The automation industry is experiencing an explosion of growth and technology capability. To explain complex technology, we use terms such as "artificial intelligence" to convey the idea that solutions are more capable and advanced than ever before. If you are an investor, business leader, or technology user who seeks to understand the technologies you are investing in, this article is for you. What follows is an explanation of vision-guided robotics and deep-learning algorithms. That's right, the article is titled "artificial intelligence" and yet by the end of the first paragraph, we've already switched to deep-learning algorithms!

A Bachelor's study usually takes six semesters; a Master's study takes four. But this is only an outline. I've witnessed people doing their BA in three semesters and some taking nine semesters. Sometimes there are so many exciting courses that you voluntarily stay longer to learn it all. Therefore, I've loosely structured the recreated curriculum into four semesters.

If you've ever shopped online, subscribed to an OTT platform, or stumbled upon a self-driving Tesla's clip on YouTube, you're probably familiar with terms such as Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision and so on. These are the driving concepts behind such technologies. While most feel that the revolution is here, the untapped potential of this domain of computer science and its impact on the global economy is a hot topic. Research by McKinsey depicts with simulations, that by 2030, about 70% of companies could be reliant on AI based services. While Artificial General Intelligence, or systems capable of performing different tasks and accumulating intellect are far from reality (like the ones we come across in sci-fi movies), AI for specific tasks give us a lot to work with.

Scientists at Duke University and the Wildlife Conservation Society (WCS) have come up with an interesting set of deep learning algorithms that could analyze more than 10,000 drone images of mixed colonies of seabirds in the Falkland Islands off Argentina's coast. The Falklands are home to the world's largest colonies of black-browed albatrosses (Thalassarche melanophris) and the second-largest colonies of southern rockhopper penguins (Eudyptes c. chrysocome). Hundreds of thousands of birds breed on the islands in densely interspersed groups. The deep-learning algorithm made by the scientists has successfully identified and counted the albatrosses with 97% and the penguins with 87% accuracy. Madeline C. Hayes, a remote sensing analyst at the Duke University Marine Lab, who led the study has a view that using drone surveys and deep learning gives them an alternative that is remarkably accurate, less disruptive, and significantly easier. One person, or a small team, can do it, and the equipment they need to do it isn't all that costly or complicated.

Traditional cybersecurity isn't necessarily bad at detecting attacks, the trouble is it often does so after they have occurred. A better approach is to spot potential attacks and block them before they can do any damage. One possible way of doing this is via'deep learning' allowing technology to identify the difference between good and bad. We spoke with Brooks Wallace, cybersecurity sales leader at Deep Instinct to find out more about this innovative solution. BW: If you look at cybersecurity, there's always been this holy grail of prevention.

Natural language processing (NLP), or NLP for short, is a branch of artificial intelligence that helps computers understand, interpret, and manipulate human language. It is broadly defined as the automatic manipulation of natural language, like speech and text, by software or technology. Natural language processing is a form of AI that is easy to understand and start using. It can also do a lot to help you in making better business decisions. In order to make your website worth your user's time, NLP can do help you a lot.

With the Deep Neural Networks (DNNs) as a powerful function approximator, Deep Reinforcement Learning (DRL) has been excellently demonstrated on robotic control tasks. Compared to DNNs with vanilla artificial neurons, the biologically plausible Spiking Neural Network (SNN) contains a diverse population of spiking neurons, making it naturally powerful on state representation with spatial and temporal information. Based on a hybrid learning framework, where a spike actor-network infers actions from states and a deep critic network evaluates the actor, we propose a Population-coding and Dynamic-neurons improved Spiking Actor Network (PDSAN) for efficient state representation from two different scales: input coding and neuronal coding. For input coding, we apply population coding with dynamically receptive fields to directly encode each input state component. For neuronal coding, we propose different types of dynamic-neurons (containing 1st-order and 2nd-order neuronal dynamics) to describe much more complex neuronal dynamics. Finally, the PDSAN is trained in conjunction with deep critic networks using the Twin Delayed Deep Deterministic policy gradient algorithm (TD3-PDSAN). Extensive experimental results show that our TD3-PDSAN model achieves better performance than state-of-the-art models on four OpenAI gym benchmark tasks. It is an important attempt to improve RL with SNN towards the effective computation satisfying biological plausibility.

The Institute of Materials and Processes, IMP, of the University of Applied Sciences in Karlsruhe, Germany in cooperation with VDI Verein Deutscher Ingenieure e.V, AEN Automotive Engineering Network and their cooperation partners present their competences of AI-based solution approaches in the production engineering field. The online congress KI 4 Industry on November 12 and 13, 2020, showed what opportunities the use of artificial intelligence offers for medium-sized manufacturing companies, SMEs, and where potential fields of application lie. The main purpose of KI 4 Industry is to increase the transfer of knowledge, research and technology from universities to small and medium-sized enterprises, to demystify the term AI and to encourage companies to use AI-based solutions in their own value chain or in their products.

Gradient flows are a powerful tool for optimizing functionals in general metric spaces, including the space of probabilities endowed with the Wasserstein metric. A typical approach to solving this optimization problem relies on its connection to the dynamic formulation of optimal transport and the celebrated Jordan-Kinderlehrer-Otto (JKO) scheme. However, this formulation involves optimization over convex functions, which is challenging, especially in high dimensions. In this work, we propose an approach that relies on the recently introduced input-convex neural networks (ICNN) to parameterize the space of convex functions in order to approximate the JKO scheme, as well as in designing functionals over measures that enjoy convergence guarantees. We derive a computationally efficient implementation of this JKO-ICNN framework and use various experiments to demonstrate its feasibility and validity in approximating solutions of low-dimensional partial differential equations with known solutions. We also explore the use of our JKO-ICNN approach in high dimensions with an experiment in controlled generation for molecular discovery.

Abstract--Few-Shot Learning (FSL) algorithms are commonly trained through Meta-Learning (ML), which exposes models to batches of tasks sampled from a meta-dataset to mimic tasks seen during evaluation. However, the standard training procedures overlook the real-world dynamics where classes commonly occur at different frequencies. While it is generally understood that class imbalance harms the performance of supervised methods, limited research examines the impact of imbalance on the FSL evaluation task. Our analysis compares 10 state-of-the-art meta-learning and FSL methods on different imbalance distributions and rebalancing techniques. Our results reveal that 1) some FSL methods display a natural disposition against imbalance while most other approaches produce a performance drop by up to 17% compared to the balanced task without the appropriate mitigation; 2) contrary to popular belief, many meta-learning algorithms will not automatically learn to balance from exposure to imbalanced training tasks; 3) classical rebalancing strategies, such as random oversampling, can still be very effective, leading to state-of-the-art performances and should not be overlooked; 4) FSL methods are more robust against meta-dataset imbalance than imbalance at the task-level with a similar imbalance ratio ( ρ < 20), with the effect holding even in long-tail datasets under a larger imbalance ( ρ = 65). We identify well to new examples. However, large datasets can be costly and examine three levels of class imbalance: task-level, to obtain and annotate [1]. This is a particularly limiting dataset-level, and combined (task-level and dataset-level) issue in many real-world situations due to the need to perform imbalance. In contrast to previous work on CIFSL [12], [13], real-time operations, the presence of rare categories, [14], [15], we explicitly attribute and quantify the impact on or the desire for a good user experience [2], [3], [4], [5]. the performance caused by class imbalance for each model. Few-Shot Learning (FSL) alleviates this burden by defining Moreover, we study multiple class imbalance distributions, a distribution over tasks, with each task containing a few giving a realistic assessment of performance and revealing labeled data points (support set) and a set of target data previously unknown strengths and weaknesses of 10 stateof-the-art (query set) belonging to the same set of classes. Additionally, we offer practical advice, way to train FSL methods is through Meta-Learning (ML). Figure 1 the model is repeatedly exposed to batches of tasks sampled shows a graphical representation of the CIFSL problem.