It is not surprising that Europe, despite having a strong industrial base and leading AI research and talent, is dragging behind the US and China. European countries are lagging behind in artificial intelligence due to the fragmentation of the EU's research space and digital market, difficulties in attracting human capital and external investment, lack of commercial competitiveness and geopolitical inequalities. Reading the ESPAS Ideas Paper Series, the Future of AI and Big Data, one could enjoy its deep insights, see the Supplement, as well as the honesty of the report as to the EU AI state of affairs. It specifically reads: "The EU will lag behind in AI for some more time, because it has a more complicated task than others. On the other hand, with a resilient and free economy, a balanced regulatory system, an interested public, intact societies and world class research it will be well-placed in the medium term... Some experts believe that the advances in machine learning are plateauing and that AI will only develop slowly and incrementally from now on. Others see much more change coming, even revolutionary jumps like super intelligent AIs that are able to be employed in many fields at the same time... While many policy makers see the question of AGI as science fiction, huge investments are made into researching it. For example, DeepMind – developers of the Go-champion AI AlphaGo and bought by Google for 500 million USD – spends up to 200 million USD each year to come closer to that goal.OpenAI, funded with an Endowment of 1 billion USD, has the same goal. Since this research is not required to be transparent, it is likely that states such as the US, Chinese and probably others are also already working on such programmes. The biggest project by the European Union is the Human Brain Project, an effort to construct a virtual human brain, although this is not exactly the same as building an AGI... Imagine, in 20 years, there will be a super intelligent, friendly, conscious AI which is a source of pride to the world and fulfils all our wishes. Would this be a paternalistic world? The difficult question goes to the core of the human condition: What are we to do, if we are not needed anymore? What then is the purpose of humanity?"

Graph deep learning aims to apply deep learning techniques to learn from complex data that is represented as graph.

Recently, Apple researchers, including C. V. Krishnakumar Iyer, Feili Hou, Henry Wang, Yonghong Wang, Kay Oh, Swetava Ganguli, Vipul Pandey, have developed Trinity, a no-code AI platform for complex spatial datasets. The platform enables machine learning researchers and non-technical geospatial specialists to experiment with domain-specific signals and datasets to solve various challenges. It tailors complex Spatio-temporal datasets to fit standard deep learning models–in this case, Convolutional Neural Networks (CNNs), and formulate disparate problems in a standard way, eg. "It creates a shared vocabulary leading to better collaboration among domain experts, machine learning researchers, data scientists, and engineers. Currently, the focus is on semantic segmentation, but it is easily extendable to other techniques such as classification, regression, and instance segmentation," as per the paper.

Hello guys, if you want to learn Machine learning and Deep learning in 2021 and look for the best online courses and tutorials, you have come to the right place. In this article, I will share some of the best free classes to learn Machine learning and Deep learning online. By the way, If you are thinking of learning Data Science, Machine learning, or Deep learning, you are not alone; more and more people are starting with these advanced skills worldwide. I have seen a lot of interest from Indian engineers in machine learning and the Artificial intelligence space. They are totally caught up with the craze of developing programs that can recognize numbers, alphabets, vehicles, and several other image scanning stuff.

This article describes the result of joint efforts by five people from different backgrounds, who came together to develop the final project of the 2021 Deep Learning course offered by Saturdays.AI, a non-profit organization. Before going into other details, we believe that it is important to answer the following questions: who are we and where do we come from? So given this short and needed context, before going deeper and explaining the project and its motivation, it may be useful to see what we have achieved. Finally, the code and implementation can be found in this github repository. Now, let's go into details!

One of the big headaches in deep learning is that models take forever to train. As an ML engineer, waiting hours or days for training to complete makes iteratively improving your model a slow and frustrating process. In this blog post, we show how to accelerate fine-tuning the ALBERT language model while also reducing costs by using Determined's built-in support for distributed training with AWS spot instances. Originally, ALBERT took over 36 hours to train on a single V100 GPU and cost $112 on AWS. With distributed training and spot instances, training the model using 64 V100 GPUs took only 48 minutes and cost only $47! That's both a 46x performance improvement and a 58% reduction in cost!

The digital world of today is growing in popularity with artificial intelligence (AI) and machine learning (ML). Machine Learning (ML) is a subset of AI and it includes the study of computer algorithms that enable computers to learn from experiences other than human influence. For many years, Python has been the choice for developers working in machine learning and artificial intelligence. Python provides developers with some of the greatest flexibility and features which not just enhance their productivity but also increase the quality of code, not to mention large libraries that assist and reduce workload. The following are characteristics that Python provides for machine learning and intelligence: an environment that is open source, soft computing, and C, works easily with C and C, optimizing code and debug time.

Computational models describing the mechanical behavior of materials are indispensable when optimizing the stiffness and strength of structures. The use of state-of-the-art models is often limited in engineering practice due to their mathematical complexity, with each material class requiring its own distinct formulation. Here, we develop a recurrent neural network framework for material modeling by introducing “Minimal State Cells.” The framework is successfully applied to datasets representing four distinct classes of materials. It reproduces the three-dimensional stress-strain responses for arbitrary loading paths accurately and replicates the state space of conventional models. The final result is a universal model that is flexible enough to capture the mechanical behavior of any engineering material while providing an interpretable representation of their state.

Solving partially-observable Markov decision processes (POMDPs) is critical when applying deep reinforcement learning (DRL) to real-world robotics problems, where agents have an incomplete view of the world. We present graph convolutional memory (GCM) for solving POMDPs using deep reinforcement learning. Unlike recurrent neural networks (RNNs) or transformers, GCM embeds domain-specific priors into the memory recall process via a knowledge graph. By encapsulating priors in the graph, GCM adapts to specific tasks but remains applicable to any DRL task. Using graph convolutions, GCM extracts hierarchical graph features, analogous to image features in a convolutional neural network (CNN). We show GCM outperforms long short-term memory (LSTM), gated transformers for reinforcement learning (GTrXL), and differentiable neural computers (DNCs) on control, long-term non-sequential recall, and 3D navigation tasks while using significantly fewer parameters.

In this paper, we demonstrate the implementation of our ultra-efficient deep convolutional neural network architecture: CondenseNeXt on NXP BlueBox, an autonomous driving development platform developed for self-driving vehicles. We show that CondenseNeXt is remarkably efficient in terms of FLOPs, designed for ARM-based embedded computing platforms with limited computational resources and can perform image classification without the need of a CUDA enabled GPU. CondenseNeXt utilizes the state-of-the-art depthwise separable convolution and model compression techniques to achieve a remarkable computational efficiency. Extensive analyses are conducted on CIFAR-10, CIFAR-100 and ImageNet datasets to verify the performance of CondenseNeXt Convolutional Neural Network (CNN) architecture. It achieves state-of-the-art image classification performance on three benchmark datasets including CIFAR-10 (4.79% top-1 error), CIFAR-100 (21.98% top-1 error) and ImageNet (7.91% single model, single crop top-5 error). CondenseNeXt achieves final trained model size improvement of 2.9+ MB and up to 59.98% reduction in forward FLOPs compared to CondenseNet and can perform image classification on ARM-Based computing platforms without needing a CUDA enabled GPU support, with outstanding efficiency.