Mapping TED’s Ideas Worth Spreading

TEDTalks duo ecologist Eric Berlow and physicist Sean Gourley questioned how to connect TEDx ideas – some 24,000 video clips in a content library in the cloud. Identifying patterns, they developed an algorithm, or mathematical structure to correlate relationships of this data.

Things to know when watching:

Natural language processing (NLP) – for speech-to-text translation to extract key concepts of an idea

Meme-ome – mathematics that underlines an idea, the results from NLP

Interestingly, tags do not generate ideas, but rather a network structure of network IDs. These architectural complexities are examples of creative synthesis, central topic points or bridging of ideas.

We constantly try to make connections in our lives from those with people and events that shape who we are. In this blog I try to make connections and with the beauty of tagging, you can see those keywords at the end of each post.

The brain behind Google Engineering – Ray Kurzweil

Ray Kurzweil, futuristic thinker, TEDTalks, SXSW speaker, and founder of Kurzweil Education Systems. He is the man who thinks he will live forever and inventor of the Kurzweil Reading Machine, developed with optical character recognition (OCR) technology and text-to-speech software for sighted and blind people.

In 2012, Larry Page hired him as the director of engineering at Google. Following my last post, Kurzweil is working on projects with machine learning (AI) and natural language processing.  By 2029, Kurzweil hopes artificial intelligence will be able to recognize human emotion and advanced syntactic parsing.

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For clarification, [Kurzweil]:

My mission at Google is to develop natural language understanding with a team and in collaboration with other researchers at Google. Search has moved beyond just finding keywords, but it still doesn’t read all these billions of web pages and book pages for semantic content. If you write a blog post, you’ve got something to say, you’re not just creating words and synonyms. We’d like the computers to actually pick up on that semantic meaning. If that happens, and I believe that it’s feasible, people could ask more complex questions.

Kurzweil is using Google as a catalyst to propel artificial intelligence into the future. With great anticipation, I can’t wait to see Google’s transcendence into machine learning research.

Applying IBM’s Watson to Big Data Analytics in Education

‘A smarter planet is built on smarter analytics’

Anyone who has watched Jeopardy! for the last twenty odd years knows Watson as the computer genius who won against Ken Jennings on the show. Despite super intelligence, Watson’s cognitive software did not answer every question correctly. In fact during the second round contestants (human and AI) were stumped with this little known fact…Category? US Cities please. Its largest airport was named for a World War II hero; its second largest, for a World War II battle.

Watson, the artificial intelligence computer designed by IBM was programmed to get most rapidly-fired questions correct, with the exception of a few trick contextual answers. Ironically, one of the system’s biggest flaws was deciphering pesty language idiosyncrasies. For example, English anomalies; idioms, conundrums, euphemisms, semantic woes and punctuation malfunctions. To resolve the slight hiccup, IBM designers focused on natural language processing to pick up on inconsistencies and ambiguities. (See Major tasks in NLP)

So with only ten seconds to answer the gameshow’s million-dollar question, how does a computer examine over 4TB or 200 million wiki sources? Did Watson use algorithms to sift through the data? This all led me to wonder, what role does big data and analytics play in the world today?

Putting Watson to Work chiefly explains research expansion in areas of finance, healthcare, mobile communication devices and engagement, or better customer relationships. It has huge potential in cross-industry disciplines. Imagine what Watson can do to drive better data analytics in education if we apply some of these principles:

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Transform education through predictive analytics
Learn from software, improving outcomes for individual students
Align vision with data
Anticipate trends that shape the future
Act on decisions that optimize results

The Jeopardy answer was Chicago. I’m sorry Watson, but Toronto is not the US city we were looking for. If you only paid attention to context clues and punctuation: semicolons and syntax; the text directly following it.