Recent efforts to leverage the Multi-modal Large Language Model (MLLM) as GUI agents have yielded promising outcomes. However, these agents still struggle with long-horizon tasks in online environments, primarily due to insufficient knowledge and the inherent gap between offline and online domains. In this paper, inspired by how humans generalize knowledge in open-ended environments, we propose a Hierarchical Multimodal Skills (HMS) module to tackle the issue of insufficient knowledge. It progressively abstracts trajectories into execution skills, core skills, and ultimately meta-skills, providing a hierarchical knowledge structure for long-horizon task planning. To bridge the domain gap, we propose the Skill-Augmented Monte Carlo Tree Search (SA-MCTS) algorithm, which efficiently leverages skills acquired in offline environments to reduce the action search space during online tree exploration. Building on HMS, we propose Mirage-1, a multimodal, cross-platform, plug-and-play GUI agent. To validate the performance of Mirage-1 in real-world long-horizon scenarios, we constructed a new benchmark, AndroidLH. Experimental results show that Mirage-1 outperforms previous agents by 32\%, 19\%, 15\%, and 79\% on AndroidWorld, MobileMiniWob++, Mind2Web-Live, and AndroidLH, respectively. Project page: https://cybertronagent.github.io/Mirage-1.github.io/

Substantial scholarship has estimated the susceptibility of jobs to automation, but little has examined how job contents evolve in the information age as new technologies substitute for tasks, shifting required skills rather than eliminating entire jobs. Here we explore patterns and consequences of changes in occupational skill and characterize occupations and workers subject to the greatest re-skilling pressure. Recent work found that changing skill requirements are greatest for STEM occupations. Nevertheless, analyzing 167 million online job posts covering 727 occupations over the last decade, we find that re-skilling pressure is greatest for low-skilled occupations when accounting for distance between skills. We further investigate the differences in skill change across employer and market size, as well as social demographic groups, and find that these differences tend to widen the economic divide. Jobs from large employers and markets experienced less change relative to small employers and markets, and non-white workers in low-skilled jobs are most demographically vulnerable. We conclude by showcasing our model's potential to precisely chart job evolution towards machine-interface integration using skill embedding spaces.

Even if you do not work in software development, being able to write code gives you a significant edge over everybody else. So if you are working in fields like biology, chemistry, finance etc, more familiarity with relevant technologies and being able to write code makes your job much faster and efficient to a point that it is almost a necessity now. Machine learning is becoming increasingly important in the software industry, and many companies are using it to improve their products and services. However, it's not necessary for every software developer to be an expert in machine learning in order to remain relevant in the industry in the coming years. While having a basic understanding of machine learning concepts and techniques can certainly be beneficial for a software developer, it's not essential for every role.

Data science is a wide field with many specializations, and an individual can have a great career with a data science degree. However, curriculums vary between schools, and the specific data science classes taught in one school may not be taught in another. There are several core skills in the data science field that recruiters and hiring managers are looking for, and you need to be sure your online data science course offers hands-on experience with those skills. Read on to see what core data science skills are most attractive to recruiters and hiring managers and whether your online data science course has them! Statistical and machine learning methods are important in any data science career.

New Delhi [India], July 21 (ANI / PNN): According to the World Economic Forum, 133 million new jobs will be created in the field of artificial intelligence (AI) by 2022. Job demand and growth is projected in three key areas: data analysts and data scientists, AImachine learning specialists (including AI software engineers), and big data specialists. At the peak of decision-intelligence companies, use software that embeds AI within organizations across sales, marketing, planning, and supply chains to transform decision-making. The company has grown rapidly in the last 12 months, expanding its teams in Jaipur (India) and the United Kingdom, as well as opening new offices in the United States and Pune (India). As a result, Peak is creating 150 new jobs worldwide this year, including roles in data science and AI software engineering.

Starting with a strong quantitative background is often extremely helpful: a good intuition for mathematics and statistics is invaluable for practitioners working in AI. Those with strong core skills in these areas often find it much easier to stay ahead of innovations and build a career in this fast-paced sector. As Koushik Kulkarni (Head of AI Engineering at Peak) says, "If you try and foster some core skills in mathematics and statistics, with a bit of computer science on the side, it should make it easier for you to start your journey as an AI software engineer."

Data science has been hot for many years now, attracting attention and talent. There is a persistent thread of commentary, though, that says data science's core skill of statistical modeling is overhyped and that managers and aspiring data scientists should focus on engineering instead. Vicki Boykis' 2019 blog post was the first article I remember along these lines. Don't do a degree in data science, don't do a bootcamp…It's much easier to come into a data science and tech career through the "back door", i.e. starting out as a junior developer, or in DevOps, project management, and, perhaps most relevant, as a data analyst, information manager, or similar… While tuning models, visualization, and analysis make up some component of your time as a data scientist, data science is and has always been primarily about getting clean data in a single place to be used for interpolation. More recently, Gartner's 2020 AI hype cycle report acknowledges the role of data scientists but says: Gartner foresees developers being the major force in AI.

Digital disruption has broken out of Silicon Valley. Any company, no matter how nuts-and-bolts, can be disrupted by a digital competitor; equally, any company could be that digital disruptor. The discussion was kick-started by two leading industry thinkers: Andrew Moore, chief transformation officer of chipmaking giant Intel, and Nigel Moulton, chief technology officer at Dell EMC, part of a corporation that services 99% of the Fortune 500 companies. Their remarks sparked lively discussion. Both Intel's Mr Moore and Dell EMC's Mr Moulton spend a lot of time talking to leading companies about their digital transformation journey, and they kicked off with a tough message: it's hard work.

Modern Reinforcement Learning (RL) algorithms promise to solve difficult motor control problems directly from raw sensory inputs. Their attraction is due in part to the fact that they can represent a general class of methods that allow to learn a solution with a reasonably set reward and minimal prior knowledge, even in situations where it is difficult or expensive for a human expert. For RL to truly make good on this promise, however, we need algorithms and learning setups that can work across a broad range of problems with minimal problem specific adjustments or engineering. In this paper, we study this idea of generality in the locomotion domain. We develop a learning framework that can learn sophisticated locomotion behavior for a wide spectrum of legged robots, such as bipeds, tripeds, quadrupeds and hexapods, including wheeled variants.

Digital disruption has broken out of Silicon Valley. Any company, no matter how nuts-and-bolts, can be disrupted by a digital competitor; equally, any company could be that digital disruptor. The discussion was kick-started by two leading industry thinkers: Andrew Moore, chief transformation officer of chipmaking giant Intel, and Nigel Moulton, chief technology officer at Dell EMC, part of a corporation that services 99% of the Fortune 500 companies. Their remarks sparked lively discussion. Both Intel's Mr Moore and Dell EMC's Mr Moulton spend a lot of time talking to leading companies about their digital transformation journey, and they kicked off with a tough message: it's hard work.