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Experts predict when machines will be better than you at your job

#artificialintelligence

The experts predict that AI will outperform humans in the next 10 years in tasks such as translating languages (by 2024), writing high school essays (by 2026), and driving trucks (by 2027). Forty years is an important number when humans make predictions because it is the length of most people's working lives. To find out if different groups made different predictions, Grace and co looked at how the predictions changed with the age of the researchers, the number of their citations (i.e., their expertise), and their region of origin. While North American researchers expect AI to outperform humans at everything in 74 years, researchers from Asia expect it in just 30 years.


Experts predict when machines will be better than you at your job

#artificialintelligence

The experts predict that AI will outperform humans in the next 10 years in tasks such as translating languages (by 2024), writing high school essays (by 2026), and driving trucks (by 2027). Forty years is an important number when humans make predictions because it is the length of most people's working lives. To find out if different groups made different predictions, Grace and co looked at how the predictions changed with the age of the researchers, the number of their citations (i.e., their expertise), and their region of origin. While North American researchers expect AI to outperform humans at everything in 74 years, researchers from Asia expect it in just 30 years.


Experts predict when machines will be better than you at your job

#artificialintelligence

The experts predict that AI will outperform humans in the next 10 years in tasks such as translating languages (by 2024), writing high school essays (by 2026), and driving trucks (by 2027). Forty years is an important number when humans make predictions because it is the length of most people's working lives. To find out if different groups made different predictions, Grace and co looked at how the predictions changed with the age of the researchers, the number of their citations (i.e., their expertise), and their region of origin. While North American researchers expect AI to outperform humans at everything in 74 years, researchers from Asia expect it in just 30 years.


How to Solve the New $1 Million Kaggle Problem - Home Value Estimates

@machinelearnbot

More specifically, I provide here high-level advice, rather than about selecting specific statistical models or algorithms, though I also discuss algorithm selection in the last section. If this is the case, an easy improvement consists of increasing value differences between adjacent homes, by boosting the importance of lot area and square footage in locations that have very homogeneous Zillow value estimates. Then for each individual home, compute an estimate based on the bin average, and other metrics such as recent sales price for neighboring homes, trend indicator for the bin in question (using time series analysis), and home features such as school rating, square footage, number of bedrooms, 2- or 3-car garage, lot area, view or not, fireplace(s), and when the home was built. With just a few (properly binned) features, a simple predictive algorithm such as HDT (Hidden Decision Trees - a combination of multiple decision trees and special regression) can work well, for homes in zipcodes (or buckets of zipcodes) with 200 homes with recent historical sales price.


Experts predict when machines will be better than you at your job

#artificialintelligence

The experts predict that AI will outperform humans in the next 10 years in tasks such as translating languages (by 2024), writing high school essays (by 2026), and driving trucks (by 2027). Forty years is an important number when humans make predictions because it is the length of most people's working lives. To find out if different groups made different predictions, Grace and co looked at how the predictions changed with the age of the researchers, the number of their citations (i.e., their expertise), and their region of origin. While North American researchers expect AI to outperform humans at everything in 74 years, researchers from Asia expect it in just 30 years.


Experts predict when machines will be better than you at your job

#artificialintelligence

The experts predict that AI will outperform humans in the next 10 years in tasks such as translating languages (by 2024), writing high school essays (by 2026), and driving trucks (by 2027). Forty years is an important number when humans make predictions because it is the length of most people's working lives. To find out if different groups made different predictions, Grace and co looked at how the predictions changed with the age of the researchers, the number of their citations (i.e., their expertise), and their region of origin. While North American researchers expect AI to outperform humans at everything in 74 years, researchers from Asia expect it in just 30 years.


Deep Learning for Chatbots, Part 2 – Implementing a Retrieval-Based Model in Tensorflow

#artificialintelligence

A positive label means that an utterance was an actual response to a context, and a negative label means that the utterance wasn't – it was picked randomly from somewhere in the corpus. Each record in the test/validation set consists of a context, a ground truth utterance (the real response) and 9 incorrect utterances called distractors. Before starting with fancy Neural Network models let's build some simple baseline models to help us understand what kind of performance we can expect. The Deep Learning model we will build in this post is called a Dual Encoder LSTM network.


Using Artificial Neural Networks to Predict the Quality and Performance of Oil-Field Cements

AI Magazine

Inherent batch-to-batch variability, aging, and contamination are major factors contributing to variability in oil-field cement-slurry performance. Such variability imposes a heavy burden on performance testing and is often a major factor in operational failure. Our approach involves predicting cement compositions, particle-size distributions, and thickening-time curves from the diffuse reflectance infrared Fourier transform spectrum of neat cement powders. Our research shows that many key cement properties are captured within the Fourier transform infrared spectra of cement powders and can be predicted from these spectra using suitable neural network techniques.