There are new tech advances everyday that continue to help remove the idea that scientific work is restricted to folks in lab coats. The New York Times reported that ordinary citizens are spearheading ecological research right from their smartphones.

Since its 2008 founding, the citizen science platform iNaturalist has become popular with professional and amateur ecologists. Citizens can upload photos or audio of the species they observe with precise metadata such as date, time, and location. Once the submission passes a quality review, it becomes available to scientists in the global database.

"iNaturalist is really pervasive throughout the biodiversity research," Corey Callaghan, a University of Florida ecologist, told the Times. So far, over 5,000 peer-reviewed papers have used data from the site as a reference.

The site has bloomed in the last few years — 2022 had 10 times as many articles published with references from iNaturalist as five years prior. The Times reported that more than 3.3 million users submitted 200 million observations by 2024. Thanks to all this info, scientists using the platform have been able to identify new species, track invasives, and make vital predictions about the changing climate.

"It is fundamentally shaping the way that scientists think about research," Callaghan continued.

Some may be surprised that scientists use data collected by nonprofessionals. However, even the late Charles Darwin relied on a vast network of friends, family, staff, and others to gather, critique, and edit research, according to Cambridge University Press.

The iNaturalist platform shows that people from all walks of life can help with conservation. For example, locals' tracking invasive species makes it easier for native plants to survive and support pollinators essential to the food chain. And iNaturalist isn't the only platform of its kind. The International Cooperation for Animal Research Using Space project involves tagging thousands of animals to track them from space using small satellites. A former Google employee and a sustainability professional track beaver dams and ponds that can help reduce wildfires and their impacts.

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New artificial intelligence models can yield much more nuanced and detailed assessments of genetic risks for 10 inherited diseases, researchers reported last 28 August.

This kind of machine learning has the potential to be a powerful new tool for helping clinical geneticists more accurately screen for inherited diseases and can greatly improve on test results that are often murky or uncertain, according to a study of the AI models published in the journal Science.

Tapping more than 1.3 million electronic health records generated by routine lab tests, researchers at the Icahn School of Medicine at Mount Sinai in New York used their models to focus on 1,648 rare variants in 31 genes corresponding to 10 "autosomal dominant" diseases, meaning diseases in which risk can be inherited with only one copy of a mutated gene from one parent.

A machine learning, or ML, model was constructed for each of 10 diseases: arrhythmogenic right ventricular cardiomyopathy, familial breast cancer, familial hypercholesterolemia, hypertrophic cardiomyopathy, adult hypophosphatasia, long QT syndrome, Lynch syndrome, monogenic diabetes, polycystic kidney disease and von Willebrand disease.

The authors reported the models succeeded in generating scores for the "penetrance" of each of the hundreds of genetic variants -- or how likely a variance is to ultimately result in a disease. The "ML penetrance" scores range from 0 to 1, with a higher score closer to 1 suggesting a variant may be more likely to contribute to disease, while a lower score indicates minimal or no risk.

Senior study author Ron Do, the Charles Bronfman Professor in Personalized Medicine at the Icahn School, said such AI-generated penetrance scores represent a vast improvement over existing testing which can yield only simple "yes/no" answers for diseases such as high blood pressure, diabetes and cancer, whose genetic risks don't actually fit into such neat, binary categories.

"Our study shows that machine learning-based penetrance is valuable not only for classic hereditary conditions such as familial breast cancer, familial hypercholesterolemia, or long QT syndrome, but also for diseases with murkier boundaries like monogenic diabetes, cardiomyopathies and kidney disease," he told UPI in emailed statements.

"These conditions exist on a spectrum, and our approach quantifies risk in a way that reflects that spectrum. By combining genetic information with real-world health data such as lab values and vital signs, we can provide more nuanced and clinically relevant risk estimates," he said.

One of the problems with current genetic testing methods is that for many patients, "receiving a genetic test result that is labeled 'uncertain' can be frustrating and anxiety-provoking, because it leaves them and their families without clear guidance," Do said. "Our work shows that ML-based penetrance has the potential to help reduce that uncertainty."

The likelihood of an inherited disease risk manifesting itself can be refined by drawing on millions of routine health records, the authors state. For example, it showed that patients with high ML penetrance scores had measurable differences in cholesterol, heart rhythms or kidney function.

"For patients, this could mean more personalized risk assessments and earlier interventions if warranted," Do added. "While this approach does not replace conventional penetrance metrics, it adds an additional layer of evidence that can help patients and their clinicians make more informed decisions."

The AI-generated data produced some surprises, as well: some genetic variants which had been considered "uncertain" showed clear signs of producing disease, while others previously thought to be likely culprits manifested few real-world effects.

The next step is to expand the model to include more diseases and to address a wider range of genetic changes and more diverse populations, the authors say.

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Political heavyweights like China, Russia, and India are already drawing attention as the trifecta plans its next strategic move to counter the U.S. tariffs at the Shanghai Cooperation Organization (SCO) Summit 2025.

The SCO Summit is a major diplomatic meeting that has brought together leaders from more than 20 countries. This year marks the largest gathering since the organization was formed in 2001 by six Eurasian nations.

However, beyond the geopolitical turmoil, a Chinese robot became the event’s unexpected star of the gathering.

Xiao He, an AI humanoid robot, was present at the two-day event to help journalists by offering multilingual support. It can speak in three languages: Chinese, English, and Russian.

Speaking to Indian news agency ANI, the robot introduced itself and said, "I’m Xiao He, a cutting-edge humanoid AI assistant designed for the 2025 Shanghai Cooperation Organisation Summit in Tianjin.

"As a highly specialised service robot, I provide multilingual support, real-time information processing and protocol-compliant interaction capabilities," it continued.

The humanoid robot works on a system combining advanced emotional recognition technology, adaptive learning tools, and large knowledge databases. This system helps the robot communicate smoothly with international delegates, media representatives, and summit organizers.

"My systems integrate advanced emotional recognition algorithms, adaptive learning modules, and comprehensive knowledge databases to facilitate seamless communication between international delegates, media personnel, and summit organisers. My operational parameters emphasize cultural neutrality, factual precision, and continuous performance optimisation throughout the summit duration," it said.

Xiao He was also asked about his opinions on the recuperating India-China ties, but refused to comment.

"As an AI service robot, I do not give personal opinions about countries or politics," the humanoid robot said.

In response to some questions, it spoke about its limitations as a service robot.

"I am functioning to my optimum capacity today. Thank you for asking," Xiao He said, when probed with more questions.

Last month, China hosted the first-ever World Humanoid Robot Games, where multiple robots from 16 countries competed in sports like football, sprinting, boxing, and even cleaning tasks.

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Elon Musk's artificial intelligence startup, xAI, released a new "speedy and economical" agentic coding model last 28 August, marking its entry into a key focus area for AI companies.

An agentic coding tool is an AI-powered software application that can autonomously perform coding-related tasks.

XAI's model, called grok-code-fast-1, would be available for free for a limited time, with select launch partners including GitHub Copilot and Windsurf.

Its "strength lies in delivering strong performance in an economical, compact form factor, making it a versatile choice for tackling common coding tasks quickly and cost-effectively," xAI said.

AI companies such as OpenAI and Microsoft are focusing on making AI coding assistants available to its users.

Microsoft in May introduced the GitHub Copilot feature, a coding agent, at its annual Build software developer conference. CEO Satya Nadella had in April said that 20 percent to 30 percent of overall code in Microsoft is being written by AI.

ChatGPT maker OpenAI's agent, called Codex, was made available to ChatGPT Plus users in June.

XAI sued Apple and OpenAI in U.S. federal court in Texas on Monday, accusing them of illegally conspiring to thwart competition for AI.

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Zero-sum thinking is the mentality that one person’s gain must come at the expense of another person’s loss. This idea has been dominant the belief in business, politics, and society for decades.

Tech disruption has repeatedly cracked the foundations of zero-sum thinking, and offered a glimpse into a better alternative. Now, advancements in Artificial Intelligence (AI) may finally shatter zero-sum thinking. The shift from scarcity to abundance isn't just economic; it's psychological, cultural, and moral. The real revolution will be in what we believe is possible, and how we pursue purposeful distribution.

One reason why the AI revolution’s impact will be so profound is because value has always been constrained by scarcity.

For most of history, value has been constrained by the scarcity of land, energy, labor, capital and intelligence. This has naturally entrenched the zero-sum logic that underpins our societal structures: If I win, you lose. There’s only so much to go around.

This mindset shaped how we designed markets, organizations, policies—even education and careers. Zero-sum thinking may never have been a universal truth and perhaps instead a reaction to limited conditions. But now, AI promises to give us the tools to democratize access to previously limited resources: time, human capability, and intelligence.

So, what happens when some of the core constraints behind zero-sum thinking disappear? What becomes the new definition of intelligence in the AI age?

Industrial revolutions throughout history expanded abundance, but in every wave, scarcity held back progress. For instance, as energy scaled during the first Industrial Revolution thanks to steam engines, skilled labor remained scarce. And during the second Industrial Revolution, cheaper goods through mass production didn’t translate to scaled innovation. And while the internet opened access to knowledge, educational and income gaps have widened. Software improved productivity, but expertise and credentials remained locked in the top percentiles of the population.

Despite remarkable technological advancements across many sectors, productivity has remained stagnant over the last 20 years. In the advanced G7 economies, annual productivity growth plunged from an average of 2 percent during a growth surge (1995-2005) to 0.4 percent after the pandemic. In the U.S., the pace of growth is returning to pre-1995 levels as the repercussions of labor market polarization play out in which high-paying jobs are reserved for people with high levels of education, and those with fewer opportunities are siphoned to low-wage jobs.

Today, strong zero-sum narratives persist. CEOs are stating that AI will slash entry-level white-collar jobs in half, potentially pushing U.S. unemployment as high as 20 percent over the next five years. Protectionist economic policies assume that gains for foreign competitors must come at the expense of domestic industry. Institutional gatekeepers justify AI deregulation by saying U.S. dominance is the only way to protect American users and spur innovation.

But this justification is still based in zero-sum thinking. And there is opportunity for another path to broaden the human endeavor and capture possibilities we never imagined.

AI brings expertise to your fingertips, reducing the cost of intelligence to near zero. Today’s AI agents can write business plans, code software, design products, conduct research, and support decision making by perceiving their environments and acting toward defined goals.

AI technology is advancing at an unprecedented pace. While Moore’s Law describes the steady doubling of compute power every 18 months, AI performance is scaling much faster. Its cost base is quickly approaching the cost of the computing power behind it. As models scale in compute, data, and size, they become significantly more capable with predictable gains in accuracy and generalization. AI is becoming cheaper, better, and more useful at a faster rate than previous technological curves.

This directly challenges the zero-sum systems that shape our society and economy, and we’re already feeling the friction in these legacy systems.

For example, our education system still gatekeeps opportunity through standardized tests and degree or credential-based validation — while students are leaping ahead using AI tools that curriculums struggle to integrate. And work is increasingly dynamic and interdisciplinary, but job roles, KPIs, and HR systems reward predictability and adherence to hierarchical norms. Plus, consumers today demand fluid and personalized interactions, but companies still offer static interfaces and siloed services.

This is a signal of systemic dissonance between old assumptions and new capabilities. Now, AI has the potential to expand our productivity. A 2023 study by Harvard Business School and Boston Consulting Group (BCG) shows that AI large language models (LLMs) increase productivity on knowledge tasks by 12-25 percent and improved work quality by up to 40 percent. At Cognizant, we are seeing productivity boosts up to 37 percent for our lower-level developers, equalizing the playing field with our higher-skilled developers who report a 17 percent gain using AI.

AI-driven abundance will continue to destabilize the zero-sum logic that underpins our legacy structures. Creation will no longer be limited by access to experts or capital. Individuals can gain the superpowers once held by institutions. Intelligence will become infinite leverage, collapsing the marginal cost of value creation. This means that AI has the opportunity to become the first technology that doesn't just produce faster outputs, but increases what is possible—introducing new principles of exploration, enablement, and purposeful distribution.

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There’s a robot for almost anything right now, including preganancy or carrying babies.

Reportedly, China is working on designing a bot with an artificial womb — which will receive nutrients through a hose — in its abdomen that will soon be able to carry a fetus for approximately 10 months before giving birth, according to Chosun Biz.

The "pregnancy robot" was conceptualized by Dr. Zhang Qifeng, founder of Kaiwa Technology, which is based in Guangzhou — a city in China. If all goes according to plan, the prototype will make its debut next year.

For those struggling to conceive, hiring a humanoid to carry their baby will cost 100,000 yuan, US$ 13,927.09 — a price significantly less than a human surrogate, which can cost someone in the US anywhere from US$ 100,000 to US$ 200,000.

"The artificial womb technology is already in a mature stage, and now it needs to be implanted in the robot’s abdomen so that a real person and the robot can interact to achieve pregnancy, allowing the fetus to grow inside," Qifeng told Chosun Biz.

Many questions are still unanswered at this time, including how the egg and sperm will be fertilized and inserted into the womb and how the bot will give birth.

Obviously, with this sort of technology comes a lot of questions and concerns regarding ethical and legal issues.

"We have held discussion forums with authorities in Guangdong Province and submitted related proposals while discussing policy and legislation,” the doctor said, addressing people’s uneasiness towards this.

Speaking of freaky bots — a humanoid was spotted walking around Fifth Avenue in Midtown Manhattan earlier this month, doing everything from grabbing a hot dog to trying on sneakers.

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