In this paper, we propose a novel deep multi-level attention model to address inverse visual question answering. The proposed model generates regional visual and semantic features at the object level and then enhances them with the answer cue by using attention mechanisms. Two levels of multiple attentions are employed in the model, including the dual attention at the partial question encoding step and the dynamic attention at the next question word generation step. We evaluate the proposed model on the VQA V1 dataset. It demonstrates state-of-the-art performance in terms of multiple commonly used metrics.

Explainable Artificial Intelligence (XAI) methods are typically deployed to explain and debug black-box machine learning models. However, most proposed XAI methods are black-boxes themselves and designed for images. Thus, they rely on visual interpretability to evaluate and prove explanations. In this work, we apply XAI methods previously used in the image and text-domain on time series. We present a methodology to test and evaluate various XAI methods on time series by introducing new verification techniques to incorporate the temporal dimension. We further conduct preliminary experiments to assess the quality of selected XAI method explanations with various verification methods on a range of datasets and inspecting quality metrics on it. We demonstrate that in our initial experiments, SHAP works robust for all models, but others like DeepLIFT, LRP, and Saliency Maps work better with specific architectures.

This article is part of Fast Company's editorial series The New Rules of AI. More than 60 years into the era of artificial intelligence, the world's largest technology companies are just beginning to crack open what's possible with AI--and grapple with how it might change our future. Click here to read all the stories in the series. Artificial intelligence is still in its youth. But some very big things have already happened.

From winning at Go to passing eighth grade level multiple choice tests, AI is making rapid advances. But its creativity still leaves much to be desired. On September 4, 2019, Peter Clark, along with several other researchers, published "From'F' to'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project " The Aristo project named in the title is hailed for the rapid improvement it has demonstrated when it tested the way eighth-grade human students in New York State are tested for their knowledge of science. The researchers concluded that this is an important milestone for AI: "Although Aristo only answers multiple choice questions without diagrams, and operates only in the domain of science, it nevertheless represents an important milestone towards systems that can read and understand. The momentum on this task has been remarkable, with accuracy moving from roughly 60% to over 90% in just three years."

Hewlett Packard Enterprise Co. today is expanding its reach into artificial intelligence development with a software platform that supports the full lifecycle of machine learning model construction and deployment using the self-contained software environments called containers. HPE ML Ops provides for the rapid rollout of machine learning workloads across on-premises, public cloud and hybrid cloud environments. The idea is to enable development teams to employ processes similar to those used in DevOps, the rapid application-building technique that that involves frequent code releases and constant refinement. The result is reductions in model deployment times from months to days, HPE said. The company is attacking a common problem with machine learning projects, which is a lack of resources and operational processes to deploy them.

AI is changing the field of synthetic biology and how we engineer biology. It's helping engineers design new ways to design genetic circuits -- and it could leave a remarkable impact on the future of humanity TVs and radios blare that "artificial intelligence is coming," and it will take your job and beat you at chess. But AI is already here, and it can beat you -- and the world's best -- at chess. In 2012, it was also used by Google to identify cats in YouTube videos. Today, it's the reason Teslas have Autopilot and Netflix and Spotify seem to "read your mind."

The world's data is doubling every year and holds the key to transforming every industry and our lives. AI and Machine Learning can help make sense of this data but require tremendous amounts of computing. With the end of Moore's law, completely new approaches are needed to process the incredible amount of data available. NVIDIA solved this challenge with the invention of GPU-accelerated computing, providing a clear path forward. In my current role, I focus on GPU-accelerated computing in the data center, helping customers transform their businesses with AI and High-Performance Computing (HPC).

This is library I made for Pytorch, for fast transfer between pinned CPU tensors and GPU pytorch variables. The inspiration came from needed to train large number of embeddings, which don't all fit on GPU ram at a desired embedding size, so I needed a faster CPU - GPU transfer method. This also allows using any optimizer for sparse training, since every embedding contained in the Pytorch embedding variable receives an update, previously only Pytorch's SGD, Adagrad, and SparseAdam were suitable for such training. In addition to augmenting parameter sizes, you can use to increase the speed of which data on your CPU is transferred to Pytorch Cuda variables. Also, SpeedTorch's GPU tensors are also overall faster then Pytorch cuda tensors, when taking into account both transferring two and from (overall 2.6x faster).

February 19, 2019 – Machine learning, which provides the ability to learn a task from data (without the need of being programmed explicitly), is a key component of any Pathology AI (Artificial Intelligence) system. There are many different approaches in machine learning, reaching from simple decision trees to complex deep learning, each with its advantages and disadvantages. Deep learning, which allows to learn highly complex visual features, has created a hype about Artificial Intelligence (AI) and Healthcare AI, as is was able to solve complex computer vision problems that we believed out-of-reach just a few years ago. As pathology is a visual task it is understandable that academia and "pure" technology companies are now working heavily on deep learning approaches for pathology.The key problem for any Pathology AI system are the variations between different patient types. In a disease state, no two patient samples look identical.

Ever wondered how data science and machine learning are revolutionizing biotech and healthcare, from drug discovery and agriculture to women's health and prenatal diagnostics? Join us on Oct 8th at Illumina's Foster City campus to find out! Daphne Koller (Insitro), Diane Wu (Trace Genomics), Hana Janebdar (Juno Bio), and Raheleh Salari (Natera) will be sharing their stories on how they're combining their expertise in genomics and machine learning to make the world a better place. The event will be sponsored by the Illumina Accelerator. Food and drinks will be served.