Before you even ask, the name has no meaning. When Google was trying to figure out what to call its language parsing technology, someone suggested Parsey McParseface; it's a bit like Apple's Liam, which has no clever backstory either. The overall AI model is called SyntaxNet (please make your SkyNet jokes now); 'ol Parsey is just for English. Combining machine learning and search techniques, Parsey McParseface is 94 percent accurate, according to Google. It also leans on SyntaxNet's neural-network framework for analyzing the linguistic structure of a sentence or statement, which parses the functional role of each word in a sentence.

In machine learning, a programmer might develop an AI that can calculate all possible consequences of a single action. Humans, however, don't have the same raw computational power; we have to efficiently create and execute a plan. We mentally invent different "layers" to organise our actions and then think about the higher levels rather than individual steps, according to a Neuron study from members of Google DeepMind and the University of Oxford. "The idea is basically to understand how humans or animals make long-term decisions," says Jan Balaguer, a PhD student at University of Oxford and member of Google DeepMind. "We're interested in trying to find machine-learning solutions to difficult tasks and real-life problems. Quite often it can be useful to draw inspiration from neuroscience."

There's been a perception that the adoption of technology in legal services has been slow over the past few years. However, according to the ALM 2015 Law Tech Survey this is certainly beginning to change as 95% of firm leaders and technologists respondents agreed or mostly agreed with recent decisions by management regarding the firm's technology. Law firms have faced a number of challenges over recent years. Although the UK is recovering well from the 2009 financial crisis this also led to stricter compliance regulations. With the increase in regulatory demands there is a need for better finance and risk mitigation.

Microsoft unveiled Twitter artificial intelligence bot @TayandYou yesterday in a bid to connect with millennials and "experiment" with conversational understanding. Billed as'AI fam from the internet that's got zero chill!' Tay was meant to engage with her peers help the tech giant explore its cognitive learning abilities through "playful conversation". "The more you chat with Tay the smarter she gets," said Microsoft. The stunt however, took an unexpected turn when Tay's verified Twitter account began issuing a series of inflammatory statements after being targeted by Twitter trolls. The conversational learning curve saw the bot tweet posts from her verified account mentioning Hitler, 9/11 and feminism, some of which (including the below) have now been deleted.

Artificial Intelligence seems to have just taken a disruptive leap forward. As reported in Wired this month, over 500M US have been invested in AI-related startups, with VIV Labs making to the feature story of this month Wired. In the meantime, IBM made the headlines just a week ago by commercialising Watson as a business-friendly AI machine, and the buzz keeps going. Now, for somebody like me, who is passionate about advancing faster towards personalised medicine, somebody who has been fighting, along with colleagues to piece together clinical data, genomics data, environmental data, you name it, to start painting a stratified, personalised view of patients, diagnosis, and treatments, what will this mean? Will AI help us make a leap forward?

In an earlier post, "What Type of Artificial Intelligence Do You Need?", two paradigms of artificial intelligence are described; deterministic and statistical. Deterministic is similar to speaking with an expert. In this blog post, we will consider how Deterministic Artificial Intelligence can be applied to Client Suitability, which is a well-known regulatory challenge in financial services. Deterministic A.I. can help financial institutions process high volumes of Client Suitability analysis. By automating large amounts of decision-making and data analysis, financial institutions can limit their regulatory risk and increase efficiency.

Clearly, the idea of Artificial Intelligence (AI) has been with us in pop culture for a while. One of their breakthroughs involves the use of a technique called deep learning. Though it takes many forms, one example of deep learning is the creation of electronic "neural networks" that can mimic different basic operations occurring in real webs of neurons. This advancement enables the machine to take direct inputs from the world without human involvement and create its own internal representation for further processing."

Move over Janet Yellen, automation in the workplace is about to get personal. Instead of relying on the Federal Reserve chair, imagine using a computer to transform mountains of raw economic data into reliable predictions for unemployment, inflation and gross domestic product. "The capability is here," says Andrew Lo, director of the Laboratory for Financial Engineering at the Massachusetts Institute of Technology, near Boston. "The biggest hurdle is the cultural barrier. You've got a lot of central bankers who are not as open to technology."

Hal 2000 of 2001 A Space Odyssey runs a deep space exploration vessel while simultaneously trying to kill its astronaut crew. The robots in Star Wars are our friends. Clearly, the idea of Artificial Intelligence (AI) has been with us in pop culture for a while. But enthusiasm among scientists for AI's reality has actually waxed and waned strongly a number of times over the last half century. Right now, researchers are riding high on another wave of enthusiasm.

The data features that you use to train your machine learning models have a huge influence on the performance you can achieve. Irrelevant or partially relevant features can negatively impact model performance. In this post you will discover automatic feature selection techniques that you can use to prepare your machine learning data in python with scikit-learn. Feature Selection For Machine Learning in Python Photo by Baptiste Lafontaine, some rights reserved. Feature selection is a process where you automatically select those features in your data that contribute most to the prediction variable or output in which you are interested.