With improvements in facial-recognition technology and the increasing popularity of smartphones, the threat to one's privacy unexpectedly posed by Internet photos posted by strangers is growing day by day. To protect against unwanted scrutiny, trading company Nissey Corp., based in Sabae, Fukui Prefecture, has developed special glasses that thwart electronic facial recognition. The Privacy Visor is a set of white titanium goggles with lenses that have a mesh-like surface providing many tiny spaces to see through. "Since the view might not be clear, driving or riding on bicycles must be avoided," a Nissey spokesman warned. The glasses were jointly developed with the National Institute of Informatics, a research institute dedicated to information technology.

I still ponder on this question "Under current US laws, can I possibly win a suit against a law firm for poor representation because they used AI on my case and I lost my case that ended up causing me to lose millions and impacted my reputation? And, could this firm lose their license through the state board resulting from my claim & suit as well as others who claimed poor representation due to AI used on their case?" I believe they can under current laws. Last week, BigLaw firm BakerHostetler announced that it was partnering with ROSS Intelligence to bring artificial intelligence to its Bankruptcy, Restructuring, and Creditor Rights practice. ROSS will be used to help BakerHostetler's non-robot lawyers research more quickly and intelligently.

Google is offering Parsey McParseface and SyntaxNet to interested developers that want to integrate artificial intelligence to their application and system projects without having to build their own platform from scratch. SyntaxNet will be an open-sourced neural network framework that can be used by developers to create software that could have the capabilities to understand how the human language works. Google is also rolling out Parsey McParseface as a parser for the English language. AI is a highly-complicated subject but there is no doubt that the advancements in technology has improved the platform. Digital assistants such as Siri, Alexa and Cortana are products of good AI technology.

A TensorFlow implementation of the models described in Andor et al. (2016). At Google, we spend a lot of time thinking about how computer systems can read and understand human language in order to process it in intelligent ways. We are excited to share the fruits of our research with the broader community by releasing SyntaxNet, an open-source neural network framework for TensorFlow that provides a foundation for Natural Language Understanding (NLU) systems. Our release includes all the code needed to train new SyntaxNet models on your own data, as well as Parsey McParseface, an English parser that we have trained for you, and that you can use to analyze English text. So, how accurate is Parsey McParseface?

Continuing the topic started last week, this week I would like to show you how these little lightweight programs called chatbots are taking their own place within organizations, doing no less than increasing employees productivity and streamlining business processes. Currently, chatbots are very popular in the consumer space. From the business perspective, eCommerce, delivery and transportation businesses boost their business opportunities by letting their end customers order services and goods even more conveniently. But if you look very closely, you will see that bots are slowly taking over the enterprise space as well, and I'm not talking about company employees using third-party bots via enterprise messaging apps like Slack. I'm talking about enterprise companies building their own corporate chatbot to add value to their product, create a better interaction experience for their B2B customers and improve own business processes.

Today, Google introduces Parsey McParseface -- a free new tool, born from Google's research division to help computers better parse and understand English sentences. "We were having trouble thinking of a good name, and then someone said, 'We could just call it Parsey McParseface!' So… yup," says a Google spokesperson. Parsey McParseface is a piece of a larger framework called SyntaxNet, itself a big part of Google's popular home-built TensorFlow software for building artificial intelligence, as explained in a blog entry. With this release, any developer anywhere can download, use, and even start to improve Google's tools in their own software. One of the biggest problems in artificial intelligence, today, is that speech recognition by computers may be better than ever, but they still have trouble understanding exactly what we mean. After all, language is complicated: Consider that "Buffalo buffalo Buffalo buffalo buffalo buffalo" is a 100% gramatically correct sentence in American English.

Most people don't realize this yet, but artificial intelligence is about to be integrated into everything. The technology, although decades old, has recently become advanced enough that a skilled programmer, even one that's not an artificial intelligence expert, can start integrating it into their consumer-facing products that you touch and feel everyday. It means we're not only going to start talking to the computer instead of pushing buttons, but the computer is going to start talking back! So, out of all the things we can tell our computer, why would we tell it our most intimate thoughts and our deepest secrets? The robot doesn't care if you are inexperienced, unhealthy, or financially illiterate.

In a recent article on 5 Computer Vision and Image Understanding Companies, we talked about how artificial intelligence is enabling computers to see as well as humans when recognizing images and in even some cases better. A company we wrote about before called Enlitic has developed a deep learning algorithm that can increase the accuracy of a radiologist's interpretation by 50-70% and at a speed 50,000 times faster. Not only that, but Affectiva's technology can also evaluate your emotions in real-time through your webcam. They have an online demo you can try and see for yourself how it works. The ability for a computer to detect human emotions falls into a field of study called "affective computing".

Mobile apps, video games, spreadsheets, and accurate weather forecasts: that's just a sampling of the life-changing things made possible by the reliable, exponential growth in the power of computer chips over the past five decades. But in a few years technology companies may have to work harder to bring us advanced new use cases for computers. The continual cramming of more silicon transistors onto chips, known as Moore's Law, has been the feedstock of exuberant innovation in computing. But it looks to be slowing to a halt. "We have to ask, is this going to be a problem for areas like mobile devices, data centers, and self-driving cars?" says Thomas Wenisch, an assistant professor at the University of Michigan.

This post looks at perhaps the most important, and often overlooked, step in learning machine learning, an aspect which can make the biggest difference in one's skill set. The web is full of good explanations of machine learning algorithms. And every second applicant for a data science position has finished the Coursera course on machine learning. Theory will not help you choose good values for the 16 parameters a standard implementation of a random forest takes. The default values are good to get started, but which parameters should you modify depending on your data?