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

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."

Here is the original post:
Slack is training its machine learning on your chat behavior unless you opt out via email - TechRadar

The following is a summary of Machine learning study of the extended drugtarget interaction network informed by pain related voltage-gated sodium channels, published in the April 2024 issue of Pain by Chen et al.

Pain is a significant global health issue. However, current treatment options lack in terms of effectiveness, side effects, and potential for addiction, raising the need for improved treatment and the development of new drugs.

Researchers conducted a prospective study identifying potential drug candidates for pain management by targeting Voltage-gated sodium channels (Nav1.3, Nav1.7, Nav1.8, Nav1.9).

They constructed a protein-protein interaction (PPI) network based on pain-related sodium channels and drug-target networks. From over 1,000 targets, 111 inhibitor data sets were selected, and machine learning (ML) was employed to select candidates using a natural language process.

The results showed 150,000 drug candidates for side effects, repurposing potential, and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of candidates.

Investigators concluded an innovative platform for the pharmacological development of pain treatments, potentially offering improved efficacy and reduced side effects.

Source: journals.lww.com/pain/abstract/2024/04000/machine_learning_study_of_the_extended_drug_target.17.aspx

Original post:
Pain-related Sodium Channels in Drug-target Interaction Network: A Machine Learning Analysis - Physician's Weekly

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

TikToks latest machine learning ad solutions are proof that the platform wants to automate as much of its advertising as possible.

The product, dubbed Performance Automation, was announced at the platforms fourth annual TikTok World product summit today its first official summit since Biden signed the TikTok divest or sell bill last month, and subsequently the entertainment app took the U.S. government to court to appeal.

Its safe to say TikTok wants advertisers to believe its not entertaining the idea of being booted out of the U.S. anytime soon. If that wasnt already obvious during its NewFront earlier this month, this latest announcement makes it clearer that its business as usual for the platform right now. Or at least trying to make it as clear as possible that advertisers can park their contingency plans and keep spending on TikTok.

TikTok is actively working to keep marketers engaged and on the platform despite the legislative challenges, said Traci Asbury, social investment lead at Goodway Group. They [TikTok] have complete confidence in their upcoming legal appeals and are actively encouraging marketers to keep adopting best practices and usage of the platforms capabilities to make positive impacts on their businesses.

Well, you probably already know about TikToks Smart Performance Campaign, which was launched last year. The campaign uses semi-automation capabilities including auto-targeting, auto-bidding and auto-creative.

But Performance Automation, which is still in early testing, goes one step further, by automating more of the process, including the creative. With this campaign, advertisers input the necessary assets, budget and goals, and TikToks predictive AI and machine learning will select the best creative asset, to ensure the best campaign is put in front of the right customer at the right time. As a TikTok spokesperson confirmed, the platform is moving toward a performance automation vision and this latest product is the next step on that journey.

And thats not all. The platform has also launched a similar capability for its TikTok Shop, dubbed TikTok Shop Marketing Automation. Like Performance Automation, this works by automating bidding, budgeting, ad management and creative for TikTok Shop products. Since TikTok Shop is only available in select regions, this latest product is currently rolled out in South-East Asia, and in testing in the U.S.

Ohio-based health and wellness brand Triquetra Health is one of those early testers. According to Adolfo Fernandez, global product strategy and operations at TikTok, the brand already achieved 4x their return on investment in TikTok Shop within the first month of using this new automation product, and increased sales on the platform by 136%. He did not provide exact figures.

To be clear, Performance Automation and TikTok Shop Marketing Automation arent their official names. These are just temporary identities the platform is using until they roll out the products officially.

Still, all sounds familiar? Thats because it is. Performance Automation is similar to what the other tech giants have been doing for a while now, and what TikTok started to dabble in with its Smart Performance Campaign last year. Think Googles Performance Max, Metas Advantage+ and now even Amazons Performance+ they all play a similar role for their respective platforms. TikTok just joining the pack simply confirms that automation is the direction that advertising as an industry is heading.

In many ways, this was inevitable. Meta, Google et al have amassed billions of ad dollars over the years by making it as easy as possible for marketers to spend on their ads. From programmatic bidders to attribution tools, the platforms have tried to give marketers fewer reasons to spend elsewhere. Machine learning technologies that essentially oversee campaigns are the latest manifestation of this. Sooner or later TikTok was always going to make a move.

Still, there are concerns aplenty over how these technologies work they are, after all, the ultimate set it and forget it type of campaign. Marketers hand over the assets and data they want the platform to work with, and the technology takes it from there. Thats it. Marketers have no way of knowing whether these campaigns are doing what the platform says theyre doing because theyre unable to have them independently verified. It remains to be seen whether TikToks own effort will take a similar stance or break with tradition.

Speaking of measurement, TikTok is also launching unified lift a new product which measures TikTok campaign performance across the entire decision journey, using brand and conversion lift studies. KFC Germany has already tried it out and drove a 25% increase in brand recall and saw an 81% increase in app installs, according to Fernandez, without providing exact figures.

Among the other announcements were:

Well for now, nothing much has changed. Marketers have contingency plans in place, but thats just standard business practice. Beyond that, everything as far as TikTok goes is pretty much business as usual.

Colleen Fielder, group vp of social and partner marketing solutions at Basis Technologies said her team is not actively recommending any of their clients discontinue spending on TikTok. Theyre continuing to include the platform on proposals.

We knew TikTok was going to sue the U.S. government, and that may push this 9-12 month timeline even further back, which gives us a longer lead time to continue running on TikTok and / or identify alternative platforms as needed, she said.

For Markacy, its a similar state of play. We have a loose partnership with digital media company Attn, which is heavily invested in TikTok, said Tucker Matheson, co-CEO of the company. Theyre still getting big proposals for work, which is a positive sign.

Link:
TikTok moves toward 'performance automation vision' with latest machine learning ad tools - Digiday