It's been an incredible year for the'New Wave' of legal technology and Artificial Lawyer has hopefully been able to bring you some of the key moments in this evolutionary journey that is unfolding week by week all around the world. Now, as we head toward 2018, many of the leading players and commentators in the legal AI, legal bot and data analysis world have been asked to give their views on what has taken place and what will happen next. Plus, next year there will be predictions from the world of smart contracts and legal blockchain, (Hi, Clause, Integra Ledger and IBM's Cognitive Legal team, to name a few!) Artificial Lawyer asked an array of experts to name what was the biggest development for legal AI and the New Wave of legal technology this year, and what they expected to see happen in 2018. They were invited to respond with text or images to illustrate their views, and if they were up for it, a haiku or longer poem. Naturally, we couldn't fit everyone in – the legal tech world is just so massive now – but hopefully you'll find this collective wisdom both inspiring and thought-provoking – and fun – I know Artificial Lawyer did. Biggest development of 2017: 'I think the most significant thing was how mainstream legal AI became – mass adoption by firms and NewLaw, and more focus on integrations, grown up security requirements, APIs and the like.' Biggest development of 2018: 'I think we're going to see more news about wider ML applicability, not just NLP/ML for litigation document review, contract review in diligence and in-house contract review (the primary use cases to date).

Unlike other machine learning algorithms, neural networks are also designed to be able to learn from their mistakes. This is another reason many deep-learning scientists believe we are finally beginning to develop machines that come close to what most people would consider artificial intelligence. Just like humans, machines can now make a prediction, act on that prediction and learn from that action, whether right or wrong. Like humans, this allows them to become more accurate over time as more decisions and feedback is collected. For the first time ever, neural networks are enabling human-like insight and learning in computers.

Human resources as a function has experienced significant changes in the last decade due to the evolution of technologies. Today, artificial intelligence (AI) is reshaping the way companies hire, manage and engage with their workforce. Advanced data-driven technology is rapidly making its way into the HR industry as businesses are focusing more on creating an employee-oriented corporate culture. Recruitment is no more a tedious process for HR practitioners as it no longer entails time-consuming activities such as manually screening the resumes of the prospective candidates, making phone calls or replying to candidates via emails. These mundane errands are now managed by smart technologies designed to replicate human conversation, thus enabling HR experts to contemplate the bigger picture.

Bear with me, it's more interesting than it sounds:). Yes, this (46-page) paper does include a catalogue of data sets with dialogues from different domains, but it also includes a high level survey of techniques that are used in building dialogue systems (aka chatbots). In particular, it focuses on data-driven systems, i.e. those that incorporate some kind of learning from data. This particular paper is focused on corpus-based learning where you have been able to build up, or have access to, a data set on which you can train your models. If you want to build a defensible machine learning based business, having access to quality sources of data that your competitors don't is a good start.

Clustering [1] is a paradigm to classify the subjects into several groups based on their similarity information. As we know that clustering is a fundamental task in machine learning, pattern recognition and data mining fields and it has widespread applications. With the obtained groups by clustering methods, further analysis tasks can be conducted to achieve different ultimate goals. However, traditional clustering methods only use one feature set or one view information of the subjects while multiple feature sets or multiple view information of these subjects are available. The subjects of interest with multiple feature sets or multiple view information are the so called multi-view data. Multi-view data are very common in real-world applications due to the innate properties, or collecting from different sources. For instance, a web page can be described by the words appearing on the web page itself and the words underlying all links pointing to the web page from other pages in nature. In multimedia content understanding, the multimedia segments can be simultaneously described by their video signals from visual camera and audio signals from voice recorder devices. The existence of such multi-view data raised the interest of multi-view learning [2], [3], [4], which has been extensively studied in semi-supervised setting.

The statistical analysis of discrete data has been the subject of extensive statistical research dating back to the work of Pearson. In this survey we review some recently developed methods for testing hypotheses about high-dimensional multinomials. Traditional tests like the $\chi^2$ test and the likelihood ratio test can have poor power in the high-dimensional setting. Much of the research in this area has focused on finding tests with asymptotically Normal limits and developing (stringent) conditions under which tests have Normal limits. We argue that this perspective suffers from a significant deficiency: it can exclude many high-dimensional cases when - despite having non Normal null distributions - carefully designed tests can have high power. Finally, we illustrate that taking a minimax perspective and considering refinements of this perspective can lead naturally to powerful and practical tests.

There's an awful lot of text data available today, and enormous amounts of it are being created on a daily basis, ranging from structured to semi-structured to fully unstructured. What can we do with it? Well, quite a bit, actually; it depends on what your objectives are, but there are 2 intricately related yet differentiated umbrellas of tasks which can be exploited in order to leverage the availability of all of this data. NLP is a major aspect of computational linguistics, and also falls within the realms of computer science and artificial intelligence. Text mining exists in a similar realm as NLP, in that it is concerned with identifying interesting, non-trivial patterns in textual data.

Recent news on Sophia the robot getting citizenship in the Saudi Arabia has widely attracted daily news and social media. Despite the debates and agitations on a robot getting recognition as humans, experts view this event as a phenomenal milestone in the research of AI. The current level of Artificial Intelligence is achieved through years of research in Machine Learning, Deep Learning and other related fields. With a lot of hype and investments around, Deep Learning technology – a subdivision of Machine Learning is now successfully applied in our daily life from speech recognition apps in smartphones to YouTube recommendations. One of the pioneers of the Deep Learning, Andrew Ng feels that AI is the new form of electricity where every AI application in future electronic devices will be fuelled by Deep Learning models.

The tech is increasingly becoming ubiquitous across all industries from the automotive industry, fintech, social media, ecommerce to even entertainment. We are living in the age of big data, as increasingly more enterprises invest in AI and machine learning -- a branch of AI which is in its simplest definition is a form of data analysis -- startups are taking notice and disrupting whole industries by employing that tech. With that in mind, here's everything you need to know about the artificial intelligence (AI) and machine learning tech ecosystem in South Africa. There are a large number of South African startups using AI-related technologies in their software solutions. Here below is a list of some of the more well-known startups -- some of which have developed cutting AI solutions, or potentially disruptive technologies using AI.

You can't understand AI until you know the categories: robotic process automation, cognitive augmentation and cognitive automation.