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Basically I underestimated chat gpt it is Basically much more powerful than I realized not just a Jetson society but it could even bring realities like we have seen in star trek the next generation where one can ask an AI anything and it can do anything given a task. This could also bring upon a superintelligence once programmed much like a wolfram alpha is for homework but for everything. It can nearly do any job and can replace all tech jobs eventually to get to universal basic income or even bring an end to the wild west of the internet it could create a near perfect cyber defense because it could simply know everything and make everything bug free. In short it can a near God like AI to answer and do any digital task. This can make nearly all jobs eventually automated:3.


It’ll be a while before ChatGPT takes your job entirely, and in the meantime you can use it to make work life easier.

Get a glimpse of the future and be amazed by the technological advancements that await us in the year 2100. Our video features top 10 predictions that will shape the world of technology in the next century. From fully immersive virtual reality to advanced artificial intelligence and nanotechnology, this video is packed with exciting insights.

We’ll dive into the possibilities of space colonization and teleportation, explore the potential of augmented reality and fusion energy, and look at the rise of robot assistants and mind uploading. Get ready to be amazed by the holographic displays that will take virtual experiences to a whole new level.

This video is perfect for anyone who wants to stay ahead of the curve and be informed about the future of technology. Subscribe now and turn on the notification bell to never miss an update. Optimize your viewing experience by turning on closed captions.

Leave a comment and let us know which prediction you’re most excited about. Join the discussion and share your thoughts on the future of technology. Don’t wait, watch now!

Generative AI represents a big breakthrough towards models that can make sense of the world by dreaming up visual, textual and conceptual representations, and are becoming increasingly generalist. While these AI systems are currently based on scaling up deep learning algorithms with massive amounts of data and compute, biological systems seem to be able to make sense of the world using far less resources. This phenomenon of efficient intelligent self-organization still eludes AI research, creating an exciting new frontier for the next wave of developments in the field. Our panelists will explore the potential of incorporating principles of intelligent self-organization from biology and cybernetics into technical systems as a way to move closer to general intelligence. Join in on this exciting discussion about the future of AI and how we can move beyond traditional approaches like deep learning!

This event is hosted and sponsored by Intel Labs as part of the Cognitive AI series.

Deepmind’s AdA shows that foundation models also enable generalist systems in reinforcement learning that learn new tasks quickly.

In AI research, the term foundation model is used by some scientists to refer to large pre-trained AI models, usually based on transformer architectures. One example is OpenAI’s large language model GPT-3, which is trained to predict text tokens and can then perform various tasks through prompt engineering in a few-shot setting.

In short, a foundation model is a large AI model that, because of its generalist training with large datasets, can later perform many tasks for which it was not explicitly trained.

How should we live when we know we must die? This question is posed by the first work of world literature, the Gilgamesh epic. More than 4,000 years ago, Gilgamesh set out on a quest for immortality. Like all Babylonian literature, the saga has survived only in fragments. Nevertheless, scholars have managed to bring two-thirds of the text into readable condition since it was rediscovered in the 19th century.

The Babylonians wrote in cuneiform characters on clay tablets, which have survived in the form of countless fragments. Over centuries, scholars transferred the characters imprinted on the pieces of clay onto paper. Then they would painstakingly compare their transcripts and—in the best case—recognize which fragments belong together and fill in the gaps. The texts were written in the languages Sumerian and Akkadian, which have complicated writing systems. This was a Sisyphean task, one that the experts in the Electronic Babylonian Literature project can scarcely imagine today.

Enrique Jiménez, Professor of Ancient Near Eastern Literatures at LMU’s Institute of Assyriology, and his team have been working on the digitization of all surviving cuneiform tablets since 2018. In that time, the project has processed as many as 22,000 text fragments.