The International Business Communication Standards (IBCS or IBCS Standards) are practical proposals for the design of reports and presentations, meaning, in most cases, the proper conceptual, perceptual and semantic design of charts and tables.

It then presents this activity to human analysts, who confirm which events are actual attacks, and incorporate that feedback into its models for the next set of data. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the machine-learning startup PatternEx have developed an AI platform called AI2 that predicts cyber-attacks significantly better than existing systems by continuously incorporating input from human experts (AI2 refers to merging AI with "analyst intuition": rules created by living experts). The team showed that AI2 can detect 85 percent of attacks --about three times better than previous benchmarks -- while also reducing the number of false positives by a factor of 5. The system was tested on 3.6 billion pieces of data known as "log lines," which were generated by millions of users over a period of three months. To predict attacks, AI2 combs through data and detects suspicious activity by clustering the data into meaningful patterns using unsupervised (automatic, no human help) machine learning.

Bots (or chatbots) seem to be taking the technology world by storm. Not a week goes by without some exciting announcements related to chatbots. Recently, at the F8 conference, Facebook announced a new chatbot platform for its messenger app. This announcement comes just a few days after Microsoft's release of its Bot Framework at the Build Conference. While we are still in the early days of the "Bot revolution," we can clearly see how chatbots are going to become an integral part of the consumer and enterprise software ecosystem.

Google's artificial intelligence (AI) division has achieved a landmark victory over the grandmaster of Chinese board game Go. The AlphaGo system beat three-time European Go champion Fan Hui 5:0 in a competition held at the company's London headquarters in October. AlphaGo was built by Google's AI division created from the 400m acquisition of London-based AI company DeepMind in 2014. The victory is notable because the complexity of Go makes it difficult for computer systems to calculate all the possible moves. Chess, for example, offers 400 possible moves after the first two, but Go offers 130,000, making it very difficult for an AI to perform those calculations to match a human opponent.

What is the difference between Machine Learning and Statistical modeling? I have been doing research for the past 2 years. Generally, it takes me not more than a day to get clear answer to the topic I am researching for. However, this was definitely one of the harder nuts to crack. When I came across this question at first, I found almost no clear answer which can layout how machine learning is different from statistical modeling.

How easy it is to build a learning machine? Shouldn't one just hire some Machine Learning PhDs and have them run their algorithms? Well, this is most probably a good idea, but it won't be enough. I'll try to explain why in this blog entry. Before answering our questions, let's define what we are dealing with.

Proponents of autonomous vehicles are in a sticky situation. Self-driving technology is expected to have a tremendous impact on public health and reduce the 1.25 million deaths every year on global roads. At the same time, this emerging technology is a threat to the employment of the millions who are paid to sit behind the wheel -- from truck drivers to cab drivers and delivery workers. Baidu chief scientist Andrew Ng, an expert in the world of artificial intelligence, acknowledges the unemployment concerns, but he sees a way forward that offers society the benefits of autonomous vehicles and blunts the negative impact of job losses. "I feel a strong moral responsibility or obligation to try to make self-driving cars a reality as quickly as possible," Ng said in a visit to The Washington Post.

Microsoft and Facebook both recently announced that they will be heavily investing in bots. Each company has created an accompanying framework for developing chatbots, which you can use to deploy your own bots on their respective services--Skype and Messenger. Microsoft's Satya Nadella is so bullish on bots that he spun up a Domino's chatbot in real time when he made the company's announcement, which is super cool and exactly why I am long MSFT! These new frameworks make that case even more strongly than we ever could. But in the rush to bring forth this new reality, what a bot is and what it can do is not so obvious.

Artificial intelligence and autonomous cars are no longer exclusive to science fiction. Google's self-driving cars have driven more than five million kilometres, while some Teslas can drive themselves under certain conditions, and Singapore could see a fully autonomous taxi hit the streets by the end of the year. While all these initiatives are focused on what's outside the car, on equipping and teaching machines to understand and react to the outside world, it's also important to understand the people inside. The solution they created – a camera that can understand when drivers are fatigued or distracted, has helped save thousands of lives. The company traces its origins to a group of roboticists at the Australian National University in 1997.

The conventional wisdom is that intelligent systems, while good with numbers and maybe facts, are not going to be able to cope with the world of judgment and decision-making. The common assumption is that computers will not be able to deal with the nuance of reasoning that drives the solely human ability to assess what is happening in the world and then make reasoned decisions in reaction to that assessment. And herein lies my problem -- the assumption hidden in this belief is that humans are actually good at this sort of reasoning. And it's not clear that this is true. In particular, we seem prone to reasoning mistakes based on biases in decision-making that hinder us every day.