Originally posted on The Horizons Tracker. The world is awash with data, and it's tempting to think that this data is what's used to train the AI systems that are increasingly prevalent around the world. New research1 from MIT highlights how not only is AI often trained on relatively small samples of curated data, but this data often contains errors that undermine the training delivered to machine learning algorithms. Indeed, across 10 of the most-cited datasets used by scientists to train machine learning systems, the researchers found that 3% of the data was mislabeled or inaccurate. It has long been suspected that the data used to train AI systems is not what it could be, but until now no one has been able to quantify just how poor it is.

Over this past summer, I was fortunate enough to be given the opportunity to deliver a speech to the State University of New York (SUNY) College of Optometry residency class of 2019. During this 20 minutes (which they likely perceived as just over an hour), I recommended that residents take a few moments and conduct a search of the world's literature using the key words "deep learning" with the disease of their choice. Conducting such a search myself gave me a better understanding of the likely direction of health care in my clinical lifetime. A study recently published in JAMA Ophthalmology describes a deep learning system which appears to show high sensitivity and specificity for the detection of glaucoma.1 Previously by Dr. Casella: Consider IOP fluctuations when diagnosing glaucoma Deep learning So, just what exactly is deep learning? In the arena of artificial intelligence, this subset of machine learning is based on so-called "neural networks" that process data into concepts.

The media has been awash with breathless prose about the capabilities of artificial intelligence in recent years. One would be forgiven for thinking that machines are practically at human levels of cognition already, or at least will be very soon. A recent study from UCLA highlights just how far there still is to go. The study illustrated a number of quite significant limitations that the researchers believe we have to understand and improve upon before we let ourselves get carried away. The researchers ran a number of experiments to test the progress made with machine vision.

Global technology company Huawei has launched a study on the similarities between the human brain and Artificial Intelligence, which reveals that the average European is unaware of 99.74% of the actual decisions they make every day, showing how hard our brain works without us having to consciously engage it. It is commonly accepted that the human brain makes approximately 35,000 decisions a day; however, the new research, polling 10,000 Europeans, reveals that we are aware of just 0.26% of these decisions with respondents on average believing they make only 92 decisions per day. Walter Ji, President, Huawei Western Europe Consumer Business Group comments, "The research shows how human intelligence works just like Artificial Intelligence, operating in the background to empower us in everything we do. While revealing a significant gap between the number of decisions we believe we make every day and the actual number we make, the results also shed light on other discrepancies between how we think we spend our time, and how we actually spend it." The research also revealed how people would like their smartphones to help with decisions and make their lives easier, with 47% saying they would like to be presented with creative ways to use up the food that's in their fridge, and 43% saying they would like automatic notifications about travel.