Artificial Intelligence technology brings a lot of benefits to various fields, including education. Many researchers claim that Artificial Intelligence and Machine Learning can increase the level of education. The latest innovations allow developers to teach a computer to do complicated tasks. It leads to the opportunity to improve the learning processes. However, it's impossible to replace the tutor or professor. AI provides many benefits for students and teachers.
Artificial Intelligence has been a hot buzz word for the past few years now. From Netflix recommendations to our favorite assistants such as Siri or Cortana, artificial intelligence (AI for short) has found it way everywhere in our life. Many people think they know artificial intelligence when really they have some weird dystopian understanding of it. In reality, artificial intelligence is a broad umbrella term for many other thing. Siri, Cortana, Alexa, Google Home, and any other voice activated device uses AI.
Billionaire investor Mark Cuban is bullish on the future of artificial intelligence and has been for years. Not only has he made it a priority to learn about and invest in AI himself, but he has consistently recommended other entrepreneurs do the same. And if the ABC's "Shark Tank" star had to start a side hustle business today, that's where Cuban would turn. "I would become an expert in scripting for Alexa and Google Home and Cortana and go to any place that sold devices they supported and show them how much more they could do with a few hours of personalization," he tells CNBC Make It. By "scripting," Cuban is referring to the process of coding voice commands to create so-called "skills," which enable devices – like Amazon's Echo or Echo Dot, which use artificial intelligence-enabled voice assistant Alexa, Google Home or Microsoft's Cortana – to complete a task.
Digital assistant such as Alexa, Siri, Cortana or the Google Assistant are some of the best examples of mainstream adoption of artificial intelligence(AI) technologies. These assistants are getting more prevalent and tackling new domain-specific tasks which makes the maintenance of their underlying AI particularly challenging. The traditional approach to build digital assistant has been based on natural language understanding(NLU) and automatic speech recognition(ASR) methods which relied on annotated datasets. Recently, the Amazon Alexa team published a paper proposing a self-learning method to allow Alexa correct mistakes while interacting with users. The rapid evolution of language and speech AI methods have made the promise of digital assistants a reality.
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.
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."
Automatic Speech Recognition (ASR) has increased in popularity in recent years. The evolution of processor and storage technologies has enabled more advanced ASR mechanisms, fueling the development of virtual assistants such as Amazon Alexa, Apple Siri, Microsoft Cortana, and Google Home. The interest in such assistants, in turn, has amplified the novel developments in ASR research. However, despite this popularity, there has not been a detailed training efficiency analysis of modern ASR systems. This mainly stems from: the proprietary nature of many modern applications that depend on ASR, like the ones listed above; the relatively expensive co-processor hardware that is used to accelerate ASR by big vendors to enable such applications; and the absence of well-established benchmarks. The goal of this paper is to address the latter two of these challenges. The paper first describes an ASR model, based on a deep neural network inspired by recent work in this domain, and our experiences building it. Then we evaluate this model on three CPU-GPU co-processor platforms that represent different budget categories. Our results demonstrate that utilizing hardware acceleration yields good results even without high-end equipment. While the most expensive platform (10X price of the least expensive one) converges to the initial accuracy target 10-30% and 60-70% faster than the other two, the differences among the platforms almost disappear at slightly higher accuracy targets. In addition, our results further highlight both the difficulty of evaluating ASR systems due to the complex, long, and resource intensive nature of the model training in this domain, and the importance of establishing benchmarks for ASR.
To many, Machine Learning may be a new word, but it was first coined by Arthur Samuel in 1952, and since then, the constant evolution of Machine Learning has made it the go-to technology for many sectors. Right from robotic process automation to technical expertise, Machine Learning technology is extensively used to make predictions and get valuable insight into business operations. It's considered as the subset of Artificial Intelligence (intelligence demonstrated by machines). If we go by the books, Machine Learning can be defined as a scientific study of statistical models and complex algorithms that primarily rely on patterns and inference. The technology works independently of any explicit instruction, and that's its strength.
Artificial Intelligence (AI) seems to be a unique technology of making a machine, a robot fully autonomous. AI is an analysis of how the machine is thinking, studying, determining and functioning when it is trying to solve problems. These so-called problems are present in all fields - the most emerging ones in 2020 and even beyond. The aim of Artificial Intelligence (AI) is to enhance machine functions relating to human knowledge, such as reasoning, learning and problems along with the ability to manipulate things. For example, virtual assistants or chatbots offer expert advice.