Under an executive order issued by President Donald Trump, AI companies such as OpenAI and Anthropic are being asked to submit advanced artificial intelligence models to the government for vetting on cybersecurity, confidentiality, “insider risk” and intellectual property protections.
But as the executive order makes clear, AI companies are under no obligation to do much of anything.
At the end of a section ordering the creation of a vetting process in which AI companies would submit their models 30 days ahead of release, it states: “Nothing in this section shall be construed to authorize the creation of a mandatory governmental licensing, preclearance, or permitting requirement for the development, publication, release, or distribution of new AI models, including frontier models.”
A White House representative did not immediately respond to a request for comment.
Frontier models are cutting-edge AI models that could pose a major security risk, such as Anthropic’s Mythos, which the company kept from public release due to cybersecurity concerns. US-based AI companies are in a race to train and release artificial intelligence models, especially amid competition from Chinese companies.
But the rapid release cycle of new models has raised concerns that they aren’t being properly tested or regulated before going public, particularly regarding their human impacts or potential use as hacking tools.
The executive order also includes provisions for agencies, such as the Pentagon and the US Department of the Treasury, to beef up their cybersecurity defenses over the next 30 days.
Over the next 60 days, agencies, including the Department of Homeland Security and the National Security Agency, are expected to create a framework for evaluating AI models, even though companies aren’t required to submit them.
(Disclosure: Ziff Davis, CNET’s parent company, in 2025 filed a lawsuit against OpenAI, alleging it infringed Ziff Davis copyrights in training and operating its AI systems.)
The ‘appearance’ of oversight?
Executive actions similar to this one were expected two weeks before the June 2 order, but the final version included several notable changes. According to CNN, the original draft called for a 90-day review period instead of 30 days. However, AI companies involved in shaping the order, including Anthropic, reportedly pushed back on the longer timeline.
CNN also reported that a Department of Commerce Institute of Standards and Technology announcement last month related to AI companies sharing their AI models with the government has disappeared from the agency’s website.
Given the lack of enforcement provisions or mandatory requirements for AI companies, the executive order received a muted reaction.
“Voluntary frameworks are not enough,” Anthony Aguirre, CEO and president of the AI safety nonprofit Future of Life Institute, said in a statement emailed to CNET. “We need a mandatory government pre-deployment review process for the most powerful AI systems, allowing the government to block the release of systems that pose an unacceptable national security risk.”
The executive order doesn’t make clear what happens if the government receives early access to an AI model and a significant problem is discovered.
John Thickstun, assistant professor of computer science at Cornell University, told CNET that this lack of clarity makes it hard to understand what the executive order will achieve.
“Without clearer answers to these questions, my read of this is that it creates some appearance of oversight while largely continuing the administration’s hands-off approach to AI governance,” Thickstun said.
Looker is defined as a self-service big data and BI(Business Intelligence) software that helps in solving the problem of SQL in creating data analytics. It plays a very important role in big companies as it tends to help in getting values from the data of the company and giving a complete 360-degree view of it to the customers. It is helpful in industries such as eCommerce, gaming, finTech, medic, AdTech, Saas, etc. The main benefit of Looker is that it helps in providing a unified view of the company, hence each department of the company is satisfied by the needs and is successful in having the desired unified view. It is not difficult to set up a looker as it can be done quickly using some pre-built applications.
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Looker is compatible working with both web-based data as well as SQL. It is known to support over 25 different applications such as Hive, Vertica, BigQuery, Spark, etc.
The main key features of Looker are stated below:
Helps in data visualization
Helps in defining business metrics
Data modeling languages
Breaks the barriers to insights
Optimizes cost
Enhances performance
It has embedded analytics
It has integrations with relational databases
Formation of reports and KPI dashboards
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Lifetime LMS & Faculty Access
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Real-world & Project Based Learning
How does Looker Work?
Looker is basically used for creating SQL queries and then passing them into the database connection. Hence, looker produces the SQL queries which are based on the project called LookML, describing the relationship between the tables and their columns present in the database. Let us understand the working of the looker in the section below:
1. Viewing the query:
A user can simply use the SQL tab present in the Looker’s data section and know what all is Looker sending into the database for getting the data. The user can even use the bottom links for viewing the query present in the SQL runner. Let us refer to the image below for a better understanding:
2. The canonical form of a Looker Query:
The dimensions, views, measures, explores and references, etc are all defined in the LookML project. Let us refer to the image below:
3. Running raw SQL in Looker’s SQL Runner:
The Looker consists of a feature called SQL runner that helps in running the SQL against DB connections the user has set up inside the Looker. The raw queries present in the SQL are executed in the SQL Runner producing an identical outcome. The SQL runner also helps in highlighting the errors present in the SQL command along with the position of the error in the message in case the SQL gets any kind of errors.
Looker Blocks
The looker blocks are defined as the pre-built pre-built models of data that are used for accelerating the sources of data and their analytics patterns. These are also called the entry points used for flexible, easy, and quick analytics.
Let us have a look at a few looker blocks and discuss their usage:
Source Blocks: These are the data sources for the analytics, working as a third party. Analytic Blocks: Helps in various types of analysis of the design patterns Data Tools: They have multiple data analytic techniques. Data Blocks: They have public data which is pre-modeled. Viz Blocks: Represents the output of the query of the type custom visualization Embedded Blocks: Embeds the data into customized apps
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Looker Dashboard
Looker Dashboard is defined as a collection of queries that can be displayed in the form of visualizations on a screen as a dashboard. Looker dashboard helps users in altering filters on the dashboards, setting up the delivery schedules on the dashboard, applying alerts to the tiles and downloading data on the dashboard, etc.
The looker dashboard helps in building a query using the steps below:
1. The name has to be given to the query 2. The field and filters needed to be assigned to the query 3. The visualization options need to be configured 4. Click Run once the query is setup 5. The query is saved as a tile using save on the dashboard.
There are a few things that users can perform using the looker dashboard:
Viewing the dashboard: It helps in a lot of ways such as changing filter values of the dashboard, pinning the e dashboard, updating data on the dashboard, drilling and exploring data points, viewing the LookML dashboard, legacy dashboard, etc.
Creating user-defined dashboard: Used for creating dashboard tiles and creation of dashboards.
Editing user-defined dashboard: The editing part such as re-arranging, editing tiles of the dashboard, settings of the dashboard, deleting the dashboard, etc.
Adding saved content to the dashboard: It performs features such as editing, configuring, addition, and deleting the dashboard.
Cross-filtering dashboard: It performs features like cross-filtering, drilling, sharing, etc.
Scheduling dashboard: It helps in sending a dashboard to the email after scheduling it.
Comparison of user-defined and LookML dashboard: Understanding various user-defined dashboard features and looker dashboard characteristics.
Usage
The looker’s Usage page is defined as the dashboard-created dashboard for presenting useful information related to the Looker instance. It enables the admins to use their data and understand it better for further utilizing it in the applications. The usage page is found under the Server section in the admin menu.
The i_ _ Looker Model
The information present in the looker model is contained in the i _ _ looker. It helps the user in building custom and useful reports.
Usage Dashboard
A user can access the usage dashboard using the looker’s Admin page.
The download can be scheduled in the usage dashboard in a similar manner to any other dashboard. The metrics can be drilled along with the elements easily.
Query by Source Title
The query source title is present at the top of the Usage page and it contains all the information about the queries and the number of queries present in the Looker. The sources for this are mentioned below:
Dashboard: It has queries related to the tiles.
Explore: It has a query related to explorer running.
Renderer: It has a query related to generating images.
Query: It has a query related to the looker’s internal database.
SQL Runner: The queries are run directly in the SQL
Saved Look: It contains queries related to looks
Public Embed: The query can run from the content accessed via a non-private URL.
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Looker is an amazing tool to work with as its features and robustness are great. Looker is defined as a self-service big data and BI (Business Intelligence) software that helps in solving the problem of SQL in creating data analytics. The looker’s dashboard is defined as a collection of queries that can be displayed in the form of visualizations on a screen as a dashboard. In this article, we have discussed more lookers such as its working and its features along with the usage model and i _ _ looker model.
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