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Judge Declares Google An Illegal Monopoly In Federal Court
- Written by Andrea Miliani Former Tech News Expert
- Fact-Checked by Justyn Newman Former Lead Cybersecurity Editor
A federal judge in the United States ruled this week that Google has been illegally maintaining its dominance in online searches.
“After having carefully considered and weighed the witness testimony and evidence, the court reaches the following conclusion: Google is a monopolist, and it has acted as one to maintain its monopoly,” declared Judge Amit P. Mehta of the U.S. District Court for the District of Columbia in the 277-page document published on Monday.
According to the judge’s ruling, Google has been paying billions of dollars to become the world’s default search engine for citizens across the world. The data provided by the judge shows that by 2020 Google held 90% of web searches and 96% on mobile. And, as reported by Reuters , Mehta noted that only in 2021, Google paid $26.3 billion to maintain dominance. In that same year, advertisers paid $150 billion to Google to reach users through its popular browser.
Judge Mehta’s decision represents the Federal Government’s first big win in the modern Internet era. According to Reuters, the verdict comes weeks after finishing hearing arguments from both sides—closing arguments were presented in May this year—. However, the case was filed by the former Trump administration—conducted by the Justice Department and the Federal Trade Commission (FTC)— years ago, in 2020 .
According to the New York Times , this decision will have a major impact on the company and the Big Tech world. “This is the most important antitrust case of the century, and it’s the first of a big slate of cases to come down against Big Tech,” said Rebecca Haw Allensworth, a professor at Vanderbilt University to the newspaper.
Image from Freepik
AI Tool Set To Enhance Surgical Training
- Written by Kiara Fabbri Former Tech News Writer
- Fact-Checked by Justyn Newman Former Lead Cybersecurity Editor
Researchers have developed an AI tool for surgical training . It is designed to improve the learning process for surgeons. The tool analyzes video recordings of surgical techniques. It provides real-time feedback to trainees.
Led by Dean Suvranu De, the team developed a platform called VBA-Net. This tool uses deep learning to differentiate between expert and novice surgeons through video analysis. The AI provides comprehensive feedback, including overall scores and specific areas for improvement.
Beyond basic assessment, VBA-Net offers personalized feedback tailored to each surgeon’s strengths and weaknesses. This approach is designed to optimize the learning process and accelerate skill development.
De explained , “The more training and feedback surgeons-in-training receive, the more their skills will improve”
Additionally, the system incorporates Explainable Artificial Intelligence (XAI), which allows users to understand the AI’s decision-making process. This transparency is intended to build trust in the AI’s assessments. Furthermore, VBA-Net operates with minimal hardware requirements, using a standard camera setup.
“Our objective is to streamline the evaluation process by guiding trainees in their focus to the most critical facets of a surgical procedure,” De said. “Our ultimate aspiration is to enhance patient outcomes, save lives and cultivate more well-trained surgeons in the future.”
While AI holds immense promise for revolutionizing surgical training, past research highlights some key limitations to consider.
One concern is that AI technology might encounter unforeseen situations during surgery, something it wouldn’t have been trained on. This underscores the importance of physician oversight. Surgeons need to be able to critically assess the AI’s decisions and take corrective actions when necessary.
Furthermore, as highlighted by Eugene Kruglik, a Healthcare Development Expert, limited and inconsistent data sets pose another significant challenge. The quality and quantity of data used to train AI models directly affects their accuracy and reliability.
By acknowledging these limitations, we can ensure a more responsible and effective integration of AI into surgical training.
