If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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.
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.
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.
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.
The nature of every industry has changed a lot with the introduction of advanced technologies. Data entry and data conversion services have supported advancement of technology in cloud adoption, big data, software defined networking, IOT (Internet of Things), virtualization, and so on. Recent reports indicate that AI and big data are driving various technological innovations and will impact the future of digital transformation. These technologies have developed rapidly over the years and will be implemented in every industry in the future. In fact, organizations need to observe and adapt to these trends to stay competitive.
A team of Google researchers has used a deep-learning algorithm to predict lung cancer accurately from computed scans. The work demonstrates the potential for Artificial Intelligence (AI) to increase both accuracy and consistency, which could help accelerate adoption of lung cancer screening worldwide. Lung cancer is the deadliest of all cancers worldwide -- more than breast, prostate, and colorectal cancers combined -- and it's the sixth most common cause of death globally, according to the World Health Organization. "Using advances in 3D volumetric modelling alongside datasets from our partners (including Northwestern University), we've made progress in modelling lung cancer prediction as well as laying the groundwork for future clinical testing," Shravya Shetty, M.S. Technical Lead at Google explained in a blog post late Monday. Google researchers created a model that can not only generate the overall lung cancer malignancy prediction (viewed in 3D volume) but also identify subtle malignant tissue in the lungs (lung nodules).
Attracting top talent is vital within any company or organisation. According to a CareerBuilder survey, 75% of employers admitted to having made a poor hiring decision, resulting in a loss of nearly US$15,000 per bad hire. In today's world, finding the right talent that will stick may seem more difficult than first imagined. Why wait for candidates to come to you when you could find them before you even click on a job ad template? As a recruiter, you could be sourcing your candidates and choosing who you add to your talent pipeline, instead of candidates deciding whether to engage with you.
Cloud and datacenter architects searching for new ways to pack more artificial intelligence horsepower into already constrained spaces will want to take a close look at Intel's new Nervana Neural Network Processors. Depending on the application, the processors may offer four times the performance or one-fifth the power draw as commercially available alternatives. The new processors are Intel's first ASIC offerings tailored specifically for deep learning workloads. The company announced last week the processors are shipping now. In addition to the NNP-T1000 for training and the NNP-I1000 for inference, Intel also announced the coming generation of the Movidius Myriad Vision Processing Unit, which is designed for AI vision and inference processing at the edge.