Machine learning (ML) and artificial intelligence (AI) have revolutionized industries and our daily lives; they help video-streaming services predict which movies we'd like to watch, allow credit card companies to identify fraudulent transactions and enable navigation apps to find the fastest routes to our destinations. For geospatial applications, AI and ML can identify objects and patterns automatically and derive meaningful insights from satellite imagery in hours--a task that previously would have required teams of analysts and months of effort. With these tools, we can gain insights about any spot on the globe, identify where things are changing most quickly and find patterns that have never before been visible in data. In machine learning, a form of AI, computer programs improve through experience, accessing data and using it to learn for themselves. Algorithms with richer data will become more effective in nature.
The insurance industry is a competitive sector representing an estimated $507 billion or 2.7 percent of the US Gross Domestic Product. As customers become increasingly selective about tailoring their insurance purchases to their unique needs, leading insurers are exploring how machine learning (ML) can improve business operations and customer satisfaction. The greatest opportunities seem to lie, perhaps unsurprisingly, in claims and underwriting. No other sources have taken a comprehensive look at the impact of AI among the leading insurance companies in the U.S. We researched this sector in depth to help answer questions business leaders are asking today: This article aims to present a comprehensive look at the four leading insurance companies and their use of AI. Our "top 4" rankings are based on the National Association of Insurance Commissioners' 2016 ranking of the top 25 insurance companies.
The Fourth Industrial Revolution is upon us. Human evolution has entered a new phase on the back of breathtaking technology advances. Professor Klaus Schwab, in his seminal book, The Fourth Industrial Revolution, talks about the blurring of the lines between the physical, digital and biological spheres. The Fourth Industrial Revolution deals with how technologies like artificial intelligence, machine learning and the internet of things change the way we live and interact with the world and each other. Terms like AI and ML are thrown around a lot and are sometimes used alternatively.
This listing marks 20 years since we started compiling an yearly choice of the year's most important technology. Some, for example mRNA vaccines, are already transforming our own lives, while some continue to be a couple of decades off. Below, you will get a brief description along with a link to a feature article that probes every technology in detail. We hope you will enjoy and research --taken collectively, we think this listing reflects a glimpse into our collective potential . Tech businesses have been shown to be poor stewards of their private information.
I study a PhD in Security within Machine Learning and this is actually an extremely dangerous thing with nearly all DNN models due to how they 'see' data and is used within many ML attacks. DNN's don't see the world as we do (Obviously) but more importantly that means images or data can appear exactly the same to us, but to a DNN be completely different.You can imagine a scenario where a DNN within a autonomous car can be easily tricked to misclassify road signs. To us, a readable STOP sign with always say STOP, even if it has scratches, and dirt on the sign, we can easily interpret what the sign should be telling us. However an attacker can use noise (Similar to the photo of another road sign) to alter the image in tiny ways to cause a DNN to think a STOP sign is actually just a speed limit sign, while to us it still looks exactly like a STOP sign. Deploy such an attack on a self driving car at a junction with a stop sign and you can imagine how the car would simply drive on rather than stopping. You'll be surprised how easy it is to trick AI, even big companies like YouTube's have issues with this within copyright music detection if you perform complex ML attacks upon the music.Here's a paper similar to the scenario I described but by placing stickers in specific places to make an AI not see stop signs; https://arxiv.org/pdf/1707.08945.pdf - _Waldy_
Summary – Undoubtedly, AI augmentation has become the core subject of the AI world. To be precise, augmented AI is here to show the world that cognitive ability is here just to support human intelligence and it is not here to replace it. Nonetheless, it is the role human intelligence possess using deep learning and machine learning algorithms to solve complex problems. Simply said, AI augmentation is here to make life much more simple as such to support, accelerate, and increase the efficiency of the tasks humans perform. Such instances include tasks like auto-transcription software and self-driving cars.
Self-driving cars can be as stubborn as a mule. Sometimes it seems as though a car is about as stubborn as a mule or perhaps acting bull-headed. Here's an example of something I witnessed first-hand the other day. A tow truck was getting ready to take a car for a tow. This was a flatbed style tow truck. You've surely seen these types of tow trucks on the roadways wherein they piggyback a car that needs to be transported. The tail end of the flatbed portion tilts at a somewhat acute angle to allow for driving a car up onto the riding platform. This forms a ramp for the car to traverse upward onto the empty and awaiting flatbed area.
Heavily funded autonomous vehicle startup Aurora Innovation Inc. has acquired Ours Technology Inc., a fellow startup developing chip-based lidar sensors based on a new approach known as frequency-modulated continuous-wave sensing. The deal was announced today. Aurora is building an autonomous driving system that can be installed on trucks and cars to let them navigate the roads without human input. The startup has raised more than $1 billion in funding from investors including Amazon.com Inc. and Sequoia Capital. Aurora entered the headlines late last year when it bought Uber Technologies Inc.'s autonomous driving unit in a deal reportedly worth $4 billion.
Though challenges and headwinds exist, we believe that the intelligent edge is poised to transform the computing landscape, propelling the world's largest technology companies toward the next generation of connectivity and operational efficiency. By bringing powerful computing capabilities closer to where data originates and needs to be consumed, the intelligent edge unlocks the potential for faster, less expensive, and more secure operations in everything from autonomous vehicles to virtual reality to the Internet of Things (IoT)--helping to accelerate the Fourth Industrial Revolution.5 The intelligent edge is the combination of advanced connectivity, compact processing power, and artificial intelligence (AI) located near devices that use and generate data.6 It represents an evolution and convergence of trends in industrial monitoring, automated manufacturing, utility management, and telecommunications, amplified by cloud computing, data analytics, and AI. The intelligent edge puts these latter capabilities physically near where data needs rapid analysis and response, enabling that data to be acted on directly or filtered to push only the most important bits to the core. In particular, the intelligent edge's ability to bring cloud capabilities to remote operations could greatly amplify their performance.