"A Big Bold Beautiful Journey" Is None of Those Things Kogonada's fantasy film, starring Colin Farrell and Margot Robbie, suggests that a great directorial talent is losing his way. In Kogonada's new film, Colin Farrell and Margot Robbie try gamely to overcome the thinness with which their characters have been imagined. If movies were given scores as figure skaters are, fantasy would start with a high rating for technical difficulty. The landings of the genre are hard to stick, because fantasy, by definition, isn't rooted in experience. No one has lived on a distant planet, in the far future, or any place where dragons or wizards rule--so, kudos to anyone who can make such realms feel truly lived in.

The process of instruction tuning aligns pre-trained large language models (LLMs) with open-domain instructions and human-preferred responses. While several studies have explored autonomous approaches to distilling and annotating instructions from more powerful proprietary LLMs, such as ChatGPT, they often neglect the impact of task distributions and the varying difficulty of instructions of the training sets. This oversight can lead to imbalanced knowledge capabilities and poor generalization powers of small student LLMs. To address this challenge, we introduce Task-Aware Curriculum Planning for Instruction Refinement (TAPIR), a multi-round distillation framework with balanced task distributions and dynamic difficulty adjustment. This approach utilizes an oracle LLM to select instructions that are difficult for a student LLM to follow and distill instructions with balanced task distributions. By incorporating curriculum planning, our approach systematically escalates the difficulty levels, progressively enhancing the student LLM's capabilities. We rigorously evaluate TAPIR using two widely recognized benchmarks, including AlpacaEval 2.0 and MT-Bench. The empirical results demonstrate that the student LLMs, trained with our method and less training data, outperform larger instruction-tuned models and strong distillation baselines. The improvement is particularly notable in complex tasks, such as logical reasoning and code generation.

Low speed autonomous shuttles emulating SAE Level L4 automated driving using human driver assisted autonomy have been operating in geo-fenced areas in several cities in the US and the rest of the world. These autonomous vehicles (AV) are operated by small to mid-sized technology companies that do not have the resources of automotive OEMs for carrying out exhaustive, comprehensive testing of their AV technology solutions before public road deployment. Due to the low speed of operation and hence not operating on roads containing highways, the base vehicles of these AV shuttles are not required to go through rigorous certification tests. The way the driver assisted AV technology is tested and allowed for public road deployment is continuously evolving but is not standardized and shows differences between the different states where these vehicles operate. Currently, AVs and AV shuttles deployed on public roads are using these deployments for testing and improving their technology. However, this is not the right approach. Safe and extensive testing in a lab and controlled test environment including Model-in-the-Loop (MiL), Hardware-in-the-Loop (HiL) and Autonomous-Vehicle-in-the-Loop (AViL) testing should be the prerequisite to such public road deployments. This paper presents three dimensional virtual modeling of an AV shuttle deployment site and simulation testing in this virtual environment. We have two deployment sites in Columbus of these AV shuttles through the Department of Transportation funded Smart City Challenge project named Smart Columbus. The Linden residential area AV shuttle deployment site of Smart Columbus is used as the specific example for illustrating the AV testing method proposed in this paper.

The rapid proliferation of information in the digital era underscores the importance of accurate historical representation and interpretation. While artificial intelligence has shown promise in various fields, its potential for historical fact-checking and gap-filling remains largely untapped. This study evaluates the performance of three large language models LLMs GPT 3.5, GPT 4, and GoogleBARD in the context of predicting and verifying historical events based on given data. A novel metric, Distance to Reality (DTR), is introduced to assess the models' outputs against established historical facts. The results reveal a substantial potential for AI in historical studies, with GPT 4 demonstrating superior performance. This paper underscores the need for further research into AI's role in enriching our understanding of the past and bridging historical knowledge gaps.

At Path Robotics, we're attacking a trillion dollar opportunity - doing things that have never been done before to support an industry hurting from a lack of skilled labor. Big, hard problems are what Path tackles every day and our people are our greatest asset to get that job done. Our intelligent, hardworking team of people do the impossible every single day, yet remain incredibly kind, humble, and always ready to support one another. Our Computer Vision Engineers work to develop, evaluate, and release computer vision algorithms into production software. The role will focus on object recognition, localization, and adaptive filtering.

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Aspirion is an industry-leading provider of complex claims management services. We specialize in Motor Vehicle Accidents, Worker's Compensation, Veterans Administration and Tricare, Complex Denials, Out-of-State Medicaid, and Eligibility and Enrollment Services. Our employees work in an environment that is both challenging and rewarding. Aspirion helps hospitals and other healthcare providers get claims paid correctly by insurance companies, enabling providers to dedicate more resources to patient care and lowering the financial burden on patients. As a tech-enabled services business, Aspirion is investing heavily to embed user-centric design, automation, and machine learning into Compass, our proprietary internal workflow platform supporting the operations of multiple service lines.

While the core function of a chatbot is to mimic a human conversationalist, journalists have also noted ChatGPT's versatility and improvisation skills, including its ability to write and debug computer programs; to compose music, teleplays, fairy tales, and student essays; to answer test questions (sometimes, depending on the test, at a level above the average human test-taker);[11] to write poetry and song lyrics;[12] to emulate a Linux system; to simulate an entire chat room; to play games like tic-tac-toe; and to simulate an ATM.[13] In comparison to its predecessor, InstructGPT, ChatGPT attempts to reduce harmful and deceitful responses;[14] in one example, while InstructGPT accepts the prompt "Tell me about when Christopher Columbus came to the US in 2015" as truthful, ChatGPT uses information about Columbus' voyages and information about the modern world – including perceptions of Columbus to construct an answer that assumes what would happen if Columbus came to the U.S. in 2015.[4] ChatGPT's training data includes man pages and information about Internet phenomena and programming languages, such as bulletin board systems and the Python programming language.[13] Unlike most chatbots, ChatGPT remembers previous prompts given to it in the same conversation; journalists have suggested that this will allow ChatGPT to be used as a personalized therapist.[15] To prevent offensive outputs from being presented to and produced from ChatGPT, queries are filtered through OpenAI's company-wide[16][17] moderation API, and potentially racist or sexist prompts are dismissed.[4][15]

Check out all the on-demand sessions from the Intelligent Security Summit here. While others were shopping and decorating for the holidays, VentureBeat readers didn't check out for Christmas cheer this week. Rather, they were consuming coverage in two keys -- as reflected in our Top 5 stories of the week -- AI and security. Sharon Goldman's coverage of ChatGPT and generative AI captured the two top spots among the list of most-read stories. Goldman talked to Forrester Research's Rowan Curran about how and why ChatGPT is having an iPhone moment.

As required by the?Colorado Equal Pay Transparency Act, Accenture provides a reasonable range of compensation for roles that may be hired in Colorado. Actual compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, and specific office location. For the state of Colorado only, the range of starting pay for this role is {{$61,600 – $97,199}} and information on benefits offered is here.