Collaborating Authors

The application of the law to machine learning and artificial intelligence - The Law Society


Jonathan Smithers, Law Society president, talks to Catherine Reed following a thought leadership event on the issue of the application of the law to machine learning and artificial intelligence. Read the transcript (PDF) Can't see the podcast? These issues and more will be explored further on 21 June at the Law Society's first ever conference on this theme "Lawyers and Robots: partnership of the future" as part of London Technology Week. For further information and to book you place visit the booking page or email

Artificial Intelligence Making Its Way into Today's Warehouse Technologies


Artificial intelligence (AI) is hot. Billions of dollars in venture capital have been invested in AI firms, including firms that focus on solving supply chain problems. Machine learning (ML), a subset of AI, is particularly hot. Interestingly, while AI is not a new technology in supply chain management, so much more data is becoming available for analysis, that we're seeing a new focus on using these techniques to improve supply chain applications, including warehouse technologies. Any device that perceives its environment and takes actions that maximize its chance of success toward some goal is using artificial intelligence in some manner.

How Can We Trust Machine Learning? - insideBIGDATA


Exploration, Evaluation and Explanation for ML Models: Machine learning technologies are at the core of a new generation of intelligent applications that differentiate disruptive businesses from established players. Today, business tasks like product recommendation, image tagging, sentiment analysis, churn prediction, fraud detection and lead scoring can only be achieved using machine learning (ML). To build these applications at scale, companies are fast adopting tools such as Dato's GraphLab Create and Predictive Services, enabling developers to accelerate the innovation cycle, and quickly take their ideas from inspiration to production.

Sapho aims to use machine learning to save employees more time navigating systems


Sapho is banking that machine learning will allow it to manage your personal enterprise applications so you don't have to. The company, which is focused on integrating enterprise applications into what it calls an Employee Experience Portal, plans to use machine learning to monitor how an employee uses business applications and then dish out the most relevant information to them. Time savings from Sapho's machine learning tools would come from less time searching, navigating various systems and completing work within legacy systems. Sapho estimates that employees spend one day a week searching enterprise systems for work information. Sapho's machine learning features are being rolled out with key features being in tech preview.

Before machine learning can become ubiquitous, here are four things we need to do now - SiliconANGLE


It wasn't too long ago that concepts such as communicating with your friends in real time through text or accessing your bank account information all from a mobile device seemed outside the realm of possibility. Today, thanks in large part to the cloud, these actions are so commonplace, we hardly even think about these incredible processes. Now, as we enter the golden age of machine learning, we can expect a similar boom of benefits that previously seemed impossible. Machine learning is already helping companies make better and faster decisions. In healthcare, the use of predictive models created with machine learning is accelerating research and discovery of new drugs and treatment regiments.