Technology peripherals
AI
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL): What's the Difference?
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL): What's the Difference?
Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are often used interchangeably. However, they are not exactly the same. Artificial intelligence is the broadest concept that gives machines the ability to imitate human behavior. Machine learning is the application of artificial intelligence to systems or machines to help them learn and continuously improve themselves. Finally, deep learning uses complex algorithms and deep neural networks to repeatedly train specific models or patterns.
Let’s look at the evolution and journey of each term to better understand what artificial intelligence, machine learning, and deep learning actually refer to.
Artificial Intelligence
Artificial intelligence has come a long way since the past 70-plus years. Whether we know it or not, whether we like it or not, it has penetrated into every aspect of our lives. Over the past decade, advances in machine learning and deep learning have created an AI boom across industries and organizations of all sizes. Cloud service providers are further driving this momentum by developing free open source services and offering new scenarios.
Figure 1: Overview of AI, ML and DL
Artificial intelligence is probably the most talked about concept since 1956 . By 2015, widespread use of GPUs made parallel processing faster, more powerful, and cheaper. And increasingly cheap storage can store big data (from plain text to images, mappings, etc.) at scale. This created a need for data analysis, which is more commonly known as data science, leading to the development of machine learning as a method to achieve artificial intelligence.
Machine Learning
Machine learning is the use of algorithms to process, learn and understand or predict patterns in available data. Recently, the low-code and no-code concepts of software development are used as a self-learning process in machine learning, which gives specific instructions to complete a specific task. Machines are “trained” using data and algorithms so that they can learn how to perform tasks and, more importantly, apply that learning to an evolving process.
Figure 2: Evolution of AI, ML and DL
Machine learning developed when the developer community focused on AI , and then developed algorithmic decision tree learning, logic programming, clustering, parallel processing, and reinforcement learning. These are good steps in the right direction, but not enough to address scenarios of interest to the world.
Deep Learning
Deep learning is the evolution of neural networks and machine learning, and is the brainchild of the artificial intelligence community. It learns how the human mind works in a specific scenario and then does it better than humans at that job! For example, IBM's Watson played chess against itself and made great progress in the game, eventually beating the world champion. Google's AlphaGo also learned how to play the game Go, playing it over and over to improve itself and become a champion.
Artificial intelligence, machine learning and deep learning are constantly evolving. Everyone involved in data science hopes to advance these concepts to improve our daily lives. And the open source community, private industry, scientists, and government agencies are all working together on this.
Figure 3: Types of AI, ML and DL
In summary, while AI helps in creating intelligent machines, machines Learning helps build AI-driven applications. Deep learning is a subset of machine learning. It trains specific models by processing large amounts of data using complex algorithms. Since narrow AI is extremely difficult to develop, machine learning is addressing opportunities in this area through rigid computation. At least for achieving general AI, deep learning helps bring AI and machine learning together.
The above is the detailed content of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL): What's the Difference?. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
AI Hentai Generator
Generate AI Hentai for free.
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
1378
52
Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions
Jun 28, 2024 am 03:51 AM
This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP
Context-augmented AI coding assistant using Rag and Sem-Rag
Jun 10, 2024 am 11:08 AM
Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application
Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations
Jun 11, 2024 pm 03:57 PM
Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it
Seven Cool GenAI & LLM Technical Interview Questions
Jun 07, 2024 am 10:06 AM
To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text
To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework
Jul 25, 2024 am 06:42 AM
Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A
Five schools of machine learning you don't know about
Jun 05, 2024 pm 08:51 PM
Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing
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
SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time
Jul 17, 2024 pm 06:37 PM
Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S
