This paper investigates the use of nonparametric kernel-regression to obtain a tasksimilarity aware meta-learning algorithm. Our hypothesis is that the use of tasksimilarity helps meta-learning when the available tasks are limited and may contain outlier/ dissimilar tasks. While existing meta-learning approaches implicitly assume the tasks as being similar, it is generally unclear how this task-similarity could be quantified and used in the learning. As a result, most popular metalearning approaches do not actively use the similarity/dissimilarity between the tasks, but rely on availability of huge number of tasks for their working. Our contribution is a novel framework for meta-learning that explicitly uses task-similarity in the form of kernels and an associated meta-learning algorithm. We model the task-specific parameters to belong to a reproducing kernel Hilbert space where the kernel function captures the similarity across tasks. The proposed algorithm iteratively learns a meta-parameter which is used to assign a task-specific descriptor for every task. The task descriptors are then used to quantify the task-similarity through the kernel function. We show how our approach conceptually generalizes the popular meta-learning approaches of model-agnostic meta-learning (MAML) and Meta-stochastic gradient descent (Meta-SGD) approaches. Numerical experiments with regression tasks show that our algorithm outperforms these approaches when the number of tasks is limited, even in the presence of outlier or dissimilar tasks. This supports our hypothesis that task-similarity helps improve the metalearning performance in task-limited and adverse settings.

Reinforcement learning (RL) [5] studies the problem of how to make sequential decisions to learn and act in unknown environments (which is usually modeled by a Markov Decision Process (MDP)) and maximize the collected rewards. There are mainly two types of algorithms to approach the RL problems: model-based algorithms and model-free algorithms. Model-based RL algorithms keep explicit description of the learned model and make decisions based on this model. In contrast, modelfree algorithms only maintain a group of value functions instead of the complete model of the system dynamics. Due to their space-and time-efficiency, model-free RL algorithms have been getting popular in a wide range of practical tasks (e.g., DQN [16], TRPO [18], and A3C [15]). In RL theory, model-free algorithms are explicitly defined to be the ones whose space complexity is always sublinear relative to the space required to store the MDP parameters [12]. For tabular MDPs (i.e., MDPs with finite number of states and actions, usually denoted by S and A respectively), this requires that the space complexity to be opS

This paper describes the NEMO submission to SIGTYP 2020 shared task which deals with prediction of linguistic typological features for multiple languages using the data derived from World Atlas of Language Structures (WALS). We employ frequentist inference to represent correlations between typological features and use this representation to train simple multi-class estimators that predict individual features. We describe two submitted ridge regression-based configurations which ranked second and third overall in the constrained task. Our best configuration achieved the micro-averaged accuracy score of 0.66 on 149 test languages.

When we train a machine learning algorithm we have to be credible and prove that it is better than another model. In this article we will see the techniques to do this. How do I split the data of my set in order to make correct estimates of the performance of my ML algorithm? We will see it also with respect to the confidence limits. We will see how to evaluate two classifiers. Since in general there is no better technique than another, you have to compare the different approaches. Depending on the problem under examination, we have preferable approaches compared to others. In general we have no guarantee that one technique works better than others if we do not use statistical tests. For example, if we have a binary problem, our choice will probably fall on a binary classifier. Another important aspect is that, in many contexts, not all errors weigh in the same way, for example in the medical field saying that a sick patient is well means that the error has a higher cost than the opposite error. We initially assume constant costs for each type of error, which means not considering the costs. We will also see the cost-sensitive performance evaluation in our scheme (model). It is also possible to make a numeric evaluation, for example, to predict the performance of our processor. While the classification works only with labels or probabilities, it is also possible to evaluate a numerical value. Although in theory I could do it, if I evaluate the performance on the errors of the training set I make a mistake. Evaluation of the training set error is not a good indicator of performance on future data.

AI is one of the hottest sectors, with its technology promising to revolutionize and automate every industry imaginable. Even AI-enabled applications can bring the next major disruption within enterprise software. While AI is still in its early stage, people across the globe are interacting with it either directly or indirectly on a daily basis via virtual assistants, facial-recognition technology, gaming platforms, chatbots, mapping applications, and a host of other software. As developments in AI accelerate, companies are looking to expand their offerings by attracting investments or series funding. Hence, this makes a perfect time for startups to find investors to further their projects and plans.

Amica's new app uses artificial intelligence (AI) to help divorce couples fairly and separate their assets.

Artificial intelligence promises to expertly diagnose disease in medical images and scans. However, a close look at the data used to train algorithms for diagnosing eye conditions suggests these powerful new tools may perpetuate health inequalities. A team of researchers in the UK analyzed 94 datasets--with more than 500,000 images--commonly used to train AI algorithms to spot eye diseases. They found that almost all of the data came from patients in North America, Europe, and China. Just four datasets came from South Asia, two from South America, and one from Africa; none came from Oceania.

Have you noticed that artificial intelligence is getting more popular among the tech industry? Do you know the reason behind it? The human generation is moving towards the platform of automation. Due to the engaged task of human work, it is important to maintain their other work and their belonging to keep it work for them and maintain a healthy life. In recent times there are many apps have been launched, which was functioned under artificial intelligence, such as the Ada app, Face Id, etc. These kinds of apps were getting attracted to smartphone users.

Humans are not perfect drivers; we are vulnerable to many physical and emotional factors influencing our driving behavior. A study suggests that many road accidents occur due to a lack of response time for drivers. In order to make informed judgments, drivers need a smart assistance system that can predict a possible event beforehand and prevent a fatal crash or serious injuries. V2X is an intelligent transport system comprising of Vehicle-to-vehicle (V2V), Vehicle-to-infrastructure (V2I), and Vehicle-to-Pedestrian (V2P) communications. Biometric seat technology; autonomously managed municipality; and highway system are also part of advanced IoT technologies.

An online app called Amica is now using artificial intelligence to help separating couples make parenting arrangements and divide their assets. For many people, the coronavirus pandemic has put even the strongest of relationships to the test. A May survey conducted by Relationships Australia found 42% of 739 respondents experienced a negative change in their relationship with their partner under lockdown restrictions. There has also been a surge in the number of couples seeking separation advice. The Australian government has backed the use of Amica for those in such circumstances.