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Keeping Machine Learning Algorithms Humble and Honest in the Ethics-First Era – Datamation

By Davide Zilli, Client Services Director at Mind Foundry

Today in so many industries, from manufacturing and life sciences to financial services and retail, we rely on algorithms to conduct large-scale machine learning analysis. They are hugely effective for problem-solving and beneficial for augmenting human expertise within an organization. But they are now under the spotlight for many reasons and regulation is on the horizon, with Gartner projecting four of the G7 countries will establish dedicated associations to oversee AI and ML design by 2023. It remains vital that we understand their reasoning and decision-making process at every step.

Algorithms need to be fully transparent in their decisions, easily validated and monitored by a human expert. Machine learning tools must introduce this full accountability to evolve beyond unexplainable black box solutions and eliminate the easy excuse of the algorithm made me do it!"

Bias can be introduced into the machine learning process as early as the initial data upload and review stages. There are hundreds of parameters to take into consideration during data preparation, so it can often be difficult to strike a balance between removing bias and retaining useful data.

Gender for example might be a useful parameter when looking to identify specific disease risks or health threats, but using gender in many other scenarios is completely unacceptable if it risks introducing bias and, in turn, discrimination. Machine learning models will inevitably exploit any parameters such as gender in data sets they have access to, so it is vital for users to understand the steps taken for a model to reach a specific conclusion.

Removing the complexity of the data science procedure will help users discover and address bias faster and better understand the expected accuracy and outcomes of deploying a particular model.

Machine learning tools with built-in explainability allow users to demonstrate the reasoning behind applying ML to a tackle a specific problem, and ultimately justify the outcome. First steps towards this explainability would be features in the ML tool to enable the visual inspection of data with the platform alerting users to potential bias during preparation and metrics on model accuracy and health, including the ability to visualize what the model is doing.

Beyond this, ML platforms can take transparency further by introducing full user visibility, tracking each step through a consistent audit trail. This records how and when data sets have been imported, prepared and manipulated during the data science process. It also helps ensure compliance with national and industry regulations such as the European Unions GDPR right to explanation clause and helps effectively demonstrate transparency to consumers.

There is a further advantage here of allowing users to quickly replicate the same preparation and deployment steps, guaranteeing the same results from the same data particularly vital for achieving time efficiencies on repetitive tasks. We find for example in the Life Sciences sector, users are particularly keen on replicability and visibility for ML where it becomes an important facility in areas such as clinical trials and drug discovery.

There are so many different model types that it can be a challenge to select and deploy the best model for a task. Deep neural network models, for example, are inherently less transparent than probabilistic methods, which typically operate in a more honest and transparent manner.

Heres where many machine learning tools fall short. Theyre fully automated with no opportunity to review and select the most appropriate model. This may help users rapidly prepare data and deploy a machine learning model, but it provides little to no prospect of visual inspection to identify data and model issues.

An effective ML platform must be able to help identify and advise on resolving possible bias in a model during the preparation stage, and provide support through to creation where it will visualize what the chosen model is doing and provide accuracy metrics and then on to deployment, where it will evaluate model certainty and provide alerts when a model requires retraining.

Building greater visibility into data preparation and model deployment, we should look towards ML platforms that incorporate testing features, where users can test a new data set and receive best scores of the model performance. This helps identify bias and make changes to the model accordingly.

During model deployment, the most effective platforms will also extract extra features from data that are otherwise difficult to identify and help the user understand what is going on with the data at a granular level, beyond the most obvious insights.

The end goal is to put power directly into the hands of the users, enabling them to actively explore, visualize and manipulate data at each step, rather than simply delegating to an ML tool and risking the introduction of bias.

The introduction of explainability and enhanced governance into ML platforms is an important step towards ethical machine learning deployments, but we can and should go further.

Researchers and solution vendors hold a responsibility as ML educators to inform users of the use and abuses of bias in machine learning. We need to encourage businesses in this field to set up dedicated education programs on machine learning including specific modules that cover ethics and bias, explaining how users can identify and in turn tackle or outright avoid the dangers.

Raising awareness in this manner will be a key step towards establishing trust for AI and ML in sensitive deployments such as medical diagnoses, financial decision-making and criminal sentencing.

AI and machine learning offer truly limitless potential to transform the way we work, learn and tackle problems across a range of industriesbut ensuring these operations are conducted in an open and unbiased manner is paramount to winning and retaining both consumer and corporate trust in these applications.

The end goal is truly humble, honest algorithms that work for us and enable us to make unbiased, categorical predictions and consistently provide context, explainability and accuracy insights.

Recent research shows that 84% of CEOs agree that AI-based decisions must be explainable in order to be trusted. The time is ripe to embrace AI and ML solutions with baked in transparency.

About the author:

Davide Zilli, Client Services Director at Mind Foundry

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Keeping Machine Learning Algorithms Humble and Honest in the Ethics-First Era - Datamation

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This Altcoin Surged 80% in Three Weeks Amid Bitcoin (BTC) and Crypto Market Reversal – The Daily Hodl

A lesser-known cryptocurrency has rallied this month, despite the widespread Bitcoin (BTC) and crypto market downturn.

Tachyon Protocols IPX token went from $0.0539 on March 1st to a high of 0.0971 on Thursday a jump of 80%. IPX is currently valued at $0.0922 with a market capitalization of $24.7 million, making it the 111th largest cryptocurrency.

In contrast, practically the entire crypto market is fighting to recover from a historic reversal.

Bitcoin began the month at $8,564 and rose to $9,136 on March 7th, before plunging as low as $4,121 as global markets collapsed amid fears of the coronavirus. Bitcoin is currently at $6,228, a 27% decrease since the start of the month.

Whether Tachyon Protocols rally is sustainable remains to be seen.

The new crypto asset launched on February 25th and is now supported on Bithumb, OceanEX and HitBTC. New cryptocurrencies are especially risky and volatile in a space thats already notoriously dangerous to invest in, with many assets having lost 90% or more of their value since their inception.

The team at Tachyon Protocol says its working to create a decentralized internet protocol. The token is unavailable in countries such as the US, where the company says it could be viewed as a security. Tachyon Protocol released a next-generation virtual private network on the App Store and Google Play last week.

According to the white paper, the IPX token is designed to be used as a means of payment between users on the network.

It will also be used for staking, which allows people who own the native cryptocurrency to help power the network and process transactions in return for rewards.

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This Altcoin Surged 80% in Three Weeks Amid Bitcoin (BTC) and Crypto Market Reversal - The Daily Hodl

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One Altcoin Is Pumping Amidst the Red Crypto Market – Bitcoin Exchange Guide

March has been a rollercoaster in the cryptocurrency market. After crashing down hard last week, the market is finally picking up speed again.

Small-cap cryptos are actually the ones leading the market, now only down 23% in March having bounced off of the lows from last week. Last Friday, the small-cap index was down over 40%, only to make a strong recovery.

One of the very few crypto assets that are currently in the green in the crypto market that has yet again turned red is Dash.

Source: Coin360

The 18th largest cryptocurrency by market cap of $670 million is currently trading at $70.22 with 24 hours gains of 4.12%. Earlier this week, the digital asset went down to $39.80 but is up 48.80% in the past 7 days. During the past week, the digital asset jumped 30% against BTC and over 44% in the ETH market.

Up over 62% so far in 2020, Dash, however, is down 96% from its all-time high of $1,726 hit in January 2018 during the bull run.

The digital asset has also started to record an uptick on its blockchain. Recently, 614.72k DASH was transacted on-chain recently.

Active addresses that fell to 42.6k at the beginning of the second week of March bounced to 181k earlier this week and are currently around 76.5k.

New addresses created have also risen to 83.2k while the number of addresses in the money increased from 19% to 23%, noted crypto analysis company IntoTheBlock.

The most important update has been the release of v0.11. The new Dash Platform release os the testing environment for platform functionality with a significant update to Evonet. Other updates Include register public data contracts, DAPI now works in web browsers, distribution package for local development and Evonet, insecure endpoints have been removed, and updated Dash core to v0.15.

Besides Dash, in the past 24 hours, Zcash is also up 1.21% while Enjin Coinis barely in the green. Top altcoins meanwhile are struggling with Ethereum and XRP down 7.44% and 6.77% respectively.

Small cap coins like Curecoin up 54.81%, Bluezelle 32.69%, Blockmason 25.77% are experiencing gains. However, these coins have extremely low volume, some having as low as $1,500.

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One Altcoin Is Pumping Amidst the Red Crypto Market - Bitcoin Exchange Guide

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Tether Now on Bitcoin Cash Network – Blockchain Technology – Altcoin Buzz

The worlds largest stablecoin by market value Tether USDT has launched on the Bitcoin Cash (BCH) network.

While the entire crypto space is experiencing bearish trends, Tether has recorded a significant increase in value. The stablecoins market cap is now more than $5.7 billion and about $180 million worth of USDT was created a few days ago.

According to a YouTube interview by Cryptofinder with Roger Ver made on Wednesday, March 18, 2020. The stablecoin will be making use of BCH Simple Ledger Protocol (SLP). The SLP is the generic token of BCH and is quite similar to Ethereums ERC-20 tokens. One of Tethers main goals is to be valued at 1:1 with the United States dollars.

Prior to this time, Tether was already available on six other blockchains. They include Ethereum, Algorand, Liquid Network, Omni, Tron, and EOS blockchains.

BCH is the seventh blockchain on which USDT has home live. Joining the BCH ecosystem, USDT will be able to effectively issue tokens with SLP. The launch will also enable bitcoin.com wallet users to send and receive USDT using SLP without the need for third-party applications.

According to treasury data, Tether currently has a market cap of over $5.6 billion. Although Tether began its journey on Omni layer protocol, a large percentage of its mode is on Ethereum. With Ethereum quite close to maxing out last year, moving a substantial portion of Tether to Bitcoin Cash is a step in the right direction. BCH also allegedly has larger blocks and lower transfer fees, making it possible to send small amounts quickly.

Paolo Ardoino, Chief Technology Officer of Tether is optimistic about the launch of the stablecoin on BCH. Pointing out that the launch will yield various benefits. Among which is easy adoption and more applications on BCH blockchain, with Tether facilitating payments for these applications.

BCHs also recently upgraded its Bitcoin.com wallet so as to create more room for its 10 million users to access SLP using the app. Users can now also gain access to USDT using the app too.

Speaking on the launch, Ger Ver said: Its extremely exciting to hear that the worlds biggest stablecoin will be using the Bitcoin Cash Blockchain. Adding that Bitcoin.com users all over the world will be able to send and receive Tether using SLP tokens.

Roger Ver, is the Executive Chair of Bitcoin.com.

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Tether Now on Bitcoin Cash Network - Blockchain Technology - Altcoin Buzz

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A Treatise on Bitcoin and Privacy Part 2: Don’t Be Misled by Red… – Bitcoin Magazine

In Part One of this treatise, we examined the fundamental relationship between Bitcoin and privacy by going back to the beginning with the whitepaper. In spite of some excellent privacy preserving options that have been available to users since those early days, we seem to have taken a few wrong turns. But to fix it, in order to make Bitcoins privacy great again, we must be able to distinguish between real privacy and red herrings that can only lead us further off the path.

Bitcoin is an effective system to transfer and store wealth, but that wealth has first to enter the system somehow, very often coming from fiat money. (Of course, you can also earn satoshis directly in exchange for goods and services you provide, instead of buying them with fiat.)

Fiat-enabled bitcoin on-ramps (often known as cryptocurrency exchanges), acting as liquidity bridges, created huge privacy problems in Bitcoin. In order to manage fiat, exchanges will have to use traditional bank accounts. In order to get those, they have to meekly accept all the rules, conditions and limitations banks require. Traditional fiat banks, in turn, will pass over the extremely complex and heavy compliance burden they received from governments and regulatory agencies, including that concentration of economic illiteracy called KYC/AML regulation.

So, fiat-to-bitcoin bridges will almost always end up demanding a scary amount of personal information from their user, linking that information to a few deposit and withdrawal addresses (often incentivizing continuous reuse) and then even hiring chain-analysis companies in order to follow, trace, tail and stalk all the previous and following economic activity on-chain.

The first and most important reason for doing so is because these on-ramps are scared to lose the privilege of having a fiat bank account. Bitcoin was, is and will always be considered a borderline reality by governments and government-sanctioned legal cartels like modern fiat banks. Thus, its realistic to assume they would close down operative accounts to any exchange which couldnt guarantee the same level of financial surveillance that fiat banks routinely enact.

For this reason, fiat-enabled gateways not only keep promoting wrong and dangerous uses of the Bitcoin protocol, discouraging security best practices and hiring chain-analysis spy companies: They often even go to great lengths to publicly praise KYC/AML nonsense regulations and to push the narrative that Bitcoin is completely traceable, marketing some probabilistic assumptions as legal proofs and ignoring even the existence of the fundamental privacy features of the protocol.

For a while now, these businesses have been freezing or confiscating users accounts because of what theoretical chain-analysis heuristics (dishonestly promoted as facts) suggest these users may have been doing way before or way after their interaction with the exchange, basically trying to break fungibility in Bitcoin.

We often see this happening for activities that arent even explicitly considered illegal in the specific jurisdiction under which they happened: online gaming, adult services, political campaigns, etc. Anything considered even remotely controversial has been depicted as forbidden, and any statistical guess about on-chain activity, based on common patterns and typical tools, has been depicted as proven.

Of course, theres nothing really proven in chain-analysis heuristics, so the spy companies arbitrarily decide how many on-chain hops to look for, arbitrarily assuming who is doing what. Even assuming that such heuristics are correct (they have never been 100 percent reliable, and they are less and less so each day, while Bitcoin developers build better tools and Bitcoin users start employing best practices), this behavior is unacceptable. It is the digital equivalent of your physical bank sending private investigators to follow your every move for days after you withdraw cash at the ATM, and then freezing or confiscating your bank account entirely if that PI comes back with a report that says that you may have, with some probability, engaged in controversial actions with that cash.

More recently, this shady behavior has extended beyond some generically controversial activities engaged by somebody somehow connected with customers to encompass even the very act of trying to use Bitcoins security and privacy best practices!

In January 2020, a company that operates a regulated exchange froze a customers account once they discovered possible hints that somebody, possibly the customer himself (but after some hops following the withdrawal transaction, that is, not even directly), was using a wallet enabling privacy best practices. Again, imagine your physical bank sending a private investigator to follow your steps for days after you withdraw some cash at the ATM, and then freezing or confiscating your bank account if that PI reports that says that you may have, with some probability, closed your shutters at home, or pulled your shower curtains while naked, or put a lock on your personal journal, or used HTTPS within your web browser!

Furthermore, the specific message to the customer was tragically hilarious: It said that the business cant condone activities such as peer-to-peer (sic!) mixing or gambling. All this while talking about Bitcoin, which is literally a peer-to-peer protocol whose transactions can natively work as mixers, and coming from a business that operates in cryptocurrency trading, which some consider not that different from gambling!

There have been many reactions from Bitcoin users and analysts to these dodgy examples of behavior, many of which are based on logical fallacies or straight-on distortion of the facts. A classical example is the absurd notion that Bitcoin users should not use privacy best practices, because thats dangerous.

The pseudo-argument goes something like this: Since some overzealous business may use unreliable heuristics to accuse you of adopting privacy and security best practices that they have arbitrarily defined as unacceptable, possibly freezing or even confiscating your account, or flagging it as suspicious, you should just stop using those security best practices and move to insecure alternatives instead. In other words, to use our physical bank example, since your bank might flag your account if the PI they sent after you comes back with a report that says that you may have, with some probability, used some privacy best practices a few days after a cash withdrawal, you should just stop closing your shutters while home, or pulling the shower curtains while naked, or putting a lock on your personal journal, or using HTTPS within your web browser.

This is nonsense, of course. If anything, its not using privacy and security best practices that would turn out to be extremely dangerous not just for your financial safety but also for your physical safety. Reminder: Bitcoins privacy is all-or-nothing! Once a business is able to attach your physical identities, not just to an on-chain address but also to all the future and past history connected with it, all it takes is a little leak (by the business itself, by its spy-contractors or by one of the countless government agencies which will receive and pass along that information) to direct very dangerous enemies to your doorstep.

Incidentally, the pseudo-argument is flawed more fundamentally as well: Even if you were so reckless as to decide to trust this third party with a complete account of your future and past transactions, in spite of the risk to your physical security (and that of your loved ones), you may achieve the very same result just by sending it the cryptographic proofs of all the inputs you ever signed (either on-chain or on upper layers), allowing the meddling gateway to read through each of your CoinJoin or Lightning Network routing all without giving up generic privacy best practices. You are still risking a leak, but at least you are not giving every random guy with an internet connection an easy way to deanonymize and stalk you (and others you interact with).

Usually this red herring comes with some distorted vision of Bitcoins utility. If users just want to invest in bitcoin as an uncorrelated financial asset with some disinflationary features, they say, then they dont need privacy at all. This pseudo-argument is severely flawed.

Heres the bad news: Gold was, for many many centuries up until 1933, a typically uncorrelated financial asset with some disinflationary features that people in the United States and elsewhere could invest in. But then came Executive Order 6102. Gold was confiscated all across the nation, and all the investors who didnt protect their privacy (which was especially hard with paper gold, kept in custody by trusted third parties eager to comply with the order, but also pretty hard with actual physical gold, difficult to hide in large amounts or to smuggle across a border) had to give it to the government.

A good general heuristic is this: If you are a privileged first-world investor, with a good KYC identity, and you are looking for some kind of investment that is politically uncontroversial now and likely to remain that way, then you will soon be able to access that type financial product from you favorite fiat bank. If that describes you, dont even concern yourself with complex stuff like private keys, blockchain fees, addresses: leave the real protocol to real users. Just call your good old bank over the phone and ask to buy some bitcoin-flavored risk: certificates, futures, ETNs, ETFs, CFDs, etc.

If, on the other hand, you are not as privileged (like the majority of the world population today, which doesnt have a KYC-friendly identity), or if you think that the financial asset you seek is a bit controversial today already or likely to become so in the future, then you will eventually need some very strong privacy techniques to acquire it and to safely store it, since legally compliant exchanges, brokers and marketplaces will do everything they can to keep you out of it or take it from you.

A second typical reaction, even more absurd, is to suggest privacy altcoins as a solution to this problem. A regulated exchange will flag your account if you use best practices such as CoinJoin, or Lightning Network, or address-reuse-avoidance. Then, instead of bitcoin, just use some illiquid bitcoin-clone whose design has been altered in such a way that its said to offer more fungibility, right?

The superficial problem with this approach is that such magic privacy coins dont actually exist in the real world. On one hand, thats because most of the changes marketed as privacy improvements are either entirely fake or greatly exaggerated. They also tend to come with serious trade-offs which make these clones otherwise unusable at scale over the long run (usually including a completely centralized development process, trivial to compromise).

On the other hand, even if such a coin were to exist, from a technological point of view, it couldnt work in practice from an economical point of view. Remember: Privacy loves company. A huge chunk of the bitcoin economy and its users would have to move to the very same bitcoin-clone as you. Otherwise, your transactions will have a lower liquidity and a smaller anonymity set, regardless of how perfect and sci-fi-worthy the privacy tech you are using is.

There are variants of this red herring which are based on some kind of bimetallic standard idea: Those proponents will suggest that you use bitcoin as your fundamental store of value (which centralized illiquid clones cant be for obvious economic reasons), and then add a particular privacy altcoin for privacy in transactions.

Of course that cant work in most real-world scenarios. Assuming that the payer and the payee both use bitcoin as a long-term store of value, the payee would have to move satoshis from his personal storage solution to some kind of market (regulated or not, it doesnt really matter here) with the same privacy issues as any other bitcoin transaction; then exchange those satoshis for altcoins on some low-liquidity shared order book with very low privacy; and then move the altcoins over their native system with a low anonymity set to an address provided by the payee. Then the payee would have to repeat the same steps in reverse.

The privacy guarantees of the whole process would be, overall, way lower than a normal bitcoin transaction performed following the best practices. Of course, these guarantees can be increased if either the payee or the payer batch many transactions in one big altcoin reserve, exchanging satoshis only once, way before or way after the single individual transactions. But this would require the altcoin to be a reliable store of value for long periods of time which illiquid and centralized bitcoin-clones (often crippled by unbalanced trade-off choices between privacy features and other very delicate aspects) cant be.

The deeper problem with this approach is that, even if feasible, it would become completely useless pretty quickly. The very same reasons that convinced some regulated exchanges to actively discourage or even prevent their customers from adopting privacy best practices on Bitcoin, would readily convince the very same exchanges to just delist any privacy-focused bitcoin-clone. The smaller the altcoin, the weaker the incentive to list it. The bigger the altcoin, the stronger the regulatory pressure to delist it. Its as simple as that.

Some weak attempts at steel-manning this approach focus on the distinction between mandatory privacy and opt-in privacy. With Bitcoin, the altcoin proponents say, you are not forced to use the fungibility features at the protocol level, so its easy for the exchange to ask you not to use them. But with my altcoin, you have no choice, so the regulated exchange will also have no choice but to allow you to use them.

Again, this is nonsense; its not true that a privacy feature can ever be mandatory at the protocol level.

As the history of Bitcoin teaches us, its mostly about tools: Even when the base protocol includes strong fungibility capabilities, if the most widespread tools dont leverage them, then people will simply not use them. Theyll just resort to using whatever is easy and available, even if that mean adopting bad practices instead.

It doesnt matter which protocol you use: If the tools are inadequate, so is your privacy. Just as you can have a bitcoin wallet that is incompatible with CoinJoin and that forces address reuse, you can also have a monero wallet that leaks confidential information about amounts and always constructs ring-signatures between every single user and himself. If such a wallet is widespread, spy companies can assume such behavior as common and build de-anonymization heuristics.

Of course, altcoin proponents may just build and market tools that actually use the privacy features already present in their clone at the protocol level. But then again they would need just as much time, money and effort that is required for building and marketing tools that actually use the privacy features already present in Bitcoin at the protocol level.

A more useful distinction to examine is the one between privacy features that are economically convenient to use and privacy features that are costly to use. The perfect (bad) example would be that of shielded transactions in the altcoin Zcash: Since they take way more space inside blocks, and way more computation time to be verified and signed (making this last action almost impossible on a light client), economic incentives push the already-few users of the coin to unshielded transactions, which are just an outdated version of the traditional bitcoin ones.

As a direct effect, many users will think they have more privacy when this process, in fact, makes tracking and deanonymizing far easier. An indirect effect will be that the very few users who do decide to pay the extra cost for shielded transactions will find themselves within an even smaller anonymity set, ending up exposed instead of protected.

An opposite example would be the Lightning Network on Bitcoin: Since block space is expensive, users often have strong economic incentives to switch to payment channels to save fees, reducing the timechain footprint to just opening and closing channels.

Ultimately, its not surprising at all that some of the most vocal proponents of the CoinJoin is risky because your account will get flagged narrative turn out to be also promoters of new, illiquid privacy altcoins, which they hope to push to profit from pump-and-dump schemes. Same old story: Bitcoins fees are too high: buy my low-fee altcoin! or Bitcoin signatures arent quantum-proof: buy my quantum-ready altcoin! or Bitcoins smart contracts arent flexible enough: buy my Turing-complete altcoin! or Bitcoin is not fungible enough: buy my privacy altcoin!

Are there real solutions and ways to mitigate the threat that regulated exchanges pose to the privacy and the security for Bitcoin users, beyond the red herrings? Yes: many.

The ultimate solution, albeit very slow, will eventually come from the evolution of the market. While more and more resources will leave the fiat world to enter Bitcoin over the years, more and more parts of the bitcoin economy will move from fiat gateways to satoshi-denominated trades among users. Gateways will still be important, but gradually less so, making their bargaining power lower and lower over time. Fiercer competition will also help: People will be happy to leave meddling PI-hiring banks who force them to keep shower curtains open if they have alternatives.

Another mitigation will come from the evolution of Bitcoin tools. While more and more modern wallets will make it harder to reuse addresses or merge inputs, and easier to coordinate CoinJoin rounds, regulated exchanges will have a harder time forcing their customers to use only old, outdated or inferior wallets instead.

Yet another mitigation will come from the adoption of the Lightning Network. Since block space in the base layer will become more expensive, users will be strongly incentivized to route transactions over payment channels instead. It will be harder for regulated exchanges to arbitrarily ban customers due to a probabilistic link between the satoshis they deposited or withdrew on the Lightning Network, especially when the latter will be ubiquitous, thanks to economic incentives.

Additional improvements may possibly come from the next protocol upgrades in Bitcoin, especially the one called cross-input Schnorr signature aggregation. This upgrade will make coordinating with several different parties within CoinJoin rounds extremely convenient, from an economical perspective.

Another hope comes from the idea of decentralized exchanges (DEXes). So far, they suffer from liquidity limitations and their security remains tricky: While the Bitcoin leg of any trade can be easily trust-minimized, the fiat leg remains ultimately trust-based, making complex and expensive escrow mechanisms necessary. (In turn, escrow mechanisms tend to prove very difficult to decentralize effectively.)

Your privacy is in your hands just keep calm and be diligent. Dont submit to dangerous privacy violations. Dont reuse addresses. Use CoinJoin. Close your shutters when youre at home. Pull the shower curtains when youre naked. Put a lock on your personal journal. Use HTTPS when surfing the web.

In the end, Bitcoin fixes this.

This is an op ed contribution by Giacomo Zucco. Views expressed are his own and do not necessarily reflect those of Bitcoin Magazine or BTC Inc.

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A Treatise on Bitcoin and Privacy Part 2: Don't Be Misled by Red... - Bitcoin Magazine

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What Is Artificial Intelligence (AI)? – IoT For All

Artificial Intelligence is a topic that has been getting a lot of attention, mostly because of the rapid improvement that this field has seen since the turn of the 21st century. Amazing innovations are laying the foundation for ongoing breakthrough achievements. In this article, Im going to focus on three specific topics:

In the 1950s, AI pioneers Minsky and McCarthy described artificial intelligence as any task performed by a program or a machine that, was it performed by a human, would have required that human to apply intelligence to accomplish the task.

This is a fairly broad description. Nowadays, all tasks associated with human intelligence are described as AI when performed by a computer. This includes planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, manipulation and, to a lesser extent, social intelligence and creativity.

Artificial intelligence is defined as the branch of science and technology that [is] concerned with the study of software and hardware to provide machines the ability to learn insights from data and [the] environment and the ability to adapt in changing situation[s] with high precision, accuracy and speed.Amit Ray, Compassionate Artificial Superintelligence AI 5.0AI with Blockchain, BMI, Drone, IOT and Biometric Technologies

Now that we know what AI actually means, lets find out what its used for!

While surfing the web, have you ever wondered how most ads are related to your interests? Thats a representation of AI, more specifically, machine learning. However, AI is more commonly associated with robots, such as the ability of a robot to think on its own and the potential for computer consciousness. While these would be astounding achievements, they involve highly complex algorithms which we still cant produce today.

Machine learning is a big part of AI, and it might be the key reason for this fields meteoric rise. Its based on the principle of trial and error; every time we try to solve a problem, like a maze, were going to fail at least once. However, failing is a good thing in machine learning, because it enables the program to learn new information. That information is stored as data, and each time an AI goes down a specific path, it will reference the data from prior trials to see which one will work best this time.

To expand on the above example, Im going to teach you one of the first AI algorithms (often used to solve mazes), the A* algorithm.

To understand this algorithm, lets visualize our maze as a chess board with inaccessible regions (like a maze) that well call nodes.

This is a fun example of AI in action, since flying cars would be reliant on AI to function properly. In the future, scientists believe were going to have autonomous cars that transport people to their desired destinations. This involves cars having some sort of artificial intelligence, more specifically, machine learning,because they need to always find the best possible course to the destination, not crash into buildings and respect other vehicles. A very basic implementation of this, although extremely ineffective and slow, could be the A* algorithm, where buildings represent inaccessible nodes. However, some good alternatives exist that we didnt review in detail due to their high levels of complexity:

This article was written to provide a fun introduction to AI and to show its potential for future technologies. More than ever, its crucial to know the principles of artificial intelligence since it will be so important in the future. We need to constantly be open to new ideas and approaches, such as artificial intelligence (AI), and be willing to challenge assumptions of what this technology can achieve.

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Stanford virtual conference to focus on COVID19 and artificial intelligence | Stanford News – Stanford University News

Russ Altman (Image credit: Courtesy Russ Altman)

The impact of COVID-19 on society and the way artificial intelligence can be leveraged to increase understanding of the virus and its spread will be the focus of an April 1 virtual conference sponsored by the Stanford Institute for Human-Centered Artificial Intelligence (HAI).

COVID-19 and AI: A Virtual Conference, which is open to the public, will convene experts from Stanford and beyond. It will be livestreamed to engage the broad research community, government and international organizations, and civil society.

Russ Altman, one of the conference chairs, is an associate director of HAI and the Kenneth Fong Professor and professor of bioengineering, of genetics, of medicine, of biomedical data science, and, by courtesy, of computer science. He is also the host of the Sirius radio show The Future of Everything. He discusses the aims of the conference.

What was the idea behind the conference?

At HAI, we felt this was an opportunity to use our unique focus on AI and humanity to serve the public in a time of crisis. The issues involved in the pandemic are both nuanced and complex. Approaching it from multiple fields of expertise will help speed us toward solutions. The goal is to make leading-edge and interdisciplinary research available, bringing together our network of experts from across different schools and departments.

We have a world-class set of doctors and biological scientists at Stanford Medical School and theyll, of course, be involved. Well also have experts on AI, as well as the social sciences and humanities, to give their scholarly perspective on the implications of this virus, now and over time. The conference will be entirely virtual with every speaker participating remotely, providing an unpolished but authentic window into the minds of thinkers we respect.

What useful information will come out of the conference?

Were asking our speakers to begin their presentation by talking about the problem theyre addressing and why it matters. They will present the methods theyre using, whether scientific or sociological or humanistic, the results theyre seeing even if their work is preliminary and the caveats to their conclusions. Then theyll go into deeper detail that will be very interesting to academic researchers and colleagues. Importantly, we intend to have a summary of key takeaways afterward along with links to information where people can learn more.

We will not give medical advice or information about how to ensure personal safety. The CDC and other public health agencies are mobilized to do that.

What do you think AI has to offer in the fight over viruses like COVID-19?

AI is extremely good at finding patterns across multiple data types. For example, were now able to analyze patterns of human response to the pressures of the pandemic as measured through sentiments on social media, and even patterns in geospatial data to see where social distancing may and may not be working. And, of course, we are using AI to look for patterns in the genome of the virus and its biology to see where we can attack it.

This interdisciplinary conference will show how the availability of molecular, cellular and genomic data, patient and hospital data, population data all of that can be harnessed for insight. Weve always examined these data sources through more traditional methods. But now for the first time, and at a critical time of global crisis, we have the ability to use AI to look deeper into data and see patterns that were otherwise not visible previously, including the social and cultural impact of this pandemic. This is what will enable us to work together as a scholarly, scientific community to help the future of humankind.

Who do you hope will attend?

The core audience is scholars and researchers. We want to have a meaningful discussion about the research challenges and opportunities in the battle against this virus. Having said that, we know that there are many people with an interest in how scientists, researchers, sociologists and humanists are helping in this time of crisis. So were making the conference open to anyone interested in attending. It will be a live video stream from a link on our website, and available as a recording afterward.

What kind of policy effect do you hope the conference can have?

Good policy is always informed by good research. A major goal of HAI is to catalyze high-quality research that we hope will be heeded by policymakers as they work to craft responses to COVID-19 and future pandemic threats. So this will give insights to policymakers on what will be published in the coming months.

Register for the April 1 conference.

Learn more about the Stanford Institute for Human-Centered AI (HAI).

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Developing new light of tomorrow with artificial intelligence – ELE Times

The research project OpenLicht was launched in September 2016 and stands for the design of smart, customized light solutions based on open source and new materials. OpenLicht was funded by the German Ministry of Education and Research (BMBF), with the goal of enabling new forms of collaboration between science, business, maker and startup community. Infineon Technologies AG is supervising the project in close cooperation with Bernitz Electronics GmbH, Deggendorf Institute of Technology and the Technical University of Dresden.

The results are now being made public and include the prototype of a smart lighting system based on artificial intelligence. It automatically adjusts the light in the room to the users position and activity, such as reading or watching TV, learns the persons preferences and can even respond to a certain degree to circumstances it has not learned previously.

The solution developed in the project is based on open source approaches like openHAB, a smart home system, and machine learning libraries. Use of freely available development environments, software frameworks and low-cost hardware solutions enables integration of a wide range of different sensor data and further development of existing results by the community.

Intelligent light design is entering the smart home and so the vision will accompany us through our everyday life with fully automated solutions. However, the ones currently available on the market so far often pose a raft of challenges for users. It is frequently the case that they are, at most, partly automated and are complicated to program. Moreover, the systems often fail to safeguard privacy or unnecessarily consume power, since light usage is not tailored ideally to the users needs, which in turn has a negative impact on CO2 emissions. OpenLicht has found answers to these challenges.

Smart light solutions with the aid of artificial intelligence (AI)

The use of AI in the local network creates smart light solutions that are safe, yet sustainable and safeguard the users privacy. The AI acts on a system that is closed off from the outside world and does not have to be connected to the Internet.An open source gateway based on a Raspberry Pi and an Infineon Trusted Platform Module (TPM) has been developed to enable that.

That means data does not have to be sent to the cloud, but can instead be processed locally, which ensures security and privacy for households. These factors are vital in increasing the acceptance of smart home solutions. In addition, automatic adjustment to the users activities makes sure the light required at a particular moment is available. That avoids unnecessary floodlighting and helps protect the climate without the need to appeal to users conscience.

Open light for everybody

Software developments have been documented by OpenLicht and published on GitHub, for example, while new light ideas have been fleshed out in do-it-yourself projects and made available for replication on platforms such as Thingiverse or Hand im Glck. The platform concept also fosters dialog within the light community. The ultimate goal is to enable everyone to develop state-of-the-art light solutions.

In the evaluation phase, OpenLicht involved various creative partners at the interface between knowledge, innovation and design in order to open up access to light development: In cooperation with Munich University of Applied Sciences and the Strascheg Center for Entrepreneurship, students of design, electrical engineering and economics developed light innovations, built demonstrators and created business models.

At the Lichtwoche Mnchen trade fair in 2019, various workshops were able to be held both with laypersons and professionals to implement modern light solutions, for example using 3D printing technology. And at an event staged in cooperation with the Technik fr Kinder association in Deggendorf, children built their own smart light in just one morning. OpenLicht therefore enables light to be grasped in a dual sense: Lighting responds to movement and can be used, developed and redesigned by everyone.

For more information, visit http://www.infineon.com

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Is it right to use artificial intelligence in aviation security? – Airport Technology

]]> Greater Toronto Airports Authority recently announced it would be testing HEXWAVE, an AI-enhanced weapon detection technology. Credit: Florian Weihmann (Pexels).

Humans, who are limited by slow biological evolution, couldnt compete and would be superseded. On the face of it, a chilling statement made by the late Stephen Hawking. The world-renowned theoretical physicist was speaking of his fears of an unleashed artificial intelligence (AI). The development of full artificial intelligence could spell the end of the human race, he said, It would take off on its own, and re-design itself at an ever-increasing rate.

The role AI will play in tomorrows world has long been debated, with supporters and sceptics often happy to promote the merits of their argument, whilst almost all the time questioning that of the other side. Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think well augment our intelligence, counters IBM CEO Ginni Rometty.

For now, what is almost irrefutable is the impact AI has on our everyday life. In many cases, however, we often dont know its there, working in the background to make daily routines that little bit easier, and even safer. They are the hopes of the Greater Toronto Airports Authority (GTAA) which recently announced it would be testing HEXWAVE, an AI-enhanced weapon detection technology.

Pearson International Airport will be one of the test sites for Liberty Defenses system, which uses 3D radar imaging to detect and identify weapons. The intention, in aviation at least, is to use the system at a facilitys perimeter with the hope of identifying threats before they reach the terminal.

Keen to stress the purpose is not to replace security measures, the companys CEO Bill Riker said it would further enhance already employed security systems. As well as its trial at the airport, HEXWAVE will be tested at other venues, such as sports stadiums, shopping centres, education facilities and government sites across North America and Europe.

It is a product that detects metal and non-metal objects, and alerts responders through the use of AI technology, says GTAA director of corporate safety and security Dwayne MacIntosh. He said the Authority is looking at testing it at multiple locations to determine how we can best operationalise such a product in the future during the week-long project, although no date has yet been set.

The AI technology looks for prohibited items and alerts airport responders, who then work to mitigate the incident.

HEXWAVEs 3D imaging capabilities mean it can identify even the smallest of weapons including concealed handguns and knives as well as the likes of bombs and suicide vests with what the company says is a low-energy radar. This scans the outline of the body to reveal any abnormal contours, as well as detecting prohibited items in baggage. While the use of technology like this in this realm isnt new, it is the speed with which HEXWAVE carries out the operation that stands out. Rather than a subject needing to stand still, it can carry out its role on moving targets.

The AI component means the system itself assesses and determines whether an individual poses a threat. The AI technology looks for prohibited items and alerts airport responders, who then work to mitigate the incident, says MacIntosh. It can be used both inside and out and is scalable to any setting, helping manage throughput screening in real-time.

He is a firm believer in the advantages AI can offer sites such as airports. He says it facilitates the tracking and identification of threats, integrated into wider airport systems in such a way that it makes response seamless. Using AI to speed the process of identifying, tracking and dispatching resources allows for faster responses and better threat mitigation, thereby protecting passengers and the airport community, he continues.

However, critics may question whether this might infringe on individuals rights and even privacy. Its a concern MacIntosh wants to dispel, saying the system will simply not collect anyones data. Privacy is as important to us as safety, and we will always take measures to preserve both for passengers and the airport community, he adds.

But are systems of this kind necessary? Since the terror attacks on New York and Washington in September 2001, airport security has changed dramatically. In the last few years, AI technologies have been rolled out, with demand continuing to grow as the sector looks to strengthen itself against evolving threats. They have become increasingly crucial in that battle to stay ahead; indeed in the security sphere, they are becoming integral to the work of personnel.

Although there are new issues such as drones, many of the same concerns that we face today existed a decade ago.

Security scanners boasting AI technologies are fast becoming commonplace. Body scanning similar to HEXWAVE is already being used by some airports; facial recognition and other biotechnologies are now complimenting these systems and assisting the increased physical security presence since 9/11. Although there are new issues such as drones, many of the same concerns that we face today existed a decade ago, says MacIntosh. The difference is that the tools we use to mitigate those issues have continued to advance, giving us greater ability to protect passengers and the airport community.

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Artificial intelligence taking lessons on how to second-guess us – Times of India

'; var randomNumber = Math.random(); var isIndia = (window.geoinfo && window.geoinfo.CountryCode === 'IN') && (window.location.href.indexOf('outsideindia') === -1 ); console.log(isIndia && randomNumber Currently, AI may do a plausible job at detecting the intent of another person. It may even have a list of predefined, possible responses that a human will respond within a given situation, they said.

However, when an AI system or machine only has a few clues or partial observations to go on, its responses can sometimes be a little, noted the researchers.

What were doing in these early phases is to help machines learn to act like humans based on our daily interactions and the actions that are influenced by our own judgment and expectations so that they can be better placed to predict our intentions, said Lina Yao, a lecturer at UNSW. This may even lead to new actions and decisions of our own, so that we establish a cooperative relationship, Yao said.

The researchers want to see awareness of less obvious examples of human behaviour integrated into AI systems to improve intent prediction. However, doing so is a tall order, as humans themselves are not infallible when trying to predict the intention of another person, the researchers said.

Sometimes people may take some actions that deviate from their own regular habits, which may have been triggered by the external environment or the influence of another persons actions, she said.

Yao and her team are developing a prototype humanmachine interface system designed to capture the intent behind human movement.

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