Landing AI, which provides tools that make building and deploying AI systems in manufacturing faster and easier than ever, announced Series A funding of $57 million led by McRock Capital, the first investment firm focused exclusively on the Industrial IoT. In addition, New York-based global private equity and venture capital firm Insight Partners, Taiwania Capital, Canada Pension Plan Investment Board (CPP Investments), Intel Capital, Samsung Catalyst Fund, Far Eastern Group's DRIVE Catalyst, Walsin Lihwa, and AI Fund all participated in the round. Landing AI, led by artificial intelligence visionary, Andrew Ng, developed LandingLens, a fast, easy to use enterprise MLOps platform. It applies AI and deep learning to help manufacturers solve visual inspection problems, find product defects more reliably, and generate business value. Landing AI sees the next era of AI as one in which all companies access the benefits of AI--not just consumer internet companies like Google and Facebook--but legacy industries such as manufacturing, healthcare, and agriculture.

Back in 2018, the Google Arts & Culture app introduced a feature that looks your doppelgänger in works of art. It's searched for matches for more than 120 million selfies so far. Now, the app can look for animals in art that resemble your pets too. Using a machine learning algorithm, Pet Portraits matches a snap of your furry, finned or feathered friend against tens of thousands of works from Google's partner institutions. The app might determine that the best match for your pet is in a piece of street art from Mexico or a cat figurine from ancient Egypt.

The graph matching problem seeks to find an alignment between the nodes of two graphs that minimizes the number of adjacency disagreements. Solving the graph matching is increasingly important due to it's applications in operations research, computer vision, neuroscience, and more. However, current state-of-the-art algorithms are inefficient in matching very large graphs, though they produce good accuracy. The main computational bottleneck of these algorithms is the linear assignment problem, which must be solved at each iteration. In this paper, we leverage the recent advances in the field of optimal transport to replace the accepted use of linear assignment algorithms. We present GOAT, a modification to the state-of-the-art graph matching approximation algorithm "FAQ" (Vogelstein, 2015), replacing its linear sum assignment step with the "Lightspeed Optimal Transport" method of Cuturi (2013). The modification provides improvements to both speed and empirical matching accuracy. The effectiveness of the approach is demonstrated in matching graphs in simulated and real data examples.

This study proposes a novel method to assess damages in the built environment using a deep learning workflow to quantify it. Thanks to an automated crawler, aerial images from before and after a natural disaster of 50 epicenters worldwide were obtained from Google Earth, generating a 10,000 aerial image database with a spatial resolution of 2 m per pixel. The study utilizes the algorithm Seg-Net to perform semantic segmentation of the built environment from the satellite images in both instances (prior and post-natural disasters). For image segmentation, Seg-Net is one of the most popular and general CNN architectures. The Seg-Net algorithm used reached an accuracy of 92% in the segmentation. After the segmentation, we compared the disparity between both cases represented as a percentage of change. Such coefficient of change represents the damage numerically an urban environment had to quantify the overall damage in the built environment. Such an index can give the government an estimate of the number of affected households and perhaps the extent of housing damage.

International collaboration has become imperative in the field of AI. However, few studies exist concerning how distance factors have affected the international collaboration in AI research. In this study, we investigate this problem by using 1,294,644 AI related collaborative papers harvested from the Microsoft Academic Graph (MAG) dataset. A framework including 13 indicators to quantify the distance factors between countries from 5 perspectives (i.e., geographic distance, economic distance, cultural distance, academic distance, and industrial distance) is proposed. The relationships were conducted by the methods of descriptive analysis and regression analysis. The results show that international collaboration in the field of AI today is not prevalent (only 15.7%). All the separations in international collaborations have increased over years, except for the cultural distance in masculinity/felinity dimension and the industrial distance. The geographic distance, economic distance and academic distances have shown significantly negative relationships with the degree of international collaborations in the field of AI. The industrial distance has a significant positive relationship with the degree of international collaboration in the field of AI. Also, the results demonstrate that the participation of the United States and China have promoted the international collaboration in the field of AI. This study provides a comprehensive understanding of internationalizing AI research in geographic, economic, cultural, academic, and industrial aspects.

Multimodal sentiment analysis (MSA) draws increasing attention with the availability of multimodal data. The boost in performance of MSA models is mainly hindered by two problems. On the one hand, recent MSA works mostly focus on learning cross-modal dynamics, but neglect to explore an optimal solution for unimodal networks, which determines the lower limit of MSA models. On the other hand, noisy information hidden in each modality interferes the learning of correct cross-modal dynamics. To address the above-mentioned problems, we propose a novel MSA framework \textbf{M}odulation \textbf{M}odel for \textbf{M}ultimodal \textbf{S}entiment \textbf{A}nalysis ({$ M^3SA $}) to identify the contribution of modalities and reduce the impact of noisy information, so as to better learn unimodal and cross-modal dynamics. Specifically, modulation loss is designed to modulate the loss contribution based on the confidence of individual modalities in each utterance, so as to explore an optimal update solution for each unimodal network. Besides, contrary to most existing works which fail to explicitly filter out noisy information, we devise a modality filter module to identify and filter out modality noise for the learning of correct cross-modal embedding. Extensive experiments on publicly datasets demonstrate that our approach achieves state-of-the-art performance.

Despite advances in hierarchical reinforcement learning, its applications to path planning in autonomous driving on highways are challenging. One reason is that conventional hierarchical reinforcement learning approaches are not amenable to autonomous driving due to its riskiness: the agent must move avoiding multiple obstacles such as other agents that are highly unpredictable, thus safe regions are small, scattered, and changeable over time. To overcome this challenge, we propose a spatially hierarchical reinforcement learning method for state space and policy space. The high-level policy selects not only behavioral sub-policy but also regions to pay mind to in state space and for outline in policy space. Subsequently, the low-level policy elaborates the short-term goal position of the agent within the outline of the region selected by the high-level command. The network structure and optimization suggested in our method are as concise as those of single-level methods. Experiments on the environment with various shapes of roads showed that our method finds the nearly optimal policies from early episodes, outperforming a baseline hierarchical reinforcement learning method, especially in narrow and complex roads. The resulting trajectories on the roads were similar to those of human strategies on the behavioral planning level.

Robots need to be able to learn concepts from their users in order to adapt their capabilities to each user's unique task. But when the robot operates on high-dimensional inputs, like images or point clouds, this is impractical: the robot needs an unrealistic amount of human effort to learn the new concept. To address this challenge, we propose a new approach whereby the robot learns a low-dimensional variant of the concept and uses it to generate a larger data set for learning the concept in the high-dimensional space. This lets it take advantage of semantically meaningful privileged information only accessible at training time, like object poses and bounding boxes, that allows for richer human interaction to speed up learning. We evaluate our approach by learning prepositional concepts that describe object state or multi-object relationships, like above, near, or aligned, which are key to user specification of task goals and execution constraints for robots. Using a simulated human, we show that our approach improves sample complexity when compared to learning concepts directly in the high-dimensional space. We also demonstrate the utility of the learned concepts in motion planning tasks on a 7-DoF Franka Panda robot.

Apple's new-model, top-of-the-line MacBook Pro laptop computer could set you back nearly $4,000 before taxes. But that will seem like a Black Friday steal when a 45-year-old Apple computer goes on sale this week in Monrovia, where it may fetch six figures or more. On Tuesday, John Moran Auctioneers will auction off a functioning Apple-1 computer hand-built by Steve Wozniak, Steve Jobs and others in a Los Altos, Calif., garage in 1976. The system was the rock upon which the trillion-dollar Apple empire was built. In his 2011 biography "Steve Jobs," Walter Isaacson quotes Wozniak as saying of the Apple-1: "We were participating in the biggest revolution that had ever happened, I thought. I was so happy to be a part of it."

While sharks are deadly marine predators, they also seem to attract fish looking for a'spa day' by rubbing against their rough skin. Drone footage recorded by marine biologists at the University of Miami captured frequent incidents of the surprising ritual. Researchers pored over underwater video, photos, drone footage, and even witness reports to find 47 different instances of fish rubbing up against a shark's body at more than a dozen locations around the world. The length of these regimens varied from eight seconds to more than five minutes, and included dozens of incidents of leerfish, also known as garrick, rubbing up against a great white shark, the ultimate oceanic apex predator, in Plettenberg Bay, South Africa. The number of fish chafing against a particular shark varied, too, from one lone swimmer to over 100 at once.