Market & RegulationInvestment · $52M
Hemispheric Raises $52M to Decode Brain Health with AI
WIRED AIhigh confidence
Why it matters
- →Hemispheric's AI model could revolutionize non-invasive brain diagnostics.
- →The startup's approach may lead to earlier and more accurate diagnoses of cognitive disorders.
- →This development highlights the growing intersection of AI and healthcare, with potential widespread impact.
Hemispheric, a startup co-founded by Gidi Littwin, a former Apple engineer, has raised $52 million to develop AI tools for diagnosing brain health. The company has collected extensive brain activity data from 100,000 individuals to train its deep learning models. These models aim to diagnose cognitive disorders non-invasively, with plans to submit their first product for FDA approval next year. Hemispheric's vision is to make brain diagnostics as accessible as blood tests, potentially revolutionizing mental health care.
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Anthropic Advocates for Stricter AI Regulations
Anthropic is actively advocating for more stringent AI regulations at the state level, asserting that current transparency laws are inadequate for addressing the risks associated with advanced AI systems. The company is backing initiatives like third-party audits and granting enforcement powers to state attorneys general. While some critics argue this could be a tactic to hinder smaller competitors, Anthropic argues that such regulations are essential for large AI developers due to the inherent risks. This approach reflects Anthropic's dedication to AI safety, as it seeks to influence the regulatory landscape in a way that prioritizes responsible AI development.
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AI Models Struggle to Match Baby Learning Skills
AI models, despite their computational power, struggle to learn as efficiently as human infants. The EgoBabyVLM Challenge, developed by researchers from institutions like Meta and Stanford, tests AI's ability to interpret the world through a baby's perspective using video data from infant head cameras. Current models falter with this realistic, unstructured input, highlighting the unique learning capabilities of the human brain. This research suggests that integrating insights from cognitive science could lead to more efficient, human-like AI learning algorithms.
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Thinking Machines Lab Releases Inkling Model
Thinking Machines Lab, founded by former OpenAI executives, has launched its first model, Inkling, which is open-weight and designed to handle audio, video, and text inputs. While not topping benchmarks, Inkling excels in advanced reasoning and coding, boasting 975 billion parameters. The model's open-weight nature allows for modification and adaptation, aligning with the company's vision of decentralized AI development. This release positions Thinking Machines as a significant contender in the AI landscape, challenging established players with its open-source approach.
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Enterprises Struggle with AI Infrastructure Costs
Enterprises are pouring resources into AI infrastructure, yet many are unable to fully grasp the financial implications of these investments. Despite significant spending, only a small percentage of organizations have AI systems operating at full scale, with the majority still in the testing phase. Interest is growing in AI-specialized clouds, suggesting a potential shift away from traditional hyperscalers. This trend reveals a disconnect between investment levels and operational maturity, as companies face challenges with underutilized resources and unclear cost structures.
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54% of Enterprises Face AI Agent Security Incidents
A recent survey reveals a significant security gap in enterprise AI agent management, with over half of the companies experiencing security incidents or near-misses. Despite the high risk, many enterprises continue to permit agents to share credentials, which increases their vulnerability to breaches. The reliance on security solutions from major providers like OpenAI and Google is common, yet satisfaction with these measures remains high even as companies plan to overhaul their security tools. This situation underscores the urgent need for more robust identity and isolation controls to prevent future breaches and ensure that AI agents are managed securely.
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New York Governor Uses AI to Review State Laws
New York Governor Kathy Hochul is turning to AI to efficiently review and update the state's legislative framework. By utilizing AI, her team has managed to analyze every rule, regulation, and policy in just a few months, a task that would have taken years manually. This effort aims to identify and eliminate outdated laws, such as those requiring permits for pregnant people to work after midnight. Hochul's initiative represents a shift towards more efficient governance, using AI to modernize and simplify state regulations. This move could set a precedent for other states to follow in utilizing AI for legislative review, showcasing its potential to enhance governmental efficiency and responsiveness.
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