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  • How AI could lead to a better understanding of the brain

How AI could lead to a better understanding of the brain

Also: Microsoft-backed study shows AI investments producing returns


Today's newsletter brings you insights from the intersection of AI and various sectors. In neuroscience, AI's emulation of neural activity holds the promise to revolutionize brain modeling, overcoming challenges of data detail and predictive accuracy. Goldman Sachs forecasts that by 2027, AI could start boosting U.S. GDP by automating a significant portion of labor. Microsoft's Azure update for startups underscores its commitment to accelerating AI breakthroughs, providing high-performance infrastructure to Y Combinator's startups. In healthcare, AI applications could save billions in annual spending and dramatically improve medical outcomes. A Microsoft-backed study reveals substantial ROI from AI investments across industries, while Qraft's latest AI-driven ETF aims to leverage AI for smarter investment strategies. Figma's AI-powered tools seek to improve meeting efficiency and IBM has announced a $500 million fund for AI startups, marking its increased engagement with the tech innovation landscape.


  • 🧠 How AI could lead to a better understanding of the brain

  • 📈 AI may start to boost US GDP in 2027

  • 🤝 Startups to access high-performance Azure infrastructure, accelerating AI breakthroughs

  • ❤️‍🩹 AI Applications in Healthcare: From Drug Discovery to Patient Care

  • 📊 Microsoft-backed study shows AI investments producing returns

  • 💰 Qraft rolls out latest AI-driven ETF with South Korea's LG

The article explores the potential of machine learning in simulating brain activity, a concept rooted in efforts to model brain neural networks since the 1940s. Advances in brain data acquisition, particularly connectomes that map neuron connectivity and morphology, coupled with enhanced functional recording techniques and transcriptomics, have set the stage for AI to enhance brain modeling. Machine learning can utilize these diverse datasets to emulate neural activity seen in biological systems. This integration could address challenges such as the detail required in current data and the predictive capabilities of models. The article acknowledges the difficulties in explaining AI model predictions and the limitations when confronted with data outside their training scope. However, machine-learning approaches in fields like weather prediction have shown promising results, suggesting potential benefits for brain modeling. Challenges ahead include the need for comprehensive brain data, collaborations to acquire multimodal datasets, and clear benchmarks for evaluating model performance. The aim is to enrich the understanding of neural activity through AI, potentially revolutionizing neuroscience research.

Goldman Sachs Research suggests that generative AI is poised to significantly automate work tasks, potentially boosting global economic growth, particularly in the U.S. where its impact on GDP could be measurable by 2027. This growth is expected to be derived from AI's ability to automate roughly 25% of labor in advanced economies and 10-20% in emerging markets, leading to labor cost savings and freeing up worker time. However, the adoption and impact of AI will vary by region and depend on several factors, including technological capabilities, implementation strategies, and the speed at which AI is embraced across different industries. The full realization of AI's economic benefits, while promising, is anticipated to unfold over the next decade and beyond, with a profound long-term influence on economic performance, financial markets, and interest rates.

Microsoft has updated its startup support program to provide startups with free access to Azure's AI infrastructure, including high-performance GPU clusters to train and run AI models. Y Combinator's startups will be the initial beneficiaries, with Microsoft's collaboration aiming to meet the infrastructure demands of AI-focused early-stage companies. Alongside hardware, Microsoft Azure will offer tools for easy deployment and model training management and secure AI solutions adhering to responsible AI principles. The initiative, backed by high demand for generative AI among U.S. CEOs, is part of Microsoft's broader commitment to facilitate rapid AI innovation and scale-up for startups across various sectors.

AI is emerging as a transformative force in healthcare, potentially improving medical outcomes by 30-40% and saving up to 10% of annual healthcare spending in the U.S. AI can enhance every step of drug discovery, reducing the traditionally long and costly process by aiding in molecule construction, maximization of investigational efforts, and enhanced patient identification. Additionally, AI can modernize clinical trials by improving trial design, participant recruitment, and data sufficiency, with potential savings of $13 billion in participant identification. AI's role in healthcare operations could lead to significant cost savings by streamlining tasks such as information extraction from medical records, assisting with notes for EMR integration, and reducing clerical burdens, with potential savings amounting to billions annually across private insurance firms, physician groups, and hospitals.

The comprehensive survey findings sponsored by Microsoft, as detailed in the infographic "The Business Opportunity of AI", reveal the significant impact artificial intelligence (AI) is making across industries. The report, put together by IDC in November 2023, highlights that organizations investing in AI are realizing substantial returns, averaging $3.5 for every $1 invested, and some even seeing an $8 return. A majority of AI implementations are completed within a year, with many taking less than six months, leading to a swift return on investment within an average of 14 months. AI is predominantly utilized for automating IT tasks, fraud detection, cybersecurity, and business processes, with 71% of organizations currently deploying AI and a further 22% planning to do so within a year. This has led to marked improvements in customer satisfaction, employee productivity, and market share.

Qraft Technologies has launched its fifth AI-driven exchange-traded fund (ETF), in collaboration with South Korea's LG, to expand the niche market for AI ETFs. The LG Qraft AI-Powered U.S. Large-Cap Core ETF integrates Qraft's AI models with LG's AI forecasting tools for selecting portfolio holdings. Qraft employs data scientists to develop these models from financial and macro-economic data. The ETF will maintain a 100-stock portfolio, rebalanced every four weeks, with an annual fee of 0.75%. The success of AI in investment strategies is gaining traction, with the expectation that the removal of human emotion from stock selection will be a significant advantage.

🛠️ AI tools updates

FigJam AI, a significant enhancement to Figma's collaborative interface. FigJam AI, which integrates OpenAI technology, aims to streamline the organization and summarization of meeting content. It operates via an icon that opens a widget for user input, functioning similarly to ChatGPT, to create meeting agendas, org charts, flow charts, and even suggest agenda items from simple prompts. The tools also include functions to sort and summarize content, addressing issues of cluttered brainstorm sessions by automatically organizing and condensing the outcomes. This development is particularly geared towards non-designers, with two-thirds of Figma's daily active users coming from non-design backgrounds. Additionally, FigJam AI addresses productivity within corporate workflows, allowing for more engaging meetings than platforms like Zoom or Slack alone. With this focus on productivity rather than design itself, FigJam AI supports middle managers and other team members by saving time and reducing the minutiae of meeting preparation.

💵 Venture Capital updates

IBM has launched a $500 million venture fund to invest in generative AI startups aimed at enterprise clients, signaling a new openness to innovation and collaboration with emerging tech companies. This move underlines IBM's transformation over the past three years into a company that actively engages with the startup ecosystem, contrasting with its historical image. The fund will not adhere to a fixed investment schedule but will support startups at various stages, particularly those creating tools for specific industries like healthcare and processes that do not directly compete with IBM's products. IBM has already made investments in companies like Hugging Face and HiddenLayer. The initiative reflects a broader trend of businesses increasingly seeking tangible returns on their AI investments and may see significant technological advancements coming from outside the U.S. IBM's strategy includes avoiding investments in direct competitors to their existing portfolio companies.

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