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) …
The market for AI (artificial intelligence) technologies is going to expand tremendously in the next decade. Grand View Research says the global AI market will reach $733.7 billion by 2027, growing at a CAGR (compound annual growth rate) of 42.2%. One of the many sectors that will increasingly look to leverage AI technologies between now and 2027 (and beyond) is first response. In fact, in some cases, the first-response industry is already engaged in piloting AI technologies for use on the front lines. What AI-related innovations are to come, and how will they make first responders' jobs easier?
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Is social media ruining the world? The common denominator of all these phenomena is that they're fueled in part by our seemingly innocuous participation in digital social networking. But how can simple acts like sharing photos and articles, reading the news, and connecting with friends have such destructive consequences? These are the questions explored in the new Netflix docu-drama The Social Dilemma. Directed by Jeff Orlowski, it features several former Big Tech employees speaking out against the products they once upon a time helped build.
The economic recession that follows as a consequence of the Covid-19 crisis and in particular the demise of certain sectors of the economy (physical retail, hospitality sector, etc) means that there will be greater pressure on politicians around the world to consider how to stimulate GPD growth in the post-pandemic world. However, there are also increasing pressures on politicians to combat the threat posed by Climate Change. Are the desired objectives of GDP and employment growth as well as reducing pollution at odds with each other? What if there is a pathway to GDP growth with the creation of new jobs and yet at the same time we are able to reduce emissions of Green House Gasses (GHGs)? A report entitled "How AI can enable a sustainable future" by PWC and commissioned by Microsoft (lead authors Celine Herweijer of PWC and Lucas Joppa of Microsoft) estimates that using AI for environmental applications across four sectors – agriculture, water, energy and transport. The report estimated that such applications could contribute up to $5.2 trillion USD to the global economy in 2030, a 4.4% increase relative to business as usual.
A new artificial intelligence tool could be the key to saving lives and reducing conflict between people and the decreasing populations of endangered Asian and African elephants. African elephant populations have dropped from 12 million to 400,000 in the past century, according to the World Wide Fund For Nature (WWF). There are also now fewer than 50,000 Asian elephants left in existence, according to WWF estimates. The gentle mammals face an onslaught of threats, from the illegal ivory trade to deforestation, which has forced the elephants to expand into human-inhabited areas and has increased conflict with people. Farmers worldwide -- including in India, Thailand, and Africa -- have frequently reported negative interactions with elephants grazing on crops or entering villages.
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.
Every word can be represented into N-Dimension Space after applying Machine Learning Algorithms on documents. The most famous algorithms are the Word2Vec built by Google and the GloVe built by Stanford University. We will work with the GloVe pre-trained model. The idea is to represent into a50-D space every Movie Plot Summary and based on this vector to find similar movies. Finally, we will do dimensionality reduction by applying the T-SNE algorithm and to represent the plot summaries into 2-D space.
AI and digital platforms challenge how we understand reality and our role in it. Because it mirrors our identity, technology provokes us to revisit outdated creeds while at the same time giving us the reins to personalize our human experience. Is AI going to take over the world? Will AI take my job? Internet users are particularly interested in how the AI-human symbiosis will shape over time.
New research finds that causal machine learning models are not only more accurate than previous AI-based symptom checkers for patient diagnosis but, in many cases, can now exceed the diagnosis accuracy of human doctors. That's mainly due to the methods used, which allow for a more "outside the box" creativity in diagnosis, and even more improved accuracy for more complex patient illness. In the peer-reviewed study, authored by researchers from Babylon Health and University College London, the new model scored higher than 72% of general practitioner doctors when tasked with diagnosing written test cases of realistic illnesses. Up until now, and despite significant research efforts, the report claims, diagnostic algorithms have struggled to achieve the diagnosis accuracy of doctors. That's because machine learning algorithms have attempted to follow the same process as doctors in symptom checking.
As machine learning has grown, one of the major bottlenecks remains labeling things so the machine learning application understands the data it's working with. Datasaur, a member of the Y Combinator Winter 2020 batch, announced a $3.9 million investment today to help solve that problem with a platform designed for machine learning labeling teams. The funding announcement, which includes a pre-seed amount of $1.1 million from last year and $2.8 million seed right after it graduated from Y Combinator in March, included investments from Initialized Capital, Y Combinator and OpenAI CTO Greg Brockman. Company founder Ivan Lee says that he has been working in various capacities involving AI for seven years. First when his mobile gaming startup, Loki Studios was acquired by Yahoo! in 2013, and Lee was eventually moved to the AI team, and most recently at Apple.