“A language model AI created proteins as good as ones honed over a million years of evolution. The implications are staggering” o.o Basically this leads to possibly a biological singularity:3.
A generative deep-learning model designs artificial proteins with desired enzymatic activities.
Early critiques pointed out that proving a network was near the critical point required improved statistical tests. The field responded constructively, and this type of objection is rarely heard these days. More recently, some work has shown that what was previously considered a signature of criticality might also be the result of random processes. Researchers are still investigating that possibility, but many of them have already proposed new criteria for distinguishing between the apparent criticality of random noise and the true criticality of collective interactions among neurons.
Meanwhile, over the past 20 years, research in this area has steadily become more visible. The breadth of methods being used to assess it has also grown. The biggest questions now focus on how operating near the critical point affects cognition, and how external inputs can drive a network to move around the critical point. Ideas about criticality have also begun to spread beyond neuroscience. Citing some of the original papers on criticality in living neural networks, engineers have shown that self-organized networks of atomic switches can be made to operate near the critical point so that they compute many functions optimally. The deep learning community has also begun to study whether operating near the critical point improves artificial neural networks.
The critical brain hypothesis may yet prove to be wrong, or incomplete, although current evidence does support it. Either way, the understanding it provides is generating an avalanche of questions and answers that tell us much more about the brain — and computing generally — than we knew before.
Session kindly contributed by Silvester Sabathiel in SEMF’s 2021 Numerous Numerosity Workshop: https://semf.org.es/numerosity/
ABSTRACT
With the rise and advances in the field of artificial intelligence, opportunities to understand the finer-grained mechanisms involved in mathematical cognition have increased. A vast scope of related research has been conducted on machine learning systems that learn solving differential equations, algebraic equations and integrals or proofing complex theorems, all for which the preprocessed symbolic representations form the input and output types. However on the search for cognitive mechanisms that match the scope of humans when it comes to generalizability and applicability of mathematical concepts in the external world, a more grounded approach might be required. This involves starting with fundamental mathematical concepts that are earliest acquired in the human development and learning these within an interactive and multimodal environment. In this talk we are going to examine how artificial neural network systems within such a framework provide a controlled setup to discover possible cognitive mechanisms for intuitive numerosity perception or culturally acquired numerical concepts, such as counting. First we review impactful research results from the past, before I present the contributions of the work myself was involved in. Finally we can discuss the upcoming challenges for the field of numerical cognition and where this research journey could evolve to.
SILVESTER SABATHIEL
NTNU Trondheim.
Personal website: https://silsab.com/
NTNU profile: https://www.ntnu.edu/employees/silvester.sabathiel.
ResearchGate: https://www.researchgate.net/profile/Silvester-Sabathiel-3
LinkedIn: https://www.linkedin.com/in/silvester-sabathiel-03368b117
SEMF NETWORKS
Nice. But I’m more concerned about the jobs that will be lost from this.
#Zoox #youtube #ai #yahoofinance.
This segment originally aired on February 17, 2023.
Zoox CEO Aicha Evans joins Yahoo Finance Live anchors Dave Briggs and Seana Smith to discuss approval of its self-driving auto by the California DMV, the state of the autonomous vehicle industry, and Amazon’s ownership of the company.
Don’t Miss: Valley of Hype: The culture that built Elizabeth Holmes.
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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.
ColdFusion Podcast:
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.
https://youtube.com/watch?v=4LevUzfdBtw&feature=share
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.
► All-New Echo Dot (5th Generation) | Smart Speaker with Clock and Alexa | Cloud Blue: https://amzn.to/3ISUX1u.
► Brilliant: Interactive Science And Math Learning: https://bit.ly/JasperAITechUniNet.
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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.
What do you think?
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