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Slack has been using customer data to power its machine learning functions, including search result relevance and ranking, leading to the company being criticized over confusing policy updates that led many to believe that their data was being used to train its AI models.

According to the company's policy, those wishing to opt out must do so through their organizations Slack admin, who must email the company to put a stop to data use.

Slack has confirmed in correspondence to TechRadar Pro that the information it uses to power its ML not its AI is de-identified and does not access message content.

An extract from the companys privacy principles page reads:

To develop non-generative AI/ML models for features such as emoji and channel recommendations, our systems analyze Customer Data (e.g. messages, content, and files) submitted to Slack as well as Other Information (including usage information) as defined in our Privacy Policy and in your customer agreement.

Another passage reads: To opt out, please have your org, workspace owners or primary owner contact our Customer Experience team at feedback@slack.com

The company does not provide a timeframe for processing such requests.

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In response to uproar among the community, the company posted a separate blog post to address concerns arising, adding: We do not build or train these models in such a way that they could learn, memorize, or be able to reproduce any customer data of any kind.

Slack confirmed that user data is not shared with third-party LLM providers for training purposes.

The company added in its correspondence to TechRadar Pro that its "intelligent features (not Slack AI) analyze metadata like user behavior data surrounding messages, content and files but they don't access message content."

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Slack is training its machine learning on your chat behavior unless you opt out via email - TechRadar

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Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience - Nature.com

Ethereum, the nerdy cryptocurrency that was lately overshadowed by Bitcoin, is in the news again, and the price is pumping. What gives?

Well, only one of the most important developments in Ethereum's history, anon. An Ethereum spot ETF (Exchange Traded-Fund) might get approved in the U.S. today that's Thursday, May 23.

Before we get into all that, here's a very short primer on Ethereum or ETH. Launched in 2015 by programmer Vitalik Buterin and others, Ethereum is the second largest cryptocurrency by market cap, behind Bitcoin, and it has been so for the better part of the past five years or so.

Ethereum is a very different beast from Bitcoin. The latter is a digital currency and a public ledger of transactions that uses a network of computers (miners) to securely verify every transaction in the system, as well as create new coins through a computing-intensive process called proof-of-work.

Ethereum is a blockchain platform for decentralized apps. Unlike Bitcoin, it uses proof-of-stake to power and secure the network, meaning there is no environmentally unfriendly mining, with validators using a stake of their ether or ETH (the underlying currency of the platform) to validate transactions. Also, unlike Bitcoin, which is all about the secure sending and receiving of bitcoins and fairly little else, Ethereum is a platform for other decentralized apps (also called smart contracts) to run on.

As you can imagine, this makes Ethereum more powerful than Bitcoin in a sense, but it also makes it more complicated, both in terms of usage and implications. These days, basically everyone the likes of large banks and pensions funds included understands Bitcoin to be a largely decentralized digital asset, which can be bought, securely stored and sold, akin to a digital version of gold. Ethereum is a lot more complicated, with the U.S. SEC (Securities and Exchange Commission) not being entirely clear on whether ETH is a security or not.

This leads us to the part about ETFs. In January 2024, after receiving the SEC's blessing, Bitcoin spot ETF funds started trading in the United States. This had immense implications as to who can buy Bitcoin; suddenly, a U.S. state pension fund or an investment fund was able to easily get exposure to Bitcoin without worrying about breaking some rule. And the "spot" part, in contrast to a futures ETF, means that the Bitcoin spot ETFs must buy actual Bitcoins when someone buys their product.

The interest was record-breaking, with more than $13 billion flowing into BTC via spot ETFs since their inception. And unsurprisingly, the price of Bitcoin soared from around $42,000 in early January to roughly $69,500 at writing time.

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Many of the same entities large investment companies such as BlackRock, VanEck, and Ark also filed for a spot Ethereum ETF, with deadlines for SEC's approval or denial starting on May 23. And up until a few days ago, analysts largely believed that the ETFs would be denied, given SEC's previous reluctance to provide clear guidance over whether ETH is a security or not.

This has changed. According to Bloomberg senior analyst Eric Balchunas, there was "chatter" that the SEC has completely reversed its stance on Ethereum, followed by a slew of potential ETF issuers submitting amended 19b-4 forms to the SEC, signaling that there's a very good chance that the ETFs are on their way for approval.

We know, the sheer mention of something like a 19b-4 form made you fall asleep instantly. But we mention it because there's another set of forms that need to be approved, the S-1 forms, and those are key for actual ETF approval.

In practice, this means we could get a very good indication that one or more (probably more) Ethereum ETFs are coming, but it might take weeks or months before they actually start trading.

As a result of these filings, the price of Ethereum rose from around $3,100 to $3,800, where it's trading at writing time.

Of course, nothing is official or set in stone. The Ethereum ETF applications could still get denied, though the consensus among experts is that it's now a question of when, not if, it will happen. A denial would surely be a cold shower for Ethereum's price, at least in the short term.

This is not just about Ethereum's price. This sudden change of sentiment by the SEC could mean that the U.S. government is suddenly far more open to everything crypto related. Indeed, an important crypto bill was just passed by the U.S. House of Representatives, despite the SEC head Gary Gensler having some very stern words about it.

Perhaps the simplest of implications of this approval is other crypto spot ETFs getting the nod in the future. But with BlackRock launching a tokenized version of its money-market fund on Ethereum, it's getting easy to envision a future in which a big chunk of global finance exists on the blockchain. In other words, your nerdy, crypto-mining neighbor who told you that one day all of finance will roll into crypto, may have actually been right.

Well, unless you're a trader looking to capitalize on price moves, you don't really have to do anything. Regardless of whether the Ethereum spot ETF is denied, approved, or delayed today, Ethereum and its ecosystem of apps will keep trudging along.

But it is important to consider that a potential approval fully legitimizes an entire new class of crypto assets. Institutions, funds, banks, perhaps even pension funds, will be looking to get in on the action, and it could spark a thriving period for Ethereum, as well as the apps and assets that reside on it. After a bit of a lull in the past couple of years, the crypto space could once again become very exciting over the next couple of years.

Read more here:

Ethereum heats up over key ETF decision. Here's what you need to know. - Mashable

The advent of AI has been hailed by many as a societal game-changer, as it opens a universe of possibilities to improve nearly every aspect of our lives.

Astronomers are now using AI, quite literally, to measure the expansion of our universe.

Two recent studies led by Maria Dainotti, a visiting professor with UNLVs Nevada Center for Astrophysics and assistant professor at the National Astronomical Observatory of Japan (NAOJ), incorporated multiple machine learning models to add a new level of precision to distance measurements for gamma-ray bursts (GRBs) the most luminous and violent explosions in the universe.

In just a few seconds, GRBs release the same amount of energy our sun releases in its entire lifetime. Because they are so bright, GRBs can be observed at multiple distances including at the edge of the visible universe and aid astronomers in their quest to chase the oldest and most distant stars. But, due to the limits of current technology, only a small percentage of known GRBs have all of the observational characteristics needed to aid astronomers in calculating how far away they occurred.

Dainotti and her teams combined GRB data from NASAs Neil Gehrels Swift Observatory with multiple machine learning models to overcome the limitations of current observational technology and, more precisely, estimate the proximity of GRBs for which the distance is unknown. Because GRBs can be observed both far away and at relatively close distances, knowing where they occurred can help scientists understand how stars evolve over time and how many GRBs can occur in a given space and time.

This research pushes forward the frontier in both gamma-ray astronomy and machine learning, said Dainotti. Follow-up research and innovation will help us achieve even more reliable results and enable us to answer some of the most pressing cosmological questions, including the earliest processes of our universe and how it has evolved over time.

In one study, Dainotti and Aditya Narendra, a final-year doctoral student at Polands Jagiellonian University, used several machine learning methods to precisely measure the distance of GRBs observed by the space Swift UltraViolet/Optical Telescope (UVOT) and ground-based telescopes, including the Subaru Telescope. The measurements were based solely on other, non distance-related GRB properties. The research was published May 23 in the Astrophysical Journal Letters.

The outcome of this study is so precise that we can determine using predicted distance the number of GRBs in a given volume and time (called the rate), which is very close to the actual observed estimates, said Narendra.

Another study led by Dainotti and international collaborators has been successful in measuring GRB distance with machine learning using data from NASAs Swift X-ray Telescope (XRT) afterglows from what are known as long GRBs. GRBs are believed to occur in different ways. Long GRBs happen when a massive star reaches the end of its life and explodes in a spectacular supernova. Another type, known as short GRBs, happens when the remnants of dead stars, such as neutron stars, merge gravitationally and collide with each other.

Dainotti says the novelty of this approach comes from using several machine-learning methods together to improve their collective predictive power. This method, called Superlearner, assigns each algorithm a weight whose values range from 0 to 1, with each weight corresponding to the predictive power of that singular method.

The advantage of the Superlearner is that the final prediction is always more performant than the singular models, said Dainotti. Superlearner is also used to discard the algorithms which are the least predictive.

This study, which was published Feb. 26 in The Astrophysical Journal, Supplement Series, reliably estimates the distance of 154 long GRBs for which the distance is unknown and significantly boosts the population of known distances among this type of burst.

A third study, published Feb. 21 in the Astrophysical Journal Letters and led by Stanford University astrophysicist Vah Petrosian and Dainotti, used Swift X-ray data to answer puzzling questions by showing that the GRB rate at least at small relative distances does not follow the rate of star formation.

This opens the possibility that long GRBs at small distances may be generated not by a collapse of massive stars but rather by the fusion of very dense objects like neutron stars, said Petrosian.

With support from NASAs Swift Observatory Guest Investigator program (Cycle 19), Dainotti and her colleagues are now working to make the machine learning tools publicly available through an interactive web application.

See original here:
Cosmic Leap: NASA Swift Satellite and AI Unravel the Distance of the Farthest Gamma-Ray Bursts - UNLV NewsCenter

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Ethereum (ETH) faced a nearly 5% crash in one hour this Thursday, despite the anticipation around the approval of spot Ethereum exchange-traded funds (ETFs) in the US. The X user identified as ai_9684xtpa pointed out that this was likely a market manipulation movement by the trading firm Symbolic Capital Partners.

The agency sold 6,968 ETH in one minute at 20:56, worth $27.38 million, with an average selling price of $3,930; one transaction sold 3,497 ETH on the chain at one time, and the bribe cost was as high as 90 ETH, explained ai_9684xtpa.

Such transactions are known as MEV, short for maximal extractable value, which consists of using on-chain resources to profit. The payment of 90 ETH suggests a hurry to sell the position at a higher price to make it crash, possibly to buy it again at a lower price.

Since the crash, Ethereum has ranged in and out of the $3,800 price level and is priced at $3,803.37 at the time of writing, nearly 22% away from its previous all-time high.

As shared by Bloomberg ETF analyst James Seyffart, an approval of spot Ethereum ETFs is happening this Thursday. Despite the low odds given to this scenario until last Monday, Seyffart and his fellow Bloomberg analyst Eric Balchunas boosted the chances to 75% after the SEC started contacting the issuers.

Since then, various asset management firms presented amends to their 19b-4 filings, and VanEcks Ethereum spot ETF even got listed on DTCC under the ticker $ETHV. The first final deadline is today, as the US regulator must decide on VanEcks application.

Moreover, according to Balchunas, the SECs decision on spot Ethereum ETFs might come at 4 pm (EST). Although a positive outcome is expected, it doesnt mean immediate permission for trading.

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Ethereum price crash attributed to MEV manipulation: Report - Crypto Briefing