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Is a rat smarter than Google? That's what two AI experts say

Is a rat smarter than Google? That's what two AI experts say | Social Foraging | Scoop.it

A rat is smarter than Google. And that's no dig at Google, according to artificial intelligence researchers Yann LeCun and Josh Tenenbaum. The two spoke at the World Science Festival in New York City after the premier of "The Creator: Alan Turing and the Future of Thinking Machines," a trippy arthouse film about 1940s and 1950s artificial intelligence visionary Alan Turing.

 

The galactic encyclopedia we know as Google is brilliant in many ways — for the amount of information it can absorb and shoot back in response to virtually any kind of question. Still, "It's rote learning; there's no understanding," said LeCun, a professor of computer and neural science at New York University.

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Dynamics of Social Interaction
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Stop Using the Excuse “Organizational Change Is Hard”

Stop Using the Excuse “Organizational Change Is Hard” | Social Foraging | Scoop.it
During nearly every discussion about organizational change, someone makes the obvious assertion that “change is hard.” On the surface, this is true: change requires effort. But the problem with this attitude, which permeates all levels of our organizations, is that it equates “hard” with “failure,” and, by doing so, it hobbles our change initiatives, which have higher success rates than we lead ourselves to believe.

Our biases toward failure is wired into our brains. In a recently published series of studies, University of Chicago researchers Ed O’Brien and Nadav Klein found that we assume that failure is a more likely outcome than success, and, as a result, we wrongly treat successful outcomes as flukes and bad results as irrefutable proof that change is difficult.
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WEST ELM LAUNCHES NEW AI TOOLS TO SCAN PINTEREST BOARDS TRANSFORMING CUSTOMER INSPIRATION INTO PRODUCTS FOR PURCHASE

WEST ELM LAUNCHES NEW AI TOOLS TO SCAN PINTEREST BOARDS TRANSFORMING CUSTOMER INSPIRATION INTO PRODUCTS FOR PURCHASE | Social Foraging | Scoop.it
West Elm, a purpose-driven furnishings retail brand, has introduced the West Elm Pinterest Style Finder, a new online tool utilizing artificial intell
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IBM Takes the Blockchain Revolution to the Next Level

IBM Brings encrypted blockchain to System Z.  Now both transaction and the content are secure.  
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Ocado trialling how Oxbotica's Autonomous Van Will Make Groceries Awesome

Oxbotica has teamed up with Ocado Technologies to trial autonomous grocery deliveries around the Greenwich area of London.
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Quantum-secured blockchain

Blockchain is a distributed database which is cryptographically protected against malicious modifications. While promising for a wide range of applications, current blockchain platforms rely on digital signatures, which are vulnerable to attacks by means of quantum computers. The same, albeit to a lesser extent, applies to cryptographic hash functions that are used in preparing new blocks, so parties with access to quantum computation would have unfair advantage in procuring mining rewards. Here we propose a possible solution to the quantum-era blockchain challenge and report an experimental realization of a quantum-safe blockchain platform that utilizes quantum key distribution across an urban fiber network for information-theoretically secure authentication. These results address important questions about realizability and scalability of quantum-safe blockchains for commercial and governmental applications.
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SHARE LAB: Mapping and Quantifying political information warfare

SHARE LAB: Mapping and Quantifying political information warfare | Social Foraging | Scoop.it
It is a battle for domination over the individual nodes (people) and their social graphs.

By instrumentalizing and conquering individual nodes, they are able to interfere and influence their social graph  (see: Human Data Banks and Algorithmic Labor, SHARE Labs 20161) consisted of their social circles, hundreds of friends, colleagues and relatives. This doctrine is about conquering information streams of others through proxies. Social network ecosystems are fertile ground for different form of disinformation or smear campaigns against opponents, or just a cheerleading activities, depending on the style of the political warfare. In such environment, political propaganda (spreading of ideas, information, or rumor for the purpose of helping or injuring an institution, a cause, or a person 2), can be executed through individual nodes that are anonymous or without visible, direct connection of their real-life identities to a political party.
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Brain interfaces open up a whole new way to get hacked

Brain interfaces open up a whole new way to get hacked | Social Foraging | Scoop.it
Malicious software could use brain interfaces to help steal passwords and other private data.
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RoboBees: Big Possibilities in Micro-robots, Including Programmable Bees

RoboBees: Big Possibilities in Micro-robots, Including Programmable Bees | Social Foraging | Scoop.it

Robots that fly. Robots you wear. Robots the size of nickels. These new classes of robots all have one thing in common—every aspect of them must be conceived and created from scratch. There are no designs, materials, manufacturing processes, or off-the-shelf components for them.

Electrical engineer Robert Wood's Microrobotics Lab at Harvard University is at the forefront of engineering such robots, which can fly lighter, slither through narrower spaces, and operate at smaller sizes than anything imagined before.

 

"Traditionally robots have been big, powerful, metallic objects that might weld doors onto cars in a factory," Wood says. "The robots we explore are dramatically different, some on a new, micro-sized scale, others made of soft rather than rigid materials."

 

The ways the robots might one day help humans are astonishing, he says, potentially transforming fields like medicine and agriculture.

 

Take RoboBees, colonies of autonomous flying micro-robots that Wood's team has been developing for years. He says that they could one day perform search-and-rescue expeditions, scout hazardous environments, gather scientific field data, even help pollinate crops. (Related "The Drones Come Home.") Like much of Wood's work, the RoboBees' design is "bio-inspired."

 

"If you want to make something a centimeter big that can fly, several hundred thousand solutions already exist in nature," he says. "We don't just copy nature. We try to understand the what, how, and why behind an organism's anatomy, movement, and behavior, and then translate that into engineering terms."

 

He and fellow researchers devised novel techniques to fabricate, assemble, and manufacture the miniature machines, each with a housefly-size thorax, three-centimeter (1.2-inch) wingspan, and weight of just 80 milligrams (.0028 ounces). The latest prototype rises on a thread-thin tether, flaps its wings 120 times a second, hovers, and flies along preprogrammed paths.

 

The manufacturing process is based on folding layered elements, an idea inspired by children's pop-up books. Now Wood's experiments are focused on finding a self-contained energy source that won't be too heavy and that can efficiently power the delicate bees.


Via Dr. Stefan Gruenwald
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Mapillary opens up 25k street-level images to train automotive AI systems

Mapillary opens up 25k street-level images to train automotive AI systems | Social Foraging | Scoop.it
As more companies wade into the business of building artificial intelligence systems to help you drive (or do the driving for you), a startup founded by an..
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AI Pathologist Zeroes in on Correct Cancer Diagnosis | NVIDIA Blog

AI Pathologist Zeroes in on Correct Cancer Diagnosis | NVIDIA Blog | Social Foraging | Scoop.it
An AI pathologist could lead to more accurate diagnoses and more effective treatments for breast cancer and prostate cancer. 
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Gaussian correlation inequality (GCI): A Long-Sought Proof, Found and Almost Lost

Gaussian correlation inequality (GCI): A Long-Sought Proof, Found and Almost Lost | Social Foraging | Scoop.it
Gaussian correlation inequality (GCI)
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How AI researchers built a neural network that learns to speak in just a few hours

How AI researchers built a neural network that learns to speak in just a few hours | Social Foraging | Scoop.it
The Chinese search giant’s Deep Voice system learns to talk in just a few hours with little or no human interference.
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Nestedness across biological scales

Nestedness across biological scales | Social Foraging | Scoop.it
Biological networks pervade nature. They describe systems throughout all levels of biological organization, from molecules regulating metabolism to species interactions that shape ecosystem dynamics. The network thinking revealed recurrent organizational patterns in complex biological systems, such as the formation of semi-independent groups of connected elements (modularity) and non-random distributions of interactions among elements. Other structural patterns, such as nestedness, have been primarily assessed in ecological networks formed by two non-overlapping sets of elements; information on its occurrence on other levels of organization is lacking. Nestedness occurs when interactions of less connected elements form proper subsets of the interactions of more connected elements. Only recently these properties began to be appreciated in one-mode networks (where all elements can interact) which describe a much wider variety of biological phenomena. Here, we compute nestedness in a diverse collection of one-mode networked systems from six different levels of biological organization depicting gene and protein interactions, complex phenotypes, animal societies, metapopulations, food webs and vertebrate metacommunities. Our findings suggest that nestedness emerge independently of interaction type or biological scale and reveal that disparate systems can share nested organization features characterized by inclusive subsets of interacting elements with decreasing connectedness. We primarily explore the implications of a nested structure for each of these studied systems, then theorize on how nested networks are assembled. We hypothesize that nestedness emerges across scales due to processes that, although system-dependent, may share a general compromise between two features: specificity (the number of interactions the elements of the system can have) and affinity (how these elements can be connected to each other). Our findings suggesting occurrence of nestedness throughout biological scales can stimulate the debate on how pervasive nestedness may be in nature, while the theoretical emergent principles can aid further research on commonalities of biological networks.

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AI suggests recipe for a dish just by studying a photo of it

AI suggests recipe for a dish just by studying a photo of it | Social Foraging | Scoop.it
An algorithm trained on over one million online recipes can tell you what's in a dish and how to make it
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Particle Raises $20 Million To Clean Up The Chaos Of 450 Separate IoT Platforms

Particle Raises $20 Million To Clean Up The Chaos Of 450 Separate IoT Platforms | Social Foraging | Scoop.it
IoT has a massive problem: too many platforms. This company just raised $20M to help solve that.
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Baidu’s Project Apollo Takes Flight, Bringing Autonomous Cars Closer to Reality

Baidu’s Project Apollo Takes Flight, Bringing Autonomous Cars Closer to Reality | Social Foraging | Scoop.it
Like its namesake, Baidu’s Project Apollo aims to redefine the possibilities for human travel. But instead of landing men on the moon like NASA’s version of the program,  vehicles in this initiative must learn to drive themselves. In April, Baidu announced Project Apollo — an open source platform for self-driving that includes hardware, software and …
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How We Save Face: Researchers Crack the Brain's Facial-Recognition Code

How We Save Face: Researchers Crack the Brain's Facial-Recognition Code | Social Foraging | Scoop.it
Our brains have evolved to recognize and remember faces. As infants, one of the first things we learn is to look at the faces of those around us, respond to eye contact and mimic facial expressions. As adults, this translates to an ability to recognize human faces better and faster than other visual stimuli. We’re able to instantly identify a friend’s face among dozens in a crowded restaurant or on a city street. And we can glean whether they’re excited or angry, happy or sad, from just a glance.
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Human Data Banks and Algorithmic Labour: Facebook Algorithmic Factory

Human Data Banks and Algorithmic Labour: Facebook Algorithmic Factory | Social Foraging | Scoop.it
This is the second story in our investigation trilogy titled Facebook Algorithmic Factory, created with the intention to map and visualise a complex and invisible exploitation process hidden behind a black box of the World’s largest social network.

The three stories are exploring four main segments of the process:

Data collection – Immaterial Labour and Data harvesting
Storage and Algorithmic processing – Human Data Banks and Algorithmic Labour
Targeting – Quantified lives on discount

The following map is one of the final results of our investigation, but it can also be used as a guide through our stories, and practically help the reader to remain in the right direction and not to get lost in the complex maze of the Facebook  Algorithmic Factory.
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SHARE LAB: Browsing Histories – Metadata Explorations

We are creatures of habits, and we tend to create repetitions and patterns in our everyday behaviour. We tend to go to bed and wake up at similar times, to create our morning routines and create rituals of our social interactions. Since many segments of our lives are mediated by technology, those patterns are replicated and visible through the different digital footprints. When patterns are recognised, anomaly detection is born. As stated by Pasquinelli8, the two epistemic poles of pattern and anomaly are the two sides of the same coin of algorithmic governance. An unexpected anomaly can be detected only against the ground of a pattern regularity.

Both pattern recognition and anomaly detection are used as methods for understanding the vast quantity of data, our digital footprints that are being collected by many actors, from government agencies around the globe, internet companies and service providers or data dealers.

Something recognised as an anomaly in the eye of the algorithm can put you on the watchlist of a government agency or some behavioral pattern can label you as a target for an online advertisement. In the case of Mr. J simple bar charts and heatmap based on the number of browsing actions in time can reveal few patterns of behaviour.
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Diffusion Geometry Unravels the Emergence of Functional Clusters in Collective Phenomena

Diffusion Geometry Unravels the Emergence of Functional Clusters in Collective Phenomena | Social Foraging | Scoop.it
Collective phenomena emerge from the interaction of natural or artificial units with a complex organization. The interplay between structural patterns and dynamics might induce functional clusters that, in general, are different from topological ones. In biological systems, like the human brain, the overall functionality is often favored by the interplay between connectivity and synchronization dynamics, with functional clusters that do not coincide with anatomical modules in most cases. In social, sociotechnical, and engineering systems, the quest for consensus favors the emergence of clusters. Despite the unquestionable evidence for mesoscale organization of many complex systems and the heterogeneity of their interconnectivity, a way to predict and identify the emergence of functional modules in collective phenomena continues to elude us. Here, we propose an approach based on random walk dynamics to define the diffusion distance between any pair of units in a networked system. Such a metric allows us to exploit the underlying diffusion geometry to provide a unifying framework for the intimate relationship between metastable synchronization, consensus, and random search dynamics in complex networks, pinpointing the functional mesoscale organization of synthetic and biological systems.
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Why Is 'Systems Thinking' So Rare?

Center for Collective Dynamics of Complex Systems (CoCo) Seminar Series April 27, 2017 Mark Sellers (Systems Science, Binghamton University / Northrop Grumman…

Via Hiroki Sayama
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Robots sorting system helps Chinese company finish at least 200,000 packages a day in the warehouse

Self-charging robots sorting system helps Chinese delivery company finish at least 200,000 packages a day in the warehouse Chinese delivery firm is moving to embrace automation. Orange robots at the company's sorting stations are able to identify the destination of a package through a code-scan, virtually eliminating sorting mistakes. The army of robots can sort up to 200,000 packages a day, and are self-charging, meaning they are operational 24/7. The company estimates its robotic sorting system is saving around 70-percent of the costs a human-based sorting line would require.

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Serendipity and strategy in rapid innovation

Innovation is to organizations what evolution is to organisms: it is how organisations adapt to changes in the environment and improve. Governments, institutions and firms that innovate are more likely to prosper and stand the test of time; those that fail to do so fall behind their competitors and succumb to market and environmental change. Yet despite steady advances in our understanding of evolution, what drives innovation remains elusive. On the one hand, organizations invest heavily in systematic strategies to drive innovation. On the other, historical analysis and individual experience suggest that serendipity plays a significant role in the discovery process. To unify these two perspectives, we analyzed the mathematics of innovation as a search process for viable designs across a universe of building blocks. We then tested our insights using historical data from language, gastronomy and technology. By measuring the number of makeable designs as we acquire more components, we observed that the relative usefulness of different components is not fixed, but cross each other over time. When these crossovers are unanticipated, they appear to be the result of serendipity. But when we can predict crossovers ahead of time, they offer an opportunity to strategically increase the growth of our product space. Thus we find that the serendipitous and strategic visions of innovation can be viewed as different manifestations of the same thing: the changing importance of component building blocks over time.
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Toward negotiable reinforcement learning: shifting priorities in Pareto optimal sequential decision-making

Existing multi-objective reinforcement learning (MORL) algorithms do not account for objectives that arise from players with differing beliefs. Concretely, consider two players with different beliefs and utility functions who may cooperate to build a machine that takes actions on their behalf. A representation is needed for how much the machine's policy will prioritize each player's interests over time. Assuming the players have reached common knowledge of their situation, this paper derives a recursion that any Pareto optimal policy must satisfy. Two qualitative observations can be made from the recursion: the machine must (1) use each player's own beliefs in evaluating how well an action will serve that player's utility function, and (2) shift the relative priority it assigns to each player's expected utilities over time, by a factor proportional to how well that player's beliefs predict the machine's inputs. Observation (2) represents a substantial divergence from na\"{i}ve linear utility aggregation (as in Harsanyi's utilitarian theorem, and existing MORL algorithms), which is shown here to be inadequate for Pareto optimal sequential decision-making on behalf of players with different beliefs.
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Insect Bio-inspired Neural Network Provides New Evidence on How Simple Feature Detectors Can Enable Complex Visual Generalization and Stimulus Location Invariance in the Miniature Brain of Honeybees

Insect Bio-inspired Neural Network Provides New Evidence on How Simple Feature Detectors Can Enable Complex Visual Generalization and Stimulus Location Invariance in the Miniature Brain of Honeybees | Social Foraging | Scoop.it
Honeybees (Apis mellifera) display an impressive visual behavioural repertoire as well as astounding learning capabilities. Foragers rely on visual and olfactory cues identifying rewarding flowers. Being able to recognise informative cues displayed by flowers can be assumed to facilitate fast and efficient decision-making. Indeed, honeybees can be trained to discriminate by an impressive range of visual cues; symmetry [1–3], arrangements of edges [4–6], size [7, 8], pattern disruption [9] and edge orientation [10–12]. These abilities are all the more impressive since trained bees are able to apply these same learnt cues to patterns which may have little or no resemblance to the original training patterns, so long as they fall into the same class of e.g. plane of symmetry, or edge orientation.

This rich visual behaviour despite a relatively tiny brain makes honeybees an ideal model species to explore how visual stimuli are processed and to determine if generalization requires a complex neuronal architecture. Using the published intracellular recordings of large-field optic ganglia neurons to achromatic stimuli [13, 14] and the known anatomical morphologies of mushroom body (learning centres) class II ‘clawed’ Kenyon cells [15] we designed two simple, but biologically inspired models. These models were not created, or indeed in any way ‘tweaked’ to replicate performance at any particular visual task. Instead they attempt to explore how well, or poorly, the known neuronal types within the bee brain could solve real behaviourally relevant problems and how much neuronal complexity would be required to do so. The initial models presented here were therefore kept very basic with limited neuronal pathways and very simple synaptic connections from the optic lobes to the mushroom bodies. In addition, to comprehend how these optic lobe neuron responses alone may explain the bees’ discrimination abilities and behavioural performance, we did not employ any form of learning in these models. Since two of the optic ganglia (medulla and lobula) of bees extend a variety of axonal fibres to both the ipsilateral and the contralateral mushroom bodies and, as opposed to axons from different regions of the optic lobes that are distinctly layered within the mushroom bodes, there is no apparent segregation of the visual inputs from the individual corresponding left and right eye regions [16, 17], we tested the discrimination and generalization performance difference between retaining independent inputs from each eye and combining the neuronal input from both eyes within our simulated mushroom body models. These models allowed us to simulate achromatic pattern experiments and compare the simulation performances of our two different bee-brain models—henceforth called ‘simulated bees’, to the performance of actual honeybees in these same specific experiments.
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