The brain produces feelings of confidence that inform decisions the same way statistics pulls patterns out of noisy data –

The brain produces feelings of confidence that inform decisions the same way statistics pulls patterns out of noisy data –

The directions, which came via cell phone, were a little garbled, but as you understood them: “Turn left at the 3rd light and go straight; the restaurant will be on your right side.” Ten minutes ago you made the turn. Still no restaurant in sight. How far will you be willing to drive in the same direction?

Research suggests that it depends on your initial level of confidence after getting the directions. Did you hear them right? Did you turn at the 3rd light? Could you have driven past the restaurant? Is it possible the directions are incorrect?

Human brains are constantly processing data to make statistical assessments that translate into the feeling we call confidence, according to a study published in Neuron. This feeling of confidence is central to decision making, and, despite ample evidence of human fallibility, the subjective feeling relies on objective calculations.

“The feeling ultimately relies on the same statistical computations a computer would make,” says Professor Adam Kepecs, a neuroscientist at Cold Spring Harbor Laboratory and lead author of the new study. “People often focus on the situations where confidence is divorced from reality,” he says. “But if confidence were always error-prone, what would be its function? If we didn’t have the ability to optimally assess confidence, we’d routinely find ourselves driving around for hours in this scenario.”

Calculating confidence for a statistician involves looking at a set of data—perhaps a sampling of marbles pulled from a bag—and making a conclusion about the entire bag based on that sample. “The feeling of confidence and the objective calculation are related intuitively,” says Kepecs. “But how much so?”

In experiments with human subjects, Kepecs and colleagues therefore tried to control for different factors that can vary from person to person. The aim was to establish what evidence contributed to each decision. In this way they could compare people’s reports of confidence with the optimal statistical answer. “If we can quantify the evidence that informs a person’s decision, then we can ask how well a statistical algorithm performs on the same evidence,” says Kepecs.

Source: Our brain uses statistics to calculate confidence, make decisions | Neuroscientist News

Today, we’re announcing a new series of workshops and an interagency working group to learn more about the benefits and risks of artificial intelligence.

There is a lot of excitement about artificial intelligence (AI) and how to create computers capable of intelligent behavior. After years of steady but slow progress on making computers “smarter” at everyday tasks, a series of breakthroughs in the research community and industry have recently spurred momentum and investment in the development of this field.

Today’s AI is confined to narrow, specific tasks, and isn’t anything like the general, adaptable intelligence that humans exhibit. Despite this, AI’s influence on the world is growing. The rate of progress we have seen will have broad implications for fields ranging from healthcare to image- and voice-recognition. In healthcare, the President’s Precision Medicine Initiative and the Cancer Moonshot will rely on AI to find patterns in medical data and, ultimately, to help doctors diagnose diseases and suggest treatments to improve patient care and health outcomes.

In education, AI has the potential to help teachers customize instruction for each student’s needs. And, of course, AI plays a key role in self-driving vehicles, which have the potential to save thousands of lives, as well as in unmanned aircraft systems, which may transform global transportation, logistics systems, and countless industries over the coming decades.

Source: Preparing for the Future of Artificial Intelligence | whitehouse.gov

$ pip install uvloop

import asyncio
import uvloop
asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())

Source: uvloop: Blazing fast Python networking — magicstack

Introduction

Question answering has recieved more focus as large search engines have basically mastered general information retrieval and are starting to cover more edge cases. Question answering happens to be one of those edge cases, because it could involve a lot of syntatic nuance that doesn’t get captured by standard information retrieval models, like LDA or LSI. Hypothetically, deep learning models would be better suited to this type of task because of their ability to capture higher-order syntax. Two papers, “Applying deep learning to answer selection: a study and an open task” (Feng et. al. 2015) and “LSTM-based deep learning models for non-factoid answer selection” (Tan et. al. 2016), are recent examples which have applied deep learning to question-answering tasks with good results.

Feng et. al. used an in-house Java framework for their work, and Tan et. al. built their model entirely from Theano. Personally, I am a lot lazier than them, and I don’t understand CNNs very well, so I would like to use an existing framework to build one of their models to see if I could get similar results. Keras is a really popular one that has support for everything we might need to put the model together.

The Github repository for this project can be found here.

Source: Deep Language Modeling for Question Answering using Keras

Source: anomaly_detection/Anomaly Detection Post.ipynb at master · fastforwardlabs/anomaly_detection · GitHub