Core Insights - The article emphasizes the rapid transformation of the world through artificial intelligence and programming technologies, highlighting the significance of Auckland University's computer science undergraduate program as a platform for students passionate about these fields [1]. Group 1: Program Advantages - Auckland University's computer science program boasts exceptional academic resources and a strong faculty, with the department recognized internationally for its research in artificial intelligence, data science, and cybersecurity [3]. - The faculty comprises professors from around the globe who have made significant academic contributions and maintain close collaborations with major tech companies like Google and Microsoft, integrating the latest industry trends into the curriculum [3]. - The university provides advanced learning resources, including high-performance computing clusters and virtual reality equipment, facilitating complex programming experiments and AI project development [3]. - Partnerships with numerous tech companies offer students internship and employment opportunities, allowing them to engage with real-world business projects during their studies [3]. Group 2: Application Requirements - Applicants to the computer science undergraduate program must meet specific academic and language criteria, with international students typically required to achieve an average high school score of over 80%, particularly excelling in mathematics and physics [4]. - For Chinese students, the Gaokao score is a critical reference, generally requiring a score above the provincial first-tier line; alternative qualifications like A-Level or IB scores are also accepted [4]. - Language proficiency is essential, with a minimum IELTS score of 6.5 (no individual score below 6.0) or a TOEFL score of 90 (with writing no less than 21) required for admission [4]. Group 3: Curriculum Content - The curriculum is diverse and designed to build a solid theoretical foundation and practical innovation skills, starting with introductory courses in computer science, programming basics (Python and Java), and discrete mathematics in the first year [6]. - As students progress, they encounter more specialized courses such as data structures and algorithms, computer systems principles, and database systems, deepening their understanding of computer science fundamentals [6]. - Elective courses in artificial intelligence, machine learning, computer graphics, and cybersecurity allow students to explore cutting-edge areas of interest, while project-based courses enable teamwork and problem-solving through real programming projects [6].

Group 1 - The core idea is that coding for artificial intelligence (AI) is becoming as essential as reading and writing, with the potential to enrich lives through data utilization [1][2] - AI and data science can provide significant value across various professions, making AI-oriented coding skills more valuable than traditional coding [2][3] - The rapid rise of AI has led to an increase in job opportunities, emphasizing the importance of foundational skills, project work, and job searching in career development [3][4][6] Group 2 - Learning foundational skills in AI is a continuous process, with a focus on understanding key concepts in machine learning and deep learning [7][8] - Mathematics is crucial for AI roles, with an emphasis on linear algebra, probability, statistics, and exploratory data analysis [8][11] - Building a portfolio of projects that demonstrate skill progression is essential for career advancement in AI [24][26] Group 3 - The job search process in AI involves predictable steps, including researching roles, conducting informational interviews, and applying for positions [27][36] - Networking and building a supportive community are vital for career growth in the AI field [43][48] - The importance of continuous learning and adapting to new technologies is highlighted as a key to success in AI careers [10][41]

Group 1 - The term "artificial intelligence" was formally introduced at a conference in 1956 at Dartmouth College, marking the beginning of efforts to replicate human intelligence through modern science and technology [1] - Alan Turing is recognized as the father of artificial intelligence due to his introduction of the "Turing Test" in 1950, which provides a method to determine if a machine can exhibit intelligent behavior equivalent to a human [1][3] - The Turing Test involves a human evaluator interacting with an isolated "intelligent agent" through a keyboard and display, where if the evaluator cannot distinguish between the machine and a human, the machine is considered intelligent [3][5] Group 2 - The Turing Test is characterized as a subjective evaluation method rather than an objective scientific test, as it relies on human judgment rather than consistent measurable criteria [6][9] - Despite claims of machines passing the Turing Test, such as Eugene Goostman in 2014, there is no consensus that these machines possess human-like thinking capabilities, highlighting the limitations of the Turing Test as a scientific standard [6][8] - Turing's original paper contains subjective reasoning and speculative assertions, which, while valuable for exploration, do not meet the rigorous standards of scientific argumentation [8][9] Group 3 - The field of artificial intelligence has been criticized for lacking a solid scientific foundation, often relying on conjecture and analogy rather than empirical evidence [10][19] - The emergence of terms like "scaling law" in AI research reflects a trend of using non-scientific concepts to justify claims about machine learning performance, which may not hold true under scrutiny [16][17] - Historical critiques, such as those from Hubert L. Dreyfus in 1965, emphasize the need for a deeper scientific understanding of AI rather than superficial advancements based on speculative ideas [18][19] Group 4 - The ongoing development of AI as a practical technology has achieved significant progress, yet it remains categorized as a modern craft rather than a fully-fledged scientific discipline [20][21] - Future advancements in AI should adhere to the rational norms of modern science and technology, avoiding the influence of non-scientific factors on its development [21]

Core Viewpoint - The TIOBE Index for May 2025 highlights Python's dominance in the programming language landscape, achieving a market share of 25.35%, a significant increase of 2.2% from the previous month, marking a rare and substantial lead over its closest competitor, C++ [1][3]. Programming Language Rankings - The top programming languages in May 2025 are as follows: 1. Python: 25.35% (+9.02%) 2. C++: 9.94% (+0.41%) 3. C: 9.71% (-0.27%) 4. Java: 9.31% (+0.62%) 5. C: 4.22% (-2.27%) 6. JavaScript: 3.68% (+0.66%) [2][24]. Python's Market Position - Python's market share surpasses C++ by over 15%, showcasing a dominant position that is uncommon in the programming language rankings [3]. - The historical context indicates that only Java in 2001 had a higher market share than Python currently does [1]. Limitations of Python - Despite its popularity, Python has two main limitations: 1. Performance issues due to being an interpreted language, which inherently runs slower [6]. 2. Higher frequency of runtime errors, as many bugs are only discovered during execution [6][4]. - Critical applications, such as aerospace control systems, still rely on languages like C++ and Java due to these limitations [5][4]. Factors Contributing to Python's Popularity - Python's simplicity and ease of learning have made it the preferred language for many entering the programming field, especially as the demand for programming talent grows amid digital transformation [11]. - The language's extensive ecosystem, including libraries like NumPy, Pandas, TensorFlow, and PyTorch, has further solidified its position in various domains [12][13]. Application Areas of Python - Python is widely used in several fields: - Data analysis: 50% of respondents use Python for this purpose [16]. - Web development: 49% [16]. - DevOps and automation: 35% [16]. - Machine learning: 31% [16]. - Educational purposes: 28% [16]. - Software testing: 26% [16]. - The language is also utilized in scientific computing, numerical simulations, and web development frameworks like Django and Flask [22].

新加坡城市景观。 新华社发 就读于电子信息科技大学的刘胜然对新加坡科技设计大学很感兴趣。他说："这所学校相对年轻，它由 新加坡政府与美国麻省理工学院合作设立，注重跨学科融合，是一所专注设计与创新研究、工程领域的 新概念大学。" 刘胜然坦言，申请新加坡排名靠前的公立大学硕士项目竞争压力不小。"据我所知，申请新加坡国立大 学和南洋理工大学的硕士，要有不错的本科GPA成绩，且雅思成绩不低于6.5或托福100分，同时要求具 备科研或相关职场经验。"刘胜然说。 《2025新加坡留学报告》中一组数据显示，在2024入学季，新加坡公立大学硕士录取的学生中，八成以 上的申请者GPA在80分以上，雅思成绩达到6.5分以上，近七成的录取学生GMAT成绩在650分以上。相 关留学专家建议，背景条件较薄弱的学生，不妨选择新加坡私立大学就读语言课程或预科类课程，逐步 完善自身条件和能力。 此外，高中生可凭高考成绩直接申请新加坡公立大学本科。启德教育相关负责人介绍："对于新加坡排 名前三的公立大学（新加坡国立大学、南洋理工大学、新加坡管理大学），建议申请者高考成绩高于当 地一本线100分以上，并具备托福93分或雅思6.5分（阅读和写作单 ...

Ingredion (INGR) Conference Call Summary Company Overview - **Company**: Ingredion - **Date**: November 14, 2024 - **Focus**: Texture and Healthful Solutions in the food industry Key Points Industry and Market Insights - The global market for texturizing ingredients is approximately **$20 billion** with a growth outlook of **2% to 5%** for ingredients and a faster growth rate for solutions combining these ingredients [12][26] - The **global packaged food retail market** is valued at **$600 billion**, with a significant opportunity for CPG companies to leverage front-of-pack texturizing claims [14] - **80%** of consumers are currently paying attention to food prices, indicating a strong demand for affordability in food products [27] - **75%** of consumers find all-natural claims appealing, and **72%** prefer products with natural ingredients [27] Texture as a Competitive Advantage - Texture is a critical factor in consumer purchasing decisions, with **greater than 50%** of consumers consciously considering texture when buying products [12] - **40%** of product launch success is linked to texture, emphasizing its importance in consumer liking and repeat purchases [62] - The company aims to be recognized as the go-to provider for texture and healthful solutions that enhance the taste of healthy foods [18] Strategic Focus Areas - Ingredion is focusing on three primary consumer benefit areas: 1. **Influencing the texture experience** to make foods more interesting and appealing [15] 2. **Affordability**, especially in light of food inflation [16] 3. **Clean label** products that meet consumer demand for natural ingredients [17] Innovations and Solutions - The company has invested in **sensory and consumer understanding** to create solutions that influence texture and flavor [15] - Examples of innovative solutions include: - A cheese product with **20% cost reduction** while maintaining texture and taste [50] - An ice cream formulation that does not melt at higher temperatures, reducing energy consumption for storage [51] Global Presence and Local Adaptation - Ingredion operates **22 plants globally** and has local teams to understand regional food preferences [42] - The company is adapting its strategies to meet the needs of both developed and developing markets, focusing on convenience and affordability [30][32] Consumer Trends - There is a growing demand for **multicultural foods**, with **60%** of consumers preferring multi-texture and multi-flavor foods [38] - The trend towards healthier foods post-pandemic is significant, with consumers seeking healthier options that also taste good [40] Challenges and Opportunities - The high failure rate of product launches (up to **80%**) presents a significant opportunity for Ingredion to assist customers in improving their product development processes [59] - The company is leveraging its extensive data and technology to enhance the probability of successful product launches [61][83] Conclusion - Ingredion is positioning itself as a leader in the texture and healthful solutions market, focusing on innovation, consumer insights, and local market adaptation to drive growth and meet evolving consumer demands [55][56]