Despite the continuous progress of Large Language Models (LLMs) across various tasks, their performance on mathematical problems and reasoning tasks remains limited. This limitation can be attributed, among other factors, to the inherent difficulty of these problems and the fact that solutions often consist of multiple steps, potentially of varying nature, making it challenging for a single prompting technique to execute all required steps. To address this, we introduce BloomWise, a new prompting technique, inspired by Bloom's Taxonomy, aiming to improve LLMs' performance in solving such problems by encouraging them to approach the problem starting from simple, i.e., remembering, and progressing to higher cognitive skills, i.e., analyzing, until the correct solution is reached. The decision regarding the need to employ more sophisticated cognitive skills is based on self-evaluation performed by the LLM. Thus, we encourage the LLM to deploy the appropriate cognitive processes. In extensive experiments across 4 popular math reasoning datasets, we have demonstrated the effectiveness of our proposed approach. We also present extensive ablations, analyzing the strengths of each module within our system.

We introduce Bitune, a method that improves instruction-tuning of pretrained decoder-only large language models, leading to consistent gains on downstream tasks. Bitune applies both causal and bidirectional attention to the prompt, to obtain a better representation of the query or instruction. We realize this by introducing two sets of parameters, for which we apply parameter-efficient finetuning techniques. These causal and bidirectional features are then combined into a weighted average with trainable coefficients, which is subsequently used to generate new tokens. We demonstrate significant improvements in zero-shot performance on commonsense reasoning, arithmetic, and language understanding tasks, while extensive ablation studies validate the role of each component and demonstrate the method's agnosticism to different PEFT techniques.

In news that will likely surprise no-one, Britain's average CEO is a 55-year-old privately educated white man who studied Economics at Cambridge and makes £4,196,000 a year. Data from the FTSE100 - a list of the 100 biggest companies listed on the London Stock Exchange - has been used to work out the most common background for a UK CEO. Researchers from People Managing People used artificial intelligence (AI) to combine the LinkedIn profile pictures of Britain's top 100 CEOs. The resulting composite image reveals the face of the average CEO – an oddly familiar man dubbed Andrew. Finn Bartram, Editor of People Managing People, said the uncanny digital portrait shows the'undeniable privileges and gender disparities for the top jobs at some of the biggest companies in the country.'

Evaluating the individual movements for teammates in soccer players is crucial for assessing teamwork, scouting, and fan engagement. It has been said that players in a 90-min game do not have the ball for about 87 minutes on average. However, it has remained difficult to evaluate an attacking player without receiving the ball, and to reveal how movement contributes to the creation of scoring opportunities for teammates. In this paper, we evaluate players who create off-ball scoring opportunities by comparing actual movements with the reference movements generated via trajectory prediction. First, we predict the trajectories of players using a graph variational recurrent neural network that can accurately model the relationship between players and predict the long-term trajectory. Next, based on the difference in the modified off-ball evaluation index between the actual and the predicted trajectory as a reference, we evaluate how the actual movement contributes to scoring opportunity compared to the predicted movement. For verification, we examined the relationship with the annual salary, the goals, and the rating in the game by experts for all games of a team in a professional soccer league in a year. The results show that the annual salary and the proposed indicator correlated significantly, which could not be explained by the existing indicators and goals. Our results suggest the effectiveness of the proposed method as an indicator for a player without the ball to create a scoring chance for teammates.

This rewarding 8 tech careers will help if you are changing your career, looking to go into tech or if you're thinking of the best tech career path to take. This article provides a breakdown for the top 8 rewarding and highly sought-after tech careers for you. Attracting top tech talent has become a real priority for most companies worldwide in the last few years as we all scramble to adapt to a tight and competitive IT job market. Reports like Robert Half Technology's 2020 IT salary report and data from the Bureau of Labour Statistics have indicated the most in-demand IT roles. Staying on top of hiring trends means companies need to start filling those positions ranging from security-related to AI engineering roles.

Data Scientist is the most promising job in the U.S according to LinkedIn. Also, the demand for Data Scientists is growing exponentially in all the industries. Out of all the openings, 19% of data science professionals jobs are secured by the Finance Industry. Python statistics is one of the most important python built-in libraries developed for descriptive statistics. Python statistics is all about the ability to describe, summarize, and represent data visually through comprehensive python statistics libraries.

Annual salaries for data scientists and machine learning engineers vary significantly across the world. Based on a 2017 Kaggle survey of data professionals, countries with the highest paid data scientists and machine learning engineers (in USD) were: US ($120K), Australia ($111K), Israel ($88K), Canada ($81K) and Germany ($80K). Countries with the lowest annual salaries were: Brazil ($35K), Poland ($29K), Ukraine ($25K), India ($14K) and Russia ($13K). In my last post, I compared at annual salaries of different data professionals in the US. Data scientists and machine learning engineers from the US reported some of the highest salaries among different data professionals.

Regardless of size, geography or industry, every organisation requires exceptional talent that is both motivated and focused to deliver on customer promises. Yet, it is getting harder to recruit and retain talented, high performing employees. When considering the costs associated with recruiting and on-boarding new employees, making the wrong decision will result in a massive impact for an organisation. "It costs an organisation 200% of a senior hires' annual salary to on board them, this includes recruitment fees, remuneration, back office administration, training and team time. It's very expensive to replace people," says Jason Davies, Africa Head of Leadership, Learning, Talent and Resourcing at Barclay's Africa Group.

It's going to take a lot of humans to create the kind of artificial intelligence that could replace truckers, financial analysts, and customer service representatives with robots. U.S. employers will spend more than $650 million on annual salaries for 10,000 jobs in AI this year, according to a study from career and hiring data firm Paysa. The 2-year-old firm touts itself as the only platform to use AI to deliver personalized job and salary recommendations. It was founded by Chris Bolte, Zachary Poley, Nikhil Raj and Patrick Harrington -- all formerly of Walmart Labs and Walmart's engineering and product teams. The firm uses millions of data points like job openings, resumes, and compensation to determine the market value of individual skills.

It's going to take a lot of humans to create the kind of artificial intelligence that could replace truckers, financial analysts, and customer service representatives with robots. U.S. employers will spend more than $650 million on annual salaries for 10,000 jobs in AI this year, according to a study from career and hiring data firm Paysa. The 2-year-old firm touts itself as the only platform to use AI to deliver personalized job and salary recommendations. It was founded by Chris Bolte, Zachary Poley, Nikhil Raj and Patrick Harrington -- all formerly of Walmart Labs and Walmart's engineering and product teams. The firm uses millions of data points like job openings, resumes, and compensation to determine the market value of individual skills.