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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

The SWR was designed specifically for automated pipe welding.

For Joe White Tank in Fort Worth, Texas, increased demand for construction projectsand more competitive bidding for the pipe fabrication jobs within those projectsrecently presented a new challenge.

The company has been in the welding industry since 1942, specializing in fabricating custom tanks, pressure vessels for industrial and ammonia refrigeration, and piping for commercial and industrial construction. It has built a reputation for quality work while consistently delivering products faster than typical market lead times.

That standard recently got put to the test, but President Jeff Yurtin and his management team arent people used to resting on their laurels. Rather, theyre normally on the hunt for new ways to clear the next hurdle.

For Yurtin, the issue at hand was scaling labor for projects that can dampen productivity if theyre not managed correctly. To meet that need, he chose to invest in a Novarc Spool Welding Robot (SWR). The machine offers accurate torch control and machine learning algorithms that can detect different features of a workpiece.

The unit was designed specifically for pipes, pressure vessels, and other types of roll-welded workpieces. It features an adaptive controls system to help ensure accurate torch control, and AI/machine learning algorithms to detect weld pool features.

The SWR can integrate smoothly with the production flow and existing manufacturing processes of customers, according to Soroush Karimzadeh, Novarcs co-founder and CEO.

The SWR is designed with a small footprint and a very long reach, enabling it to be adopted in almost any fabrication shop, no matter the layout or various requirements, Karimzadeh said. Its designed to be minimally intrusive to the production flow.

The nature of Joe White Tanks bread-and-butter projects can throw a kink into its work process, however. And that includes persistent labor issues.

Piping projects can require hard starts and stops with little time to ramp your labor up and down, Yurtin said. Hiring and firing welders for jobs was not our idea of success. We pursued growing our business with a long-term mindset. By adding the SWR to our shop floor, we added capacity strategically and avoided many of the negative implications that come from a short-term, job-to-job labor force. And it has a small footprint; we would have had to install four manual weld cells to do the job of the SWR.

The SWR also helps to address the nationwide shortage of skilled welders by helping less-experienced operators produce high-quality welds.

Novarcs machine includes a user interface that has proven easy to learn for operators, regardless of experience level.

Balancing a stable workforce with changing customer and industry demands can be difficult, Yurtin said. Our organizations culture is very important to our management team. So, as we have grown our companys market presence, we have worked to limit high employee turnover.

The benefits of workforce continuity are legion. Not only does it give employees a greater sense of job security, but it also results in a more willing commitment to corporate goals. At a time when fabrication shops across North America are experiencing a shortage of skilled welders, the SWR helped limit the impact of this challenge, Yurtin said.

We used to have a department dedicated to pipe welding, but now we have our SWR operators working with fitters, supporting them, to ramp up efficiency, he said. This has freed up welders to work on other projects in our backlog, shrinking our market lead time and significantly increasing our capacity. The Novarc SWR increased our capacity by 400% without reducing quality.

The SWR accommodates users with a set of requirements for the fit-up process and provides comprehensive training for fitters.

This is another way to ensure the integration of the SWR into our clients manufacturing processes is as smooth as possible, Karimzadeh said.

With a nod to sustaining their strong corporate culture, the companys employees are buying in, according to Yurtin.

Anyone thats been in the welding business for more than 10 minutes knows that the physical demands are significant, Yurtin said. Welders get tired.

The ergonomics of the SWR are an immediate benefit for the welder, he added. They're still using their hands, but they dont have to wear a hood, and its much easier with the joystick control.

The increased productivity also created an unexpected effect on the shop floor that Yurtin recounted with a smile:

Sometimes its like a game, where the welders see how much they can get through in one day, and were all pumped when we have a super productive day.

With that sort of team reaction, the machine could even be seen as an aid in recruitment.

Its a more attractive place to work, and the younger generation of welders is really excited about automation and working with the SWR, Yurtin said. Even the older workers find the learning curve easy to handle.

Often with automation comes an initial hesitancy, either about using the new technology or the need to make a change that could be perceived as high risk. Yurtin, however, chooses to focus on the ROI.

Quick payback, he said. As we are able to operate four times faster, we have been able to take on more work. We would need four weld cells previously to deliver the same capacity as one SWR. And the SWR takes 25% to 30% less space. The SWR has increased our ability to accept jobs with shorter lead times, win more projects, and pursue larger bids.

At the end of the day, safety always comes first for Karimzadeh.

Novarc cobots are designed to follow the standard for collaborative robots and collaborative robot applications, governed by the ISO 15066 standard, so the cobot is basically equipped with force- and speed-limiting sensors to ensure that if there is a safety event, it can safely stop the work, Karimzadeh said. In addition, the health hazards for welders are significantly reduced, as the welding torch is moved by the cobot, and welders are not exposed to weld fumes and arc light.

As helpful as those benefits are, the one that stands out for Yurtin is quality.

We are mainly using the SWR to weld pipe for pressure vessels, industrial refrigeration, ammonia refrigerationbasically pipe for industrial applications, Yurtin noted. These need to be ASME-quality, X-ray-quality welds. And the SWR, besides being super easy to operate, has increased the quality and consistency of the welds. The SWR lays the root pass itself, and the penetration is perfect from root to cap. The SWR can handle it all. And it always passes X-ray inspection.

Yurtin also credits the SWR for helping to position Joe White Tank as a future-friendly welding shop.

Were excited to be a showcase for innovation and believe the manufacturing industry needs to adopt new technology to be successful and meet increasing demands for productivity and competitive bidding, he said. Our clients are really impressed by the technology of the collaborative robot in our shop, not to mention the quality, productivity, output, and efficiency.

Karimzadeh added, The end users of pipe spools are pushing harder and harder regarding project delivery timelines, cost of production, and quality of welds. This is ultimately pushing the industry to automate. Its the only way to meet the timelines, manage the cost, and maintain the quality of the work.

For Joe White Tank, the search for new solutions to welding challenges is a constant quest for answers that improve its product, its work environment, and, ultimately, its bottom line while reflecting positively on its reputation in the industry.

The rest is here:
Pipe welding integrates machine learning to boost production - TheFabricator.com

PE: Do you see the FDA placing any restrictions on the use of AI and machine learning as times goes on? What may prompt such actions?

Fakhouri: Like I mentioned during the keynote interview, we get asked, does FDA regulate large language models? Are you going to ban generative AI use? My response is that we typically don't regulate linear regression. We look at the data and the information that any modeling technique is producing, and we want to make sure that the information is trustworthy. So, I wouldn't say that we would be banning or prohibiting a certain AI or machine learning type of algorithm, what we're actually interested in is how robust how accurate, how credible, the information from these models is.

PE: What do you think the future may hold for AI and machine learning in pharma R&D in both the short- and long-term?

Fakhouri: We're actually very excited about AI use, I think we're seeing that it's increasing efficiencies in different parts of the drug development process. If you think about things such as discovery or protein folding, which again, is outside of what we normally look at, it could potentially cut the development time by years. This is all very exciting, because it could translate into faster, safe and effective drugs coming into the market. It can also fill in certain gaps for rare diseases, for example, where we can see a lot of potential use for AI to accelerate the development of drugs. In this type of situation, that's what I would say would be the long term. With the short term, I think what we're all doing, whether it's industry, whether it's the regulator's academia, is we're going through this adoption curve. You need to train your staff, you need to bring in the right expertise, and you need to develop the right tools to solve the right problems. That's going to take some time and that's why I think the short term uses of AI are going to be mostly low hanging type of fruits where you're increasing operational efficiency, but then that will translate into the development of safe and effective drugs faster.

Read the original:
US Pharma and Biotech Summit 2024: Artificial Intelligence and Machine Learning Through the Eyes of the FDA Part II - Pharmaceutical Executive

NeuroSense Therapeutics is collaborating with PhaseV for insights into how to better design the protocol for the planned Phase 3 trial that will test PrimeC for amyotrophic lateral sclerosis (ALS).

A specialist in machine learning technology for clinical trials, PhaseV used data from the ongoing Phase 2b PARADIGM trial (NCT05357950) as input to a causal machine learning model. This is a form of artificial intelligence that can help unlock insights and identify features that may contribute to a treatment response.

As part of its independent analysis, the company found that PrimeC could work well in multiple subgroups of patients in the Phase 3 study, which should start in the coming months.

Being able to predict treatment outcomes in certain patients may help optimize the design of the upcoming trial by selecting the patients most likely to respond, while reducing costs.

ALS is a complex disease that manifests in unique ways in each patient. Although there is an improved understanding of the underlying mechanisms of ALS, therapeutic options remain limited, Raviv Pryluk, CEO and co-founder of PhaseV, said in a press release.

NeuroSense plans to submit an end-of-Phase 2 package for review by the U.S. Food and Drug Administration (FDA) and the European Medicines Agency, the FDAs European counterpart, and discuss the clinical protocol for the Phase 3 trial with the regulators.

There remains a critical need for new innovative approaches to address this devastating neurodegenerative disease, said Alon Ben-Noon, CEO of NeuroSense. We plan to continue to collaborate with PhaseV as we develop our Phase 3 trial.

PrimeC contains fixed doses of two FDA-approved oral medications: the antibiotic ciprofloxacin and celecoxib, a pain killer that reduces inflammation. Both are expected to work together to slow or stop disease progression by blocking key mechanisms that lead up to ALS, such as inflammation, iron accumulation, and RNA processing.

PARADIGM is testing a long-acting formulation of PrimeC in 68 adults with ALS who started to see symptoms up to 2.5 years before enrolling. While continuing their standard ALS treatments, the participants were randomly assigned to PrimeC or a placebo, taken as two tablets twice daily for six months.

An analysis of PARADIGMs per-protocol population 62 adults with ALS who adhered well to the clinical protocol showed a significant 37.4% reduction in functional decline, as measured by the ALS Functional Rating Scale-Revised (ALSFRS-R).

A subgroup of those patients who were at a higher risk for rapid disease progression had the most clinical benefit, with those treated with PrimeC for six months showing a significant, 43% reduction in functional decline over a placebo. High-risk patients made up about half the adults in the Phase 2b trial.

Another subgroup of newly diagnosed patients whod had their first symptoms of ALS within a year of enrollment showed a 52% reduction in the rate of disease progression. This translated to a 7.76-point difference in favor of PrimeC on a maximum total of 48 points in the ALSFRS-R.

Through our initial collaboration with PhaseV, we gained an even greater understanding of the effect of PrimeC across multiple patient subgroups, Ben-Noon said. We will apply these insights to optimize the design of our Phase 3 study with the aim of maximizing meaningful clinical results that will differentiate PrimeC in the market.

Through a unique combination of causal [machine learning], real-world data, and advanced statistical methods, we confirmed the potential clinical benefit of PrimeC, Pryluk said. Our analysis predicted a high rate of success for PrimeC in the Phase 3 clinical trial for multiple recommended subgroups.

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Machine learning drafted to aid Phase 3 testing of ALS therapy PrimeC - ALS News Today