A Br(AI)ve New World
When chatting with colleagues or clients, we often concur that technology is a prime sector, constantly presenting a new wave to surf, hence new knowledge to acquire and new business opportunities. Not every wave is worth riding, but there’s a big one rolling in right now, and it’s all about AI. This wave is reshaping our day-to-day lives in countless ways. The potential for a shift towards machine learning (ML) has been around for years, but with the rise of powerful computing — thanks in part to innovative GPUs from companies like NVIDIA — and an avalanche of data, we’re seeing rapid improvements in ML technologies. These changes are causing businesses across the board to transform. One of the standout elements of this wave is the emergence of AI applications like ChatGPT, which can create fresh, original content. We’re currently at an exciting turning point with the widespread adoption of ML, and it’s clear that this wave of generative AI is set to reimagine many customer experiences and applications.
At Parsectix, we’re invigorated by this emerging landscape and aim to stand at the vanguard of this technological revolution. Thus, we’ve committed ourselves to become leaders in our field, and over the past few months, we’ve diligently focused on enhancing our skills and knowledge.
So, I’m planning to put out a series of articles, covering everything from the bare bones of ML to real-world examples of how customers use it. Here’s the first one for you to have a gander at:
What is Artificial Intelligence?
Artificial intelligence (AI) is any system that can take in knowledge on a human level and use it to speed up and automate jobs that humans can do with their natural smarts. AI comes in two types: narrow, where the AI copies human brain power in one specific area, and general, where the AI can learn and show its smarts in loads of different areas.
What is Machine Learning?
Machine learning (ML) is a technique where we teach computers to spot and make sense of patterns in information, using maths and statistics. After these patterns have been spotted, ML then makes and updates models, so they can make guesses about future events that get better and better over time. These guesses are based on data from the past and present. For instance, ML could be used to work out how likely it is that a customer will buy a certain item, based on what the customer has bought before or how well the item has sold in the past.
Constructing ML applications is a cyclical procedure comprising a series of stages. To build an ML application, follow the general steps above.
What is the difference between ML and AI?
Artificial intelligence ingests data, such as human-level knowledge, and imitates natural intelligence. Machine learning is a subset of AI, where data and algorithms continuously improve the training model to help achieve higher-quality output predictions. Deep learning is a subset of machine learning. It is an approach to realizing ML that relies on a layered architecture, mimicking the human brain to identify data patterns and train the model.
Deep learning is a subset of machine learning. ML is a technique for realizing AI.
What is Generative AI?
Generative AI is a sort of AI that can come up with fresh content and ideas, like chats, tales, pictures, videos, and tunes. Like all AI, generative AI is driven by ML models — massive models that are pre-trained on heaps of data and often called Foundation Models (FMs). Recent leaps forward in ML have brought about models with billions of parameters or variables. (More about MFs on the upcoming articles 😉)
Fifty-four percent of the surveyed executives acknowledge the potential of AI simulations as a strategic instrument for the innovation of new products and the identification of new market segments — PWC, “2022 AI Business Survey”
How can Machine Learning help me?
Machine learning, with its capacity for continuous improvement of results, implies that training models can be a cornerstone of almost any decision-making process. Able to process limitless volumes of data, it can produce immediate analyses and assessments, discern trends and patterns, and generate predictive forecasts. In the evolving landscape of business and technology, the role of machine learning is practically limitless. It can be incorporated into software engineering practices through MLOps, which bridges the gap between machine learning and traditional operations, by implementing algorithms in low-code or no-code development environments. Additionally, ML can be a driving force for quantum computing, enhancing data processing speeds and significantly accelerating model training.
As we steer towards the heart of this revolution, it’s essential to recognize the seismic shift AI and ML are causing in our world. They are not mere technical jargon, but catalysts driving change across industries and societies. Here at Parsectix, we’re equipped to help you navigate through these groundbreaking developments. With a series of upcoming articles, we’ll be exploring the nitty-gritty and the vast potentials of these technologies, touching on their real-world implications and how they can be leveraged. So, whether you’re an industry veteran, a curious newbie, or somewhere in between, we invite you to join us on this journey. Remember, knowledge is power, and in the world of AI, it’s the power to shape the future.