Maroon 5 keeps popping up on my Pandora stations, so artificial intelligence (AI) and machine learning still have a ways to go. Even if AI can beat us at Go. But, wow, that aside, the technologies showcased at the 2017 Consumer Electronics Show (#CES2017, actually the 50th annual, sponsored by the Consumer Technology Association [CTA]), from countless robots to Hyundai exoskeletons, are incredible. Voice recognition, virtual and augmented reality, smart home technologies and drones are everywhere. AI and machine learning comprise major threads. Which raises the question: Will all that AI be Democrat or Republican?

The Managing Partner of Intergroup Partners AG, Montserrat Corominas, has recently been published in Inc Magazine. This is her article "Artificial Intelligence and Virtual Reality are about to transform business. Here's how to be prepared" KEY OBSERVATIONS Most business leaders are not quite sure yet how AI and VR are relevant to their companies and what is in it for them. To my understanding, these are only contemporary descriptions of state of the art technologies. The most important topic to discuss for boards, founders and management teams in regards to AI/VR is the inherent new way of communication through digitization.

In recent years, computers have gotten remarkably good at recognizing speech and images: Think of the dictation software on most cellphones, or the algorithms that automatically identify people in photos posted to Facebook. But recognition of natural sounds -- such as crowds cheering or waves crashing -- has lagged behind. That's because most automated recognition systems, whether they process audio or visual information, are the result of machine learning, in which computers search for patterns in huge compendia of training data. Usually, the training data has to be first annotated by hand, which is prohibitively expensive for all but the highest-demand applications. Sound recognition may be catching up, however, thanks to researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).

A scarf designed to filter out harmful elements in city air. A breast pump that fits into a bra and keeps track of pumping volume. A drone that can dive into water and help anglers catch a big one. CES 2017, the consumer tech event held in Las Vegas this week, featured vendors with automated baubles, humanoid robots and "smart" everything -- a dishwasher, hairbrush and lawnmower, to name a few. But it's not so much the gadgets as their underlying technology that will make CIOs -- who seek out new tools for business, not the home or yard -- stop, look and listen.

Looking at chatbot builders, I see a lot of technologists. People who are interested in AI and conversational UI, NLP/NLU and machine learning. I am by no means an expert in any of these fields. The little I do know though is around the idea of syntax and the difficulties of NLP in parsing language. Last year google released SyntaxNet to help with NLU.

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty Take some time to learn about the human brain! This is my favorite intro to neuroscience course: https://www.mcb80x.org/

As long as the bias is not too strong, you are better off with a robust, outlier-insensitive estimate, than with an unbiased one. It would be interesting to do some analysis, to figure out the impact that a 10% bias has on your yield metric (measured as correctness of predictions, or revenue). The impact might be much smaller than 10%. Your model might be a bad model. It is better to reduce the variance generated by your model, rather than picking up a kosher (perfect) statistical estimate.

Artificial intelligence is moving from science fiction to practical reality fast. AI -- technology that teaches machines to learn so they can perform cognitive tasks and interact with people -- is suddenly accessible to many companies. Costs associated with the advanced computing and data-storage hardware behind AI are plummeting. A growing number of vendors also offer AI tools such as robotic processing automation that can be configured without the help of a rocket scientist. So this is clearly an area more banks will need to pay attention to going forward.

This is the second installment in a three-part series covering AI2 and machine learning. Be sure to read Part 1 for an introduction to AI2. AI2 is an "analyst-in-the-loop" system, meaning that it exploits the expertise of a security analyst to improve itself. A "human-in-the-loop" system is used to generate more supervised examples for the machine learning stage to use in an iterative training algorithm. This is exactly what AI2 does, allowing feedback to make the machine gradually smarter in the security domain.

The pace of technological change continues its inexorable march forward, with the pace steadily clicking faster. After decades of attending CES and riding the ebbs and flows it seems to me that this year finally shows a convergence of many waves of development. Artificial intelligence, voice recognition, virtual reality and internet of things are converging in ways that portend major changes in the way we will interact 24/7. From the seemingly arcane (a chess board that allows you to play anyone in the world, or against the best computers) to autonomous cars to the HAL-like ubiquity of Alexa, we are yet again in a brave new world. Consciously or otherwise, often in slippery slope fashion, we have individually opened the kimono to our personal world in exchange for seeming convenience.