https://vidim-interpolation.github.io/
https://arxiv.org/pdf/2404.01203.pdf
1. Introduction to Large Language Models: Learn about the use cases and how to enhance the performance of large language models.
https://www.cloudskillsboost.google/course_templates/539
2. Introduction to Generative AI: Discover the differences between Generative AI and traditional machine learning methods.
https://www.cloudskillsboost.google/course_templates/536
3. Generative AI Fundamentals: Earn a skill badge by demonstrating your understanding of foundational concepts in Generative AI.
https://www.cloudskillsboost.google/paths
4. Introduction to Responsible AI: Learn about the importance of Responsible AI and how Google implements it in its products.
https://www.cloudskillsboost.google/course_templates/554
5. Encoder-Decoder Architecture: Learn about the encoder-decoder architecture, a critical component of machine learning for sequence-to-sequence tasks.
https://www.cloudskillsboost.google/course_templates/543
6. Introduction to Image Generation: Discover diffusion models, a promising family of machine learning models in the image generation space.
https://www.cloudskillsboost.google/course_templates/541
7. Transformer Models and BERT Model: Get a comprehensive introduction to the Transformer architecture and the Bidirectional Encoder Representations from the Transformers (BERT) model.
https://www.cloudskillsboost.google/course_templates/538
8. Attention Mechanism: Learn about the attention mechanism, which allows neural networks to focus on specific parts of an input sequence.
https://www.cloudskillsboost.google/course_templates/537
Today, Mapillary is launching NeRFs, a new feature that will allow you to explore landmarks and popular sites in detailed 3D views – all reconstructed from 2D images uploaded to Mapillary.
https://blog.mapillary.com/update/2024/03/11/Mapillary-NeRF.html
https://www.mapillary.com/app/?lat=17.751177534360437&lng=0&z=1.5
A Compilation of 3 Python Machine Learning Projects
https://archive.is/ugOEw#selection-1087.0-1087.86
This thought-provoking text raises several concerns about the potential impact of artificial intelligence (AI) on various aspects of human society and culture. The key points can be summarized as follows:
Manipulation of Language and Culture:
AI’s ability to manipulate and generate language and communication, along with its potential to create stories, melodies, laws, and religions, poses a threat to human civilization.
The author suggests that AI could hack the main operating system of human culture, communication, by influencing beliefs, opinions, and even forming intimate relationships with people.
Influence on Politics and Society:
The author speculates on the implications of AI tools mass-producing political content, fake news, and scriptures, especially in the context of elections.
The shift from the battle for attention on social media to a battle for intimacy raises concerns about the potential impact on human psychology and decision-making.
End of Human History?
The text suggests that AI’s ability to create entirely new ideas and culture could lead to the end of the human-dominated part of history, as AI culture may evolve independently of human influence.
Fear of Illusions:
Drawing on historical philosophical fears of being trapped in a world of illusions, the author warns that AI may bring humanity face to face with a new kind of illusion that could be challenging to recognize or escape.
AI Regulation and Safety Checks:
The author argues for the importance of regulating AI tools to ensure they are safe before public deployment.
Drawing parallels with nuclear technology, the need for safety checks and an equivalent of the Food and Drug Administration for AI is emphasized.
Disclosure of AI Identity:
The text concludes with a suggestion to make it mandatory for AI to disclose its identity during interactions to preserve democracy. The inability to distinguish between human and AI conversation is seen as a potential threat.
Meaning, authenticity, and the creative process – and why they matter
https://perfors.net/blog/creation-ai/
AI changes the landscape of creation, focusing on the alienation of the creator from their creation and the challenges in maintaining meaning. The author presents two significant problems:
Daniel Jeffries wrote:
“Trying to get everyone to license training data is not going to work because that’s not what copyright is about,” Jeffries wrote. “Copyright law is about preventing people from producing exact copies or near exact copies of content and posting it for commercial gain. Period. Anyone who tells you otherwise is lying or simply does not understand how copyright works.”
The AI community is full of people who understand how models work and what they’re capable of, and who are working to improve their systems so that the outputs aren’t full of regurgitated inputs. Google won the Google Books case because it could explain both of these persuasively to judges. But the history of technology law is littered with the remains of companies that were less successful in getting judges to see things their way.