In a new short video that has surfaced on TikTok, apes have been spotted flying drones. The drone is an Autel Robotics Evo and the apes are located in a Myrtle Beach Safari in South Carolina. The video was taken by photographer Nick B. and shows two apes flying a drone. One is standing up using the drone's controller while the other sits beside him holding the drone's case. The video is particularly impressive as the ape seems very much in control of the drone.
Google Cloud is rolling out an "AI Hub" supplying machine learning content ranging from data pipelines and TensorFlow modules. It also announced a new pipeline component for the Google-backed Kubeflow open-source project, the machine learning stack built on Kubernetes that among other things packages machine learning code for reuse. The AI marketplace and the Kubeflow pipeline are intended to accelerate development and deployment of AI applications, Google said Thursday (Nov. The new services follow related AI efforts such as expanding access to updated Tensor processing units (TPUs) on the Google Cloud. The AI Hub is described as a community for accessing "plug-and-play" machine learning content.
WekaIO (Weka), the innovation leader in high-performance and scalable file storage, and an NVIDIA Partner Network Solution Advisor introduced Weka AI, a transformative storage solution framework underpinned by the Weka File System (WekaFS) that enables accelerated edge-to-core-to-cloud data pipelines. Weka AI is a framework of customizable reference architectures (RAs) and software development kits (SDKs) with leading technology alliances like NVIDIA, Mellanox, and others in the Weka Innovation Network (WIN) . Weka AI enables chief data officers, data scientists and data engineers to accelerate genomics, medical imaging, the financial services industry (FSI), and advanced driver-assistance systems (ADAS) deep learning (DL) pipelines. In addition, Weka AI easily scales from entry to large integrated solutions provided through VARs and channel partners. Artificial Intelligence (AI) data pipelines are inherently different from traditional file-based IO applications.
Thanks to the Internet of Things (IoT), artificial intelligence (AI), and advances in sensor technology, a whole host of everyday products are getting smarter. We have smart TVs and smartwatches. We have smart running shoes – or rather, smart insoles – that gather data on your running performance. You can even get smart nappies that send an alert to your phone when your baby's nappy needs changing. For product manufacturers, there's no doubt we've reached a tipping point in the smart product trend, meaning it's no longer possible (or wise) to ignore consumer demand for smart, AI-loaded products.
You may be wondering since when did GitHub get into the business of Automated Machine Learning. Well, it didn't but you can use it for testing your personalized AutoML software. In this tutorial, we will show you how to build and containerize your own Automated Machine Learning software and test it on GitHub using Docker container. We will use PyCaret 2.0, an open source, low-code machine learning library in Python to develop a simple AutoML solution and deploy it as a Docker container using GitHub actions. If you haven't heard about PyCaret before, you can read official announcement for PyCaret 2.0 here or check the detailed release notes here.
Clearwater Analytics, a Software-as-a-service (Saas) buy-side data aggregation and portfolio reporting specialist, this week launches a new machine learning-based information extraction service. The data extraction solution focuses on data aggregation and normalization, drilling into transactional data to create a service that automates the ingestion of many types of data which are traditionally manually entered. The solution uses advanced AI techniques including natural language processing (NLP) and deep learning to identify key data elements in a variety of document types, then extracts the data and feeds it into Clearwater's data aggregation engine to be reconciled. "We are committed to providing our clients with the most accurate data possible for their reporting needs," says Warren Barkley, Chief Technology Officer at Clearwater Analytics. "Machine learning-backed data extraction eliminates the need for manual intervention with unstructured data and allows our clients faster access to more accurate information."
Artificial intelligence can help internet service providers prevent DDoS attacks before they happen, say researchers. Findings from the National University of Singapore and Ben-Gurion University of the Negev, Israel, presented a new method in the peer-reviewed journal Computers & Security. The method uses machine learning to detect vulnerable smart home devices, which are an attractive target for hackers who assemble botnets to launch DDoS attacks. The machine learning detector does not invade customers' privacy and can pinpoint vulnerable devices even if they're not compromised. "To the best of my knowledge, telcos monitor the traffic and can only detect DDoS attacks once they are executed, which might be too late," Yair Meidan, Phd student at Ben-Gurion and the research team lead, told The Daily Swig. "In contrast, our method proposes means to detect potentially vulnerable IoT devices before they are compromised and being used to execute such attacks.
Vice President and Chief Evangelist of SAP, Shailendra Kumar joined us on the show to talk about Industry 4.0. We had a conversation about how prepared the world is for Industry 4.0 and how businesses of the future will leverage AI and automation. Shailendra or Shaily is the Vice President and Chief Evangelist of SAP and is on the Advisory Board of Aegis School of Business, Data Science, Cyber Security, and Telecommunication. A keynote speaker across Asia and Oceania around Emerging Technologies which are also showcased through #TheShailyShow. With over a quarter of a century's experience in the field of Emerging Technologies like Artificial Intelligence, Machine Learning, Advanced Analytics and Data Science, Shailendra has built upon extensive knowledge of data-driven analytics strategies for revenue growth, cost reduction, marketing and customer behaviour management to drive business outcomes.
Data annotation consists, text annotation, image annotation, and video annotation using the various techniques as per the project requirements and machine learning algorithms compatibility. Data annotation is done to create the training data sets for AI and ML while image annotation is a very important type of image annotation. A task of marking and outlining objects and entities on an image and offering various keywords to classify it which is readable for machines. Presently, Image annotation is growing very fast as image annotation is a very important task as this data helps to create accurate datasets that help computer vision models work in a real-world scenario and get effective results. We annotate & tag images with respective labels & keywords for easy and accurate categorization & help you in creating your customized image annotation services.
Jumper.ai is an AI-based platform that enables brands and SME's to instantly auto-reply and engage with customers. Its platform features conversational commerce, live chat, automated replies, bot builder, and abandoned cart recovery. Boxx.ai is a Bengaluru based artificial intelligence startup that helps e-commerce companies increase their conversion rates by displaying the most personalised products for each user. Boxx.ai predicts what each visitor is likely to buy next using its proprietary algorithms. This helps consumer internet companies curate a line of products and extend a highly personalised experience to each of its customers.