The ecological effects of accidental or malicious radioactive contamination are insufficiently understood because of the hazards and difficulties associated with conducting studies in radioactively-polluted areas. Data sets from severely contaminated locations can therefore be small. Moreover, many potentially important factors, such as soil concentrations of toxic chemicals, pH, and temperature, can be correlated with radiation levels and with each other. In such situations, commonly-used statistical techniques like generalized linear models (GLMs) may not be able to provide useful information about how radiation and/or these other variables affect the outcome (e.g. Ensemble machine learning methods such as random forests offer powerful alternatives.

It's poised to be an exciting year in artificial intelligence. Last year, artificial intelligence deals topped a billion for the first time. The White House started publishing reports on AI and the economy. Samsung just announced a new $150 million fund for early-stage AI startups. Their recent acquisition of "super-Siri" Viv Labs must have whetted their appetite. There's are even AI startup incubators popping up, like deep learning mastermind Yoshua Bengio's Element in Montreal.

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.

Alpha Go's victory over Lee Sedol was perhaps one of the most important, but we saw advancements in self-driving cars, the continued embrace of bots and personal assistants for retail, adoption and competition around in-house assistants like Amazon Echo, along with frequent, sometimes weekly, breakthroughs on the academic side, mainly relating to machine learning. With the biggest tech companies in the world--Google, Facebook, Amazon, Microsoft, and others--devoting more and more resources to AI, the momentum is going to increase. For those of us who've been in the field for a while, it's an incredibly exciting time. AI and Machine Learning have come to the fore in the recent past, but we believe that the next few years hold even more far reaching successes. So what advances will we be seeing exactly?

Thank you for coming here and taking a look at my view on where we are headed as a society in 2017 with exponential technologies, and the companies that build them, drawing society down a new path. It's the culmination of hundreds of hours of reading articles and research, watching talks, documentaries, and movie, and discussing how the world is changing with the people building the future. In this report I cover a number of areas including a brief history of 1994 through to 2017, VR & Mixed Reality, Artificial Intelligence, Intelligence Amplification, Commerce Revolution, Automation, and Cyberwar. But, what is'The Psychedelic Cultural Reality?' With the advancing of these aforementioned technology areas, the world will feel artificial, visually-overloaded, and uncomfortable. The alt-view of the world will seem like an artificial vision – and many will welcome it with open arms. Nikolas Badminton is a world-respected futurist speaker that researches, speaks, and writes about the future of work, how technology is affecting the workplace, how workers are adapting, the sharing economy, and how the world is evolving.

By 2035, developers will have learned to automate many jobs. Investments in artificial intelligence (A.I.) and robotics will surge, displacing workers. And a more connected world will increase -- not reduce -- differences, increasing nationalism and populism, according to a new government intelligence assessment prepared just in time for President-elect Donald Trump's administration. The "Global Trends" report, unveiled Monday, is produced every four years by the National Intelligence Council. It is released just before the inauguration of a new or returning president.

Foreign residents in Japan may be at a disadvantage in some ways, but they are by no means powerless nor on their own, says Tokyo-based nonprofit organization Asian People's Friendship Society (APFS). In a recently launched program series, the organization is nurturing a new group of volunteers it calls "foreign community leaders" who will assist fellow non-Japanese trying to navigate life amid a different and foreign culture. "Long-term foreign residents have incredible know-how on how to get by in their everyday lives in Japan," says Jotaro Kato, the head of APFS. "I want people to know that there are foreigners out there who can speak perfect Japanese" and who can provide guidance if needed. Targeting long-term foreign residents with a high level of proficiency in the Japanese language, the 30-year-old organization is spearheading the project to groom such veterans so they can help newcomers overcome a variety of everyday obstacles, such as dealing with language barriers, cultural differences and visa conundrums. For its part, APFS has organized a series of lectures and workshops that are currently taking place every other Saturday in a community hall in Itabashi Ward, Tokyo, in which experts from many different fields discuss topics important to foreign residents.

Cybersecurity was all over the news in 2016 – whether it was email breaches that compromised the Democrat campaign for the elections, or revelations towards the end of the year that planes were vulnerable to hacking through in-flight entertainment systems. The British government boasted that it had the capabilities to launch cybersecurity offensives and was committing a huge chunk of its budget to developing these further. Yahoo suffered from an attack that potentially gained access to 1 billion accounts, the largest known breach of all time. Vendors, hackers, banks, businesses, countries and shadowy state actors all seem locked in a perpetual game of cat and mouse – and highly sophisticated and organised malicious attackers seem to have the upper hand. According to the experts, here are some of the cybersecurity nightmares organisations will have to wrangle with in 2017.

No statistical algorithm can be the master of all machine learning application domains. That's because the domain knowledge encoded in that algorithm is specific to the analytical challenge for which it was constructed. If you try to apply that same algorithm to a data source that differs in some way, large or small, from the original domain's training data, its predictive power may fall flat. That said, a new application domain may have so much in common with prior applications that data scientists can't be blamed for trying to reuse hard-won knowledge from prior models. This is a well-established but fast-evolving frontier of data science known as "transfer learning" (but goes by other names such as knowledge transfer, inductive transfer, and meta learning).

Last Friday, the University of Ca' Foscari in Venice organized an IEEE workshop on the Human Use of Machine Learning (HUML 2016). The workshop, held at the European Centre for Living Technology, hosted roughly 30 participants and broadly addressed the social impacts and ethical problems stemming from the wide-spread use of machine learning. HUML joins a growing number workshops for critical voices in the ML community. These include Fairness, Accountability and Transparency in Machine Learning (FAT-ML), the #Data4Good at ICML 2016, and Human Interpretability of Machine Learning (WHI), held this year at ICML and Interpretable ML for Complex Systems, held this year at NIPS. Among this company, HUML was notable especially notable for diversity of perspectives.