The Traveling Salesman Problem (TSP) is a classic mathematical problem in which one tries to find the shortest route that passes through a set of points. The TSP was first defined in the 1800s, it is regarded as difficult to solve and has intrigued mathematicians ever since.
With the advent of mega distribution centers, we may conclude that finding an optimal collection route positively impacts the speed of execution, as well as the aggregated cost of the merchandise.
To solve the TSP we must consider two facts:
a) The modern traveling salesman is IoT connected.
b) There is a never-ending stream of orders to fulfill.
As artificial intelligence advances, the goal for modern tech companies is to build AI software that thinks for itself without human intervention.
Towards that end, Amazon Web Services just picked MXNet, as its favored deep-learning framework to facilitate that work, according to a blog post Tuesday by Amazon chief technology officer Werner Vogels.
Group cohesion and consensus have primarily been studied in the context of discrete decisions, but some group tasks require making serial decisions that build on one another. We examine such collective problem solving by studying obstacle navigation during cooperative transport in ants. In cooperative transport, ants work together to move a large object back to their nest. We blocked cooperative transport groups of Paratrechina longicornis with obstacles of varying complexity, analyzing groups' trajectories to infer what kind of strategy the ants employed. Simple strategies require little information, but more challenging, robust strategies succeed with a wider range of obstacles. We found that transport groups use a stochastic strategy that leads to efficient navigation around simple obstacles, and still succeeds at difficult obstacles. While groups navigating obstacles preferentially move directly toward the nest, they change their behavior over time; the longer the ants are obstructed, the more likely they are to move away from the nest. This increases the chance of finding a path around the obstacle. Groups rapidly changed directions and rarely stalled during navigation, indicating that these ants maintain consensus even when the nest direction is blocked. Although some decisions were aided by the arrival of new ants, at many key points, direction changes were initiated within the group, with no apparent external cause. This ant species is highly effective at navigating complex environments, and implements a flexible strategy that works for both simple and more complex obstacles.
Spur Projects, an Australian organization focusing on suicide prevention, has published the data from its “How Is the World Feeling?” mental health survey. This survey gathered data about people’s emotional well-being, including emotions such as “happy,” “anxious,” and “powerful,” as well as demographic information about participants, such as employment status and sexual orientation. Spur Projects conducted the survey from October 10-16, 2016, and received responses from 10,144 people from 104 countries.
Most organizations have well established procedures for vetting and sharing computer code. But what about data analysis?
Important findings are often held in "a mixed bag of presentations, emails, and Google Docs," two members of Airbnb's engineering and data science team blogged at Medium in February. When someone in the organization wants to locate and use that existing work, they often have to track down updated code and waste time checking and reproducing earlier results. And then they'll typically distribute their own findings "through a presentation, email, or Google Doc, perpetuating the cycle."
ERP heads for the cloud On-premises ERP is destined for legacy status. How can IT ensure a smooth transition to cloud? READ NOW After considering various ideas on how to solve this problem, Airbnb created an internal Knowledge Repo, combining git version control and Markdown templates for reporting results. Airbnb recently open-sourced its Knowledge Repository Beta, seeking contributors to help move the project forward.
Git allows the same sort of peer review and version control that developers typically use to collaborate on code, while Markdown offers a mixture of text and code in a single, easily reproducible file. You can see RStudio's tutorial on R Markdown for more info of what Markdown in general can do. Markdown is available for other languages such as Python as well.
At Google I/O in 2016 there were two browser focused technologies from the company. These are the Polymer project and Angular 2. It might be a bit hard to make sense of why the company is investing in these two overlapping and competing projects.
Angular 2 is a compelete web framework that allows developers to build client side applications that run both on the server with Node.js as well as in the browser. It's a major revision to the wildly successful Angular 1.x and while making major changes internally, it's still the same product.
Polymer on the other hand is a project that aims to let developers use the latest native Web Platform features today. It's essentially an interim solution to provide a layer that will enable technologies that only exist today as specifications. In essence Polymer does not create any new features.
Pattern classification of human brain activity provides unique insight into the neural underpinnings of diverse mental states. These multivariate tools have recently been used within the field of affective neuroscience to classify distributed patterns of brain activation evoked during emotion induction procedures. Here we assess whether neural models developed to discriminate among distinct emotion categories exhibit predictive validity in the absence of exteroceptive emotional stimulation. In two experiments, we show that spontaneous fluctuations in human resting-state brain activity can be decoded into categories of experience delineating unique emotional states that exhibit spatiotemporal coherence, covary with individual differences in mood and personality traits, and predict on-line, self-reported feelings. These findings validate objective, brain-based models of emotion and show how emotional states dynamically emerge from the activity of separable neural systems.
The ability to modulate brain states using targeted stimulation is increasingly being employed to treat neurological disorders and to enhance human performance. Despite the growing interest in brain stimulation as a form of neuromodulation, much remains unknown about the network-level impact of these focal perturbations. To study the system wide impact of regional stimulation, we employ a data-driven computational model of nonlinear brain dynamics to systematically explore the effects of targeted stimulation. Validating predictions from network control theory, we uncover the relationship between regional controllability and the focal versus global impact of stimulation, and we relate these findings to differences in the underlying network architecture. Finally, by mapping brain regions to cognitive systems, we observe that the default mode system imparts large global change despite being highly constrained by structural connectivity. This work forms an important step towards the development of personalized stimulation protocols for medical treatment or performance enhancement.
The weight with which a specific outcome feature contributes to preference quantifies a person’s ‘taste’ for that feature. However, far from being fixed personality characteristics, tastes are plastic. They tend to align, for example, with those of others even if such conformity is not rewarded. We hypothesised that people can be uncertain about their tastes. Personal tastes are therefore uncertain beliefs. People can thus learn about them by considering evidence, such as the preferences of relevant others, and then performing Bayesian updating. If a person’s choice variability reflects uncertainty, as in random-preference models, then a signature of Bayesian updating is that the degree of taste change should correlate with that person’s choice variability. Temporal discounting coefficients are an important example of taste–for patience. These coefficients quantify impulsivity, have good psychometric properties and can change upon observing others’ choices. We examined discounting preferences in a novel, large community study of 14–24 year olds. We assessed discounting behaviour, including decision variability, before and after participants observed another person’s choices. We found good evidence for taste uncertainty and for Bayesian taste updating. First, participants displayed decision variability which was better accounted for by a random-taste than by a response-noise model. Second, apparent taste shifts were well described by a Bayesian model taking into account taste uncertainty and the relevance of social information. Our findings have important neuroscientific, clinical and developmental significance.
Collective behaviour in biological systems pitches us against theoretical challenges way beyond the borders of ordinary statistical physics. The lack of concepts like scaling and renormalization is particularly grievous, as it forces us to negotiate with scores of details whose relevance is often hard to assess. In an attempt to improve on this situation, we present here experimental evidence of the emergence of dynamic scaling laws in natural swarms. We find that spatio-temporal correlation functions in different swarms can be rescaled by using a single characteristic time, which grows with the correlation length with a dynamical critical exponent z~1. We run simulations of a model of self-propelled particles in its swarming phase and find z~2, suggesting that natural swarms belong to a novel dynamic universality class. This conclusion is strengthened by experimental evidence of non-exponential relaxation and paramagnetic spin-wave remnants, indicating that previously overlooked inertial effects are needed to describe swarm dynamics. The absence of a purely relaxational regime suggests that natural swarms are subject to a near-critical censorship of hydrodynamics.
Retailers looking to make smarter decisions should take inspiration from the Large Hadron Collider at Cern. Not convinced? That’s exactly what professor Michael Feindt, founder and chief scientific adviser at BlueYonder is trying to do.
"When we do predictions, how can we then form optimal decisions?" he asked the audience at WIRED Retail 2016. The answer, he explained, was to trust the data and then automate decision making. "A lot of decisions you make will never be automated. But I think in operational decisions in retail we can automate up to 99 per cent."
READ MORE When AR takes over we will all rent virtual diamonds and have AI personal shoppers at home
When AR takes over we will all rent virtual diamonds and have AI personal shoppers at home By LIAT CLARK What should a store order? At what price? What quantity? When? All of these decisions are repeated over time. "This is usually done by hand or gut feeling, but it can be done better," said Feindt. "These are decisions that are very regular and that we have a lot of historic data about."
Raise your hand if you’re running Adobe Analytics on your site. Okay, now keep your hands up if you also are running Google Analytics. Wow. Not very many hands went down there! There are lots of reasons that organizations find themselves running multiple web analytics platforms on their sites. Some are good. Many aren’t. Who’s to judge? …
The creaking of an opening gate followed by a dog attack can disturb otherwise pleasant evening walks. The sound of that gate opening on subsequent walks will elicit an emotional response, and the power of this response will be different if the dog was a German shepherd or a poodle. Through repeated experiences, the neighborhood, the gate and the dog all become part of the brain’s emotional memory system. The core of this system–the amygdala–forges indelible links of experience when we are attacked or threatened but, thanks to the power of expectation, the strength of these emotional memories is proportional to the unpleasantness of the experience.
“Forming an emotional memory is all about learning and calibrating our internal expectations with repeated external stimuli from the environment,” says Joshua Johansen, a team leader at the RIKEN Brain Science Institute. An instructive signal like a dog attack should startle you–and your amygdala–the first time it happens, but over time, both your brain activity and your behavior will temper the reaction to the dog attack once you learn to expect when and how it happens, for example on a particular street, outside of a particular house. In a study published in Nature Neuroscience, Johansen and colleagues discovered a neural circuit that can temper the strength of emotional memories by restraining the amygdala’s over-responsiveness to expected but unpleasant stimuli.
Decision making is hard. Decision making in a group is even harder. The vultures from Disney’s The Jungle Book come to mind. What we gonna do? I don’t know, whatcha wanna do? And so it goes.
Honey bees are an example of a superorganism. Not only do they work together to run their large and complex societies, they also work together to decide on a new home.
When honey bees decide it’s getting too cozy in their hive, half of the bees will leave with the old queen and swarm to an intermediate location. The remaining bees will stay home with a newly raised queen.
Web developer Jill Hubley has created a data visualization mapping the most common languages in New York City, broken down by census tract. Users can select individual languages to see where they are the most common, as well as filter out English and Spanish—the most common languages—to see the widely varied demographic makeup of the city’s population. The visualization uses data from the U.S. Census Bureau’s 2014 American Community Survey
Microsoft has made a major breakthrough in speech recognition, creating a technology that understands a conversation as well as a person does.
In a paper published Monday, a team of researchers and engineers in Microsoft Artificial Intelligence and Research reported a speech recognition system that makes the same or fewer errors than professional transcriptionists. The researchers reported a word error rate (WER) of 5.9 percent, down from the 6.3 percent WER the team reported just last month.
The 5.9 percent error rate is about equal to that of people who were asked to transcribe the same conversation, and it’s the lowest ever recorded against the industry standard Switchboard speech recognition task.
Traffic congestion varies spatially and temporally. The observation of the formation, propagation and dispersion of network traffic congestion can lead to insights about the network performance, the bottleneck dynamics etc. While many researchers use the traffic flow data to reconstruct the congestion profile, the data missing problem is bypassed. Current methods either omit the missing data or supplement the missing part by average etc. Great error may be introduced during these processes. Rather than simply discarding the missing data, this research regards the data missing event as a result of either the severe congestion which prevent the floating vehicle from entering the congested area, or a type of feature of the resulting traffic flow time series. Hence a new traffic flow operational index time series similarity measurement is expected to be established as a basis of identifying the dynamic network bottleneck. The method first measures the traffic flow operational similarity between pairs of neighboring links, and then the similarity results are used to cluster the spatial-temporal congestion. In order to get the similarity under missing data condition, the measurement is implemented in a two-stage manner: firstly the so called first order similarity is calculated given that the traffic flow variables are bounded both upside and downside; then the first order similarity is aggregated to generate the second order similarity as the output. We implement the method on part of the real-world road network; the results generated are not only consistent with empirical observation, but also provide useful insights.
Platforms are all the rage these days. Powered by online technologies, they are sweeping across the economic landscape, striking down companies large and small. Uber’s global assault on the taxi industry is well known. Many platforms, some household names and others laboring in obscurity, are doing the same in other sectors.
Surveying these changes, you might conclude that if your business isn’t a platform, you had better worry that one is coming your way. Everyone from automakers to plumbers should count their days as traditional businesses. And maybe you should jump on the platform bandwagon too. If it worked for Airbnb, why not you?
Based on our research into the wave of online platforms that have started in the last two decades, we don’t necessarily disagree. Traditional businesses should worry, and maybe they should think about platform strategies. But we think these conclusions are overwrought — and miss what’s really going on.
The mammalian neocortex has a repetitious, laminar structure and performs functions integral to higher cognitive processes, including sensory perception, memory, and coordinated motor output. What computations does this circuitry subserve that link these unique structural elements to their function? Potjans and Diesmann (2014) parameterized a four-layer, two cell type (i.e. excitatory and inhibitory) model of a cortical column with homogeneous populations and cell type dependent connection probabilities. We implement a version of their model using a displacement integro-partial differential equation (DiPDE) population density model. This approach, exact in the limit of large homogeneous populations, provides a fast numerical method to solve equations describing the full probability density distribution of neuronal membrane potentials. It lends itself to quickly analyzing the mean response properties of population-scale firing rate dynamics. We use this strategy to examine the input-output relationship of the Potjans and Diesmann cortical column model to understand its computational properties. When inputs are constrained to jointly and equally target excitatory and inhibitory neurons, we find a large linear regime where the effect of a multi-layer input signal can be reduced to a linear combination of component signals. One of these, a simple subtractive operation, can act as an error signal passed between hierarchical processing stages.
Influencing social change on a broad scale is a chronically difficult problem. But what if you could identify – and then target and train at exactly the right time – those members of a population most likely to have the greatest influence on their peers?
Sharing your scoops to your social media accounts is a must to distribute your curated content. Not only will it drive traffic and leads through your content, but it will help show your expertise with your followers.
How to integrate my topics' content to my website?
Integrating your curated content to your website or blog will allow you to increase your website visitors’ engagement, boost SEO and acquire new visitors. By redirecting your social media traffic to your website, Scoop.it will also help you generate more qualified traffic and leads from your curation work.
Distributing your curated content through a newsletter is a great way to nurture and engage your email subscribers will developing your traffic and visibility.
Creating engaging newsletters with your curated content is really easy.