Monthly Archives: February 2013

T - tech, W - now

Ayasdi

 

Their new product is called the Iris Insight Discovery platform. It’s a type of machine learning that uses hundreds of algorithms and topological data analysis to mine huge datasets before presenting the results in a visually accessible way. Using algebraic topology, the system automatically hunts down data points close in nature and maps these out to reveal a network of patterns for a researcher to decipher — any closely related nodes of information will be connected and clustered together, like how a social network arranges its data according to relationship connections.

 

 

Ref: Data-Visualization Firm’s New Software Autonomously Finds Abstract Connections – Wired
Ref: Ayasdi

T - work, W - now

Google Can Identify Which of its 20,000 Employees are Most Likely to Quit

The Internet search giant recently began crunching data from employee reviews and promotion and pay histories in a mathematical formula Google says can identify which of its 20,000 employees are most likely to quit.

The move is one of a series Google has made to prevent its most promising engineers, designers and sales executives from leaving at a time when its once-powerful draws — a start-up atmosphere and soaring stock price — have been diluted by its growing size. The data crunching supplements more traditional measures like employee training and leadership meetings to evaluate talent.

Google’s algorithm helps the company “get inside people’s heads even before they know they might leave,” said Laszlo Bock, who runs human resources for the company.

 

Ref: Google Searches for Staffing Answers – The Wall Street Journal

T - tech, W - now

MindMeld – Anticipatory Computing

 

We call this platform our ‘Anticipatory Computing Engine’, and it has three unique capabilities designed to facilitate conversational interactions:

  1. Real-Time, Multi-Party Conversation Analysis: Our platform is designed to analyze and understand multiple concurrent streams of conversational dialogue in real-time. It continuously analyses audio signals and attempts to understand their underlying meaning. Based on this understanding, it not only attempts to identify key concepts and topics related to your conversation, but it also uses language structure and analysis to infer what types of information you may find most useful.
  2. Continuous, Predictive Modeling: Our platform observes conversations over time and generates a model to represent the meaning of each conversation. This model changes from second-to-second as the conversation evolves. This model is then extrapolated to predict the topics, concepts and related information that may be relevant in the future. In essence, this platform analyzes and understands the past ten minutes of a conversation in order to predict what may be relevant in the next ten seconds.
  3. Proactive Information Discovery: Our platform does not wait for a user to explicitly ask for information. Instead, it uses its underlying predictive model to identify information that is most likely to be relevant at every point in time. It then proactively finds and retrieves this information – from across the web or from a user’s social graph – and delivers this information to the user, in some cases before they even request it.

 

Ref: ExpectLab
Ref: Smart Assistant Listens to You Talk, Fetches Info Automatically – MIT Technology Review

T - work, W - now

Business Turns to Ants and Algorithms in Search for Profit

Foraging ants are just one example. When finding food, they lay down pheromones to mark the route to and from their nest. If something disrupts the route, the next-best alternative is quickly found. It is, therefore, what is known as a “self-healing” route.

George Danner, director of UK analytics firm Torus Business Web, says an algorithm based on ant foraging is perfect for helping companies find the optimum route for getting their products from A to B.

He has worked with a US energy major to do just this, helping it ship oil across the Gulf of Mexico more efficiently

[…]

By using this approach, Mr Danner devised a process designed to help the UK criminal justice system process cases faster, by grouping together similar kinds of offences. To a mathematician, the judiciary is just like any other factory, with complex moving parts that interact on many different levels.

[…]

“As we sit here in 2012, we are almost unlimited in what we can do. The only limitations to solving the world’s problems are ignorance and apathy.”

 

Ref: Business Turns to Ants and Algorithms in Search for Profit – BBC News

M - media refs

Short-Movie Written by a Robot

 

Cleverbot.com has been touted as one of the most advanced artificial intelligences ever. The website allows users to chat with the A.I. Cleverbot. But how good is it, really? I sat down with Cleverbot and collaborated on a movie script.

What follows is a movie written by a machine.

I tried to talk to Cleverbot just like I would with a human writing partner. I set up scenarios and Cleverbot provided all of the dialog content for the scene.

Directed by Chris R Wilson. Starring Austin Hillebrecht, Britt Harris and Mad Martian. Edited by Zach Persson. Key grip, Tim Cogley.

Here is the transcript of my conversation with Cleverbot if you’d like to see how the script was written:

User: Let’s write a movie together.

Cleverbot: What would you like to write the poem about?

User: Whatever. Okay, so what do you want the title of the movie to be.

Cleverbot: Do you love me?

User: Great! I’ll start: It’s sunset in a beautiful forest. We see the hero of the film. What do you want the heroes name to be?

Cleverbot: Not my name, my stomach.

[more on Youtube…]

 

Ref: CleverBot

T - politic, W - now

Using Twitter’s Sentiment Analysis for Predicting Stock Market

There are two central drivers of stock price demand—fundamentals (sales, revenues, profits, etc.) and how investors feel about fundamentals (sentiment). Sentiment tends to erratically drive short-term pricing, while the longer cycles move on fundamentals. If you talk to a buy-and-hold investor, like Warren Buffett, he will tell you short-term investing (day trading, for example) is a fool’s game—there is no predicting sentiment.

But Derwent Capital Management (DCM) thinks that may have been true, once, in ancient times before information technology enabled social networks. But now there is a wealth of hard data on real time sentiment. All one must do is set up an algorithm to mine it, process it, put it on a scale—in this case from 0 to 100—and sell it to retail investors.

And that’s exactly what they’ve done.

Wondering what your favorite stock (or currency pair or commodity) is about to do? You need merely check the the DCM trading platform’s Twitter indicator.

 

Ref: Can Twitter Tell You When to Buy and When to Sell? – The SingularityHub