OpenAI is rolling out the ability to carry on conversations with a human-sounding robot on the ChatGPT app. As artificial intelligence grows in popularity, the latest tech tools are creeping into just about every industry and endeavor -- including wedding planning. A luxury wedding planner this spring shared how brides and grooms are making use of sophisticated AI tools to ease the stress of their big day. Lisa Lafferty, a wedding and event planner in Beverly Hills, California, said she's helped throw some extravagant parties in her decade of experience -- which has given her an up-close look at AI's use in the wedding space. Owner of Beverly Hills Premier Catering, Lafferty expanded her catering business to event planning in 2018 and has since planned events for celebrities, real estate moguls, Fortune 500 brands and more, she said.

At Chicago, I recall undergraduate students gawking about deep learning to Professor Lafferty after class. I recall professor Lafferty had hesitation in his voice at the time. It felt as though he was discussing a controversial, politically-sensitive issue. At that time, we knew only a fraction of what we know now and many of us were still wondering how deep learning could be anything more than non-linear regression. I had no motivation or curiosity to understand the subject and even the trio at Stanford--the ones who gave us the best-selling ML book of all time--only put a few paragraphs in the first edition of their textbook saying just that.

This work remains a milestone in AI research. Samuel's program reportedly beat a master and "solved" the game of checkers. Both journalistic claims were false, but they created the impression that there was nothing of scientific interest left in the game (Samuel himself made no such claims). Consequently, most subsequent game-related research turned to chess. Other than a program from Duke University in the 1970s (Truscott 1979), little attention was paid to achieving a world championship-caliber checker program.

The trucking industry scored a victory this week when Republican lawmakers effectively blocked Obama administration safety rules aimed at keeping tired truckers off the highway. The American Trucking Associations is pledging to come back next month, when Republicans will control the White House and Congress, and try to block state laws that require additional rest breaks for truckers beyond what federal rules require. The group says there should be one uniform national rule on work hours for interstate truckers. The trucking industry's latest triumph has caused concern among safety advocates that it may signal the start of a broad rollback of transportation safety regulations once there's no longer a Democratic president to check the tendency of Republican lawmakers to side with industry. "Unfortunately, it's going to be an open season on safety in this coming Congress."

We show that maximum entropy (maxent) models can be modeled with certain kinds of HMMs, allowing us to construct maxent models with hidden variables, hidden state sequences, or other characteristics. The models can be trained using the forward-backward algorithm. While the results are primarily of theoretical interest, unifying apparently unrelated concepts, we also give experimental results for a maxent model with a hidden variable on a word disambiguation task; the model outperforms standard techniques.

Topic models have proven to be a useful tool for discovering latent structures in document collections. However, most document collections often come as temporal streams and thus several aspects of the latent structure such as the number of topics, the topics' distribution and popularity are time-evolving. Several models exist that model the evolution of some but not all of the above aspects. In this paper we introduce infinite dynamic topic models, iDTM, that can accommodate the evolution of all the aforementioned aspects. Our model assumes that documents are organized into epochs, where the documents within each epoch are exchangeable but the order between the documents is maintained across epochs. iDTM allows for unbounded number of topics: topics can die or be born at any epoch, and the representation of each topic can evolve according to a Markovian dynamics. We use iDTM to analyze the birth and evolution of topics in the NIPS community and evaluated the efficacy of our model on both simulated and real datasets with favorable outcome.

An important challenge in understanding climate change is to uncover the dependency relationships between various climate observations and forcing factors. Graphical lasso, a recently proposed L 1 penalty based structure learning algorithm, has been proven successful for learning underlying dependency structures for the data drawn from a multivariate Gaussian distribution. However, climatological data often turn out to be non-Gaussian, e.g. cloud cover, precipitation, etc. In this paper, we examine nonparametric learning methods to address this challenge. In particular, we develop a methodology to learn dynamic graph structures from spatial-temporal data so that the graph structures at adjacent time or locations are similar. Experimental results demonstrate that our method not only recovers the underlying graph well but also captures the smooth variation properties on both synthetic data and climate data. An important challenge in understanding climate change is to uncover the dependency relationships between various climate observations and forcing factors. Graphical lasso, a recently proposed An important challenge in understanding climate change is to uncover the dependency relationships between various climate observations and forcing factors. Graphical lasso, a recently proposed L 1 penalty based structure learning algorithm, has been proven successful for learning underlying dependency structures for the data drawn from a multivariate Gaussian distribution. However, climatological data often turn out to be non-Gaussian, e.g. cloud cover, precipitation, etc. In this paper, we examine nonparametric learning methods to address this challenge. In particular, we develop a methodology to learn dynamic graph structures from spatial-temporal data so that the graph structures at adjacent time or locations are similar. Experimental results demonstrate that our method not only recovers the underlying graph well but also captures the smooth variation properties on both synthetic data and climate data.

We propose a novel approach to context sensitive semantic smoothing by making use of an intermediate, "semantically light" representation for sentences, called Semantically Relatable Sequences (SRS). SRSs of a sentence are tuples of words appearing in the semantic graph of the sentence as linked nodes depicting dependency relations. In contrast to patterns based on consecutive words, SRSs make use of groupings of non-consecutive but semantically related words. Our experiments on TREC AP89 collection show that the mixture model of SRS translation model and Two Stage Language Model (TSLM) of Lafferty and Zhai achieves MAP scores better than the mixture model of MultiWord Expression (MWE) translation model and TSLM. Furthermore, a system, which for each test query selects either the SRS or the MWE mixture model based on better query MAP score, shows significant improvements over the individual mixture models.

In 1992, the seemingly unbeatable World Checker Champion Marion Tinsley defended his title against the computer program CHINOOK. After an intense, tightly contested match, Tinsley fought back from behind to win the match by scoring four wins to CHINOOK's two, with 33 draws. This match was the first time in history that a human world champion defended his title against a computer. This article reports on the progress of the checkers (8 3 8 draughts) program CHINOOK since 1992. Two years of research and development on the program culminated in a rematch with Tinsley in August 1994. In this match, after six games (all draws), Tinsley withdrew from the match and relinquished the world championship title to CHINOOK,citing health concerns. CHINOOK has since defended its title in two subsequent matches. It is the first time in history that a computer has won a human-world championship.