One example of deep learning in action is in the healthcare industry. By analyzing a large amounts of medical data, deep learning algorithms can help doctors and experts to make more accurate diagnoses and treatment plans. This lead to improved patient outcomes and reduced costs for healthcare industry overall. For example, we can train deep learning algorithms to identify early signs of diseases such as cancer, allowing doctors to begin treatment earlier and potentially improve patient outcomes. In a 2021 study, researchers at Johns Hopkins Kimmel Cancer Center developed and used a new artificial intelligence blood testing technology to successfully detect lung cancer. Now, in a new study of 724 people, this technology has successfully detected more than 80% of liver cancers. Other examples from the healthcare industry: https://www.philips.com/a-w/ab...
Another example of deep learning in action is in the finance industry, where it can identify fraudulent transactions and improve risk management. By analyzing large amounts of data, deep learning algorithms can detect patterns and anomalies that may show fraudulent activity. This can help businesses to avoid losses and improve their bottom line. The Deutsche Bank recently announced a partnership with NVIDIA to implement several to accelerate the use of artificial intelligence (AI) and machine learning (ML) in the financial services sector.
In the manufacturing industry, deep learning algorithms can optimize production processes and reduce waste. By analyzing data from sensors and other sources, deep learning algorithms can identify inefficiencies and suggest improvements. This can help businesses to reduce their operating costs and improve their competitiveness. Many executives are unsure about how to apply AI solutions in a way that creates real bottom-line impact. As a first step, industrial leaders can gain a better understanding of AI technology and how it can be used to solve specific business problems. To do this, they should define an overall direction and road map, then narrow their focus to areas where AI can create tangible value. One way that AI can create value is by augmenting the capabilities of knowledge workers, particularly engineers, through the use of predictive capabilities to process data, detect patterns, and make recommendations. Examples of AI technologies that can be used to solve business problems in the industrial sector include AI scheduling agents, which can optimize complex manufacturing lines, and machine learning models that can predict and prevent equipment failures.
Deep learning can automate a wide range of tasks, from data entry and analysis to customer service. ChatGPT and AI image generators are examples of how deep learning is already transforming various industries and the way we work. It will reduce the need for expensive labor and the risk of human error, which can help businesses save money and improve efficiency. For example, a content creator using AI may be able to produce 10 times more output; a designer or developer may be able to deliver a new product or feature 10 times faster; a lawyer can handle 10 time more cases at a time etc. etc. (Factor 10 just an example!)
Today, it is impossible to imagine our everyday life without AI. Many end users now use AI in their apps on a daily basis. Many companies use tools that are supported by AI. Larger AI projects and partnerships are being entered into. Developments in the last 2-3 years have made tremendous progress. Using custom models for custom problems no longer needs a 10 person team with PhDs. A new era has already begun, we are at the beginning, but still in the middle of it. If your company is not using AI today, they will have to give in to their competitors in the long run.
