Businesses in every industry are scrambling to launch Artificial Intelligence (AI) and Deep Learning initiatives. Yet, overall success of these plans is highly dependent on their workforce, and the biggest challenge for businesses is a real scarcity of people who have production-level experience with AI. Plenty of reports – from Accenture, Deloitte and others – show the primary obstacle to successful adoption of deep learning is a severe shortage of talent. The talent landscape is affected by several trends, but is even more compounded as digital giants like Microsoft, Google and Apple tussle for the lion's share of expertise. For startups and even established companies, it's clear attracting the right AI talent is difficult at best.

Learn to apply Reinforcement Learning and Artificial Intelligence algorithms using Python, Pytorch and OpenAI Gym Artificial Intelligence is dynamically edging its way into our lives. It is already broadly available and we use it - sometimes even not knowing it - on daily basis. Soon it will be our permanent, every day companion. And where can we place Reinforcement Learning in AI world? Definitely this is one of the most promising and fastest growing technologies that can eventually lead us to General Artificial Intelligence!

There are loads of Data Science courses at Udemy, not just the ones listed above. If none of these take your fancy, have a look around and I'm sure you'll find others that might just hit the spot. I also recommend taking a look at courses in Statistics, Artificial Intelligence, Machine Learning and Deep Learning too. Udemy's list changes every 30 days, so I will update this post regularly to reflect these changes. Final word - when you've done any of these courses, please return and leave some feedback and a review in the comments below.

The challenge of the latest work can be summed up as how to give neural networks an ability not just to generalize from one learned task to another, but to continually sharpen that ability to generalize over time, with exposure to new tasks. And, to do so with a minimum of data required as examples, given that many new tasks a neural network confronts over time may not have a lot of training data available, or, at least, not a lot of "labeled" training data. The result is described in a paper out last week, "Online Meta-Learning," posted on the arXiv pre-print server. The current research has echoes in Levine's other work that's closer to robotics per se. ZDNet back in October related how Levine trains robot simulations -- agents -- to infer movement from multiple frames of video from YouTube. There's a parallel with online meta-learning, in that the computer is learning how to extend its understanding across examples in time, sharpening its ability to understand, in a sense. The approach that lead authors Finn and Rajeswaran pursue is to combine two different approaches that the teams have explored extensively in recent years: meta-learning and online learning.

The challenge of the latest work can be summed up as how to give neural networks an ability not just to generalize from one learned task to another, but to continually sharpen that ability to generalize over time, with exposure to new tasks. And, to do so with a minimum of data required as examples, given that many new tasks a neural network confronts over time may not have a lot of training data available, or, at least, not a lot of "labeled" training data. The result is described in a paper out last week, "Online Meta-Learning," posted on the arXiv pre-print server. The current research has echoes in Levine's other work that's closer to robotics per se. ZDNet back in October related how Levine trains robot simulations -- agents -- to infer movement from multiple frames of video from YouTube. There's a parallel with online meta-learning, in that the computer is learning how to extend its understanding across examples in time, sharpening its ability to understand, in a sense. The approach that lead authors Finn and Rajeswaran pursue is to combine two different approaches that the teams have explored extensively in recent years: meta-learning and online learning.

Navigating and understanding the real world remains a key challenge in machine learning and inspires a great variety of research in areas such as language grounding, planning, navigation and computer vision. We propose an instruction-following task that requires all of the above, and which combines the practicality of simulated environments with the challenges of ambiguous, noisy real world data. StreetNav is built on top of Google Street View and provides visually accurate environments representing real places. Agents are given driving instructions which they must learn to interpret in order to successfully navigate in this environment. Since humans equipped with driving instructions can readily navigate in previously unseen cities, we set a high bar and test our trained agents for similar cognitive capabilities. Although deep reinforcement learning (RL) methods are frequently evaluated only on data that closely follow the training distribution, our dataset extends to multiple cities and has a clean train/test separation. This allows for thorough testing of generalisation ability. This paper presents the StreetNav environment and tasks, a set of novel models that establish strong baselines, and analysis of the task and the trained agents.

Collective intelligence is manifested when multiple agents coherently work in observation, interaction, decision-making and action. In this paper, we define and quantify the intelligence level of heterogeneous agents group with the improved Anytime Universal Intelligence Test(AUIT), based on an extension of the existing evaluation of homogeneous agents group. The relationship of intelligence level with agents composition, group size, spatial complexity and testing time is analyzed. The intelligence level of heterogeneous agents groups is compared with the homogeneous ones to analyze the effects of heterogeneity on collective intelligence. Our work will help to understand the essence of collective intelligence more deeply and reveal the effect of various key factors on group intelligence level.

Long Short Term Memory Fully Convolutional Neural Networks (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN) have shown to achieve state-of-the-art performance on the task of classifying time series signals on the old University of California-Riverside (UCR) time series repository. However, there has been no study on why LSTM-FCN and ALSTM-FCN perform well. In this paper, we perform a series of ablation tests (3627 experiments) on LSTM-FCN and ALSTM-FCN to provide a better understanding of the model and each of its sub-module. Results from the ablation tests on ALSTM-FCN and LSTM-FCN show that the these blocks perform better when applied in a conjoined manner. Two z-normalizing techniques, z-normalizing each sample independently and z-normalizing the whole dataset, are compared using a Wilcoxson signed-rank test to show a statistical difference in performance. In addition, we provide an understanding of the impact dimension shuffle has on LSTM-FCN by comparing its performance with LSTM-FCN when no dimension shuffle is applied. Finally, we demonstrate the performance of the LSTM-FCN when the LSTM block is replaced by a GRU, basic RNN, and Dense Block.

When she was an MIT undergraduate studying electrical engineering, Jeannette Wing '78, SM '79, PhD '83 took a required computer science class and began thinking about changing her major. But before making the decision, she called her father, a professor of electrical engineering at Columbia University, to ask one big question: Is computer science just a fad? "I literally remember asking him that question," Wing said, drawing chuckles from an audience of MIT students and faculty. Wing's father assured her that computer science was here to stay. "So I switched," said Wing, who is herself now the Avanessians Director of the Data Science Institute and professor of computer science at Columbia. "And I've never looked back."