While smart cities and smart homes have become mainstream buzzwords, few people outside the IT and machine learning communities know about TensorFlow, PyTorch, or Theano. These are the open-source machine learning (ML) frameworks on which smart systems are built to integrate Internet of Things (IoT) devices among other things. ML algorithms and code are often found in publically available repositories, or data stores, that draw heavily on the aforementioned frameworks. In a December 2019 analysis of code hosting site GitHub, SMU Professor of Information Systems David Lo found over 46,000 repositories that were dependent on TensorFlow, and over 15,000 used PyTorch. Because of these frameworks' popularity, any vulnerability in them can be exposed to cause widespread damage.
The Netatmo Smart Video Doorbell is the latest from one of Europe's biggest smart home security product makers. It was first shown at CES 2019, so it's been a long time coming. The product is now available in the U.S. for $300. That makes it much more expensive up front than many of its competitors, but it could easily work out cheaper over time because you don't need to pay any subscription charges for cloud-based video storage or other services. Netatmo's device is larger than some competitors, but it has a smart design with the facia split evenly into three sections: the top is the camera, the center is the speaker, and the lower third is the doorbell button.
It is true that IoT or Internet of Things revolution is going on, and AI or Artificial Intelligence can play a vital role in it. The goal of applying AI to IoT systems is effectively placing an additional layer of intelligence across the entire IoT stack -- from infrastructure all the way to applications. The Internet of Things (IoT) is a term that has been introduced in recent years to define objects that are able to connect and transfer data via the Internet. 'Thing' refers to a device that is connected to the internet and transfers the device information to other devices. The cloud-based IoT is used to connect a wide range of things such as vehicles, mobile devices, sensors, industrial equipment and manufacturing machines to develop various smart systems it includes smart city and smart home, smart grid, smart industry, intelligent vehicle, smart health, and smart environmental monitoring.
This branch of artificial intelligence curates your social media and serves your Google search results. Soon, deep learning could also check your vitals or set your thermostat. MIT researchers have developed a system that could bring deep learning neural networks to new--and much smaller--places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the "internet of things" (IoT). The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving data security.
Our new normal has created an even greater need for simplification and very crisp outcomes. Company executives and technology leaders can use artificial intelligence (AI) and internet of things (IoT) technology to enable desired outcomes -- not just for experimental efforts -- and can use visualization to prioritize and communicate the value of security investments. Here are a few examples of how organizations can do that. Understand that cybersecurity complexity is a widespread problem. In the world of security, there is no dearth of point tools.
In the last few years, the smart home has been built up as a kind of domestic nirvana. Consumers simply need to purchase multiple smart devices, such as televisions or appliances, and, presto!--they all work together to create a personalized and ultra-connected home. Recent research shows four major barriers impacting the adoption of smart homes. Let's take a closer look at each. The cost of a home automation system typically ranges from $404 to $1,830, with a national average of $1,045, according to HomeAdvisor.
Many times AI has been put on a pedestal as the future of x y & z, however, many seem to agree that education is a sector in particular which will see stark changes in both admin, teaching styles, personalisation and more. I had the pleasure of speaking to three individuals working in the field, including, Vinod Bakthavachalam, Senior Data Scientist at Coursera, Kian Katanforoosh, Lecturer at Stanford University & Sergey Karayev, Co-Founder and CTO of Gradescope. We began by having Sergey of Gradescope walk us through his product, which has been recently acquired by turnitin. The concept, it seemed was formed from the simple and widespread issue of both lack of consistency, lack of insight through time constraint and delayed feedback on academic work. Sergey found that scanning the papers onto an online interface when paired with a rubric can allow for accurate marking in seconds across several papers.
If you look around yourself, you will find at least one object which is connected to Internet. It might be a smartphone, television, air conditioner, or even door bells. Collection of these things can be called as IoT or Internet of Things. Its ability to collect, share and receive data, via Internet, is transforming everyday objects into smart devices. However, analyzing massive incoming data from countless IoT devices can make the process much complex.
The manufacturing industry is undergoing a new age of evolution, with major changes occurring on multiple fronts. Companies keen on digital transformation are taking inspiration from the Internet of Things (IoT) to power their factories of the future. As a growing subcategory of IoT, the industrial Internet of Things (IIoT) leverages smart sensors and actuators to connect humans and machines with the Internet, boosting manufacturing and industrial processes in terms of efficiency, productivity, and safety. Along with cyber-physical systems (CPS), cloud computing and cognitive computing, the IIoT is key to building the Industry 4.0 era. The market opportunities for IIoT are massive.
Consumers are the most positive and excited for AI technologies that benefit their lives outside of work, research from O'Reilly shows. The survey, which delves into the opinions of consumers and compares them to that of AI-creators – those working to develop AI driven solutions including CTOs, data scientists, software engineers, solutions architects and IT Directors – reveals a wider indifference to the potential of AI in a work setting. The results suggest that while AI may be inserting itself into our lives in more ways than we recognise, to encourage adoption, developers should focus their efforts on leveraging AI to make consumers lives easier, augmenting existing experiences to make them more seamless. Adoption and acceptance outside the office, will ultimately lead to the same in a work setting, alleviating fears of job loss and instead focusing on job enhancement. Rachel Roumeliotis, Vice President of Data and AI at O'Reilly said: "Consumer conceptions of AI are still very much influenced by popular culture, science fiction and the virtual assistants they use every day. However, there are strong areas of overlap between AI developers and AI users. Both groups appreciate the success of smart home technology and are watching the development of autonomous vehicles very closely. It's up to these sectors to capitalise on the hype, but the results are also a call for the creators of work-focused AI to make solutions that capture the imagination and generate excitement."