Just released this week is a doozy of a game concept from Luden.io that's picking up pretty positive user reviews. While True: Learn() will have you puzzling together neural networks using actual machine learning techniques. There's a story about your cat, too, which is apparently a better machine learning specialist than you--so you set about making a cat to human translation software. Its primary selling point, however, is that it's designed to teach you actual concepts in machine learning. Puzzles in the game are based on real world problems that could be solved by machine learning, like self-driving cars.
The number of techy tools that brokers have access to in their toolbox is growing as broker management systems include more and more features, technology vendors offer solutions that allow for the analysis of data coming in from carriers, and insurers plan out brokerage environments of the future that are focused on the customer experience. Not all technology will prove to be useful to brokers, at least not at the outset when these tools are still being honed, and some will fail to be adopted widely – one only has to look at Segways to realize that technology is not always as transformative as it promises. In the case of one brokerage, chatbots were that tech tool that didn't quite take off. "We were one of the brokerages experimenting with chatbots and we never really got to a point where it was there for us to go all in on them," said John McClelland, broker and director of digital for McClelland Insurance, and founder of miBroker, adding that it wasn't a complete enough solution. While the firm's experiments involved using chatbots for lead generation, the next piece of the puzzle was missing.
Learn how you can generate CUDA code from a trained deep neural network in MATLAB and leverage the NVIDIA TensorRT library for inference on NVIDIA GPUs. The video demonstrates this by using a pedestrian detection application as an example. The NVIDIA TensorRT library is a high-performance deep learning inference optimizer and runtime library. The generated code leverages the network-level and layer-level TensorRT APIs to get the best performance, and you see the neural network for pedestrian detection running on a NVIDIA Titan XP around 700 fps. You can export the generated code along with the rest of the application and deploy the algorithm on embedded GPU targets such as Jetson Tegra or Drive PX platforms.
Maoyan Entertainment, China's biggest online movie ticketing platform, is seeking to raise as much as US$345 million in a Hong Kong initial … By target, the hottest sectors were tech, entertainment and culture, e-commerce, … as regulators relaxed restrictions on M&A and IPO approvals. Tencent and Alibaba, China's biggest tech groups and two of the country's most acquisitive buyers, have stepped on the brakes after a … Let's start with Honor, a sub-brand of Chinese tech giant Huawei, … with super low-cost phones made by Chinese tech giant TCL under the … China's push for technology self-reliance faces reality check, says … The problem is that a large proportion of suppliers in China's technology industry are foreign based, often with headquarters in Taiwan, South … Tencent Holdings, the largest video game publisher in the world and owner of China's top messaging app WeChat, made 163 investments in … China has been scrambling to catch up to the U.S. cloud computing … intelligence research and so-called "smart cities," which generate a lot of … As the trade war between China and the United States rumbles on, its focus has shifted from deficits and surpluses towards more technological .. The Alibaba effect: Chinese e-tailer tightens grip over 600m lives … It is also a world leader in technologies like artificial intelligence. Artificial Intelligence Is Powerful--And Misunderstood. The potential applications for AI are extremely exciting. World's most valuable AI startup SenseTime unveils self-driving center … Xiaomi Corp. will invest at least 10 billion yuan ($1.5 billion) on artificial intelligenceand smart devices over the next five years, as the … According to the Chinese media, the joint venture between Riot and Tencent will be established in Shanghai.
Pratim is Head of Solutions Architecture, where he runs a team focused on Data & AI for the Customer Success Unit. Prior to that he was at AWS, as a Specialist SA for Big Data & Analytics, where he advised customers on big data architecture, migration of big data workloads to the cloud, and implementing best practices and guidelines for analytics. Pratim is particularly interested in operational excellence for petabyte to exabyte scale operations, and design patterns covering "good" data architecture including governance, catalog, and lineage. He's also passionate about advanced analytics, planet scale NoSQL database like Cosmos DB, and using the right mix of technology, business, and pragmatism to ultimately make customers successful. AI is already showing its potential for good causes.
The Army envisions acquiring technologies that use machine learning to autonomously detect and address software vulnerabilities and network misconfigurations – routine mistakes that could offer attackers an entry point onto its systems. Another reason organizations are turning to AI-powered cyber defenses: to counter the threat posed by intelligent cyber weapons. In February 2018, a group of more than two dozen researchers representing the Washington-based Center for a New American Security, the universities of Oxford and Cambridge, and nonprofit organizations including the Electronic Frontier Foundation and OpenAI, issued a groundbreaking report warning that AI technologies could amplify the destructive power available to nation-states and criminal enterprises. The report outlines dozens of ways attackers could use artificial intelligence to their advantage, from generating automated spear-phishing attacks capable of reliably fooling their human targets, to triggering ransomware attacks using voice or facial recognition, to designing malware that mimics normal user behavior to evade detection. Although there haven't yet been confirmed cases of AI-enabled cyberattacks, the researchers conclude that, "the pace of progress in AI suggests the likelihood of cyber attacks leveraging machine learning capabilities in the wild soon, if they have not done so already."
Alphabet's multi-billion dollar subsidiary Waymo plays a leading role in the modern autonomous driving industry, and the company's cutting-edge self-driving system has allowed it to maintain this high valuation on Wall Street. Is the core method that enables self-driving vehicles to visualize their surroundings, sense the world, predict behaviors, and calculate actions and movement etc. To make ML-based solutions available for a wider variety of deployment scenarios, Waymo's autonomous driving team has collaborated with Google AI Brain Team researchers on a system that automates the creation of high quality and low latency neural networks on existing AutoML architectures. Building on its previous research with Neural Architecture Search (NAS), the team used NAS cells to automatically build new machine learning models for tasks specific to self-driving, and transferred CIFAR-10 model learning results directly to the system. The team then set up an automatic search algorithm to enable different NAS cell combinations with convolutional neural networks (CNN) for training and evaluating of suitable models for LiDAR point identification segmentation tasks.
Artificial intelligence has now entered every sphere of our lives. Autonomous robots are self-dictated, have emotional intelligence and can analyse their surroundings and react accordingly. Robots like Smartibot, Vector by Anki and Evie are some of the coolest robots in the market. They can perform walk, perform chores and even play games. AI has been in vogue since its ability to mimic human-like game moves was discovered.
This is the CTOvision assessment on the megatrend of Artificial Intelligence. This report gives a high level overview of the most important factors of the trend, gives updated insights into the activities of the major AI companies, and succinct descriptions of AI tech. The field is growing dramatically with the proliferation of high powered computers into homes and businesses and especially with the growing power of smartphones and other mobile devices. AI requires lots of data to be effective and with the proliferation of mobile devices there is more data now than ever. There are many key technologies used in fielding AI.
Tomaso Poggio is a professor at MIT and is the director of the Center for Brains, Minds, and Machines. Cited over 100,000 times, his work has had a profound impact on our understanding of the nature of intelligence, in both biological neural networks and artificial ones. He has been an advisor to many highly-impactful researchers and entrepreneurs in AI, including Demis Hassabis of DeepMind, Amnon Shashua of MobileEye, and Christof Koch of the Allen Institute for Brain Science. This conversation is part of the Artificial Intelligence podcast and the MIT course 6.S099: Artificial General Intelligence. The conversation and lectures are free and open to everyone.