In your mind, does 985 ben still carry the halo of "the glory of heaven"? Does it mean holding a large factory offer and becoming a “life winner” easily
Recently, however, near-intellectual intelligence has seen a 985-motor graduate posted on the internet: with an outstanding academic background, he has never had access to a large factory and has only a few notifications of recruitment to a medium plant. He's in deep self-doubt when he sees his fellow students entering bytes, ali, etc. — am i ashamed
New suit, new private website
Behind this confusion is the subversive shift in the recruitment logic of businesses of the ai era. Manus xiaohong's recruitment principle: no more academic studies, whether or not it's an ai fanatic, whether it's a super-heavy use of ai and whether it has an ai native mind. This has led more and more job seekers to ask: what is the extent to which ai is learning to find a job? Can zero get started
I. The dilemma of higher education: ai-era education is no longer a “hard currency” job search
In the soybean “985 waste introduction program” group, over 120,000 members were assembled, mostly young people who graduated from famous schools and faced unemployment or confusion. There is a similar voice in the group: holding a prestigious school diploma and repeatedly failing to find a job, even beginning to question their value。
In contrast, a 27-year-old student from the “four non-general” institute of higher education, with his outstanding skills in the field, emerged out of a fierce competition and succeeded in the byte-breeding back-end development offer。
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This contrast is the true picture of the new recruitment trends described by manus xiaohong: the core yardstick for enterprise selection has shifted from “academic origin” to “ai competence landing”. Education may help you get an interview, but getting an offer ultimately depends on whether you can use ai to solve real problems。
Ii. Core competencies for the transition to ai: different jobs need to learn these levels
The demand for ai talent in an enterprise is not “just by using an ai tool”, but by having the ability to think and practice in an ai nature with a focus on the depth of learning and core requirements of different positions:
Base threshold: skilled application of ai tools
Whichever ai post is transitioned needs the ability to use the base tool of the big model:
• skilled preparation of efficient pRopt, capable of giving precise instructions to large models
• mastery of the mainstream large model api call method to achieve a simple functional interface
• understanding the underlying logic of the large model, allowing for the selection of suitable models based on demand。
This is an introductory requirement for entry into the ai field and a basic criterion for enterprises to judge whether a job seeker “knows ai”。
Progress requirements: technology development and landscape land
If the target is technical posts such as aigc large model applications development engineer, multi-model engineer, the degree to which the project development will be independently completed needs to be learned:
• a mastery of the core technologies for large model integration, application development, such as incremental pre-training, self-monitoring learning, gpt architecture, etc
• the ability to develop customized ai application solutions based on the actual business landscape of the enterprise
• integrated application capabilities for multi-modular technology (text, image, audio) for cross-media creation and development。
High-level requirements: integration of business with ai
For the aigm products manager post, in addition to the technical base, it is important to learn the extent of bridging technology and business:
• dismantling 50+ top-of-the-art aigc applications and mastering the design logic and landing method of the ai product
• understanding the technological literacy of large models and being able to communicate efficiently with technical teams
• develop commercially valuable ai product concepts and facilitate landings, taking into account industry needs。
Hard-on-the-ground indicators: available project experience
Manus xiao hong refers to the “super-intensive use of ai”, which essentially requires job seekers to apply ai technology to their work. Therefore, the transfer of ai must build on the operational experience of enterprise-level projects, such as the completion of large model application development, the development of an ai test system, etc., which is the core evidence of its ability。
Iii. Is ai training suitable for zero base? Selective agencies need to avoid these pits
Many fear that zero basics will not be ai and that a quality ai training system will set up a ladder of learning paths for zero basics who can make the transition by finding the right way:
Zero basic ai feasibility: stage development is key
The formal ai training course will start with the ai foundation concept, tool operation, avoid complex bottom algorithms and allow participants to develop ai application thinking before moving into code development and project practice. For example, courses designed for zero-basic participants are phased in to advance learning difficulties and ensure that participants are able to keep pace。
Guide for selection of ai training institutions to avoid pits
A number of people have completed ai training and are unable to find a job. The core is to step on the pits of training institutions and focus on these three points when choosing:
• pioneer site 1: theory only, no real-world projects. (b) lack of training in practical ethics at the enterprise level, which can only be based on superficial knowledge and cannot be applied locally
• pipepoint 2: the curriculum is disconnected from the job. (a) old content, which does not match the latest agc job needs of the enterprise and does not use the skills learned
• site 3: untargeted development. • directly explain high-level content, without distinction between zero basic and advanced students, leading to learning faults。
Iv. Neighborhood intelligence: helping you meet corporate ai standards with hands-on training
Understanding the capacity requirements of the ai era is only the first step, and translating them into their own core competitiveness is key to winning the job market。
The near-earth intelligence deep farming model and aig field training are tailored to meet the core needs of enterprise recruitment and develop targeted learning programmes for different-based trainees:
• series a: aigc large model application development project division
Designing a stept culture system for zero-basic-to-step trainees, focusing on large model integration, application development and command training, and developing technical skills that can break the operational ceiling of large models。
• series b: aig multi-model model application division
In-depth exploration of the use of mlllm tools, api call-up and tool development, covering multi-modular technologies such as ai creation, visual arts, and building capacities for technology application and innovation。
• series c: manager of aigm products
From product base to large model technology literacy, combined with 50+ field case teaching, the full range of experts in ai product management。
• series d: ai test engineer
Focus on the "ai+ test" frontier area, which covers all-chamber skills, such as traditional testing bases and large model integration tests, to develop a full-linker talent capable of achieving smart upgrading of the testing system through an enterprise-level project。
Concluding remarks: breaking the academic chain and winning the job with ai
Whether you come from 985 schools or from ordinary colleges, the halo of education will eventually give way to the hard power of power in the arena of the ai era. As fussing says, what businesses really need is someone who can use ai, and who has an ai native mind。
Close-mindedness is willing to accompany each and every one of those who have gone forward, to be effective and technology-oriented, to help build the core competitiveness of the ai era and to meet new career challenges and opportunities。
Action is the answer. Together with near-intelligence, unlock the new possibilities of the ai era。
