Posted in robotics/AI
It seems it has significant bias.
In this episode we look at the problem of ChatGPT’s political bias, solutions and some wild stories of the new Bing AI going off the rails.
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Materials scientists are often inspired by nature and therefore use biological compounds as cues to design advanced materials. It is possible to mimic the molecular structure and functional motifs in artificial materials to offer a blueprint for a variety of functions. In a new report in Science Advances, Tae Hyun Kim and a research team at the California Institute of Technology and the Samsung Advanced Institute of Technology in the U.S. and South Korea, created a flexible biomimetic thermal sensing polymer, abbreviated BTS, which they designed to mimic ion transport dynamics of pectin; a plant cell wall component.
The researchers used a versatile synthetic procedure and engineered the properties of the polymer to be elastic, flexible and stretchable in nature. The flexible polymer outperformed state-of-the-art temperature sensing materials such as vanadium oxide. Despite mechanical deformations, the thermal sensor-integrated material showed high sensitivity and stable functionality between 15° and 55° Celsius. The properties of the flexible BTS polymer made it well suited to map temperature variations across space-time and facilitate broadband infrared photodetection relevant for a variety of applications.
Organic electronic materials are competitive alternatives to conventional silicon-based microelectronics due to their cost-effective, multifunctional nature. Materials scientists seek to tailor the properties of such materials at the molecular level for a range of sensing applications for wearable and implantable devices with specific characteristics such as flexibility and elasticity. At present, there is an increasing demand for all-organic electronic devices to form a range of soft and active materials. For instance, organic thermal sensors are suited for remote health care and robotics, albeit with limitations.
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In this video, I’ll discuss some of the most advanced humanoid robots currently in development and reveal if the future really is bright for Robotics.
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Other related terms are artificial intelligence, the future of ai, ai, timelapse of ai, timelapse of artificial intelligence, AI timelapse, artificial intelligence timeline, AI timeline, future technology, future tech, the future of robotics, superintelligence, super intelligent ai, super intelligent artificial intelligence, artificial general intelligence, general intelligence, artificial superintelligence, agi, asi.
Since OpenAI has not open-sourced the code for ChatGPT, replicating the chatbot is a herculean task, and even the big-tech are struggling. But, AI startup Colossal-AI has found a way to build your own ChatGPT with less computing resources.
Towards this goal, the company has leveraged a PyTorch-based implementation that covers all three stages from pre-training, reward model training, and reinforcement learning. They offer a demo version of the training process that requires only 1.62 GB of GPU memory and can be done on a single consumer-grade GPU, with 10.3x growth on one GPU model capacity.
Check out the GitHub repository here.
A research team led by Dr. Yong-hun Kim and Dr. Jeong-Dae Kwon has successfully developed the world’s first neuromorphic semiconductor device with high-density and high-reliability by developing a thin film of lithium-ion battery materials. They achieved this by producing ultra-thin lithium ions, a key material of lithium-ion batteries that have been in the spotlight recently, and combining it with two-dimensional nano-materials. The research team is from the Surface & Nano Materials Division at the Korea Institute of Materials Science (KIMS).
A neuromorphic semiconductor device has synapses and neurons similar to the human brain, which processes and memorizes information. The synaptic device receives signals from neurons and modulates the synaptic weight (connection strength) in various ways to simultaneously process and store information. In particular, the linearity and symmetry of synaptic weights enables various pattern recognition with low power.
Traditional methods for controlling synaptic weights use charge traps between interfaces of heterogeneous materials or oxygen ions. In this case, however, it is difficult to control the movement of ions in the desired direction according to the external electric field. The researchers solved this problem with an artificial intelligence semiconductor device with high density by developing a thin film process while maintaining the mobility of lithium ions according to the external electric field. The thin film—with a thickness of several tens of nanometers—enables fine pattern processing while controlling the thickness of the wafer scale.
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Watch this next video about the Future of Artificial Intelligence (2030 — 10,000 A.D.+): https://youtu.be/cwXnX49Bofk.
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But with so many signals that could happen, how can scientists possibly sift through all of them to try to find a possible alien transmission?
Well, it turns out, scientists found the answer: Don’t. Instead, let AI do it for you.
That’s exactly what the researchers behind this study did, utilizing an AI algorithm to look through signals from a designated 820 different star systems. These targets were made from the Hipparcos catalogue, a collection of data from 118,200 stars made by the European Space Agency’s (ESA) Hipparcos satellite, and totaled over 480 hours of data.
In a stunning turn of events, a top-ranking AI system was soundly defeated by a human player in the strategic board game Go. This marks a significant reversal of the 2016 victory by the computer, which was widely celebrated as a landmark achievement in the field of artificial intelligence. The victorious player, Kellin Pelrine, holds a rank just below the top amateur level and was able to exploit a previously unidentified vulnerability in the AI system that had been detected by another computer. The games were played without any direct computer assistance, and Pelrine emerged as the clear winner by winning 14 out of 15 games.
Financial Times mentioned that this victory, which has not been previously disclosed, brought to light a flaw in the top Go computer programs that is common to many of today’s prevalent AI systems, including San Francisco-based OpenAI’s ChatGPT chatbot. The strategy used to win the Go match was proposed by a computer program that investigated the AI systems to discover any weaknesses. Pelrine, who executed the proposed plan with merciless precision, was able to use the tactics to put the human player back on top of the Go board.
This video explores Super Intelligent AI and 5 reasons it will be unstoppable. Watch this next video about the Timelapse of Artificial Intelligence (2030 — 10,000 A.D.+): https://youtu.be/cwXnX49Bofk.
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SOURCES:
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• https://www.popularmechanics.com/technology/robots/a35267508…-machines/
• https://www.businessinsider.com/mankind-will-not-be-able-to-…g-to-study.
• https://www.analyticsinsight.net/superintelligent-ai-can-we-…-humanity/
• https://www.mpg.de/16231640/0108-bild-computer-scientists-we…s-149835-x.
• https://www.nytimes.com/2019/10/31/opinion/superintelligent-…gence.html.
• https://www.nickbostrom.com/superintelligence.html.
• https://codebots.com/artificial-intelligence/the-3-types-of-…n-possible.
• https://spectrum.ieee.org/super-artificialintelligence.
• https://www.forbes.com/sites/cognitiveworld/2019/06/19/7-typ…f6d649233e.
• https://en.wikipedia.org/wiki/Superintelligence.
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