The spread of hackathon-style events and "project sprints" has created an explosion of new business ideas across industries. Despite the flood, the pipeline for developing nascent businesses is broken. Whether you're developing a new line of business inside a corporation or building the next Silicon Valley mega-startup, it can be hard to transition from the high-speed, high-intensity environment of a hackathon to the slow, methodical process of finding product-market fit. Hackathon judges sometimes pick "winners" with great stories but not great business plans, making it even more confusing to tell which ideas have potential. Experienced entrepreneurs know that the customer is the only judge that really matters.

The Debut Jobs App L'oreal, Ernst and Young, Microsoft, and Deutsche Bank have all signed up to support a smartphone app called Debut, which promises to let young people fast-track the recruitment process and land roles in big companies just by playing mobile games. Users download the game, they play, and they win(or are recognized) based on characteristics that employers are looking for, which will then connect them to fast-track interviews or international internships. You can visit the game here. DeepMind's AI Labyrinth Google's AI company, DeepMind, which was founded by Demis Hassabis, created a virtual world called Labyrinth. It's part of its Apollo program and it's designed to put software into a generalized world to see what it can learn while it's there.

We are going to introduce the Markov Switching Regimes (MSR) model which, as its name indicates, tries to capture when a regimen has changed to another one. This would be a change between opposite trends or it could consist in passing from "being in trend" to "not being in trend" and vice versa. The name of Markov could sound familiar to some of you as j3 introduced what the Markov chains were a couple of years ago. The main characteristic of this stochastic process is that in a stage t, the probability of occurrence only depends on what happened in the immediately previous stage, t-1. In our post we will assume that the trend of an index today will depend only on which trend was living yesterday, this means, the index will be governed by a Markov chain.

"I think being radical is the only way of doing things, because slow iteration doesn't really work." Claned Co-founder Vesa Perala believes that instead of attempting to retrofit technology to out-dated educational systems, EdTech start-ups should be helping to write a new rulebook. "Our pitch pretty much begins with education reform. The starting point is that the Finnish schooling system might be perceived as being the best in the world, but we're still overhauling it," he says. For the past 3 years, Claned has been in what he describes as semi-stealth mode, focusing on developing a robust artificial intelligence system that uses machine-learning algorithms to map out what factors most impact individual learning.

Google executive chairman Eric Schmidt has questioned whether renowned scientist Stephen Hawking and SpaceX billionaire Elon Musk are in a position to accurately predict the future of artificial intelligence. Hawking told the BBC in 2014 that AI could end mankind, while Musk tweeted that same year that AI could be more dangerous than nuclear weapons after reading a book called "Superintelligence." Schmidt was asked at the Brilliant Minds conference in Stockholm on Thursday what he made of their predictions. In response, he said: "In the case of Stephen Hawking, although a brilliant man, he's not a computer scientist. Elon [Musk] is also a brilliant man, though he too is a physicist, not a computer scientist."

Welcome to my home page. I specialize in artificial intelligence, machine learning, and data visualization, and regularly write about my latest work on my personal blog. I also occasionally collaborate with the media and private companies on projects that I believe are important, many of which have been featured all over the world and in the news. If you'd like to talk data, hire me to consult on a data project, commission me to teach a workshop in your area, or anything else, please feel free to contact me by email.

Even research which is conducted within the university setting is increasingly pushing up against new ethical frontiers in the creation of machine learning algorithms based on vast pools of human-created training data. For example, several researchers I spoke with mentioned situations where colleagues had taken large datasets licensed to the university for strictly non-commercial use or collected from human subjects for strictly academic research and used them to construct large machine learning computer models. These models were then licensed from the university to the faculty member's private startup, where they were then used for commercial gain. In at least some cases, protected human subjects data was used to create a computer model for academic research, which was approved by IRB, but that model was then allegedly subsequently licensed by the university for commercial use to the faculty member's startup. None of the researchers were privy to whether IRB had approved the commercial licensing or if that occurred without IRB knowledge and they argued that the very nature of a machine learning model deidentifies such data to the point that it should no longer be considered human subjects data.

Kaggle competitions are a fantastic way to learn data science and build your portfolio. I personally used Kaggle to learn many data science concepts. I started out with Kaggle a few months after learning programming, and later won several competitions. Doing well in a Kaggle competition requires more than just knowing machine learning algorithms. It requires the right mindset, the willingness to learn, and a lot of data exploration. Many of these aspects aren't typically emphasized in tutorials on getting started with Kaggle, though. In this post, I'll cover how to get started with the Kaggle Expedia hotel recommendations competition, including establishing the right mindset, setting up testing infrastructure, exploring the data, creating features, and making predictions.

When learning was still a predominantly human undertaking. "If a dataset can be downloaded from the web, regardless of whether it originated from a breach or other illegal activity, it is considered to be in the public domain and falls under the IRB exemption for public domain datasets." This is only one of many staggering assessments Kalev Leetaru got in response to his requests to data scientists and researchers, universities, research institutions, and research funders, asking them to elaborate on how they assured that their use of big data sets were ethically tenable. Leetaru, Senior Fellow at the George Washington University Center for Cyber & Homeland Security, was confronted with a plethora of answers – almost all explaining why the addressed would be unable to explain their reasoning. All this at a time when a number of high profile studies had to be retracted after publication in reaction to a firestorm of criticism by other researchers (who have a more accurate ethical compass, apparently).

I recently had my first, live experience failing the Turing test, but in the currently evolving chat interfaces, does this even matter any more? Amy of https://x.ai is a virtual assistant: she schedules meetings over email. I was introduced to her after being invited to a meeting that spanned multiple time-zones, and the interaction I had with Amy was so genuine that I initially did not realize, and then treated with great scepticism that this wasn't a human! As we begin to populate our lives with co-workers who may not be 100% human, it's time to start asking some questions of these tools as if interviewing a human. It's a question that hides many facets: Do you think this person is someone you'd want to work with every day?