The rules of the game called psychological science.

Machine Learning is not new, but its application across memory-optimized distributed systems has led to an explosion in both the number and capability of its uses. Pandora develops personalized content recommendations with machine learning algorithms, Tesla has produced the first widely distributed autonomous vehicle, and Amazon uses autonomous robots to move packages within its warehouses and even deliver packages. When coupled with real-time data, advanced analytics approaches like machine learning and deep learning create immediate business opportunities. Machine learning has never been more accessible--if your data pipelines support real-time analysis. Attendees will learn tools and techniques for integrating machine learning models across industries and organizations.

Artificial Intelligence (AI) is used everywhere today, from self-driving cars, space travel to financial investments. So, when we think about the most exciting application for AI, we can't help but think of using AI to tell us how to make money in the currency markets (which has a $5 trillion a day turnover). More than 80% of trades in the currency markets are made by computers using AI. Big investment banks/trading firms use AI extensively. However, to build an AI system, a very strong background in Computer Science and Advanced Mathematics is essential.

LinkedIn cofounder Reid Hoffman, Omidyar Network, and John S. and the James L. Knight Foundation have today joined forces to create the Ethics and Governance of Artificial Intelligence Fund. The research fund will focus on AI for the public good and will bring a more diverse range of voices, like faith leaders and policymakers, to AI research and development. Issues the fund may address include ethical design, potential harmful and beneficial impacts of AI, and AI that works in the public interest. "Artificial intelligence and complex algorithms, fueled by big data and deep-learning systems, are quickly changing how we live and work -- from the news stories we see, to the loans for which we qualify, to the jobs we perform," the group said in a statement provided to VentureBeat. "Because of this pervasive but often concealed impact, it is imperative that AI research and development be shaped by a broad range of voices -- not only by engineers and corporations, but also by social scientists, ethicists, philosophers, faith leaders, economists, lawyers and policymakers."

It's been more than a decade since an international team of scientists sequenced the human genome. But the genomics revolution is only just beginning, Color Genomics CEO Othman Laraki says. "Each one of us, we have a torrent of data in our bodies," Laraki said on the latest episode of Recode Decode, hosted by Kara Swisher. "We're generating a tremendous amount of data, and today, we make use of almost none of that." He said most health data used to be recorded by hand and stored at the hospital.

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).

And so the insurrection is beginning. Last week Japanese insurance Fukoku Mutual Life Insurance announced that it was going to be replacing 30 staff with an artificial intelligence that would be calculating payouts (although, it noted, with human oversight still making final approvals). The technology would improve productivity by 30% and the firm expected to save some 140m Yen a year (around £1m) after the 200m Yen investment. Now I'm sure that this implementation of IBM's Watson technology (remember: Watson was the man who predicted in 1943 a global market for maybe five computers) will be very whizzy. But excuse me whilst I contend that Fukoku's PR make this AI sound like every IT business case I've ever seen: cost savings through headcount reduction blah blah, productivity gains blah blah.

"Jia Jia" can hold a simple conversation and make specific facial expressions when asked, and her creator believes the eerily life-like robot heralds a future of cyborg labour in China. Billed as China's first human-like robot, Jia Jia was first trotted out last year by a team of engineers at the University of Science and Technology of China. Team leader Chen Xiaoping sounded like a proud father as he and his prototype appeared Monday at an economic conference organised by banking giant UBS in Shanghai's futuristic financial centre. Chen predicted that perhaps within a decade artificially intelligent (AI) robots like Jia Jia will begin performing a range of menial tasks in Chinese restaurants, nursing homes, hospitals and households. "In 5-10 years there will be a lot of applications for robots in China," Chen said.

The most potent and under-appreciated corporate function today is Corporate Communications. Their continued reliance on a pre-digital, centrally-governed model that delivers tightly-edited messages through pre-approved channels does not help. This approach is colliding with the new digital ways in which people search, find, consume and share news (including corporate information, real or fake). It's time for Chief Communications Officers to update their standard operating model to calibrate for the proliferation of decentralized digital networks as well as conversational user interfaces that are reshaping public and private modes of communication. The purpose of Corporate Communications is to be the primary source of truth for stakeholders, a role crucial for executives and society alike.