Systems & Languages


Top Graphical Models Applications in Real World

@machinelearnbot

Now we are going to explain the various Graphical Models Applications in real life such as – Manufacturing, finance, Steel Production, Handwriting Recognition etc. At last, we will discuss the case study about the use of Graphical Models in the Volkswagen. Making the production of low cost and most reliable components at a high rate is possible. Graphs, because they are pictures. They are particularly appropriate for presentation of financial information.


Robust distributed decision-making in robot swarms

Robohub

Reaching an optimal shared decision in a distributed way is a key aspect of many multi-agent and swarm robotic applications. As humans, we often have to come to some conclusions about the current state of the world so that we can make informed decisions and then act in a way that will achieve some desired state of the world. Of course, expecting every person to have perfect, up-to-date knowledge about the current state of the world is unrealistic, and so we often rely on the beliefs and experiences of others to inform our own beliefs. We see this too in nature, where honey bees must choose between a large number of potential nesting sites in order to select the best one. When a current hive grows too large, the majority of bees must choose a new site to relocate to via a process called "swarming" – a problem that can be generalised to choosing the best of a given number of choices.


Arm announces PSA security architecture for IoT devices

ZDNet

Arm has unveiled PSA, a new systems architecture designed to help secure and protect today's connected devices. The British semiconductor firm said on Monday ahead of TechCon 2017 that the new system, Platform Security Architecture (PSA), is intended to act as a common industry framework for developers, hardware, and silicon providers as a means to enhance the security of Internet of Things (IoT) devices built on system-on-a-chip (SoC) Arm Cortex processors. Last year, Arm and SoftBank Chairman Masayoshi Son predicted a trillion connected devices could be in play by 2035. These devices will require protection at not only the network but hardware level, to prevent them being used for more nefarious purposes such as in the case of the Mirai botnet. This is where PSA comes in, according to Arm.



Building--and scaling--a reliable distributed architecture

#artificialintelligence

I recently asked Joseph Breuer and Robert Reta, both Senior Software Engineers at Netflix, to discuss what they have learned through implementing a service at scale at Netflix. Joseph and Robert will be presenting a session on Event Sourcing at Global Scale at Netflix at O'Reilly Velocity Conference, taking place October 1-4 in New York. The primary challenge when operating a service in a distributed architecture at scale is managing for the behavior of your downstream dependencies. Continue reading Building--and scaling--a reliable distributed architecture.


To Code or Not to Code with KNIME

@machinelearnbot

Many modern data analysis environments allow for code-free creation of advanced analytics workflows. The advantages are obvious: more casual users, who cannot possibly stay on top of the complexity of working in a programming environment, are empowered to use existing workflows as templates and modify them to fit their needs, thus creating complex analytics protocols that they would never have been able to create in a programming environment. In some areas this may not be as dramatic, as the need for new ways of solving (parts of) problems isn't as critical anymore and a carefully designed visual environment may capture everything needed. The screenshot below shows how expert code written in those two languages can be integrated in a KNIME analytical workflow.


A Distributed AI Lab – AI Grant

#artificialintelligence

Together, we're excited to announce AI Grant 2.0! AI Grant 2.0 Fellows will receive some new treats, including: We've learned from the previous cohort that $2,500 will satisfy the needs of most projects. Our aspiration with AI Grant is to build a distributed AI lab. Stop reading, and click here to start the application.


A Simple Introduction To Data Structures: Part One – Linked Lists

#artificialintelligence

When many developers first realize how important data structures are (after trying to write a system that processes millions of records in seconds) they are often presented with books or articles that were written for people with computer science degrees from Stanford. The second field (the Pointer field) is storing the location in memory to the next node (memory location 2000). Hopefully, this was a quick and simple introduction to why data structures are important to learn and shed some light on when and why Linked List are an important starting point for data structures. If you can think of any better ways of explaining Linked Lists or why data structures are important to understand, leave them in the comments!


Cisco: Distributed AI Development Using Blockchain

#artificialintelligence

Jack Clark of OpenAI believes that this situation seems to benefit large-scale cloud providers like Amazon, Microsoft, and Google. This is also why our data center people are working with NVIDIA to add GPUs to our Unified Computing System (UCS) line (Dec 2016). The addition of GPUs makes it likely that each cloud/appliance will specialize around one or more particular frameworks to add value as well as services that play to each provider's strengths. And Google: TensorFlow integrated with ecosystem ML services.


A Knowledge Graph-based Semantic Database for Biomedical Sciences

@machinelearnbot

In short: BioGrakn is a graph-based semantic database that takes advantage of the power of knowledge graphs and machine reasoning to solve problems in the domain of biomedical science. We address the major issue of semantic integrity, that is, interpreting the real meaning of data derived from multiple sources or manipulated by various tools. We've discussed how BioGrakn takes advantage of the power of knowledge graphs and machine reasoning to solve problems in the domain of biomedical science. We address the major issue of semantic integrity, that is, interpreting the real meaning of data derived from multiple sources or manipulated by various tools.