The Innovation Ultimatum. Steve Brown

The Innovation Ultimatum - Steve Brown


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weight, cost, and so on—and the tool generates options and evaluates each one using simulation tools. The designer simply picks the option that best meets their needs—perhaps the one that's cheapest to make, easiest to manufacture, or has the lightest weight.

      Generative AI will help to design lighter-weight aircraft, cars that are more resilient to crashes, and stronger, lighter robots. Generative architecture will improve the structural integrity and design of new buildings.

      Researchers at the University of California, Berkeley, working in partnership with Glidewell Dental Lab, use GANs to design dental crowns. The AI uses digital x-rays of a patient's upper and lower jaw to design a crown that perfectly fills the gap in the patient's tooth line, optimizes bite contact, and looks aesthetically pleasing. Researchers claim that AI-generated crowns outperform those designed by humans. The approach should speed crown production, reduce costs, and free dentists to spend more time generating revenue by working in patients’ mouths, rather than designing crowns on a CAD machine in a back office.

      Generative AI is an example of a broader category of AI that I refer to as “collaborative AI.” Collaborative AIs operate in partnership with humans in a creative process. Humanity's use of tools distinguishes us from most other species. Traditional tools are subordinate—we wield a hammer, drive a car, and program a computer. Collaborative AI changes our relationship with tools. They are no longer subordinate, they now co-create with us. Collaborative AIs aren't just tools, they're partners.

      Collaborative AI will co-create visuals for presentations, advertisements, and marketing brochures. Collaborative email software will auto-compose responses. Collaborative management software will co-create plans for complex projects. Many job functions will benefit from collaborative AI in the coming years.

      Future Uses of AI

      AI is a big deal. Every leader should pay close attention. Every organization must understand how AI will shape product development, business operations, customer service, and workforce management.

      How AI Works

      You don't have to understand how AI works to use it. But such insight can help you to understand the capabilities and limitations of today's technology. While the following description is designed to be accessible to nontechnical types, feel free to skip to the next section if it gets too far into the weeds for you.

      Neural Networks, Training, and Models

      Neural networks underpin most of today's artificial intelligence. They operate quite differently from traditional digital computers. Traditional computers are glorified adding machines. Neural nets are organized more like the highly interconnected structures found in our brains.

      Typically, the more layers there are, and the more nodes in each layer, the more capable the neural network. Neural networks with many layers are known as “deep” neural networks. This is where the term deep learning comes from.

      Example: A Radiology AI

      To train a neural network to read radiology charts and look for tumors, you would expose it to many example charts (the input), each tagged with a radiologist's diagnosis—tumor or no tumor (the desired output). The output of the network is a single number, the probability that an image contains a tumor. Each time the neural net is exposed to a new image, the output of the network is compared with the correct result. If an image of a tumor is presented, the result should be close to 100%. If there's no tumor the result should be close to zero. The backprop process is used to tweak the network's model (the weightings of the connections between the nodes), strengthening the weightings of links that lead to the correct result, and weakening those that don't. Once trained with enough data, the neural network will predict the right diagnosis with impressive accuracy. A more complex network might have several outputs. One could be the percentage chance of a tumor, another the probability of an embolism, another the probability of a broken bone, and so on.

      If this all seems too difficult to understand, that's okay. The key thing to understand is that neural nets can infer how to perform tasks from examples, without the need of a domain expert to supply explicit rules on how to perform that task.

      Some radiologists already use AI-based tools to offer a “second opinion” as they read charts. As the accuracy of these tools surpasses that of human radiologists on routine charts, radiologists will be able to focus their attention on more


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