All of us are familiar with how Artificial Intelligence (AI) is changing our workplace but what we don't realize is how AI is also impacting our lives outside of the office. AI is disrupting every area of our lives, whether we're trying to read our emails, get driving directions, find good restaurants, or get music or movie recommendations. Here are some of the best examples of how AI is used in our everyday lives. Deep Learning, a subset of AI, is the key component used in self-driving and parking vehicles to recognize the space around a vehicle. There are many companies that are implementing AI in cars to give them the power to see, think, and learn, so they can navigate a nearly infinite range of possible driving scenarios.

New evidence of voice recognition's racial bias problem has emerged. Speech recognition technologies developed by Amazon, Google, Apple, Microsoft, and IBM make almost twice as many errors when transcribing African American voices as they do with white American voices, according to a new Stanford study. All five systems produced these error rates even when the speakers were of the same gender and age, and saying the exact same words. We can't know for sure if these technologies are used in virtual assistants, such as Siri and Alexa, as none of the companies disclose this information. If they are, the products will be offering a vastly inferior service to a huge chunk of their users -- which can have a major impact on their daily lives.

INFerence-as-a-Service (INFaaS) in the cloud has enabled the prevalent use of Deep Neural Networks (DNNs) in home automation, targeted advertising, machine vision, etc. The cloud receives the inference request as a raw input, containing a rich set of private information, that can be misused or leaked, possibly inadvertently. This prevalent setting can compromise the privacy of users during the inference phase. This paper sets out to provide a principled approach, dubbed Cloak, that finds optimal stochastic perturbations to obfuscate the private data before it is sent to the cloud. To this end, Cloak reduces the information content of the transmitted data while conserving the essential pieces that enable the request to be serviced accurately. The key idea is formulating the discovery of this stochasticity as an offline gradient-based optimization problem that reformulates a pre-trained DNN (with optimized known weights) as an analytical function of the stochastic perturbations. Using Laplace distribution as a parametric model for the stochastic perturbations, Cloak learns the optimal parameters using gradient descent and Monte Carlo sampling. This set of optimized Laplace distributions further guarantee that the injected stochasticity satisfies the -differential privacy criterion. Experimental evaluations with real-world datasets show that, on average, the injected stochasticity can reduce the information content in the input data by 80.07%, while incurring 7.12% accuracy loss.

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.

"We build models and machines in AI that are more complicated than we can understand" Only 13% of finance organizations are using artificial intelligence, analytics, and automation to transform multiple processes across their enterprises; when it comes to meeting business expectations to generate actionable insights from company data, not a single survey respondent states that their function can do so significantly; none of the executives say they have a structured way to generate predictive insights to meet businesses' changing and varied demands; no one is using AI, analytics, and automation to fundamentally reimagine their finance function [Genpact survey of 500 CFOs and senior finance executives] The Indiana Donor Network can immediately spot anomalies in its donor network and boost contributions. The machine learning-based system from Sisense Inc. can spot, for example, when a hospital that regularly produces organ donations doesn't deliver any in a particular week. After an anomaly is spotted, the organization can arrange a meeting the next day with the hospital to review protocols. Before, a problem in the system would have come to light 30 to 45 days after the fact. I want to emphasize, though, that we haven't solved language understanding yet in any satisfying way"--Sam Bowman "This is a new period in the world's history--we build models and machines in AI that are more complicated than we can understand"--Jason Yosinski, co-founder of Uber AI Labs

Ever wonder why virtual assistant Siri can easily tell you what the square root of 1,558 is in an instant but can't answer the question "what happens to an egg when you drop it on the ground?" Artificial intelligence (A.I.) interfaces on devices like Apple's iPhone or Amazon's Alexa often fall flat on what many people consider to be basic questions, but can be speedy and accurate in their responses to complicated math problems. That's because modern A.I. currently lacks common sense. "What people who don't work in A.I. everyday don't realize is just how primitive what we call'A.I.' is nowadays," machine-learning researcher Alan Fern of Oregon State University's College of Engineering told KOIN 6 News. "We have A.I.s that do very specialized, specific things, specific tasks, but they're not general purpose. They can't interact in general ways because they don't have the common sense that you need to do that."

IoT or Internet of Things is slowly making its way in every aspect of our lives. If you don't own an IoT device yet, you will surely own one soon. But it is highly unlikely that you even wouldn't have heard of such devices. From smart televisions, fridges, thermostats to smart coffee makers, IoT devices have infiltrated our daily lives and are slowly gaining mainstream recognition. According to recent studies as of 2019, the number of active IoT devices peaked at a significant 26.66 billion.

Recent advances in research have demonstrated the effectiveness of knowledge graphs (KG) in providing valuable external knowledge to improve recommendation systems (RS). A knowledge graph is capable of encoding high-order relations that connect two objects with one or multiple related attributes. With the help of the emerging Graph Neural Networks (GNN), it is possible to extract both object characteristics and relations from KG, which is an essential factor for successful recommendations. In this paper, we provide a comprehensive survey of the GNN-based knowledge-aware deep recommender systems. Specifically, we discuss the state-of-the-art frameworks with a focus on their core component, i.e., the graph embedding module, and how they address practical recommendation issues such as scalability, cold-start and so on. We further summarize the commonly-used benchmark datasets, evaluation metrics as well as open-source codes. Finally, we conclude the survey and propose potential research directions in this rapidly growing field.

The last decade of tech was to a large part defined by the advent of Deep Supervised Learning (DL). The availability of cheap data at scale, computational power, and researcher interest have made it the de-facto school of algorithms used for most pattern recognition problems. Face recognition on social media, product recommendations on sites, voice assistants like Google Assistant, Alexa, and Siri are some examples largely powered by DL. The issue with deep learning is that the resources that led to its rise are also giving rise to inequities. Today, it is tough for startups to beat'big tech' like Apple, Google, Amazon, and Microsoft in deep learning through better research capabilities or better data.

Even Apple's voice assistant, Siri, is being forced to adapt to an ongoing health crisis. As reported by CNBC, Siri is now capable of responding to users who ask the assistant about whether they have novel coronavirus, COVID-19. Queries like, 'Hey Siri, do I have the coronavirus?' will now elicit a questionnaire asking users if they have a fever or a cough and will recommend those that are experiencing potentially fatal symptoms to call 911. CNBC reports that if the symptoms appear to be more mild, Siri will instruct users to stay home and avoid contact with others instead. It may also redirect some users to the App Store where they can download apps that let them consult with a doctor digitally.