Newspapers are a popular form of written discourse, read by many people, thanks to the novelty of the information provided by the news content in it. A headline is the most widely read part of any newspaper due to its ap - pearance in a bigger font and sometimes in colour print. In this paper, we sug - gest and implement a method for computing inferences from English news headlines, excluding the information from the context in which the headlines appear. This method attempts to generate the possible assumptions a reader formulates in mind upon reading a fresh headline. The generated inferences could be useful for assessing the impact of the news headline on readers includ - ing children. The understandability of the current state of social affairs depends greatly on the assimilation of the headlines. As the inferences that are indepen - dent of the context depend mainly on the syntax of the headline, dependency trees of headlines are used in this approach, to find the syntactical structure of the headlines and to compute inferences out of them.

A few months ago, Fr Philip Larrey published his book called "Artificial Humanity". In this article, we will explain what would happen if we have an inhumane AI. First of all, what does inhumane mean? Primarily, when we say Artificial Inhumanity, we are referring to an AI which is not concerned with humans. It does not exhibit any human feeling, and humans are just animate objects roaming our world. Even though AI was initially conceived to serve humans, we do not exclude the possibility of eventually having an AI, which ultimately only serves its interests. If that happens, then we are definitely in big trouble. The question of whether machines can think is about as relevant as the question of whether submarines can swim. Using the same line of thought, if machines exhibit humanity, does that mean that they are human?

Machine Learning is an enormous field, and today we'll be working to analyze just a small subset of it. Supervised learning is one of Machine Learning's subfields. The idea behind Supervised Learning is that you first teach a system to understand your past data by providing many examples to a specific problem and desired output. Then, once the system is "trained", you can show it new inputs in order to predict the outputs. How would you build an email spam detector?

Chris discusses how rule-based systems are moving to learning-based systems in various enterprise use cases. Sramana Mitra: Let's start by having you introduce yourself and Genesys and what work you're doing around artificial intelligence. Christopher Connolly: I am the Vice President of our Solutions Strategy Group. Genesys is the number one customer experience platform. It enables companies to create exceptional omni-channel experiences and relationships.

The multiple challenges of operating ever more complex environments are well known. The most common we hear when we are speaking with our customers and partners are based around the vast amount of data now being produced and the quality of it. These aren't new problems when it comes to network availability and performance monitoring. When I started working in this area in the late 1990s, Network Operations Centers (NOCs) were already drowning in the amount of data being produced. Back then, in the early days of systems and network management solutions, data would simply be discarded to avoid overloading the network management system.

Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chatbots, or search engines. Given the high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google's Tensorflow). Today, machine learning has come to play an integral role in many phases of the financial ecosystem, from approving loans, credit scores, to managing assets, to assessing risks. Yet, few technically-savvy professionals have an accurate view of just how many ways machine learning finds its way into their daily financial lives.

Our SMS search engine, called Text Engine, was originally created in 2013. The idea was to create a utility that would enable users to search the Web without needing to use a web browser and without using data. Text Engine accomplishes this by giving you access to vital, basic web information just by sending and receiving text messages. To keep Text Engine relevant for an ever-changing mobile market, our investors suggested that we think about adding a chatbot experience to Text Engine. So that's what we did.

Arabot, an Amman-based'Bot as a Service' startup has raised $1 million in seed funding led Saudi's Riyad Taqnia Fund (RTF) with participation from existing investors, RTF announced in a statement to MENAbytes. Founded in 2016 by Abdallah Faza and Kais Hassan in 2016, Arabot allows businesses to integrate and use its intelligent Arabic bot (that also speaks English) through their website, mobile app, or contact center platform to engage with customers. Arabot according to its website is built upon Arabic NLP engine which can understand and analyze Arabic content and conversation in different Arabic dialects. "We are building a conversation/dialogue management as a hybrid rule-based system fused with Deep Machine Learning to reach needed levels of Natural Language Understanding (NLU)," notes Arabot's website. The service that could be used by businesses mainly for customer service and promotion can be integrated in their website, mobile app, contact center platform, or messaging platform.

If everyone had the time and desire to go to college and get an AI degree, you very likely wouldn't be reading this blog. AI works in mysterious ways, but these five AI principles ought to help you avoid errors when dealing with this tech. More data leads to better models 3. An ounce of clean data is worth a pound of dirty data 4. Start with stupid baselines 5. AI isn't magic Brief caveat -- this post will make much more sense with a basic understanding of machine learning. We wrote a blog post a few weeks ago explaining those basics.

Scenario discovery is the process of finding areas of interest, commonly referred to as scenarios, in data spaces resulting from simulations. For instance, one might search for conditions - which are inputs of the simulation model - where the system under investigation is unstable. A commonly used algorithm for scenario discovery is PRIM. It yields scenarios in the form of hyper-rectangles which are human-comprehensible. When the simulation model has many inputs, and the simulations are computationally expensive, PRIM may not produce good results, given the affordable volume of data. So we propose a new procedure for scenario discovery - we train an intermediate statistical model which generalizes fast, and use it to label (a lot of) data for PRIM. We provide the statistical intuition behind our idea. Our experimental study shows that this method is much better than PRIM itself. Specifically, our method reduces the number of simulations runs necessary by 75% on average.