There was a time when we heard terms like Artificial Intelligence and Machine Learning only in sci-fi movies. But today, technological advances have brought us to a point where businesses across verticals are not only talking about, but also implementing artificial intelligence and machine learning in everyday scenarios. AI is everywhere, from gaming stations to maintaining complex information at work. Computer Engineers and Scientists are working hard to impart intelligent behavior in the machines making them think and respond to real-time situations. AI has evolved from being a research topic to being at the early stages of enterprise adoption.
To have a conversation about artificial intelligence (AI), we need a practical definition of human intelligence (HI). Let's consider that human intelligence is the ability to reason, solve problems, and learn. These activities involve a complex interaction between cognitive functions like perception, memory, language, and planning. People do these things naturally because human intelligence enables us to learn from past experiences, adapt to new situations, and handle abstract ideas. Humans can use learned knowledge to adapt to, shape, and change their environment.
A British company has developed an artificial voice that can speak with'deep human emotion' -- and even cry -- with complete realism. The digital helpers that we are used to -- like Alexa and Google Assistant -- tend to speak in close-to monotones, without real inflection to convey emotion. While this may suffice for voice assistants, such flat computer-generated voices are unsuitable for applications like producing dialogue for video games or film. However, technology developed by the ten-person team at the London-based firm Sonantic allows the creation of authentic-sounding lines of speech in minutes. A British company has developed an artificial voice that can speak with'deep human emotion' -- and even cry -- with complete realism (stock image) 'We create hyper-realistic artificial voices.
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.
Siri, Cortana, Alexa, Watson, Bixby, Viv, M, Google Assistant and the list goes on and on……. Along with all the above personal assistants in the real world, one of the most famous personal assistants of 21st century is the one and only'Jarvis' from the movie'Iron man' & 'Avengers', big ticket projects from the Marvel Cinematic Universe. The sheer concept of a personal assistant facilitated by technology is groundbreaking and similar is the stature of breakthrough technology which has been leveraged to accomplish it, which goes by the name of, Cognitive Computing. Cognitive Computing was used for the very first time by IBM. It developed Watson, a unique response-capable computing system, which was built to compete against humans on the popular game show called, Jeopardy.
Here's a look at the top technology trends that will influence us. AI is now part of everyday life, driven by the emergence of a device ecosystem including Alexa, Siri, and Google Assistant. In 2020, emotion recognition and computer vision will scale and AI will have a breakout moment in manufacturing. U.S. startups Vicarious, Kindred, and Osaro stand out in using AI technologies for manufacturing. Kindred's technology is used to automate part of distribution for apparel brands such as GAP.
Among fictional buzzwords like "telepathy," "cyberspace," "parallel universe," and so on – what's undeniably popular and real is "AI" -- artificial intelligence. The idea that a machine can exhibit the same level of intelligence and sentience as a human being has captured much interest today. This idea has increasingly become popular in the workplace: The World Economic Forum forecasts that due to technologies like machines and algorithms, "133 million new jobs [are] expected to be created by 2022 compared to 75 million that will be displaced." A report published by Tractica, showed that AI revenue could grow from $643.7 million in 2016 to $36.8 billion by 2025. These billion-dollar facts could leave a surprisingly awakening question -- is the future going to be AI-oriented, and will humans be left out of it?
We introduce dodecaDialogue: a set of 12 tasks that measures if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by utilizing knowledge resources, discuss topics and situations, and perceive and converse about images. By multi-tasking on such a broad large-scale set of data, we hope to both move towards and measure progress in producing a single unified agent that can perceive, reason and converse with humans in an open-domain setting. We show that such multi-tasking improves over a BERT pre-trained baseline, largely due to multi-tasking with very large dialogue datasets in a similar domain, and that the multi-tasking in general provides gains to both text and image-based tasks using several metrics in both the fine-tune and task transfer settings. We obtain state-of-the-art results on many of the tasks, providing a strong baseline for this challenge.
Many mobile applications and virtual conversational agents now aim to recognize and adapt to emotions. To enable this, data are transmitted from users' devices and stored on central servers. Y et, these data contain sensitive information that could be used by mobile applications without user's consent or, maliciously, by an eavesdropping adversary. In this work, we show how multimodal representations trained for a primary task, here emotion recognition, can unintentionally leak demographic information, which could override a selected opt-out option by the user. We analyze how this leakage differs in representations obtained from textual, acoustic, and multimodal data. We use an adversarial learning paradigm to unlearn the private information present in a representation and investigate the effect of varying the strength of the adversarial component on the primary task and on the privacy metric, defined here as the inability of an attacker to predict specific demographic information. We evaluate this paradigm on multiple datasets and show that we can improve the privacy metric while not significantly impacting the performance on the primary task. To the best of our knowledge, this is the first work to analyze how the privacy metric differs across modalities and how multiple privacy concerns can be tackled while still maintaining performance on emotion recognition.