Is AI leading to a financial crisis?

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SEC CHAIR SAYS AI MIGHT LEAD TO A FINANCIAL CRISIS 🙃

Sound the alarms! AI is coming for everyone’s wallet. 💸

What’s up? The Chair of the U.S. Securities and Exchange Commission (SEC), Gary Gensler, has sounded a cautionary note regarding the rapid integration of AI in the financial industry. In a recent interview with the Financial Times, Gensler expressed concerns that the adoption of AI on Wall Street could potentially trigger a financial crisis.

What was said? Gensler emphasized that it is "nearly unavoidable" that AI might lead to a market crash in the late 2020s or early 2030s. This risk is associated with the heavy reliance on AI models developed by tech companies, which could result in economic chaos if those models fail.

What can be done? Amidst major banks like Morgan Stanley and JPMorgan leveraging AI models to enhance their trading and investment strategies, Gensler called for the implementation of comprehensive AI regulation, addressing both the underlying AI models created by tech companies and their usage within financial institutions. He termed this a "cross-regulatory challenge," highlighting the complexity of ensuring financial stability in an era of widespread AI adoption.

NEW AI TOOL DETECTS PAIN OF THOSE UNDERGOING SURGERY 😷

Pain, pain, go away. Come again another day. 🤕

What’s new? Presented at the annual ANESTHESIOLOGY 2023 meeting in San Francisco, a new AI pain recognition tool may detect patients’ pain before, during, and after surgery.

What is the tool? This pain recognition system combines computer vision and deep learning, effectively giving the computer "eyes" to analyze visual cues and assess patients' pain. Traditional methods like the Visual Analog Scale (VAS) and the Critical-Care Pain Observation Tool (CPOT) can be influenced by biases, potentially affecting pain management and overall health outcomes. This system offers a solution based on raw data and machine learning.

How was the model trained? Researchers provided the AI model with 143,293 facial images from 69 patients who underwent various elective surgical procedures, including knee and hip replacements as well as complex heart surgeries. They trained the AI to differentiate between 115 pain episodes and 159 non-pain episodes by presenting each raw facial image. The AI identified patterns, with its focus primarily on facial expressions and specific areas like the eyebrows, lips, and nose, as revealed through heat maps.

Why is this important? Early pain detection and treatment can reduce hospital stays and prevent long-term suffering. Integrating cameras in recovery rooms with this model would allow for the continuous monitoring of the pain of patients, saving time for staff and freeing them up for those who need immediate attention. Of course, emerging privacy issues will need to be addressed.

HOW AI IS HELPING BRAIN TUMOR TREATMENT 🧠

It is truly incredible what AI can do when it puts its mind to it. 🤖

What’s up? In the Netherlands, a team of scientists is pioneering the use of AI to aid surgeons in making smarter decisions during brain tumor operations, reducing stress and improving patient outcomes. Their research was recently published in Nature.

What is the system? This AI system, named Sturgeon, harnesses deep learning to analyze DNA and rapidly provide results that previously took days to obtain. One of the key benefits of this technology is early-stage tumor identification, which empowers surgeons to determine the best approach for a given tumor during lengthy procedures. This individualized approach may eventually lead to targeted therapies for different tumor subtypes.

How effective is it? In testing, the Sturgeon system correctly diagnosed 45 out of 50 frozen tumor samples from previous brain cancer procedures in just 40 minutes. Real-world testing involved 25 brain surgeries, primarily on young patients. The AI system delivered 18 accurate diagnoses and, in seven cases, couldn't meet the required confidence level. Nevertheless, the Sturgeon system managed to complete its diagnostics in under 90 minutes, allowing it to guide crucial decisions during the surgery.

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