Explore the rapidly evolving world of artificial intelligence, machine learning, and automation. From AI ethics to real-world applications, this category delivers insights that matter for today and tomorrow.
Welcome back to The State of AI, a new collaboration between the Financial Times and MIT Technology Review. Every Monday for the next two weeks, writers from both publications will debate one aspect of the generative AI revolution reshaping global…
Large language models (LLMs) are mainly trained to generate text responses to user queries or prompts, with complex reasoning under the hood that not only involves language generation by predicting each next token in the output sequence, but also entails…
A US telecom company trained an AI model on years of inmates’ phone and video calls and is now piloting that model to scan their calls, texts, and emails in the hope of predicting and preventing crimes. Securus Technologies president…
With some needed infrastructure now being developed for agentic commerce, enterprises will want to figure out how to participate in this new form of buying and selling. But it remains a fragmented Wild West with competing payment protocols, and it’s…
As AI, cloud, and other technology investments soar, organizations have to make investment decisions with increased speed and clarity. Practices like FinOps, IT financial management (ITFM), and strategic portfolio management (SPM) help stakeholders evaluate opportunities and trade-offs for maximum value.…
Hybrid cloud security was built before the current era of automated, machine-based cyberattacks that take just milliseconds to execute and minutes to deliver devastating impacts to infrastructure. The architectures and tech stacks every enterprise depends on, from batch-based detection to…
This article is divided into four parts; they are: • Optimizers for Training Language Models • Learning Rate Schedulers • Sequence Length Scheduling • Other Techniques to Help Training Deep Learning Models Adam has been the most popular optimizer for…
Enterprises are investing billions of dollars in AI agents and infrastructure to transform business processes. However, we are seeing limited success in real-world applications, often due to the inability of agents to truly understand business data, policies and processes. While…
This article is divided into two parts; they are: • Fine-tuning a BERT Model for GLUE Tasks • Fine-tuning a BERT Model for SQuAD Tasks GLUE is a benchmark for evaluating natural language understanding (NLU) tasks.
Agent memory remains a problem that enterprises want to fix, as agents forget some instructions or conversations the longer they run. Anthropic believes it has solved this issue for its Claude Agent SDK, developing a two-fold solution that allows an…