Artificial Intelligence (AI) has become a buzzword in recent times and with the hype surrounding it, chief technology officers (CTOs) and chief information officers (CIOs) are constantly pressured into having an AI mandate--wisely or unwisely for the fear of being left out or even worse come out as not being in the leading edge of tech. Executives who are intent on capitalizing the power of AI must be able to address it in an informed way. I want an AI bot for my customers to download a document and notify users on the status. This is a request we received from a CXO. Problems where the goals and the means to achieve the goals, are well understood, don't require AI, you just need a simple mechanism.

Just let me code, already! You know it's out there. You know there's free GPU somewhere, hanging like a fat, juicy, ripe blackberry on a branch just slightly out of reach. Wondering how on earth to get it to work? For anyone who doesn't already know, Google has done the coolest thing ever by providing a free cloud service based on Jupyter Notebooks that supports free GPU.

An analysis of more than 400 use cases across 19 industries and nine business functions highlights the broad use and significant economic potential of advanced AI techniques. Artificial intelligence (AI) stands out as a transformational technology of our digital age--and its practical application throughout the economy is growing apace. For this briefing, Notes from the AI frontier: Insights from hundreds of use cases (PDF–446KB), we mapped both traditional analytics and newer "deep learning" techniques and the problems they can solve to more than 400 specific use cases in companies and organizations. Drawing on McKinsey Global Institute research and the applied experience with AI of McKinsey Analytics, we assess both the practical applications and the economic potential of advanced AI techniques across industries and business functions. Our findings highlight the substantial potential of applying deep learning techniques to use cases across the economy, but we also see some continuing limitations and obstacles--along with future opportunities as the technologies continue their advance.

Rasa Core is an open source machine learning-based dialogue system for building level 3 contextual AI assistants. In version 0.11, we shipped our new embedding policy (REDP), which is much better at dealing with uncooperative users than our standard LSTM (Rasa users will know this as the KerasPolicy). We're presenting a paper on it at NeurIPS, and this post explains the problem that REDP solves and how it works. Our new dialogue model (REDP) outperforms our standard LSTM policies at dealing with uncooperative users. These plots are explained in more detail below. The hard thing about building a good AI assistant is dealing with the infinite ways that your users stray from the happy path.

If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" Here is a reading roadmap of Deep Learning papers! You will find many papers that are quite new but really worth reading. I would continue adding papers to this roadmap. After reading above papers, you will have a basic understanding of the Deep Learning history, the basic architectures of Deep Learning model(including CNN, RNN, LSTM) and how deep learning can be applied to image and speech recognition issues.

What's the best platform for hosting your code, collaborating with team members, and also acts as an online resume to showcase your coding skills? Ask any data scientist, and they'll point you towards GitHub. It has been a truly revolutionary platform in recent years and has changed the landscape of how we host and even do coding. It acts as a learning tool as well. How, you ask? I'll give you a hint – open source! The best part about these releases is that the researchers behind the code also provide pretrained models so folks like you and I don't have to waste time building difficult models from scratch. Additionally, we regularly see the top trending repositories aimed towards coders and developers – this includes resources like cheatsheets, video links, e-books, research paper links, among other things. No matter which level you are at in your professional career (beginner, established or advanced), you will always find something new to learn on GitHub. Natural Language Processing (NLP) was easily the most talked about domain within the community with the likes of ULMFiT and BERT being open-sourced.

After winning its three-game match against Chinese grandmaster Ke Jie, the world's top Go player, AlphaGo is retiring. Demis Hassabis, the CEO and founder of DeepMind, the Google artificial intelligence lab that built this historic machine, tells WIRED he will now move the machine's designers to other projects. "This is some of the top people in the company," Hassabis says. "The idea is to really explore what we can do in other domains." Considering the world-shaking success of AlphaGo, that is a very powerful idea.

The depth completion and depth prediction evaluation are related to our work published in Sparsity Invariant CNNs (THREEDV 2017). It contains over 93 thousand depth maps with corresponding raw LiDaR scans and RGB images, aligned with the "raw data" of the KITTI dataset. Given the large amount of training data, this dataset shall allow a training of complex deep learning models for the tasks of depth completion and single image depth prediction. Also, we provide manually selected images with unpublished depth maps to serve as a benchmark for those two challenging tasks. Make sure to unzip annotated depth maps and raw LiDaR scans into the same directory so that all corresponding files end up in the same folder structure.

The possibilities seem almost endless as artificial intelligence continues to evolve: so far, it's in the process of automating the manufacturing, finance and construction industries, and even making inroads into making art, literature and music. But perhaps the biggest leap forward will be in the field of medicine, where AI is already helping medical professionals with diagnosing disease, identifying dangerous drug interactions and even performing surgery more accurately. And in a stunning win during the bi-annual Critical Assessment of Structure Prediction (CASP) competition, Google-affiliated AI research lab DeepMind showed that AI could potentially unlock one of the biggest mysteries of all: predicting how proteins fold themselves in three-dimensional space. Best known for creating AI programs capable of beating human champions of games like chess and go, DeepMind's AlphaFold AI bested the other 97 entrants in correctly predicting how 25 out of 43 proteins would fold in three dimensions. In comparison, the second-place winner was only able to solve three out of 43 proteins.

Model averaging is an ensemble technique where multiple sub-models contribute equally to a combined prediction. Model averaging can be improved by weighting the contributions of each sub-model to the combined prediction by the expected performance of the submodel. This can be extended further by training an entirely new model to learn how to best combine the contributions from each submodel. This approach is called stacked generalization, or stacking for short, and can result in better predictive performance than any single contributing model. In this tutorial, you will discover how to develop a stacked generalization ensemble for deep learning neural networks. How to Develop a Stacking Ensemble for Deep Learning Neural Networks in Python With Keras Photo by David Law, some rights reserved. A model averaging ensemble combines the predictions from multiple trained models.