This consideration is important because a machine learning model may not be interpretable by a user. Second, scientists often have initial models that should influence the discovery process.
Domain-specific knowledge can be critical to the discovery process. Third, scientific datasets are often rare and difficult to obtain. It often takes years to collect and process the data before it can be analyzed. As such, it is important that the analysis is carefully planned and executed, and that any general feedback about the performance the learning process is not lost between studies. Fourth, scientists want models that move beyond description and provide explanations of the data.
Explanation and interpretation are paramount to the user. Finally, scientists want computational assistance rather than a complete replacement of themselves. Langley [ 16 ] further suggests that users want interactive discovery environments that help them understand their data while at the same time giving them control over the modeling process.
Collectively, these five lessons suggest that synergy between the user and the AI is critical. With this in mind, our proposed AI system includes a graphical user interface GUI that allows the user to easily launch analyses, view the results, and give the AI feedback about what results are useful or interesting.
As described above, a key component of PennAI is human-computer interaction. The first important feature is to make it easy for the user to directly launch machine learning analyses by choosing a method and its parameter settings from an intuitive push-button menu implemented through the web using JavaScript.
The user can launch single analyses or, in an advanced mode, launch a grid search across multiple methods and parameter settings. The methods and the controller that keep track of these analyses is described below.
Figures 3 and 4 show prototypes of our GUI for uploading and viewing datasets for analysis and launching machine learning analyses on those datasets, respectively.
Our JavaScript implementation is compatible with mobile devices, which allows the user to interact with the AI system from any Internet-connected device. The second key feature of PennAI is the ability to toggle the AI on and off for automated analysis, shown in Figure 3. An AI toggle allows the user to turn the AI on and set parameters controlling the maximum number of runs the AI can launch, as well as the frequency of updates the user would like to receive by email or text message.
Our first application of PennAI is for data mining using machine learning in the biomedical domain. Here, we make use of an extensive open source machine learning library in Python called scikit-learn [ 28 ].
Scikit-learn provides peer-reviewed implementations of several common supervised and unsupervised machine learning algorithms, data preprocessing methods, feature engineering and selection methods, hyperparameter optimization procedures, and more. To most users, scikit-learn is considered to be the standard machine learning library in Python. Of course, there are dozens of machine learning algorithms, preprocessors, etc.
To simplify the algorithm selection process for PennAI users, we currently limit PennAI to six machine learning algorithms that we believe will handle most supervised classification use cases, shown in Table 1.
We also limit the parameter choices for each algorithm to a handful of the most important parameters and parameter options, which makes it easier for users to choose a parameter configuration at the expense of algorithm customizability. An example of the interface to the Machine Learning Engine can be found in Figure 4 , where only a handful of the most important parameters and parameter options are available for the k-Nearest Neighbors classification algorithm. In an upcoming PennAI implementation, we will provide simplified descriptions of the machine learning algorithms and parameters so users can make use of the algorithms without fully understanding their implementation.
Instead, it is more important for the user to understand that adding more decision trees to the random forest i. Once the Machine Learning Engine finishes training and evaluating a machine learning model, it stores the machine learning model, the model predictions, and an analysis of the model in the Graph Database Engine, which are used in the Visualization Engine both described below. The Controller Engine acts as the interface between the high-performance computing system and the user or AI.
This component is hidden from the user but facilitates the automatic launching of jobs on a multi-CPU machine, computing cluster, or cloud computing system. The controller must not only coordinate the launching of jobs but also keep track of when they finish and deposit the results in the Graph Database Engine described below that serves as the memory of the system.
FGLab uses node. Another key component of PennAI is a memory system that keeps track of every analysis that is run on each data set. We keep track of the details of the machine learning method, the parameter settings, the data set analyzed, and results such as the model, model error, and area under the receiver operating characteristic curve AUC.
The advantage of using a NoSQL database is that new data elements can be added without creating tables and without strict format specifications. This flexibility is important for the rapidly changing landscape of machine learning. MongoDB can also function as a graph database that allows the documents to be linked in a network according to shared index terms related to the analysis and data. The Graph Database Engine serves as the memory of PennAI and provides the raw materials for the AI to learn which methods and parameter settings are working better than others for particular kinds of problems.
The initial knowledge base consists of results from a previously published benchmark of scikit-learn algorithms [ 24 ] , in which 14 machine learning algorithms were run with full hyperparameter optimization on a suite of supervised classification problems.
The results are combined with meta-information about the datasets e. This data can then be modelled to extract rules that represent the knowledge used by the Artificial Intelligence Engine to make informed analyses.
The knowledge base will be updated with all future analyses. Each component described above provides the raw materials for the Artificial Intelligence Engine which then 1 searches the graph database for results related to one or more data sets, 2 performs statistical analysis comparing algorithms and their parameters, 3 combines facts and rules in an expert system to make new analysis recommendations, 4 communicates findings to the user, and 5 automatically launches new analyses using suggestions from the expert system.
The first function uses the search capabilities of the MongoDB graph database to identify relevant machine learning results in the form of JSON files. All returned JSON files can be parsed to extract the machine learning algorithm, parameters, and information about the model performance. These results are collated in a tab-delimited file and a statistical analysis performed to determine the best algorithm configurations for certain problem types, similar to meta-learning techniques [ 8 ].
New statistical results are used to populate the knowledge base of an expert system that has a set of decision rules provided by developers and advanced machine learning practitioners. The user can access these suggestions manually or PennAI can use the suggestions to automatically launch new jobs, thus continually growing the PennAI knowledge base.
Essentially, the Artificial Intelligence Engine becomes a research assistant who tinkers with new ways of modeling the dataset and reports back to the user with their best findings. The accessibility standards for software applications set forth in Section include those that follow. They are soon to be revised. Although Section standards specifically apply to federal agencies, they provide a model of accessibility that has been adopted by other organizations as they create policies to meet their obligations under the ADA and other federal and state legislation.
Such efforts will result in a greater number of software products accessible to people with disabilities. When software is designed to be accessible to individuals with a broad range of disabilities, it is more usable by others.
For example, providing captions to a multimedia presentation can provide access to the content for a user who is deaf, that is using the product in a noise-free environment, who wants to search for specific content, or for whom English is a second language.
And, making educational software available to a student who has a learning disability that affects reading ability, can make it accessible to younger users as well.
Applying accessibility standards in the design of software products helps level the playing field in education and employment. DO-IT Disabilities, Opportunities, Internetworking, and Technology serves to increase the successful participation of individuals with disabilities in challenging academic programs such as those in science, engineering, mathematics, and technology.
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