Understanding Artificial Intelligence, Machine Learning, and Deep Learning
Dive into the fundamentals of AI, ML, and deep learning - Explore how our ScholarAI elevates research in science and academia by providing trusted, cutting-edge AI solutions while learning the basics.
Artificial intelligence (AI), recently popularized by ChatGPT and other mainstream tools, is poised to revolutionize professional productivity across industries, but, what is AI, how does it relate to machine learning and deep learning, and how is ScholarAI pushing AI forward? Here we provide an overview of these foundational terms and provide select references for further reading.
Artificial Intelligence (“AI”)
AI can be difficult to define, but generally refers to a broad field of computer science concerned with building smart machines capable of performing tasks that would otherwise require human intelligence. AI integrates a variety of mathematical algorithms, techniques, and disciplines, including machine learning and deep learning, to enable machines to learn, reason, perceive, understand natural language and engage in problem-solving. Modern AI can largely be traced back to the seminal paper “Attention Is All You Need” by Vaswani et al., where the transformer architecture that enables large language models like GPT-4 is introduced and described.
Branches of AI
There are several important branches of AI. We will not cover all of them here but we have provided a list of 3 important concepts with brief definitions:
- Machine Learning (ML): Focuses on the development of algorithms that can learn and make predictions and decisions based on data.
- Deep Learning (DL): A subset of ML that uses neural networks with many layers (sometimes referred to as deep neural networks) to analyze various factors of data.
- Natural Language Processing (NLP): Enables machines to understand and interpret human language.
Machine Learning (“ML”)
Machine learning is a fundamental concept within the broader field of AI. ML focuses on the development of algorithms and statistical models to enable computers to perform tasks without explicit instructions, instead relying on patterns and inference. Machine learning algorithms build a model based on sample data, known as "training data," to make predictions or decisions without being explicitly programmed to perform the task.
Types of Machine Learning
Machine learning can be broadly divided into three major functional categories:
- Supervised Learning: Algorithms are trained on labeled data, where the input data is tagged with the correct output. The algorithm learns to predict the output from the input data.
- Unsupervised Learning: Algorithms are used on data without labeled responses. The system tries to learn patterns and structures from the data.
- Reinforcement Learning: A type of machine learning where an agent learns to behave in an environment by performing actions and seeing the results.
Deep Learning (“DL”)
Deep learning is a subset of machine learning that is often referenced with respect to advanced AI systems. It is widely believed that deep learning represents one of the most significant advancements in the field of AI. Deep learning uses multiple layers to progressively extract higher-level features from raw inputs. Deep learning models, particularly deep neural networks, are composed of layers of nodes or “neurons”, with each layer transforming the input data into more abstract and composite representations. “Neurons” are named due to the inspiration neural networks draw from the structure and function of the human brain, namely the interconnecting of many neurons. The key characteristic of deep learning is its ability to perform feature extraction automatically, learning complex patterns in large amounts of data. Traditional machine learning techniques often require manual feature extraction, but deep learning models learn these features directly from the data, making them particularly effective for tasks like image and speech recognition.
Natural Language Processing (“NLP”)
Natural language processing is a critical area of AI that focuses on the interaction between computers and human language. It involves enabling computers to understand, interpret, and manipulate human language in a valuable way. Specifically, natural language processing combines computational linguistics (i.e., rule-based modeling of human language) with statistical, machine learning, and deep learning models. These technologies enable computers to process human language in the form of text or voice data and understand its full meaning, complete with the speaker or writer's intent and sentiment.
Evolution and Impact of Deep Learning
The field of NLP has been significantly advanced by the advent of deep learning models, which have propelled forward the capabilities of language processing. Deep learning in NLP involves using neural networks with multiple layers to automatically learn and extract linguistic features and semantics from large amounts of text data. These advancements have led to natural language processing being used in a variety of applications including:
- Speech Recognition: Translating spoken language into text.
- Machine Translation: Automatically translating text from one language to another.
- Sentiment Analysis: Identifying and categorizing opinions expressed in text.
- Chatbots and Virtual Assistants: Interacting with users in human-like conversations.
- Information Retrieval: Finding relevant responses to queries in large databases.
ScholarAI’s contribution to AI
ScholarAI is committed to increasing the trustworthiness, and the therefore utility of advanced AI systems for professional researchers. To do this, ScholarAI is building towards the notion of artificial general intelligence (AGI) for science. ScholarAI’s science AGI is enabled by the world’s first AI-native collection of technical documents, featuring over 200M peer-reviewed scientific articles, pre-print documents, textbooks, and future patents and clinical trial data. ScholarAI’s science AGI will catalyze easier, faster, and cheaper 1) synthesis of scientific concepts––identifying knowledge gaps and therefore important research questions to be answered; 2) paper writing and information sourcing––accelerating the publication and review processes; and 3) the further field objective of more fluent research workflows beyond the keyboard––more clearly defining the most critical wet lab/benchtop experiments to be run by professional scientists.
As we journey through the fascinating realms of AI, machine learning, and deep learning, it's clear that these technologies are not just the future; they are the present, reshaping how we approach complex problems and tasks. ScholarAI stands at the forefront of this evolution, offering a unique blend of these advanced techniques with a focus on enhancing the trustworthiness and utility of AI for professional researchers.
With our expansive database and commitment to developing a science-focused AGI, we are bridging critical knowledge gaps, streamlining research processes, and redefining scientific exploration.
If you're ready to take the next step in elevating your research game, join our journey toward reliable and accurate AI with over 200M+ scholarly articles accessible - sign up for ScholarAI here.
Select Resources for Further Reading:
- Artificial Intelligence Review by A. Kilani, A. Hamida, H. Hamam (2022)
- Explainable Artificial Intelligence: An Analytical Review by P. Angelov, E. Soares, et al. (2021)
- The Role of Artificial Intelligence in Healthcare: A Structured Literature Review by Silvana Secinaro, D. Calandra, et al. (2021)
- Trustworthy Artificial Intelligence: A Review by Davinder Kaur, Suleyman Uslu, et al. (2022)
- Machine Learning - A Probabilistic Perspective by Kevin P. Murphy (2012)
- An Introduction to Machine Learning by M. Kubát (2017)
- Practical Bayesian Optimization of Machine Learning Algorithms by Jasper Snoek, H. Larochelle, Ryan P. Adams (2012)
- Towards A Rigorous Science of Interpretable Machine Learning by F. Doshi-Velez, Been Kim (2017)
- A Survey of the Usages of Deep Learning for Natural Language Processing by Dan Otter, Julian R. Medina, J. Kalita (2020)
- Recent Trends in Deep Learning Based Natural Language Processing by Tom Young, Devamanyu Hazarika, et al. (2017)
- Foundations of Statistical Natural Language Processing by Christopher D. Manning, Hinrich Schütze (1999)
- Speech and Language Processing - An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition by Dan Jurafsky, James H. Martin (2019)