Every CIO knows about the key role that artificial intelligence (AI) plays in the importance of information technology and the fantastic things that it is going to be able to do in the future. Already our homes are being flooded with devices that can listen to what we want them to do and perform a variety of actions to meet our every need. However, in the workplace it’s starting to look like AI may have hit a wall – a limit in what it can do for us. How are CIOs going to find a way to realize the true potential of AI?
The Problem With AI
CIOs understand that there is a problem with AI. This all comes down to how to make AI do things it can’t, at present. This problem was once merely an academic concern. However, it now has consequence for billions of dollars’ worth of talent and infrastructure. That debate comes down to whether or not the current approaches to how CIOs go about building AI are enough. With a few tweaks and the application of enough brute computational force, will the technology we currently have be capable of true “intelligence,” in the sense we imagine it exists in something like an animal or a human?
On one side of this debate are the proponents of “deep learning”—an approach that, since a landmark paper in 2012 by a trio of researchers at the University of Toronto, has exploded in popularity. While far from the only approach to artificial intelligence, it has demonstrated abilities beyond what previous AI tech could accomplish. The “deep” in “deep learning” refers to the number of layers of artificial neurons in a complete network of them. The thinking is that as in their biological equivalents, artificial nervous systems with more layers of neurons are capable of more sophisticated kinds of learning. To understand artificial neural networks, picture a bunch of points in space connected to one another like the neurons the in our brains are. Adjusting the strength of the connections between these points is a rough analog for what happens when your brain learns. The result is a neural wiring diagram, with favorable pathways to desired results, such as being able to correctly identify an image.
The problem that the person with the CIO job is facing is that today’s deep-learning systems don’t resemble our brains. At best, they look like the outer portion of the eye’s retina, where a scant few layers of neurons do initial processing of an image. It’s very unlikely that such an AI network could be bent to all the tasks our brains are capable of. Because these networks don’t know things about the world the way a truly intelligent creature does, they are brittle and can be easily confused.
How To Move Forward With AI
Despite its limitations, deep learning currently powers the gold-standard software in image and voice recognition, machine translation and is beating humans at board games. It’s the driving force behind Google’s custom AI chips and the AI cloud service that runs on them, as well as Nvidia Corp.’s self-driving car tech. The problem with the deep learning systems that we have today is that to get to “general intelligence” – which requires the ability to reason, learn on one’s own and build mental models of the world – will take more than what today’s AI can achieve.
To go further with AI, the person with the CIO job needs to take inspiration from nature. That means coming up with other kinds of artificial neural networks, and in some cases giving them innate, pre-programmed knowledge—like the instincts that all living things are born with. Many researchers agree with this, and are working to supplement deep-learning systems in order to overcome their limitations. One area of intense research is determining how to learn from just a few examples of a phenomenon—instead of the millions that deep-learning systems typically require.
Until CIOs figure out how to make our AIs more intelligent and robust, we’re going to have to hand-code into them a great deal of existing human knowledge. That is, a lot of the “intelligence” in artificial intelligence systems like self-driving software isn’t really artificial at all. As much as CIOs need to train their vehicles on as many miles of real roads as possible, for now, making these systems truly capable will still require inputting a great deal of logic that reflects the decisions made by the engineers who build and test them.
What All Of This Means For You
I think that everyone can agree that AI technology is some wonderful stuff. There are a lot of devices in our lives right now that have some AI embedded in them that allow us to do things that we have never been able to do before. However, CIOs are starting to see the limitations of today’s AI systems that they have been building. Knowing what AI currently can’t do, CIOs are starting to search for ways that will allow them to extend what AI is going to be able to do in the future.
The problem with AI as it stands today is that there are things that AI simply can’t do. The goal is to make today’s AI behave more like an animal or a human. Deep learning is a form of AI that is designed to mimic how humans learn things. The problem with today’s AI systems is that they are brittle and can be easily fooled. Today’s AI systems cannot be programmed with “general intelligence”. CIOs are starting to turn to nature in order to find ways to program their deep learning systems. Until CIOs find a way to do this, they are going to have to continue to hand code their AI systems.
AI has hit a wall. The systems that we have designed today that use AI are a great help for a number of the things that we need to have done. However, there is a lot more that we need help with. It currently does not look like our existing AI systems are going to be up to the task of helping us to do more than they already are. In order to solve this problem it’s going to be up to CIOs to take a look at the currently state of AI and come up with ways to move it forward. The good news is that this can be done. It’s just going to take some creative thinking on the part of CIOs.
– Dr. Jim Anderson
Blue Elephant Consulting –
Your Source For Real World IT Department Leadership Skills™
Question For You: Do you think that just creating bigger and bigger deep learning networks would solve this problem?
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