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AI and the Future of Intelligent Investing - State Street
Enabling AI at scale for investment firms Organizations can now build AI applications at a scale that was unimaginable just five years ago. In concert, the proliferation of new data sources, rapidly growing volumes of financial data and the ability to extract information from complex unstructured data sources have opened new possibilities for investment firms, asset owners and wealth managers. To turn these possibilities into reality, AI and machine learning (ML) applications require vast quantities of data to build their underlying models. Deep learning models are trained on millions of historical data records to make predictions, forecasts and classifications. Large language models (LLMs) are trained on thousands of documents and other unstructured sources to generate natural language content. Terabyte data volumes are the norm with some applications ingesting petabytes of data. You can’t get there from here: the imperative for high-quality data Ensuring that massive volumes of disparate structured and unstructured data is fit for purpose requires a solid data management foundation capable of capturing, curating, enriching and delivering data sets to AI and ML modeling engines. Time to information is also critical. Many use cases require forecasts and predictions in near real time due to the short shelf life of actionable investment and risk data. Legacy databases, spreadsheets and data silos present major obstacles to leveraging the benefits of AI. The considerable cost and effort of connecting these disparate data sources to a centralized, AI-ready repository rarely delivers the expected benefits. A new data management paradigm is required. The ability to harness AI at scale is underpinned by significant advances in cloud computing. Data scientists can train massive AI models at whatever scale and speed they want, simply by requisitioning more computing resources from the cloud. Transparent usage metering enables firms to make informed decisions balancing computing cost with model training times. Additionally, investment firms can improve operational effectiveness by outsourcing non-core activities like server maintenance, performance monitoring and software upgrades to their cloud provider, thereby reducing the need for costly internal operations teams. Deploying technology like the Snowflake Data Cloud also increases scale, by eliminating data movement – data is accessed in-place – enabling complex data sets for AI/ML modeling to be assembled in se...
The Nasdaq Just Touched New Highs, and BlackRock Says the AI Trade is ...
The AI trade continues to pull in a greater range of companies—from semiconductor suppliers to firms building datacenters, said Blackrock's Head of iShares Investment Strategy for the Americas ...
Stock Market Today, April 21: Opendoor Technologies Rises as ...
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Weekly market commentary | BlackRock Investment Institute
Stay tuned for insights on hot topics and latest trends in the financial market via the Weekly commentary by the BlackRock Investment Institute.
