The field of Computer Vision (CV) has faced challenges. Initially, it relied on handcrafted features and rule-based algorithms, resulting in limited accuracy. The introduction of machine learning (ML) has brought progress, particularly Transfer Learning (TL), which addresses various CV problems by reusing pre-trained models. TL requires less data and computing while delivering nearly equal accuracy, making it a prominent technique in the CV landscape. Our research focuses on TL development and how CV applications use it to solve real-world problems. We discuss recent developments, limitations, and opportunities.

Original aim: Our first milestone will be to publish a draft framework on a preprint server such as medRxiv at month 12 of the project. This will be updated based on relevant changes in direction to HDR UK priorities, or new insights gained after its deployment in use-case scenarios for work packages 2 and 3. Current progress: The TRIPOD-AI protocol has been recently published in BMJ Open1. An additional article has recently been published in the Journal of Clinical Oncology2 on the critical need for better reporting of machine learning methods in health data science for risk prediction. Original aim: We anticipate pre-registering this research project by month 9 of the project. A manuscript will then be submitted to a preprint server such as medRxiv, by month 24, to report the utility and pitfalls of synthetic data generation models to support reproducible machine learning in wearable sensor data and electronic health records.

Many people claim current technological progress as happening at a faster and faster pace (exponential even), with no end in sight. The merits and detriments of technology can be argued ad nauseum, but I won't be getting into that in this post (I generally view technology itself as neutral -- it can be used to improve human life or terribly misused to oppress, control, and kill). What I am going to briefly explore here is the question: is current progress in AI exponential? And if so, what implications does that have for estimates on the arrival of human level or superhuman level AI? Before I dive in, it's worth asking (if you didn't study mathematics): why does it matter if something is changing exponentially? Frequently people think the word "exponential" means "really fast", which is sometimes true, but doesn't capture much of the meaning of the concept.

Customer Support: providing answers to frequently asked questions or replying to simple factual questions such as "when's the next train to Cardiff?" or "what's my credit card balance?" A more complex, application-driven chatbot could handle account applications, insurance claims or other situations where the customer needs to be guided through filling out a form. Concierge Services: performing repetitive tasks where the user needs to specify some parameters such as time and place. For example, a travel chatbot could help them find and book a hotel room near all the attractions they want to visit, and a booking chatbot could make an appointment at the hairdresser or GP on a convenient day. Over time, the chatbot could learn the user's preferences and ask, "shall I book your usual?"