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Category: robotics/AI – Page 955

GPT, one of the most interesting and important developments in the world of #artificialintelligence. In this video, we will talk about #GPT3 and the upcoming version #GPT4 next week! Also we will discuss how #GPT5 could look like. in addition, We will explain what #GPT is, what it is used for and why it can be a threat for us.

GPT stands for Generative Pre-trained Transformer. It is a deep learning model that is trained on a huge amount of text, making it capable of understanding and generating human language. GPT can be used for various applications such as text generation, translation, summarization, chatbots, and much more.

Although GPT-4 has yet to be released, we can already start speculating about what GPT-5 might look like and what it might be able to do. GPT-5 will likely have trillions of parameters, which means it can process even more data and produce even better results and maybe even be self-aware… #samaltman #microsoft #openai #selfaware #selfconscious #selfawareness #openai #chatgpt #aiscarystories #scarystories #scarystory #realstories #realscarystories #truestories #truestory #creapystories #AIScarystory #AIHorror #artificialintelligence #scaryai #scaryartificialintelligence #trueaiscarystories #truescarystories.

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GPT-4 from OpenAI has launched, as a multimodal AI capable of taking 25,000 tokens as input, makes it the most powerful consumer facing artificial intelligence model on earth.

AI news timestamps:
0:00 GPT-4 intro.
0:30 Pricing.
0:58 Improvements and scoring.
2:01 GPT-4 example applications.
2:57 GPT-4 vs ChatGPT
3:48 GPT-4 further functionality.
5:56 GPT-5 future implications.
7:29 Is GPT-4 already AGI?

#ai #technology #tech

Continue reading “GPT-4 AI Full Breakdown: How Smart vs AGI? ( GPT-5 + IQ TEST + PRICE + NEW FEATURES )” | >

Algorithms are complex mathematical formulas used to perform tasks in our digital world. They are programmed to process information, make decisions, and take actions. Algorithms are used in various applications, such as search engines, social media, autonomous vehicles, and digital assistants.

But not all algorithms are innocent. Some algorithms have a sinister #scary side that poses a threat to our privacy, our freedom, and our humanity… #aiscarystories #aihorrorstories #scarystories #scarystory #horrorstories #horrorstory #realstories #realhorrorstories #realscarystories #truestories #truestory #creapystories #AIScarystory #AIHorror #artificialintelligence #scaryai #scaryartificialintelligence #trueaiscarystories #truescarystories.

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Continue reading “Sinister Algorithms: The dark side of our future” | >

Tang Yu, the AI CEO of the gaming company NetDragon Websoft, was assigned typical duties of the company that included reviewing high-level analytics, making leadership decisions, assessing risks, and fostering an efficient workplace. Tang Yu was the first CEO of a company that worked 24×7, without receiving compensation. The company said while appointing the AI chatbot as CEO that it will play a crucial role in the development of talents and ensuring a far and efficient workplace for all employees.

“Tang Yu’s appointment highlights the Company’s “AI + management” strategy and represents a major milestone of the Company towards being a “Metaverse organization”. Tang Yu will streamline process flow, enhance the quality of work tasks, and improve speed of execution. Tang Yu will also serve as a real-time data hub and analytical tool to support rational decision-making in daily operations, as well as to enable a more effective risk management system,” NetDragon Websoft said in a blog post.

Interestingly, the AI bot helped the company in generating revenue. As per reports, the company outperformed the Hang Seng Index, which tracks the biggest companies listed in Hong Kong.

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Experts at the Technical University of Munich (TUM) have pioneered the world’s first ethical algorithm for autonomous vehicles, which could see autonomous driving become the norm globally.

The researchers’ ethical algorithm is significantly more advanced than its predecessors, as it fairly distributes levels of risks instead of operating on an either/or principle. The algorithm has been tested in 2,000 scenarios of critical conditions in various settings, such as streets in Europe, the US, and China. The innovation could improve the safety and uptake of autonomous vehicles worldwide.

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Breakthroughs don’t often happen in neuroscience, but we just had one. In a tour-de-force, an international team released the full brain connectivity map of the young fruit fly, described in a paper published last week in Science. Containing 3,016 neurons and 548,000 synapses, the map—called a connectome—is the most complex whole-brain wiring diagram to date.

“It’s a ‘wow,’” said Dr. Shinya Yamamoto at Baylor College of Medicine, who was not involved in the work.

Why care about a fruit fly? Far from uninvited guests at the dinner table, Drosophila melanogaster is a neuroscience darling. Although its brain is smaller than a poppy seed—a far cry from the 100 billion neurons that power human brains—the fly’s neural system shares similar principles to those that underlie our own brains.

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Graph neural networks (GNNs) are promising machine learning architectures designed to analyze data that can be represented as graphs. These architectures achieved very promising results on a variety of real-world applications, including drug discovery, social network design, and recommender systems.

As graph-structured data can be highly complex, graph-based machine learning architectures should be designed carefully and effectively. In addition, these architectures should ideally be run on efficient hardware that support their computational demands without consuming too much power.

Researchers at University of Hong Kong, the Chinese Academy of Sciences, InnoHK Centers and other institutes worldwide recently developed a software-hardware system that combines a GNN architecture with a resistive memory, a that stores data in the form of a resistive state. Their paper, published in Nature Machine Intelligence, demonstrates the potential of new hardware solutions based on resistive memories for efficiently running graph machine learning techniques.

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