This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks. We investigate two questions related to the use of unlabeled data in production SSL context: 1) how to select samples from a huge unlabeled data pool that are beneficial for SSL training, and 2) how do the selected data affect the performance of different state-of-the-art SSL techniques. We compare four widely used SSL techniques, Pseudo-Label (PL), Knowledge Distillation (KD), Virtual Adversarial Training (VAT) and Cross-View Training (CVT) in conjunction with two data selection methods including committee-based selection and submodular optimization based selection. We further examine the benefits and drawbacks of these techniques when applied to intent classification (IC) and named entity recognition (NER) tasks, and provide guidelines specifying when each of these methods might be beneficial to improve large scale NLU systems.

Seeking the equivalent entities among multi-source Knowledge Graphs (KGs) is the pivotal step to KGs integration, also known as \emph{entity alignment} (EA). However, most existing EA methods are inefficient and poor in scalability. A recent summary points out that some of them even require several days to deal with a dataset containing 200,000 nodes (DWY100K). We believe over-complex graph encoder and inefficient negative sampling strategy are the two main reasons. In this paper, we propose a novel KG encoder -- Dual Attention Matching Network (Dual-AMN), which not only models both intra-graph and cross-graph information smartly, but also greatly reduces computational complexity. Furthermore, we propose the Normalized Hard Sample Mining Loss to smoothly select hard negative samples with reduced loss shift. The experimental results on widely used public datasets indicate that our method achieves both high accuracy and high efficiency. On DWY100K, the whole running process of our method could be finished in 1,100 seconds, at least 10* faster than previous work. The performances of our method also outperform previous works across all datasets, where Hits@1 and MRR have been improved from 6% to 13%.

Since many real world networks are evolving over time, such as social networks and user-item networks, there are increasing research efforts on dynamic network embedding in recent years. They learn node representations from a sequence of evolving graphs but not only the latest network, for preserving both structural and temporal information from the dynamic networks. Due to the lack of comprehensive investigation of them, we give a survey of dynamic network embedding in this paper. Our survey inspects the data model, representation learning technique, evaluation and application of current related works and derives common patterns from them. Specifically, we present two basic data models, namely, discrete model and continuous model for dynamic networks. Correspondingly, we summarize two major categories of dynamic network embedding techniques, namely, structural-first and temporal-first that are adopted by most related works. Then we build a taxonomy that refines the category hierarchy by typical learning models. The popular experimental data sets and applications are also summarized. Lastly, we have a discussion of several distinct research topics in dynamic network embedding.

A robotic fruit packing machine that uses artificial intelligence could be adapted to harvesting in rural and regional Australia.

As a single person wandering through the world, it can be difficult to find someone who loves all the right things: parks, subways, bike lanes, human-scale buildings, high-density housing, debates over the ideal length of a city block. Even on a dating app, you can't always tell from a profile who might be thinking, behind a smile, I hate cars. But if this is exactly the sort of partner--or friend or fling--you're looking for, there is a solution: Join the wildly popular Facebook meme group and leftist community NUMTOTs ("New Urbanist Memes for Transit-Oriented Teens," which isn't really just for teens) and request access to its private spin-off group, NUMTinder. With about 8,000 members living mostly in North America, the United Kingdom, and Australia, NUMTinder is a makeshift dating environment for those who consider liking public transportation to be a core part of their personality, or those for whom a lack of interest in urban planning is a deal breaker. Almost everyone in the group posts at least one selfie with a bike or a subway entrance to demonstrate commitment to the lifestyle, and when a new member introduces herself, it's not uncommon for her to brag about the fact that she doesn't have a driver's license.

Pre-training models such as BERT have achieved great success in many natural language processing tasks. However, how to obtain better sentence representation through these pre-training models is still worthy to exploit. Previous work has shown that the anisotropy problem is an critical bottleneck for BERT-based sentence representation which hinders the model to fully utilize the underlying semantic features. Therefore, some attempts of boosting the isotropy of sentence distribution, such as flow-based model, have been applied to sentence representations and achieved some improvement. In this paper, we find that the whitening operation in traditional machine learning can similarly enhance the isotropy of sentence representations and achieve competitive results. Furthermore, the whitening technique is also capable of reducing the dimensionality of the sentence representation. Our experimental results show that it can not only achieve promising performance but also significantly reduce the storage cost and accelerate the model retrieval speed.

In the last few years, the interest in knowledge bases has grown exponentially in both the research community and the industry due to their essential role in AI applications. Entity alignment is an important task for enriching knowledge bases. This paper provides a comprehensive tutorial-type survey on representative entity alignment techniques that use the new approach of representation learning. We present a framework for capturing the key characteristics of these techniques, propose two datasets to address the limitation of existing benchmark datasets, and conduct extensive experiments using the proposed datasets. The framework gives a clear picture of how the techniques work. The experiments yield important results about the empirical performance of the techniques and how various factors affect the performance. One important observation not stressed by previous work is that techniques making good use of attribute triples and relation predicates as features stand out as winners.

An expert on the social implications of technology responds to Shiv Ramdas' "The Trolley Solution." Imagine a university without any teachers, just peer learners, open-access resources, and an office space full of high-speed internet-enabled computers, accessible to anyone between 18–30 years of age, regardless of any prior learning. That university is called 42. It does not have any academic instructors; the teachers are the self-starting students who have their eyes set on a job in Big Tech. Aided only by a problem-based learning curriculum, students gain a certificate of completion about three to five years after starting out.

"I wanted to create something that brought a humanness to the robotic or technological," he said. "But I didn't want to just make a gold robot, it was about making a figure that was a little bit human, a little bit robotic, and then a little bit spirit like too, bringing that environmental, natural theme in as well. He freely admits he might have been a poor choice for the project, as he knew nothing about artificial intelligence before talking to Dr Shrapnel, and lives on a small Sunshine Coast property, far from the urban environments championed by the street rrt festival. "But I think that works in my favour in a way, because I'm an outsider on both ends of it. I could bring a fresh perspective to both sides," he said.

Most experts have settled on a description of Artificial Intelligence as being the scientific endeavor of building computers that mimic the capabilities of the human brain. To put that into perspective, we know that Human Intelligence started to evolve 7–8 million years ago when our oldest ancestors had a brain volume of about 450 cubic centimeters. In the next 3.5 million years our ancestors' brain volume increased to about 1350 cubic centimeters. Modern humans (average brain volume of about 1200 cubic centimeters) evolved from the Homo Sapiens species during a period of dramatic climate change 300,000 years ago. Like other early humans that were living at this time, they gathered and hunted food, and evolved behaviors that helped them respond to the challenges of survival in unstable environments.