How to innovate algorithm models

With the advancement of digital transformation, the demand for distributed and decentralized AI models and algorithms has become increasingly prominent, and the organic combination of different algorithms and models has become a mainstream choice in practical applications. . In addition, multi-modality, unsupervised, interpretability, self-learning, self-evolution, etc. are all research directions that need to be focused on in the current AI field.

So, what are the new developments in these "soul" features in the AI ​​field? How do major domestic and foreign AI giants maximize model performance in actual implementation? If you want to understand the development and cutting-edge exploration of artificial intelligence algorithm models, the AISummit "Innovation of Algorithm Models" special session is not to be missed!

Special Session of the Summit

From August 6th to 7th, the AISummit Global Artificial Intelligence Technology Conference will be held as scheduled in the form of online live broadcast on the official website of the conference. It is expected that 100,000 people will attend the conference. With the theme of "Drive·Innovation·Digital Intelligence", this conference is mainly aimed at mid-to-high-end technology managers and technical practitioners of technology companies, business managers undergoing digital intelligence transformation, as well as people and entrepreneurs interested in the field of artificial intelligence. . The conference will also invite nearly a hundred technical elites from well-known Internet technology companies, managers of traditional companies in the digital-intelligence transformation period, and experts and scholars from cutting-edge academic institutions to jointly discuss the industry driving forces of artificial intelligence and discuss cutting-edge innovations in artificial intelligence. Technology, let’s talk about the wave of “digital intelligence” in the era of artificial intelligence.

In this issue of the AISummit conference, the "Innovation of Algorithm Models" special session, was hosted by many senior technical leaders and experts in the industry from Byte, Kuaishou, Alibaba Damo Academy, and Tencent From the perspective of business practice, share advanced cases and technical thinking on machine learning algorithm model innovation.

Topic details

Topic 1: Applications and challenges of byte AI machine translation technology

Speaker: Wang Mingxuan Head of Machine Translation at Jiedu AI Lab

Content preview:

Nowadays, machine translation can be used in many scenarios such as information release and information exchange. Artificial intelligence technology has improved In addition to the creation of information content, machine translation still faces some challenges, such as translation of scarce resources, multi-language translation, chapter translation, etc. However, increasing the amount of data, establishing a unified representation, and creating a new machine translation paradigm are still issues that need to be solved in the future of machine translation.

This sharing was conducted by Wang Mingxuan, head of machine translation at ByteDance AI Lab, who brought about the application of Byte AI machine translation technology and the challenges that machine translation will face in the future.

Topic 2: On-device rearrangement system recommended by Kuaishou short videos

## Speaker: Ding Weijie Kuaishou senior algorithm expert

Content preview:

The mainstream recommendation system deployed in the cloud can achieve near real-time at the minute level, while the recommendation system deployed on the end benefits from its link characteristics , which can achieve real-time feedback in seconds.

This sharing introduces the application and innovation of end-to-end real-time rearrangement in the Kuaishou short video recommendation system from several aspects:

( 1) The unique infrastructure of the end-to-end rearrangement system, a model selection solution combined with the cloud under extremely small computing power and bandwidth constraints;

# (2) End-to-end rearrangement The system's characteristic modeling method, under the extremely small parameter space limit, the refinement of feature engineering and model structure, the AUC evaluation of single-point prediction is significantly better than the published SOTA algorithm;

(3) The unique sorting mechanism of the on-end rearrangement system, the refined processing of the listwise sorting scheme under the limitation of extremely small candidate space.

Topic 3:

Alibaba’s large-scale pre-training dialogue model practice

Speaker: Li Yongbin, senior algorithm expert at Alibaba DAMO Academy, dialogue intelligence technology Person in charge

Content preview:

How to inject human knowledge into the pre-training model so that knowledge and data can be organically integrated has always been a difficult problem in AI research. A model can only solve one task, and poor versatility is a big problem in AI.

The pre-trained model may be the solution. It can draw inferences from one example and solve a variety of tasks.

However, knowledge injection is not easy. Since knowledge is much smaller than unlabeled data in terms of order of magnitude, simple mixing can easily lead to knowledge being overwhelmed or serious overfitting.

Using semi-supervised learning to inject knowledge into pre-trained dialogue models to achieve the organic integration of knowledge and data will be the first solution to inject knowledge into pre-trained models in the field of human-computer dialogue.

This sharing is provided by Li Yongbin, a senior algorithm expert at Alibaba DAMO Academy and head of conversational intelligence technology, to explain the practice of Alibaba's large-scale and training dialogue models, and how to use semi-supervised learning to inject annotated human knowledge into pre-training. Dialogue model to explore new paths for knowledge and data integration.

Topic 4: Exploration and development of video content understanding

Speaker: Xie Xiaohui, Tencent online video technology expert

Content preview:

All people who are deeply involved in the field of AI will find that the semantic gap is a very challenging problem, and it is necessary to use technologies such as knowledge graphs to help the entire AI recognize Know new progress.

In this sharing, Xie Xiaohui, an online video technology expert from Tencent, will share the cutting-edge exploration and development of video content understanding. The content includes the current status and challenges of video content understanding technology, as well as the latest practice of video content understanding in Tencent's business.

Reservation method

Click to enter the ​

​AISummit Global Artificial Intelligence Technology Conference​​ official website, follow the prompts to completely fill in and submit the information to complete the registration.

Scan the QR code to join the official group of the conference, participate in the lottery, and win exquisite gifts such as SONY speakers, Bingdundun, and AI technology books, as well as red envelopes.

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