Last week, researchers at the Allen Institute for Artificial Intelligence demonstrated in a new paper that an AI they'd designed could ace an eighth-grade multiple-choice science test with more than 90 percent correct answers -- and do quite well on a 12th-grade science test, too, with more than 80 percent correct answers. The system, called Aristo, took the New York Regents Science Exam (a standardized test for students across New York State), with a few limitations: it didn't have to solve the problems that involved looking at diagrams. Nonetheless, the researchers tested the program on different versions of the test as well as on tests from different years and found that its performance was pretty consistent: It's an A student. Aristo demonstrates how quickly AI is advancing. As recently as 2016, the paper's authors note, no one in the field could manage to score as well as 60 percent on a similar eighth-grade science exam.

Manifold learning now plays a very important role in machine learning and many relevant applications. Although its superior performance in dealing with nonlinear data distribution, data sparsity is always a thorny knot. There are few researches to well handle it in manifold learning. In this paper, we propose Hierarchic Neighbors Embedding (HNE), which enhance local connection by the hierarchic combination of neighbors. After further analyzing topological connection and reconstruction performance, three different versions of HNE are given. The experimental results show that our methods work well on both synthetic data and high-dimensional real-world tasks. HNE develops the outstanding advantages in dealing with general data. Furthermore, comparing with other popular manifold learning methods, the performance on sparse samples and weak-connected manifolds is better for HNE.

In this paper, we propose an AdaBoost - assisted extreme learning machine for efficient online sequential classification (AOS - ELM) . In order to achieve better accuracy in online sequential learning scenarios, we utilize the cost - sensitive algorithm - AdaBoost, which diversifying the weak classifiers, and addin g the forgetting mechanism, which stabilizing the performance during the training procedure . Hence, AOS - ELM adapt s bet ter to sequentially arrived data compared with other voting based methods. The experim ent results show AOS - ELM can achieve 9 4.41 % accuracy on MNIST dataset, which is the theoretical accuracy bound performed by original batch learning algorithm, AdaBoost - EL M. Moreover, with the forgetting mechanism, the standard deviation of accuracy during the online sequential learning process is reduced to 8.26x.

Transfer learning aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-domain marginal or conditional distributions. However, in real applications, the marginal and conditional distributions usually have different contributions to the domain discrepancy. Existing methods fail to quantitatively evaluate the different importance of these two distributions, which will result in unsatisfactory transfer performance. In this paper, we propose a novel concept called Dynamic Distribution Adaptation (DDA), which is capable of quantitatively evaluating the relative importance of each distribution. DDA can be easily incorporated into the framework of structural risk minimization to solve transfer learning problems. On the basis of DDA, we propose two novel learning algorithms: (1) Manifold Dynamic Distribution Adaptation (MDDA) for traditional transfer learning, and (2) Dynamic Distribution Adaptation Network (DDAN) for deep transfer learning. Extensive experiments demonstrate that MDDA and DDAN significantly improve the transfer learning performance and setup a strong baseline over the latest deep and adversarial methods on digits recognition, sentiment analysis, and image classification. More importantly, it is shown that marginal and conditional distributions have different contributions to the domain divergence, and our DDA is able to provide good quantitative evaluation of their relative importance which leads to better performance. We believe this observation can be helpful for future research in transfer learning.

Through multi-agent competition, the simple objective of hide-and-seek, and standard reinforcement learning algorithms at scale, we find that agents create a self-supervised autocurriculum inducing multiple distinct rounds of emergent strategy, many of which require sophisticated tool use and coordination. We find clear evidence of six emergent phases in agent strategy in our environment, each of which creates a new pressure for the opposing team to adapt; for instance, agents learn to build multi-object shelters using moveable boxes which in turn leads to agents discovering that they can overcome obstacles using ramps. We further provide evidence that multi-agent competition may scale better with increasing environment complexity and leads to behavior that centers around far more human-relevant skills than other self-supervised reinforcement learning methods such as intrinsic motivation. Finally, we propose transfer and fine-tuning as a way to quantitatively evaluate targeted capabilities, and we compare hide-and-seek agents to both intrinsic motivation and random initialization baselines in a suite of domain-specific intelligence tests.

Traditional Reinforcement Learning (RL) problems depend on an exhaustive simulation environment that models real-world physics of the problem and trains the RL agent by observing this environment. In this paper, we present a novel approach to creating an environment by modeling the reward function based on empirical rules extracted from human domain knowledge of the system under study. Using this empirical rewards function, we will build an environment and train the agent. We will first create an environment that emulates the effect of setting cabin temperature through thermostat. This is typically done in RL problems by creating an exhaustive model of the system with detailed thermodynamic study. Instead, we propose an empirical approach to model the reward function based on human domain knowledge. We will document some rules of thumb that we usually exercise as humans while setting thermostat temperature and try and model these into our reward function. This modeling of empirical human domain rules into a reward function for RL is the unique aspect of this paper. This is a continuous action space problem and using deep deterministic policy gradient (DDPG) method, we will solve for maximizing the reward function. We will create a policy network that predicts optimal temperature setpoint given external temperature and humidity.

CHALET includes 58 rooms and 10 house configuration, and allows to easily create new house and room layouts. CHALET supports a range of common household activities, including moving objects, toggling appliances, and placing objects inside closeable containers. The environment and actions available are designed to create a challenging domain to train and evaluate autonomous agents, including for tasks that combine language, vision, and planning in a dynamic environment.

Energy-Based Models(EBM) is one of the most promising areas of deep learning that hasn't seen a tremendous level of adoption yet. Conceptually, EBMs are a form of generative modeling that learns the key characteristics of a target dataset and tries to generate similar datasets. While EBMs results appealing because of its simplicity they have experienced many challenges when applied in real world applications. Recently, AI-powerhouse OpenAI published a new research paper that explores a new technique to create EBM model that can scale across complex deep learning topologies. EBMs are typically used in one of the most complex problems of real world deep learning solutions: generating quality training datasets.

Chapter 7: To evaluate the market by segments, by countries and by manufacturers with revenue share and sales by key countries in these various regions.

What this will mean, in the short term, is that AI will become significantly more capable, in less time due to dramatically faster prototyping and larger scale training. In addition, there will be a growth in practical applications of AI because the new paradigm of training at the edge avoids the huge upfront costs of centralized training in the cloud. Millions more developers can now participate in advancing AI solutions. Because training can be coordinated between devices using the IoT (Internet of Things), the cloud infrastructure will have a diminished role. One of the early applications of AI in the construction industry is for training workers and improving their skills.