In fact, in many countries, the internet, the very thing that was supposed to smash down the walls of authoritarianism like a sledgehammer of liberty, has been instead been co-opted by those very regimes in order to push their own agendas while crushing dissent and opposition. And with the emergence of conversational AI -- the technology at the heart of services like Google's Allo and Jigsaw or Intel's Hack Harassment initiative -- these governments could have a new tool to further censor their citizens. Turkey, Brazil, Egypt, India and Uganda have all shut off internet access when politically beneficial to their ruling parties. Nations like Singapore, Russia and China all exert outsized control over the structure and function of their national networks, often relying on a mix of political, technical and social schemes to control the flow of information within their digital borders. The effects of these policies are self-evident.

The machines haven't taken over. However, they are seeping their way into our lives, affecting how we live, work and entertain ourselves. From voice-powered personal assistants like Siri and Alexa, to more underlying and fundamental technologies such as behavioral algorithms, suggestive searches and autonomously-powered self-driving vehicles boasting powerful predictive capabilities, there are several examples and applications of artificial intellgience in use today. However, the technology is still in its infancy. What many companies are calling A.I. today, aren't necessarily so.

One of the highlights at this year's Consumer Electronics Show (CES) was the parade of new connected vehicle technologies. Automakers and their suppliers rolled out a number of innovative capabilities that promise to shape the next generation of driving, make transportation safer and more efficient, revitalize our cities, and reduce air pollution. Often lost amidst the "oohs" and "ahhs" these new capabilities inspire, however, is their dependence on radio spectrum and the policies that govern its use. The new connected vehicle capabilities come in decidedly different flavors. Some, for example, seek to enhance the automobile user's experience.

YouTube recommendations are a prominent example of applying advanced analytics on a massive scale to improve a service, the experience users get out of it, and the bottom line of the vendor behind it -- Google. Previously, we explored the rationale behind it and pondered as to how this type of analytics could be classified. It's time to pick up where we left off and explore how it works under the hood. Inspiration came from a hit moment for YouTube recommendations: one of those times when it succeeded in picking up the track that the person curating an ad-hoc, spur-of-the-moment playlist was about to play next. The wow effect induced by this successful prediction/recommendation of a rarity, triggered a spur-of-the-moment discussion which may serve to illuminate different aspects of analytics.

Your devices will know everything about you. Millions of people own smart devices that do simple things like adjust temperature, turn down lights or lock doors when they go out. You can buy a connected fridge that tells you when you have run out of food or set up security cameras that tell you when suspicious movement is detected. There is even a toothbrush that tells you what areas of the mouth you have neglected and an intelligent hair brush that does … something. Depending on which market forecast you believe, the install base for connected devices could be anywhere between 20 billion to just over 30 billion by 2020.

As financial companies have begun employing automated advisors aimed at helping customers manage their money, and oncologists have started using the artificial intelligence system known as Watson to identify new treatments, the prominent role that sophisticated computer programs have begun to occupy in our lives has become undeniable. Government agencies are also harnessing the powers of automation. The U.S. Environmental Protection Agency and the U.S. Food and Drug Administration, for example, are starting to use complex computer models that can predict environmental exposure to chemicals and drug interactions across patient groups. As agencies begin to enter this brave new world of automation, questions have begun to emerge about how government officials should delegate important tasks to machines. For one, will automation negatively affect the level and quality of human deliberation and dialogue that are integral to democratic governance? Furthermore, when adjudicating individual determinations, like the awarding of disability benefits, will machines prove incapable of providing much needed empathy to claimants?

This post is'not' intended to teach people how to use popular predictive modelling APIs for free. Although, to your surprise, this isn't a far fetched possibility. Trained Machine learning models are basically a function that maps feature vectors to the output variable. Upon querying with a test instance, the model predicts an outcome, assigning probability scores to all the possible classes. Google, Amazon etc provides public facing APIs to train predictive models on the subscriber's data, the model can further be used for prediction purposes .

A new app can give you the answers to your math homework and even explain how to solve it just by taking a picture. Called Socratic, the free app uses artificial intelligence to determine what information you need, and returns'explainers' and videos to give you step-by-step help. The firm says it's like having a'digital tutor in your pocket,' generating answers from a community of teachers and students. A new app can give you the answers to your math homework and even explain how to solve it just by taking a picture. The tutor app can also help with questions in science, history, English, economics.

I recently gave a free webinar on Principal Component Analysis. We had almost 300 researchers attend and didn't get through all the questions. This is part of a series of answers to those questions. If you missed it, you can get the webinar recording here. Principal Component Analysis specifically could be used with a training and test data set, but it doesn't make as much sense as doing so for Factor Analysis.

At the heart of deep learning lies a hard optimization problem. So hard that for several decades after the introduction of neural networks, the difficulty of optimization on deep neural networks was a barrier to their mainstream usage and contributed to their decline in the 1990s and 2000s. Since then, we have overcome this issue. In this post, I explore the "hardness" in optimizing neural networks and see what the theory has to say. In a nutshell: the deeper the network becomes, the harder the optimization problem becomes.