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A machine learning-based study by Caltech reveals that habit formation varies greatly, with gym habits taking six months to establish on average, while healthcare workers form a hand-washing habit in a few weeks. The study emphasized the power of machine learning in researching human behavior outside lab conditions.

New machine learning study finds different habits take varying amounts of time to take root.

Putting on your workout clothes and getting to the gym can feel like a slog at first. Eventually, you might get in the habit of going to the gym and readily pop over to your Zumba class or for a run on the treadmill. A new study from social scientists at Caltech now shows how long it takes to form the gym habit: an average of about six months.

The same study also looked at how long it takes healthcare workers to get in the habit of washing their hands: an average of a few weeks.

There is no magic number for habit formation, says Anastasia Buyalskaya (PhD 21), now an assistant professor of marketing atHEC Paris. Other authors of the study, which appears in the journalProceedings of the National Academy of Sciences,include CaltechsColin Camerer, Robert Kirby Professor of Behavioral Economics and director and leadership chair of the T&C Chen Center for Social and Decision Neuroscience, and researchers from the University of Chicago and the University of Pennsylvania. Xiaomin Li (MS 17, PhD 21), formerly a graduate student and postdoctoral scholar at Caltech, is also an author.

You may have heard that it takes about 21 days to form a habit, but that estimate was not based on any science, Camerer says. Our works supports the idea that the speed of habit formation differs according to the behavior in question and a variety of other factors.

The study is the first to use machine learning tools to study habit formation. The researchers employed machine learning to analyze large data sets of tens of thousands of people who were either swiping their badges to enter their gym or washing their hands during hospital shifts. For the gym research, the researchers partnered with 24 Hour Fitness, and for the hand-washing research, they partnered with a company that used radio frequency identification (RFID) technology to monitor hand-washing in hospitals. The data sets tracked more than 30,000 gymgoers over four years and more than 3,000 hospital workers over nearly 100 shifts.

With machine learning, we can observe hundreds of context variables that may be predictive of behavioral execution, explains Buyalskaya. You dont necessarily have to start with a hypothesis about a specific variable, as the machine learning does the work for us to find the relevant ones.

Machine learning also let the researchers study people over time in their natural environments; most previous studies were limited to participants filling out surveys.

The study found that certain variables had no effect on gym habit formation, such as time of day. Other factors, such as ones past behavior, did come into play. For instance, for 76 percent of gymgoers, the amount of time that had passed since a previous gym visit was an important predicator of whether the person would go again. In other words, the longer it had been since a gymgoer last went to the gym, the less likely they were to make a habit of it. Sixty-nine percent of the gymgoers were more likely to go to the gym on the same days of the week, with Monday and Tuesday being the most well-attended.

For the hand-washing part of the study, the researchers looked at data from healthcare workers who were given new requirements to wear RFID badges that recorded their hand-washing activity. It is possible that some health workers already had the habit prior to us observing them, however, we treat the introduction of the RFID technology as a shock and assume that they may need to rebuild their habit from the moment they use the technology, Buyalskaya says.

Overall, we are seeing that machine learning is a powerful tool to study human habits outside the lab, Buyalskaya says.

Reference: What can machine learning teach us about habit formation? Evidence from exercise and hygiene by Anastasia Buyalskaya, Hung Ho, Katherine L. Milkman, Xiaomin Li, Angela L. Duckworth and Colin Camerer, 17 April 2023, Proceedings of the National Academy of Sciences.DOI: 10.1073/pnas.2216115120

The study was funded by the Behavior Change for Good Initiative, the Ronald and Maxine Linde Institute of Economics and Management Sciencesat Caltech, and theTianqiao and Chrissy Chen Institute for Neuroscienceat Caltech.

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Breaking the 21-Day Myth: Machine Learning Unlocks the Secrets of ... - SciTechDaily

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Published June 17, 2023

Background

A brain tumour can be defined as the uncontrolled development of cancerous cells in the brain. According to previous research, if left unchecked, a brain tumour can lead to cancer. Therefore, it is essential for a radiologist to be accurate about the existence of brain tumours from magnetic resonance images (MRI) for their analysis.

With the advent of e-health and machine/deep learning techniques, medical specialists are able to provide better health care and quick responses to their patients. Using machine learning (ML) techniques, an ML model can be trained to know if brain tumours are in MRI images. Machine learning is a branch of artificial intelligence that can by themselves learn how to solve specific problems if given the right access to data. Furthermore, ML has been effective in making decisions and predictions from data produced by healthcare industries.

This article will critically review different ML pipelines and models used in detecting brain tumours from MRI images and evaluate their strengths and limitations. The datasets used for analysis in this article are the T1-CE MRI image dataset, TCIA (The Cancer Imaging Archive), and Rembrandt database for brain cancer imaging.

Methods and analysis

In this article, a deep neural network called Convolutional neural network and two traditional machine learning algorithms called K-Nearest Neighbours and Nave Bayes' for detecting cancer tumours in the human brain using MRI images in this study.

Method 1.

Convolutional Neural Networks (CNN):

A Convolutional Neural network is a method of deep learning that uses convolutions on a kernel that slides through an image and produces a feat map to better understand segments and objects within an image. A convolutional neural network is used here to segment brain tumour into one of various four classes:

This article will not emphasise which architecture performs best, but on some aspects that are worth taking note of when training a CNN.

A CNN's basic structure consists of an input image, a kernel or filter (usually a 3 x3) matrix that slides horizontally across the image repeatedly moving X strides at a time and generating an output. The weights are then adjusted depending on how alike the newly generated feature map compares to the original input image. The basic structure might sound simple, but many actors come into play for the algorithm to be able to segment and locate brain tumour cells accurately. This study summarizes some of these aspects:

CNN architecture: figure 1 below shows the CNN architecture used to classify the different tumours.

The figure above shows how the CNN architecture processes an image pixel by pixel and automatically extracts the features needed and classifies the tumours using one of four different labels from 0 to 3; 0 healthy region, 1 meningioma tumour, 2 glioma tumour, and 3 pituitary tumour.

Overfitting: This is a very important issue for CNN. When the machine learning algorithm overlearns or memorises the train data, it cannot generalise properly on unseen data. This issue can be taken care of by using more artificially generated data in data augmentation, which is a popular method for this. Another method to avoid overfitting is using dropouts, which is dropping out a certain percentage of the neurons in the network to prevent overlearning. Other methods of dealing with this issue include batch normalisation and pooling.

Batch normalisation is a method of normalisation in the data that employs mini batches, which speeds up the training process by reducing the normal of epochs to be trained and stabilising the training process.

Pooling is another important aspect that downsizes the image and causes the machine learning algorithm to learn features on a downsized or less detailed image. Different pooling methods exist, such as max pooling, which uses the maximum value from the pool to estimate, while mean pooling uses the mean as an estimator.

As the data is non-linear, they need a function to introduce non-linearity in the data. The right activation function for this is the ReLU function or the rectilinear unit. After several layers of convolutions and rectifying using the RelU, the data is completely flattened using pooling into a columnar matrix which is then passed through a fully connected layer. Using a SoftMax activation, the fully connected layers can then be classified based on the classes initiated. The feature map gotten from this will then be used to classify MRI images based on the features it has learned. Keras API was used here as it is a framework for object detection and segmentation.

Method 2

K-Nearest Neighbours (KNN):

K-Nearest Neighbours (KNN) is a classical shallow machine learning algorithm used for brain tumour segmentation and classification. In this study, MRI images undergo segmentation via k-means clustering, an unsupervised algorithm. Features extracted from these clusters are then analyzed using the Gray level Co-Occurrence matrix (GLCM) and inputted into the KNN classifier for classification.

KNN requires extensive data pre-processing to achieve significant results. The study focuses on key pre-processing techniques, including image enhancement through filtering and resizing. Filtering techniques such as mean and median filters are employed to eliminate noise like salt and pepper, Gaussian noise, speckle, and Brownian noise.

Image segmentation involves creating clusters based on color, texture, contrast, and brightness. Cluster analysis using the unsupervised algorithm k-means facilitates easy feature extraction.

Feature extraction utilizes the Gray level Co-occurrence matrix, which measures the spatial dependence of grey-level intensities between pixels. This method has shown accurate results (89.9%) in classifying brain tumour cells using MRI images.

Once features are extracted, they are fed into the KNN classifier, with each segment representing a distinct class. The focus of this article is not on the specific configurations or steps taken by KNN for classification of the feature set.

Method 3

Nave Bayes:

The Nave Bayes algorithm is a supervised machine learning technique used for classification based on the probabilistic theory of Bayes. It assumes that all features (pixels) are independent of each other, making it suitable for applications with randomness.

Similar to the KNN method discussed earlier, the Nave Bayes algorithm requires important pre-processing steps to prepare the data for the machine learning process. The accuracy of the model heavily relies on these pre-processing steps. This article focuses on the following pre-processing techniques:

By implementing these pre-processing techniques, the Nave Bayes algorithm can be applied for accurate brain tumour classification.

After all the above pre-processing steps, the data is now ready to be fed into the Nave Bayes classification algorithm, whose configuration shall not be discussed in this article.

Discussion & evaluation

When looking at the results from the three different methods, it is clearly seen that the use of machine learning in detecting a brain tumour from MRI scans is very promising, with all three methods producing a high level of accuracy. In the classification process, model validation is used to divide the data into training and testing in order to obtain the accuracy of testing results.

The result of the three models in this study can be seen in the figure below. However, all three models were trained and tested on different datasets, which should be noted when comparing accuracy.

Final thoughts

As machine learning gains traction in technology and e-health industries, it's vital to recognize how different models and pipelines impact performance.

The deep learning model Convolutional Neural Network (CNN) outperformed K-Nearest Neighbour Network (KNN) and Nave Bayes models in this study, despite lacking spatial information and potential pooling issues. However, performance comparison was based on three distinct datasets, limiting accurate assessment.

When selecting a model, dataset characteristics like size and complexity are crucial. Deep learning models excel in large datasets with intricate patterns, but require powerful GPUs. In contrast, traditional machine learning models thrive with smaller data volumes.

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Artificial intelligence (AI) and machine learning (ML) have been hailed as revolutionary technologies that will reshape industries, improve productivity, and enhance our daily lives. However, behind the AI veil lies a hidden cost that is often overlooked: the energy intensity of machine learning. As AI and ML applications continue to grow, so does the demand for computational power, leading to an increase in energy consumption and environmental impact. This article will delve into the energy intensity of machine learning and explore the implications of this growing concern.

Machine learning, a subset of AI, involves training algorithms to learn from data and make predictions or decisions. This process requires vast amounts of computational power, particularly for deep learning models, which use artificial neural networks to mimic the human brains decision-making process. Training these models can take days, weeks, or even months, depending on the complexity of the task and the size of the dataset. During this time, the energy consumption of the computers running these algorithms can be immense.

One of the most striking examples of the energy intensity of machine learning is the training of large-scale language models like OpenAIs GPT-3. GPT-3, which has been described as one of the most powerful language models ever created, consists of 175 billion parameters and required hundreds of powerful GPUs to train. According to a study by researchers at the University of Massachusetts Amherst, training a single large-scale AI model like GPT-3 can generate as much carbon emissions as five cars over their entire lifetimes, including manufacturing and fuel consumption.

The energy intensity of machine learning is not only an environmental concern but also a barrier to entry for smaller organizations and researchers. The cost of training large-scale models can be prohibitive, with some estimates suggesting that training GPT-3 could cost around $4.6 million in electricity alone. This creates a competitive advantage for large tech companies with deep pockets, potentially stifling innovation and exacerbating existing inequalities in the AI research community.

To address the energy intensity of machine learning, researchers and industry leaders are exploring various strategies. One approach is to develop more energy-efficient hardware, such as specialized AI chips that can perform complex calculations with less power. Companies like Google, NVIDIA, and Graphcore are at the forefront of this effort, developing custom chips designed specifically for AI and ML workloads.

Another strategy is to improve the efficiency of machine learning algorithms themselves. Researchers are exploring techniques such as pruning, quantization, and knowledge distillation, which can reduce the computational complexity of models without sacrificing performance. These techniques can help make AI models more accessible to a wider range of users and reduce the overall energy consumption of the machine learning ecosystem.

In addition to these technical solutions, there is a growing awareness of the need for more sustainable AI practices. This includes considering the environmental impact of AI research and development, as well as incorporating sustainability metrics into the evaluation of AI systems. Organizations like the Partnership on AI and the AI for Good Foundation are working to promote responsible AI development and ensure that the benefits of AI are shared broadly across society.

In conclusion, the energy intensity of machine learning is a critical issue that must be addressed as AI and ML technologies continue to advance. By developing more energy-efficient hardware, improving the efficiency of algorithms, and promoting sustainable AI practices, the AI research community can help mitigate the environmental impact of machine learning and ensure that these transformative technologies are accessible to all. As we continue to push the boundaries of AI and ML, it is essential that we also consider the hidden costs behind the AI veil and work towards a more sustainable future for our planet.

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Behind the AI Veil: The Energy Intensity of Machine Learning - EnergyPortal.eu