Menu

Blog

Archive for the ‘robotics/AI’ category: Page 623

Nov 27, 2022

Super Intelligent A.I. is Neither Necessary nor Desirable

Posted by in category: robotics/AI

A.I. Risk and the dangers of the cult of AGI.

Nov 27, 2022

Researchers are building robots that can build themselves

Posted by in categories: particle physics, robotics/AI

Researchers at MIT’s Center for Bits and Atoms are working on an ambitious project, designing robots that effectively self-assemble. The team admits that the goal of an autonomous self-building robot is still “years away,” but the work has thus far demonstrated positive results.

At the system’s center are voxels (a term borrowed from computer graphics), which carry power and data that can be shared between pieces. The pieces form the foundation of the robot, grabbing and attaching additional voxels before moving across the grid for further assembly.

Continue reading “Researchers are building robots that can build themselves” »

Nov 27, 2022

Robotics Breakthrough Builds Anything

Posted by in categories: information science, robotics/AI, transportation

MIT researchers have devised an algorithm using voxels robotics devices to build anything from houses to planes to cars and even other robots by using a grid system that transfers knowledge to determine when to build what, and when to build other robot builders. New Google Deepmind video game artificial intelligence develops agents that can talk, listen, ask questions, navigate, search and retrieve information, control things, and do a range of other intelligent tasks in real-time. New Non-invasive brain computer interface device transmits information through optic nerve to compete with Neuralink BCI.

Tech News Timestamps:
0:00 Robotics Breakthrough Builds Anything — Even Robots.
2:44 New Google Deepmind Video Game AI
5:25 New Neuralink BCI Competitor.

#robot #ai #neuralink

Nov 27, 2022

How Will AI And 5G Power the Next Wave Of Innovation?

Posted by in categories: internet, robotics/AI

The combined force of these disruptive technologies (AI and 5G) enables fast, secure, and ubiquitous connectivity of cost-efficient smart networks and IoT (Internet-of-Things) devices. This convergence point is essential to concepts like intelligent wireless edge.

5G and AI, the connected digital edge

Artificial intelligence and 5G are the two most critical elements that would empower futuristic innovations. These cutting-edge technologies are inherently synergistic. The rapid advancements of AI significantly improve the entire 5G ecosystem, its performance, and efficiency. Besides, 5G-connected devices’ proliferation helps drive unparalleled intelligence and new improvements in AI-based learning and inference. Moreover, the transformation of the connected, intelligent edge has commenced as on-device intelligence has garnered phenomenal traction. This transformation is critical to leveraging the full potential of 5G’s future. With these prospects, these technologies hold enough potential to transform every industry. Here’s how the combination of AI and 5G has been reshaping industries.

Nov 27, 2022

Google AI Introduces ‘SegCLR,’ a Self-Supervised Machine Learning Technique that Produces Highly Informative Representations of Cells Directly from 3D Electron Microscope Imagery and Segmentations

Posted by in categories: mapping, robotics/AI

If we can analyze the organization of neural circuits, it will play a crucial role in better understanding the process of thinking. It is where the maps come into play. Maps of the nervous system contain information about the identity of individual cells, like their type, subcellular component, and connectivity of the neurons.

But how do we obtain these maps?

Volumetric nanometer-resolution imaging of brain tissue is a technique that provides the raw data needed to build these maps. But inferring all the relevant information is a laborious and challenging task because of the multiple scales of brain structures (e.g., nm for a synapse vs. mm for an axon). It requires hours of manual ground truth labeling by expert annotators.

Nov 26, 2022

History Of AI In 33 Breakthroughs: Digital Storage

Posted by in categories: business, robotics/AI

On September 14, 1956, IBM announced the 305 and 650 RAMAC (Random Access Memory Accounting) “data processing machines,” incorporating the first-ever disk storage product. The 305 came with fifty 24-inch disks for a total capacity of 5 megabytes, weighed 1 ton, and could be leased for $3,200 per month.

In 1953, Arthur J. Critchlow, a young member of IBM’s advanced technologies research lab in San Jose, California, was assigned the task of finding a better data storage medium than punch-cards.


The information explosion (a term first used in 1941, according to the Oxford English Dictionary) has turned into the big digital data explosion. And the data explosion enabled deep learning, an advanced data analysis method, to perform today’s AI breakthroughs in image identification and natural language processing.

Continue reading “History Of AI In 33 Breakthroughs: Digital Storage” »

Nov 26, 2022

New robots in Europe can be workers’ best friends

Posted by in categories: employment, food, robotics/AI

Just as car created job for drivers, computer created job for data entry operator.robots will also create new types of high paying jobs.


For decades, the arrival of robots in the workplace has been a source of public anxiety over fears that they will replace workers and create unemployment.

Now that more sophisticated and humanoid robots are actually emerging, the picture is changing, with some seeing robots as promising teammates rather than unwelcome competitors.

Continue reading “New robots in Europe can be workers’ best friends” »

Nov 26, 2022

History of the Universe from a Neural Network

Posted by in categories: alien life, ethics, existential risks, quantum physics, robotics/AI

Vitaly Vanchurin, physicist and cosmologist at the University of Minnesota Duluth speaks to Luis Razo Bravo of EISM about the world as a neural network, machine learning, theories of everything, interpretations of quantum mechanics and long-term human survival.

Timestamp of the conversation:

Continue reading “History of the Universe from a Neural Network” »

Nov 26, 2022

Artificial Intelligence (AI) Researchers from Cornell University Propose a Novel Neural Network Framework to Address the Video Matting Problem

Posted by in categories: mapping, robotics/AI

Image and video editing are two of the most popular applications for computer users. With the advent of Machine Learning (ML) and Deep Learning (DL), image and video editing have been progressively studied through several neural network architectures. Until very recently, most DL models for image and video editing were supervised and, more specifically, required the training data to contain pairs of input and output data to be used for learning the details of the desired transformation. Lately, end-to-end learning frameworks have been proposed, which require as input only a single image to learn the mapping to the desired edited output.

Video matting is a specific task belonging to video editing. The term “matting ” dates back to the 19th century when glass plates of matte paint were set in front of a camera during filming to create the illusion of an environment that was not present at the filming location. Nowadays, the composition of multiple digital images follows similar proceedings. A composite formula is exploited to shade the intensity of the foreground and background of each image, expressed as a linear combination of the two components.

Although really powerful, this process has some limitations. It requires an unambiguous factorization of the image into foreground and background layers, which are then assumed to be independently treatable. In some situations like video matting, hence a sequence of temporal-and spatial-dependent frames, the layers decomposition becomes a complex task.

Nov 26, 2022

Researchers From Stanford And Microsoft Have Proposed An Artificial Intelligence (AI) Approach That Uses Declarative Statements As Corrective Feedback For Neural Models With Bugs

Posted by in category: robotics/AI

The methods currently used to correct systematic issues in NLP models are either fragile or time-consuming and prone to shortcuts. Humans, on the other hand, frequently reprimand one another using natural language. This inspired recent research on natural language patches, which are declarative statements that enable developers to deliver corrective feedback at the appropriate level of abstraction by either modifying the model or adding information the model may be missing.

Instead of relying solely on labeled examples, there is a growing body of research on using language to provide instructions, supervision, and even inductive biases to models, such as building neural representations from language descriptions (Andreas et al., 2018; Murty et al., 2020; Mu et al., 2020), or language-based zero-shot learning (Brown et al., 2020; Hanjie et al., 2022; Chen et al., 2021). For corrective purposes, when the user interacts with an existing model to enhance it, language has yet to be properly utilized.

The neural language patching model has two heads: a gating head that determines if a patch should be applied and an interpreter head that forecasts results based on the information in the patch. The model is trained in two steps: first on a tagged dataset and then through task-specific fine-tuning. A set of patch templates are used to create patches and synthetic labeled samples during the second fine-tuning step.

Page 623 of 2,040First620621622623624625626627Last