The conventional deep learning model is a supervised model. It takes months to develop and train the model before it is ready for the production line. Here, Karina Odinaev, Co-Founder and CEO of Cortica and Co-Founder of Lean AI, explains why the conventional deep learning model is broken and what the alternatives are.
Back in 1868, George Boole’s wife paraphrased his thoughts on the capabilities of machines: “Between them, they have conclusively proved, by unanswerable logic of facts, that calculation and reasoning, like weaving and ploughing, are work, not for human souls, but for clever combinations of iron and wood. If you spend time doing work that a machine could do faster than yourselves, it should only be for exercise.”
As labour shortages continue to bite, food processors are backing AI-powered vision systems to automate the last remaining manual operations on packaging and production lines.
According to Salesforce, simple tasks like data entry and paperwork once took up so many hours that sales teams only spent one-third of their time actually making sales. But times are changing...
Since IoT devices started to gain popularity in 2010, the technology has provided the manufacturing industry with sensors, automated gateway connectivity, and more. Now, IoT is being combined with artificial intelligence (AI) to create a fully autonomous data collection analysis ecosystem.
As its capabilities and reach continues to evolve, Artificial Intelligence (AI) plays an increasingly important role for engineers as they are tasked with integrating AI into systems.