Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans. Today, Artificial Intelligence is a very popular subject that is widely discussed in the technology and business circles. Many experts and industry analysts argue that AI or machine learning is the future – but if we look around, we are convinced that it's not the future – it is the present. With the advancement in technology, we are already connected to AI in one way or the other – whether it is Siri, Watson or Alexa. Yes, the technology is in its initial phase and more and more companies are investing resources in machine learning, indicating a robust growth in AI products and apps in the near future.

Many will be flocking to the UK capital this week – all in the name of fashion. London Fashion Week is making a return on Friday, with fashionistas from all around the world gathering in celebration of fashion, community, diversity and creativity. In light of this, I wanted to take a look at the evolution of the fashion industry and how technology is transforming the sector at a pace faster than ever before. Wearable tech has been on the market for some time now, from the earliest days of the Fitbit to the latest iterations of the Apple Watch and Google Glass. Today, fashion leaders are merging form and function to make wearables more stylish and, well, wearable.

Artificial Intelligence is the future of growth. There is sure to be at least one article in the newspaper/internet/blogs daily on the revolutionary advancements made in the field of Artificial Intelligence or its subfield disrupting standard industries like Fintech, Banking, Law, or any other. In banking domain digital banking teams of all modern banks planning to transform the customer experience with their AI based chat-driven intelligent virtual assistant i.e. bots. Amalgamating the latest technology of artificial intelligence, predictive analytics and cognitive messaging to serve millions of customers is now a new winning strategy? AI and regulation are paving the way for Fintech.

Today, 2020 Democratic presidential candidate and Massachusetts Rep. Seth Moulton is expected to introduce a bill that would limit how smart device manufacturers like Amazon and Google can collect your data. The Automatic Listening and Exploitation Act, or the ALEXA Act for short, would empower the Federal Trade Commission to seek immediate penalties if a smart device is found to have recorded user conversations without the device's wake word being triggered. For Google's home devices, for instance, that would mean recording a conversation without being prompted by "Hey, Google." Moulton's bill also addresses smart doorbells and their video capabilities. "Smart speakers and doorbells are great, but consumers should have a way to fight back when tech companies collect more data than Americans have agreed to give up," Moulton said.

Session-based Recurrent Neural Networks (RNNs) are gaining increasing popularity for recommendation task, due to the high autocorrelation of user's behavior on the latest session and the effectiveness of RNN to capture the sequence order information. However, most existing session-based RNN recommender systems still solely focus on the short-term interactions within a single session and completely discard all the other long-term data across different sessions. While traditional Collaborative Filtering (CF) methods have many advanced research works on exploring long-term dependency, which show great value to be explored and exploited in deep learning models. Therefore, in this paper, we propose ASARS, a novel framework that effectively imports the temporal dynamics methodology in CF into session-based RNN system in DL, such that the temporal info can act as scalable weights by a parallel attentional network. Specifically, we first conduct an extensive data analysis to show the distribution and importance of such temporal interactions data both within sessions and across sessions. And then, our ASARS framework promotes two novel models: (1) an inter-session temporal dynamic model that captures the long-term user interaction for RNN recommender system. We integrate the time changes in session RNN and add user preferences as model drifting; and (2) a novel triangle parallel attention network that enhances the original RNN model by incorporating time information. Such triangle parallel network is also specially designed for realizing data argumentation in sequence-to-scalar RNN architecture, and thus it can be trained very efficiently. Our extensive experiments on four real datasets from different domains demonstrate the effectiveness and large improvement of ASARS for personalized recommendation.

In matrix factorization, available graph side-information may not be well suited for the matrix completion problem, having edges that disagree with the latent-feature relations learnt from the incomplete data matrix. We show that removing these $\textit{contested}$ edges improves prediction accuracy and scalability. We identify the contested edges through a highly-efficient graphical lasso approximation. The identification and removal of contested edges adds no computational complexity to state-of-the-art graph-regularized matrix factorization, remaining linear with respect to the number of non-zeros. Computational load even decreases proportional to the number of edges removed. Formulating a probabilistic generative model and using expectation maximization to extend graph-regularised alternating least squares (GRALS) guarantees convergence. Rich simulated experiments illustrate the desired properties of the resulting algorithm. On real data experiments we demonstrate improved prediction accuracy with fewer graph edges (empirical evidence that graph side-information is often inaccurate). A 300 thousand dimensional graph with three million edges (Yahoo music side-information) can be analyzed in under ten minutes on a standard laptop computer demonstrating the efficiency of our graph update.

Big Data is the unexpected resource bonanza of the current century. Moore's Law driven advances in computing power, the rise of cheap storage and advances in algorithm design have enabled the capture, storage, and processing of many types of data previously that were unavailable for use in computing systems. Documents, email, text messages, audio files, and images are now able to transform into a usable digital format for use by analysis systems, especially artificial intelligence. The AI systems can scan massive amounts of data and find both patterns and anomalies that were previously unthinkable and do so in a timeframe that was unimaginable. While most of the uses of Big Data have been coupled with AI/machine learning algorithms so companies and understand their customer's choices and improve their overall experience (think about recommendation engines, chatbots, navigation apps and digital assistants among others) there are uses that are truly industry transforming.

Recent surveys, studies, forecasts and other quantitative assessments of the impact and progress of AI highlighted the need to retrain many workers, improving AI's score from F to A on 8th-grade science exam, and the $97.9 billion the AI market will reach in 2023. In the next three years, as many as 120 million workers in the world's 12 largest economies may need to be retrained or reskilled as a result of AI and intelligent automation; only 41% of CEOs surveyed say that they have the people, skills and resources required to execute their business strategies; the time it takes to close a skills gap through training has increased from 3 days on average in 2014 to 36 days in 2018 [IBM] Top drivers for investing in robotics and automation: Reduced cost (80%), improved quality (55%), increased productivity (54%), improved capabilities of robots (54%). "I was at MIT for another fifteen years after I graduated…twenty years after I went and asked to do my bachelor's thesis [with Victor Zue on speech recognition], Siri comes out… twenty years ago, we [wanted to] have a device where you can talk to it and it gives you answers and twenty years later there it was. So, that, for me, that was a cue that maybe it's time to go where the action is, which was in companies that were building these things. Once you have a large company like Microsoft or Google throwing their resources behind these hard problems, then you can't compete when you're in academia for that space. You know, you have to move on to something harder and more far out… So, I joined Microsoft to work on Cortana…"--T.J. Hazen The worldwide market for AI systems will reach $97.9 billion in 2023, up from $37.5 billion in 2019.

One of the most significant emerging tech trends this year is around emerging tech convergence. Instead of focusing on one of the essential eight technologies, businesses are combining them to solve problems in powerful new ways. We're seeing this come to life in the growing use of conversational interfaces, which combine artificial intelligence (AI) and the internet of things (IoT). Like most people, I prefer talking over typing on a keyboard, tapping on a screen or clicking with a mouse. We've all become accustomed to simply asking our phones or other personal or home devices to give directions, answer questions, and find the information we need.

Our voices are diverse, complex, and variable. Voice commands are even more daunting to process -- even between people, let alone computers. The way we frame our thoughts, the way we culturally communicate, the way we use slang and infer meaning… all of these nuances influence the interpretation and comprehensibility of our words. So, how are designers and engineers tackling this challenge? This is where VUIs come into play.