If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
This project provides high-performance character-aware sequence labeling tools, including Training, Evaluation and Prediction. Details about LM-LSTM-CRF can be accessed here, and the implementation is based on the PyTorch library. Our model achieves F1 score of 91.71 /-0.10 on the CoNLL 2003 NER dataset, without using any additional corpus or resource. The documents would be available here. As visualized above, we use conditional random field (CRF) to capture label dependencies, and adopt a hierarchical LSTM to leverage both char-level and word-level inputs.
Abstract: A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or object part. We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters. When multiple predictions agree, a higher level capsule becomes active. We show that a discriminatively trained, multi-layer capsule system achieves state-of-the-art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits. To achieve these results we use an iterative routing-by-agreement mechanism: A lower-level capsule prefers to send its output to higher level capsules whose activity vectors have a big scalar product with the prediction coming from the lower-level capsule.
We now live in an age where machine learning is a hot topic. Machines can learn on their own without human intervention, and at the same time, it can bring humans closer to machines by enabling humans to "teach" machines. Machine learning has been around for several decades, but only recently have we been able to take advantage of this technology thanks to the recent advancements made in computing power. Now, let's look at a quick timeline of machine learning: Machine learning (ML) deals with systems and algorithms focused on identifying patterns within data and making predictions by finding hidden patterns in data. It is worth mentioning here that machine learning falls under the artificial intelligence (AI) umbrella, which in turn intersects with the broader fields of data mining and knowledge discovery.
A new report from Forrester advises CIOs to leverage machine learning to turn the tsunami of data obtained in Internet of Things (IoT) deployments into actionable insights. Successful companies in the industrial sector that are doing this are not only predicting problems and opportunities before they occur, but are also developing new revenue streams during their digital transformation. Large volumes of data are required to train and then exploit machine learning algorithms, and fortunately that data is now easily accessible, especially as IoT gains traction in industries. According to Forrester's Paul Miller, senior analyst serving CIO professionals and lead author of the report, "Put Data to Work in the Industrial Internet of Things," machine learning is becoming a powerful tool in efforts to win, serve, and retain customers. "It's easy to focus on automating or augmenting existing processes with IoT, and this can deliver real cost savings and efficiency gains.
This post was co-authored with Duncan Gilchrist and is Part 1 of our "Best of Both Worlds: An Applied Intro to ML for Causal Inference" series (Part 2 here). We're grateful to Evan Magnusson for his strong thought-partnership and excellent insights throughout. Over the last couple years, we've been excited to see -- and leverage -- a range of new methods that significantly improve our ability to glean causal relationships from data, especially big data. Many of these methods marry the best of machine learning and econometrics to unlock deeper and more correct inference. Applied correctly, they help us get the insights we need to make better decisions for our companies and our communities.
Whenever you spot a trend plotted against time, you would be looking at a time series. The de facto choice for studying financial market performance and weather forecasts, time series are one of the most pervasive analysis techniques because of its inextricable relation to time--we are always interested to foretell the future. One intuitive way to make forecasts would be to refer to recent time points. Today's stock prices would likely be more similar to yesterday's prices than those from five years ago. Hence, we would give more weight to recent than to older prices in predicting today's price.
Recently there has been a great buzz around the words "neural network" in the field of computer science and it has attracted a great deal of attention from many people. But what is this all about, how do they work, and are these things really beneficial? Essentially, neural networks are composed of layers of computational units called neurons, with connections in different layers. These networks transform data until they can classify it as an output. Each neuron multiplies an initial value by some weight, sums results with other values coming into the same neuron, adjusts the resulting number by the neuron's bias, and then normalizes the output with an activation function.
MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to partially complementary regions within the 3'UTR of their target genes. Computational methods play an important role in target prediction and assume that the miRNA "seed region" (nt 2 to 8) is required for functional targeting, but typically only identify 80% of known bindings. Recent studies have highlighted a role for the entire miRNA, suggesting that a more flexible methodology is needed. We present a novel approach for miRNA target prediction based on Deep Learning (DL) which, rather than incorporating any knowledge (such as seed regions), investigates the entire miRNA and 3'UTR mRNA nucleotides to learn a uninhibited set of feature descriptors related to the targeting process. We collected more than 150,000 experimentally validated homo sapiens miRNA:gene targets and cross referenced them with different CLIP-Seq, CLASH and iPAR-CLIP datasets to obtain 20,000 validated miRNA:gene exact target sites.
Use Machine Learning to boost IoT efficacy says Forrester. A new report from Forrester, "Put Data to Work in the Industrial Internet of Things" advises CIOs to leverage machine learning to turn the tsunami of data obtained in Internet of Things (IoT) deployments into actionable insights. Successful companies in the industrial sector that are doing this are not only predicting problems and opportunities before they occur but are also developing new revenue streams during their digital transformation. Large volumes of data are required to train and then exploit machine learning algorithms, and fortunately, that data is now easily accessible, especially as IoT gains traction in industries. Machine learning is becoming a powerful tool in efforts to win, serve, and retain customers.
– Any one working within industries like the mobility, fintech, mobile money, payments, banking or InsureTech with little knowledge of data science is actually sitting on gold mine to explore and show what Data Science / AI can do for that company. Today every company on this planet collect vast quantities of data on a daily basis or even per second. For example credit card issuers with every credit card swipe and completed transaction capture critical customer information, In case of mobile payments/money the same thing happen or even in banks same scenarios. However, the raw data alone does not generate the insights needed to drive business decisions or simply not good enough at all. It's the proper analysis of this data that unlocks its true value.