Today we posted a new Tech Note in which Greg Diamos, a research scientist at Baidu's Silicon Valley AI Lab, discusses a new technique for speeding up the training of deep recurrent neural networks. At SVAIL, our mission is to create AI technology that lets us have a significant impact on hundreds of millions of people. We believe that a good way to do this is to improve the accuracy of speech recognition by scaling up deep learning algorithms on larger datasets than what has been done in the past. These algorithms are very compute intensive, so much so that the memory capacity and computational throughput of our systems limits the amount of data and the size of the neural network that we can train. So a big challenge is figuring out how to run deep learning algorithms more efficiently.

I am thinking about using this method to train a model for this as my master thesis. But I didn't had anything about it during my classes, I just found the subject and found it interesting. But honestly I don't understand as of yet where you can use this type of models in practice.

Recently, when I was attending AINL-ISMW FRUCT 2015 conference, I found out that Google open-sourced TensorFlow. That is cool, but may provoke some questions. Here, I try to answer some of them. It is definitely not the same as, say, cloud-based machine-learning service (like, for example, Azure ML is). It is a machine-learning library using data flow graphs to build models. The main purpose of the library is to create models to solve various NLP and image recognition tasks.

The idea of environmental determinism once made a lot of sense. Hostile climates and habitats prevented the expansion of human populations. The conceptual opposite of determinism is called possibilism. These days, human populations can found living in many inhospitable habitats. This isn't because humans have physically evolved. But rather, we normally occupy built-environments. We exist through our technologies and advanced forms of social interaction: a person might not be able to build a house, but he or she can arrange for financing to have a house constructed. "Social possibilism" has enabled our survival in inhospitable conditions. Because humans today almost always live within or in close proximity to built-environments, among the most important factors affecting human life today is data. The systems that support human society make use of data in all of its multifarious forms; this being the case, data science is important to our continuation and development as a species. This blog represents a discussion highlighting the need for a universal data model. I find that the idea of "need" is highly subjective; and perhaps the tendency is to focus on organizational needs specifically.

But, as Kahn points out, collecting and translating data is only half the story. The other half is connecting to devices, which will be automatically instructed by the analyzed data. Google's Nest offers a home app that uses sensors, analytics and the internet to connect everything from your thermostat to your fire alarms and home security system. Apple has launched a similar IoT application called HomeKit. According to Kahn, the Smart Bed will have the ability to turn your bedroom thermostat down when your body is at rest and turn the heat back up when the bed senses you are waking. It will lift the shades in your bedroom, signal the hot water heater to ready the shower, and the coffee machine to prepare your coffee just the way you like it.

Most golfers feel the trip from the green to the clubhouse for refreshments can be a pain. Now Rakuten, a Japanese firm, has unveiled a snack-delivery service for golfers that uses drones. Golfers order anything from drinks to replacement balls through an app and receive the items from a quadcopter in just minutes - which means they never have to leave the course. Rakuten, a Japanese firm, has unveiled a snack-delivery service for golfers that uses drones. Golfers place their order using an accompanied at that they download on their smartphones.

A good vendor should be able to explain both how they manage data and how they solve your specific business problem. Iyengar suggests asking some questions to see if a predictive company will be a good fit: "Ask a [ML provider] how they handle unclean data. Their answer will show you how well they know their work. You can also ask about the variety of algorithms they use, since they should have a good variety of fairly robust algorithms. They should be comfortable explaining how they deploy a model structure, what their web stack looks like, and how that will work with customer architecture."

One thing I'm missing from this awesome site are courses in these topics, both highly relevant to the IT-industry these days. I'm aware that this might be straying somewhat from Treehouse's profile of teaching web-centric skills, but seeing as Treehouse already have courses in SQL and databases, this site could really take it to the next level by delving into Machine Learning or Data Analysis. Have the Treehouse staff thought about this?

Usually when people imagine a self-aware machine, they picture a device that emerges through deliberate effort and that then makes its presence known quickly, loudly, and (in most scenarios) disastrously. Even if its inventors have the presence of mind not to wire it into the nuclear missile launch system, the artificial intelligence will soon vault past our capacity to understand and control it. If we're lucky, the new machine will simply break up with us, like the operating system in the movie Her. If not, it might decide not to open the pod bay doors to let us back into the spaceship. Regardless, the key point is that when an artificial intelligence wakes up, we'll know. But who's to say machines don't already have minds? What if they take unexpected forms, such as networks that have achieved a group-level consciousness? What if artificial intelligence is so unfamiliar that we have a hard time recognising it?

The web is full of good explanations of machine learning algorithms. And every second applicant for a data science position has finished the Coursera course on machine learning. Theory will not help you choose good values for the 16 parameters a standard implementation of a random forest takes. The default values are good to get started, but which parameters should you modify depending on your data? Choosing the right features, algorithms and parameters is an art.