Personal assistant chatbots are used to handle administrative tasks such as scheduling meetings, setting reminders, retrieving information to answer questions, or extracting information from Internet in a summary. These types of chatbots have a variety of different use cases and can be used internally within the company to manage employees, customer-facing to schedule meetings with clients in a business-to-business context, or consumer-facing as a personal assistant individual. Areas where personal assistant chatbots can add value to a business are similar to customer service and e-commerce chatbots. While low-involvement AI can still be deployed in these companies, the full potential of AI can only be harnessed by companies with the resources to train more robust systems. Sure, this could be considered a benefit to larger companies for growing their businesses effectively, but this uneven distribution of technology can create strong competitive barriers for smaller companies. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay Beyond its impact on preventing a fair competitive landscape, AI's overreliance on data represents a fundamental inefficiency in the technology. Many discussions regarding advances in artificial intelligence that have been made public in recent years may lead some people to question whether the technology is even remotely “intelligent” compared to humans. For example, you shouldn't show a child 10,000 pictures of a dog to make him realize it's a dog. Humans have adopted heuristics that allow us to take mental shortcuts and process information much faster. For AI to fulfill its promise of replicating human intelligence, it will need to adopt similar shortcuts to learning. Improvements to the technology that allow AI to learn with less data need to be made before it becomes a practical solution for marketers. Developments to improve this flaw in artificial intelligence have been underway for years. The Bayesian program learning (BPL) framework, developed by Brenden M. Lake, Ruslan Salakhutdinov, and Joshua B. Tenenbaum (2015), allows an artificial intelligence system to replicate the human. similar behavior after being exposed to just one dataset. While not yet perfected, the BPL framework represents an exponential improvement over the massive data requirements of deep learning algorithms. Gary Marcus is also working to improve the efficiency of deep learning algorithms with his company Geometric Intelligence. Its XProp software was able to recognize numbers with an error rate of 0.2% after being exposed to just 150 examples, compared to the 700 examples needed for a deep learning algorithm to perform the same task. Despite its performance in recognizing handwritten numbers with a 0.2% error rate that isn't even an improvement over current deep learning algorithms, Marcus' software brings AI technology closer to less reliance on huge quantities of data to be effective. More recently, a growing number of AI developers have recognized the inefficiency of the amount of data needed to power AI and machine learning. As Charles Bergan, vice president of engineering, said.