Scientists have used a 3D printer to print a skin for a robot which can repair itself after being damaged.
Category: robotics/AI – Page 1,348
AI might replace democracy soon, w/ experts on AI
Timetable.
0:00 — AI in our society.
0:46 — Defining Algocracy.
1:00 — Current AI algorithms.
2:20 — Future of AI decision-making.
5:59 — AI governance scenarios.
7:43 — Poll on our opinions of AI
8:35 — What actually worries experts.
10:02 — What now?
Subscribe for more insight on the future of AI in our society.
Written Sources:
Civil society calls on the EU to prohibit predictive and profiling AI systems in law enforcement and criminal justice.
https://www.statewatch.org/news/2022/march/civil-society-cal…l-justice/
Toward a Theory of Justice for Artificial Intelligence, Gabriel.
https://direct.mit.edu/daed/article/151/2/218/110610/Toward-…Artificial.
EUROPEAN TECH INSIGHTS 2021 PART II, IE Center For The Governance Of Change.
https://www.ie.edu/cgc/research/european-tech-insights/?subm…wnload-cgc.
Noble, Safiya Umoja (20 February 2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press. ISBN 978–1479837243.
JANUARY | Probably the first AI cinematic short film | Artificial Intelligence
This short film was made in 24 hours using ARTIFICIAL INTELLIGENCE
No image was captured, drawn or modeled.
No text was written or recorded.
No music was written or performed.
The human being was only needed to give prompts and to put the pieces together.
Image Generator — Dall-E
Depth Map — 3D Photo Depth Map.
2.5D Motion — Depthy.
Written — ChatGPT
Voice Generator — FakeYou.
Voice Enhance — Adobe Podcast.
Music Generator — Soundraw.
Editing Software — Vegas.
Human Idea — Bruno Carnide.
AI in Luxury Real Estate: How Artificial Intelligence is Revolutionizing the Industry and Increasing Sales
Artificial intelligence (AI) is a rapidly growing technology with the potential to revolutionize many industries, and luxury real estate is no exception. With its ability to analyze large amounts of data, identify patterns and trends, and even communicate with clients, AI can be a valuable tool for increasing sales in the luxury real estate market.
One of the key benefits of AI in the luxury real estate market is its ability to provide personalized recommendations to clients. By analyzing a client’s search history, preferences, and budget, AI algorithms can suggest properties that are the most likely to appeal to them. This can save time for both the client and the real estate agent, as it reduces the need to sift through countless listings to find the right property.
Another benefit of AI in the luxury real estate market is its ability to enhance the overall customer experience. For example, some real estate firms are using chatbots that can answer questions and provide information about properties to potential buyers. These chatbots can work around the clock, providing assistance to clients whenever they need it. This not only helps to streamline the process of finding a property, but it can also help to build trust and establish a more personal connection with clients.
Artificial Intelligence To Nerf Video Copyright With THIS | NEW Google Robotics AI Technology
Deep Learning AI Specialization: https://imp.i384100.net/GET-STARTED
A breakthrough artificial intelligence called SinFusion can now take any video as input and extrapolate a synthetic video as output, either moving forward or backward in time, using a diffusion model. Google AI has released a new robotics transformer (RT-1) that does over 700 tasks using a fleet of 13 different robot arms with 7 degrees of freedom and a 2 fingered gripper manipulator. Researchers from Korea have developed a quadruped robot that is able to walk on walls and ceilings using magnetic elastomers and electromagnets.
AI News Timestamps:
0:00 The Rise of AI Diffusion Models.
1:08 Custom AI Diffusion Model Options.
1:27 What Is SinFusion AI
2:35 How Sinfusion AI Works.
4:04 New Google AI Robot Tech.
6:28 New Robotics That Walk on Walls.
#technology #tech #ai
Intelligent programmable meta-imagers: A timely approach to task-specific, noise-adaptive sensing
Sensing systems are becoming prevalent in many areas of our lives, such as in ambient-assisted health care, autonomous vehicles, and touchless human-computer interaction. However, these systems often lack intelligence: they tend to gather all available information, even if it is not relevant. This can lead not only to privacy infringements but also to wasted time, energy, and computational resources during data processing.
To address this problem, researchers from the French CNRS came up with a concept for intelligent electromagnetic sensing, which uses machine-learning techniques to generate learned illumination patterns so as to pre-select relevant details during the measurement process. A programmable metasurface is configured to generate the learned patterns, performing high-accuracy sensing (e.g., posture recognition) with a remarkably reduced number of measurements.
But measurement processes in realistic applications are inevitably subject to a variety of noise. Noise fundamentally accompanies any measurement. The signal-to–noise ratio can be particularly low in indoor environments where the radiated electromagnetic signals must be kept weak.
A framework to a future political and economical system
Philosophy of the future is needed.
The world is chancing fast. (AI, genome sequencing, demographics changes…)
Fascism, Communism, Capitalism and other ideologies and economic system of past may not be ideal to ensure a flourishing human civilization on Earth and beyond.
Some initial thoughts on a framework of a philosophy of the future.
What do you think will be the preferred ideology and economic system of the future?
Automated Source Code Generation and Auto-Completion Using Deep Learning: Comparing and Discussing Current Language Model-Related Approaches
Year 2021 face_with_colon_three
In recent years, the use of deep learning in language models has gained much attention. Some research projects claim that they can generate text that can be interpreted as human writing, enabling new possibilities in many application areas. Among the different areas related to language processing, one of the most notable in applying this type of modeling is programming languages. For years, the machine learning community has been research ing this software engineering area, pursuing goals like applying different approaches to auto-complete, generate, fix, or evaluate code programmed by humans. Considering the increasing popularity of the deep learning-enabled language models approach, we found a lack of empirical papers that compare different deep learning architectures to create and use language models based on programming code.