If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
To answer questions on complex policies, handle claims, premium reminders, payments and to make sales, service and support much more effective in insurance? Indeed, there are chatbots that we are developing to do this and a lot more. Currently, a wide variety of chatbots exists across industries, functions and domains. There are a lot of generic bots that have a great design but their bot design is still evolving. However, domain specific bots or good industry bots are few and far between. The leverage of NLP technologies provides for new interaction channels that are not possible in traditional web or mobile apps.
Artificial intelligence (AI) and machine learning technologies are becoming increasingly incorporated into consumer products and enterprise solutions alike. As AI applications quickly advance into large-scale and more diverse use cases, it's becoming imperative that ethics guide its development, deployment and applications. This is especially important as we increasingly apply AI to use cases that impact individual lives and livelihoods -- including healthcare, criminal justice, public welfare and education. It's clear that to continue the widespread adoption of AI on both a consumer and enterprise level -- and subsequently spur continued innovation in the technology -- AI technologies and applications need to be trustworthy and transparent. Survey after survey have revealed substantial consumer mistrust of AI technologies.
How does Amazon help Alexa understand what people mean and not just what they say? And, we couldn't be talking about Alexa, smart home tech, and AI at a better time. During this week's Amazon Devices event, the company made a host of smart home announcements, including a new batch of Echo smart speakers, which will include Amazon's new custom AZ1 Neural Edge processor. In August this year, I had a chance to speak with Evan Welbourne, senior manager of applied science for Alexa Smart Home at Amazon, about everything from how the company is using AI and ML to improve Alexa's understanding of what people say, Amazon's approach to data privacy, the unique ways people are interacting with Alexa around COVID-19, and where he sees the future of voice and smart tech going in the future. The following is an transcript of our conversation edited for readability. Bill Detwiler: So before we talk about maybe IoT, we talk about Alexa, and kind of what's happening with the COVID pandemic, as people are working more from home, and as they may have questions that they're asking about Alexa, about the pandemic, let's talk about kind of just your role there at Amazon, and what you're doing with Alexa, especially with AI and ML. So I lead machine learning for Alexa Smart Home. And what that sort of means generally is that we try to find ways to use machine learning to make Smart Home more useful and easier to use for everybody that uses smart home. It's always a challenge because we've got the early adopters who are tech savvy, they've been using smart home for years, and that's kind of one customer segment. But we've also got the people who are brand new to smart home these days, people who have no background in smart home, they're just unboxing their first light, they may not be that tech savvy.
Suppose you're trying to engage in a conversation with a founder or CEO, you'll probably hear them speaking about artificial intelligence (AI) and machine learning (ML). And they'll probably tell you how these innovative technologies can transform their business. Machine learning (ML) has real-life applications, so typically that we often tend to overlook it! From switching on the phone by facial recognition to more complicated recommender algorithms that influence your decision to watch or shop next, machine learning is making quite a noise for now. ML is described as making machines learn to imitate human actions through complex coding started in Python, R, C, C#, Java, etc.
The insurance industry is seeing a welcome disruption via artificial intelligence (AI), but only a few companies might benefit from this breakthrough. Most organizations lack cognitive technologies to process insight, and this makes the data almost useless. But insurtech companies can connect the potential of the AI data streams available. In this complete introduction to artificial intelligence, you'll be learning: And although artificial intelligence is massively popular, other complex tech topics like big data and deep learning can often cause confusion. So if you want to leverage AI and get the best out of this breakthrough, this article is for you.
While I was building prototypes with different chatbot platforms and environments, I saw clear patterns starting to emerge. One could see how most platforms have a very similar approach to Conversational AI. Even though one might lead the other in certain elements, each were trying to solve common problems in a very similar fashion. In this pursuit of solving Conversational AI problems, Rasa stands alone in many areas with their unique approach. Here are eight things they do differently, and do exceptionally well.
Transformers have now become the defacto standard for NLP tasks. Originally developed for sequence transduction processes such as speech recognition, translation, and text to speech, transformers work by using convolutional neural networks together with attention models, making them much more efficient than previous architectures. And although transformers were developed for NLP, they've also been implemented in the fields of computer vision and music generation. However, for all their wide and varied uses, transformers are still very difficult to understand, which is why I wrote a detailed post describing how they work on a basic level. It covers the encoder and decoder architecture, and the whole dataflow through the different pieces of the neural network.
Roku has long been adept at staying above the fray in the platform wars. For example, it was the only third-party video device to support Prime Video as Amazon rolled out its own Fire TV. With its announcement today that it now supports Apple HomeKit (and thus Siri), in addition to Amazon Alexa and Google Assistant, its players will now be able to be controlled by all of the major voice agents. But while it has steered clear of alienating the industry's giants, it has nonetheless been mounting an offensive, one that is now clear in its pursuit of capturing the home theater. It all began innocently enough with the launch of the Roku Wireless Speakers.
For the past couple of decades, there has been a loneliness pandemic, marked by rising rates of suicides and opioid use, lost productivity, increased health care costs and rising mortality. The COVID-19 pandemic, with its associated social distancing and lockdowns, have only made things worse, say experts. Accurately assessing the breadth and depth of societal loneliness is daunting, limited by available tools, such as self-reports. In a new proof-of-concept paper, published online September 24, 2020 in the American Journal of Geriatric Psychiatry, a team led by researchers at University of California San Diego School of Medicine used artificial intelligence technologies to analyze natural language patterns (NLP) to discern degrees of loneliness in older adults. "Most studies use either a direct question of ' how often do you feel lonely,' which can lead to biased responses due to stigma associated with loneliness or the UCLA Loneliness Scale which does not explicitly use the word'lonely,'" said senior author Ellen Lee, MD, assistant professor of psychiatry at UC San Diego School of Medicine.
The Fourth Industrial Revolution just took a huge step forward, thanks to a breakthrough artificial intelligence (AI) model that can learn virtually anything about the world -- and produce the content to tell us about it. The AI program is GPT-3 by OpenAI, which started out as a language model to predict the next word in a sentence and has vastly exceeded that capability. Now, drawing from voluminous data -- essentially all of Wikipedia, links from Reddit, and other Internet content -- GPT-3 has shown it can also compose text that is virtually indistinguishable from human-generated content. Asger Alstrup Palm, Area9's chief technology officer, explained that GPT-3 was tasked with testing the "scaling hypothesis" -- to see if a bigger model with ever-increasing amounts of information would lead to better performance. Although it's too early to call the scaling hypothesis proven, there are some strong indications that this is, indeed, the case. Further validating the potential of GPT-3, Microsoft recently announced it will exclusively license the model from OpenAI, with the intention of developing and delivering AI solutions for customers and creating new solutions using natural language generation.