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.

Summary: Based on a McKinsey study we reported that 47% of companies had at least one AI/ML implementation in place. Looking back at the data and the dominance of RPA as the most widely reported instance makes us think that the number is probably significantly lower. We've been trying to get a handle on who has actually adopted AI/ML and to what extent. So we've been combing through these great new data sources from the good folks at McKinsey in their AI Adoption Survey, and Stanford's Human-Centered AI Institute 2018 AI Index which we used in our previous reporting. But one thing kept bothering me.

Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. Think of how efficiently (or not) Gmail detects spam emails, or how good text-to-speech has become with the rise of Siri, Alexa, and Google Home. 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.

Manually checking models for compliance against building regulation is a time-consuming task for architects and construction engineers. There is thus a need for algorithms that process information from construction projects and report non-compliant elements. Still automated code-compliance checking raises several obstacles. Building regulations are usually published as human readable texts and their content is often ambiguous or incomplete. Also, the vocabulary used for expressing such regulations is very different from the vocabularies used to express Building Information Models (BIM). Furthermore, the high level of details associated to BIM-contained geometries induces complex calculations. Finally, the level of complexity of the IFC standard also hinders the automation of IFC processing tasks. Model chart, formal rules and pre-processors approach allows translating construction regulations into semantic queries. We further demonstrate the usefulness of this approach through several use cases. We argue our approach is a step forward in bridging the gap between regulation texts and automated checking algorithms. Finally with the recent building ontology BOT recommended by the W3C Linked Building Data Community Group, we identify perspectives for standardizing and extending our approach.

The blockchain wave is gathering strength. More and more enterprises are embarking on concrete initiatives, either alone or in collaboration with their peers, their business partners, their clients, or their suppliers. Most of the use cases I've seen so far, apart from a couple of exceptions, are exploratory and mostly focus on using blockchain as a shared and trusted database of assets and transactions. The really interesting use cases that realize the exponential and disruptive benefit of the blockchain are yet to come. The most visionary players have revolutionary use cases in their roadmaps, but right now, enterprises are taking foundational steps necessary to pave the way for innovative use cases that might disrupt entire industries.