Commodity Chemicals

How to use machine learning to identify "good" customers vs "bad" customers - BDO Canada - IT Solutions


Good profitable customers rarely become unprofitable. This method has become simplistic, as customers rarely consume overhead costs equally. ABC bases customer profitability analysis on the principle customers consume activities. A model can be built which calculates loss ratio based on Activity Based Costing.

Carbon Black warns that artificial intelligence is not a silver bullet


The research, which Carbon Black says looked "Beyond the Hype" found that the roles of AI and ML in preventing cyber-attacks have been met with both hope and skepticism. The vast majority (93 percent) of the 400 security researchers interviewed while conducting this research said non-malware attacks pose more of a business risk than commodity malware attacks, and more importantly that these are often not stopped by traditional anti-virus offerings. Mike Viscuso, co-founder and CTO of Carbon Black told SC Media UK: "Researchers have reported seeing an increase in the number, and sophistication, of non-malware attacks. These attacks are specifically designed to evade file-based prevention mechanisms and leverage native operating system tools to keep attackers under the radar." One respondent explained: "Most users seem to be familiar with the idea that their computer or network may have accidentally become infected with a virus, but rarely consider a person who is actually attacking them in a more proactive and targeted manner."

Artificial Intelligence: BASF partner with Nuritas on 'next gen' functional peptides


The first step of the partnership will see Nuritas, a biotech and R&D start-up that uses artificial intelligence and new technologies for the discovery of novel food and health ingredients, grant an exclusive royalty-based license to BASF to commercialise one of its existing peptides. A second part of the deal will focus on the discovery of new functional peptides, based on health areas that are strategically important to BASF, using Nuritas' technological expertise and AI platform. According to BASF, peptide networks of focus in the collaboration will be natural, food-derived, patented and of significant benefit to health – including peptides that bring about anti-inflammatory responses. "Cooperating with an innovative and agile start-up like Nuritas enables us to further expand our already broad portfolio of health solutions," commented Saori Dubourg, head of BASF's Nutrition & Health Business. Nuritas' unique platform combines DNA analysis and artificial intelligence (AI) to predict, unlock, and validate peptides from natural sources.

Headlines for the Next 50 Years : Plastics Technology

AITopics Original Links

As micro-molding gives way to "nano-molding," processors will need creative answers to the problems of handling flyspeck-sized parts. Farms may replace oil wells as the source of new plastics. Biopolymers made from cornstarch or other renewable feedstocks will supple-ment petrochemical-derived polymers in a wide range of applications. What if you could change the color of every part right at the machine? Instant color changes may be part of the coming era of "mass customization."

The Care and Feeding of Machine Learning - Carbon Black


Ingest incoming binaries: extract and compute features, statistics, and abstractions from incoming binaries. Binaries come from customers, partners, and trawls of the web for the diverse goodware and malware samples. The output of this task is a series of predictions about binaries' potential maliciousness and relationships to known malware families. Intelligence comes from our partners, our customers, and Carbon Black malware analysts.

Use of 3D Vision and Artificial Intelligence Predicted to Drive the Global Industrial Robotics Market in the Rubber and Plastic Industries Until 2020, Says Technavio


The industrial robotics market in the rubber and plastic industry for material handling application was valued USD 1.06 billion in 2015. The industrial robotics market in the rubber and plastic industry for assembling and the disassembling application was valued USD 480.9 million in 2015. The industrial robotics market in the rubber and plastic industry for dispensing and painting application was valued at USD 412.4 million in 2015. Manual gluing, painting, and adhesive-dispensing operations demand high precision and consistent quality.

These futuristic driverless pods will run on Singapore's roads by end of the year


The pods run on electricity, and are able to travel autonomously on smaller roads, such as those within a gated community or school campus. The pods look like they're going to be larger versions of the ones that already run in Abu Dhabi's cleantech business park, Masdar City -- also produced by 2getthere and SMRT back in 2010. The futuristic petrol car-free park has 10 electric pods, which seat between four and six passengers each, and the system marked its millionth passenger carried in 2014. In this video, you can see Masdar City's pods in operation, exiting their charging blocks and moving seamlessly to the next station.

Report 81 12 Stanford KSL

Classics (Collection 2)

Problems related to an inadequate data base of interpretation rules. The same set of production rules can suggest possible structural interpretations of 13C spectral features. Any individual 13C feature permits a great variety of st,:uctural interpretations. This paper presents an "expert system" devised to aid organic chemists in determining the structure (i.e. the arrangement of atoms and bonds) of newly isolated, naturally occurring compounds. The system exploits a data base of rules for analyzing.013

Accurate, fully-automated NMR spectral profiling for metabolomics Artificial Intelligence

Many diseases cause significant changes to the concentrations of small molecules (aka metabolites) that appear in a person's biofluids, which means such diseases can often be readily detected from a person's "metabolic profile". This information can be extracted from a biofluid's NMR spectrum. Today, this is often done manually by trained human experts, which means this process is relatively slow, expensive and error-prone. This paper presents a tool, Bayesil, that can quickly, accurately and autonomously produce a complex biofluid's (e.g., serum or CSF) metabolic profile from a 1D1H NMR spectrum. This requires first performing several spectral processing steps then matching the resulting spectrum against a reference compound library, which contains the "signatures" of each relevant metabolite. Many of these steps are novel algorithms and our matching step views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable metabolic profile. Our extensive studies on a diverse set of complex mixtures, show that Bayesil can autonomously find the concentration of all NMR-detectable metabolites accurately (~90% correct identification and ~10% quantification error), in <5minutes on a single CPU. These results demonstrate that Bayesil is the first fully-automatic publicly-accessible system that provides quantitative NMR spectral profiling effectively -- with an accuracy that meets or exceeds the performance of trained experts. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of NMR in clinical settings. Available at

Random forest models of the retention constants in the thin layer chromatography Artificial Intelligence

In the current study we examine an application of the machine learning methods to model the retention constants in the thin layer chromatography (TLC). This problem can be described with hundreds or even thousands of descriptors relevant to various molecular properties, most of them redundant and not relevant for the retention constant prediction. Hence we employed feature selection to significantly reduce the number of attributes. Additionally we have tested application of the bagging procedure to the feature selection. The random forest regression models were built using selected variables. The resulting models have better correlation with the experimental data than the reference models obtained with linear regression. The cross-validation confirms robustness of the models.