While there exists many methods for manipulating rigid objects with parallel-jaw grippers, grasping with multi-finger robotic hands remains a quite unexplored research topic. Reasoning and planning collision-free trajectories on the additional degrees of freedom of several fingers represents an important challenge that, so far, involves computationally costly and slow processes. In this work, we present Multi-FinGAN, a fast generative multi-finger grasp sampling method that synthesizes high quality grasps directly from RGB-D images in about a second. We achieve this by training in an end-to-end fashion a coarse-to-fine model composed of a classification network that distinguishes grasp types according to a specific taxonomy and a refinement network that produces refined grasp poses and joint angles. We experimentally validate and benchmark our method against a standard grasp-sampling method on 790 grasps in simulation and 20 grasps on a real Franka Emika Panda. All experimental results using our method show consistent improvements both in terms of grasp quality metrics and grasp success rate. Remarkably, our approach is up to 20-30 times faster than the baseline, a significant improvement that opens the door to feedback-based grasp re-planning and task informative grasping. Code is available at https://irobotics.aalto.fi/multi-fingan/.

Edge computing is now possible in so many use cases we previously never dreamed of. And thanks to decision-making close to the device, these applications are reliable, private, and faster than ever.

Generative art has made significant strides in the past few years. One need only look at the countless artworks posted across the Internet (e.g. The implications this has for artists, moving forward, is up for debate -- but one positive thing we can all agree upon is that it opens the door to profoundly new visual and auditory experiences. Likewise, it makes the creation of art accessible even to the untrained. Thus, enter Lucid Sonic Dreams: a Python package that syncs generative art to music in only a few lines of code!

The term artificial intelligence has acquired immense popularity in the past few years. A number of applications today have incorporated AI. This technology is going to take us far in the world, and will also overtake us in a lot of ways. If this is the new era of the Industrial Revolution, AI is surely going to be one of its driving forces. This article will help one understand the whole family that comes under AI including -- general artificial intelligence, artificial narrow intelligence, machine learning and deep learning, among others.

Tensor2Tensor, shortly known as T2T, is a library of pre-configured deep learning models and datasets. The Google Brain team has developed it to do deep learning research faster and more accessible. It uses TensorFlow throughout and aims to improve performance and usability strongly. Models can be trained on any of the CPU, single GPU, multiple GPU and TPU either locally or in the cloud. Tensor2Tensor models need minimal or zero configuration or device-specific code. It provides support for well-acclaimed models and datasets across different media platforms such as images, videos, text and audio.

This article is a first of a series that I'm writing, and where I will try to address the topic of using Deep Learning in NLP. First of all, I was writing an article for an example of text classification using a perceptron, but I was thinking that will be better to review some basics before, as activation and loss functions. Activation functions are introduced in neural networks to capture complex relationships in data. There are nonlinear functions added in general at the end of a network to transform data from complex to a simple format, which makes it easy to be interpreted depending on the main purpose of the model. There are many types of activation functions.

In the age of the intelligent era, more and more innovative concepts emerge daily. One of them is undoubtedly the field of "artificial intelligence." In this case, specifically, "deep learning," a sub-branch of AI, will encounter us when we need to dive deeper into complex problems, and it is used in many areas today. We develop deep learning models to achieve specific tasks, and in some cases, surpass human, repetitive capabilities. In this article, we will explore how the GPU is used for deep learning with code examples.

Open Neural Network Exchange (ONNX) is a powerful and open format built to represent machine learning models. The final outcome of training any machine learning or deep learning algorithm is a model file that represents the mapping of input data to output predictions in an efficient manner. These models are stored in different file formats depending on the framework they were created in .pkl Therein lies the problem, you can't take a model created and trained in one framework and use it or deploy it in a different framework. The intent behind ONNX is to be like the "USB standard" of the machine learning world.

In the animal kingdom, brains come in all shapes and sizes. So, in a new machine learning approach, engineers did away with the human brain and all its beautiful complexity--turning instead to the brain of a lowly worm for inspiration. Turns out, simplicity has its benefits. The resulting neural network is efficient, transparent, and here's the kicker: It's a lifelong learner. Whereas most machine learning algorithms can't hone their skills beyond an initial training period, the researchers say the new approach, called a liquid neural network, has a kind of built-in "neuroplasticity." That is, as it goes about its work--say, in the future, maybe driving a car or directing a robot--it can learn from experience and adjust its connections on the fly.

The deep learning company, Deci, has announced a broad strategic business and technology collaboration with Intel to optimise deep learning inference on Intel Architecture (IA) CPUs. As one of the first companies to participate in the Intel Ignite startup accelerator, Deci will now work with Intel to deploy innovative AI technologies to mutual customers. The collaboration is intended to take a significant step towards enabling deep learning inference at scale on Intel CPUs, reducing costs and latency, and enabling new applications of deep learning inference. New deep learning tasks can be performed in a real-time environment on edge devices and companies that use large scale inference scenarios can dramatically cut cloud or datacentre cost, simply by changing the inference hardware from GPU to Intel CPU. "By optimising the AI models that run on Intel's hardware, Deci enables customers to get even more speed and will allow for cost-effective and more general deep learning use cases on Intel CPUs," said Deci CEO and co-founder Yonatan Geifman.