The sharing economy needs machine intelligence to set prices
How much should you charge someone to live in your house? Or how much would you pay to live in someone else’s house? Would you pay more or less for a planned vacation or for a spur-of-the-moment getaway?
Answering these questions isn’t easy. And the struggle to do so, my colleagues and I discovered, was preventing potential rentals from getting listed on our site -- Airbnb, the company that matches available rooms, apartments, and houses with people who want to book them.
In focus groups, we watched people go through the process of listing their properties on our site—and get stumped when they came to the price field. Many would take a look at what their neighbors were charging and pick a comparable price; this involved opening a lot of tabs in their browsers and figuring out which listings were similar to theirs. Some people had a goal in mind before they signed up, maybe to make a little extra money to help pay the mortgage or defray the costs of a vacation. So they set a price that would help them meet that goal without considering the real market value of their listing. And some people, unfortunately, just gave up.
Clearly, Airbnb needed to offer people a better way—an automated source of pricing information to help hosts come to a decision. That’s why we started building pricing tools in 2012 and have been working to make them better ever since. This June, we released our latest improvements. We started doing dynamic pricing—that is, offering new price tips daily based on changing market conditions. We tweaked our general pricing algorithms to consider some unusual, even surprising characteristics of listings. And we’ve added what we think is a unique approach to machine learning that lets our system not only learn from its own experience but also take advantage of a little human intuition when necessary.
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Showing posts with label algorithms. Show all posts
Showing posts with label algorithms. Show all posts
Wednesday, 17 February 2016
Sunday, 7 February 2016
Google AI algorithm masters ancient game of Go
Deep-learning software defeats human professional for first time.
A computer has beaten a human professional for the first time at Go — an ancient board game that has long been viewed as one of the greatest challenges for artificial intelligence (AI).
The best human players of chess, draughts and backgammon have all been outplayed by computers. But a hefty handicap was needed for computers to win at Go. Now Google’s London-based AI company, DeepMind, claims that its machine has mastered the game.
DeepMind’s program AlphaGo beat Fan Hui, the European Go champion, five times out of five in tournament conditions, the firm reveals in research published in Nature on 27 January1. It also defeated its silicon-based rivals, winning 99.8% of games against the current best programs. The program has yet to play the Go equivalent of a world champion, but a match against South Korean professional Lee Sedol, considered by many to be the world’s strongest player, is scheduled for March. “We’re pretty confident,” says DeepMind co-founder Demis Hassabis.
“This is a really big result, it’s huge,” says Rémi Coulom, a programmer in Lille, France, who designed a commercial Go program called Crazy Stone. He had thought computer mastery of the game was a decade away.
The IBM chess computer Deep Blue, which famously beat grandmaster Garry Kasparov in 1997, was explicitly programmed to win at the game. But AlphaGo was not preprogrammed to play Go: rather, it learned using a general-purpose algorithm that allowed it to interpret the game’s patterns, in a similar way to how a DeepMind program learned to play 49 different arcade games.
Read the news article
A computer has beaten a human professional for the first time at Go — an ancient board game that has long been viewed as one of the greatest challenges for artificial intelligence (AI).
The best human players of chess, draughts and backgammon have all been outplayed by computers. But a hefty handicap was needed for computers to win at Go. Now Google’s London-based AI company, DeepMind, claims that its machine has mastered the game.
DeepMind’s program AlphaGo beat Fan Hui, the European Go champion, five times out of five in tournament conditions, the firm reveals in research published in Nature on 27 January1. It also defeated its silicon-based rivals, winning 99.8% of games against the current best programs. The program has yet to play the Go equivalent of a world champion, but a match against South Korean professional Lee Sedol, considered by many to be the world’s strongest player, is scheduled for March. “We’re pretty confident,” says DeepMind co-founder Demis Hassabis.
“This is a really big result, it’s huge,” says Rémi Coulom, a programmer in Lille, France, who designed a commercial Go program called Crazy Stone. He had thought computer mastery of the game was a decade away.
The IBM chess computer Deep Blue, which famously beat grandmaster Garry Kasparov in 1997, was explicitly programmed to win at the game. But AlphaGo was not preprogrammed to play Go: rather, it learned using a general-purpose algorithm that allowed it to interpret the game’s patterns, in a similar way to how a DeepMind program learned to play 49 different arcade games.
Read the news article
Friday, 22 January 2016
The arrival of Algorithmic business
What happens when 30 billion things connect with business and 3 billion people with smartphones? All these things and people generate vast amounts of rich data, and what companies do with that data – how they turn it into proprietary algorithms – will determine how well they maximize the opportunity presented by digital business.
“Algorithms are where the real value lies,” said Peter Sondergaard, senior vice president at Gartner and global head of Research, in the opening keynote to more than 8,500 CIOs and IT leaders at the sold out Gartner Symposium/ITxpo in Orlando. “Algorithms define action.” Digital revenues have risen, IT organizations have gone bimodal, and the increased density of connections promises smart agents and algorithms that can do very complex things, including spawning their own, new algorithms and agents.
In today’s digital era, dynamic, digital algorithms are at the core of new customer interactions. Back in the day, Coco-Cola created a secret recipe; essentially, an algorithm; for a fountain drink that would build an empire.
Today, Amazon’s recommendation engine prompts people to buy more products, or the Waze algorithms give cars better routes based on thousands of independent inputs, changing traffic patterns dynamically in real time. Moving forward, companies will be valued not just on their big data, but on the algorithms that turn that data into actions and impact customers.
Companies will be valued not just on their big data, but on the algorithms that turn that data into actions and impact customers.
“Algorithms are where the real value lies,” said Peter Sondergaard, senior vice president at Gartner and global head of Research, in the opening keynote to more than 8,500 CIOs and IT leaders at the sold out Gartner Symposium/ITxpo in Orlando. “Algorithms define action.” Digital revenues have risen, IT organizations have gone bimodal, and the increased density of connections promises smart agents and algorithms that can do very complex things, including spawning their own, new algorithms and agents.
In today’s digital era, dynamic, digital algorithms are at the core of new customer interactions. Back in the day, Coco-Cola created a secret recipe; essentially, an algorithm; for a fountain drink that would build an empire.
Today, Amazon’s recommendation engine prompts people to buy more products, or the Waze algorithms give cars better routes based on thousands of independent inputs, changing traffic patterns dynamically in real time. Moving forward, companies will be valued not just on their big data, but on the algorithms that turn that data into actions and impact customers.
Companies will be valued not just on their big data, but on the algorithms that turn that data into actions and impact customers.
Deep Machine Learning libraries and frameworks
At the end of 2015, all eyes were on the year’s accomplishments, as well as forecasting technology trends of 2016 and beyond. One particular field that has frequently been in the spotlight during the last year is deep learning, an increasingly popular branch of machine learning, which looks to continue to advance further and infiltrate into an increasing number of industries and sectors. Here are a list of Deep Learning libraries and frameworks that will gain momentum in 2016.
1. Theano is a python library for defining and evaluating mathematical expressions with numerical arrays. It makes it easy to write deep learning algorithms in python. On the top of the Theano many more libraries are built.
· Keras is a minimalist, highly modular neural network library in the spirit of Torch, written in Python, that uses Theano under the hood for optimized tensor manipulation on GPU and CPU.
· Pylearn2 is a library that wraps a lot of models and training algorithms such as Stochastic Gradient Descent that are commonly used in Deep Learning. Its functional libraries are built on top of Theano
· Lasagne is a lightweight library to build and train neural networks in Theano. It is governed by simplicity, transparency, modularity, pragmatism , focus and restraint principles.
· Blocks a framework that helps you build neural network models on top of Theano.
· Keras is a minimalist, highly modular neural network library in the spirit of Torch, written in Python, that uses Theano under the hood for optimized tensor manipulation on GPU and CPU.
· Pylearn2 is a library that wraps a lot of models and training algorithms such as Stochastic Gradient Descent that are commonly used in Deep Learning. Its functional libraries are built on top of Theano
· Lasagne is a lightweight library to build and train neural networks in Theano. It is governed by simplicity, transparency, modularity, pragmatism , focus and restraint principles.
· Blocks a framework that helps you build neural network models on top of Theano.
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