The difficulty and expense of collecting river water samples in remote areas has led to significant -- and in some cases, decades-long -- gaps in available water chemistry data, according to a Penn State-led team of researchers. The team is using artificial intelligence (AI) to predict water quality and fill the gaps in the data. Their efforts could lead to an improved understanding of how rivers react to human disturbances and climate change. The researchers developed a model that forecasts dissolved oxygen (DO), a key indicator of water's capability to support aquatic life, in lightly monitored watersheds across the United States. They published their results in Environmental Science & Technology.

As data accessibility and analysis capabilities have rapidly advanced in recent years, new digital platforms driven by artificial intelligence (AI) and machine learning (ML) are increasingly finding practical applications in industry. "Data are so readily available now. Several years ago, we didn't have the manipulation capability, the broad platform or cloud capacity to really work with large volumes of data. We've got that now, so that has been huge in making AI more practical," says Paige Morse, industry marketing director for chemicals at Aspen Technology, Inc. (Bedford, Mass.; www.aspentech.com). While AI and ML have been part of the digitalization discussion for many years, these technologies have not seen a great deal of practical application in the chemical process industries (CPI) until relatively recently, says Don Mack, global alliance manager at Siemens Industry, Inc. (Alpharetta, Ga.; www.industry.usa.siemens.com). "In order for AI to work correctly, it needs data. Control systems and historians in chemical plants have a lot of data available, but in many cases, those data have just been sitting dormant, not really being put to good use. However, new digitalization tools enable us to address some use cases for AI that until recently just weren't possible." This convergence of technologies, from smart sensors to high-performance computing and cloud storage, along with advances in data science, deep learning and access to free and open-source software, have enabled the field of industrial AI to move beyond pure research to practical applications with business benefits, says Samvith Rao, chemical and petroleum industry manager at MathWorks (Natick, Mass.; www.mathworks.com).

The emerging subject of deep learning mathematical analysis [1] has been tasked with answering some "mysterious" facts that appear to be inexplicable using traditional mathematical methodologies. They are attempting to comprehend what a neural network actually does. Deep Neural Networks (DNN) transform data at each layer, producing a new representation as output. DNN attempts to divide data in a classification problem, enhancing this action layer by layer until it reaches an output layer when DNN provides its best possible result. Under the manifold hypothesis (natural data creates lower-dimensional manifolds in its embedding space), this task can be viewed as the separation of lower-dimensional manifolds in a data space. DNN layers are linked by a realization function, Φ (an affine transformation) and a component-wise activation function, ρ. Consider the fully connected feedforward neural network depicted in Figure 2. The network architecture can be described by defining the number of layers N, L, the number of neurons, and the activation function.

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In this tutorial, you will learn how to tune the hyperparameters of a deep neural network using scikit-learn, Keras, and TensorFlow. Optimizing your hyperparameters is critical when training a deep neural network. There are many knobs, dials, and parameters to a network -- and worse, the networks themselves are not only challenging to train but also slow to train as well (even with GPU acceleration). Failure to properly optimize the hyperparameters of your deep neural network may lead to subpar performance. Luckily, there is a way for us to search the hyperparameter search space and find optimal values automatically -- we will cover such methods today.

Digital Art created via Convolutional Neural Networks and Deep Learning. Style: Knife Painting Image: Cat Original Image by Nezhelskayadi@pixabay

In May 2020 OpenAI presented GPT-3 in a paper titled Language Models are Few Shot Learners. GPT-3, the largest neural network ever created, revolutionized the AI world. OpenAI released a beta API for people to play with the system and soon the hype started building up. People were finding crazy results. GPT-3 could transform a description of a web page into the corresponding code.

Artificial intelligence (AI) is a large subject of computer science devoted to the development of intelligent computers capable of doing activities that would typically need human intelligence. While AI is a heterogeneous discipline with numerous methodologies, developments in machine learning and deep learning are transforming practically every aspect of the IT sector.

The best way to compare two frameworks is to code something up in both of them. I've written a companion jupyter notebook for this post and you can get it here. All code will be provided in the post too. First, let's code a simple approximator for the following function in both frameworks: We will try to find unknown parameter phi given data x and function values f(x). Yes, using stochastic gradient descent for this is an overkill and analytical solution may be found easily, but this problem will serve our purpose well as a simple example.

Recently, the combination of robust one-dimensional convolutional neural networks (1-D CNNs) and Raman spectroscopy has shown great promise in rapid identification of unknown substances with good accuracy. Using this technique, researchers can recognize a pure compound and distinguish it from unknown substances in a mixture. The novelty of this approach is that the trained neural network operates automatically without any pre- or post-processing of data. Some studies have attempted to extend this technique to the classification of pure compounds in an unknown mixture. However, the application of 1-D CNNs has typically been restricted to binary classifications of pure compounds. Here we will highlight a new approach in spectral recognition and quantification of chemical components in a multicomponent mixture. Two 1-D CNN models, RaMixNet I and II, have been developed for this purpose. The former is for rapid classification of components in a mixture while the latter is for quantitative determination of those constituents. In the proposed method, there is no limit to the number of compounds in a mixture. A data augmentation method is also introduced by adding random baselines to the Raman spectra. The experimental results revealed that the classification accuracy of RaMixNet I and II is 100% for analysis of unknown test mixtures; at the same time, the RaMixNet II model may achieve a regression accuracy of 88% for the quantification of each component.