Technology AI This AI thinks it's the 1800s What happens when you train an LLM only on limited historical data? Breakthroughs, discoveries, and DIY tips sent six days a week. An interesting thing about contemporary artificial intelligence models, specifically large language models (LLMs): They can only output text based on what's in their training dataset. Models, including ChatGPT and Claude, are "trained" on large databases of text. The models, when asked a question, statistically create a response by calculating, one word at a time, what the most likely next word should be.

In the task of Knowledge Graph Completion (KGC), the existing datasets and their inherent subtasks carry a wealth of shared knowledge that can be utilized to enhance the representation of knowledge triplets and overall performance. However, no current studies specifically address the shared knowledge within KGC. To bridge this gap, we introduce a multi-level Shared Knowledge Guided learning method (SKG) that operates at both the dataset and task levels. On the dataset level, SKG-KGC broadens the original dataset by identifying shared features within entity sets via text summarization. On the task level, for the three typical KGC subtasks - head entity prediction, relation prediction, and tail entity prediction - we present an innovative multi-task learning architecture with dynamically adjusted loss weights. This approach allows the model to focus on more challenging and underperforming tasks, effectively mitigating the imbalance of knowledge sharing among subtasks. Experimental results demonstrate that SKG-KGC outperforms existing text-based methods significantly on three well-known datasets, with the most notable improvement on WN18RR.

Conventional Knowledge Graph Construction (KGC) approaches typically follow the static information extraction paradigm with a closed set of pre-defined schema. As a result, such approaches fall short when applied to dynamic scenarios or domains, whereas a new type of knowledge emerges. This necessitates a system that can handle evolving schema automatically to extract information for KGC. To address this need, we propose a new task called schema-adaptable KGC, which aims to continually extract entity, relation, and event based on a dynamically changing schema graph without re-training. We first split and convert existing datasets based on three principles to build a benchmark, i.e., horizontal schema expansion, vertical schema expansion, and hybrid schema expansion; then investigate the schema-adaptable performance of several well-known approaches such as Text2Event, TANL, UIE and GPT-3.5. We further propose a simple yet effective baseline dubbed \textsc{AdaKGC}, which contains schema-enriched prefix instructor and schema-conditioned dynamic decoding to better handle evolving schema. Comprehensive experimental results illustrate that AdaKGC can outperform baselines but still have room for improvement. We hope the proposed work can deliver benefits to the community. Code and datasets available at https://github.com/zjunlp/AdaKGC.

I explore two questions in this thesis: how can computer science be used to fight human trafficking? And how can computer vision create a sense of touch? I use natural language processing (NLP) to monitor the United States illicit massage industry (IMI), a multi-billion dollar industry that offers not just therapeutic massages but also commercial sexual services. Employees of this industry are often immigrant women with few job opportunities, leaving them vulnerable to fraud, coercion, and other facets of human trafficking. Monitoring spatiotemporal trends helps prevent trafficking in the IMI. By creating datasets with three publicly-accessible websites: Google Places, Rubmaps, and AMPReviews, combined with NLP techniques such as bag-of-words and Word2Vec, I show how to derive insights into the labor pressures and language barriers that employees face, as well as the income, demographics, and societal pressures affecting sex buyers. I include a call-to-action to other researchers given these datasets. I also consider how to creating synthetic financial data, which can aid with counter-trafficking in the banking sector. I use an agent-based model to create both tabular and payee-recipient graph data. I then consider the role of computer vision in making tactile sensors. I report on a novel sensor, the Digger Finger, that adapts the Gelsight sensor to finding objects in granular media. Changes include using a wedge shape to facilitate digging, replacing the internal lighting LEDs with fluorescent paint, and adding a vibrator motor to counteract jamming. Finally, I also show how to use a webcam and a printed reference marker, or fiducial, to create a low-cost six-axis force-torque sensor. This sensor is up to a hundred times less expensive than commercial sensors, allowing for a wider range of applications. For this and earlier chapters I release design files and code as open source.

Ezra founder and CEO Emi Gal explains on'Fox & Friends Weekend' how artificial intelligence can'enhance' MRI scans, image quality, analysis, and comprehension. Potentially "practice-changing" results from two new cancer drug studies were introduced at the American Society of Clinical Oncology (ASCO)'s annual meeting this week in Chicago. For lung cancer patients, a drug called osimertinib -- taken by pill once daily -- was shown to reduce the risk of deaths by more than 50% in a long-running international study. For breast cancer patients, a new drug called ribociclib significantly increased survival rates and prevented recurring disease in a separate study. "Targeted therapies have been a major advance in treating deadly cancers," Dr. Marc Siegel, professor of medicine at NYU Langone Medical Center, told Fox News Digital.

Self-supervised pre-training recently demonstrates success on large-scale multimodal data, and state-of-the-art contrastive learning methods often enforce the feature consistency from cross-modality inputs, such as video/audio or video/text pairs. Despite its convenience to formulate and leverage in practice, such cross-modality alignment (CMA) is only a weak and noisy supervision, since two modalities can be semantically misaligned even they are temporally aligned. For example, even in the (often adopted) instructional videos, a speaker can sometimes refer to something that is not visually present in the current frame; and the semantic misalignment would only be more unpredictable for the raw videos collected from unconstrained internet sources. We conjecture that might cause conflicts and biases among modalities, and may hence prohibit CMA from scaling up to training with larger and more heterogeneous data. This paper first verifies our conjecture by observing that, even in the latest VATT pre-training using only narrated videos, there exist strong gradient conflicts between different CMA losses within the same sample triplet (video, audio, text), indicating them as the noisy source of supervision. We then propose to harmonize such gradients during pre-training, via two techniques: (i) cross-modality gradient realignment: modifying different CMA loss gradients for one sample triplet, so that their gradient directions are in more agreement; and (ii) gradient-based curriculum learning: leveraging the gradient conflict information on an indicator of sample noisiness, to develop a curriculum learning strategy to prioritize training with less noisy sample triplets. Applying those gradient harmonization techniques to pre-training VATT on the HowTo100M dataset, we consistently improve its performance on different downstream tasks. Moreover, we are able to scale VATT pre-training to more complicated non-narrative Youtube8M dataset to further improve the state-of-the-arts.

ShipHero, a shipping and logistics platform for more than 5,000 eCommerce brands and third-party logistics (3PL) providers, announced that it would implement inVia Robotics' robotic system at two more fulfillment centers. The company has been using inVia's automated picking and replenishment robots at its Jacksonville, Florida location, and its now adding that technology to its Allentown, Pennsylvania and Las Vegas warehouses. ShipHero and inVia have already built a native integration between inVia's warehouse execution system (WES) and ShipHero's warehouse management system (WMS) software, ensuring rapid and seamless deployments in the new facilities. "Over the last two years we've seen demand for 3PL services grow dramatically, which has led to a greater need for technology that can help keep products moving quickly through the order process," Lior Elazary, CEO and co-founder of inVia Robotics, said. "The native integration between our WES and ShipHero's WMS will allow us to very rapidly expand both of our technologies into additional warehouses, bypassing the need for time-consuming custom systems integration. We're excited to expand our AI and automation services across ShipHero's strategic distribution network."

The Feinstein Institutes for Medical Research has spun out a startup whose artificial-intelligence device could help paralyzed patients regain the use of their hands. Earlier this month, the startup, Neuvotion Inc., announced a $1.1 million funding round from the Long Island Angel Network and the Good Shepherd Rehabilitation Network based in Allentown, Pennsylvania. The Darien, Connecticut, startup is in the process of transferring research developed in the laboratory of Chad Bouton, vice president of advanced engineering at the Feinstein Institutes, a unit of Northwell Health. Bouton also is founder of Neuvotion. The company's initial device, NeuStim, is worn as a patch on the patient's forearm and is being positioned for use in clinics and at home.

As of March 17th, 2020, more than 188,297 people have been infected with COVID-19. How can technology aid in curtailing the spread of infectious diseases that have the potential to create panic and infirm thousands of people? The Internet of Things (IoT), a network of interconnected systems and advances in data analytics, artificial intelligence, and connectivity, can help by providing an early warning system to curb the spread of infectious diseases. China's efforts to control the coronavirus have meant many residents stayed at home and factories just shut down. That had an unintended effect: less air pollution.