Chain-of-Thought (CoT) reasoning has significantly advanced state-of-the-art AI capabilities. However, recent studies have shown that CoT reasoning is not always faithful, i.e. CoT reasoning does not always reflect how models arrive at conclusions. So far, most of these studies have focused on unfaithfulness in unnatural contexts where an explicit bias has been introduced. In contrast, we show that unfaithful CoT can occur on realistic prompts with no artificial bias. Our results reveal non-negligible rates of several forms of unfaithful reasoning in frontier models: Sonnet 3.7 (16.3%), DeepSeek R1 (5.3%) and ChatGPT-4o (7.0%) all answer a notable proportion of question pairs unfaithfully. Specifically, we find that models rationalize their implicit biases in answers to binary questions ("implicit post-hoc rationalization"). For example, when separately presented with the questions "Is X bigger than Y?" and "Is Y bigger than X?", models sometimes produce superficially coherent arguments to justify answering Yes to both questions or No to both questions, despite such responses being logically contradictory. We also investigate restoration errors (Dziri et al., 2023), where models make and then silently correct errors in their reasoning, and unfaithful shortcuts, where models use clearly illogical reasoning to simplify solving problems in Putnam questions (a hard benchmark). Our findings raise challenges for AI safety work that relies on monitoring CoT to detect undesired behavior.

Some of 2024's most interesting science, technology, and history stories could be found in international auctions. Regardless of their final winning bids, each of the following items and artifacts are impressive in their own right. From AI-painted artwork to hunks of coal, these auction items highlight the wide range of not just artifacts from the past, but future-forward items, as well. If nearly 45 million sounds like a lot for a dinosaur skeleton to you, you aren't alone. Although billed as one of the "finest" known examples, a stegosaurus named "Apex" almost immediately drew controversy over the summer for a final bid that came in at over 10 times Sotheby's initial estimation.

GPR models can also incorporate prior knowledge through selecting an appropriate kernel function. GPR commonly assumes a homoscedastic Gaussian distribution for observation noise because this yields an analytical form for the posterior predictive prediction. However, Bayesian inference based on Gaussian noise distributions is known to be sensitive to outliers which are defined as observations that strongly deviate from model assumptions. In regression, outliers can arise from relevant inputs being absent from the model, measurement error, and other unknown sources. These outliers are associated with unconsidered sources of variation that affect the target variable sporadically. In this case, the observation model is unable to distinguish between random noise and systematic effects not captured by the model. In the context of GPR under Gaussian noise, outliers can heavily influence the posterior predictive distribution, resulting in a biased estimate of the mean function and overly confident prediction intervals. Therefore, robust observation models are desired in the presence of potential outliers.

We describe a method for the neural decoding of memory from EEG data. Using this method, a concept being recalled can be identified from an EEG trace with an average top-1 accuracy of about 78.4% (chance 4%). The method employs deep representation learning with supervised contrastive loss to map an EEG recording of brain activity to a low-dimensional space. Because representation learning is used, concepts can be identified even if they do not appear in the training data set. However, reference EEG data must exist for each such concept. We also show an application of the method to the problem of information retrieval. In neural information retrieval, EEG data is captured while a user recalls the contents of a document, and a list of links to predicted documents is produced.

Query Rewriting (QR) plays a critical role in large-scale dialogue systems for reducing frictions. When there is an entity error, it imposes extra challenges for a dialogue system to produce satisfactory responses. In this work, we propose KG-ECO: Knowledge Graph enhanced Entity COrrection for query rewriting, an entity correction system with corrupt entity span detection and entity retrieval/re-ranking functionalities. To boost the model performance, we incorporate Knowledge Graph (KG) to provide entity structural information (neighboring entities encoded by graph neural networks) and textual information (KG entity descriptions encoded by RoBERTa). Experimental results show that our approach yields a clear performance gain over two baselines: utterance level QR and entity correction without utilizing KG information. The proposed system is particularly effective for few-shot learning cases where target entities are rarely seen in training or there is a KG relation between the target entity and other contextual entities in the query.

Chris Lister, vice president of operations of the Tesla Gigafactory, provides insight during a tour on Dec. 3, 2018. Big numbers are one way to appreciateTesla's gargantuan Nevada Gigafactory. Operating 24-hours per day in shifts, workers produce enough battery packs and drive units in a week to power 5,300 of Tesla's Model 3 sedans. Tesla says at 5.4 million square feet, roughly equivalent to 50 Home Depot stores, the factory is just 30 percent of its potential size and is already producing more batteries than all other carmakers combined. With more than 7,000 Tesla workers, the factory is responsible for increasing manufacturing employment in the Reno-Sparks area by 55 percent since 2014, according to the Governor's Office of Economic Development.

According to Al Fiegehen, chief executive officer of Cubix Corporation in Carson City, the computer industry is about to explode at the same rate it did in the 1980s when computer technology forever changed the world. The online magazine, Wired, refers to the 1980s as the "Decade That Invented the Future." IBM introduced the PC in 1981 when Microsoft invented MS-Dos; three years later, the Apple McIntosh was introduced. Time magazine referred to 1982 as "The Year of the Computer" and by 1983, about 10 million computers were in use. Fiegehen and his then partner, Don Lehr, were right at the forefront of this technological explosion when computers went from the second-generation memory systems developed by IBM between 1947-1962 to third generation systems with integrated circuits introduced in 1963.

Many people in Silicon Valley believe in the Singularity--the day in our near future when computers will surpass humans in intelligence and kick off a feedback loop of unfathomable change. When that day comes, Anthony Levandowski will be firmly on the side of the machines. In September 2015, the multi-millionaire engineer at the heart of the patent and trade secrets lawsuit between Uber and Waymo, Google's self-driving car company, founded a religious organization called Way of the Future. Its purpose, according to previously unreported state filings, is nothing less than to "develop and promote the realization of a Godhead based on Artificial Intelligence." Mark Harris is a freelance journalist reporting on technology from Seattle. Sign up to get Backchannel's weekly newsletter, and follow us on Facebook and Twitter. Way of the Future has not yet responded to requests for the forms it must submit annually to the Internal Revenue Service (and make publically available), as a non-profit religious corporation. However, documents filed with California show that Levandowski is Way of the Future's CEO and President, and that it aims "through understanding and worship of the Godhead, [to] contribute to the betterment of society." A divine AI may still be far off, but Levandowski has made a start at providing AI with an earthly incarnation. The autonomous cars he was instrumental in developing at Google are already ferrying real passengers around Phoenix, Arizona, while self-driving trucks he built at Otto are now part of Uber's plan to make freight transport safer and more efficient. He even oversaw a passenger-carrying drones project that evolved into Larry Page's Kitty Hawk startup. Levandowski has done perhaps more than anyone else to propel transportation toward its own Singularity, a time when automated cars, trucks and aircraft either free us from the danger and drudgery of human operation--or decimate mass transit, encourage urban sprawl, and enable deadly bugs and hacks. But before any of that can happen, Levandowski must face his own day of reckoning.

Many people in Silicon Valley believe in the Singularity--the day in our near future when computers will surpass humans in intelligence and kick off a feedback loop of unfathomable change. When that day comes, Anthony Levandowski will be firmly on the side of the machines. In September 2015, the multi-millionaire engineer at the heart of the patent and trade secrets lawsuit between Uber and Waymo, Google's self-driving car company, founded a religious organization called Way of the Future. Its purpose, according to previously unreported state filings, is nothing less than to "develop and promote the realization of a Godhead based on Artificial Intelligence." Mark Harris is a freelance journalist reporting on technology from Seattle. Sign up to get Backchannel's weekly newsletter. Way of the Future has not yet responded to requests for the forms it must submit annually to the Internal Revenue Service (and make publically available), as a non-profit religious corporation. However, documents filed with California show that Levandowski is Way of the Future's CEO and President, and that it aims "through understanding and worship of the Godhead, [to] contribute to the betterment of society." A divine AI may still be far off, but Levandowski has made a start at providing AI with an earthly incarnation. The autonomous cars he was instrumental in developing at Google are already ferrying real passengers around Phoenix, Arizona, while self-driving trucks he built at Otto are now part of Uber's plan to make freight transport safer and more efficient. He even oversaw a passenger-carrying drones project that evolved into Larry Page's Kitty Hawk startup. Levandowski has done perhaps more than anyone else to propel transportation toward its own Singularity, a time when automated cars, trucks and aircraft either free us from the danger and drudgery of human operation--or decimate mass transit, encourage urban sprawl, and enable deadly bugs and hacks. But before any of that can happen, Levandowski must face his own day of reckoning.

Commuting may never be the same again in Nevada, the US state that is home to Las Vegas, legalised gambling and huge amounts of desert. Besides gambling, it is now legal there to have a self-driven car – providing it matches up to the specification achieved by Google's autonomous models. The first fully licensed self-driven car – a modified Toyota Prius – won a special permit on Tuesday, the first of three applied for by Google, which allows it to be used on the state's roads, including the famous Las Vegas strip. Autonomous vehicles are the "car of the future," said Bruce Breslow, the director of Nevada's Department of Motor Vehicles (DMV), in a statement. The state also has plans to eventually license autonomous vehicles owned by members of the public, the DMV said.