We are all familiar with Amazon and the regular suggestions which pop up in our Amazon accounts for new products which we might like to purchase. These suggestions are compiled by AI which collects data based on our browsing/purchasing history. Consumers are now interacting with bots on a regular basis. This form of AI is now prevalent in online trading and customer service, be it chatbots, informational bots or transactional bots. Another form of AI which is impacting the purchasing process is the voice technology application. The rapid rise of AI voice assistants such as Siri, Alexa and Google Assist mean consumers are becoming more used to performing tasks with their voice which could have an impact with respect to trade mark law. Indeed use of voice recognition as a means for consumers to interact with brands and purchase products and services raises even more fundamental questions from a trade mark perspective.

We've seen no shortage of scandals when it comes to AI. In 2016, Microsoft Tay, an AI bot built to learn in real time from social media content turned into a misogynist, racist troll within 24 hours of launch. A ProPublica report claimed that an algorithm -- built by a private contractor -- was more likely to rate black parole candidates as higher risk. A landmark U.S. government study reported that more than 200 facial recognition algorithms -- comprising a majority in the industry -- had a harder time distinguishing non-white faces. The bias in our human-built AI likely owes something to the lack of diversity in the humans who built them.

As the world of emerging technologies such as big data, robotics, disruptive tech and Internet of Things is quickly becoming part of our everyday lives, Artificial intelligence will continue to play a fundamental contributor to the future of these innovative technologies. With this in mind, AI is already impacting the long term future of virtually every industry and this will certainly have an impact on the legal profession however, it will also create opportunity too. With all Technologies regardless of its purpose, human brainpower will play a fundamental part in its creation from R&D to deploying artificial intelligence. Over the past 5yrs, I've constantly advised my junior lawyers to focus on in-house roles with a focus on software, algorithms, Data Cloud (SaaS, PaaS, IaaS) and FinTech as it'd the future and we're certainly very dependent on it even more than ever during this pandemic. Regardless of our current dependency, once we all return to normality, we have now become accustomed to using and embracing technology so that won't change.

Climate change communication in the mass media and other textual sources may affect and shape public perception. Extracting climate change information from these sources is an important task, e.g., for filtering content and e-discovery, sentiment analysis, automatic summarization, question-answering, and fact-checking. However, automating this process is a challenge, as climate change is a complex, fast-moving, and often ambiguous topic with scarce resources for popular text-based AI tasks. In this paper, we introduce \textsc{ClimaText}, a dataset for sentence-based climate change topic detection, which we make publicly available. We explore different approaches to identify the climate change topic in various text sources. We find that popular keyword-based models are not adequate for such a complex and evolving task. Context-based algorithms like BERT \cite{devlin2018bert} can detect, in addition to many trivial cases, a variety of complex and implicit topic patterns. Nevertheless, our analysis reveals a great potential for improvement in several directions, such as, e.g., capturing the discussion on indirect effects of climate change. Hence, we hope this work can serve as a good starting point for further research on this topic.

Change Point Detection techniques aim to capture changes in trends and sequences in time-series data to describe the underlying behaviour of the system. Detecting changes and anomalies in the web services, the trend of applications usage can provide valuable insight towards the system, however, many existing approaches are done in a supervised manner, requiring well-labelled data. As the amount of data produced and captured by sensors are growing rapidly, it is getting harder and even impossible to annotate the data. Therefore, coming up with a self-supervised solution is a necessity these days. In this work, we propose TSCP2 a novel self-supervised technique for temporal change point detection, based on representation learning with Temporal Convolutional Network (TCN). To the best of our knowledge, our proposed method is the first method which employs Contrastive Learning for prediction with the aim change point detection. Through extensive evaluations, we demonstrate that our method outperforms multiple state-of-the-art change point detection and anomaly detection baselines, including those adopting either unsupervised or semi-supervised approach. TSCP2 is shown to improve both non-Deep learning- and Deep learning-based methods by 0.28 and 0.12 in terms of average F1-score across three datasets.

Following the revolutions in military affairs brought about by gunpowder and nuclear weapons, we find ourselves once again at the dawn of a new era of warfare: The Age of Autonomous Systems. Using cutting-edge technologies for military purposes, especially from the field of Artificial Intelligence, will radically transform how wars will be fought in the near future. LAWS (Lethal Autonomous Weapon Systems) is a critical acronym to understand warfare in the 21st century. LAWS encompass any weapon system with autonomy in its critical functions, namely one which can select (i.e., search for or detect, identify, track, and select) and attack (i.e., use force against, neutralise, damage or destroy) targets without human intervention[1]. While technically accurate, 'LAWS' is admittedly a less emphatic term than that used by a global coalition of Human Rights Watch-coordinated non-governmental organisations formed in October 2012 who are working to fully ban LAWS -- or as they call them, 'Killer Robots'.

SANTA CLARA, CALIF., Nov. 30, 2020 – ServiceNow (NYSE: NOW) today announced it has signed an agreement to acquire Element AI, a leading artificial intelligence (AI) company with deep AI capabilities and some of the world's brightest AI minds. Element AI will significantly enhance ServiceNow's commitment to build the world's most intelligent workflow platform, enabling employees to work smarter and faster, streamline business decisions, and unlock new levels of productivity. A pioneer in the AI industry, Element AI has world‑class scientists and practitioners who will bring expertise in applying modern AI to text and language, chat, images, search, question response, and summarization and will accelerate AI innovation natively in the Now Platform. Element AI Co‑founder and Lead Fellow, Dr. Yoshua Bengio, a winner of the 2018 ACM A.M. Turing Award for his pioneering contributions to modern AI, will serve as a technical advisor for ServiceNow. With the acquisition of Element AI, ServiceNow will create an AI Innovation Hub in Canada to accelerate customer‑focused AI innovation in the Now Platform. The new investment deepens ServiceNow's commitment to the Canadian market, which has long been a leader in AI research and represents one of the world's most significant locations for AI talent.

In a new report released on October 27 by the United States Patent and Trademark Office (USPTO), more than 42% of all technology areas in 2018 incorporate Artificial Intelligence (AI) in their new inventions. The majority of these improvements come in knowledge processing and planning/control, which involve analyzing information to gain new insights and using those insights to manage a business process. CIOs continue to talk about how vital AI technologies are, but this new report confirms that if companies aren't already putting that talk into action, they are behind the curve. The danger of falling behind is even greater for companies that haven't started adoption since the statistics only cover till the end of 2018. In the last 18 months, the percentage of technologies that include AI has undoubtedly continued to increase. The report also confirms an increased interest by the office in this technology and a higher willingness to consider new applications that include them.

Facebook allegedly violated Illinois state law by using consumers' facial features to improve its photo-tagging software. Nearly one and a half million Illinois residents have filed claims to part of a $650 million privacy settlement offered by Facebook. According to NBC Chicago, the law firm responsible for the social media lawsuit said that 1.42 million Illinois residents have already filed claims. Eligible claimants could receive awards ranging between $200 and $400. The lawsuit, says NBC, alleged that Facebook broke Illinois' "strict biometric privacy law."

Despite its utopian promises as a disruptive equalizer, AI - like most tools deployed under the guise of neutrality - has tended to simply reinforce existing social structures. To counter this trend, radical AI calls for centering on the marginalized. We argue that gaps in key infrastructure are preventing the widespread adoption of radical AI, and propose a guiding principle for both identifying these infrastructure gaps and evaluating whether proposals for new infrastructure effectively center marginalized voices.