Alphabet's Google needs strong measures imposed on it to prevent it from using its artificial intelligence products to extend its dominance in online search, a U.S. Department of Justice attorney said as a trial in the historic antitrust case began on Monday. The outcome of the case could fundamentally reshape the internet by unseating Google as the go-to portal for information online. The Justice Department is seeking an order that would require Google to sell its Chrome browser and take other measures to end what a judge found was its monopoly in online search. Prosecutors have compared the lawsuit to past cases that resulted in the breakup of AT&T and Standard Oil.

Help-seeking is a critical aspect of learning and problem-solving for computing students. Recent research has shown that many students are aware of generative AI (GenAI) tools; however, there are gaps in the extent and effectiveness of how students use them. With over two years of widespread GenAI usage, it is crucial to understand whether students' help-seeking behaviors with these tools have evolved and how. This paper presents findings from a repeated cross-sectional survey conducted among computing students across North American universities (n=95). Our results indicate shifts in GenAI usage patterns. In 2023, 34.1% of students (n=47) reported never using ChatGPT for help, ranking it fourth after online searches, peer support, and class forums. By 2024, this figure dropped sharply to 6.3% (n=48), with ChatGPT nearly matching online search as the most commonly used help resource. Despite this growing prevalence, there has been a decline in students' hourly and daily usage of GenAI tools, which may be attributed to a common tendency to underestimate usage frequency. These findings offer new insights into the evolving role of GenAI in computing education, highlighting its increasing acceptance and solidifying its position as a key help resource.

Large Language Models have revolutionized code generation ability by converting natural language descriptions into executable code. However, generating complex code within real-world scenarios remains challenging due to intricate structures, subtle bugs, understanding of advanced data types, and lack of supplementary contents. To address these challenges, we introduce the CoCoST framework, which enhances complex code generation by online searching for more information with planned queries and correctness testing for code refinement. Moreover, CoCoST serializes the complex inputs and outputs to improve comprehension and generates test cases to ensure the adaptability for real-world applications. CoCoST is validated through rigorous experiments on the DS-1000 and ClassEval datasets. Experimental results show that CoCoST substantially improves the quality of complex code generation, highlighting its potential to enhance the practicality of LLMs in generating complex code.

In decision-making problems with limited training data, policy functions approximated using deep neural networks often exhibit suboptimal performance. An alternative approach involves learning a world model from the limited data and determining actions through online search. However, the performance is adversely affected by compounding errors arising from inaccuracies in the learnt world model. While methods like TreeQN have attempted to address these inaccuracies by incorporating algorithmic structural biases into their architectures, the biases they introduce are often weak and insufficient for complex decision-making tasks. In this work, we introduce Differentiable Tree Search (DTS), a novel neural network architecture that significantly strengthens the inductive bias by embedding the algorithmic structure of a best-first online search algorithm. DTS employs a learnt world model to conduct a fully differentiable online search in latent state space. The world model is jointly optimised with the search algorithm, enabling the learning of a robust world model and mitigating the effect of model inaccuracies. We address potential Q-function discontinuities arising from naive incorporation of best-first search by adopting a stochastic tree expansion policy, formulating search tree expansion as a decision-making task, and introducing an effective variance reduction technique for the gradient computation. We evaluate DTS in an offline-RL setting with a limited training data scenario on Procgen games and grid navigation task, and demonstrate that DTS outperforms popular model-free and model-based baselines.

What makes all of this all the more shocking is that it came as a surprise to precisely no one who has been paying attention to AI language models. Here's the problem: the technology is simply not ready to be used like this at this scale. AI language models are notorious bullshitters, often presenting falsehoods as facts. They are excellent at predicting the next word in a sentence, but they have no knowledge of what the sentence actually means. That makes it incredibly dangerous to combine them with search, where it's crucial to get the facts straight.

Since OpenAI released ChatGPT, there has been a lot of speculation about what its killer app will be. And perhaps topping the list is online search. According to The New York Times, Google's management has declared a "code red" and is scrambling to protect its online search monopoly against the disruption that ChatGPT will bring. ChatGPT is a wonderful technology, one that has a great chance of redefining the way we create and interact with digital information. It can have many interesting applications, including for online search.

It is a lack of knowledge about the AV due to confusing terminology from the industry. Industry stakeholders must work together to ensure clear and consistent messaging and the use of consumer-facing terminology is part of this. Understanding which words and phrases resonate with consumers can help manage misconceptions and improve consumer understanding of AV. Some 56% of study respondents thought current driver technologies are the same as fully automated #selfdrivingcars systems. Consumers showed further confusion when asked about terminology used to describe different levels of #automation.

My co-authors were Christine Strutt of Von Seidels in Cape Town, South Africa and Francine Ward of the Law Office of Francine D. Ward, Palm Desert, California. The article published by INTA in its February 9, 2022, Bulletin, explains how artificial intelligence (AI) is replacing trademark's function in brand selection. Here is a summary of the article. Traditionally, trademarks were shortcuts, identifying and distinguished goods in the marketplace in response to a buyer's needs and self-selected criteria. Trademarks have also protected against human frailty by alleviating confusion, imitation, disparagement and misrepresentation. AI is altering a consumer's browsing, selection and purchasing process.

There are notable examples of online search improving over hand-coded or learned policies (e.g. AlphaZero) for sequential decision making. It is not clear, however, whether or not policy improvement is guaranteed for many of these approaches, even when given a perfect evaluation function and transition model. Indeed, simple counter examples show that seemingly reasonable online search procedures can hurt performance compared to the original policy. To address this issue, we introduce the choice function framework for analyzing online search procedures for policy improvement. A choice function specifies the actions to be considered at every node of a search tree, with all other actions being pruned. Our main contribution is to give sufficient conditions for stationary and non-stationary choice functions to guarantee that the value achieved by online search is no worse than the original policy. In addition, we describe a general parametric class of choice functions that satisfy those conditions and present an illustrative use case of the framework's empirical utility.

Taken in aggregate, the billions of online searches we make every day say a lot about our most private thoughts and biases. Taken in aggregate, the billions of online searches we make every day say a lot about our most private thoughts and biases. When we have a question about something embarrassing or deeply personal, many of us don't turn to a parent or a friend, but to our computers: We ask Google our questions. As millions of us look for answers to questions, or things to buy, or places to meet friends, our searches produce a map of our collective hopes, fears, and desires. Seth Stephens-Davidowitz, a former data scientist at Google, analyzes the information we leave behind on search engines, social media, and even pornography sites.