Interesting post that suggests that in deep learning algorithms, questioning things may lead to higher quality conclusions.
Researchers at Uber and Google are working on modifications to the two most popular deep-learning frameworks that will enable them to handle probability. This will provide a way for the smartest AI programs to measure their confidence in a prediction or a decision—essentially, to know when they should doubt themselves.
Deep learning, which involves feeding example data to a large and powerful neural network, has been an enormous success over the past few years, enabling machines to recognize objects in images or transcribe speech almost perfectly. But it requires lots of training data and computing power, and it can be surprisingly brittle.
Somewhat counterintuitively, this self-doubt offers one fix. The new approach could be useful in critical scenarios involving self-driving cars and other autonomous machines.
“You would like a system that gives you a measure of how certain it is,” says Dustin Tran, who is working on this problem at Google. “If a self-driving car doesn’t know its level of uncertainty, it can make a fatal error, and that can be catastrophic.”
This post is a refreshing counterpoint to the breathless ‘AI will take over everything’ reporting that is increasingly common of late.
The first area is that “we won’t be riding in self-driving cars”. As Dr. Reddy explains: “While many are predicting a driverless future, we’re a long ‘road’ away from autonomous vehicles.” This is is terms of cars that will take commuters to work, a situation where the commuter can sit back and read his or her iPad while paying little attention to the traffic outside.
He adds: “For a number of years ahead, human operators and oversight will still rule the roads, because the discrete human judgments that are essential while driving will still require a person with all of his or her faculties — and the attendant liability for when mistakes happen. Besides technical challenges, humans tend to be more forgiving about mistakes made by human intelligence as opposed to those made by artificial intelligence.”
The second ‘unprediction’ is that people will not be replaced by AI bots this year. Dr. Reddy states: “While it is possible that artificial intelligence agents might replace (but more likely supplement) certain administrative tasks, the reality is that worker displacement by AI is over-hyped and unlikely.” So robots won’t be taking over most jobs any time soon.
This is because, the analyst states: “Even in an environment where Automated Machine Learning is helping machines to build machines through deep learning, the really complex aspects of jobs will not be replaced. Thus, while AI will help automate various tasks that mostly we don’t want to do anyway, we’ll still need the human knowledge workers for thinking, judgment and creativity. But, routine tasks beware: AI is coming for you!”
The third aspect is that we won’t get AI-powered medical diagnoses. This is, Dr. Reddy says “Due to a lack of training data and continued challenges around learning diagnosis and prognosis decision-making through identifying patterns, AI algorithms are not very good at medical decision automation and will only be used on a limited basis to support but not replace diagnosis and treatment recommendations by humans.”
He adds: “AI will be increasingly deployed against sporadic research needs in the medical arena, but, as with fraud detection, pattern recognition by machines only goes so far, and human insight, ingenuity and judgment come into play. People are still better than machines at learning patterns and developing intuition about new approaches.”
Importantly: “People are still better than machines at learning patterns and developing intuition about new approaches.”
AI at work
The fourth and final area is that we will still struggle with determining where artificial intelligence should be deployed. Dr. Reddy states: “Despite what you might be hearing from AI solution vendors, businesses that want to adopt AI must first conduct a careful needs assessment. As part of this process, companies also must gain a realistic view of what benefits are being sought and how AI can be strategically deployed for maximum benefit.”
The analyst adds: “IT management, business users and developers should avoid being overly ambitious and carefully assess the infrastructure and data required to drive value from AI. Best practices and ‘buy versus build’ analysis also should be part of the conversations about implementing AI applications.”