The project pix2code is a research project demonstrating an application of deep neural networks to generate code from visual inputs. We could not emphasize enough that this project is experimental and shared for educational purposes only. Any difference in length between the generated token sequence and the expected token sequence is also counted as error. TL;DR Not anytime soon will AI replace front-end developers.
Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking). Most ensemble methods use a single base learning algorithm to produce homogeneous base learners, i.e. In subsequent boosting rounds, the weighting coefficients are increased for data points that are misclassified and decreased for data points that are correctly classified. The figure also shows how the test accuracy improves with the size of the ensemble and the learning curves for training and testing data.
For AI engineers, however, soft skills are simply the next frontier. In a world programmed in 0s and 1s, things like empathy, self-awareness, and social skills are about as far from binary as you can get. That means computers don't understand emotions in the same way humans do. Emotional intelligence and soft skills are closely related.
Recent applications of machine learning with big data are able to predict diseases--such as Alzheimer's and diabetes--with incredible accuracy, years before the onset of symptoms. To assess the likelihood of a patient developing a certain condition, physicians have traditionally relied on risk calculators such as this one. Bringing together the data collected in many large-scale studies across diverse medical specialties, together with information from our medical records and other sources, doctors can accurately calculate the likelihood of suffering from a disease, a patient's possible outcome, and even figure out what the main predictors for each illness are. The CS experts have brought to the table the capacity to identify, develop, and fine-tune machine learning algorithms and techniques to predict conditions with better accuracy and speed.
Using huge amounts of data, it is possible to have a neural network learn good vector representations of words that have some desirable properties like being able to do math with them. In the field of machine learning, transfer learning is the ability of the machine to use some of the learned concepts in one task for another different task. The idea behind this algorithm is the following: in the same way we can get a good word vector representation by using a neural network that tries to predict the surrounding words of a word, they use a neural network to predict the surrounding sentences of a sentence. Facebook's InferSent uses a similar approach, but instead of using machine translation, they use a neural network that learns to classify the Stanford Natural Language Inference (SNLI) Corpus and while doing this, they also get good text vectorization.
With the help of Mª Asunción Jiménez Cordero, a PhD student specialized in Machine Learning, we developed a Spark application written in Scala to analyze real-time tweets and classify them as negative, positive or neutral using Support Vector Machine. We just needed to use the already trained model with our testing dataset, and evaluate if the tweets classified as positive or negative were correct. It was time to develop a simple web page to show the results in real time and run our Spark app in a cluster. The process, starting from a point where we had no idea about machine learning or Spark to develop a real-time machine learning app in Scala, running on a cluster, was hard but totally worth it.
The AI was trained to correctly spot the difference between diseased and healthy brains, before being tested on its accuracy abilities on a second set of 148 scans – 52 of which were healthy, 48 had Alzheimer's and the other 48 had a mild cognitive impairment that was known to develop into Alzheimer's within 10 years. The algorithm correctly distinguished between healthy and diseased brains 86% of the time, according to the researchers, who added that it was also able to spot the difference between a healthy brain and a mild impairment with an 84% accuracy rating. Last month mobile game Sea Hero Quest – which uses navigation challenges to gather data about spatial movement as part of research into the disease – was expanded to virtual reality for the first time. The game sets users navigation challenges, and they can opt-in to share their data with the researchers behind the game, who can use player performance data to plot spatial navigation skills of different ages groups and genders.
They've developed a social sentiment technology based on deep learning that lets brands capture customer sentiment with 90% accuracy. This AI technology for the first time truly understands the meaning of full sentences and is able to accurately determine customer attitudes and contextual reactions in tweets, posts and articles. There are two main approaches most vendors use today: sentiment analysis based on keyword scoring, or a calculation based on predefined categories. For the first time, the algorithm understands the meaning of full sentences and is able to accurately determine customer attitudes and contextual reactions in tweets, posts and articles.
Salesforce thinks it has found a way to improve forecasting accuracy in its Einstein Sales Cloud product with everyone's go-to tool of late, artificial intelligence. Einstein Forecasting "allows business leaders, sales leaders, to be able to make company decisions because you have predictive information at hand," she said. The technology uses 24 months of trailing sales data to model current sales pipelines for both individual sales representatives and teams, and surfaces that through a dashboard that lets managers detect budding problems before the last two weeks of the quarter. Forecasting is part of Sales Cloud Einstein in Salesforce's flagship customer-relationship management product.
By convention, the rare class is usually positive, so this means the True Positive (TP) rate is 0.78, and the False Negative rate (1 – True Positive rate) is 0.22. The Non-Large Loss recognition rate is 0.79, so the True Negative rate is 0.79 and the False Positive (FP) rate is 0.21. They don't report a False Positive rate (or True Negative rate, from which we could have calculated it). This result means that, using their Neural network, they must process 28 uninteresting Non-Large Loss customers (false alarms) for each Large-Loss customer they want.