Many technology commentators got all excited a few months ago when Microsoft launched how-old.net, a website where users could upload a photo and the site would guess the age of the person in the picture. The service was a great way to showcase the opportunity that applying artificial intelligence to a problem set introduces. Insilico hopes to deliver a similar sort of an offering, but with a far more important purpose. Insilico Medicine is an organization focused on aging research. Headquartered at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore, it has R&D resources in Belgium, Poland, Russia and China employing 39 scientists worldwide.

Machine learning is being applied in recommendation engines, marketing automation, financial fraud detection, language translation, and text-to-speech applications. Apple recently announced that the iPhone 7 would use machine learning in its camera to recognize faces, imagery, and even the lighting in a room, making Apple the latest tech company to give primacy to its use of machine learning. But machine learning is no longer exclusive to digital companies: Businesses in every industry are utilizing this technology to improve processes. The NFL uses machine learning to gather deep insights into player movements, positions, and passes to reorganize play style. In the medical sector, machine learning analyzes patients and predicts the likelihood of their returning. Even hiring and talent management in most companies is now handled by algorithms that dig out desired characteristics and, hopefully, remove biases.

There are a number of machine learning models to choose from. We can use Linear Regression to predict a value, Logistic Regression to classify distinct outcomes, and Neural Networks to model non-linear behaviors. When we build these models, we always use a set of historical data to help our machine learning algorithms learn what is the relationship between a set of input features to a predicted output. But even if this model can accurately predict a value from historical data, how do we know it will work as well on new data? Or more plainly, how do we evaluate whether a machine learning model is actually "good"?

Stream water temperature is considered both a dominant factor in determining the longitudinal distribution pattern of aquatic biota and as a general metabolic indicator for the water body, since so many biological processes are temperature dependent. Moreover, the plunging depth of stream water, its associated pollutant load, and its potential impact on lake/reservoir ecology is dependent on water temperature. Lack of detailed datasets and knowledge on physical processes of the stream system limits the use of a phenomenological model to estimate stream temperature. Rather, empirical models have been used as viable alternatives. In this study, an empirical model (artificial neural networks (ANN)), a statistical model (multiple regression analysis (MRA)), and the chaotic non-linear dynamic algorithms (CNDA) were examined to predict the stream water temperature from the available solar radiation and air temperature.

Well, here's something you might want to prepare yourself for. By the time wedding bells are ringing your new son- or daughter-in-law could very well be a robot. That's what Dr. David Levy believes, at least. You might recognize Levy's name, either because of his chess-playing prowess or his decades-long involvement with artificial intelligence research. Levy, who happens to be a very good friend of computing pioneer Clive Sinclair, says that humans and robots will be tying the knot "before, not after, the year 2050."

Summary: A great story about an AI-powered massive on-line open learning platform focused on STEM education. The platform and its content is to be available across many languages to serve students anywhere in preparing for a better life in STEM careers. If you're from the US you're probably feeling some angst as our K-12 students seem to slip further and further back on STEM studies. Imagine how bad it is in the lesser developed countries where shortages of STEM teachers and basic tech resources make it almost impossible for young people to prepare for a better life through a tech career. Worse still, UNESCO says there are 100 million young people around the world who do not attend school at all.

It's been a year of change in the enterprise software market. SaaS providers are fighting to compete with one another, machine learning is becoming a reality for businesses at a larger scale, and containers are growing in popularity. Here are some of the top trends from 2016 that we'll likely still be talking about next year. As more companies adopt software-as-a-service products like Office 365, Slack and Box, there is increasing pressure for companies that compete with each another to collaborate. After all, nobody wants to be stuck using a service that doesn't work with the other critical systems they have.

Due to Apple's success with a closed-platform solution, many software companies have opted to forgo an open-platform to provide a more consistent user experience, and of course, for the benefit of increased profits. But, when it comes to chatbots, a closed-platform completely defeats the purpose of a chatbot solution. Closed platforms are walled gardens that isolate the chatbot from a world of possibility. A chatbot on a closed platform can never become the ultimate solution because it can't be customized to address the specific problems a company faces. For example, integrating with internally developed software would likely be a lengthy process and at the discretion of the software developer. Any changes or added functionality to the platform have to be reviewed, approved, and implemented by the vendor.

The gold rush for artificial intelligence (AI) is officially in full swing. Big players like Google and Facebook and small teams alike are in an all-out sprint toward the goal of creating the next generation of AI assistants that will fundamentally change how we live and work. I am in awe at the pace of progress, because every week it feels like a new barrier is breached, a tool grows more robust, or a new startup is launched with the ability to transform an industry. However, the most surprising observation continues to be people's underestimation of AI. Specifically how the general population seems so unable, or unwilling, to imagine that a machine could ever match a human's ability in any job -- particularly their own.

Google isn't the only company trying to figure out eye-tracking for virtual reality -- Oculus VR is on the case too. The Facebook-backed VR company has confirmed that it recently acquired Danish startup The Eye Tribe, a firm best known for creating software developer kits that bring gaze-based controls to smartphones, tablets and PCs. Now, that technology belongs to one of the highest profile VR headset makers on the market. Although Oculus was happy to confirm the acquisition, mums the word on details: we don't know how much the company was acquired for, what the Eye Tribe's future is outside of Oculus or when we might see this technology in a future product. Still, the aim of the purchase seems obvious -- The Eye Tribe has been working on a foveated rendering for VR, which increases VR performance by only rendering the part of the simulation the user is directly looking at.