In the era of large models, the development trend and technology expansion of algorithm engineers

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In the era of large models, the development trend and technology expansion of algorithm engineers present an impressive prospect. With the rapid development of the field of artificial intelligence and the wide application of large-scale models, algorithm engineers are also in an era full of opportunities and challenges. They will play a role in the intersection of multiple fields and shoulder the important mission of promoting the advancement of artificial intelligence technology. Then in the following blog, Zhouzhou will combine some live broadcast content of Hehe Information and core corporate ideas to discuss several key points, and spy on the future direction of algorithm engineers in the era of large models.

1. What is each stage of an artificial intelligence algorithm engineer like?

Phase 1: Pattern Recognition Phase

During the period from 2000 to 2012 before the large-scale application of deep learning, known as the period of pattern recognition, the field of artificial intelligence is in an initial stage of exploration and development. In this period, deep learning has not yet appeared, and there is no clear consensus on the definition of artificial intelligence. This stage focuses on the research and application of various pattern recognition techniques aimed at solving specific problems in different professional directions. However, due to the limitations of technical level and data resources, there are significant differences in pattern recognition in each field. For example, in character recognition and face recognition, the threshold is generally very high.

At this early stage, research in pattern recognition has largely focused on traditional machine learning methods such as support vector machines, hidden Markov models, and decision trees. While these methods achieve modest results on some tasks, their performance is often unsatisfactory for complex real-world problems. At this stage, the lack of deep learning such as end-to-end artificial intelligence algorithm models has led to bottlenecks in large-scale applications and cross-domain challenges.

(1) Traditional Machine Learning – Support Vector Machine

In machine learning, a vector machine (SVM, Support Vector Machine) usually refers to a supervised learning algorithm for classification and regression problems. SVM was developed by Vladimir Vapnik and Alexey Chervonenkis in the late 1970s to late 1990s. It excels in dealing with high-dimensional data and complex features, and has achieved remarkable success in many practical problems.

The basic principle of SVM is to find an optimal hyperplane (or decision boundary) in a high-dimensional space to separate sample points of different categories. In a binary classification problem, the goal of this hyperplane is to maximize the separation between the sample points of the two classes closest to the hyperplane. These closest sample points are called support vectors (Support Vectors), so the name of SVM comes from this.

(2) Traditional Machine Learning – Hidden Markov Model

Hidden Markov Model (HMM) is a probabilistic graphical model for modeling sequence data. HMM is mainly used to describe the probability relationship between the time-varying state sequence and the corresponding observation sequence. In HMMs, states are unobservable (hidden), while sequences of observations are visible. The model assumes the existence of a hidden Markov process that evolves over a sequence of discrete time steps and produces corresponding observations based on states.

(3) A new start! –AlexNet

In 2006, an important study opened new doors for the development of deep learning. Geoffrey Hinton et al. proposed the concept of Deep Belief Network (DBN), which is a multi-layer neural network structure that can automatically extract features from data. The proposal of DBN is of great significance for solving the limitations of traditional machine learning methods when dealing with complex data. It provides theoretical support and method guidance for the construction of deep neural networks, and lays the foundation for the emergence and rise of the second stage of deep learning.

Then, in 2012, the amazing performance of Hinton's student Alex Krizhevsky and others in the ImageNet image classification competition completely changed the pattern of algorithmic models in artificial intelligence. Alex built a deep convolutional neural network (CNN-AlexNet) by using graphics processing unit (GPU) and other hardware acceleration technologies, and defeated many outstanding and well-known opponents in the international artificial intelligence competition ImageNet competition and finally won the champion.

AlexNet is the first deep convolutional neural network applied to image classification proposed by Alex Krizhevsky. The network won the first place with a top-5 test error rate of 15.3% in the 2012 ILSVRC (ImageNet Large Scale Visual Recognition Competition) image classification competition. one. Also after that year, more and more neural network models were proposed, such as the excellent VGG and GoogLeNet.

The breakthrough achievement of AlexNet has attracted extensive attention from the global scientific and industrial circles to deep learning. The emergence of CNN not only enables computers to efficiently process image recognition tasks, but also shows the great potential of deep learning in processing complex data and realizing artificial intelligence. This victory marked the beginning of the era of deep learning. Since then, deep learning has become a hot topic in the field of artificial intelligence, attracting the attention and investment of a large number of researchers.

The rise of deep learning not only benefits from the improvement of algorithms and models, but also depends on the support of computing hardware and massive data. With the continuous improvement of computer hardware performance, especially the development of parallel computing devices such as GPU, the training speed of deep learning models has been significantly improved. In addition, the popularity of the Internet and the Internet of Things has led to an explosive growth of data, providing a large amount of training data for deep learning, which is conducive to the improvement of model learning and generalization capabilities.

Stage 2: Deep learning alchemy stage

Through the accumulation of the first stage and the deep learning in some competitions, it has led to a wave of large-scale applications of deep learning during the period from 2012 to 2022, which has significantly raised the threshold for algorithm research in the field of artificial intelligence. reduce.

This era is vividly called the "deep learning alchemy period", because the wide application of deep learning models is like alchemy, creating a new era of artificial intelligence in continuous exploration and optimization. With the popularization of open source data sets and algorithms, more people can participate in the research of artificial intelligence, and the focus has gradually shifted from model development to how to better adjust parameters and make full use of the application of algorithms.

Some differences between deep learning and the traditional machine learning pattern recognition stage of stage one are as follows:

1. Learning of Feature Representations
2. Handle large-scale data
3. deal with complex problems
4. end-to-end learning

(1) Deep Learning Model – Convolutional Neural Network CNN

A convolutional neural network is a deep learning model for image processing and computer vision tasks. It extracts local features of images through multi-layer convolutional layers and pooling layers, and classifies them through fully connected layers and Softmax layers. CNN has made remarkable achievements in the fields of image classification, object detection, and image segmentation, such as models such as AlexNet, VGG, ResNet, and Inception.

(2) Deep Learning Model – Recurrent Neural Network RNN

A recurrent neural network is a deep learning model for processing sequence data. It introduces cyclic connections in the network, making the network memorable and able to process variable-length sequence data. RNN performs well in tasks such as natural language processing (NLP) and speech recognition, such as the Seq2Seq model for machine translation and the LSTM (Long Short-Term Memory) model for text generation.

Stage Three: The Era of Large Models

From 2022, artificial intelligence has entered a new stage, and the emergence of large models such as ChatGPT has completely changed the pattern of artificial intelligence algorithms. This period can be called "the era of artificial intelligence explosion". In this era, all walks of life began to quickly embrace artificial intelligence technology, and the continuous improvement of computing power and models has brought unprecedented opportunities for the application of artificial intelligence.

(1) GPT-4 multimodal model

Since the first year of ChatGPT this year, Microsoft has continued to make efforts in the field of multimodal models. On February 28, it published a paper and launched an all-round artificial intelligence model - Kosmos-1, compared with ChatGPT limited to plain text content (LLM) , the Kosmos-1 backbone is based on Transformer's causal language model, which belongs to the multi-modal large-scale language model (MLLM). In addition to natural language tasks, it can understand text and image content at the same time. In the future, more input modes will be integrated, such as audio and video.

(2) Landing of diversified applications

In March of this year, Microsoft open-sourced the ChatGPT AI interactive application Visual ChatGPT. By calling ChatGPT and a series of visual basic models, it realized the sending and receiving of images during the chat process, as well as the dynamic processing of images. On the basis of ChatGPT, it has VQA visual Questions and answers and the ability of AI to draw. Just one day after the release of Visual ChatGPT, it reached 4K+ stars on Github.

Differences and commonalities of engineers in three different stages

The differences between the above three stages are as follows:

• In the first stage, because deep learning is not yet popular and computing resources are relatively limited, it is very difficult for algorithm engineers to train large-scale models. Algorithm engineers may focus primarily on traditional machine learning methods such as support vector machines, decision trees, etc. The technical background is relatively traditional.

• In the second stage, with the rise of deep learning, the popularity of open source datasets and open source algorithms allows algorithm engineers to obtain data and algorithms more easily, and have more opportunities to conduct experiments and research. Algorithm engineers begin to master deep learning technology, and have a deeper understanding of the construction and parameter adjustment of neural networks and large models.

• In the third stage, due to the explosion of computing power and models, the popularization of cloud computing and distributed computing technology provides strong support for algorithm engineers. With the emergence of large models such as ChatGPT, algorithm engineers need to have more advanced natural language processing techniques and a deeper understanding of the training and deployment of large-scale models.

Correspondingly, I think the constant commonality of algorithm engineers is algorithm engineering ability and continuous learning ability. In the three stages, algorithm engineers need to maintain an attitude of continuous learning. The most necessary innovative spirit and problem-solving ability of algorithm engineers Capability is the key to promoting the development of artificial intelligence technology.

Hehe Information, an excellent and well-known enterprise in the field of artificial intelligence, has also gone through the above three stages in its development process. These stages are the process of its continuous growth and expansion. The first is the research stage of the vertical field. At this time, the enterprise needs to focus on the improvement and in-depth research of its own field. In the early days, Hehe Information focused on solutions in specific fields, such as image processing and text recognition processing. By concentrating resources and expertise, the company has accumulated advantages in specific fields and contributed to the development of the industry.

With the emergence of neural networks and deep learning, Hehe Information has also taken the lead in conducting continuous and in-depth understanding and research on data and algorithms. During this period, the company increased its investment in the research team, carried out more in-depth academic research, and explored more complex algorithms and models. The collection, processing and labeling of data has become crucial, and the introduction of emerging algorithms such as deep learning has also helped Hehe Information to make important breakthroughs in the industry market.

The emergence of large-scale models makes algorithm engineers need to focus on more efficient training and reasoning strategies, and abundant data sources also provide support for the generalization performance and stability of the model. At this stage, Hehe Information also attaches great importance to the training of algorithm engineers and engineering teams, focusing on technology implementation and practical application.

As an outstanding enterprise in the field of artificial intelligence, Hehe Information has experienced research in vertical fields, continuous in-depth research on data and algorithms, and in-depth research on engineering capabilities and data source requirements in the large model stage. At each stage, the company constantly adapts to the pulse of technological development, constantly explores new possibilities, and makes active contributions to the advancement and application of artificial intelligence. With the continuous development of the field of artificial intelligence, Hehe Information will continue to lead the wave of technological innovation and make more efforts to promote the progress of artificial intelligence technology and the development of society.

In addition to algorithm engineers, what other related jobs can you do?

In the era of large-scale models, students majoring in algorithms will indeed face some anxiety, worrying that the only way to become an algorithm engineer is to find their own value in the field of algorithms when the large-scale models are so powerful.

Hehe Information gave the following constructive answers to the general anxiety of students: If we look at the application of algorithms from a broader perspective, we will find that the work that we can carry out around algorithms is actually very extensive. Broaden our professional boundaries.

In the era of large models, how to industrialize technological breakthroughs has become an important issue. We can optimize and expand the career choices of algorithm students from the following aspects: First, the emergence of large models brings the need for understanding of algorithm models and parameter tuning skills, prompting engineers to become a hot profession. In the U.S., there are already recruiting tips engineers. It reminds engineers that they must have a deep understanding of the mechanism of the large model, so as to exert the corresponding value. As shown in the picture below, the AI ​​​​artificial intelligence engineer who has recently become popular.

The product manager in the era of large models has also become a very important role. Product managers need to understand the principle of large-scale model algorithms, and design products that meet the needs from the perspective of users and the market based on large-scale models. A change in the product design paradigm of the current era is the simplification of complex processes to just one dialog box, which requires the product manager's understanding and application of large model technology.

At the same time, in the era of large models, the pre-sales and marketing teams are responsible for explaining and promoting algorithmic products, so that they can better explain the functions and advantages of products to customers and expand the influence of products. Data engineers also play an important role in collecting and processing data to ensure the smooth progress of model training and optimization.

In addition to the above examples, the wide application of large models has also brought new career opportunities such as model monitoring and interpretation experts, model security experts, and model interpretability researchers.

2. In the era of large models, what capabilities do algorithm engineers and transformation need to possess?

The excellent characteristics that an excellent algorithm engineer should possess in the era of large models

Hehe Information believes that an algorithm engineer should have the following main excellent qualities! ~

1. Ability to understand algorithms: Algorithm engineers need to have a deep understanding of different types of algorithms, including traditional machine learning algorithms and deep learning algorithms, as well as their principles and applicable scenarios. For large models, it is necessary to master complex neural network structures, activation functions, loss functions, etc., as well as parameter tuning and optimization methods. Only by deeply understanding the essence of the algorithm can we flexibly select and adjust the algorithm in practical applications to make it perform optimally.

2. The ability to understand data: In the era of large models, data is the cornerstone of algorithms. Algorithm engineers need to have a deep understanding of data and the ability to process it. This includes the cognition of the quality, scale, and characteristics of the data, and the ability to perform data preprocessing, feature engineering, etc., to improve model performance and generalization capabilities. At the same time, for the processing of large-scale data, algorithm engineers need to master distributed computing and storage technologies to efficiently process massive data.

3. Algorithm engineering capabilities: Algorithm engineers need to have efficient algorithm implementation and engineering capabilities. This includes familiarity with commonly used deep learning frameworks and libraries, and the ability to flexibly build, train, and optimize large models. At the same time, be familiar with technologies such as distributed computing environment and GPU acceleration to improve the efficiency of model training and reasoning. In addition, algorithm engineers also need to master technologies such as model monitoring and interpretation, and model interpretability to ensure the reliability and interpretability of the model.

How to transfer existing algorithm knowledge and capabilities during transformation?

First of all, regarding this issue, Hehe Information pointed out that in the era of large models, existing algorithm knowledge and capabilities are very valuable resources. In addition to functioning in a purely algorithmic engineer role, these knowledge and abilities can be applied across multiple domains by transferring technical understanding and interpretation capabilities. In large-scale product design, in-depth understanding and clear explanation of algorithm working principle and application are the prerequisites to ensure the success of the product. During the pre-sales and marketing process, explaining the algorithm can show customers the value and advantages of the product and improve product acceptance.

Secondly, data is becoming more and more important in the era of large models, and the ability to migrate data to drive decision-making has also become a key skill. Algorithm engineers’ sensitivity to data and mindsets that drive decision-making can be transferred to multiple roles such as product managers and operations. Among these functions, data perception helps to better understand user needs, optimize product performance, and formulate effective market strategies.

3. In the era of large models, the improvement and expansion of algorithms and related workers

What do algorithm engineers need to learn in transformation?

In the era of large models, the content and majors that algorithm engineers need to integrate and learn mainly depend on their target positions. First of all, they should fully integrate into large-scale model technology and really apply it in their study and work. Secondly, the target position of the transformation will determine the job skills that need to be mastered:

1. Product-Related Capabilities: Understanding the entire life cycle of a product is critical to transforming into a product manager. This includes market research, requirements gathering, product design, project management, and product promotion. Having business awareness, understanding user needs, and mastering the basic methods of product design and management are essential skills.

2. Technical support and marketing: If transitioning into a pre-sales engineer or marketing role, you will need to learn customer service skills, understand marketing strategies, and master public speaking and customer service skills.

3. Data Science: Those who plan to transform into data engineers may need to strengthen their study of statistics, predictive models, machine learning, etc., and be familiar with how to use related tools and platforms for data analysis.

What kind of training does Hehe Information provide for algorithmic newcomers?

The relevant person in charge of Hehe Information said, first of all, the company will be very cautious in the talent selection stage, and select those with sufficient abilities, because there will be a growth period of nearly three years. Secondly, the founder and senior management of the company attach great importance to the growth of fresh, and expect newcomers to become members with core competitiveness in about three years .

What are the advantages of fresh graduates joining Hehe in the future market competition?

First of all, the current algorithm personnel of Hehe Information are very stable. There are two main reasons for this: First, everyone has enough opportunities to develop their abilities. He is an expert in a certain field. dreams, and dreams need soil to germinate. The company pursues advanced technology, and the pure technical gene makes this soil very fertile. The second is because the algorithm personnel who left after growing up from the synthesis are recognized in the society, and even robbed.

Secondly, everyone can seriously think about a question, how can individuals maintain their leadership and competitiveness in algorithm technology?

In the Internet age, there is a lot of information. If you compare this information to a gust of wind, the professionals in it can be butterflies or pieces of paper. The difference between them is: butterflies can fly farther and farther along the strong wind, but they can also slightly resist the wind direction and fly in their own direction. But the piece of paper has only one way to go, and it goes with the wind.

In Hehe Information, you will become that butterfly.