Credit: AI Trends
Part truth, part sci-fi, the notion of rogue artificial intelligence is gaining attention for good reason. As the creators of AI, humans hold responsibility over sentient machines in a manner similar to parental duties. But as AI matures into programs that can spawn and train themselves, things become less explainable by humans and more autonomous for machines. AI’s complexities have led to the call for more diversity of backgrounds and soft skills in the field in hopes of curbing bias and an all-out derailment of AI through more thoughtful training. So are parents uniquely equipped to train AI, and could a parenting method better train AI to abstract and adapt in today’s rapidly advancing business world?
Recent developments have catapulted AI’s advancements, enabling AI’s degree of cognition for the Gestalt test, realistic text generators, and accurate medical diagnoses. And for every type of AI, there’s an engineer behind the scenes, programming the software to recognize patterns in massive data sets to achieve a particular objective. To this end, a parent-like approach is already in use with reinforcement learning, which guides an AI’s initial development so that it can learn quickly from its mistakes and self-correct accordingly. At the same time, developments in neural networking for AI have imbued machines with even more human-like qualities, begging the question: Will AI soon be able to think abstractly enough to generalize beyond niche use cases for broader business applications?
To gain a better understanding of parental-style training methods in the dizzying world of AI, I recently spoke with Wikibon Inc.’s lead analyst for data science, James Kobielus. For more than a decade, Kobielus has been closely analyzing the depths of AI, from computing infrastructure to ethical frameworks. It will take a village of skill sets and AI training methods, spanning supervised and unsupervised models, to prepare AI for the grownup tasks of modern business, according to Kobielus.
[Editor’s note: The following has been condensed for clarity.]
How could parental experience contribute to the soft skills helpful in AI training?
Kobielus: Any sentient creature, from an octopus to a tree, engages in some degree of learning. They have to, as it were, adapt to different environments. We’ve evolved to learn, and that’s why we’re still here. So how does a person learn? There’s nature, and there’s nurture.
Let’s talk about nature. As an invention of man, AI is refined and adapted to various tasks. When we’re talking about learning in the AI context, it’s able to adapt its behavior to changes in the environment, meet the challenges it faces in that environment, and has some degree of success in reaching its outcomes. In the past 10 years, AI has shifted almost entirely from rule-based systems to what we now call machine learning.
In parenting, to some degree, a parent doesn’t need to give their child a lot of things. Babies are born with cognitive skills built into their wiring. Learning isn’t training in the sense that there’s some specific task to do to ensure unsupervised learning. But for AI, supervised learning is building a model to see how it can process data in a way to predict what it will see next based on how it’s been trained on historic data. Examples include the prediction of age, race, gender, or facial recognition.
In the context of parenting and AI, building predictive models reasonably well is a soft skill, as you have to understand what causes what. For reinforcement learning, you’ve got to understand the task, and you want to make sure people aren’t being harmed and objects aren’t being damaged. There has to be extensive simulations in reinforcement learning — with autonomous cars, for example. It’s the same for parenting; there’s lots and lots and lots of rules. Parents give a long or short reach based on the risks of the environment. Kids don’t learn all on their own, but they do have innate limiters.
How could reinforcement learning help curb bias in AI models?
Kobielus: When it comes to bias in AI, it’s defined as a set of outcomes you want to avoid. Every AI has a bias toward the task for which it’s trained. When we talk in a broad sense of the perfect AI model, it comes down to the soft skills of the AI builders understanding the task to be achieved. There’s lots of variables, such as protected attributes, when building AI for home loan approvals, as an example. While there may be valid predictors in a loan, if they’re baked into the AI model, it could effectively unfairly bias entire groups of people that didn’t have historic advantages like wealthy parents or private schooling.
How AI can curb bias is: focus on the data. It reflects the bias in society at large so the AI can be designed against unwanted bias. The AI model must be tested for potential bias, which can be done by a human workforce to evaluate those outliers identified by the AI.
For reinforcement learning to avoid bias, it’s actually not something I’ve come across yet, so let’s explore it. If an AI is programmed to avoid the steps that might be correlated with unfair discrimination, it’s possible to use reinforcement learning to train an AI model to take steps to avoid overt bias.
What can machines do better than toddlers? What can toddlers do better than machines?
Kobielus: Let’s just say humans, because toddlers are humans. Well, humans aren’t programmed — we’re not machines. At no point has someone written code to be directly inserted into my brain. I instead take information and assess it. That’s how we learn.
AI must achieve logic from humans, who can program in hard logic and soft logic. And logic is becoming increasingly statistical. That’s what the AI revolution is all about. Machines run 24/7, don’t sleep or burn out. Machines can process far more data than humans and can keep an updated, precise data log, while I can barely recall what I said two seconds ago.
For AI, what’s so amazing is the chipset. The industry is moving toward AI-optimized chipsets, [graphics processing units], and Tensor Core processing. The logic that drives machines of all sorts, especially edge devices like my smartphone and Alexa sitting on my desk, is able to learn from its environment with amazing versatility to engage with humans.
But what humans can do better than machines is analogize. We can compare what we saw before to what we’re seeing now. Analogies are the foundation of human intelligence, and AI is now being programmed to do analogies under supervised learning with statistical representations.
You could even look at AI as increasing the refinement of statistical analysis for humans. It’s taking all of our senses — vision, auditory, etc. — to build and refine statistical analysis. What machines can do is rapid, across vast troves of data possibly invisible to humans. We have intuition and innate skills. Machines are symbiotic with us, pumping our own intuition with data.
Read the source article in siliconAngle.