Large Language Models have become a valuable source of information for our daily inquiries. However, after training, its data source quickly becomes out-of-date, making RAG a useful tool for providing even more recent or pertinent data. In this work, we investigate how RAG pipelines, with the course materials serving as a data source, might help students in K-12 education. The initial research utilizes Reddit as a data source for up-to-date cybersecurity information. Chunk size is evaluated to determine the optimal amount of context needed to generate accurate answers. After running the experiment for different chunk sizes, answer correctness was evaluated using RAGAs with average answer correctness not exceeding 50 percent for any chunk size. This suggests that Reddit is not a good source to mine for data for questions about cybersecurity threats. The methodology was successful in evaluating the data source, which has implications for its use to evaluate educational resources for effectiveness.

Georgiana Strait is a Digital Marketing Manager at Link Labs with 6 years of experience in the technology and software industries, with specialized interest and expertise in the IoT field. She has helped market emerging technologies such as Machine Learning/Artificial Intelligence, analytics programs, virtual assistants, software for regulatory compliance needs, and much more. Prior to her professional career as a marketer, Georgiana served in the United States Military in the Active Duty Army as a Chemical Biological Radiological Nuclear Specialist. She was stationed in El Paso, Texas at Fort Bliss and had one deployment overseas during her time in the Army.

On the street outside Joseph Baca's home in El Paso, Texas, there is a traffic light that always seems to be red. Whether the intersection is clear, the traffic waits. He knows that, like most traffic lights in El Paso, this one has a camera. Why, he often said to his family, couldn't the camera be used to monitor the road and control the signal? That question eventually led to the development of an idea that could save not only time but also, potentially, lives.

That plan will include highway lanes enabled for self-driving trucks and terminals stretching from Los Angeles to Jacksonville, Fla., over the next two to three years, and eventually across the Lower 48 states, said Cheng Lu, TuSimple's president. Top news and in-depth analysis on the world of logistics, from supply chain to transport and technology. The company's fleet of 40 trucks now operates autonomously on seven routes between Phoenix, Tucson, Ariz., El Paso, Tex. and Dallas, with a human operator on board each vehicle to take over if needed. TuSimple plans to pilot fully autonomous driverless service next year, the company said, and aims to expand those operations nationwide in 2023 and 2024 with the help of commercialized technology it is developing with German car-parts maker ZF Friedrichshafen AG. To get there, TuSimple is building out lanes and terminals connected by high-definition routing maps that function "like virtual railroad tracks" for its retrofitted big rigs, Mr. Lu said.

EL PASO, Texas (KTSM) Microsoft is working in El Paso to educate students and businesses about computer science. Microsoft's President compares artificial intelligence to the impact the combustion engine had years ago. "Over the next three decades artificial intelligence will probably have a bigger impact on our lives than any other single technology," said Brad Smith the President of Microsoft. Microsoft is helping companies in both Juarez and El Paso grow and learn about technology to be competitive in a global market. Microsoft says the younger generation needs to be taught about artificial intelligence and the older generation must adapt.

The tragic events of the past weekend – back-to-back mass shootings in El Paso, Texas, and Dayton, Ohio leaving at least 31 dead and more than 50 wounded – has reignited the debate over the alleged correlation between video games and violent behavior. "We must stop the glorification of violence in our society," President Trump said in remarks from the White House on Monday. "This includes the gruesome and grisly video games that are now commonplace." Thousands subsequently turned to social media to challenge this claim, citing easy access to assault-style weapons without background checks as the core problem. Video games are immensely popular in several countries that do not see mass shootings, many noted.

Talking about acts of violence like mass shootings with your children is not easy. If you have to have that difficult talk, remember the four S's. Video games again have been invoked as one of the causes of violence in the U.S. in the wake of mass shootings this weekend in El Paso, Texas, and Dayton, Ohio. President Donald Trump, who last year held a video game summit after the February 2018 Parkland, Florida, shooting that killed 17 people at Marjory Stoneman Douglas High School, was among several public officials who called out video games as a potential factor in shootings, mentioning video games and violence. President Donald Trump on Monday condemned white nationalism and said he supported "red flag" laws, which could limit a person's access to firearms if the person is determined to be a potential threat to the public.

In the multiple linear regression setting, we propose a general framework, termed weighted orthogonal components regression (WOCR), which encompasses many known methods as special cases, including ridge regression and principal components regression. WOCR makes use of the monotonicity inherent in orthogonal components to parameterize the weight function. The formulation allows for efficient determination of tuning parameters and hence is computationally advantageous. Moreover, WOCR offers insights for deriving new better variants. Specifically, we advocate weighting components based on their correlations with the response, which leads to enhanced predictive performance. Both simulated studies and real data examples are provided to assess and illustrate the advantages of the proposed methods.

Assessing heterogeneous treatment effects has become a growing interest in advancing precision medicine. Individualized treatment effects (ITE) play a critical role in such an endeavor. Concerning experimental data collected from randomized trials, we put forward a method, termed random forests of interaction trees (RFIT), for estimating ITE on the basis of interaction trees (Su et al., 2009). To this end, we first propose a smooth sigmoid surrogate (SSS) method, as an alternative to greedy search, to speed up tree construction. RFIT outperforms the traditional `separate regression' approach in estimating ITE. Furthermore, standard errors for the estimated ITE via RFIT can be obtained with the infinitesimal jackknife method. We assess and illustrate the use of RFIT via both simulation and the analysis of data from an acupuncture headache trial.

Researchers have warned that millions of human workers in the US will be replaced by robots over the next few decades, leaving Americans to wonder what areas are at the highest risk. Now, a new map has shown where the most'automatable' jobs are in the nation - and almost every metropolitan area is set to experience a robot takeover. However, it is the low-wage cities like Las Vegas, Nevada, El Paso, Texas and San Bernardino, California that will be hit the hardest – robots are predicted to take more than 60% of jobs in these cities by 2035. A new map has shown where the most'automatable' jobs are in the nation - and almost every metropolitan area is set to experience a robot takeover. The bubble size shows the number of workers employed in the metropolitan areas in December 2016.