J-WEL AI Knowledgebase

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J-WEL AI Knowledgebase

How to use J-WEL's AI Knowledgebase

This guide is to hopefully explain the intended way to use the databases here. Now, this is not the only way to use it, merely just the recommended way to start with. By following this guide, you will gain an understanding of how to use the databases.

But first lets look at the contents of each database:

MIT’s AI Resources database consists of resources developed by MIT on AI. These resources include research papers, online courses and seminar recordings, of which many are free to view. There is a wide array of topics to select from, including but not limited to; AI in finance, healthcare, ethics and machine learning. Said resources are typically associated with MIT faculties and labs, due to them being sourced from there.

MIT’s AI Researchers database consists of a list of faculty, researchers, and affiliates who are involved Artificial Intelligence or related fields across the various departments and labs at MIT. The list highlights many people at MIT, some of which work on the ethical, social and creative aspects of AI technology.

The MIT Initiatives database is a collection of MIT’s AI work from shaping AI’s future through research, ethics, and collaborations.

The DLCI database lists various MIT Departments, Labs, Centers, and initiatives (DLCI) that are doing work in the Artificial Intelligence and Machine Learning fields, with links to associated researchers and resources.

Now that you know what each is about, lets learn about how to use them. Databases MIT’s AI Resources, MIT MIT’s AI Resources List and DLCI work pretty much the same, but Database 3 (Initiatives) works a bit differently, but we’ll start with Database 1.

At the top of each database section, there will be a short description of the contents of each database, give them a read if you want to know just a bit more about each database.

The Database works fairly simply, you can scroll through the resources looking for a specific one, or you can use the provided features to filter down the list and make it easier to find what you’re looking for. Let’s start with the search function.

If you look over to the right side of the table, you’ll see the search bar. The system will scan each document for whatever string (set of characters such as “apple” or “aoiqeipo”) you type into the search and return the documents that contain said string.

(Example displayed to the right)

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Example of using the search bar

Example of using the search bar

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You can further narrow the results by filtering using the dropdowns on the left side of the table, labeled “DLCI”, “Cost” and “Resource Type”.

You can further narrow the results by filtering using the dropdowns on the left side of the table, labeled “DLCI”, “Cost” and “Resource Type”. These allow you to filter not just for any document that has the selected attribute(s), but also allows you to specify things such as “has one of the following attributes” (contains any of), “has all of the following attributes” (contains all of), “doesn’t have the following attributes” (does not contain), “contains only the following attributes, no more, no less” (equals exactly), and/or whether the specified attribute category is blank or not for that document.

(Example displayed to the right)

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Basic Attribute Filtering Example

Basic Attribute Filtering Example

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Let’s go over the categories our first resource has in a bit more detail then.

The first attribute category is “DLCI”, which represents the MIT Department, Lab, Center, or Initiative that the resource is from.

How to start a search:

Start by selecting a department or group from the DCLI pull down list.

The database will display all resources for that DCLI currently in the J-WEL AI Knowledgebase

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DLCI Attribute Filtering Example

DLCI Attribute Filtering Example

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Two additional filters are available The “Costs” pull down selects resources bast on the resource costs to access

Open Access are completely free resources

Partially Free (incl paid component) are resources that have some free components but also have some paid components, such as access to a book.

Behind paywall are resources that require a paid account

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Costs Attribute Filtering Example

Costs Attribute Filtering Example

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The third filter, Resource Type, narrows down the search results based on the kind of resource selected. For example: Multimedia, Online courses, Tools, Certificate courses, Webinars, etc.

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Resource Type Attribute Filtering Example

Resource Type Attribute Filtering Example

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Whenever you select any attribute to filter by, a new button pops up next to the search bar on the right of the database. This button is labeled “Reset” and it disabled any enabled filters, while keeping the search string. You can also disable filters by clicking on them again.

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Resetting Search Filters Example

Resetting Search Filters Example

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That about wraps up the first database.

The second and third databases, “MIT AI Research List” and “DLCI” function pretty much the exact same, just with a few keyword differences, so let’s go over them.

Instead of having attributes for DLCI, Cost and Resource Types, the documents in “MIT AI Research List” allow you to filter by the Name of the person detailed in the document and the “DLC” they belong to. The Name attribute functions like the search bar and filters based on the string input into it.

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Name Attribute Example

Name Attribute Example

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DLC Attribute Example

DLC Attribute Example

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The DLCI has 2 filterable attributes “MIT AI Researcher List”, “MIT AI Resources”, which allow you to filter the department/program/source based on; people who work there (Researcher List) and/or the work they’ve produced (AI Resources).

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MIT AI Researcher List Example

MIT AI Researcher List Example

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MIT AI Resources Example

MIT AI Resources Example

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