Große Sprachmodelle sind erstaunlich leistungsfähig. Sie fassen zusammen, übersetzen, schlussfolgern und programmieren (besser als ich). Aber im Gegensatz zu mir sind sie auch dafür berüchtigt geworden, mit beunruhigendem Selbstvertrauen Fakten zu erfinden.
Während Unternehmen sich beeilen, AI in die Praxis umzusetzen, stellen die meisten fest, dass ihre data-Infrastruktur nie für autonomes Schlussfolgern ausgelegt war. Heute entfallen bis zu 80 % der AI-Implementierungszeit auf die Aufbereitung von data-Daten und die Schemaanpassung – ein Symptom für Infrastrukturen, die auf Speicherung ausgelegt sind, nicht auf Verständnis. Ohne eine Grundlage, die Beziehungen und Bedeutungen erfasst, bleiben Agenten zwar leistungsstark, aber blind. Da AI-Agenten zu aktiven Teilnehmern in Unternehmensworkflows werden, verändern sich Art und Umfang der data-Abfragen.
Assortment optimization is a critical process in retail that involves curating the ideal mix of products to meet consumer demand while taking into account the many logistics constraints involved. The retailers need to make sure that they offer the right products, in the right quantities, at the right time. By leveraging data and consumer insights, retailers can make informed decisions on which items to stock, how to manage inventory, and what products to prioritize based on customer preferences, seasonal trends, and sales patterns.
MotherDuck extends DuckDB's analytical performance to the cloud with collaborative features, delivering 4x faster performance than BigQuery and cost savings over traditional data warehouses through serverless, pay-per-use pricing. Following the announcement of MotherDuck’s new European cloud region, we were impressed by its performance and attractive pricing. MotherDuck can already be integrated into your gold layers in order to accelerate the serving of data use cases while saving costs at the same time. See performance benchmark.
Assortment optimization is a critical process in retail that involves curating the ideal mix of products to meet consumer demand while taking into account the many logistics constraints involved. The retailers need to make sure that they offer the right products, in the right quantities, at the right time. By leveraging data and consumer insights, retailers can make informed decisions on which items to stock, how to manage inventory, and what products to prioritize based on customer preferences, seasonal trends, and sales patterns.
Neuronale Metriken für die maschinelle Übersetzung (MT) zur Evaluierung sind aufgrund ihrer überlegenen Korrelation mit menschlichen Urteilen im Vergleich zu traditionellen lexikalischen Metriken immer prominenter geworden.
Discrete choice models aim at predicting choice decisions made by individuals from a menu of alternatives, called an assortment. Well-known use cases include predicting a commuter’s choice of transportation mode or a customer’s purchases.
The abundance and diversity of responses to ChatGPT and other generative AIs, whether skeptical or enthusiastic, demonstrate the changes they're bringing about and the impact they're having far beyond the usual technology circles. This is in stark contrast to previous generations of AI, which were essentially predictive and generally the subject of articles or theses confined to the realm of research and innovation.
In today’s dynamic and ever-evolving tech industry, a career track can often feel like a winding path through a dense forest of opportunities. With rapid advancements in technology, the possibilities are endless, but so are the challenges.
