Stanford professor Fei-Fei Li is a pioneer in artificial intelligence. Her research helped lead to breakthroughs like allowing computers to recognize images. Now, AI has spread to every economic sector. This episode, hear Fei-Fei's thoughts on how humans can play a compassionate role in shaping AI's future. Plus, Caroline Fairchild brings reporting on some surprising jobs in this emerging industry. JESSI HEMPEL: From the editorial team at LinkedIn, I'm Jessi Hempel, and this is Hello Monday, a show where I investigate the changing nature of work, and how that work is changing us. Last year, I got to test-drive a self-driving car, which of course means I got to sit behind the wheel and not drive. In this one test, a human-size dummy walked out onto the track, imitating a pedestrian, jaywalking. SELF-DRIVING CAR TAPE: So here it comes...so we pass this trigger…do we see him? The car saw the pedestrian and slowed down to let him pass. This is just one of the many, many things that have become possible now that computers can recognize images. That's why this week, I wanted to talk to Fei-Fei Li.

The 33rd annual conference on Neural Information Processing Systems (NeurIPS) is going to be held at Vancouver Convention Center, Vancouver, Canada from December 8th to 14th, 2019. The primary focus of the Foundation is the presentation of a continuing series of professional meetings known as the Neural Information Processing Systems Conference, held over the years at various locations in the United States, Canada and Spain. NeurIPS received a record-breaking 6743 submissions this year, of which 1428 were accepted. A popular learning paradigm is hypergraph-based semi-supervised learning (SSL) where the goal is to assign labels to initially unlabeled vertices in a hypergraph. Motivated by the fact that a graph convolutional network (GCN) has been effective for graph-based SSL, the authors propose HyperGCN, a novel GCN for SSL on attributed hypergraphs.

Artificial Intelligence (AI) can be quite a challenging topic to truly comprehend, especially for business managers, entrepreneurs and investors that lack a deep academic background in the field. They may instinctively sense the massive potential of AI -- all the science fiction movies and TV shows that Hollywood churns out probably plays a part in this -- but they are often left wondering, how should I think about AI? How does AI actually work? The follow article addresses this gap by presenting two broad and fairly dominant intuitions of AI -- cognitive and statistical. Despite the relative fragmentation of the field and varied backgrounds of AI practitioners, the cognitive and statistical intuitions seem to reflect the ways of approaching AI today. If you can grasp one or both of these intuitions, then you will be better positioned to meaningfully participate in discussions around AI as a business stakeholder, as well as build and invest in AI opportunities. Think about the last time you had to study for a test with multiple choice questions. Figure 1 shows a very simple example of such a question.

The strength of the text is in the large number of examples and the step by step explanation of each topic as it is introduced. It is compiled in a way that allows distance learning, with explicit solutions to set problems freely available online. The miscellaneous exercises at the end of each chapter comprise questions from past exam papers from various universities, helping to reinforce the reader's confidence. Also included, generally at the beginning of sections, are short historical biographies of the leading players in the field of linear algebra to provide context for the topics covered. The dynamic and engaging style of the book includes frequent question and answer sections to test the reader's understanding of the methods introduced, rather than requiring rote learning.

At the time I'm writing this, every single trending article in my Towards Data Science home page is talking about applying or learning a particular skill in data science. At the top are big-picture skills such as How to Work With Stakeholders as a Data Scientist and How to Become a Data Engineer, followed by a litany of very specific skills including technical primers on Batch Gradient Descent vs. Stochastic Gradient Descent, Multi-Class Text Classification, Faster R-CNN, et cetera. As a dedicated Medium platform for "sharing concepts, ideas, and codes" in data science, it is not surprising that such learning resources attain high popularity amongst Towards Data Science followers, who are probably navigating data-centric projects and professions. But to a novice looking to prioritize what is essential, it can quickly become daunting. Should one train to become a master Kaggler?

Using deep learning and artificial intelligence (AI), FAU scientists are bringing to life one of about a handful of these quadruped robots in the world. Astro is unique because he is the only one of these robots with a head, 3D printed to resemble a Doberman pinscher, that contains a (computerized) brain. What would you get if you combined Apple's Siri and Amazon's Alexa with Boston Dynamic's quadruped robots? You'd get "Astro," the four-legged seeing and hearing intelligent robodog. Using deep learning and artificial intelligence (AI), scientists from Florida Atlantic University's Machine Perception and Cognitive Robotics Laboratory (MPCR) in the Center for Complex Systems and Brain Sciences in FAU's Charles E. Schmidt College of Science are bringing to life one of about a handful of these quadruped robots in the world.

This week the Allen Institute for Artificial Intelligence announced a breakthrough for a BERT-based model, passing an eighth-grade science test. The GPU-accelerated system called Aristo can read, learn, and reason about science, in this case emulating the decision making of students. For this milestone, Aristo answered more than 90 percent of the questions on an eighth-grade science exam correctly, and 83 percent on a 12th-grade exam. "Although Aristo only answers multiple choice questions without diagrams, and operates only in the domain of science, it nevertheless represents an important milestone towards systems that can read and understand," the researchers stated in a newly published paper on ArXiv. "The momentum on this task has been remarkable, with accuracy moving from roughly 60% to over 90% in just three years," Though no diagrams were used for this particular task, the work as a whole integrates multiple AI-based technologies including natural language processing, information extraction, knowledge representation and reasoning, commonsense knowledge, and diagram understanding.

The social enterprise is on the rise--a signal of a broader shift in consumer culture and a growing expectation that private companies will make a positive impact on the world. But, how do we measure impact? For years, business leaders have clamored for actionable, often automated, data to measure the return on investment (ROI) of their initiatives. As a result, the business world has become infused with the buzzword, "data-driven." Measuring the effectiveness of solutions on outcomes is important for all businesses, but it is particularly critical for companies whose outcomes are intended to improve our society.

Antifreeze proteins (AFPs) are the sub-set of ice binding proteins indispensable for the species living in extreme cold weather. These proteins bind to the ice crystals, hindering their growth into large ice lattice that could cause physical damage. There are variety of AFPs found in numerous organisms and due to the heterogeneous sequence characteristics, AFPs are found to demonstrate a high degree of diversity, which makes their prediction a challenging task. Herein, we propose a machine learning framework to deal with this vigorous and diverse prediction problem using the manifolding learning through composition of k-spaced amino acid pairs. We propose to use the deep neural network with skipped connection and ReLU non-linearity to learn the non-linear mapping of protein sequence descriptor and class label. The proposed antifreeze protein prediction method called AFP-CKSAAP has shown to outperform the contemporary methods, achieving excellent prediction scores on standard dataset. The main evaluater for the performance of the proposed method in this study is Youden's index whose high value is dependent on both sensitivity and specificity. In particular, AFP-CKSAAP yields a Youden's index value of 0.82 on the independent dataset, which is better than previous methods.

Forums play an important role in providing a platform for community interaction. The introduction of irrelevant content or spam by individuals for commercial and social gains tends to degrade the professional experience presented to the forum users. Automated moderation of the relevancy of posted content is desired. Machine learning is used for text classification and finds applications in spam email detection, fraudulent transaction detection etc. The balance of classes in training data is essential in the case of classification algorithms to make the learning efficient and accurate. However, in the case of forums, the spam content is sparse compared to the relevant content giving rise to a bias towards the latter while training. A model trained on such biased data will fail to classify a spam sample. An approach based on Synthetic Minority Over-sampling Technique(SMOTE) is presented in this paper to tackle imbalanced training data. It involves synthetically creating new minority class samples from the existing ones until balance in data is achieved. The enhanced data is then passed through various classifiers for which the performance is recorded. The results were analyzed on the data of forums of Spoken Tutorial, IIT Bombay over standard performance metrics and revealed that models trained after Synthetic Minority oversampling outperform the ones trained on imbalanced data by substantial margins. An empirical comparison of the results obtained by both SMOTE and without SMOTE for various supervised classification algorithms have been presented in this paper. Synthetic oversampling proves to be a critical technique for achieving uniform class distribution which in turn yields commendable results in text classification. The presented approach can be further extended to content categorization on educational websites thus helping to improve the overall digital learning experience.