Assistant Professor Wim van Rees and his team have developed simulations of self-propelled undulatory swimmers to better understand how fish-like deformable fins could improve propulsion in underwater devices, seen here in a top-down view. MIT ocean and mechanical engineers are using advances in scientific computing to address the ocean's many challenges, and seize its opportunities. There are few environments as unforgiving as the ocean. Its unpredictable weather patterns and limitations in terms of communications have left large swaths of the ocean unexplored and shrouded in mystery. "The ocean is a fascinating environment with a number of current challenges like microplastics, algae blooms, coral bleaching, and rising temperatures," says Wim van Rees, the ABS Career Development Professor at MIT. "At the same time, the ocean holds countless opportunities -- from aquaculture to energy harvesting and exploring the many ocean creatures we haven't discovered yet."

The benefits of analytics are well-documented. Analytics has helped organisations transform retail experiences, map pathways for trains and trucks, discover extraterrestrial life, and even predict diseases. However, over the past few years, organisations across the globe have wrestled with just how much human error has permeated their analytics attempts, often ending with disastrous results. From crashing spacecraft to sinking ships, transferring billions of dollars to unintended recipients, and causing deaths due to overdose of medication, human error in data analysis has far-reaching ramifications for organisations. The reason for human error in data analysis could be many, such as lack of experience, fatigue or loss of attention, lack of knowledge, or the all-too-common biases in interpreting data. However, what's common among these errors is that they are related to humans reading, processing, analysing, and interpreting data.

There are few environments as unforgiving as the ocean. Its unpredictable weather patterns and limitations in terms of communications have left large swaths of the ocean unexplored and shrouded in mystery. "The ocean is a fascinating environment with a number of current challenges like microplastics, algae blooms, coral bleaching, and rising temperatures," says Wim van Rees, the ABS Career Development Professor at MIT. "At the same time, the ocean holds countless opportunities -- from aquaculture to energy harvesting and exploring the many ocean creatures we haven't discovered yet." Ocean engineers and mechanical engineers, like van Rees, are using advances in scientific computing to address the ocean's many challenges, and seize its opportunities. These researchers are developing technologies to better understand our oceans, and how both organisms and human-made vehicles can move within them, from the micro scale to the macro scale.

The complete paper discusses risk reduction and increased fabric-maintenance (FM) efficiency using artificial-intelligence (AI) and machine-learning (ML) algorithms to analyze full-facility imagery for atmospheric corrosion detection and classification. With this tool, a comprehensive and objective analysis of a facility's health is achievable in a matter of weeks from the time of data collection. This application of AI and ML is a novel approach aimed at gaining a comprehensive understanding of facility-coating integrity and external corrosion threats. Atmospheric corrosion is the most-significant asset-integrity threat in the Gulf of Mexico (GOM). Offshore facilities require constant inspection and FM--and the significant financial obligation of these activities--to stay ahead of rapid equipment degradation.

Enterprises continued to accelerate the adoption of AI and machine learning to solve product and business challenges and improve revenues in 2021. Meanwhile, AI startups have experienced significant growth, roping in major investments to improve their product offerings and meet the growing demand for AI solutions across sectors. In fact, data from CB Insights Research shows that while the number of equity funding deals in the global AI space this year is just slightly less than the last (2,384 deals in 2021 versus 2,450 in 2020), the amount of capital invested has almost doubled to $68 billion. As we head into 2022, here's a quick look back at the milestones that shaped the AI space over the past 12 months. To start the year, OpenAI announced DALL-E, a multimodal AI system that generated images from text.

We base our work on the teleosemantic modelling of concepts as abilities implementing the distinct functions of recognition and classification. Accordingly, we model two types of concepts - substance concepts suited for object recognition exploiting visual properties, and classification concepts suited for classification of substance concepts exploiting linguistically grounded properties. The goal in this paper is to demonstrate that object recognition can be construed as classification via visual properties, as distinct from work in mainstream computer vision. Towards that, we present an object recognition process based on Ranganathan's four-phased faceted knowledge organization process, grounded in the teleosemantic distinctions of substance concept and classification concept. We also briefly introduce the ongoing project MultiMedia UKC, whose aim is to build an object recognition resource following our proposed process.

Modern neural language models widely used in tasks across NLP risk memorizing sensitive information from their training data. As models continue to scale up in parameters, training data, and compute, understanding memorization in language models is both important from a learning-theoretical point of view, and is practically crucial in real world applications. An open question in previous studies of memorization in language models is how to filter out "common" memorization. In fact, most memorization criteria strongly correlate with the number of occurrences in the training set, capturing "common" memorization such as familiar phrases, public knowledge or templated texts. In this paper, we provide a principled perspective inspired by a taxonomy of human memory in Psychology. From this perspective, we formulate a notion of counterfactual memorization, which characterizes how a model's predictions change if a particular document is omitted during training. We identify and study counterfactually-memorized training examples in standard text datasets. We further estimate the influence of each training example on the validation set and on generated texts, and show that this can provide direct evidence of the source of memorization at test time.

What AI and data analytics trends are taking the industry by storm this year? This comprehensive review highlights upcoming directions in AI to carefully watch and consider implementing in your personal work or organization.

The graph represents a network of 3,049 Twitter users whose tweets in the requested range contained "#cx", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 13 December 2021 at 01:26 UTC. The requested start date was Sunday, 12 December 2021 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 3-day, 21-hour, 47-minute period from Wednesday, 08 December 2021 at 03:11 UTC to Sunday, 12 December 2021 at 00:59 UTC.

This paper presents a grounded language-image pre-training (GLIP) model for learning object-level, language-aware, and semantic-rich visual representations. GLIP unifies object detection and phrase grounding for pre-training. The unification brings two benefits: 1) it allows GLIP to learn from both detection and grounding data to improve both tasks and bootstrap a good grounding model; 2) GLIP can leverage massive image-text pairs by generating grounding boxes in a self-training fashion, making the learned representation semantic-rich. In our experiments, we pre-train GLIP on 27M grounding data, including 3M human-annotated and 24M web-crawled image-text pairs. The learned representations demonstrate strong zero-shot and few-shot transferability to various object-level recognition tasks. 1) When directly evaluated on COCO and LVIS (without seeing any images in COCO during pre-training), GLIP achieves 49.8 AP and 26.9 AP, respectively, surpassing many supervised baselines. 2) After fine-tuned on COCO, GLIP achieves 60.8 AP on val and 61.5 AP on test-dev, surpassing prior SoTA. 3) When transferred to 13 downstream object detection tasks, a 1-shot GLIP rivals with a fully-supervised Dynamic Head. Code will be released at https://github.com/microsoft/GLIP.