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APPLICATIONS OF AI IN YOUR HOUSEHOLD: TOP 10 USE OF AI AT HOME

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You might think that artificial intelligence is only something the tech giants are focused on and that it doesn't have any impact on your household or everyday life. But the reality is different. Whether you realize it or not, Artificial Intelligence is everywhere. The application of AI is not only for big sectors or finance or manufacturing, it is also impacting our daily lives. So, let's find out about the applications of AI in your daily life.


6 auto insurance predictions for 2021

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With 2021 just around the corner and all of us wondering what's in store for auto insurance, here are some of the trends that I believe are likely to take hold of the sector in the year to come. The aftereffects of COVID-19 will continue to shake up the insurance industry. Customers will not only favor flexible service providers, but "modular" services - meaning those that they can pick and choose features from based on their needs or even their current financial standing. For auto insurers, this could translate into policies based on the actual mileage driven as opposed to the number of drivers, or even discounts for drivers that participate in ridesharing programs. Personalization will become the new normal for insurance. Relatively, the insurance industry is pretty far behind the pack in terms of the scope of applications to intelligently process or integrate real-time data.


Age of Empires IV and Real-Time Strategy Games' Rocky History

WIRED

Real-time strategy is having a moment. And gaming's largest companies, including Microsoft and Tencent, are bankrolling studios behind new RTS entries like Age of Empires IV, which is set for release on October 28. This resurgence is good news for fans of real-time strategy games, but the genre must adapt to tastes of modern gamers. Fortunately, the developers behind tomorrow's blockbuster real-time strategy games are mindful of the genre's past mistakes. The seed of the real-time strategy genre was planted when Chris Crawford published a treatise on the future of real-time gaming, titled "The Future of Computer Wargaming," in the debut winter 1981 issue of Computer Gaming World.


Data Pipelines with Apache Beam

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Apache Beam is one of the latest projects from Apache, a consolidated programming model for expressing efficient data processing pipelines as highlighted on Beam's main website [1]. Throughout this article, we will provide a deeper look into this specific data processing model and explore its data pipeline structures and how to process them. Apache Beam can be expressed as a programming model for distributed data processing [1]. It has only one API to process these two types of data of Datasets and DataFrames. While you are building a Beam pipeline, you are not concerned about the kind of pipeline you are building, whether you are making a batch pipeline or a streaming pipeline. For its portable side, the name suggests it can be adjustable to all. In Beam context, it means to develop your code and run it anywhere. To use Apache Beam with Python, we initially need to install the Apache Beam Python package and then import it to the Google Colab environment as described on its webpage [2]. In this section, the architecture of the Apache Beam model, its various components, and their roles will be presented. Primarily, the Beam notions for consolidated processing, which are the core of Apache Beam. The Beam SDKs are the languages in which the user can create a pipeline. Users can choose their favorite and comfortable SDK. As the community is growing, new SDKs are getting integrated [3]. Once the pipeline is defined in any supported languages, it will be converted into a generic language standard. This conversion is done internally by a set of runner APIs. I would like to mention that this generic format is not fully language generic, but we can say a partial one. This conversion only generalizes the basic things that are the core transforms and are common to all as a map function, groupBy, and filter. For each SDK, there is a corresponding SDK worker whose task is to understand the language-specific things and resolve them.


Journal of Research on Technology in Education special issue

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With the emerging opportunities of artificial intelligence (AI), learning and teaching may be supported in situ and in real-time for more efficient and valid solutions. Hence, AI have the potential to further revolutionise the integration of human and artificial intelligence and impact human and machine collaboration during learning and teaching (Seeber et al., 2020; Wesche & Sonderegger, 2019). The discourse around utilisation of AI in education shifted from being narrowly focused on automation-based tasks to augmentation of human capabilities linked to learning and teaching (Chatti et al., 2020). As such, AI systems are capable of analysing large datasets, including unstructured data, in real-time, and detect patterns or structures that can be used for intelligent human decision-making in learning and teaching situations (Baker, 2016). This special issue will address the reciprocal issues when augmenting human intelligence with machine intelligence in K-12 and higher education.


Applications of AI in Your Household: Top 10 Use of AI at Home

#artificialintelligence

You might think that artificial intelligence is only something the tech giants are focused on and that it doesn't have any impact on your household or everyday life. But the reality is different. Whether you realize it or not, Artificial Intelligence is everywhere. The application of AI is not only for big sectors or finance or manufacturing, it is also impacting our daily lives. So, let's find out about the applications of AI in your daily life.


Recognition of faces in real time

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At the moment of writing this article, there is approximately 7,899,797,315 living person on this planet, approx. I will try to cope up with the difficult task of face recognition because despite the large numbers, recognizing a face and even recognizing that a specific face was seen before is a very difficult task for even a computer. Fortunately, we live in 2021, with beautiful algorithms already discovered, useful tools, and human-friendly programming languages already made for us to use, all this for free.


Swarm Intelligence: AI Inspired By Honeybees Can Help Us Make Better Decisions - AI Summary

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But when groups are involved, with many people grabbing the wheel at once, we often find ourselves in a fruitless stalemate headed for disaster, or worse, lurching off the road and into a ditch, seemingly just to spite ourselves. It turns out that Mother Nature has been working on this problem for hundreds of millions of years, evolving countless species that make effective decisions in large groups. A human business team trying to select the ideal location for a new factory would face a similarly complex problem and find it very difficult to choose optimally, and yet simple honeybees achieve this. They do so by forming real-time systems that efficiently combine the diverse perspectives of the hundreds of scout bees that explored the available options, enabling group deliberation that considers their differing levels of conviction until they converge on a single unified decision. It enables groups of all sizes to connect over the internet and deliberate as a unified system, pushing and pulling on decisions while swarming algorithms monitor their actions and reactions.


The Rapid Expansion of AI Surveillance: What You Need to Know

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AI surveillance is increasing at a rapid pace around the world. The East Asia/Pacific, Americas, and the Middle East/North Africa regions are robust adopters of these tools. Even liberal democracies in Europe have installed automated border controls, predictive policing, "safe cities", and facial recognition systems. China is the biggest supplier of these technologies which can be found in 63 countries. Huawei alone is responsible for providing AI surveillance technology to at least fifty countries and its leadership has strong ties with the Chinese government.


Why Do I Think There Will be Hundreds of Billions of TinyML Devices Within a Few Years?

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A few weeks ago I was lucky enough to have the chance to present at the Linley Processor Conference. I gave a talk on "What TinyML Needs from Hardware", and afterwards one of the attendees emailed to ask where some of my numbers came from. In particular, he was intrigued by my note on slide 6 that "Expectations are for tens or hundreds of billions of devices over the next few years". I thought that was a great question, since those numbers definitely don't come from any analyst reports, and they imply at least a doubling of the whole embedded system market from its current level of 40 billion devices a year. Clearly that statement deserves at least a few citations, and I'm an engineer so I try to avoid throwing around predictions without a bit of evidence behind them.