MIT Technology Review Insights has published a playbook for crafting an AI strategy

Highlights

? AI's Economic Impact: AI is projected to significantly boost global GDP by 14% by 2030, contributing an estimated $15.7 trillion.

? Automation of Mundane Tasks: Research by the Oxford University indicates that 40% of routine tasks could be automated by AI by 2030.

? Substantial AI Investment: Investment in AI is expected to reach $200 billion by 2025, according to Goldman Sachs.

? Widespread AI Influence: Experts assert that AI will impact every job and function within organizations. “No job, no function will remain untouched by AI,” says SP Singh, senior vice president and global head, enterprise application integration and services, at technology company Infosys.

? Challenges in Scaling AI: Despite optimistic predictions, only 5.4% of U.S. businesses had fully integrated AI into their operations by 2024.

? Barriers to Enterprise-Wide AI Adoption: Moving from AI pilots to full deployment requires strategic changes in #infrastructure, #datagovernance, and supplier ecosystems.

? Importance of Strategic Planning: Organizations must address uncertainties in AI performance and ROI to scale AI across business functions effectively.

? Rising AI Readiness Spending: Companies are planning to significantly increase spending on AI-related activities, including #data readiness and platform modernization.

? Data Liquidity as a Key Factor: The ability to access, combine, and analyze data seamlessly is critical for effective AI deployment.

? Governance and Security Concerns: #Governance, #security, and #privacy issues are major obstacles, slowing down AI deployment for 45% of companies.

? Data Quality Issues: Half of the respondents consider data quality as a significant barrier to AI deployment, especially in large firms with complex IT infrastructure.

? Cautious AI Adoption: Nearly all organizations (98%) prefer to delay AI deployment to ensure it is implemented safely and securely, with larger companies particularly concerned about governance and security.

Cosine introduced AI software engineer, achieving 30.08% on SWE-Bench – ahead of Amazon and Cognition.

This model is so much more than a benchmark score: it was trained from the start to think and behave like a human SWE.

Inside the investor’s mind.

Ilia Eremeev, founder of the games fund shared presentation about how they make investment decisions, identifying what they perceive as risks and opportunities.

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1. ?????????? ???????: AI has been a part of medicine since the mid-20th century, with its role significantly expanding in fields like radiology, cardiology, and neurology.
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3. ????????? ????????????: While 65% of surveyed physicians see definite or some advantage in using AI, 70% express concerns about potential biases, privacy risks, and liability issues.
4. ?????????-???????? ??? ?????: Each medical specialty leverages AI differently, from emergency medicine's vitals monitoring to family medicine's focus on personalized patient education and medication adherence.
5. ?????????????? ???????: 56% of surveyed physicians identify administrative burden reduction through automation as AI's biggest opportunity.

Scale launched SEAL Leaderboards—private, expert evaluations of leading frontier models.

Evaluations are a critical component of the AI ecosystem.

Evals are incentives for researchers, and our evaluations set the goals for how we aim to improve our models.

Trusted 3rd party evals are a missing part of the whole ecosystem, which is why Scale built these.

They eval'd many of the leading models:

- GPT-4o
- GPT-4 Turbo
- Claude 3 Opus
- Gemini 1.5 Pro
- Gemini 1.5 Flash
- Llama3
- Mistral Large

On Coding, Math, Instruction Following, and Multilinguality (Spanish).

Scale

SEAL leaderboards

model-eval-public by Scale

What is going on? OpenAI has said its mission is not to build "superintelligence" in an apparent backtrack from previous comments by Sam Altman, as it readies its new model.

Bing's API has been down since late last night, and is still down. Copilot is down as well. As a consequence of this, Chat's search is also down. This is quite a long downtime for Microsoft, now coming up on seven hours.

Late last night before the outage. Source reddit r/Bing.

If you have Meta glasses their search will also be down. Their Ray-Ban wearable also uses Bing.

It's possible this is part of the 4o transition. Copilot, for now, still runs 4-Turbo. It took them a very long time to move Bing over to 4T last time. Months. It might be different this time, as they have a new team lead.

This is a very interesting paper focused on transparency around the use of #AI and #machinelearning embedded and enabling #medicaldevices from an #FDA regulatory standpoint.

The authors acknowledge the great potential of #AI and #ML in medical devices.

These include early disease detection, new insights into human physiology, improved and personalized diagnostics and therapeutics, as well as a unique ability to learn, adapt, and improve device performance.

At the same time, AI and ML enabled medical devices offer new regulatory challenges.

These include their usability, equity of access, management of performance bias, the potential for continuous learning, and stakeholder (manufacturer, patient, caregiver, healthcare provider, etc.) accountability.

Financial Statement Analysis with Large Language Models

This paper feeds financial statements to GPT-4 and finds that it outperforms financial analysts in predicting the sign of earnings changes. Interestingly, this is achieved despite only feeding GPT-4 with numbers, leaving out any textual context. Moreover, GPT-4’s earnings predictions seem to contain predictive power for future stock returns.

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Financial Statement Analysis with Large Language Models

We investigate whether an LLM can successfully perform financial statement analysis in a way similar to a professional human analyst. We provide standardized an