The CamemBERT Transformer model (by Facebook AI, Inria and Sorbonne Université), trained on 138GB of French text was added this morning to the huggingface/transformers model repository, and is now usable in both PyTorch and TensorFlow 2! Install the library from source to play around with it! It is available alongside chinese and german BERT models and other multi-lingual models. CamemBERT improves the state of the art on several French NLP tasks, outperforming multi-lingual models in several tasks. It's based on RoBERTa's training scheme but uses whole-word masking as well as sentence-piece tokenization.

ML-driven insights from automated systems can significantly enhance the decision-making capabilities of hedge fund managers. FREMONT, CA: Conventionally, hedge fund managers were deploying machines and algorithms to encourage quantitative investment. However, most of these algorithms were designed with predefined conditions that were guiding the various investment decisions. Lately, machine learning (ML) has started gaining traction by offering an alternative approach to quantitative hedge fund investments. ML-driven algorithms work in a dynamic fashion which analyzes the data patterns and adapts accordingly.

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Our post on the 100,000 registered customers milestone this summer included an infographic of sample use cases being explored by BigML users, which naturally span many different sectors and industries. Today, we'd like to start a series of posts that further highlight a subset of those business problems to give our readers some clues on how a comprehensive platform as ours can be utilized in different business contexts in case they're considering new Machine Learning solutions. There are many ways to organize use cases, e.g., by industry, function, geography. In this post, we will focus on startups and SMBs as we give you a glimpse of the motivation behind solving each reference use case. Startups and SMBs have good reasons to prefer the BigML platform because it lets them to affordably step into Machine Learning with ample room to further scale efforts as data volumes and the number of use cases implemented grow over time.

This is the implementation of semantic target driven navigation training and evaluation on Active Vision dataset. We used Active Vision Dataset (AVD) which can be downloaded from here. To make our code faster and reduce memory footprint, we created the AVD Minimal dataset. AVD Minimal consists of low resolution images from the original AVD dataset. In addition, we added annotations for target views, predicted object detections from pre-trained object detector on MS-COCO dataset, and predicted semantic segmentation from pre-trained model on NYU-v2 dataset.

Machine learning (ML) is a domain of artificial intelligence that allows computer algorithms to learn from experience without being explicitly programmed. To summarize neurosurgical applications of ML where it has been compared to clinical expertise, here referred to as "natural intelligence." A systematic search was performed in the PubMed and Embase databases as of August 2016 to review all studies comparing the performance of various ML approaches with that of clinical experts in neurosurgical literature. Twenty-three studies were identified that used ML algorithms for diagnosis, presurgical planning, or outcome prediction in neurosurgical patients. Compared to clinical experts, ML models demonstrated a median absolute improvement in accuracy and area under the receiver operating curve of 13% (interquartile range 4-21%) and 0.14 (interquartile range 0.07-0.21), In 29 (58%) of the 50 outcome measures for which a P-value was provided or calculated, ML models outperformed clinical experts (P .05). In 18 of 50 (36%), no difference was seen between ML and expert performance (P .05), All 4 studies that compared clinicians assisted by ML models vs clinicians alone demonstrated a better performance in the first group. We conclude that ML models have the potential to augment the decision-making capacity of clinicians in neurosurgical applications; however, significant hurdles remain associated with creating, validating, and deploying ML models in the clinical setting.

Connect, download a free E-Book, watch a keynote, or browse my blog. Recently, I discussed how Artificial Intelligence (AI) and a new breed of Creative Machines was being used to help design everything from cities to NASA planetary rovers, and now architecture studio Wallgren Arkitekter and Swedish construction company BOX Bygg have created an AI design tool called Finch that can generate new building floor plans and adapt them according to the space available – and while this might sound like quirky work, as we begin to 3D print everything from military barracks through to family homes and 80 storey skyscrapers, having an AI that can help design buildings will no doubt come in very handy indeed. Furthermore, as AI and drone technology helps us develop the world's first fully autonomous construction sites this additional development could mean one day machines control the entire construction process – from initial building concept and design, through to final construction and fit outs. Finch, that you can see working below, will be launched in 2020 as a plug-in to visual programming tool Grasshopper within 3D computer graphics software Rhino. "The idea of Finch is to create a more user-friendly tool for architects to be able to enjoy the benefits of parametric design without any knowledge of Grasshopper or coding," said Pamela Wallgren, co-founder of Wallgren Arkitekter.

PULP Platform Youtube channel (subscribe it!): PULP-DroNet is a deep learning-powered visual navigation engine that enables autonomous navigation of a pocket-size quadrotor in a previously unseen environment. Thanks to PULP-DroNet the nano-drone can explore the environment, avoiding collisions also with dynamic obstacles, in complete autonomy -- no human operator, no ad-hoc external signals, and no remote laptop! This means that all the complex computations are done directly aboard the vehicle and very fast. The visual navigation engine is composed of both a software and a hardware part.

Automation Anywhere has announced advances to its IQ Bot, designed to accelerate intelligent automation to aid in intelligent document processing. IQ Bot is the world's only web-based, cloud-native RPA-integrated Intelligent Document Processing (IDP) solution. The new version, delivered both via the cloud and on premises, expands AI-driven document processing capabilities to users. With a large number of pre-packaged use cases available out of the box, users can now automate business processes that involve documents such as invoices, purchase orders, loan applications, insurance claims and many others across multiple industries. The new release also expands the user interface to support 10 languages and enables extraction from identification documents such as passports and industry standard documents for insurance, health claims and others.

Intelligent automation has a massive potential to streamline the complex trading process by precisely capturing the market patterns. FREMONT, CA: The capital market landscape is on the path to transformation with the emergence of new technologies. Investment firms are aiming to enhance their business and operational strategies. Technologies such as artificial intelligence (AI), with robotic process automation (RPA), is being considered the torchbearer in this regard. In fact, the above-mentioned technologies are often collectively referred to as intelligent automation.