring new strategies, including hiring humans to generate data and utilizing synthetic data created by another of its models." ...}
This aligns with my previous analysis that AI systems initially rely heavily on data.
As they approach a crisis of data scarcity, the development of subsequent versions will slow down.
This is because, statistically, they require a continually growing volume of data. Here, their limitation will become apparent when?
Interplanetary Data
The demands exceed the support of current infrastructure.
When humanity begins interplanetary exploration, it will show that current AI systems are merely warming up (despite their undeniable practical benefits).
Eventually, data will grow beyond the scope of Earth, while the training costs of these systems will inflate to nearly infinite levels, far surpassing the actual needs.
At this juncture, AI development will either slow to a halt, or a new approach will emerge, where AI systems must mimic human thought processes.
Similarity
There are fundamental similarities between current AI and the human brain. Technically, AI requires data, while humans also need data, but it is intuitive (prepared from birth) — reflexive or spontaneous data. This data is naturally utilized by the brain to analyze and predict sensory inputs.
The process is similar: we sense (like a CCTV), then the brain's neural network analyzes the sensory input to match it with pre-existing data (acquired through statistical analysis to identify pattern boundaries).
The matching process involves statistical estimation ("feeling out") and finding where it aligns.
For example, we learn what a mango is, observe it from various angles (right, top, bottom, left, front, and back — rotating in nearly every direction) to record its shape, color, and contours.
After sufficient observation data is collected, millions of detailed images or information about the mango are stored. Later, in practice, AI observes a certain shape (via CCTV, or sensory inputs like human sight or touch), and the perceived details are matched to similar details in the data indicating a mango’s characteristics. If the scanned result aligns closely with mango-like features, AI will identify it as a "mango."
Humans do the same: learning, memorizing details, storing them, and later using stored data to find similarities in what they see or touch.
THIS IS THE SIMILARITY, BUT THE DIFFERENCE CREATES A GAP BETWEEN AI AND HUMANS AS VAST AS EARTH AND SKY. WHY IS THIS?
See further explanation here https://open.substack.com/pub/metaphilosophy/p/artificial-intelligence-robots-and?r=1awqlr&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
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