During the mid-twentieth century, Alan Turing developed a test of a machine’s ability to exhibit intelligent behaviour indistinguishable from that of a human. Since then, AI machines have leapt from novelty to practical application, becoming prevalent in society, and utilised both to complement intelligence, and empower existence.
While large language models (LLMs) can mimic human responses, grasping the ‘why’ behind actions remains uniquely human. We believe that this difference will be fundamental to extracting the true benefit of AI.
After the internet, the mobile telephone, and the cloud, the rapid progression of generative artificial intelligence (GenAI) is the latest revolution in technology, driving how we will interact with machines for years to come. Man is finally able to give speech to machine, as traditional work and effort are beginning to morph into processes augmented by GenAI. The concept of ‘AI factories’ will therefore become increasingly important, adapting constantly to the speed of AI innovation.
Upon the creation of ChatGPT, reports emerged warning of significant disruption to process-based jobs. While caution is necessary, we face a considerable opportunity. The promise of a revolution amongst the disruption. Of untold access to upskilling, relief from repetitive work, and to improvements in the customer experience, all available with a shift toward the ‘AI factory’.
A model’s effectiveness is dependent upon the quality of its data. Our belief is that a model’s access to data must, therefore, be carefully controlled, as governance of information, alongside respect for privacy and security, is fundamental to maintaining information control and compliance
To improve information retrieval and reduce the risk of model hallucinations, organisations must rethink the way that they index and structure information. A capable and efficient GenAI model requires a context, and management of information upstream of a model can guarantee its success.
To improve information retrieval and reduce the risk of model hallucinations, organisations must rethink the way that they index and structure information. A capable and efficient GenAI model requires a context, and management of information upstream of a model can guarantee its success.
Once a company considers appropriate use cases, understands the opportunities available, and trains employees accordingly, the benefit of GenAI to worker processes can be realised. We believe that, by infusing these processes with AI, a goal fundamental to modern prosperity can be achieved: harmony between employees and technology, with employee effort supported from the cutting edge of innovation.
Since the end of the twentieth century, we have been interacting with computers through graphical user interfaces. But, despite progress in UX/UI design, fluidity in human-machine interactions has remained far from optimal. In offering the first true opportunity to converse seamlessly with computers, we believe that GenAI can and will fundamentally transform legacy applications.
In 2017, Google published a deep learning architecture called Transformer. It revolutionised the fields of Artificial Intelligence and Machine Learning. Fast forward to 2024: transformer-based Large Language Models (LLMs) power routinely used chatbots able to understand and answer using natural language. The Transformer architecture is so successful that some believe it won’t be dethroned for the next few years.
How will digital architecture enhance the adoption of MCB?
Large Language Model (LLM, e.g., ChatGPT, LLaMA, Mistral, Falcon… and many others), became increasingly popular in the past few months. The demonstrated breathtaking capabilities, being able to sustain human-like conversations and understand unstructured text.
How will Customers and Employees benefit from using MCB?
Going beyond the Generative AI hype. We believe we can infuse AI throughout all eligible processes, with a fundamental goal: helping the employee work better and faster.
