Tesla vehicles are apparently going to talk to people not only inside the car but also outside. CEO Elon Musk even released a quick preview video.
It’s no secret that Tesla wants to use more artificial intelligence in its business.
Two years ago, Tesla hired Andrej Karpathy to lead its computer vision and AI team and they have been expanding their team since then.
“A new model based on the blood-vessel network in a rat brain shows that the vessel position within its circulatory network does not influence the blood flow nor how nutrients are transported. Instead, transport is controlled mostly by the dilation of vessels. As well as providing new insights into the circulatory system, the model could lead to better artificial tissues and brain-scanning techniques – and might even improve the performance of solar panels.”
Nutrient flow in the brain is controlled by blood-vessel dilation, reveals network model
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New understanding of blood transport could lead to better solar panels.
Interesting research paper on a new nanobot technology. I’m watching for ways in which suitable substrates for mind uploading can be constructed, and DNA self-guided assembly has potential.
Here are some excerpts and a weblink to the paper:
“…Chemical approaches have opened synthetic routes to build dynamic materials from scratch using chemical reactions, ultimately allowing flexibility in design…”
… As a realization of this concept, we engineered a mechanism termed DASH—DNA-based Assembly and Synthesis of Hierarchical materials—providing a mesoscale approach to create dynamic materials from biomolecular building blocks using artificial metabolism. DASH was developed on the basis of nanotechnology that uses DNA as a generic material ranging from nanostructures to hydrogels, for enzymatic substrates, and as linkers between nanoparticles…”
“…Next, to illustrate the potential uses of self-generated materials, we created various hybrid functional materials from the DASH patterns. The DASH patterns served as a versatile mesoscale scaffold for a diverse range of functional nanomaterials beyond DNA, ranging from proteins to inorganic nanoparticles, such as avidin, quantum dots, and DNA-conjugated gold nanoparticles (AuNPs) (Fig. 4D, figs. S37 and S38, and Supplementary Text). The generated patterns were also rendered functional with catalytic activity when conjugated with enzymes (figs. S39 and S40 and Supplementary Text). We also showed that the DNA molecules within the DASH patterns retained the DNA’s genetic properties and that, in a cell-free fashion, the materials themselves successfully produced green fluorescent proteins (GFPs) by incorporating a reporter gene for sfGFP (Fig. 4E and figs. S9 and S41) (40). The protein production capability of the materials established the foundation for future cell-free production of proteins, including enzymes, in a spatiotemporally controlled manner.
…” Our implementation of the concept, DASH, successfully demonstrated various applications of the material. We succeeded in constructing machines from this novel dynamic biomaterial with emergent regeneration, locomotion, and racing behaviors by programming them as a series of FSAs. Bottom-up design based on bioengineering foundations without restrictions of life fundamentally allowed these active and programmable behaviors. It is not difficult to envision that the material could be integrated as a locomotive ele-ment in biomolecular machines and robots. The DASH patterns could be easily recognized by naked eyes or smartphones, which may lead to better detection technologies that are more feasible in point-of-care settings. DASH may also be used as a template for other materials, for example, to create dynamic waves of protein expression or nanoparticle assemblies. In addition, we envision that further expansion of artificial metabolism may be used for self-sustaining structural components and self-adapting substrates for chemical production pathways. Ultimately, our material may allow the construction of self-reproducing machines through the production of enzymes from generated materials that, in turn, reproduce the material. Our biomaterial powered by artificial metabolism is an important step toward the creation of “artificial” biological systems with dynamic, life-like capabilities.”…
Metabolism is a key process that makes life alive—the combination of anabolism and catabolism sustains life by a continuous flux of matter and energy. In other words, the materials comprising life are synthesized, assembled, dissipated, and decomposed autonomously in a controlled, hierarchical manner using biological processes. Although some biological approaches for creating dynamic materials have been reported, the construction of such materials by mimicking metabolism from scratch based on bioengineering has not yet been achieved. Various chemical approaches, especially dissipative assemblies, allow the construction of dynamic materials in a synthetic fashion, analogous to part of metabolism. Inspired by these approaches, here, we report a bottom-up construction of dynamic biomaterials powered by artificial metabolism, representing a combination of irreversible biosynthesis and dissipative assembly processes. An emergent locomotion behavior resembling a slime mold was programmed with this material by using an abstract design model similar to mechanical systems. Dynamic properties, such as autonomous pattern generation and continuous polarized regeneration, enabled locomotion along the designated tracks against a constant flow. Furthermore, an emergent racing behavior of two locomotive bodies was achieved by expanding the program. Other applications, including pathogen detection and hybrid nanomaterials, illustrated further potential use of this material. Dynamic biomaterials powered by artificial metabolism could provide a previously unexplored route to realize “artificial” biological systems with regenerating and self-sustaining characteristics.
Characteristic properties of life, such as dynamic self-generation of organisms, are sustained by metabolism. Using a flux of matter and energy, molecules are irreversibly synthesized from ingredients and then further dynamically assembled into macromolecules and beyond by series of biological reactions, resulting in the structural hierarchy of life’s materials (2–4). Mimicking metabolism as a material generation system may lead to the engineering of novel dynamic biomaterials with characteristic properties of life. Although various approaches have been reported to bioengineer such dynamic materials, mimicking metabolism from the ground up is still under development. For instance, engineered living materials allow material generation by life (5, 6). However, this approach relies on external living systems, such as cells, to generate the material. Similarly, other dynamic biomaterials, such as active cytoskeletons, directly use already-existing metabolism designed by life (7–10).
Zume Pizza, the Mountain View company that used robots to make its pizzas, has made its last delivery.
In filings with the state Employment Development Department, Zume said it is cutting 172 jobs in Mountain View, and eliminating another 80 jobs at its facility in San Francisco. Zume Chief Executive Alex Garden made the annoucement about Zume in an email to company employees on Wednesday.
“With admiration and sadness, we are closing Zume Pizza today,” Garden said in his email “Over the last four years this business has been our invention test bed and has been our inspiration for many of the growth businesses we have at Zume today.”
Analog machine learning hardware offers a promising alternative to digital counterparts as a more energy efficient and faster platform. Wave physics based on acoustics and optics is a natural candidate to build analog processors for time-varying signals. In a new report on Science Advances Tyler W. Hughes and a research team in the departments of Applied Physics and Electrical Engineering at Stanford University, California, identified mapping between the dynamics of wave physics and computation in recurrent neural networks.
The map indicated the possibility of training physical wave systems to learn complex features in temporal data using standard training techniques used for neural networks. As proof of principle, they demonstrated an inverse-designed, inhomogeneous medium to perform English vowel classification based on raw audio signals as their waveforms scattered and propagated through it. The scientists achieved performance comparable to a standard digital implementation of a recurrent neural network. The findings will pave the way for a new class of analog machine learning platforms for fast and efficient information processing within its native domain.
The recurrent neural network (RNN) is an important machine learning model widely used to perform tasks including natural language processing and time series prediction. The team trained wave-based physical systems to function as an RNN and passively process signals and information in their native domain without analog-to-digital conversion. The work resulted in a substantial gain in speed and reduced power consumption. In the present framework, instead of implementing circuits to deliberately route signals back to the input, the recurrence relationship occurred naturally in the time dynamics of the physics itself. The device provided the memory capacity for information processing based on the waves as they propagated through space.
A drone is an autonomous unmanned aerial vehicle (UAV) that can be programmed for automatic routing and delivery. These come handy in delivery medicines which is easier to carry and can add value to the pharma supply chain. Drone helps to deliver to places with the high expense involved or poor infrastructure and thereby plays a significant role in last-mile delivery.
The pace with which they are now being used for delivery, even Amazon is experimenting with the delivery mechanism offered by drone as its logistics and transport market is forecast to grow 20% in coming times.
Originally a bunch of children’s toys, then comic books, cartoons and movies, robot action figures than morph into vehicles and back again have proved immensely popular over the years. After a successful Kickstarter last year, Robosen Robotics has launched the T9, a robot that transforms into a vehicle through voice commands or via an app.
There are many Transformer-like robot toys already available, but most require the user to manually change the thing from action figure to vehicle, animal, device or whatever, and back again. Like the bots from the cartoons and movies, the T9 is an actual transforming robot designed to stimulate a child’s interest in programming, robotics and artificial intelligence.
The T9 is claimed to be the first robot in the consumer space that can automatically move from vehicle to robot and back again, can walk on two legs when in robot form, race on its wheels when in vehicle form, involves coding and program development, and can be controlled by voice commands or through a mobile app. It can even bust some funky dance moves if you want it to.
During Intel’s keynote at the 2020 Consumer Electroncics Show, the chip giant gave an update on its various AI and machine learning ventures.
Posted in biotech/medical, life extension, neuroscience, robotics/AI | Leave a Comment on We all will experience it at some point, unfortunately: The older we get the more our brains will find it difficult to learn and remember new things
What the reasons underlying these impairments are is yet unclear but scientists at the Center for Regenerative Therapies of TU Dresden (CRTD) wanted to investigate if increasing the number of stem cells in the brain would help in recovering cognitive functions, such as learning and memory, that are lost during ageing.”
https://tu-dresden.de/tu-dresden/newsportal/news/verjuengungskur-fuers-gehirn-zusaetzliche-stammzellen-verbessern-lernen-und-gedaechtnis-von-alten-maeusen
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Ein jeder wird es irgendwann erleben: Je älter wir werden, desto schwieriger wird es für unser Gehirn, neue Dinge zu lernen und sich an sie zu erinnern. Die Gründe hinter diesen Beeinträchtigungen sind oft unklar. Nun haben Wissenschaftler des Zentrums für Regenerative Therapien der TU Dresden (CRTD) untersucht, ob eine Erhöhung der Anzahl von Hirnstammzellen helfen würde, kognitive Funktionen wie Lernen und Gedächtnis wiederzuerlangen, die im Laufe des Alterns verloren gehen.
Die Forschungsgruppe von Prof. Federico Calegari hat dazu eine im eigenen Labor entwickelte Methode verwendet: Im Gehirn alter Mäuse stimulierten die Wissenschaftler den dort vorhandenen kleinen Pool neuronaler Stammzellen so, dass sich die Menge dieser Stammzellen und damit auch die Anzahl der aus ihnen erzeugten Gehirnzellen erhöhte. Das Team beobachtete, dass diese zusätzlichen Neuronen überleben und sogar neue Kontakte zu benachbarten Zellen knüpfen können. In einem nächsten Schritt untersuchten die Wissenschaftler eine wichtige Aufgabe des Gehirns, die ähnlich wie bei der Maus auch beim Menschen im Laufe des Alterns verloren geht: die Navigationsfähigkeit.
Es ist vom Alter abhängig, auf welche Art man sich in einer neuen Umgebung zurechtzufinden lernt. In der Jugend erstellt das Gehirn eine kognitive Landkarte und erinnert sich an diese. Diese Fähigkeit schwindet im Alter – statt mit der Landkarte im Kopf navigieren ältere Individuen anhand fester Abfolgen von Richtungswechseln, um ein bestimmtes Ziel zu erreichen. Die zuverlässigere Strategie von beiden ist jedoch die kognitive Landkarte, sprich: Die Strategie des jungen Gehirns.
