There is vast scope for agricultural productivity to improve. Traditional farming practices are still shockingly outdated in many parts of the world. AI is one tool that can help best achieve this, and a number of startups are already making progress. Switzerland-based agricultural tech firm Gamaya, for example, this year announced $3.2 million in funding for its AI project - drones equipped with hyperspectral cameras that capture changes in water and fertilizer use, crop yields, and pests, data from which is analyzed using AI algorithms to highlight potential issues to farmers. The technology can also be used for finding patterns that can predict outcomes of farmers' decisions, giving them a better idea of where to invest and apply appropriate resources.

There is also the issue of safety around using such AI-driven robots in customer facing roles. Google's recent paper examining the likely issues that will arise from AI technology, 'Concrete Problems in AI Safety,' noting among them the fairly basic'Avoiding Negative Side Effects'. This asked how tech companies could ensure that AI system not disturb its environment in negative ways while pursuing its goals, for example a cleaning robot knocking over a vase because it can clean faster by doing so. This problem may seem petty, but if it were to happen in a hotel it could proof fatal to public trust.

In the short term, data scientists are unlikely to be replaced. Kevin Murphy, a Senior Research Scientist at Google notes that: 'The first problem is that current Machine Learning methods still require considerable human expertise in devising appropriate features and models. The second problem is that the output of current methods, while accurate, is often hard to understand, which makes it hard to trust.' Murphy cites the'automatic statistician' project from Cambridge, which'aims to address both problems, by using Bayesian model selection strategies to automatically choose good models/ features, and to interpret the resulting fit in easy-to-understand ways, in terms of human readable, automatically generated reports.' Their project won a 750,000 Google Focused Research Award, but it still has a number of challenges to overcome if it going to be a success. What Murphy says initially still stands true, and Machine Learning methods require considerable expertise at the point of origination.

Humans behind the scenes are vital for helping virtual assistants to learn how to interact with people as if they were real people themselves. At the moment, one of the main things holding adoption of virtual assistants back is the public's discomfort with communicating machines using their own voice. Amazon's Alexa is working to humanize its responses by adding in'hmms' and'ums' into her responses. Apple's Siri assistant too is renowned for making wry jokes. To do this, companies are hiring writers to help build the assistant's personality.