Any far-reaching new technology is always uneven in its adoption, but few have been more uneven than generative AI. That makes it hard to assess its likely impact on individual businesses, let alone on productivity across the economy as a whole. At one extreme, AI coding assistants have revolutionized the work of software developers. At the other extreme, most companies are seeing little if any benefit from their initial investments. That has provided fuel for the skeptics who maintain that--by its very nature as a probabilistic technology prone to hallucinating--generative AI will never have a deep impact on business. To students of tech history, though, the lack of immediate impact is normal.

Recent outages have revealed how vulnerable the internet is, but there seems to be no official plan in the event of a catastrophic failure. Vladimir Lenin is said to have warned that all societies are three square meals from chaos. But in the modern world, it is only a Wi-Fi signal that separates us from anarchy. Every aspect of our lives is reliant on computers and the internet, and when they fail, they do so with disorientating speed. This became abundantly clear during power cuts across Spain and Portugal earlier this year.

Plus: OpenAI has unveiled estimates of how many of ChatGPT's weekly users are experiencing severe mental health symptoms "We will never build a sex robot," says Mustafa Suleyman Mustafa Suleyman, CEO of Microsoft AI, is trying to walk a fine line. On the one hand, he thinks that the industry is taking AI in a dangerous direction by building chatbots that present as human: He worries that people will be tricked into seeing life instead of lifelike behavior. On the other hand, Suleyman runs a product shop that must compete with those peers. Last week, Microsoft announced a string of updates to its Copilot chatbot designed to make Copilot more expressive, engaging, and helpful. Will Douglas Heaven, our senior AI editor, talked to Suleyman about the tension at play when it comes to designing our interactions with chatbots and his ultimate vision for what this new technology should be. A few weeks ago, I set out on what I thought would be a straightforward reporting journey.

Northeastern University, Boston, US A Abstract: Large Language Models (LLMs) are capable of solving complex math problems or answer difficult questions on almost any topic, but can they generate random street addresses for European cities? Large Language Models (LLMs) have shown impressive performance across a wide range of task s, such as answering questions on virtually any topic. However, there remain areas in wh ich their performance falls short, for example, seemingly simple tasks like counting the letters in a word. In this column, we explore another such challenge: generatin g random street addresses for four major European cities. Our results reveal that LLMs exhibit strong biases, repeatedly selecting a limited set of streets and, for some models, even specific street numbers. Surprisingly, so me of the more prominent and ico nic streets are not selected by the models and the most frequent numbers in the responses lack any clear significance.

Productivity culture encourages us to live inside our tasks and projects. But nature offers its own organizational system. This summer, I reread the novel " Aurora," by Kim Stanley Robinson, a science-fiction writer whom I profiled a few years ago. Robinson has an ecological orientation, and "Aurora" is basically a book about how we fit into nature. It ends on a beach, with an extended description of swimming in big waves. It's early morning, and the waves, as they rise, "turn a deep translucent green."

Recommender systems for niche and dynamic communities face persistent challenges from data sparsity, cold start users and items, and privacy constraints. Traditional collaborative filtering and content-based approaches underperform in these settings, either requiring invasive user data or failing when preference histories are absent. We present DeepNaniNet, a deep neural recommendation framework that addresses these challenges through an inductive graph-based architecture combining user-item interactions, item-item relations, and rich textual review embeddings derived from BERT. Our design enables cold start recommendations without profile mining, using a novel "content basket" user representation and an autoencoder-based generalization strategy for unseen users. We introduce AnimeULike, a new dataset of 10,000 anime titles and 13,000 users, to evaluate performance in realistic scenarios with high proportions of guest or low-activity users. DeepNaniNet achieves state-of-the-art cold start results on the CiteULike benchmark, matches DropoutNet in user recall without performance degradation for out-of-matrix users, and outperforms Weighted Matrix Factorization (WMF) and DropoutNet on AnimeULike warm start by up to 7x and 1.5x in Recall@100, respectively. Our findings demonstrate that DeepNaniNet delivers high-quality, privacy-preserving recommendations in data-sparse, cold start-heavy environments while effectively integrating heterogeneous content sources.

One of the key challenges to predict odor from molecular structure is unarguably our limited understanding of the odor space and the complexity of the underlying structure-odor relationships. Here, we show that the predictive performance of machine learning models for structure-based odor predictions can be improved using both, an expert and a data-driven odor taxonomy. The expert taxonomy is based on semantic and perceptual similarities, while the data-driven taxonomy is based on clustering co-occurrence patterns of odor descriptors directly from the prepared dataset. Both taxonomies improve the predictions of different machine learning models and outperform random groupings of descriptors that do not reflect existing relations between odor descriptors. We assess the quality of both taxonomies through their predictive performance across different odor classes and perform an in-depth error analysis highlighting the complexity of odor-structure relationships and identifying potential inconsistencies within the taxonomies by showcasing pear odorants used in perfumery. The data-driven taxonomy allows us to critically evaluate our expert taxonomy and better understand the molecular odor space. Both taxonomies as well as a full dataset are made available to the community, providing a stepping stone for a future community-driven exploration of the molecular basis of smell. In addition, we provide a detailed multi-layer expert taxonomy including a total of 777 different descriptors from the Pyrfume repository.

In this very personal workography, I relate my 40-year experiences as a researcher and educator in and around Artificial Intelligence (AI), more specifically Natural Language Processing. I describe how curiosity, and the circumstances of the day, led me to work in both industry and academia, and in various countries, including The Netherlands (Amsterdam, Eindhoven, and Utrecht), the USA (Stanford), England (Brighton), Scotland (Aberdeen), and China (Beijing and Harbin). People and anecdotes play a large role in my story; the history of AI forms its backdrop. I focus on things that might be of interest to (even) younger colleagues, given the choices they face in their own work and life at a time when AI is finally emerging from the shadows.