"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
The 1.5 meter, silvery gray velociraptor lunges forward, interrupting the flight of the tennis ball with its head before the ball can get to the soccer net at the end of the gym. Its tail stretches out, stopping another ball. It pivots, somewhat clumsily, and runs three steps in the other direction to intercept a third ball. Robots building Teslas aren't as sophisticated as AI velociraptors that tend goals It's been doing this for an hour, running back and forth as a trio of tennis ball machines toss yellow balls in various loopy ways toward the net. It's a game that its creators have invented to rapidly improve its coordination. But then it stops trying to intercept the balls, although it still twitches toward them.
This year, we saw a dazzling application of machine learning. The OpenAI GPT-2 exhibited impressive ability of writing coherent and passionate essays that exceed what we anticipated current language models are able to produce. The GPT-2 wasn't a particularly novel architecture – it's architecture is very similar to the decoder-only transformer. The GPT2 was, however, a very large, transformer-based language model trained on a massive dataset. In this post, we'll look at the architecture that enabled the model to produce its results. We will go into the depths of its self-attention layer. My goal here is to also supplement my earlier post, The Illustrated Transformer, with more visuals explaining the inner-workings of transformers, and how they've evolved since the original paper. My hope is that this visual language will hopefully make it easier to explain later Transformer-based models as their inner-workings continue to evolve.
H2O is a fully open-source, distributed in-memory machine learning platform with linear scalability. H2O supports the most widely used statistical & machine learning algorithms, including gradient boosted machines, generalized linear models, deep learning, and many more. H2O also has an industry-leading AutoML functionality (available in H2O 3.14) that automates the process of building a large number of models, to find the "best" model without any prior knowledge or effort by the Data Scientist. H2O AutoML can be used for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit. Some of the important features of H2O's AutoML are: H2O's AutoML can also be a helpful tool for the novice as well as advanced users.
This is 4R's sixth consecutive year participating in the show and will present its latest Merchant Analytics approach. NextPoint provides an exclusive annual event which offers retailers and solution providers an experience uncommon from other industry events and trade shows. Mark Garland, Executive Vice President Sales, Marketing & Solutions, said, "It is hard to believe this will be our sixth year presenting at NextPoint. The last five years at NextPoint have provided excellent networking opportunities. We are looking forward to sharing how 4R positions retailers to earn more profit from their inventory with proven success stories."
We first formulate the MDP for our problem, M S,? QWeb solves the above problem using deep Q network(DQN) to generate Q values for each state and for each atomic action. The training process is almost the same as traditional DQN with the help of reward augmentation and some curriculum learning approaches, which we will discuss later. But for now let's first focus on the architecture of QWeb, which is essentially the most fruitful part of this algorithm. Encoding user instructions: As we've seen in the preliminaries, a user instruction consists of a list of fields, i.e.,key-value pairs K, V .
Logistics is a mature, technologically-advanced, and analytically-sophisticated industry. Still, even after decades of improvements coming from the Industrial Engineering and Operations Research fields, major efficiencies can still be realized by applying advanced analytics, data infrastructure, and computing power. All business processes in logistics rely on accurate demand forecasting in the short, medium, and long-term to inform resourcing, planning, and staffing to support future needs. In three weeks we delivered a functioning production time-series forecasting framework using R and Spark. After six months we had scaled to a refined framework that produces timely forecasts on over several thousand locations in our client's network.
One of the most promising applications of deep learning is image analysis (as part of computer vision), e.g. for image segmentation or classification. Whereas segmentation yields a probability distribution (also known as mask) for each class per pixel (i.e. each pixel belongs to 1 of K classes), classification does so for the whole image (i.e. each image belongs to 1 of K classes). Software solutions can be encountered nearly everywhere nowadays, for example in medical image analysis. In clinical research, where novel medications are tested, sometimes it is of interest if a drug can change the condition of a tissue, e.g. Medical images are created by imaging techniques such as medical ultrasound, X-ray, computed tomography (CT), magnetic resonance imaging (MRI), or even regular microscopes.
Economists have been studying the relationship between technological change, productivity and employment since the beginning of the discipline with Adam Smith's pin factory. It should therefore not come as a surprise that AI systems able to behave appropriately in a growing number of situations - from driving cars to detecting tumours in medical scans - have caught their attention. In September 2017, a group of distinguished economists gathered in Toronto to set out a research agenda for the Economics of Artificial Intelligence (AI). They covered questions such as what is economically unique about AI, what will be its impacts, and what are the right policies to enhance its benefits. I recently had the privilege of attending the third edition of this conference in Toronto, and to witness first-hand how this agenda has evolved in the last two years.
Since our recent release of Transformers (previously known as pytorch-pretrained-BERT and pytorch-transformers), we've been working on a comparison between the implementation of our models in PyTorch and in TensorFlow. We've released a detailed report where we benchmark each of the architectures hosted on our repository (BERT, GPT-2, DistilBERT, ...) in PyTorch with and without TorchScript, and in TensorFlow with and without XLA. We benchmark them for inference and the results are visible in the following spreadsheet. We would love to hear your thoughts on the process.