It's not completely impossible that robots could turn against us, so it's no surprise killer robot movies have been popular for decades. We could be about to add another flick to the canon, as the new trailer for the Child's Play reboot suggests. Plot details haven't been confirmed yet, so it's not totally certain this Chucky is a robot, but there's enough to back up rumors it's a defective doll "whose programming code was hacked so that he has no limitations to learning and also violence." The evidence in the trailer is pretty conclusive. It opens up like an ad for a big tech company, before showing some moving metal parts amid some familiar-looking clothing and a kid getting his face scanned after unwrapping a certain doll (which you don't get a good look at here, unfortunately).

PLEASE NOTE: that RSVPing to this page DOES NOT GRANT YOU ACCESS to this meetup, Spaces are limited! DESCRIPTION How do we design Ai systems that we trust? Algorithmic Bias, Algorithmic Transparency, Technological Unemployment, Data Privacy & Algorithmic Misinformation (fake news) are just some of the issues facing the fair and ethical use of Machine Learning. In collaboration with Microsoft for this DSAi special edition Ethics & Interpretability event - come along to learn from industry leaders how issues such as Algorithmic Bias might affect you & what is being done to address the ethical use of Machine Learning in 2019. 'Ethics for Artificial Intelligence' In this 20 minute presentation, Aurelie will provide a formal introduction as to what ethical and responsible AI is.

I don't know about you, I've never been comfortable with the use of the term Artificial Intelligence (AI) by mainstream media, and the way the ubiquitous sales and marketing folk have recently begin to fling it around only add to my general angst over it all. When we talk about Artificial Intelligence, to many, this conjures up thoughts of killer robots, a dystopian future and being enslaved to mega-corporations (gulp, is that bit already here?!). Apart from all that, there is a general misconception that you need a Ph.D. to be able to use these technologies, let alone understand them. To all of the above, I simply say - balderdash good sir! balderdash! I have recently taken a deep dive into the field of AI by involving myself (yet again) in some further university study.

Intelligent connectivity is the combination of high-speed, low-latency 5G networks, cutting-edge artificial intelligence (AI) and the linking of billions of devices through the Internet of Things (IoT). As these three revolutionary technologies combine they will enable transformational new capabilities in transport, entertainment, industry and public services, and much more beyond. As operators expand beyond provision largely of network access to facilitation of holistic services, they are rapidly bringing into view a world of technological ease and sophistication which not long ago still seemed a long way off. The GSMA estimates that, by 2025, there will be 25 billion connected devices. This hyperconnectivity will be enabled by undisturbed mobile broadband, which will make the number of connected devices communicating with one other will be virtually limitless.

The workplace chatbots are coming! The workplace chatbots are coming! And in a few years, there will be even more. According to Gartner, by 2021 the daily use of virtual assistants in the workplace will climb to 25 percent. That will be up from less than 2 percent this year.

It's an undeniable fact how a mobile application can add more simplicity and comfort to our lives. There are positively plethora of apps out there that demonstrate this point. Customization has added another layer to applications that can adjust and change following the client. Organizations like Apple and Amazon have shown the effect that human-made brain power can have on a machine unit with developments like Siri and Alexa. These voice-controlled AI applications assist users with their daily errands and chores.

It is a complicated process and companies are turning to new technologies like artificial intelligence to optimize their operations.

The last year I attended the field of machine learning and at the end of 2018 achieved some results that I want to share. I trained a neural network to recognize a car by a photo and created this simple demo for illustration. The starting point for the task was the Stanford Cars Dataset. Some classes of this dataset contain quite a lot of errors (e.g. So I took only 48 classes from the dataset and cleaned them up.

Music genre can be hard to describe: many factors are involved, such as style, music technique, and historical context. Some genres even have overlapping characteristics. Looking for a better understanding of how music genres are related to musical harmonic structures, we gathered data about the music chords for thousands of popular Brazilian songs. Here, 'popular' does not only refer to the genre named MPB (Brazilian Popular Music) but to nine different genres that were considered particular to the Brazilian case. The main goals of the present work are to extract and engineer harmonically related features from chords data and to use it to classify popular Brazilian music genres towards establishing a connection between harmonic relationships and Brazilian genres. We also emphasize the generalisation of the method for obtaining the data, allowing for the replication and direct extension of this work. Our final model is a combination of multiple classification trees, also known as the random forest model. We found that features extracted from harmonic elements can satisfactorily predict music genre for the Brazilian case, as well as features obtained from the Spotify API. The variables considered in this work also give an intuition about how they relate to the genres.

A wide class of machine learning algorithms can be reduced to variable elimination on factor graphs. While factor graphs provide a unifying notation for these algorithms, they do not provide a compact way to express repeated structure when compared to plate diagrams for directed graphical models. To exploit efficient tensor algebra in graphs with plates of variables, we generalize undirected factor graphs to plated factor graphs and variable elimination to a tensor variable elimination algorithm that operates directly on plated factor graphs. Moreover, we generalize complexity bounds based on treewidth and characterize the class of plated factor graphs for which inference is tractable. As an application, we integrate tensor variable elimination into the Pyro probabilistic programming language to enable exact inference in discrete latent variable models with repeated structure. We validate our methods with experiments on both directed and undirected graphical models, including applications to polyphonic music modeling, animal movement modeling, and latent sentiment analysis.