Artificial intelligence and machine learning are revolutionising the future of businesses, finance, and public services. With the help of Generative AI, machines can draw, write and perform other tasks that were previously limited to humans. ChatGPT is no longer just an amusing tool; it has become essential for knowledge workers. Healthcare has also benefited from technological advancements in AI, enabling faster cancer detection. Every industry is experiencing a seismic shift due to the rapid pace of AI development, and we are only at the beginning of this exciting revolution. PepTalk's top AI speakers are pioneers in these fields, leading the way towards a brighter future.
Expert insights from artificial intelligence speakers
We’re proud to offer an inspiring line-up of artificial intelligence speakers, each with diverse experiences and expertise. Pioneers and thought leaders, our speakers boast a diverse AI background, encompassing ethicists, developers, neuroscientists, cybersecurity experts and more. Your organisation can quickly upskill and help create a competitive advantage with their insights. Speaking topics include:
- The latest trends and innovations in AI technology.
- The ethical and societal implications of AI advancements.
- Strategies for integrating AI into business operations to drive growth and innovation.
- The future of AI and its potential to transform industries and societies.
What is Artificial Intelligence?
For those who remember HAL 9000, Artificial Intelligence (AI) may bring images of blood-thirsty robots to mind. However, more simply, the field of computer science aims to create intelligent machines capable of performing tasks similarly to humans. AI involves the development of algorithms and computational processes that enable computers to learn, reason, solve problems, perceive, and understand language. AI seeks to automate intellectual tasks and is a pivotal technology in driving innovation across various industries, including healthcare, education, finance, and transportation.
The essence of AI lies in its ability to quickly process vast amounts of data, make decisions, and predict outcomes based on that data. Through techniques like machine learning and deep learning, AI systems can learn from experience and adjust to new inputs. With increasing accuracy, AI can perform tasks that were once only possible for humans. As AI continues to evolve, it promises to enhance human capabilities, automate tedious or hazardous tasks, and solve complex problems beyond human reach. However, its use raises important ethical and societal issues that require careful consideration.
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What are some of the key terms in the field of Artificial Intelligence?
Here's a straightforward and clear glossary of common AI-related terms and acronyms that you might encounter in an AI keynote.
AI (Artificial Intelligence): The simulation of human intelligence in machines that are programmed to think and learn. The overarching discipline encompasses everything from robotic process automation to actual robotics.
ML (Machine Learning): A subset of AI that includes algorithms and statistical models that computer systems use to perform tasks without explicit instructions, relying on patterns and inference instead.
DL (Deep Learning): A subset of machine learning that structures algorithms in layers to create an "artificial neural network" that can independently learn and make intelligent decisions. Deep learning is behind most of the breakthroughs powering AI advancements today.
NLP (Natural Language Processing): A branch of AI that helps computers understand, interpret, and manipulate human language. NLP involves applying algorithms to identify and extract the natural language rules such that the unstructured language data is converted into a form that computers can understand.
GPT (Generative Pre-trained Transformer): A type of language model that uses deep learning to produce human-like text. It's pre-trained on a large corpus of text and then fine-tuned for specific tasks. GPT models are known for generating coherent and contextually relevant text based on a given prompt.
LLM (Large Language Model): A type of deep learning model designed to understand, generate, and translate human language. LLMs are "large" because they are trained on vast amounts of text data, enabling them to understand various language nuances, contexts, and styles.
ANN (Artificial Neural Network): A computing system comprising several simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs. ANNs are inspired by the biological neural networks that constitute animal brains.
CNN (Convolutional Neural Network): A class of deep neural networks most commonly applied to analysing visual imagery. CNNs are known for their ability to recognise patterns and structures in images, making them particularly useful in image and video recognition, recommender systems, and image classification.
RNN (Recurrent Neural Network): An artificial neural network where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behaviour. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs. This makes them ideal for tasks such as speech recognition or language translation.
Transformer: A model architecture designed to handle sequential data, but unlike RNNs, it doesn't require data to be processed in order. It uses mechanisms called attention to weigh the influence of different input parts on each output part. Transformers have been highly successful, particularly in NLP tasks.
Prompt: A prompt is a text input given to an AI model designed to elicit a specific output or response. It acts as a starting point or instruction that guides the AI in generating text, answering questions, completing a task, or creating content that follows a certain theme or style. Prompts can range from simple questions or statements to more complex and detailed instructions, depending on the desired outcome. The effectiveness of a prompt in generating a relevant and accurate response often depends on how well it is constructed and how much context it provides to the AI model. In essence, a prompt is the user's way of communicating with the AI, telling it what is expected in its response.
Fine-tuning: The process of taking a pre-trained model and adapting it to a specific task by continuing the training process with data from that task. This allows for leveraging pre-learned patterns on a new, often smaller, dataset, improving performance on specific tasks.
This list covers some of the foundational terms and acronyms in the field of AI. Each term is a significant area of study and application within the broader landscape of artificial intelligence and machine learning.
