A recent study has used machine learning analysis techniques to chart the readability, usefulness, length and complexity of more than 50,000 privacy policies on popular websites in a period covering 25 years from 1996 to 2021. The research concludes that the average reader would need to devote 400 hours of'annual reading time' (more than an hour a day) in order to penetrate the growing word counts, obfuscating language and vague language use that characterize the modern privacy policies of some of the most-frequented websites. 'The average policy length has almost doubled in the last ten years, with 2159 words in March 2011 and 4191 words in March 2021, and almost quadrupled since 2000 (1146 words).' The mean word count and sentence count among the corpus studied, over a 25 year period. Though the rate of increase in length spiked when the GDPR and the California Consumer Privacy Act (CCPA) protections came into force, the paper discounts these variations as'small effect sizes' which appear to be insignificant against the broader long-term trend.

Mobile artificial intelligence is disrupting the already breakneck-paced mobile app development game. In 2020, the mobile AI sector reached a valuation of 2.14 billion dollars, and that number is expected to grow 4.5x by the year 2026. It's safe to say that mobile artificial intelligence is here to stay, so let's find out how this innovative technology is used in mobile app development. Mobile artificial intelligence aims at making mobile technology smarter and more functional for users. A well-known example of the power of mobile AI is Amazon's Alexa Shopping product, which has freed up countless hours of customer support grunt work for Amazon.

When we talk about Computer vision products, most of them have required the configuration of multiple things including the configuration of GPU and Operating System for the implementation of different problems. This sometimes causes issues for customers and even for the development team. Keeping these things in mind, Nvidia released Jetson Nano, which has its own GPU, CPU, and SDKs, that help to overcome problems like multiple framework development, and multiple configurations. Jetson Nano is good in all perspectives, except memory, because it has limited memory of 2GB/4GB, which is shared between GPU and CPU. Due to this, training of custom Computer Vision models on Jetson Nano is not possible.

Machine learning (ML) has empowered businesses to scale up to modern business demands. From training artificial Intelligence (AI) to answering customer concerns, optimizing processes to detecting and analyzing fraud, the advent of technology in business has been exquisite. While the full impact of machine learning is yet unknown, ethical issues are becoming more prevalent. ML has already experienced some unexpected catastrophic events. Therefore, debates over ML and AI ethics and risk assessments are far from over.

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In brief Miscreants can easily steal someone else's identity by tricking live facial recognition software using deepfakes, according to a new report. Sensity AI, a startup focused on tackling identity fraud, carried out a series of pretend attacks. Engineers scanned the image of someone from an ID card, and mapped their likeness onto another person's face. Sensity then tested whether they could breach live facial recognition systems by tricking them into believing the pretend attacker is a real user. So-called "liveness tests" try to authenticate identities in real-time, relying on images or video streams from cameras like face recognition used to unlock mobile phones, for example.

AiM Future, a leader in embedded machine learning intellectual property (IP) for edge computing devices, announced it has joined the Edge AI and Vision Alliance. AiM Future is accelerating the transition from centralized cloud-native AI to the distributed intelligent edge. Its market-proven NeuroMosAIc Processor (NMP) family of machine learning hardware accelerators and software, NeuroMosAIc Studio, enables the efficient execution of deep learning models common to computer vision applications. "It is our company's pleasure to join the Edge AI and Vision Alliance," said ChangSoo Kim, founder, and CEO of AiM Future. "As a premier organization for technology innovators revolutionizing artificial intelligence across the edge computing spectrum, the partnership is a natural fit. It is clear AiM Future's vision of bringing the impossible to reality is shared by the Alliance and its ecosystem. The field of edge AI is rapidly advancing and partnerships are fundamental to addressing the many challenges and limitations of today's edge devices."

GrAI Matter Labs unveils life-ready AI with GrAI VIP at GLOBAL INDUSTRIE. GrAI Matter Labs is a company in brain-inspired ultra-low latency computing that specializes in Life-Ready AI. Artificial Intelligence is the closest thing to natural intelligence. Artificial intelligence that feels alive. They make brain-inspired chips that act like people.

While global economic and social uncertainties in 2020 caused significant stress, progress in intelligent technologies continued. The digital and intelligent transformation of all industries significantly accelerated, with AI technologies showing great potential in combatting COVID-19 and helping people resume work. Understanding future technology trends may never have been as important as it is today. Baidu Research is releasing our prediction of the 10 technology trends in 2021, hoping that these clear technology signposts will guide us to embrace the new opportunities and embark on new journeys in the age of intelligence. In 2020, COVID-19 drove the integration of AI and emerging technologies like 5G, big data, and IoT.

The rising number of innovative start-up operations working within the domain of AI powered tools and services is one of the key factors driving the growth within the global artificial intelligence as a service market. The solutions offered by the players and vendors functioning within the global artificial intelligence as a service market are utilized in a number of end use industry verticals, such as healthcare and life sciences, telecommunications, manufacturing, education, transportation, media and entertainment, banking, financial services, and insurance or BFSI, retail, government and defence, energy, and agriculture, among others. Some of the key technologies used by the players in the global artificial intelligence as a service market include deep learning, natural language processing or NLP, and machine learning or ML. The rising demand from the BFSI industry vertical is positively influencing the growth in the global artificial intelligence as a service market. On the other hand, healthcare and life sciences end use industry vertical is also expected to contribute heavily in the development of the global artificial intelligence as a service market in coming years.