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) …
A good deep learning model has a carefully carved architecture. It needs enormous training data, effective hardware, skilled developers, and a vast amount of time to train and hyper-tune the model to achieve satisfactory performance. Therefore, building a deep learning model from scratch and training is practically impossible for every deep learning task. Here comes the power of Transfer Learning. Transfer Learning is the approach of making use of an already trained model for a related task.
"Artificial intelligence (AI)I will automate everything and put people out of work." "AI is a science-fiction technology." "Robots will take over the world." The hype around AI has produced many myths, in mainstream media, in board meetings and across organizations. Some worry about an "almighty" AI that will take over the world, and some think that AI is nothing more than a buzzword.
At the peak of the Covid-19 pandemic in 2020, Australian transport agency Transport for New South Wales (NSW) had to restore public confidence in the state's transportation network and curb the spread of the disease. One of the ways it did that was to analyse the travel history recorded by Opal transit cards – with an individual's permission – and inform the commuter if the regular buses and train services that they had been taking were Covid-safe. Chris Bennetts, executive director for digital product delivery at Transport for NSW, said those insights were derived using a machine learning model that predicts how full a bus or train carriage was going to be at a given time. Based on the predictions, commuters would be advised if they could continue using their regular services or switch to a different service or mode of transport. "That was interesting for us because it was our first foray into personalisation to offer more choices for customers," said Bennetts.
AI and machine learning systems have become increasingly competent in recent years, capable of not just understanding the written word but writing it as well. But while these artificial intelligences have nearly mastered the English language, they have yet to become fluent in the language of computers -- that is, until now. IBM announced during its Think 2021 conference on Monday that its researchers have crafted a Rosetta Stone for programming code. Over the past decade, advancements in AI have mainly been "driven by deep neural networks, and even that, it was driven by three major factors: data with the availability of large data sets for training, innovations in new algorithms, and the massive acceleration of faster and faster compute hardware driven by GPUs," Ruchir Puri, IBM Fellow and Chief Scientist at IBM Research, said during his Think 2021 presentation, likening the new data set to the venerated ImageNet, which has spawned the recent computer vision land rush. "Software is eating the world," Marc Andreessen wrote in 2011.
Anomaly detection can be treated as a statistical task as an outlier analysis. But if we develop a machine learning model, it can be automated and as usual, can save a lot of time. There are so many use cases of anomaly detection. Credit card fraud detection, detection of faulty machines, or hardware systems detection based on their anomalous features, disease detection based on medical records are some good examples. There are many more use cases.
In this blog, we shall discuss about how to build a neural network to translate from English to German. This problem appeared as the Capstone project for the coursera course "Tensorflow 2: Customising your model", a part of the specialization "Tensorflow2 for Deep Learning", by the Imperial College, London. The problem statement / description / steps are taken from the course itself. We shall use the concepts from the course, including building more flexible model architectures, freezing layers, data processing pipeline and sequence modelling. Here we shall use a language dataset from http://www.manythings.org/anki/
Organizations need to recognize a range of biases in their data that can end up in their machine learning models. Knowing the types of bias that can exist can help organizations identify and potentially resolve some of the issues resulting in skewed, inaccurate or inappropriate results for the machine learning models. Many modern organizations collect data in different forms, or modalities, such as numerical, text, images, graphs or audio, in structured and unstructured formats. The way organizations collect their data can introduce bias, and there can be bias in the language used in each of these different data formats. For example, a mislabeled graph can lead to incorrect input data, resulting in skewed conclusions from a machine learning model.
If you have built Deep Neural Networks before, you might know that it can involve a lot of experimentation. In this article, I will share with you some useful tips and guidelines that you can use to better build better deep learning models. These tricks should make it a lot easier for you to develop a good network. You can pick and choose which tips you use, as some will be more helpful for the projects you are working on. Not everything mentioned in this article will straight up improve your models' performance.
In this role as a Data Engineer, you will lead the design and implementation of a large-scale, low latency, end to end platform which will serve reporting (near real-time as well as historical) and predictive analytics needs of the Worldwide Consumer HR org. You will partner with scientists, analysts, engineers and senior leaders to deliver scientific solutions that improve employee experience across Amazon. A day in the life The Data Engineer for this role will collaborate with stakeholders on Org Research & Measurement science and engineering teams to build ML platforms, data ingestion processes and service integrations. You will design and implement scalable and efficient ETL extract/load strategies using AWS tools in development and production environments. The Data Engineer will develop code to acquire/transform datasets for machine learning algorithms, analysis and reporting using Python/PySpark/SQL.
Why do Neural networks fail to generalize to new environments, and how can this be fixed? Many real world data analysis problems exhibit in-variant structure, and models that take advantage of this structure have shown impressive empirical performance, particularly in deep learning. Most machine learning problems have an invariant structure. Image classification tasks, for example, are usually invariant to translation, rotation, scale, viewpoint, illumination etc. An example of statue class is shown below. It seems intuitive the machine learning models should capture the invariances of the problem at hand to perform better. We will look at why is it so in details below. Anyways, there are many works that empirical support of this over range of applications (Cohen & Welling, 2016; Fawzi et al., 2016; Salamon & Bello, 2017).