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.

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

AI

The bill increases regulatory requirements for machine learning systems in California.

May 24, 2024 In a Tuesday press release, Haltia AI, an artificial intelligence startup based in Dubai, warned leaders in machine learning that Californias new AI bill will cripple innovation with overly burdensome regulations.

Haltia said that the bill throws a wrench into the growth of AI startups with its unrealistic requirements and stifling compliance costs.

The legislation, titled the Understanding the Safe and Secure Innovation for Frontier Artificial Intelligence Act, was introduced in February and passed the California State Senate on Tuesday. The act mandates that developers of AI tools comply with various safety requirements and report any safety concerns.

AI systems are defined by the act as machine-based systems that can make predictions, recommendations, decisions, and formulate options. Safety tests include ensuring that an AI model does not have the capability to enable harms, such as creation of chemical and biological weapons or cyberattacks on critical infrastructure. Third party testers will be required to determine the safety of these systems.

Haltia said that on the surface, the act aims for responsible AI development. However, its implementation creates a labyrinth of red tape that disproportionately impacts startups. Because the bill requires ongoing annual reviews, Haltia argues that it adds significant technical and financial burdens.

Arto Bendiken, co-founder and CTO at Haltia, said that the act is a prime example of how well-intentioned regulations can morph into a bureaucratic nightmare. He added that the financial penalties for non-compliance only exacerbate the issue, potentially deterring groundbreaking ideas before they even take flight.

Haltia called for other AI startups to follow its lead and move operations to the United Arab Emirates where its thriving ecosystem, coupled with its commitment to the future of AI, makes it the ideal launchpad for the next generation of groundbreaking AI technologies in the Silicon Valley of the East.

In 2023, California Governor Gavin Newson signed an executive order that announced new directives aimed at understanding the risks of machine learning technologies in order to ensure equitable outcomes when used and to prepare the states workforce for its use.

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AI Startup Says California AI Bill Will Hamper Innovation - BroadbandBreakfast.com

image:

The study is part of the PsychENCODE Consortium, which brings together multidisciplinary teams to generate large-scale gene expression and regulatory data from human brains across several major psychiatric disorders and stages of brain development. (From left: first authors Sean Whalen and Chengyu Deng, and senior authors Katie Pollard and Nadav Ahituv.)

Credit: Gladstone Institutes / Michael Short

SAN FRANCISCOMay 24, 2024In a scientific feat that broadens our knowledge of genetic changes that shape brain development or lead to psychiatric disorders, a team of researchers combined high-throughput experiments and machine learning to analyze more than 100,000 sequences in human brain cellsand identify over 150 variants that likely cause disease.

The study, from scientists at Gladstone Institutes and University of California, San Francisco (UCSF), establishes a comprehensive catalog of genetic sequences involved in brain development and opens the door to new diagnostics or treatments for neurological conditions such as schizophrenia and autism spectrum disorder. Findings appear in the journal Science.

We collected a massive amount of data from sequences in noncoding regions of DNA that were already suspected to play a big role in brain development or disease, says Senior Investigator Katie Pollard, PhD, who also serves as director of the Gladstone Institute for Data Science and Biotechnology. We were able to functionally test more than 100,000 of them to find out whether they affect gene activity, and then pinpoint sequence changes that could alter their activity in disease.

Pollard co-led the sweeping study with Nadav Ahituv, PhD, professor in the Department of Bioengineering and Therapeutic Sciences at UCSF and director of the UCSF Institute for Human Genetics. Much of the experimental work on brain tissue was led by Tomasz Nowakowski, PhD, associate professor of neurological surgery in the UCSF Department of Medicine.

In all, the team found 164 variants associated with psychiatric disorders and 46,802 sequences with enhancer activity in developing neurons, meaning they control the function of a given gene.

These enhancers could be leveraged to treat psychiatric diseases in which one copy of a gene is not fully functional, Ahituv says: Hundreds of diseases result from one gene not working properly, and it may be possible to take advantage of these enhancers to make them do more.

Organoids and Machine Learning Take the Spotlight

Beyond identifying enhancers and disease-linked sequences, the study holds significance in two other key areas.

First, the scientists repeated parts of their experiment using a brain organoid developed from human stem cells and found that the organoid was an effective stand-in for the real thing. Notably, most of the genetic variants detected in the human brain tissue replicated in the cerebral organoid.

Our organoid compared very well against the human brain, Ahituv says. As we expand our work to test more sequences for other neurodevelopmental diseases, we now know that the organoid is a good model for understanding gene regulatory activity.

Second, by feeding massive amounts of DNA sequence data and gene regulatory activity to a machine learning model, the team was able to train the computer to successfully predict the activity of a given sequence. This type of program can enable in-silico experiments that allow researchers to predict the outcomes of experiments before doing them in the lab. This strategy enables scientists to make discoveries faster and using fewer resources, especially when large quantities of biological data are involved.

Sean Whalen, PhD, a senior research scientist in the Pollard Lab at Gladstone and a co-first author of the study, says the team tested the machine learning model using sequences held out from model training to see if it could predict the results already gathered on gene expression activity.

The model had never seen this data before and was able to make predictions with great accuracy, showing it had learned the general principles for how genes are impacted by noncoding regions of DNA in developing brain cells, Whalen says. You can imagine how this could open up a lot of new possibilities in research, even predicting how combinations of variants might function together.

A New Chapter for Brain Discoveries

The study was completed as part of the PsychENCODE Consortium, which brings together multidisciplinary teams to generate large-scale gene expression and regulatory data from human brains across several major psychiatric disorders and stages of brain development.

Through the consortiums publication of multiple studies, it seeks to shed light on poorly understood psychiatric conditions, from autism to bipolar disorder, and ultimately jumpstart new treatment approaches.

Our study contributes to this growing body of knowledge, showing the utility of using human cells, organoids, functional screening methods, and deep learning to investigate regulatory elements and variants involved in human brain development, says Chengyu Deng, PhD, a postdoctoral researcher at UCSF and a co-first author of the study.

About the Study

The study, Massively Parallel Characterization of Regulatory Elements in the Developing Human Cortex, appears in the May 24, 2024 issue of Science. Authors include: Chengyu Deng, Sean Whalen, Marilyn Steyert, Ryan Ziffra, Pawel Przytycki, Fumitaka Inoue, Daniela Pereira, Davide Capauto, Scott Norton, Flora Vaccarino, PsychENCODE Consortium, Alex Pollen, Tomasz Nowakowski, Nadav Ahituv, and Katherine Pollard.

The work was funded in part by the National Institute of Mental Health, the New York Stem Cell Foundation, the National Human Genome Research Institute, and Coordination for the Improvement of Higher Education Personnel. The data generated was part of thePsychENCODE Consortium.

About Gladstone Institutes

Gladstone Institutesis an independent, nonprofit life science research organization that uses visionary science and technology to overcome disease. Established in 1979, it is located in the epicenter of biomedical and technological innovation, in the Mission Bay neighborhood of San Francisco. Gladstone has created a research model that disrupts how science is done, funds big ideas, and attracts the brightest minds.

Massively parallel characterization of regulatory elements in the developing human cortex

24-May-2024

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Scientists leverage machine learning to decode gene regulation in the developing human brain - EurekAlert

Ed Hicks, business development manager for federal and artificial intelligence at Dell Technologies (NYSE: DELL), said government agencies that intend to implement AI at the edge should consider adopting federated learning to quickly glean insights from data while ensuring the security of critical data.

In an article published on Carahsoft.com, Hicks noted that federated learning could enable agencies to leverage larger datasets at a decreased bandwidth.

Federated models require significantly less bandwidth than other models because the information isnt being sent back to a data center for processing, he wrote. If an agency has a rich dataset in the cloud and a small amount of compute at the edge, it can use federated learning to train the edge device without having to move all the data from the cloud.

Hicks cited some of the key considerations for agencies that plan to apply AI at the edge, including the need to identify the end goal of the AI or a machine learning model.

Other key questions include how much data an agency is trying to process and how quickly it needs the results, he added.

The Dell Technologies executive discussed how the company works to help agencies that intend to incorporate AI at the edge, such as creating a roadmap for AI implementation, providing federated learning and analytics tools to agencies and finding ways for agencies to make the most of their data.

We also provided a validated, containerized solution that agencies can use to quickly and easily deploy a federated learning solution in a Kubernetes environment, Hicks added.

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Dell Technologies' Ed Hicks: Federated Learning Could Help Agencies Advance AI at the Edge - GovCon Wire