![]() What is the law when AI makes the 'decisions'? Particularly, service chatbots may introduce themselves as a ‘virtual bot’, but if consumers don’t know what a virtual bot is they won’t know when they are talking to AI as opposed to a person. Some of our concerns are around an overall lack of transparency when it comes chatbot use – as opposed to a problem of sentience. We are currently analysing our findings in a project funded by the Australian Communications Consumers Action Network on the use of chatbots providing customer support in Australian telcos. Smart chatbots decipher the ‘rules’ or ‘patterns’ for themselves which can mean greater unpredictability and less control. Ongoing concernsĪll of this however doesn’t mean customer service chatbots are free from risk. This is why chatbots in customer service roles currently use simple, pre-determined rules which are sufficient for most purposes. They are far less predictable and harder to maintain. This example highlights the difficulty operators face when it comes to controlling chatbots with AI neural networks. In less than 24 hours, Tay began mimicking Twitter users with its own racist and antisemitic statements. Tay learned its responses from human posts on Twitter. Challenging decisions made by algorithmĪ now infamous example of this problem was Microsoft’s chatbot Tay. But it also means that this flexibility can be accompanied by greater unpredictability and less control. It means that smart chatbots can respond to a wider range of inputs. The AI in smart chatbots is still performing pattern recognition, but unlike simple rules-based chatbots that rely on pre-programmed instructions, smart chatbots decipher the ‘rules’ or ‘patterns’ for themselves. The neural network must be ‘trained’ on sample data – meaning the algorithm iteratively compares its predictions to what the ‘correct’ output should be and then performs complex calculations, adjusting its parameters to improve its predictions over time. Picture: ShutterstockĪ ‘neural network’ is a type of algorithm that has connections that are very roughly like biological neurons. Google says its AI chatbot system LaMDA isn’t sentient. The complex type of algorithm they typically employ is a ‘neural network’. They employ machine learning algorithms that perform Natural Language Processing (NLP). ‘Smart’ chatbots using more advanced techniques can overcome this limitation. Any sequence of letters can be used to address the chatbot, but the chatbot can only respond to patterns that it ‘recognises’ with a corresponding ‘rule’.Īnd this is the key disadvantage of the simpler pattern matching chatbots. The difficulty is that the range of possible inputs (things that can be said to or asked of the chatbot) is practically infinite. TikTok captures your faceįor example, the program may detect the word ‘hello’ in the user’s input “Hello there!” and match it to an appropriate greeting like, “Hi, my name is ChatBot!”. They do this by following a pre-determined rule. ![]() Rules-based chatbots detect certain words or word combinations and pattern match them to a response or class of responses. This makes them a good choice for many practical purposes, like online banking. They are easy to build, predictable in output and relatively simple to maintain. Rules-based chatbots are the simplest kind. Picture: Shutterstock Rules-based chatbots Chatbots are essentially computer programs that interpret written or spoken language. Regardless of approach, chatbots are of two basic types: ‘rules-based’ chatbots, like ELIZA, and ‘smart’ chatbots, like LaMDA. Many programming languages and technologies can be used to build them. AI enables the kind of ‘conversations’ that LamDA has had with humans.Ĭhatbots are essentially computer programs that interpret written or spoken language and provide appropriate responses. The seminal advancement was the invention of data-driven algorithms – otherwise known as machine learning algorithms or artificial intelligence (AI). The technology underpinning chatbots has evolved since the 1960s. So how do these chatbots engage with us in human-like ways? And some of the answers can feel quite human. They are usually found in a little box-shaped icon that pops up in the lower right-hand corner of the screen, asking you to type a question. You may have encountered them in communicating online with telecommunication companies, banks, airlines and shopping sites.
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