Home Technology peripherals AI Six major trends in the next stage of artificial intelligence development

Six major trends in the next stage of artificial intelligence development

Nov 13, 2023 pm 02:17 PM
Big Data automation deep learning

Artificial intelligence is gradually changing our current lives, whether it is the reform of productivity or the pursuit of a more intelligent life experience. All of this seems to be increasingly closely related to the development of artificial intelligence. Next, let us take a look at the six major trends in the future development of artificial intelligence and see which aspects will bring changes to our future production and life

Six major trends in the next stage of artificial intelligence development

Algorithm learning: Let employees understand the working principle of artificial intelligence and the calculation process of the algorithm, thereby improving the overall cognitive ability of artificial intelligence and making more critical suggestions on its output results. Translated into Chinese: Train employees to learn algorithms so that they can understand the working principles of artificial intelligence and the calculation process of algorithms, thereby improving their overall cognitive ability of artificial intelligence and being able to make more critical suggestions on its output results

Human-machine collaboration: Enhance the collaborative capabilities of artificial intelligence and humans, integrate AI into the work process, and become an important anchor for improving work efficiency. At the same time, it emphasizes the leading role of people in work and gives full play to the application of subjective decision-making ability.

Six major trends in the next stage of artificial intelligence development

Data explanation: Limited by the current development level of artificial intelligence, its output content may be contrary to common sense. Cultivating talents who can explain this part of the content can assist artificial intelligence in enhancing its explainability and enable it to be implemented as soon as possible.

Moral supervision: As artificial intelligence continues to integrate into every corner of society, various moral, legal and other regulatory issues accompanying its birthplace are becoming increasingly acute. This requires strengthening the planning of the R&D and application process of artificial intelligence, as well as enhancing artificial intelligence's ability to understand so-called human ethics.

Six major trends in the next stage of artificial intelligence development

Creative innovation: By using technologies such as generative artificial intelligence and AI illusion, we can further broaden the ways to obtain creative ideas and reduce creative costs. With the rich knowledge reserve of artificial intelligence, new expressions can be quickly and easily created to enhance unique competitiveness

Interactive prompts: Limited by the current interpretability of artificial intelligence, when providing corresponding services, enhancing interactive prompts for related content can improve users' understanding during use, thereby obtaining a better user experience.

Six major trends in the next stage of artificial intelligence development

The application of artificial intelligence can undoubtedly improve the work efficiency of the entire society. While it may change our current work structures in the short term, as a tool it will ultimately drive a shift in the way we work and liberate the workforce

The above is the detailed content of Six major trends in the next stage of artificial intelligence development. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Beyond ORB-SLAM3! SL-SLAM: Low light, severe jitter and weak texture scenes are all handled Beyond ORB-SLAM3! SL-SLAM: Low light, severe jitter and weak texture scenes are all handled May 30, 2024 am 09:35 AM

Written previously, today we discuss how deep learning technology can improve the performance of vision-based SLAM (simultaneous localization and mapping) in complex environments. By combining deep feature extraction and depth matching methods, here we introduce a versatile hybrid visual SLAM system designed to improve adaptation in challenging scenarios such as low-light conditions, dynamic lighting, weakly textured areas, and severe jitter. sex. Our system supports multiple modes, including extended monocular, stereo, monocular-inertial, and stereo-inertial configurations. In addition, it also analyzes how to combine visual SLAM with deep learning methods to inspire other research. Through extensive experiments on public datasets and self-sampled data, we demonstrate the superiority of SL-SLAM in terms of positioning accuracy and tracking robustness.

Super strong! Top 10 deep learning algorithms! Super strong! Top 10 deep learning algorithms! Mar 15, 2024 pm 03:46 PM

Almost 20 years have passed since the concept of deep learning was proposed in 2006. Deep learning, as a revolution in the field of artificial intelligence, has spawned many influential algorithms. So, what do you think are the top 10 algorithms for deep learning? The following are the top algorithms for deep learning in my opinion. They all occupy an important position in terms of innovation, application value and influence. 1. Deep neural network (DNN) background: Deep neural network (DNN), also called multi-layer perceptron, is the most common deep learning algorithm. When it was first invented, it was questioned due to the computing power bottleneck. Until recent years, computing power, The breakthrough came with the explosion of data. DNN is a neural network model that contains multiple hidden layers. In this model, each layer passes input to the next layer and

PHP's big data structure processing skills PHP's big data structure processing skills May 08, 2024 am 10:24 AM

Big data structure processing skills: Chunking: Break down the data set and process it in chunks to reduce memory consumption. Generator: Generate data items one by one without loading the entire data set, suitable for unlimited data sets. Streaming: Read files or query results line by line, suitable for large files or remote data. External storage: For very large data sets, store the data in a database or NoSQL.

AlphaFold 3 is launched, comprehensively predicting the interactions and structures of proteins and all living molecules, with far greater accuracy than ever before AlphaFold 3 is launched, comprehensively predicting the interactions and structures of proteins and all living molecules, with far greater accuracy than ever before Jul 16, 2024 am 12:08 AM

Editor | Radish Skin Since the release of the powerful AlphaFold2 in 2021, scientists have been using protein structure prediction models to map various protein structures within cells, discover drugs, and draw a "cosmic map" of every known protein interaction. . Just now, Google DeepMind released the AlphaFold3 model, which can perform joint structure predictions for complexes including proteins, nucleic acids, small molecules, ions and modified residues. The accuracy of AlphaFold3 has been significantly improved compared to many dedicated tools in the past (protein-ligand interaction, protein-nucleic acid interaction, antibody-antigen prediction). This shows that within a single unified deep learning framework, it is possible to achieve

Five major development trends in the AEC/O industry in 2024 Five major development trends in the AEC/O industry in 2024 Apr 19, 2024 pm 02:50 PM

AEC/O (Architecture, Engineering & Construction/Operation) refers to the comprehensive services that provide architectural design, engineering design, construction and operation in the construction industry. In 2024, the AEC/O industry faces changing challenges amid technological advancements. This year is expected to see the integration of advanced technologies, heralding a paradigm shift in design, construction and operations. In response to these changes, industries are redefining work processes, adjusting priorities, and enhancing collaboration to adapt to the needs of a rapidly changing world. The following five major trends in the AEC/O industry will become key themes in 2024, recommending it move towards a more integrated, responsive and sustainable future: integrated supply chain, smart manufacturing

TensorFlow deep learning framework model inference pipeline for portrait cutout inference TensorFlow deep learning framework model inference pipeline for portrait cutout inference Mar 26, 2024 pm 01:00 PM

Overview In order to enable ModelScope users to quickly and conveniently use various models provided by the platform, a set of fully functional Python libraries are provided, which includes the implementation of ModelScope official models, as well as the necessary tools for using these models for inference, finetune and other tasks. Code related to data pre-processing, post-processing, effect evaluation and other functions, while also providing a simple and easy-to-use API and rich usage examples. By calling the library, users can complete tasks such as model reasoning, training, and evaluation by writing just a few lines of code. They can also quickly perform secondary development on this basis to realize their own innovative ideas. The algorithm model currently provided by the library is:

Application of algorithms in the construction of 58 portrait platform Application of algorithms in the construction of 58 portrait platform May 09, 2024 am 09:01 AM

1. Background of the Construction of 58 Portraits Platform First of all, I would like to share with you the background of the construction of the 58 Portrait Platform. 1. The traditional thinking of the traditional profiling platform is no longer enough. Building a user profiling platform relies on data warehouse modeling capabilities to integrate data from multiple business lines to build accurate user portraits; it also requires data mining to understand user behavior, interests and needs, and provide algorithms. side capabilities; finally, it also needs to have data platform capabilities to efficiently store, query and share user profile data and provide profile services. The main difference between a self-built business profiling platform and a middle-office profiling platform is that the self-built profiling platform serves a single business line and can be customized on demand; the mid-office platform serves multiple business lines, has complex modeling, and provides more general capabilities. 2.58 User portraits of the background of Zhongtai portrait construction

Discussion on the reasons and solutions for the lack of big data framework in Go language Discussion on the reasons and solutions for the lack of big data framework in Go language Mar 29, 2024 pm 12:24 PM

In today's big data era, data processing and analysis have become an important support for the development of various industries. As a programming language with high development efficiency and superior performance, Go language has gradually attracted attention in the field of big data. However, compared with other languages ​​such as Java and Python, Go language has relatively insufficient support for big data frameworks, which has caused trouble for some developers. This article will explore the main reasons for the lack of big data framework in Go language, propose corresponding solutions, and illustrate it with specific code examples. 1. Go language

See all articles