text:
How do systemic approaches to IT operations impact the business culture?
How Is Data Affecting Your Dating Life?
Have we lost the cancer battle? No! – say Big Data and Machine Learning
Where does Europe stand in the development of AI?
German radiologists are eager to co-create the future of AI in their field.
Here is a look at where Fintech is leading us and Why
What are the SportsTech opportunities in Europe?
How to attract and retain the important, but elusive, data scientist
Data Natives 2019
Data Science 101
It's All Data
Candidate Database
Artificial IntelligenceData ScienceMachine Learning
How Blockchains could transform Artificial Intelligence
Trent McConaghy · December 21, 2016
3	12 31.5k 17
Trent McConaghy 2016-12-21
From Decentralized Model Exchanges to Model Audit Trails
[This is based on a talk I first gave on Nov 7, 2016. Here are the slides.]
In recent years, AI (artificial intelligence) researchers have finally cracked problems that they've worked on for decades, from Go to human-level speech recognition. A key piece was the ability to gather and learn on mountains of data, which pulled error rates past the success line.
In short, big data has transformed AI, to an almost unreasonable level.
Blockchain technology could transform AI too, in its own particular ways. Some applications of blockchains to AI are mundane, like audit trails on AI models. Some appear almost unreasonable, like AI that can own itself — AI DAOs. All of them are opportunities. This article will explore these applications.
Blockchains as Blue Ocean Databases
Before we discuss applications, let's first review what's different about blockchains compared to traditional big-data distributed databases like MongoDB.
We can think of blockchains as "blue ocean"databases: they escape the "bloody red ocean" of sharks competing in an existing market, opting instead to be in a blue ocean of uncontested market space. Famous blue ocean examples are Wii for video game consoles (compromise raw performance, but have new mode of interaction), or Yellow Tail for wines (ignore the pretentious specs for wine lovers; make wine more accessible to beer lovers).
By traditional database standards, traditional blockchains like Bitcoin are terrible: low throughput, low capacity, high latency, poor query support, and so on. But in blue-ocean thinking, that's ok, because blockchains introduced three new characteristics: centralized / shared control, immutable / audit trails, and native assets / exchanges. People inspired by Bitcoin were happy to overlook the traditional database-centric shortcomings, because these new benefits had potential to impact industries and society at large in wholly new ways.
These three new "blockchain" database characteristics are also potentially interesting for AI applications. But most real-world AI works on large volumes of data, such as training on large datasets or high-throughput stream processing. So for applications of blockchain to AI, you need blockchain technology with big-data scalability and querying. Emerging technologies like BigchainDB, and its public network IPDB do exactly that. You no longer need to compromise on the the benefits of traditional big-data databases in order to have the benefits of blockchains.
Overview of Blockchains for AI
Having blockchain tech that scales unlocks its potential for AI applications. Let's now explore what those might be, by starting with the three blockchain benefits.
These blockchain benefits lead to the following opportunities for AI practitioners:
Decentralized / shared control encourages data sharing:
(1) Leads to more data, and therefore better models.
(2) Leads to qualitatively new data, and therefore qualitatively new models.
(3) Allows for shared control of AI training data & models.
Immutability / audit trail:
(4) Leads to provenance on training/testing data & models, to improve the trustworthiness of the data & models. Data wants reputation too.
Native assets / exchanges:
(5) Leads to training/testing data & models as intellectual property (IP) assets, which leads to decentralized data & model exchanges. It also gives better control for upstream usage of your data.
There's one more opportunity: (6) AI with blockchains unlock the possibility for AI DAOs (Decentralized Autonomous Organizations). These are AIs that can accumulate wealth, that you can't shut off. They're Software-as-a-Service on steroids.
There are almost surely more ways that blockchains can help AI. Also, there are many ways that AI can help blockchains, such as mining blockchain data (e.g. Silk Road investigation). That's for another discussion:)
Many of these opportunities are about AI's special relationship with data. So let's first explore that. Following this, we'll explore the applications of blockchains for AI in more detail.
AI & Data
Here, I will describe how much of modern AI leverages copious quantities of data for its impressive results. (This isn't always the case, but it is a common theme worth describing.)
"Ancient" History of AI & Data
When I started doing AI research in the 90s, a typical approach was:
Here's your fixed dataset (typically small).
Design an algorithm to improve performance, for example design a new kernel for a support vector machine classifier that reduces area-under-curve (AUC).
Publish that algorithm in a conference or journal. Ten percent relative improvement is about the "minimum publishable unit", as long as your algorithm itself was fancy enough. If you got a 2x-10x, you're looking at best-paper territory, especially if the algorithm was really fancy.
If this sounds academic, that's because it was. Most AI work was still in academia, though there were real-world applications too. In my experience, it was like this in many sub-fields of AI, including neural networks, fuzzy systems (remember those?), evolutionary computation, and even slightly less AI-ish techniques like nonlinear programming or convex optimization.
In my first published paper (1997), I proudly showed how my freshly-invented algorithm had the best results compared to state-of-the-art neural networks, genetic programming, and more — on a small fixed dataset. Oops.
Towards Modern AI & Data
But, the world shifted. In 2001, Microsoft researchers Banko and Brill released a paper with remarkable results. First, they described how most work in their domain of natural language processing was on less than a million words — small datasets. Error rates were 25% for the old / boring / least fancy algorithms like Naive Bayes and Perceptrons, whereas fancy newer memory-based algorithms achieved 19% error. That's the four datapoints on the far left of the plot below.
From Banko and Brill, 2001
So far, no surprises. But then Banko and Brill showed something remarkable: as you added more data — not just a bit more data but orders of magnitude more data — and kept the algorithms the same, then the error rates kept going down, by a lot. By the time the datasets were three orders of magnitude larger, error was less than 5%. In many domains, there's a world of difference between 18% and 5%, because only the latter is good enough for real-world application.
Moreover, the best-performing algorithms were the simplest; and the worst algorithm was the fanciest. Boring old perceptrons from the 1950s were beating state-of-the-art techniques.
Modern AI & Data
Banko and Brill weren't alone. For example, in 2007, Google researchers Halevy, Norvig and Pereira of Google published a paper showing how data could be "unreasonably effective" across many AI domains.
Screenshot of Halevy, Norvig, and Pereira, 2007
This hit the AI field like an atom bomb.
"It's the data, silly."
The race was on to gather way more data. It takes significant effort to get mountains of good data. If you have the resources, you can get data. Sometimes you can even lock up data. In this new world, data is a moat, and AI algorithms a commodity. For these reasons, "more data" is a key imperative for Google, Facebook, and many others.
"Mo' data, mo' money." — Everyone
Once you understand these dynamics, specific actions have simple explanations. Google doesn't buy satellite imaging companies simply because it likes space; and Google gives away TensorFlow.
Deep learning directly fits in this context: it's the result of figuring out how, if given a massive enough dataset, to start to capture interactions and latent variables. Interestingly, backprop neural networks from the '80s are sometimes competitive with the latest techniques, if given the same massive datasets. See here. It's the data, silly.
My own coming-of-age as an AI researcher was similar. As I was attacking real-world problems, I learned how to swallow my pride, abandon the "cool" algorithms, build only as much was needed to solve the problem at hand, and learned to love the data and the scale. It happened in my first company, ADA(1998–2004), as we pivoted from automated creative design to "boring" parameter optimization; which incidentally became less boring in a hurry as our users asked us to go from 10 variables to 1000. It happened in my second company, Solido (2004-present), as well, as we pivoted from fancier modeling approaches to super-simple but radically scalable ML algorithms like FFX; and once again was un-boring as our users pulled us from 100 variables to 100,000, and from 100 million Monte Carlo samples to 10 trillion (effective samples). Even BigchainDB, the product of my third and current company, emerged from the need for scale (2013-present). Zoom in on features, zoom up on scale.
Opportunity 1: Data Sharing → Better Models
In short: decentralized / shared control encourages data sharing, which in turns lead to better models, which in turns leads to higher profit / lower cost / etc. Let's elaborate.
Left image (silos): Doc Searls, CC-BY-2.0. Right image (silo): Gnangarra, CC-BY 3.0
AI loves data. The more data, the better the models. Yet data is often siloed, especially in this new world where data can be a moat.
But blockchains encourage data sharing among traditional silos, if there is enough up-front benefit. The decentralized nature of blockchains encourages data sharing: it's less friction to share if no single entity controls the infrastructure where the data is being stored. I give more benefits later on.
This data sharing might happen within an enterprise (e.g. among regional offices), within an ecosystem (e.g. for a "consortium" database), or across the planet (e.g. for a shared planetary database, a.k.a. public blockchain). Here's an example for each:
Within-enterprise: data from different regional offices is merged using a blockchain technology, because it lowers the cost for the enterprise to audit their own data, and to share that data with auditors. With that new data in place, the enterprise can build AI models that, for example, predict customer churn better than their previous models which were only built at the level of regional office. A "data mart" for each regional office?
Within-ecosystem: competitors (say, banks or music labels) traditionally would never share their data. But it would be straightforward to show how, with combined data from several banks, one could make better models for, credit card fraud prevention. Or for organizations along a supply chain, that share data via a blockchain, one could better identify root causes of failures later in the supply chain, using AI on data from earlier in the supply chain. For example, where exactly did that strain of E. coli emerge?
Within-planet (public blockchain database): Consider the sharing of data among different ecosystems (e.g. energy usage data + auto parts supply chain data); or of individual participants in a planet-scale ecosystem (e.g. the Web). More data from more sources could improve the models. For example, spikes in energy usage in some factories in China might be correlated with fraudulent auto parts emerging on the market a day's worth of shipping later. Overall, we're seeing signs of this with companies that aggregate data, sanitize it, and repackage and sell it; from good old Bloomberg terminals to the dozens (or hundreds) of startups selling data through http APIs. I explore this further in a later point.
Enemies sharing their data to feed an AI. 2016 is fun!
Opportunity 2: Data Sharing → Qualitatively New Models
In some cases, when data from silos is merged, you don't just get a better dataset, you get a qualitatively new dataset. Which leads to a qualitatively new model, from which you can glean new insights and have new business applications. That is, you can that do something you couldn't do before.
Here's an example, for identifying diamond fraud. If you're a bank providing diamond insurance, you'd like to create a classifier that identifies whether a diamond is fraudulent. There are four trusted diamond certification labs on the planet (depending who you ask, of course:). If you only have access to the diamond data for one of these labs, then you're blind about the other three houses, and your classifier could easily flag one of those other houses' diamonds as fraud (see picture below, left). Your false positive rate would make your system unusable.
Consider instead if blockchains catalyze all four certification labs to share their data. You'd have all the legitimate data, from which you would build a classifier (below, right). Any incoming diamond, for example seen on eBay, would be run through the system and be compared to this all-data one-class classifier. The classifier can detect legitimate fraud and avoid false positives, therefore lowering the fraud rate, to benefit of insurance providers and certification labs. This could be simply framed as a lookup, i.e. not needing AI. But using AI improves it further, for example by predicting price based on color, carats, etc. then using "how close is price to expected value" as an input to the main fraud classifier.
Top left image (silos): Doc Searls, CC-BY-2.0. Top right image (silo): Gnangarra, CC-BY 3.0
Here's a second example. An appropriate token-incentive scheme in a decentralized system could incentivize datasets to get labeled that could not be previously labeled, or labeled in a cost-effective fashion. This would be basically a decentralized Mechanical Turk. With new labels we get new datasets; we train on the new datasets to get new models.
Here's a third example. A token-incentive scheme could lead to direct data input from IoT devices. The devices control the data and can exchange it for assets, such as energy. Once again, this new data can lead to new models (Thanks to Dimi de Jonghe for these last two examples.)
Hoard vs. share? There's a tension between two opposite motivations here. One is to hoard data — the "data is the new moat" perspective; the other is to share data, for better/new models. To share, there must be a sufficient driver that outweighs the "moat" benefit. The technology driver is better models or new models, but this driver must lead to business benefit. Possible benefits include reduced fraud for insurance savings in diamonds or supply chains; making money on the side in Mechanical Turk; data/model exchanges; or collective action against a powerful central player, like the music labels working together against Apple iTunes. There are more; it requires creative business design..
Centralized vs. decentralized? Even if some organizations decide to share, they could share without needing blockchain technology. For example, they could simply pool it into an S3 instance and expose the API among themselves. But in some cases, decentralized gives new benefits. First is the literal sharing of infrastructure, so that one organization in the sharing consortium doesn't control all the "shared data" by themselves. (This was a key stumbling block a few years back when the music labels tried to work together for a common registry.) Another benefit is that it's easier to turn the data & models into assets, which can then be licensed externally for profit. I elaborate on this below. (Thanks to Adam Drake for drawing extra attention to the hoard-vs-share tension.)
As discussed, data & model sharing can happen at three levels: within an enterprise (which for multinationals is harder than you might think); within an ecosystem or consortium; or within the planet (which amounts to becoming a public utility). Let's explore planet-scale sharing more deeply.
Opportunity 2a: New planet-level data → new planet-level insights
Planetary-level data sharing is potentially the most interesting level. Let's drill further into this one.
IPDB is structured data on a global scale, rather than piecemeal. Think of the World Wide Web as a file system on top of the internet; IPDB is its database counterpart. (I think the reason we didn't see more work on this sooner is that semantic web work tried to go there, from the angle of upgrading a file system. But it's pretty hard to build a database by "upgrading" a file system! It's more effective to say from the start that you're building a database, and designing as such.) "Global variable" gets interpreted a bit more literally:)
So, what does it look like when we have data sharing with a planet-scale shared database service like IPDB? We have a couple points of reference.
The first point of reference is that there's already a billion-dollar market (recently), for companies to curate and repackage public data, to make it more consumable. From simple APIs for the weather or network time, to financial data like stocks and currencies. Imagine if all this data was accessible through a single database in a similar structured fashion (even if it's just a pass through of the API). Bloomberg x 1000. Without worrying that there was a single choke point controlled by a single entity.
The second point of reference comes from the blockchain, in the concept of "oraclizing" outside data to make it consumable by a blockchain. But we can oraclize it all. Decentralized Bloomberg is just the start.
Overall, we get a whole new scale for diversity of datasets and data feeds. Therefore, we have qualitatively new data. Planetary level structured data. From that, we can build qualitatively new models, that make relations which among inputs & outputs which weren't connected before. With the models and from the models, we will get qualitatively new insights.
I wish I could be more specific here, but at this point it's so new that I can't think of any examples. But, they will emerge!
There's also a bot angle. We've been assuming that the main consumers of blockchain APIs will be humans. But what if it's machines? David Holtzman, creator of the modern DNS, said recently "IPDB is kibbles for AI". Unpacking this, it's because IPDB enables and encourages planet-level data sharing, and AI really loves to eat data.
Image: Isabelle Blanchemain, CC-BY 2.0
Opportunity 3: Audit trails on data & models for more trustworthy predictions
This application addresses the fact that if you train on garbage data, then you'll get a garbage model. Same thing for testing data. Garbage in, garbage out.
Garbage could come from malicious actors / Byzantine faults who may be tampering with the data. Think Volkswagen emissions scandal. Garbage may also come from non-malicious actors / crash faults, for example from defective IoT sensor, a data feed going down, or environmental radiation causing a bit flip (sans good error correction).
How do you know that the X/y training data doesn't have flaws? What about live usage, running the model against live input data? What about the model predictions (yhat)? In short: what's the story of the data, to and from the model? Data wants reputation too.
Image (etching): Irakli Chumburidze, CC-BY-SA 2.0
Blockchain technology can help. Here's how. At each step of the process to build models, and to run models in the field, the creator of that data can simply time-stamp that model to the blockchain database, which includes digitally signing it as a claim of "I believe this data / model to be good at this point". Let's flesh this out even more…
Provenance in building models:
Provenance on sensor data (including IoT). Do you trust what your IoT sensor is telling you?
Provenance on training input/output (X/y) data.
Provenance on model building itself, if you like, via trusted executioninfrastructure, or TrueBit-style markets that double-check computation. At the very least, have evidence of model-building with the model-building convergence curve (e.g. nmse vs. epoch).
Provenance on the models themselves.
Provenance in testing / in the field:
Provenance on testing input (X) data.
Provenance on model simulation. Trusted execution, TrueBit etc.
Provenance on testing output (yhat) data.
We get provenance in both building the models, and applying them. The result is more trusted AI training data & models.
And we can have chains of this. Models of models, just like in semiconductor circuit design. Models all the way down. Now, it all has provenance.
Catch leaks in data supply chain (in the broadest sense), at all the levels. For example, you can tell if a sensor is lying.
You know the story of the data and model, in a cryptographically verifiable fashion.
You can catch leaks in the data supply chain. That way, if an error happens, we'll have a much better idea of how and where. You can think of it as banking-style reconciliation, but for AI models.
Data gets a reputation, because multiple eyes can check the same source, and even assert their own claims on how valid they believe the data to be. And, like data, models get reputations too.
Opportunity 4: Shared global registry of training data & models
A specific challenge in the AI community is: where are the datasets? Traditionally, they have been scattered throughout the web, though there are some lists here and there pointing to main datasets. And of course many of the datasets are proprietary, precisely because they have value. The data moat, remember?
But, what if we had a global database that made it easy to manage another dataset or data feed (free or otherwise)? This could include the broad set of Kaggle datasets from its various ML competitions, the Stanford ImageNetdataset, and countless others.
That's exactly what IPDB could do. People could submit datasets, and use others' data. The data itself would be in a decentralized file system like IPFS; and the meta-data (and pointer to the data itself) would be in IPDB. We'd get a global commons for AI datasets. This helps to realize the dream of the open data community.
We don't need to stop at the datasets; we can include the models built from those datasets too. It should be easy to grab and run others' models, and submit your own. A global database can greatly facilitate this. We can get models that are owned by the planet.
Opportunity 5: Data & models as IP assets →data & model exchange
Let's build on the application of "shared global registry" of training data and models. Data & models can be part of the public commons. But they can also be bought & sold!
Data and AI models can be used as an intellectual property (IP) asset, because they are covered by copyright law. Which means:
If you have created the data or model, you can claim copyright. This is whether or not you want do do anything commercially with it.
If you have copyright of data or model, then you can license usage rights to others. For example, you can license your data to someone else to build their own model. Or, you could license your model for someone to include in their mobile application. Sub-licensing, sub-sub-licensing, etc is possible too. Of course, you can license data or models from others too.
I think it's pretty awesome that you can claim copyright of an AI model, and license it. Data is already recognized as a potentially huge market; models will follow suit.
Claiming copyright of and licensing data & models was possible before blockchain technology. The laws have served this for a while. But blockchain technology makes it better, because:
For your claim of copyright, it offers a tamper-resistant global public registry; where your claim is digitally / cryptographically signed by you. This registry can include data & models too.
For your licensing transaction, it once again offers a tamper-resistant global public registry. This time, it's not just digitally signed; rather, you cannot even transfer the rights unless you have the private key. The rights transfer occurs as a blockchain-style transfer of assets.
IP on the blockchain is near and dear to my heart, with my work on ascribe going back to 2013 to help digital artists get compensated. The initial approach had issues with scale and flexibility of licensing. Now, these have been overcome, as I recently wrote about. The technology that makes this possible includes:
Coala IP is a flexible, blockchain-friendly protocol for IP.
IPDB (with BigchainDB) is a shared public blockchain database to store rights information & other metadata at Web scale.
IPFS plus physical storage like Storj or FileCoin is a decentralized file system to store the large data & model blobs
With this, we get data and models as IP assets.
To illustrate, using ascribe, I claimed copyright of an AI model that I'd made years ago. The AI model is a CART (decision tree) for deciding which analog circuit topology to use. Here. Here's its cryptographic Certificate of Authenticity (COA). If you'd like to license an edition from me, just email me :)
Once we have data and models as assets, we can start to make exchanges for those assets.
An exchange could be centralized, like DatastreamX already does for data. But so far, they are really only able to use publicly-available data sources, since many businesses see more risk than reward from sharing.
What about a decentralized data & model exchange? By decentralizing data sharing in an "exchange" context, new benefits arise. In being decentralized, no single entity controls the data storage infrastructure or the ledger of who-owns-what, which makes it easier for organizations to work together or share data, as described earlier in this essay. Think OpenBazaar, for Deep Nets.
With such a decentralized exchange, we'll see the emergence of a truly open data market. This realizes a long-standing dream among data and AI folks, including yours truly:)
And of course we'll have AI-based algorithmic trading on those exchanges: AI algorithms to buy AI models. The AI trading algorithms could even be buying algorithmic trading AI models, then updating themselves!
Opportunity 5a: Control the Upstream of Your Data & Models
This riffs on the previous application.
When you sign on to use Facebook, you're granting Facebook very specific rights about what they can and can't do with any data that you enter into their system. It's licenses on your personal data.
When a musician signs with a label, they're granting the label very specific rights, to edit the music, to distribute it, and so on. (Usually the label tries to grab all of copyright, which is super onerous but that's another story!)
It can be the same thing for AI data, and for AI models. When you create data that can be used for model-building, and when you create models themselves, you can pre-specify licenses that restricts how others use them upstream.
Blockchain technology makes this easy, for all the use cases, from personal data to music, from AI data to AI models. In the blockchain database, you treat permissions as assets where for example a read permissions or the right to view a particular slice of data or model. You as the rights holder can transfer these permissions-as-assets to others in the system, similar to how you transfer Bitcoin: create the transfer transaction and sign it with your private key. (Thanks to Dimitri de Jonghe for this.)
With this, you have far better control for the upstream of your AI training data, your AI models, and more. For example, "you can remix this data but you can't deep-learn it."
This is likely part of DeepMind's strategy in their healthcare blockchain project. In data mining healthcare data puts them at risk of regulation and antitrust issues (especially in Europe). But if users can instead truly own their medical data and control its upstream usage, then DeepMind can simply tell consumers and regulators "hey, the customer actually owns their own data, we just use it". My friend Lawrence Lundy provided this excellent example (thanks Lawrence!). He then extrapolated further:
It's entirely possible that the only way governments will allow private ownership (human or AGI) of data is with a shared data infrastructure with "network neutrality" rules, as with AT&T and the original long lines. In that sense, increasingly autonomous AI requires blockchains and other shared data infrastructure to be acceptable to the government, and therefore to be sustainable in the long term. -Lawrence Lundy
Opportunity 6: AI DAOs — AI that can accumulate wealth, that you can't turn off
This one's a doozy. An AI DAO is AI that owns itself, that you can't turn off. I've previously discussed AI DAOs in three posts (I, II, III); I'll summarize the "how" below. I encourage the interested reader to dive deeper.
So far, we've talked about blockchains as decentralized databases. But we can decentralize processing too: basically, store state of a state machine. Have a bit of infrastructure around this to make it easier to do, and that's the essence of "smart contracts" technologies like Ethereum.
We've had decentralized processes before, in the form of computer viruses. No single entity owns or controls them, and you can't turn them off. But they had limits — they basically try to break your computer, and that's about all.
But what if you could have richer interactions with the process, and the process itself could accumulate wealth on its own? That's now possible via better APIs to the process such as smart contracts languages, and decentralized stores of value such as public blockchains.
A Decentralized Autonomous Organization (DAO) is a process that manifests these characteristics. It's code that can own stuff.
Which brings us to AI. The AI sub-field called "Artificial General Intelligence" (AGI) is most relevant. AGI is about autonomous agents interacting in an environment. AGI can be modeled as a feedback control system. This is great news, because control systems have many great qualities. First, they have strong mathematical foundations going back to the 1950s (Wiener's "Cybernetics"). They capture the interaction with the world (actuating and sensing), and adapting (updating state based on internal model and external sensors). Control systems are widely used. They govern how a simple thermostat adapts to a target temperature. They cancel noise in your expensive headphones. They're at the heart of thousands of other devices from ovens to the brakes in your car.
The AI community has recently embraced control systems more strongly. For example, they were key to AlphaGo. And, AGI agents themselves are control systems.
An AI DAO is an AGI-style control system running on a decentralized processing & storage substrate. Its feedback loop continues on its own, taking inputs, updating its state, actuating outputs, with the resources to do so continually.
We can get an AI DAO by starting with an AI (an AGI agent), and making it decentralized. Or, we can start with a DAO and give it AI decision-making abilities.
AI gets its missing link: resources. DAO gets its missing link: autonomous decision-making. Because of this, AI DAOs could be way bigger than AIs on their own, or DAOs on their own. The potential impact is multiplicative.
Here are some applications:
An ArtDAO, which creates its own digital art and sells it. To generalize, it could do 3D designs, music, videos, and even entire films.
Self-driving, self-owning cars. To generalize, any previous application of AI, but now the AI "owns itself". We might have a future where humans own nothing, we're just renting services from AI DAOs.
Any DAO app, with AI infused.
Any SaaS app, with more automation, and decentralized.
More in AI DAOs Part II piece. Some of these are, well, scary…
This essay has described how blockchain technology can help AI, by drawing on my personal experiences in both AI and blockchain research. The combination is explosive! Blockchain technologies — especially planet-scale ones — can help realize some long-standing dreams of AI and data folks, and open up several opportunities.
Let's summarize:
If you liked this, you might like the following:
Blockchain-Enabled Convergence, whitepaper by Lawrence Lundy. How blockchain tech can help AI, VR, and other emerging technologies.
Parts I, II, III of AI DAO essays, by Trent McConaghy. Culminates in a worrisome thought: we give AIs control of our resources. I do offer some crumbs of hope, however:)
Daemon and FreedomTM (sequel), novels by Daniel Suarez. How "dumb" software agents cause a chain reaction of events ending with a radical restructuring of society.
Blockchains for Big Data, essay by Trent McConaghy. Big data powers much of modern AI. Blockchains can help big data, even without the AI angle.
Google's DeepMind Healthcare A.I. Will Use Blockchain, news article by Jamie Redman. Cool! This came out just as I was putting the finishing touches on this post.
Thanks to the following reviewers / editors: Ali Soy, Adam Drake, Carly Sheridan, Dimitri de Jonghe, Gaston Besanson, Jamie Burke, Lawrence Lundy, Philipp Banhardt, Simon de la Rouviere, Troy McConaghy. If you have more feedback, please let me know and I'll be happy to update the article.
Like this article? Subscribe to our weekly newsletter to never miss out!
Follow @DataconomyMedia
Image: Simon Evans, CC BY 2.0
Tags:artificial intelligenceBlockchainMachine LearningTrent McConaghy
3 days to save you 2 years - Berlin's first IoT weekend
Our vision of the future: The Dataconomy Manifesto
Trent McConaghy
Trent McConaghy is an AI researcher and blockchain engineer. He is the founder & CTO of BigchainDB, IPDB, ascribe, and Solido. He started his career by doing AI research for national defense in the 1990s, while still an undergraduate, and went on to obtain a PhD from KU Leuven and receive an award for the #1 thesis worldwide in the field. He has done Machine Learning research for the Canadian Department of National Defense, and has written two books and 35 papers, and holds 20 patents on Machine Learning, circuits and creativity.
Artificial IntelligenceBI & AnalyticsData ScienceFeaturedinfographicsMachine LearningTopics
Artificial IntelligenceBI & AnalyticsContributorsData ScienceFeaturedHealthcareIndustryMachine LearningNewsTopics
Artificial IntelligenceBlockchainFeatured
Rule-Based Bots or AI Bots? Which One Do You Prefer?
Artificial IntelligenceBI & AnalyticsContributorsFeaturedMachine LearningTechnology & IT
Not Accounting for Bias in AI Is Reckless
BI & AnalyticsBlockchainData NativesMachine LearningStartupsTech TrendsTechnology & IT
"With proliferation of digitized technologies, the public is becoming aware of data-collecting sensors & it's concerns"
BI & AnalyticsBlockchainContributorsData NativesEventsFeaturedInternet of ThingsMachine LearningStartups
Call to all developers, programmers, entrepreneurs: Three challenges await you
Dataconomy Dataconomy Jobs Data Natives
76 Views By Bhanu Singh
409 Views By Arwa Sutarwala
312 Views By Abhimanyu Sundar
Artificial IntelligenceBI & AnalyticsContributorsData ScienceIndustryInternet of ThingsMachine LearningNewsProfessional ServicesResourcesTech TrendsTechnology & ITUnderstanding Big Data
Tweets by @DataconomyMedia
Home - About - Imprint - Contact - Site Map - Legal & Privacy
Copyright © Dataconomy Media GmbH, All Rights Reserved.
This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish.Accept Privacy Statement


Skill building, comradery and athleticism for all ages.
Established in 2012, the North Fork United Soccer Club brought together the North Fork school districts to form multiple leagues for girls and boys of all ages.
More than just soccer teams, this is where kids learn skills to be great athletes and good sportsmanship. But it's also a place to have fun, grow and make forever friendships!


text:
Ed's Notes
PI's Notes
Life School
Craft Therapy
Travis' Notes
Bible Study and Homeschool Resources
Hand Crafts
Land Property
Prayer Community
Camp Llena
Life Notes From Llena Family
Home Sermons
Preacher: P.I. Llena (2), Edmun Llena (1), Dr. David Jeremiah (1), Darrow Miller & Bob Moffit (1), Dan Cathcart (1), Colin Smith (1).
Book: Numbers (2), Psalm (1), Joel (1), Philippians (1).
Series: Bedtime Conversations (1), Psalms (1), Major Themes of the Pentateuch (1), A Tour of Prophesy (1), Transforming Cultures (1), Shadows of the Messiah (1), Restored: Study on Book of Joel (1).
Service: Sunday Morning (2), Special event (2), Devotion Time (2), Bedtime Conversations (1).
Date: 2013 (1), 2015 (1), 2016 (1), 2018 (1), 2019 (1), 2020 (2)
iTunes • Other
Filtered sermons
On Arts, Movies, Values, and Philippians 4
Philippians 4:8 (Part of the Bedtime Conversations series).
Preached by Edmun Llena on December 26, 2020 (Bedtime Conversations).
Psalm 90 - A Prayer of Moses
Psalm 90:1-17 (Part of the Psalms series).
Preached by P.I. Llena on January 26, 2020 (Devotion Time).
The Priestly Blessing
Numbers 6:22-27 (Part of the Major Themes of the Pentateuch series).
Preached by P.I. Llena on December 9, 2019 (Devotion Time).
The Prophetic Times
(Part of the A Tour of Prophesy series).
Preached by Dr. David Jeremiah on August 14, 2018 (Sunday Morning).
Transforming Cultures: A Coram Deo Course
(Part of the Transforming Cultures series).
Preached by Darrow Miller & Bob Moffit on December 30, 2016 (Special event).
The Aaronic Blessing
Numbers 6:22-26 (Part of the Shadows of the Messiah series).
Preached by Dan Cathcart on September 28, 2015 (Special event).
Restored: A Study on the Book of Joel
Joel 1:1-3:21 (Part of the Restored: Study on Book of Joel series).
Preached by Colin Smith on September 2, 2013 (Sunday Morning).
Powered by Sermon Browser
Music to Inspire
https://youtu.be/URIPZR57yxQ
Camp Llena on YouTube
Let's Learn Together
On Arts, Movies, Values, and Philippians 4 (Philippians 4:8) by Edmun Llena.
Psalm 90 - A Prayer of Moses (Psalm 90:1-17) by P.I. Llena.
The Priestly Blessing (Numbers 6:22-27) by P.I. Llena.
The Prophetic Times by Dr. David Jeremiah.
Transforming Cultures: A Coram Deo Course by Darrow Miller & Bob Moffit.
At the Place of Utmost Surrender
Triumphal Procession
List of Homeschool Resources
Our Favorite Free to Low-Cost Homeschool Curriculum Resources
How We Homeschool Our Two Kids for Less Than ₱19,000
Shop CampLlena Souvenirs
Craft Therapy Resources
Llena Travel & Tours
Join our Homeschoolers Support Group
Homeschool Curriculum for K-12
CrossWired Science Curriculum
Online LifeCoach and Tutors Available
Lifecoach
Plant-Based, Eco-Friendly, Highly Effective Household Products
Message US on How to be Preferred Shopper
Support Our Missionaries as You Shop From Amazon
Wanting Homebased Business? Message us at Kabuhayan Festival
Discover Benefits of Preferred Membership
Own Your JC Worldwide Online Franchise
Mountain Views, Wineries, and in the Lake in Minutes! $14,450.00
Cats Bookmark $12.00 $9.50
Wearable Arts $12.00 $9.50
Our Handcrafts
Choose Kindness, Short-Sleeve Unisex T-Shirt $19.75 – $22.25
Choose Patience, Short-Sleeve Unisex T-Shirt $19.75 – $22.25
Choose Joy, Short-Sleeve Unisex T-Shirt $19.75 – $22.25
© Copyright 2022 Camp Llena. All Rights Reserved. Fashion Lifestyle | Developed By Blossom Themes. Powered by WordPress.Privacy Policy

redpajama_set_name: RedPajamaCommonCrawl


DETROIT – The Business and Community Law Clinic at Wayne State University Law School and TechTown Detroit are joining forces to offer free neighborhood legal office hours to qualifying community-based businesses, social enterprises and non-profit organizations who cannot afford an attorney.
The free neighborhood legal office hours include a 30-minute consultation with a supervised, upper-level student attorney who can provide basic legal information and referrals in the area of transactional law. During office hours, the Wayne Law Business and Community Law Clinic will not provide legal advice or counsel. Interested parties can sign up and fill out the required application at go.wayne.edu/bclcevents. The application must be filled out no later than 48 hours after scheduling the appointment or it may be subject to cancellation.
Wednesday, Feb. 21 (2 to 4 p.m.) – Grand River Workplace, 19120 Grand River Ave.
Monday, Feb. 26 (3 to 7 p.m.) – Eastside Community Network, 4401 Conner St.
For more information, visit law.wayne.edu/clinics/business.
The Business and Community Law Clinic at Wayne State University Law School provides free legal services to qualifying community-based businesses as well as for-profit and non-profit organizations that aspire to create social good to revitalize Detroit and Michigan. To learn more about the clinic and upcoming collaborative workshops, visit law.wayne.edu/clinics/business.
TechTown is Detroit's entrepreneurship hub, with a full suite of programs for both tech and neighborhood small businesses. TechTown works with businesses at all stages, helping startup, emerging and established companies develop, launch and grow. For more information, visit techtowndetroit.org.


BUILDERS' CLUB – NEW FOR 2018!
At Forest Baptist Church on we have been running a Children's Club for a number of years. We are glad to welcome children of any faith or none. The club runs between 3.30pm and 5.00pm every Friday afternoon during term time. No charge is made for those who attend. The age range for the club is 5 to 11 years old.
If you would like your children to come to the club or if you would simply like to know more about it then please get in touch using our email address or by ringing Gill on 020 8556 2893.


You can choose a nice coloring page from BIRD coloring pages for kids. Enjoy our free coloring pages! Find your favorite coloring page on Hellokids! We have selected the most popular coloring pages, like Turkey coloring page for you!


text:
County school bus system to change
A major shift for students who ride buses in the Jackson County School System is in the planning stages.
Among the proposals being discussed for next fall is the transporting of K-5th grade students on one bus, and 6th-12th grade students on a separate bus. In addition, the district may add more time to the length of the day for elementary school students, officials said. It's also possible that middle and high school days will be impacted in some minor way.
Superintendent Shannon Adams said that despite an efficient transportation department, too many students are spending too much time on school buses after school.
"We would like to get kids home earlier in the afternoons," Adams said. "We have a lot of kids on the bus too long."
Adams called the proposed changes "significant."
"It will affect a lot of people next year," he said. "Communication will be critical when we do have something to put out as a way to address the issue."
A committee to address the transportation changes will meet on Wednesday, Nov. 12.
Defined tags for this entry: BOE
Related entries by tags:
School system to add back four furlough days
County BOE to trim days from school calendar
Most Jackson Co. schools make AYP
Low revenues make more county school cuts likely
Jefferson BOE to apply for charter school status
County BOE axes people, programs
Crossing guard to aid WJ students
Evans: Two-day furlough won't be necessary
EDITORIAL: School Bible class is not a good idea
Hudgens promotes Bible class in county schools
What is one plus six?


How To Inject Great Wedding Songs And Music Without Blowing Your Budget?
A wedding without music seems incomplete, probably because we have been singing and dancing at weddings throughout history. While the decision to have music at your wedding is a no-brainer today there is an array of options to choose from. By far the best is to have a great band play at your wedding but bands will charge you several hundred dollars for just a few hours' worth of performance. There are cheaper bands but those will probably just be a waste of money. It goes without saying that if you're going to hire a band to play wedding songs always hire the best.
DJs are a lot cheaper than having to hire a band. The other upside to hiring a DJ is that they will bring along lighting and sound equipment. They will set everything up so you do not have to worry about getting spot lights, disco lights and laser lights etc. Hiring a DJ can be up to 30% cheaper as compared to a band but then again there are good DJs and great DJs. Expect to pay a great DJ around 50% or half of what you'd pay a good band to play at your wedding.
The only downside to DJs is that they rarely take requests. You'll also have to do a bit of research to find one that will play wedding songs that your guests will like. Bear in mind the fact that if the majority of people at your wedding are older than 45, you're better off hiring a DJ that has experience playing for this audience.
Now, if you're on a really slim budget you may want to hire a jukebox for the duration of your celebrations. However, you need to find a jukebox that is loaded with mostly wedding songs and soft music. This can be a challenge since many jukeboxes carry a mixed bag of music and it's up to the person operating the machine to select what he or she wants to hear. Adding a jukebox to your wedding celebrations can possibly add a kind of irregularity to the celebration. Searching for a jukebox that mostly contains the music you want can be challenging and requires lots of research. That said it's not a bad option for around $300.
Even though the singing should be left up to the professionals there are some wedding songs that can be sung by everyone provided that the music is familiar and lyrics delivered on a screen. A Karaoke system is a great idea especially if you want the wedding songs to double as a wedding activity. The system is cheap to hire and most people will find that signing along is a lot more fun than listening to music only. The genres can be changed at the flip of a button and almost anyone can try their hand at singing popular tunes, with the applause or boos providing the required feedback.
This too is an option if you cannot spend more than just two or three hundred dollars on wedding music. Unlike what most people may think this is an excellent option often used at high profile weddings. Simply load you iPod with all the classic and latest wedding songs, hire professional sound and amplification equipment with lighting. The equipment hire company will send over a professional who will setup everything from the lights to the music equipment and then all you have to do is to hook up your iPod to the amplifier and let the music play. The beauty of this idea is that everyone can enjoy professional quality music delivered via a professional sound system. If the sound system is good it would be like being at a concert. Modern Weddings recommend that people pre-book the sound and light equipment at least six months in advance especially if they are getting married during the wedding season.
Wedding songs are an integral part of any wedding celebration. However, you shouldn't go overboard and blow your entire budget on music. Many people leave it for the last movement which too is not a good idea. Draft a budget in advance and plan on what idea will work best for your event accordingly.
How To Plan A Great Pre-wedding Photography Session? Music Video Production Tips For A Newbie Band Best Forms of Design Inlays in Tungsten Wedding Bands for Men Tips For Making A Wedding Video More Exciting How Worthy Is It to Join the Music Production Institute in Mumbai?


text:
Christopher S. Porrino, Attorney General
Division of Consumer Affairs
Steve C. Lee, Director
Division of Law
Michelle Miller, Acting Director
For Further Information and Media Inquiries:
Lisa Coryell (973) 504-6327
New Jersey Board of Medical Examiners Revokes License of Essex County Cardiologist Who Shortchanged His Partners by Falsifying Pension Information
View Order
NEWARK – Attorney General Christopher S. Porrino and the Division of Consumer Affairs today announced that the State Board of Medical Examiners ("the Board") has permanently revoked the license of an Essex County Cardiologist whose oversight of his practice's retirement plan shortchanged his partners by underreporting the plan's value more than $12 million, in violation of his fiduciary duties under federal law.
Dr. Mario Criscito, a former partner in the Diagnostic and Clinical Cardiology ("DCC") medical practice in West Orange, voluntarily surrendered his license, to be deemed a permanent revocation, under the terms of a Consent Order with the Board.
The settlement resolves the State's bid to revoke Criscito's license after he was found civilly liable for violating the federal Employment Retirement Income Security Act ("ERISA") by fraudulently understating the balance in DCC's pension account and claiming the unreported assets for his personal use.
"This is a clear cut case of a greedy doctor who enriched himself by abusing the trust that his partners and employees placed in him as steward of their pension fund," said Attorney General Porrino. "Dr. Criscito was found to have spent millions of their retirement savings on personal investments, resort memberships, and to pad his own bank account."
The revocation of Criscito's license stems from a 2008 civil suit between Criscito and his DCC partners, which was litigated in the U.S. District Court for the District of New Jersey and the Third Circuit Court of Appeals (the "Civil Action"). Decisions rendered in those respective courts in January 2011 and March 2012 found that Criscito violated his fiduciary duty under ERISA by providing inaccurate and false numbers to American Pension Corporation ("APC"), the company managing the pension fund for his medical practice. The District Court awarded Criscito's partners $4.18 million dollars in damages and interest, along with additional attorney's fees and costs. This award was upheld by the Third Circuit Court of Appeals prior to the matter being settled between the parties.
As sole trustee of DCC's pension plan, Criscito controlled the two accounts that commingled the retirement savings of the 17 physicians and staff members who worked there. In December 1999, one month before the commingled funds were transferred from the group accounts to individual accounts, Criscito fraudulently understated the value of the group accounts by more than $12 million in a year-end report to APC, according to the findings in the Civil Action. The fraudulently understated values were then used to create the segregated accounts for individual members.
"As a result of Dr. Criscito's failure to reveal the true value of DCC's commingled pension account, plan members were shortchanged millions of dollars when the assets were divided up," said Steve Lee, Director of the Division of Consumer Affairs. "This kind of dishonesty and deceptive behavior are an egregious violation of professional standards that warrant the permanent revocation of Dr. Criscito's medical license."
The District Court concluded that Criscito's conduct was intentional and "cannot reasonably be viewed as the result of 'mere accident,'" noting that the physician took "affirmative steps" to conceal his breach of his fiduciary duty pertaining to the pension plan, including directing APC to send all documentation pertaining to the plan to his home address; requesting that APC refrain from speaking to anyone but him about the commingled accounts; and certifying that the untrue information he gave APC was accurate by signing multiple inaccurate federal reports.
In January 2014, citing the District Court's findings, the State filed an Administrative Complaint seeking the revocation of Criscito's license to practice medicine. The Complaint asserted that Criscito's actions constituted the use of dishonesty, fraud, acts of moral turpitude, and professional misconduct in violation of New Jersey's laws and regulations pertaining to the practice of medicine in New Jersey.
In August 2014, the State sought a Summary Decision from the Office of Administrative Law, which was granted in December 2016. In granting summary decision, Administrative Law Judge Jeffrey Gerson held "The fact that Criscito fraudulently misreported the account balances of the commingled accounts in 1999 by a staggering amount of more than $12,000,000 constitutes dishonest, fraudulent, deceptive, or a misrepresentative conduct under (state law). Additionally, misrepresenting account balances by more than $12,000,000 is conduct that may lower the standing and trustworthiness of the medical profession in the eyes of the public…"
Prior to the Board's consideration of ALJ Gerson's decision, Criscito consented to the revocation of his license. Under the terms of the Consent Order, Criscito was required to surrender his medical license to be deemed a permanent revocation effective July 1. He was also required to pay the State $35,000 in attorney's fees.
Despite consenting to the permanent revocation of his license, Criscito sought an eleventh hour stay of the effective date. After an unsuccessful application in the Chancery Division of Superior Court on June 30, a similar application for a stay pending appeal was denied by the Appellate Division that same day. As a result, the permanent of revocation of Criscito's license took effect as scheduled. Criscito's appeal remains pending before the Appellate Division.
Deputy Attorney General David M. Puteska, Assistant Chief of the Professional Boards Prosecution Section in the Division of Law, represented the State in this matter.
Shelia M. Mints, Esq. of Capehart Scatchard is Criscito's current counsel of record in this matter.
Patients who believe that they have been treated by a licensed health care professional in an inappropriate manner can file an online complaint with the State Division of Consumer Affairs by visiting its website or by calling 1-800-242-5846 (toll free within New Jersey) or 973-504- 6200.
###
Last Modified: 7/11/2017 10:41 AM


Arnold's has been delivering bulk wholesale fresh fruit and vegetables on the Border and beyond to cafes, restaurants, clubs, hospitals and schools for more than a century. Arnold's Wholesale is located in a customised warehouse in Kendall St Wodonga behind the Retail store.
The warehouse features offices and the latest technology including LED lighting,automated doors, a customised vegetable processing plant and multi-room climate control for optimum produce conditions. Tours of the facilities are available for chefs and owners.
Delivery areas include Albury, Lavington, Thurgoona, Wodonga, Baranduda, Beechworth, Yackandandah, Myrtleford, Bright, Chiltern, Holbrook, Falls Creek, Culcairn, Henty, Wagga, Howlong, Rutherglen, Corowa and more. Please our office and see if we dleiver to your town.
Arnold's Wholesale delivers Monday to Friday as well as Saturday morning. Closed on all Public Holidays.
Orders can be placed by filling in the form below (for current Wholesale customers) or over the phone to the office on 02 6024 5322 or by fax on 02 6024 5211.
Please note Arnold's preferred method of taking orders is over the phone to provide market information and prices.
New Wholesale customers are welcome to call our office to introduce themselves and seek an account application form.


text:
Digital Learning Standards
Organization Standards
Google Search EMPLOYMENT CALENDAR NEWSROOM GET UPDATES QUICK LINKS
There are three types of standards in this section. Professional Standards have been established for North Carolina Media Coordinators and Instructional Technology Facilitators and are used in the evaluation of these professionals. The Information and Technology Essential Standards, implemented during the 2011-2012 school year, replaced the Technology/Computer Skills and the Information Skills Standard Course of Study. The American Association of School Librarians and the International Society for Technology in Education Standards are voluntary standards published by respective professional organizations including student, professional and administrator standards organizations. Follow the links in the left navigation to pages with more information.


Lodging is when you rent a room and share some accommodation with your landlord such as the bathroom or kitchen. Lodgers often pay for other services such as meals and cleaning too.
These pages provide information and advice on what you need to think about before taking in a lodger, what rights certain tenants have to take in lodgers and what a lodger's general housing rights are.
Lodging and the rules around taking in a lodger.
Rights that different tenants have to take in a lodger.
Lodgers' (excluded occupiers) rights and responsibilities if you share accommodation with your landlord.


text:
6 How a Norris F4 rival's early GT move was vindicated
10 Peterhansel: Al-Attiyah lost Dakar '21 due to "ego"
Nicky Hayden: Remembering motorsport's much-loved "one-off"
Published on Friday May 22nd 2020
On the anniversary of his untimely death in 2017, Autosport sat down with two former team-mates - Dani Pedrosa and Michael van der Mark - to remember 2006 MotoGP world champion Nicky Hayden and the qualities which made him a motorsport hero
The high-pressure nature of professional sport and the personal sacrifices that must be made to become elite often make sportsmen and women polarising characters. It seems that fact is only heightened in motorsport. But, very occasionally, one comes along who totally bucks the trend, who is universally appreciated.
Nicky Hayden typified the latter category.
One of five born into the Hayden family in Owensboro, Kentucky, on 30 July 1981, racing flowed through Nicky's veins. Just like so many American bike racing heroes before him, Hayden cut his teeth - and proved successful - on the dirt track scene. He blazed a trail through the AMA series, winning the Supersport championship in 1999 and clinching the Superbike title in 2002, becoming the youngest winner of the series.
His exploits were enough to capture Honda's attention, and he moved to MotoGP in 2003 as team-mate to Valentino Rossi. It would be two years before he tallied up his first win, doing so at Laguna Seca in 2005, but the following season would prove to be the 'Kentucky Kid's' breakout campaign.
In MotoGP's most dramatic championship tussle, a consistent Hayden - who only won twice compared to Rossi's five, but retired only once in a crash-strewn season for the Italian, now at Yamaha - beat Rossi by five points in a Valencia finale in which his rival crashed. The pictures of Hayden, his face dripping with tears, as he brandishes the Star Spangled Banner on his cool-down lap are some of motorcycling's most memorable of modern times.
It was the realisation of a lifelong dream - one which appeared to have ended in an Estoril gravel trap a week earlier after Honda team-mate Dani Pedrosa crashed and took the American with him.
A total accident in the heat of battle, and one the Spanish rookie was unfairly vilified for in the press, Hayden's furious immediate reaction may well have been the defining image of that 2006 season had Rossi go onto win the title. Hayden's outburst was uncharacteristic, but his reaction later wasn't.
"Sure, he felt very bad because all his hopes were gone," Pedrosa tells Autosport when asked about his memories of that day. "But I just went to his motorhome, I apologised to him. I just tried to explain [what happened], it was not intentional of course. He actually took it well.
"He was crying, but he took it well. He was crying out of anger, out of not being able to change the situation. But I told him 'OK, this is what happened and I will try to help you at the next round'."
Hayden was Pedrosa's first team-mate in MotoGP, and the Spaniard was tipped for greatness after three straight seasons of title glory in the 125cc and 250cc classes. Typically, this is a recipe for high tensions and falling outs between team-mates. Indeed, Pedrosa beat Hayden to second in the 2006 Jerez season-opener and was the first of the pair to win a race that season.
But never did things descend to Rossi/Jorge Lorenzo levels of tensions, where that pairing had a wall erected in their Yamaha garage to stop set-up secrets spilling out. As Pedrosa explains, that was in equal parts down to Hayden's nature as a person and the way he operated as a racer.
Retiring from MotoGP at the of 2015, Hayden had nothing left to prove. But his passion for motorsport and his belief of still being able to be competitive on the right machinery led him to WSBK on the Ten Kate-run CBR1000RR
"Always we got along well," the 31-time MotoGP race winner says. "Aside from the [fact the] competition was extremely strong between us, [the rivalry was] nice, never any bad words or anything.
"But the heart was like 'no, no, I don't want Dani to beat me', and in the end I had to beat Nicky. So I could feel that in every test we did, and anytime I was finishing the first day in front of him, the first thing he would do in the second day of testing, the first outing, he would go out and smash my best lap from the day before. So, I could feel this tension between us throughout 2006, and I think that also helped him a lot to achieve the championship."
This was a trait Michael van der Mark, Hayden's first team-mate in World Superbikes at the Ten Kate Honda squad in 2016 and fan of the American's growing up (van der Mark raced with Hayden's famous 69 in his early career), noticed.
"I remember one weekend in Misano, I was quite strong and in the practice Nicky wasn't even close to my laptime," Dutchman van der Mark tells Autosport. "But he was working on sectors, he was improving every sector and then the last few minutes he put it all together and he was in front of me. So, the way he was working was really impressive."
Hayden wouldn't win again in MotoGP following 2006. Honda struggled in the early years of the 800cc bikes upon the formula switch for 2007, and Hayden's move to the works Ducati squad in 2009 on the difficult Desmosedici yielded only sparse podium visits between then and 2013.
His final two years in MotoGP were spent on the uncompetitive Aspar-run Honda RCV1000R and RC213V-RS Open class machines, while he also battled a nasty right wrist injury.
Retiring from MotoGP at the of 2015, Hayden had nothing left to prove. But his passion for motorsport and his belief of still being able to be competitive on the right machinery led him to WSBK on the Ten Kate-run CBR1000RR.
Solid in the early part of the season, Hayden backed up his belief in himself at a wet Sepang. The second race of the Malaysian WSBK round, Hayden - determined to bag a good result for his ill father and biggest supporter Earl - got his Honda to the chequered flag 1.2 seconds clear of the field to claim his first win in a decade. The outpouring of emotion was exactly what you'd expect from Hayden, as was his reaction once the adrenaline had subsided.
"It was a bit strange weekend," van der Mark recalls. "It was a rain race and I remember I struggled quite a lot when the track started to get dry, and he was just pulling away from me. So it was really frustrating for me, but it was great to see him on top of the podium - even though I wanted to be there. I think it was a big motivation for the whole team.
"But Nicky was Nicky, he just stayed calm and said 'yeah, it was a wet race and could have been anyone' and he said had it been a couple of laps more... he stayed so calm. Of course, he was happy, but he was also down to Earth."
Hayden would finish on the podium twice more that year - once at home at Laguna Seca, and at the following round at the Lausitzring. He made two MotoGP appearances as an injury replacement later in the season too, at Aragon at Marc VDS, and donning the Repsol colours one more time at Phillip Island to fill in for Pedrosa. He scored a point at Aragon, and was on course for a handful more in Australia before he was taken down. No matter, he proved he could still cut it at the elite level.
On 17 May 2017, Hayden was struck by a speeding car while out riding his bicycle in Italy. He succumbed to his injuries five days later, his tragic passing cutting the motorcycle racing world deep. Dorna Sports, MotoGP's promoters, retired Hayden's 69 race number a year later, while motorcycles, leathers and helmets across the world's paddocks continue to carry the #69 in tribute.
The death of someone always tends to elevate them into a kind of faux legend, with people who paid very little attention beforehand all of a sudden becoming devout fans. That's never been the case with Hayden. Up and down bike racing paddocks, you'd struggle to find anyone with a bad word to say about him. The same is true today, and that's because you'd have struggled to hear him speak a bad word about anyone.
Prior to leaving MotoGP in 2015, Dorna made Hayden an official MotoGP legend. There are a lot of one-time and non-champions in that list that really probably shouldn't be considering some of the genuine legends they stand alongside. Hayden is the exception, though. Not only was he a brilliant rider and one of the most determined, but he was a brilliant human being.
"He was a special combination, being so competitive but not having a problem with the rivals or press or whatever" Dani Pedrosa
It's this "special" - and rare - combination which made him one of motorsport's true greats.
"He was one of the strongest competitors when you fought, when you get to compete for the fastest lap time, for position, and he didn't want to give up any bit of anything," Pedrosa fondly recalls.
"He was always trying to be competitive to you. But at the same time, he never had a bad word and he could be so competitive without it becoming personal with him.
"But, at the same time, it's difficult to guess from the outside because when that thing happened in Estoril, or one week later in Valencia, you could see he was emotional. He was crying both times, and this meant that for him it was so important. That's why I think it's a special combination, being so competitive but not having a problem with the rivals or press or whatever."
As van der Mark summed up, "he's a one-off".
How a Norris F4 rival's early GT move was vindicated The latest addition to Lamborghini's factory GT roster faced the tough prospect of abandoning his F1 dream while still at school. But 2020 British GT champion Sandy Mitchell has proved that the move was absolutely the right one, as he told JAMES NEWBOLD 1610798732 GT


text:
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">

<title>ParentPlayHandler</title>
<meta name="date" content="2016-10-22">
<link rel="stylesheet" type="text/css" href="../../../../stylesheet.css" title="Style">

JavaScript is disabled on your browser.

Skip navigation links

Overview

Package

Class

Use

Tree

Deprecated

Index

Help

Prev Class

Next Class

Frames

No Frames

All Classes

Summary:

Nested |

Field |

Constr |

Method

Detail:

Field |

Constr |

Method

com.vangav.backend.play_framework

Class ParentPlayHandler

java.lang.Object

com.vangav.backend.play_framework.ParentPlayHandler

Direct Known Subclasses:

CommonPlayHandler

public abstract class ParentPlayHandler
extends java.lang.Object

ParentPlayHandler is the parent class for all play framework's
   REST requests' handlers
 How it works for a new service:
   new_service.controllers
     abstract CommonPlayHandler extends ParentPlayHandler
       Handler extends CommonHandler
 CommonPlayHandler overrides the common methods and the methods that service
   doesn't need while each Handler class overrides only the methods it needs
   to handle the corresponding controller's requests

Constructor Summary

Constructors

Constructor and Description

ParentPlayHandler()

Method Summary

All Methods Instance Methods Concrete Methods

Modifier and Type
Method and Description

play.libs.F.Promise<play.mvc.Result>
handleRequestAsync(play.mvc.Http.Request request)

handleRequestAsync
 this is where request processing a request starts (gets called from
   a controller)

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

ParentPlayHandler

public ParentPlayHandler()

Method Detail

handleRequestAsync

public final play.libs.F.Promise<play.mvc.Result> handleRequestAsync(play.mvc.Http.Request request)

handleRequestAsync
 this is where request processing a request starts (gets called from
   a controller)

Parameters:

request - (play framework request)

Returns:

Result (containing response)

Skip navigation links

Overview

Package

Class

Use

Tree

Deprecated

Index

Help

Prev Class

Next Class

Frames

No Frames

All Classes

Summary:

Nested |

Field |

Constr |

Method

Detail:

Field |

Constr |

Method

redpajama_set_name: RedPajamaGithub


text:
TOWN OF ERA, TEXAS
Compiled by Norman L. Newton, of the Cross Timbers Genealogical Society, August 2010
THE TOWN OF ERA
Located in a fertile farming region, Era was settled in the 1870s. Merchant J.N. Gist (1827-1893) platted the townsite. The post office was opened in 1881 and named for Era Hargroves (1875-1880), daughter of an early resident; the village soon had a blacksmith shop, cotton gin, hotel, several stores, and three churches. Era Institute was chartered in 1897 with primary through college level classes. Among the town's prominent citizens was Robert E. Thomason (1879-1973), U.S. Congressman and Federal Judge. Era won first place in the Texas Community Improvement Program in 1973.
Texas Historical Commission, 1978.
Excerpts taken from the Era Centennial book
In the Era Centennial Book published in 1878 has, "Mr. and Mrs. Hargrove, Father and Mother of the girl for whom Era was named.........Era Hargrove. Mr. Hargrove began the Post Office in a cigar box on an old fashioned bureau in his home here in Era about the year of 1877 or 1878."
On August 22, 1872 records show William and Virginia Daniels and Wade Horton deeded 180 acres of land on Duck Creek to J.W. Weeks for $480.00. J.W. Weeks sold 100 acres of land to A.H. Hargrove (1836- ) for $500.00. A.H. Hargrove built a log house on this property.
The new community needed a name and a post office. The men gathered together to select a name. While it was being discussed little Era Hargrove (1875-1880) tugged at her daddy's trouser leg, and upon getting his attention asked, "Why not name the post office for me?" The group followed the suggestion and in 1878 the settlement became "Era."
Little Era Hargrove died in 1880. She was buried just east of the town. On January 10, 1881 the grief stricken family sold the 100 acres of land to J.N. Guist (1827-1893). It is not known where the Hargrove family finally settled. Mr. Guist laid out the first town.
The town began to grow. A blacksmith shop owned by Bill Higgins was the first in Era. It was later sold to John P. Bickel in 1885. A general store, larger hotel, cotton gin and three churches were added. The town had grown to the grave of Era Hargrove. Her body was moved to the new cemetery and buried on the J.N. Guist lot where it rests today.
Because of curiosity about the Hargrove family I began a journey .....................a research journey to find out more about this family. I have compiled my research into family story and have titled it, "Little Era Hargrove A Research Journey." To read this story please click the link below.
Little Era Hargrove A Research Journey


We are not responsible for uninsured orders that will be lost, damaged or undelivered. Please make sure to correct and complete the address.
Most orders will be shipped from our office within 1 - 2 business days of payments received and cleared.
Delivery time for international orders is 5 to 15 business days.
We know you will love your accessories.
Wearing our products you will notice the envious look of other women admiring your beautiful accessories.
We are very confident that our fine products will meet and surpass your expectations, as your satisfaction is our utmost priority and goal. However, if for any reason you are not completly satisfied you may return or exchange your item within 14 days from the date of purchase.
Be assured that we will always honor our guarantee of your 100% satisfaction with your accessories purchase. We will speedily process your refund and credit your card for the purchase amount, no questions asked.
If you are not happy with your purchase for any reason, please return the accessories within 30 days and we will exchange or refund the purchase price.
The accessories must be returned in its orginal condition, unworn and unaltered in any way. We do not cover the return shipping cost unless the item is damaged or broken due to our company's negligence.
Please email us at aurazone@att.net to let us know you are returning the accessories. Place all the original packaging in the return box, including your return accessories and a copy of the invoice.
The accessories should be mailed in a box, not an envelope as the accessories may be crushed in an envelope.
Your return will be processed within 7 business days. A credit will be issued to your original method of payment within 7 business days of receiving your returned accessories. You will receive an email confirming your refund.
We are not responsible for items damaged or lost in transit. In case of damage, alteration or wear, a credit will not be issued. We suggest shipping insurance as we will not replace or refund accessories lost or stolen in the mail while in delivery from you to us.
We will be happy to exchange your purchased items.
When you send your items back to us please tell us (on a copy of the invoice) what items you would like to receive for an exchange. Be sure to include the item style name, type and the price. We will credit your account for the amount of the returned items, then charge you for the replacement items and your new items will be on its way to your door.
If you would like your items to be exchanged faster - just order your new items online and also, at the same time, mail your returned items to us. Your new items will arrive within 2-3 business days and when we receive your returned items, we will credit your card for the amount of purchase. See the section above for the address and details for returning your purchases.
Payments may be made with Google checkout, just select that button on the order form.
We accept all checks and money orders (in USD only). All personal and business checks will be held until they are cleared.
If you prefer to send us money using a wire transfer method, please contact us for more information.


text:
% This code reproduces the analyses in the paper
% Urai AE, Braun A, Donner THD (2016) Pupil-linked arousal is driven
% by decision uncertainty and alters serial choice bias.
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the "Software"),
% to deal in the Software without restriction, including without limitation
% the rights to use, copy, modify, merge, publish, distribute, sublicense,
% and/or sell copies of the Software, and to permit persons to whom the
% Software is furnished to do so, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included
% in all copies or substantial portions of the Software.
% If you use the Software for your own research, cite the paper.
%
% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
% OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
% AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
% FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
% DEALINGS IN THE SOFTWARE.
%
% Anne Urai, 2016
% anne.urai@gmail.com

clearvars -except mypath;
global mypath;
close all;

% use nice shades of red and green
cols = cbrewer('qual', 'Set1', 8);
cols = cols([1 2], :);
nbins = 3;
mods = {'pupil', 'rt'};
cors = [1 0];
cnt = 1;
subjects = 1:27;

for m = 1:length(mods),
    for c = 1:length(cors),
        grandavg = postPupilBehaviour(mods{m}, nbins, cors(c));
        subplot(4,4,cnt); cnt = cnt + 1;

        % Use HOLD and ERRORBAR, passing axes handles to the functions.
        colors = cbrewer('qual', 'Set1', 8);
        switch cors(c)
            case 1
                thismarker = '.';
                thismarkersize = 14;
                thiscolor = colors(2,:);
            case 0
                thismarker = '^';
                thismarkersize = 4;
                thiscolor = colors(1,:);
        end

        % line to indicate 50% repetition
        y = grandavg.repetition;
        plot([1 nbins], [0.5 0.5], 'k:', 'linewidth', 0.5); hold on;

        % errorbar
        h = ploterr(1:nbins, nanmean(y), [], nanstd(y) ./sqrt(27), 'k-',  'abshhxy', 0);
        set(h(1), 'color', thiscolor, 'markersize', thismarkersize, 'marker',thismarker);

        % only for error markers, triangle
        if cors(c) == 0, set(h(1), 'markerfacecolor', 'w', 'markeredgecolor', thiscolor); end
        set(h(2), 'color', thiscolor); % line color

        xticklabs       = repmat({' '}, 1, nbins);
        xticklabs{1}    = 'low';
        xticklabs{end}  = 'high';
        if nbins == 3, xticklabs{2} = 'med'; end

        set(gca, 'xlim', [0.5 nbins+0.5], 'xtick', 1:nbins,  'xticklabel', xticklabs, ...
            'xcolor', 'k', 'ycolor', 'k', 'linewidth', 0.5, 'box', 'off', 'xminortick', 'off', 'yminortick', 'off');
        axis square;

        % determine y label and limits
        set(gca, 'ylim', [0.45 0.56], 'ytick', 0.45:0.05:0.55);
        ylabel('P(repeat)');
        xlim([0.5 nbins+0.5]);

        % do Bayesian ANOVA to get Bayes Factors
        statdat             = table;
        statdat.DV          = y(:);
        sj                  = repmat(1:27, nbins, 1)';
        statdat.subjnr      = sj(:);
        ft                  = repmat(transpose(1:nbins), 1, 27)';
        statdat.prevPupilBins = ft(:);
        writetable(statdat, sprintf('%s/Data/CSV/ANOVAdat.csv', mypath));
        system('/Library/Frameworks/R.framework/Resources/bin/R < bayesFactorANOVA.R --no-save');
        statres{cnt} = readtable(sprintf('%s/Data/CSV/ANOVAresults.csv', mypath)); % fetch results

        yval = max(get(gca, 'ylim'));
        if statres{cnt}.pvalue < 0.05, % only show if significant
            mysigstar(gca, [1 nbins], [yval yval], statres{cnt}.pvalue, 'k', 'down');
        end
        fprintf('\n %s, correct %d, ANOVA F(%d,%d) = %.3f, p = %.3f, bf10 = %.3f \n', ...
            mods{m}, cors(c), statres{cnt}.df1, statres{cnt}.df2, statres{cnt}.F, statres{cnt}.pvalue, statres{cnt}.bf10);

        switch mods{m}
            case 'pupil'
                xlabel('Previous trial pupil');
            case 'rt'
                xlabel('Previous trial RT');
            otherwise
                xlabel(sprintf('Previous trial %s', mods{m}));
        end
    end
end

%% also show the bar graphs of the error and correct fruend-derived model
load(sprintf('%s/Data/GrandAverage/historyweights_%s.mat', mypath, 'pupil+rt'));

colors = cbrewer('qual', 'Set1', 8);
subplot(4,4,5); plotBetas([dat.correct(:, 1) dat.incorrect(:, 1)], colors([2 1], :));
set(gca, 'xtick', 1:2, 'xticklabel', {'Correct', 'Error'}, 'xticklabelrotation', -30); %ylim([-0.35 0.3]);
axis square; ylim([-0.1 0.2]);

colors = cbrewer('qual', 'Set1', 8);
subplot(4,4,6); plotBetas([dat.correct_pupil(:, 1) dat.incorrect_pupil(:, 1)], colors([2 1], :));
set(gca, 'xtick', 1:2, 'xticklabel', {'Pupil x correct', 'Pupil x error'}, 'xticklabelrotation', -30); %ylim([-0.35 0.3]);
axis square;
ylim([-0.1 0]);

subplot(4,4,7); plotBetas([dat.correct_rt(:, 1) dat.incorrect_rt(:, 1)], colors([2 1], :));
set(gca, 'xtick', 1:2, 'xticklabel', {'RT x correct', 'RT x error'}, 'xticklabelrotation', -30); %ylim([-0.35 0.3]);
axis square; ylim([-0.1 0.01]);

print(gcf, '-dpdf', sprintf('%s/Figures/FigureS6.pdf', mypath));


WARNING: This post contains tropical scenes which may be damaging to your well being if you are in the Northern climates affected by the storm. Then again, these scenes may lift your spirits. You decide.
Usually I set aside sometime to ponder a New Year — to reflect, project. Last year I rang in 2018 at a live Jimmy Buffett concert in Nashville — very far from my norm. This year I rang in 2019 downtown in Paradise South at 9pm. Yes, 9pm. You see "Paradise South" drops the ball at 9pm instead of midnight so everyone is sure to "make it." It was a rather large celebration for a small town and the ball is homemade and dropped via fishing pole by the mayor from a second story balcony. It is not my norm to celebrate in any sort of crowd but the rest of it is keeping in line with my lifestyle from homemade to the fishing pole, all quite unconventional yet attainable. I'm beginning to wonder if maybe I'm developing a new norm? A crowd, loud?
It is now January 15, and I am just beginning my reflecting and projecting upon 2018 and 2019 (and it will probably take me days to pull this post together). Whereas 2018 began as a party, 2019 began busy. I spent New Year's Day not recovering from an early 9pm night, but by matting and framing six new art works I painted throughout 2018 for an art show at the community art center. Artists know what work it is to mat and frame if art admirers may not. I am fortunate to have my own mat cutter that I acquired several years ago and that I finally dragged down to Paradise South where the main energy of all my painting is now my focus. In this bit of minimal reflection I realize that 2019 will be a year to carry on aka keep on keeping on what I started in 2018 — paint, plant, cook. The past couple years have been working up to this mantra and now that my life has finally clicked into place, I hope to really enjoy the process of painting, planting, and cooking.
Art show at the community arts center.
The next morning began at 4am with a trip to the Gainesville airport only to arrive, on time, and wait to be checked in to, er bumped from our flight and placed on stand by for another flight to be missed again and to stand by to make a third flight to Atlanta. Then we arrived in Atlanta to the news that there weren't any more flights that day to our final destination, Puerto Vallarta, Mexico.  We took the sky train to a nice hotel and made the best of it knowing we were guaranteed a flight the next morning to Puerto Vallarta.
I haven't been to Puerto Vallarta in a good while — maybe five years running now. My first few trips were due to my parents discovering and making PV their winter stomping ground. My parents are also the reason for this current trip. I want to spend precious time with them. We are all getting older. That evening upon arrival enveloped us from the balcony of our rented condo and we collectively sighed with deep contentment and realized what paradise Puerto Vallarta is cradled between the ocean and mountains. We breathed in rhythm to the sound of the surf and completely relaxed.
I will be here until the end of January (pinch me!) and so far it has been absolutely rejuvenating. Now, I can pause and reflect on the past year which has been life changing and gather my thoughts. Once again, I reflect on how selling our big house up North and becoming FL residents has allowed me to get on with life! Obstacles have been removed. It is somewhat of a shock that the very thing I thought so important to me — the house and Violet Fern garden — was the very thing that was in my way and weighing me down.
Previous visits to Puerto Vallarta inspired several paintings especially when I visited the botanical gardens.
This painting, although recently painted just this past summer was also inspired by my previous trip to Jardin Botanico Vallarta years ago.
This visit is no different! I see inspiration everywhere and I am excited to paint more botanicals. Because I packed so haphazardly I don't have any painting supplies or even a sketchbook — arrrgh! What I do have is the ever present phone to record pictures of what inspires me. I will be thrilled to get back to painting when we return to Paradise South and also curious to see what paintings develop five years later. Sort of like the aging trend on Facebook going on now, I will compare paintings then with the new paintings now. I also want to explore painting botanicals with acrylics as well as watercolor.
Oh yes, I would like this to be my new norm! Starting the year off with a pause and rejuvenating in beautiful Puerto Vallarta. I can project that this will be a year I am kinder to my body and will concentrate on better health and fitness like so many of us. I need to drop some weight — that's what is weighing me down now. Mornings I have been going down to the marina for gentle yoga 50+ classes with Michelle Williamson (she is awesome!), and I am becoming somewhat addicted! I am amazed how much mobility I have lost and hope to get back.
I also love the food here, of course, being the foodie that I am. I consume food but food also consumes me. I hope to turn my constant cravings to more whole plant-based foods and not addicting process foods. I love this area where we can walk to the marina and pick from the many restaurants. I am finding that there are many more vegetarian and vegan options than I remember. One of my favorite places for brunch after yoga class is The Yogi Bar. They also offer yoga classes but I haven't worked up to their level yet — maybe next year.
Unfortunately, I can't just eat out for every meal so I am also cooking: beans for less than a dollar, chopping fruit, homemade pico de galo, and guacamole all fresh. I purchase warm corn tortillas out of a cooler (!!!) at the commercial (grocery store) after a good 30 minute walk there. We cab it back and unload our groceries into available shopping carts at the condo and wheel it right into our kitchen! The tortilla chips are not coated in granular flavor enhancers that mess with your brain and taste buds. They are simply tortilla chips. I feel I am in detox and I'm feeling good! Tequila has been my beverage of choice. No wine, no gin martinis or tonics. Just lots and lots of tequila — straight, with club soda and lime, margaritas, palomas ... hmmm, maybe that's why I'm feeling so good!
Did you catch that I wrote "maybe next year?" We have already reserved our spot in beautiful Puerto Vallarta for next January 2020. You kinda have to if you want to stay near the marina. You can bet next year I will have painting supplies! If this is carrying on, well then let me carry on! I think 2019 will be another great year and I wish you the same.