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) …
Reinforcement learning is field that keeps growing and not only because of the breakthroughs in deep learning. Sure if we talk about deep reinforcement learning, it uses neural networks underneath, but there is more to it than that. In our journey through the world of reinforcement learning we focused on one of the most popular reinforcement learning algorithms out there Q-Learning. This approach is considered one of the biggest breakthroughs in Temporal Difference control. In this article, we are going to explore one variation and improvement of this algorithm – Double Q-Learning.
A guide showing how to train TensorFlow Lite object detection models and run them on Android, the Raspberry Pi, and more! TensorFlow Lite is an optimized framework for deploying lightweight deep learning models on resource-constrained edge devices. TensorFlow Lite models have faster inference time and require less processing power, so they can be used to obtain faster performance in realtime applications. This guide provides step-by-step instructions for how train a custom TensorFlow Object Detection model, convert it into an optimized format that can be used by TensorFlow Lite, and run it on Android phones or the Raspberry Pi. The guide is broken into three major portions. Each portion will have its own dedicated README file in this repository. This repository also contains Python code for running the newly converted TensorFlow Lite model to perform detection on images, videos, or webcam feeds.
Medical robots have demonstrated the ability to manipulate percutaneous instruments into soft tissue anatomy while working beyond the limits of human perception and dexterity. Robotic technologies further offer the promise of autonomy in carrying out critical tasks with minimal supervision when resources are limited. Here, we present a portable robotic device capable of introducing needles and catheters into deformable tissues such as blood vessels to draw blood or deliver fluids autonomously. Robotic cannulation is driven by predictions from a series of deep convolutional neural networks that encode spatiotemporal information from multimodal image sequences to guide real-time servoing. We demonstrate, through imaging and robotic tracking studies in volunteers, the ability of the device to segment, classify, localize and track peripheral vessels in the presence of anatomical variability and motion.
I recently asked the Twitter community about their biggest machine learning pain points and what work their teams plan to focus on in 2020. One of the most frequently mentioned pain points was deploying machine learning models. More specifically, "How do you deploy machine learning models in an automated, reproducible, and auditable manner?" The topic of ML deployment is rarely discussed when machine learning is taught. Boot camps, data science graduate programs, and online courses tend to focus on training algorithms and neural network architectures because these are "core" machine learning ideas.
The European Patent Office recently turned down an application for a patent that described a food container. This was not because the invention was not novel or useful, but because it was created by artificial intelligence. By law, inventors need to be actual people. This isn't the first invention by AI – machines have produced innovations ranging from scientific papers and books to new materials and music. That said, being creative is clearly one of the most remarkable human traits.
One thing is certain: The explosion of data creation in our society will continue as far as pundits and anyone else can forecast. In response, there is an insatiable demand for more advanced high performance computing to make this data useful. The IT industry has been pushing to new levels of high-end computing performance; this is the dawn of the exascale era of computing. Recent announcements from the US Department of Energy for exascale computers represent the starting point for a new generation of computing advances. This is critical for the advancement of any number of use cases such as understanding the interactions underlying the science of weather, sub-atomic structures, genomics, physics, rapidly emerging artificial intelligence applications, and other important scientific fields.
It seems that every year Google plans to shock the artificial intelligence(AI) world with new astonishing progress in natural language understanding(NLU) systems. Last year, the BERT model definitely stole the headlines of the NLU research space. Just a few weeks into 2020, Google Research published a new paper introducing Meena, a new deep learning model that can power chatbots that can engage in conversations about any domain. NLU has been one of the most active areas of research of the last few years and have produced some of the most widely adopted AI systems to date. However, despite all the progress, most conversational systems remain highly constrained to a specific domain which contrasts with our ability as humans to naturally converse about different topics.
Segmentation of cognitive computing market by technology comprises natural language processing, automated reasoning, machine learning, and semantic analysis. Machine learning is anticipated to have the highest CAGR as it is widely used across various applications of cognitive computing and artificial intelligence. Machine learning is deployed by various industries in their operations. Cognitive computing market segmentation on industry verticals include BFSI, healthcare, construction and engineering, oil and gas, retail, education, government and defense, transportation, and others. The healthcare industry is anticipated to experience a high growth during the forecast time period as it allows doctors and specialists to have access to the data collected from disparate and exogenous sources, take informed decisions, and examine critical attributes of a patient case.
Rice University engineers have developed a deep learning computer system that can accurately predict extreme weather events. Rice University engineers have developed a deep learning computer system that can accurately predict extreme weather events, like heatwaves, up to five days in advance using minimal information about current weather conditions. The new network uses an analog method of weather forecasting that computers made obsolete in the 1950s. The Phys.org website reported that the system was fed hundreds of maps that show surface temperatures and air pressures at five kilometers height, and each map shows those conditions several days apart. The new system is able to make five-day forecasts of extreme weather events like heat waves or winter storms with 85 percent accuracy, the German news agency reported.