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) …
Navenio, the UK company that has pioneered indoor location-based artificial intelligence to revolutionise workflows and can double the throughput of hospital teams, has been successful in securing funding in the latest round of the Artificial Intelligence in Health and Care Award. Navenio was one of 38 organisations to receive the funding in the second round of the competition, with the AI Award making £140 million available to multiple applicants over four years to accelerate the testing and evaluation of artificial intelligence technologies which meet the aims set out in the NHS Long Term Plan. Through world-leading University of Oxford research, Navenio creates unique indoor location-based services, solving the problem that GPS doesn't work indoors without requiring any new infrastructure. The Award's aim is to increase the impact of AI-driven technologies to help solve clinical and operational challenges across the NHS and care settings. It aligns perfectly with Navenio's mission to help transform hospitals through ensuring the right person is in the right place, at the right time.
Tesla has gone all-in on vision-only autonomous driving, to the point of even phasing out radar sensors in some of its EVs. Now at a CVPR 2021 workshop, Tesla senior director of AI Andrej Karpathy has explained how it's planning to do this by using an in-house supercomputer called "Dojo," as TechCrunch has reported. Karparthy explained that with vision-only tech, computers must respond to new environments with the same speed and acuity as a human. However, doing that requires AI training on a massive dataset with a powerful supercomputer to crunch it. Tesla has one of those in house with "Dojo," a next-gen model with 1.8 exaflops of performance and 10 petabytes of NVME storage running at 1.6 terabytes per second.
Pandas library comes in handy while performing data-related operations. Everyone starting with their Data Science journey has to get a good understanding of this library. Pandas can handle a significant amount of data and process it most efficiently. But at the core, it is still running on CPUs. Parallel processing can be achieved to speed up the process but it is still not efficient to handle large amounts of data.
Azure Machine Learning (AML) team is excited to announce the availability of Azure Arc-enabled Machine Learning (ML) public preview release. All customers of Azure Arc-enabled Kubernetes now can deploy AzureML extension release and bring AML to, and the edge using Kubernetes on their hardware of choice. The design for Azure Arc-enabled ML helps IT Operators leverage native Kubernetes concepts such as namespace, node selector, and resources requests/limits for ML compute utilization and optimization. By letting the IT operator manage ML compute setup, Azure Arc-enabled ML creates a seamless AML experience for data scientists who do not need to learn or use Kubernetes directly. Data scientists now can focus on models and work with tools such as Azure Machine Learning AML Studio, AML 2.0 CLI, AML Python SDK, productivity tools like Jupyter notebook, and ML frameworks like TensorFlow and PyTorch.
Today, people live in the age of technology--a more comfortable and convenient life thanks to digital tools and machinery that are transforming industries. One of the latest technologies that are disrupting businesses is artificial intelligence (AI). Many people still associate artificial intelligence with science fiction, but it is no longer a new concept and buzzword that holds ambiguous meaning. AI has gradually become commonplace in people's daily lives, continuing to break into industries such as food, transportation, security, and many more. Soon, AI will enter the call center industry, and people are looking forward to the promising integration of AI and call center.
Amazon is testing a variety of robotic and smart technology solutions designed to create a safer workplace. At its Amazon Robotics and Advanced Technology labs located near Seattle, in Boston, and in Northern Italy, the e-tail giant is working on new technologies to help move totes, carts, and packages through its facilities. In the Seattle-area research and innovation lab, one project in early development involves the use of motion-capture technology to assess the movement of volunteer employees in a lab setting. These employees perform tasks that are common in many Amazon facilities, such as the movement of totes, which carry products through robotic fulfillment centers. Motion-capture software enables Amazon scientists and researchers to more accurately compare data captured in a lab environment to industry standards, rather than other traditional ergonomic modeling tools.
Imagine you are buying a new car, and you have shortlisted 3 different models to finalize from. How would you go about it? There are so many aspects to consider, like -- power, safety, price, service network, looks, brand, colour, space and so on. How would you take a balanced decision? Would you use some tool for evaluation?
The two frameworks I use for TinyML inference are TensorFlow Lite for Microcontrollers and GLOW (more specifically, the GLOW Ahead of Time (AOT) compiler). Since I haven't really seen a comparison between the two, I decided to compare the implementation of both frameworks and perform some benchmarks. While both frameworks offer tools to quantize models, I will focus on the inference engine since the approach taken by the two frameworks for the inference is very different. TensorFlow converts the model to a FlatBuffer containing serialized steps required to perform the inference, used along with a library that runs on target MCU, which interprets the FlatBuffer. The information contained in the FlatBuffer is the weights and operations used in the model.