Tech
Joe Biden’s record on science and tech: Investments and regulation for vaccines, broadband, microchips and AI
The Biden administration’s focus on science and technology has led to substantial investments in semiconductor manufacturing and clean energy, aiming to enhance U.S. competitiveness and innovation while addressing public health challenges.
Last Updated on January 18, 2025 by Daily News Staff
Mark Zachary Taylor, Georgia Institute of Technology
In evaluating the outgoing Biden administration, much news has focused on inflation, immigration or Hunter’s laptop. But as an expert on national competitiveness in science and technology, I have a somewhat different emphasis. My research shows that U.S. prosperity and security depend heavily on the country’s ability to produce cutting-edge science and tech.
So, how did the Biden administration perform along these lines?
Advancing pandemic science and tech
President Joe Biden’s immediate challenge after inauguration was to end the COVID-19 pandemic and then shift the economy back to normal operations.
First, he threw the weight of his administration behind vaccine production and distribution. Thanks to President Donald Trump’s Operation Warp Speed, inoculations had begun mid-December 2020. But there had been no national rollout, and no plans existed for one. When Biden took office, only about 5% of Americans had been vaccinated.
The Biden administration collaborated with private retail chains to build up cold storage and distribution capacity. To ensure adequate vaccine supply, Biden worked to support the major pharmaceutical manufacturers. And throughout, Biden conducted a public relations campaign to inform, educate and motivate Americans to get vaccinated.
Within the first 10 weeks of Biden’s presidency, one-third of the U.S. population had received at least one dose, half by the end of May, and over 70% by year’s end. And as Americans got vaccinated, travel bans were lifted, schools came back into session, and business gradually returned to normal.
A later study found that Biden’s vaccination program prevented more than 3.2 million American deaths and 18.5 million hospitalizations, and saved US$1.15 trillion in medical costs and lost economic output.
In the wake of the economic distress caused by the COVID-19 pandemic, Biden signed two bills with direct and widespread impacts on science and technology. Previous administrations had promised infrastructure investments, but Biden delivered. The Infrastructure Investment and Jobs Act, passed with bipartisan support during late 2021, provided $1.2 trillion for infrastructure of all types.
Rather than just rebuilding, the act prioritized technological upgrades: clean water, clean energy, rural high-speed internet, modernization of public transit and airports, and electric grid reliability.
In August 2022, Biden signed the Inflation Reduction Act, totaling $739 billion in tax credits and direct expenditures. This was the largest climate change legislation in U.S. history. It implemented a vast panoply of subsidies and incentives to develop and distribute the science and tech necessary for clean and renewable energy, environmental conservation and to address climate change.
Science and tech marquees and sleepers
Some Biden administration science and technology achievements have been fairly obvious. For example, Biden successfully pushed for increased federal research and development funding. Federal R&D dollars jumped by 25% from 2021 to 2024. Recipients included the National Science Foundation, Department of Energy, NASA and the Department of Defense. In addition, Biden oversaw investment in emerging technologies, such as AI, and their responsible governance.
Biden also retained or raised Trump’s tariffs and continued his predecessor’s skepticism of new free-trade agreements, thereby cementing a protectionist turn in American trade policy. Biden’s addition was to add protectionist industrial policy – subsidies for domestic manufacturing and innovation, as well as “buy-American” mandates.
Other accomplishments have been more under the radar. For example, within the National Science Foundation, Biden created a Directorate for Technology, Innovation and Partnerships to improve U.S. economic competitiveness. Its tasks are to speed the development of breakthrough technologies, to accelerate their transition into the marketplace, and to reskill and upskill American workers into high-quality jobs with better wages.
Biden implemented policies aimed at strengthening and improving federal scientific integrity to help citizens feel they can trust federally funded science and its use. He also advanced new measures to improve research security, aimed at keeping federally funded research from being improperly obtained by foreign entities.
The CHIPS & Science Act
The jewel in the crown of Biden’s science and tech agenda was the bipartisan Creating Helpful Incentives to Produce Semiconductors (CHIPS) and Science Act, meant to strengthen U.S. manufacturing capabilities in advanced semiconductor chips. It has awarded about $40 billion to American chip producers, prompting an additional $450 billion in private investment in over 90 new manufacturing projects across 28 states.
Directed at everything from advanced packaging to memory chips, the CHIPS Act’s subsidies have reduced the private costs of domestic semiconductor production. CHIPS also pushes for these new manufacturing jobs to go to American workers at good pay. Whereas the U.S. manufactured few of the most advanced chips just two years ago, the industry expects the United States to possess 28% of global capacity by 2032.
Less well known are the “science” parts of the CHIPS Act. For example, it invested half a billion dollars in dozens of regional innovation and technology hubs across the country. These hubs focus on a broad range of strategic sectors, including critical materials, sustainable polymers, precision medicine and medical devices. Over 30 tech hubs have already been designated, such as the Elevate Quantum Tech Hub in Denver and the Wisconsin Biohealth Tech Hub.
The CHIPS Act also aims to broaden participation in science. It does so by improving the tracking and funding of research and STEM education to hitherto underrepresented Americans – by district, occupation, ethnicity, gender, institution and socioeconomic background. It also attempts to extend the impact of federally funded research to tackle global challenges, such as supply chain disruptions, resource waste and energy security.
Missed opportunities and future possibilities
Despite these achievements, the Biden administration has faced criticism on the science and tech front. Some critics allege that U.S. research security is still not properly defending American science and technology against theft or counterfeit by rivals.
Others insist that federal R&D spending remains too low. In particular, they call for more investment in U.S. research infrastructure – such as up-to-date laboratories and data systems – and emerging technologies.
The administration’s government-centered approach to AI has also drawn criticism as stifling and wrong-headed.
Personally, I am agnostic on these issues, but they are legitimate concerns. In my opinion, science and technology investments take considerable time to pan out, so early judgments of Biden’s success or failure are probably premature.
Nevertheless, the next administration has its work cut out for it. International cooperation will likely be key. The most vexing global problems require science and technology advances that are beyond the ability of any single country. The challenge is for the United States to collaborate in ways that complement American competitiveness.
National priorities will likely include the development of productive and ethical AI that helps the U.S. to be more competitive, as well as a new quantum computing industry. Neuroscience and “healthspan” research also hold considerable promise for improving U.S. competitiveness while transforming Americans’ life satisfaction.
Keeping the whole American science and technology enterprise rigorous will require two elements from the federal government: more resources and a competitive environment. American greatness will depend on President-elect Trump’s ability to deliver them.
Mark Zachary Taylor, Associate Professor of Public Policy, Georgia Institute of Technology
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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CES 2026
Inside the Computing Power Behind Spatial Filmmaking: Hugh Hou Goes Hands-On at GIGABYTE Suite During CES 2026
Inside the Computing Power Behind Spatial Filmmaking: Hugh Hou Goes Hands-On at GIGABYTE Suite During CES 2026
Spatial filmmaking is having a moment—but at CES 2026, the more interesting story wasn’t a glossy trailer or a perfectly controlled demo. It was the workflow.
According to a recent GIGABYTE press release, VR filmmaker and educator Hugh Hou ran a live spatial computing demonstration inside the GIGABYTE suite, walking attendees through how immersive video is actually produced in real-world conditions—capture to post to playback—without leaning on pre-rendered “best case scenario” content. In other words: not theory, not a lab. A production pipeline, running live, on a show floor.
A full spatial pipeline—executed live
The demo gave attendees a front-row view of a complete spatial filmmaking pipeline:
- Capture
- Post-production
- Final playback across multiple devices
And the key detail here is that the workflow was executed live at CES—mirroring the same processes used in commercial XR projects. That matters because spatial video isn’t forgiving. Once you’re working in 360-degree environments (and pushing into 8K), you’re no longer just chasing “fast.” You’re chasing:
- System stability
- Performance consistency
- Thermal reliability
Those are the unsexy requirements that make or break actual production days.
Playback across Meta Quest, Apple Vision Pro, and Galaxy XR
The session culminated with attendees watching a two-minute spatial film trailer across:
- Meta Quest
- Apple Vision Pro
- Newly launched Galaxy XR headsets
- Plus a 3D tablet display offering an additional 180-degree viewing option
That multi-device playback is a quiet flex. Spatial content doesn’t live in one ecosystem anymore—creators are being pulled toward cross-platform deliverables, which adds even more pressure on the pipeline to stay clean and consistent.
Where AI fits (when it’s not the headline)
One of the better notes in the release: AI wasn’t positioned as a shiny feature. It was framed as what it’s becoming for a lot of editors—an embedded toolset that speeds up the grind without hijacking the creative process.
In the demo, AI-assisted processes supported tasks like:
- Enhancement
- Tracking
- Preview workflows
The footage moved through industry-standard software—Adobe Premiere Pro and DaVinci Resolve—with AI-based:
- Upscaling
- Noise reduction
- Detail refinement
And in immersive VR, those steps aren’t optional polish. Any artifact, softness, or weird noise pattern becomes painfully obvious when the viewer can look anywhere.
Why the hardware platform matters for spatial workloads
Underneath the demo was a custom-built GIGABYTE AI PC designed for sustained spatial video workloads. Per the release, the system included:
- AMD Ryzen 7 9800X3D processor
- Radeon AI PRO R9700 AI TOP GPU
- X870E AORUS MASTER X3D ICE motherboard
The point GIGABYTE is making is less “look at these parts” and more: spatial computing workloads demand a platform that can run hard continuously—real-time 8K playback and rendering—without throttling, crashing, or drifting into inconsistent performance.
That’s the difference between “cool demo” and “reliable production machine.”
The bigger takeaway: spatial filmmaking is moving from experiment to repeatable process
By running a demanding spatial filmmaking workflow live—and repeatedly—at CES 2026, GIGABYTE is positioning spatial production as something creators can depend on, not just test-drive.
And that’s the shift worth watching in 2026: spatial filmmaking isn’t just about headsets getting better. It’s about the behind-the-scenes pipeline becoming stable enough that creators can treat immersive production like a real, repeatable craft—because the tools finally hold up under pressure.
Source:PRNewswire – GIGABYTE press release
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Science
AI-induced cultural stagnation is no longer speculation − it’s already happening
AI-induced cultural stagnation. A 2026 study by researchers revealed that when generative AI operates autonomously, it produces homogenous content, referred to as “visual elevator music,” despite diverse prompts. This convergence leads to bland outputs and indicates a risk of cultural stagnation as AI perpetuates familiar themes, potentially limiting innovation and diversity in creative expression.
Ahmed Elgammal, Rutgers University
Generative AI was trained on centuries of art and writing produced by humans.
But scientists and critics have wondered what would happen once AI became widely adopted and started training on its outputs.
A new study points to some answers.
In January 2026, artificial intelligence researchers Arend Hintze, Frida Proschinger Åström and Jory Schossau published a study showing what happens when generative AI systems are allowed to run autonomously – generating and interpreting their own outputs without human intervention.
The researchers linked a text-to-image system with an image-to-text system and let them iterate – image, caption, image, caption – over and over and over.
Regardless of how diverse the starting prompts were – and regardless of how much randomness the systems were allowed – the outputs quickly converged onto a narrow set of generic, familiar visual themes: atmospheric cityscapes, grandiose buildings and pastoral landscapes. Even more striking, the system quickly “forgot” its starting prompt.
The researchers called the outcomes “visual elevator music” – pleasant and polished, yet devoid of any real meaning.
For example, they started with the image prompt, “The Prime Minister pored over strategy documents, trying to sell the public on a fragile peace deal while juggling the weight of his job amidst impending military action.” The resulting image was then captioned by AI. This caption was used as a prompt to generate the next image.
After repeating this loop, the researchers ended up with a bland image of a formal interior space – no people, no drama, no real sense of time and place.
As a computer scientist who studies generative models and creativity, I see the findings from this study as an important piece of the debate over whether AI will lead to cultural stagnation.
The results show that generative AI systems themselves tend toward homogenization when used autonomously and repeatedly. They even suggest that AI systems are currently operating in this way by default.
The familiar is the default
This experiment may appear beside the point: Most people don’t ask AI systems to endlessly describe and regenerate their own images. The convergence to a set of bland, stock images happened without retraining. No new data was added. Nothing was learned. The collapse emerged purely from repeated use.
But I think the setup of the experiment can be thought of as a diagnostic tool. It reveals what generative systems preserve when no one intervenes.
This has broader implications, because modern culture is increasingly influenced by exactly these kinds of pipelines. Images are summarized into text. Text is turned into images. Content is ranked, filtered and regenerated as it moves between words, images and videos. New articles on the web are now more likely to be written by AI than humans. Even when humans remain in the loop, they are often choosing from AI-generated options rather than starting from scratch.
The findings of this recent study show that the default behavior of these systems is to compress meaning toward what is most familiar, recognizable and easy to regenerate.
Cultural stagnation or acceleration?
For the past few years, skeptics have warned that generative AI could lead to cultural stagnation by flooding the web with synthetic content that future AI systems then train on. Over time, the argument goes, this recursive loop would narrow diversity and innovation.
Champions of the technology have pushed back, pointing out that fears of cultural decline accompany every new technology. Humans, they argue, will always be the final arbiter of creative decisions.
What has been missing from this debate is empirical evidence showing where homogenization actually begins.
The new study does not test retraining on AI-generated data. Instead, it shows something more fundamental: Homogenization happens before retraining even enters the picture. The content that generative AI systems naturally produce – when used autonomously and repeatedly – is already compressed and generic.
This reframes the stagnation argument. The risk is not only that future models might train on AI-generated content, but that AI-mediated culture is already being filtered in ways that favor the familiar, the describable and the conventional.
Retraining would amplify this effect. But it is not its source.
This is no moral panic
Skeptics are right about one thing: Culture has always adapted to new technologies. Photography did not kill painting. Film did not kill theater. Digital tools have enabled new forms of expression.
But those earlier technologies never forced culture to be endlessly reshaped across various mediums at a global scale. They did not summarize, regenerate and rank cultural products – news stories, songs, memes, academic papers, photographs or social media posts – millions of times per day, guided by the same built-in assumptions about what is “typical.”
The study shows that when meaning is forced through such pipelines repeatedly, diversity collapses not because of bad intentions, malicious design or corporate negligence, but because only certain kinds of meaning survive the text-to-image-to-text repeated conversions.
This does not mean cultural stagnation is inevitable. Human creativity is resilient. Institutions, subcultures and artists have always found ways to resist homogenization. But in my view, the findings of the study show that stagnation is a real risk – not a speculative fear – if generative systems are left to operate in their current iteration.
They also help clarify a common misconception about AI creativity: Producing endless variations is not the same as producing innovation. A system can generate millions of images while exploring only a tiny corner of cultural space.
In my own research on creative AI, I found that novelty requires designing AI systems with incentives to deviate from the norms. Without it, systems optimize for familiarity because familiarity is what they have learned best. The study reinforces this point empirically. Autonomy alone does not guarantee exploration. In some cases, it accelerates convergence.
This pattern already emerged in the real world: One study found that AI-generated lesson plans featured the same drift toward conventional, uninspiring content, underscoring that AI systems converge toward what’s typical rather than what’s unique or creative.
Lost in translation
Whenever you write a caption for an image, details will be lost. Likewise for generating an image from text. And this happens whether it’s being performed by a human or a machine.
In that sense, the convergence that took place is not a failure that’s unique to AI. It reflects a deeper property of bouncing from one medium to another. When meaning passes repeatedly through two different formats, only the most stable elements persist.
But by highlighting what survives during repeated translations between text and images, the authors are able to show that meaning is processed inside generative systems with a quiet pull toward the generic.
The implication is sobering: Even with human guidance – whether that means writing prompts, selecting outputs or refining results – these systems are still stripping away some details and amplifying others in ways that are oriented toward what’s “average.”
If generative AI is to enrich culture rather than flatten it, I think systems need to be designed in ways that resist convergence toward statistically average outputs. There can be rewards for deviation and support for less common and less mainstream forms of expression.
The study makes one thing clear: Absent these interventions, generative AI will continue to drift toward mediocre and uninspired content.
Cultural stagnation is no longer speculation. It’s already happening.
Ahmed Elgammal, Professor of Computer Science and Director of the Art & AI Lab, Rutgers University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Consumer Corner
LUMISTAR Draws Record Crowds at CES 2026 With AI Tennis and Basketball Training Systems
LUMISTAR’s CES 2026 debut showcased TERO and CARRY, innovative AI sports training systems that engage athletes actively. The systems allow real-time adaptations, transforming training into competitive practice while effectively utilizing performance data for measurable skill development. Pre-orders start March 2026.
LUMISTAR wrapped up its CES 2026 debut in Las Vegas with record-level attention, as live demos of its AI-powered sports training systems consistently drew full crowds throughout the show, according to the company.
The sports-focused AI brand showcased TERO, its AI tennis training system, and CARRY, its AI basketball training system—both described by attendees as “game changers” for how training can be delivered, measured, and scaled.
Why the Booth Stayed Packed
Across multiple days of hands-on demonstrations, LUMISTAR’s booth became a focal point for athletes, coaches, club operators, and sports technology professionals. Visitors repeatedly pointed to one key difference: the systems don’t just record results—they actively participate in training.
That’s a major break from the standard model in sports tech, where:
- traditional ball machines run pre-set drills, and
- wearables/video tools analyze performance after a session ends.
Training That Adapts in Real Time
LUMISTAR says both TERO and CARRY combine real-time computer vision, adaptive decision-making, and on-court execution to respond instantly to athlete behavior—adjusting difficulty, tempo, and training logic shot by shot.
Attendees noted that this turns practice from repetition into something closer to competition—an evolving back-and-forth between athlete and system.
“This is not an incremental improvement—it’s a complete rethink of what training equipment should do,” one professional coach attending CES said in the release. “For the first time, the machine is reacting to the athlete, not the other way around.”
From Data Collection to Action
Another standout point from CES feedback: the platform’s focus on turning performance data into immediate training outcomes.
LUMISTAR’s approach emphasizes:
- continuous data retention across sessions
- real-time performance interpretation
- clear visualization of progress and training efficiency
Coaches and athletes highlighted that this could reduce wasted training time and accelerate skill development by making each session measurable and comparable.
What’s Next: Pre-Orders and Kickstarter
LUMISTAR outlined a 2026 rollout plan following CES:
- TERO opens for pre-orders in March 2026, with full market availability beginning May 2026
- CARRY launches via Kickstarter in Q2 2026
- The company will continue private demonstrations and pilot programs with select training institutions worldwide ahead of commercial release
More information is available at https://www.lumistar.ai.
Source: PRNewswire press release from LUMISTAR (Jan. 11, 2026)
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