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  1. 8 Cool New AI Papers: From Anxious LLMs to Fake News Detection

    Bilal Hafeez

    Assessing and Alleviating State Anxiety in LLMs: Finds that trauma prompts can change the ‘state’ anxiety of LLMs, while relaxation ones reduce anxiety! Structured Outputs Enable General-Purpose LLMs to Be Medical Experts: The paper does away with fine-tuning an LLM on medical data and instead introduces a seven-step cognitive process (chain-of-thought) to answer questions. E.g., […]

  2. AI Reflections: Smarter, Faster, Cheaper – But Still Not Ready

    Viresh Kanabar

    Summary Despite significant strides in speed, cost, intelligence, and multi-modal capabilities, AI has yet to move from testing to widespread adoption. Key barriers to adoption include hallucinations, limited memory, reliance on good prompts, and poor real-world adaptability. For AI to deliver real business value, there must be a shift in focus from just improving models […]

  3. AI Reflections: DeepResearch, Grok and Disappointment

    Bilal Hafeez

    If you spend time on X or Twitter, you would think the release of Grok 3.0 and ChatGPT’s DeepResearch models marks the dawn of superintelligence (AGI). In reality, these advances highlight the limitations of large language models (LLMs) and how far we still are from AGI. At their core, these models are only as good […]

  1. AI Reflections: 3 Things Everyone Is Missing About DeepSeek

    Bilal Hafeez

    Everyone has a take on DeepSeek. In most cases, it simply re-affirms their prior views (US is weak and China is winning; US is strong, and China is cheating, etc.). But here are three things that most people are missing (well, two things really): 1). Lower Costs DeepSeek has delivered way lower inference costs for […]

  2. AI Reflections: 4 Thoughts on the DeepSeek Model

    Bilal Hafeez

    Listen to an AI-generated audio summary of this article:   New Kid on the Block The AI world is abuzz with the Chinese DeepSeek LLM model that apparently costs much less to build than OpenAI’s ChatGPT, performs better than the OpenAI o1 model and is cheaper for users to run. That is at least what […]

  3. Ep. 248: Shawn Edwards on How Bloomberg Is Using AI (Replay)

    Bilal Hafeez

    Shawn Edwards is Bloomberg’s Chief Technology Officer. Based in New York, he oversees the development of Bloomberg’s global technology strategy.

Ep. 235: Christian Schwarz on AI Bubbles, Using AI in Trading and Synthetic Data

Bilal Hafeez

Christian is Managing Director and Head of Data Science in the financial markets division at Standard Chartered. He focuses on credit and previously worked at Mizuho and Credit Suisse.

Ep. 230: Shawn Edwards on How Bloomberg Is Using AI

Bilal Hafeez

Shawn Edwards is Bloomberg’s Chief Technology Officer. Based in New York, he oversees the development of Bloomberg’s global technology strategy.

  1. Ep. 213: Bilal Hafeez on Fed Cuts, Dollar Strength and AI Hype

    Bilal Hafeez

    Bilal is the CEO and Head of Research at Macro Hive. Before that, Bilal was Global Head of International Fixed Income Strategy at Nomura, and Head of Multi-Asset Research and Advisor to the CEO at Deutsche Bank.

  2. 3 Ways AI Is Revolutionizing Finance

    Matthew Tibble

    Everyone from Main Street to Wall Street has been scrambling to deploy AI since ChatGPT dropped in late 2022. By one estimate, 2023 saw AI-related sales jump 29% to hit a whopping $166 billion. By 2027, we will be staring at a colossal $400 billion market. And the financial sector will be responsible for a […]

  3. How Smart Am AI?

    Matthew Tibble

    AI is all anyone can talk about since the advent of ChatGPT. From revolutionizing healthcare with personalized medicine to generating trading signals in financial markets, every industry is claiming a new future for itself thanks to the technology. And perhaps rightly so. But how smart is this new technology? And perhaps more fundamentally, how do […]

Brain Power: Is the Key to AI Efficiency Inside Our Heads?

Matthew Tibble

AI has a power problem. Even simple graphics processors for AI applications consume several hundred watts. But the most powerful computer we use for machine learning? That award goes to Frontier, which consumes 20 megawatts a year. That is $40 million of electricity. Frontier boasts an exaflop of compute: a billion, billion floating point mathematical […]

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