Almost every day, there's news about a massive data leak -- a breach at Yahoo that reveals millions of user accounts, a compromise involving Gmail phishing scams. Security professionals are constantly moving the chess pieces around, but it can be a losing battle. Yet, there is one ally that has emerged in recent years. Artificial intelligence can stay vigilant at all times, looking for patterns in behavior and alerting you to a new threat. While AI is not anywhere close to being perfect, experts tell CSO that machine learning, adaptive intelligence, and massive data models that can spot hacking much faster than any human are here to help.

She asks about you, affirms things you have said, however finding out about her feelings is an elusive task. She is one of the earliest chatbots created by computer scientist Joseph Weizenbaum at MIT. Weizenbaum considered ELIZA to demonstrate an example of communication between machine and human, particularly highlighting the lack of depth in this exchange. Surprisingly, even with her short and repetitive utterances, some people attributed to her a level of human understanding. It wasn't that she could read the user's mind, rather, she affirms what the user says through a cascade of regular expression subsitutions used to tweak the user's input. An example is seen below, where ELIZA has picked up on the user's sentence structure and used substitution for her output as seen below.

IBM Watson is one of the most fascinating projects I'm following closely. Originally designed for a quiz game Jeopardy!, Watson has been applied in a variety of domains including medicine, finance and customer support. Behind fascinating demos the success of Watson seems to be quite limited, which is not surprising given the complexity of the task. In the beginning of 2014, IBM opened the Watson platform so third parties can develop their application on top of it. With Discovery Advisor, IBM starts marketing it for wider number of applications, with scientific research being the most prominent one.

Friday, 3rd of February, 2017, Baltimore, MD - Insilico Medicine today announced that it signed a Memorandum of Understanding (MOU) and started the first collaborative research project with one of the largest research and medical networks, Gachon University and Gil Medical Center. The intent of the long-term collaboration is to develop artificially intelligent multimodal biomarkers of aging and health status as well as interventions intended to slow down or even reverse the processes leading to the age-related loss of function. "We are happy to collaborate with Insilico Medicine, one of the leaders in AI with a specific focus on practical aging research in the pharmaceutical and healthcare industries. The field of artificial intelligence is rapidly evolving and in addition to our own cutting-edge research programs, we collaborate with other leaders to expedite progress and ensure that we can save and extend human life sooner", said Dr. Lee Uhn, Director of AI-based Precision Medicine at Gachon University, Gil Medical Center. The first MOU between the companies was signed on November 18th, but the first project launched and data exchange transpired in January 2017.

Babies learn about their world by pushing and poking objects, putting them in their mouths and throwing them. Carnegie Mellon University scientists are taking a similar approach to teach robots how to recognize and grasp objects around them. Manipulation remains a major challenge for robots and has become a bottleneck for many applications. But researchers at CMU's Robotics Institute have shown that by allowing robots to spend hundreds of hours poking, grabbing and otherwise physically interacting with a variety of objects, those robots can teach themselves how to pick up objects. In their latest findings, presented last fall at the European Conference on Computer Vision, they showed that robots gained a deeper visual understanding of objects when they were able to manipulate them.

Science fiction author Isaac Asimov famously predicted that we'll one day have to program robots with a set of laws that protect us from our mechanical creations. But before we get there, we need rules to ensure that, at the most fundamental level, we're developing AI responsibly and safely. At a recent gathering, a group of experts did just that, coming up with 23 principles to steer the development of AI in a positive direction--and to ensure it doesn't destroy us. The new guidelines, dubbed the 23 Asilomar AI Principles, touch upon issues pertaining to research, ethics, and foresight--from research strategies and data rights to transparency issues and the risks of artificial superintelligence. Previous attempts to establish AI guidelines, including efforts by the IEEE Standards Association, Stanford University's AI100 Standing Committee, and even the White House, were either too narrow in scope, or far too generalized.

Finding the music of the moment can often be a challenging problem, even for humans with well-versed musical tastes. These challenges further explode into a myriad of complexities when attempting to construct algorithmic approaches for automatic playlist generation. A variety of factors play a role in influencing a listener's perception of what music is appropriate on a given seed (e.g. Erik Schmidt, Senior Scientist at Pandora will be presenting at the Machine Intelligence Summit in San Francisco, 23-24 March. Erik will present an overview of recommendations at Pandora, followed by a deep dive into the challenges of recommending content.

The hospitality industry has not always been at the forefront of high-tech innovation or implementation. Until recently, most of the bookings, transactions and administrative tasks at a hotel were handled manually. Revenue management – the process by which a revenue manager determines the best room rate at a given time, in order to maximise bookings and revenue – was a particularly difficult task. Revenue managers had to manually collect, review and analyse numerous data sets each time the rate needed to be updated, and then calculate the ideal room rate based on those variables. Even before the Internet, this was a very time-consuming task, which meant that revenue managers could not update rates as often as necessary (to ensure a property's continued financial success).

The safety standards to be introduced in Japan also include setting speed limits depending on road curvature and functionality to enable a driver to switch from automated to manual driving when there is a risk of an accident.

Predictive Analytics – We can leverage the power of AI based predictive algorithms to analyze stress and emotion response. This can be used by analyzing data from images via deep learning micro-expression analysis e.g. Google's, Im2Calories leverages deep learning algorithm to analyze food and estimate calories on the plate. Medical imagery is especially amenable to machine-learning. Moorfields Eye Hospital in London announced that it was working with Google's AI research division, DeepMind, to develop an AI system to spot sight-threatening conditions in digital scans of the eye.