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) …
If we want humans to trust artificial intelligence (AI), then we need to teach the machines empathy, according to John Roese, CTO and president of products and operations at Dell Technologies. Roese joined two other Dell Technologies' companies CTOs on a panel at last week's Dell Technologies Summit: Dell Boomi's Michael Morton and RSA's Zulfikar Ramzan. Boomi is a data management company that lets businesses integrate and transfer data between cloud and on-premises applications. RSA is a security company whose founders pioneered public-key cryptography. The three CTOs discussed three big problems in the data era: what does infrastructure look like in an artificial intelligence (AI) driven, real-time data environment?
In a world in which "big data" and "data science" seem to adorn every technology-related news article and social media post, have the terms finally reached public interest saturation? As the use of large amounts of data has become mainstream, is the role of "data science" replacing the hype of "big data?" Looking back over the past decade and a half, English language web searches reported by Google Trends for both "social media" and "cloud computing" begin at the latter half of the last decade, with cloud computing rising in late 2007 and social media taking flight in early 2009. Yet, while the phrase "social media" has increased linearly in the decade since, "cloud computing" has followed a very different trajectory, peaking in March 2011, decreasing through the end of 2016 and leveling off in the three years since. It seems the idea of renting computing power in the "cloud" has become so mainstream we no longer even talk about it, even as social media, despite its ubiquity, still captures our search attention.
Google became what it is by creating advanced new technology and throwing it open to all. Giant businesses and individuals alike can use the company's search and email services, or tap its targeting algorithms and vast audience for ad campaigns. Yet Google's progress on artificial intelligence now appears to have the company rethinking its do-what-you-will approach. The company has begun withholding or restricting some of its AI research and services, to protect the public from misuse. Google CEO Sundar Pichai has made "AI first" a company slogan, but the company's wariness of AI's power has sometimes let its competitors lead instead.
What they did: Cape Analytics analyzed visual data on tens of millions of homes in major metro areas nationwide by working with partners like the location data company Nearmap. That enabled a fine-grain analysis of residential solar power at a neighborhood level. Why it matters: The firm intends its localized data to help policymakers better understand where solar power is being adopted and why -- and help homeowners understand if they can get state-specific incentives for going solar. What they found: Every "super solar" neighborhood in the U.S. -- those with over 500 homes and solar systems -- is in California, except for one in Saint Petersburg, Florida, which is 13.2% solar. The big picture: Cape Analytics examined the entire U.S., Farzaneh tells Axios.
The graph represents a network of 2,353 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 13 November 2019 at 19:32 UTC. The requested start date was Monday, 11 November 2019 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 5-day, 13-hour, 33-minute period from Tuesday, 05 November 2019 at 11:26 UTC to Monday, 11 November 2019 at 01:00 UTC.
Without emotional bonding, there is no flow. People don't remember what you say, they remember how you made them feel. Naturally, the words we choose influence the outcomes. "The single biggest problem in communication is the illusion that it has taken place." How often do we struggle to clearly communicate our message to someone?
At Walmart Labs, we utilize meta-learning every day -- whether it's in our robust item catalog or item recommendations. This article will walk through what meta-learning is and how it is being used to solve practical industry problems. Meta-learning is an exciting area of research that tackles the problem of learning to learn. The goal is to design models that can learn new skills or rapidly adapt to new environments with minimal training examples. Not only does this dramatically speed up and improve the design of Machine learning (ML) pipelines or neural architectures, but it also allows us to replace hand-engineered algorithms with novel approaches learned in a data-driven way (Vanschoren, 2018).