AI Insights

Contributor: The human brain doesn’t learn, think or recall like an AI. Embrace the difference

Published

3 hours ago

July 9, 2025

Recently, Nvidia founder Jensen Huang, whose company builds the chips powering today’s most advanced artificial intelligence systems, remarked: “The thing that’s really, really quite amazing is the way you program an AI is like the way you program a person.” Ilya Sutskever, co-founder of OpenAI and one of the leading figures of the AI revolution, also stated that it is only a matter of time before AI can do everything humans can do, because “the brain is a biological computer.”

I am a cognitive neuroscience researcher, and I think that they are dangerously wrong.

The biggest threat isn’t that these metaphors confuse us about how AI works, but that they mislead us about our own brains. During past technological revolutions, scientists, as well as popular culture, tended to explore the idea that the human brain could be understood as analogous to one new machine after another: a clock, a switchboard, a computer. The latest erroneous metaphor is that our brains are like AI systems.

I’ve seen this shift over the past two years in conferences, courses and conversations in the field of neuroscience and beyond. Words like “training,” “fine-tuning” and “optimization” are frequently used to describe human behavior. But we don’t train, fine-tune or optimize in the way that AI does. And such inaccurate metaphors can cause real harm.

The 17th century idea of the mind as a “blank slate” imagined children as empty surfaces shaped entirely by outside influences. This led to rigid education systems that tried to eliminate differences in neurodivergent children, such as those with autism, ADHD or dyslexia, rather than offering personalized support. Similarly, the early 20th century “black box” model from behaviorist psychology claimed only visible behavior mattered. As a result, mental healthcare often focused on managing symptoms rather than understanding their emotional or biological causes.

And now there are new misbegotten approaches emerging as we start to see ourselves in the image of AI. Digital educational tools developed in recent years, for example, adjust lessons and questions based on a child’s answers, theoretically keeping the student at an optimal learning level. This is heavily inspired by how an AI model is trained.

This adaptive approach can produce impressive results, but it overlooks less measurable factors such as motivation or passion. Imagine two children learning piano with the help of a smart app that adjusts for their changing proficiency. One quickly learns to play flawlessly but hates every practice session. The other makes constant mistakes but enjoys every minute. Judging only on the terms we apply to AI models, we would say the child playing flawlessly has outperformed the other student.

But educating children is different from training an AI algorithm. That simplistic assessment would not account for the first student’s misery or the second child’s enjoyment. Those factors matter; there is a good chance the child having fun will be the one still playing a decade from now — and they might even end up a better and more original musician because they enjoy the activity, mistakes and all. I definitely think that AI in learning is both inevitable and potentially transformative for the better, but if we will assess children only in terms of what can be “trained” and “fine-tuned,” we will repeat the old mistake of emphasizing output over experience.

I see this playing out with undergraduate students, who, for the first time, believe they can achieve the best measured outcomes by fully outsourcing the learning process. Many have been using AI tools over the past two years (some courses allow it and some do not) and now rely on them to maximize efficiency, often at the expense of reflection and genuine understanding. They use AI as a tool that helps them produce good essays, yet the process in many cases no longer has much connection to original thinking or to discovering what sparks the students’ curiosity.

If we continue thinking within this brain-as-AI framework, we also risk losing the vital thought processes that have led to major breakthroughs in science and art. These achievements did not come from identifying familiar patterns, but from breaking them through messiness and unexpected mistakes. Alexander Fleming discovered penicillin by noticing that mold growing in a petri dish he had accidentally left out was killing the surrounding bacteria. A fortunate mistake made by a messy researcher that went on to save the lives of hundreds of millions of people.

This messiness isn’t just important for eccentric scientists. It is important to every human brain. One of the most interesting discoveries in neuroscience in the past two decades is the “default mode network,” a group of brain regions that becomes active when we are daydreaming and not focused on a specific task. This network has also been found to play a role in reflecting on the past, imagining and thinking about ourselves and others. Disregarding this mind-wandering behavior as a glitch rather than embracing it as a core human feature will inevitably lead us to build flawed systems in education, mental health and law.

Unfortunately, it is particularly easy to confuse AI with human thinking. Microsoft describes generative AI models like ChatGPT on its official website as tools that “mirror human expression, redefining our relationship to technology.” And OpenAI CEO Sam Altman recently highlighted his favorite new feature in ChatGPT called “memory.” This function allows the system to retain and recall personal details across conversations. For example, if you ask ChatGPT where to eat, it might remind you of a Thai restaurant you mentioned wanting to try months earlier. “It’s not that you plug your brain in one day,” Altman explained, “but … it’ll get to know you, and it’ll become this extension of yourself.”

The suggestion that AI’s “memory” will be an extension of our own is again a flawed metaphor — leading us to misunderstand the new technology and our own minds. Unlike human memory, which evolved to forget, update and reshape memories based on myriad factors, AI memory can be designed to store information with much less distortion or forgetting. A life in which people outsource memory to a system that remembers almost everything isn’t an extension of the self; it breaks from the very mechanisms that make us human. It would mark a shift in how we behave, understand the world and make decisions. This might begin with small things, like choosing a restaurant, but it can quickly move to much bigger decisions, such as taking a different career path or choosing a different partner than we would have, because AI models can surface connections and context that our brains may have cleared away for one reason or another.

This outsourcing may be tempting because this technology seems human to us, but AI learns, understands and sees the world in fundamentally different ways, and doesn’t truly experience pain, love or curiosity like we do. The consequences of this ongoing confusion could be disastrous — not because AI is inherently harmful, but because instead of shaping it into a tool that complements our human minds, we will allow it to reshape us in its own image.

Iddo Gefen is a PhD candidate in cognitive neuroscience at Columbia University and author of the novel “Mrs. Lilienblum’s Cloud Factory.”. His Substack newsletter, Neuron Stories, connects neuroscience insights to human behavior.

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AI Insights

Podcast: Training artificial intelligence – National Science Foundation (.gov)

Published

43 minutes ago

July 9, 2025

The Editors

Podcast: Training artificial intelligence National Science Foundation (.gov)

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AI Insights

Artificial Intelligence (A.I.) in Beauty and Cosmetics Market

Published

45 minutes ago

July 9, 2025

The Editors

The newly published report titled “Global Artificial Intelligence (A.I.) in Beauty and Cosmetics Market- By Trends, Industry Competition/Company Profiles Analysis, Revenue (US$ Billions) and Forecast Till 2034.” features in-depth analysis and an extensive study on the market, exploring its significant factors.

According to the latest market intelligence research report by InsightAce Analytic, the global Artificial Intelligence (AI) In Beauty and Cosmetics Market Size is valued at USD 4.43 Billion in 2024 and is predicted to reach USD 27.65 Billion by the year 2034 at a 20.2% CAGR during the forecast period for 2025-2034.

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https://www.insightaceanalytic.com/request-sample/1051

In recent years, the beauty and cosmetics industry has experienced a transformative shift with the growing integration of Artificial Intelligence (AI) technologies. As a highly personalized and data-rich sector, the beauty market is ideally positioned to benefit from AI-driven innovation. These advancements enable brands to make informed, data-backed decisions and adapt strategies to stay competitive in an increasingly dynamic marketplace.

Over the past decade, the industry has undergone significant change, driven by technological innovation and a marked evolution in consumer shopping behavior. As part of a broader digital transformation, leading beauty brands have adopted AI tools to offer personalized skincare solutions and customized beauty products, meeting the specific needs of individual consumers.

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Competitive Analysis:

There has been an influx of Beauty Tech implementations on the global market with the rapid expansion of the beauty and cosmetic industry. Key companies are constantly testing and launching new features with key strategic partners with innovative services, covering the market’s demands. Their focus on serving their clients’ needs, both brands and end-consumers, and the constant technological development are the key factors in boosting market growth. Companies like L’Oréal, and PROVEN, among others, have already recognized such potential and are applying A.I. in different ways. For instance, L’Oréal is implementing A.I. strategies on their business. Followed L’Oréal, by PROVEN has the largest skincare database and, with the input from the consumer, matches their data, creating unique and customized products using A.I. mechanisms.

The prominent players in the Artificial Intelligence (A.I.) in Beauty and Cosmetics

industry include:

Beiersdorf (NIVEA SKiN GUiDE), L’Oréal’s (Modiface, Hair Coach), Olay (Skin Care App), CRIXlabs (DBA Quantified Skin), Shiseido (Optune System), Procter & Gamble (Opte Wand), My Beauty Matches, Yours Skincare, EpigenCare Inc., mySkin, Haut.AI, Luna Fofo, Revieve, ANOKAI. CA., Pure & Mine, Youth Laboratories, Spruce Beauty, Nioxin, New Kinpo Group, Perfect Corp, Symrise (Philyra), Sephora USA, Inc. (Virtual Artist), Function of Beauty LLC, Estée Lauder, Coty Inc. (Rimmel), Givaudan, Beautystack and Polyfins Technology Inc and Other Prominent Players.

Key Industry Developments from Leading Players:

• In Dec 2021, Nykaa introduced L’Oréal’s advanced, Al-powered virtual try-on technology, ModiFace. Through the launch of this new technology, Company is expected to create an enhanced beauty experience for makeup enthusiasts while buying beauty products online. ModiFace technology allows photo-realistic results and AI-enabled shade calibration. The ModiFace Technology enables virtual try-on on Nykaa’s website and mobile app, helping shoppers purchase their desired beauty products across categories, starting with the L’Oréal range of products.

• In Oct 2021, PROVEN Skincare launched of $60 Billion Regulation A+ Offering. The funds are used to invest in Company’s further A.I. innovation and talent, expand domestic and global marketing strategies for its existing product line, and invest in further research and development of new product lines and categories

• In Sept 2021, Coty, Inc. announced a multi-channel partnership agreement with Perfect Corp. Through this partnership, Company plans to strengthen its offering in virtual try-ons, online skin diagnostics and data-driven personalization for brands including CoverGirl, Sally Hansen and philosophy, among others, as well as for Coty’s broad fragrance portfolio

• In June 2021, The technology division of L’Oréal, a longtime Google enterprise customer, subscribes to Google Cloud’s Vertex A.I. platform to speed up the production of its A.I. models for cosmetic services. Google’s Vertex platform enables customers to accelerate the development, deployment, and maintenance of machine learning models by unifying Google Cloud Services, machine learning systems, and machine learning operations (MLOps) under one user interface and API.

• In Jun 2021, LVMH and Google Cloud collaborated on cloud-based A.I. solutions to create new, personalized customer experiences that foster long-term growth. Through this collaboration, the Company is anticipated to augment demand forecasting and inventory optimization, support inclusive upskilling and certification programs for employees, and elevate customer experiences through personalization.

• In June 2021, Perfect Corp. announced an integration with Google for the launch of an A.R. interactive beauty try-ons. AR-powered virtual beauty try-on experiences can help online shoppers discover and experiment with various beauty products and shades online through Google search.

• In Dec 2020, Function of Beauty received a $150 Billion strategic minority investment from L Catterton. The funds used to speed up product development, support the Company’s continuous global expansion, and expand on the Company’s already industry-leading bespoke manufacturing capabilities.

Artificial Intelligence (A.I.) in Beauty and Cosmetics Market Regional Analysis:

North America is expected to dominate the growth of A.I. in the beauty and cosmetic market due to the expansion of the beauty and cosmetic industry and prominent e-commerce companies like Amazon and Sephora. Asia Pacific region is expected to experience the fastest growth in the global A.I. in beauty and cosmetic market due to rapidly increasing consumer spending and expansion of the e-commerce sector across the region. In emerging countries like China, India, and Japan, the beauty e-commerce space is adapting to multiple models to enhance the e-commerce shopping experience for consumers.

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The Global Artificial Intelligence (A.I.) in Beauty and Cosmetics Market Segments

The Global Artificial Intelligence (A.I.) in Beauty and Cosmetics Market Estimates (Value US$ Billion) & Trend and Forecast Analysis, 2025 to 2034 based on Service/Product

• Personalized Recommendation Tools

• Performance Marketing Measurement Platforms

• Demand Forecasting and Supply Chain Tools

• Real-time Customer Service Platforms

• AI-based Beauty Devices

The Global Artificial Intelligence (A.I.) in Beauty and Cosmetics Market Estimates (Value US$ Billion) & Trend and Forecast Analysis, 2025 to 2034 based on Application

• Skincare

• Haircare

• Make-up

• Fragrances

• Others

The Global Artificial Intelligence (A.I.) in Beauty and Cosmetics Market Estimates (Value US$ Billion) & Trend and Forecast Analysis, 2025 to 2034 based on Region

• Europe

• North America

• Latin America

• Asia Pacific

• Middle East & Africa

North America Artificial Intelligence (A.I.) in Beauty and Cosmetics Market Estimates Revenue (US$ Billion) by Country, 2025 to 2034

• U.S.

• Canada

Europe Artificial Intelligence (A.I.) in Beauty and Cosmetics Market Estimates Revenue (US$ Billion) by Country, 2025 to 2034

• Germany

• France

• Italy

• Spain

• Russia

• Rest of Europe

Asia Pacific Artificial Intelligence (A.I.) in Beauty and Cosmetics Market Estimates Revenue (US$ Billion) by Country, 2025 to 2034

• India

• China

• Japan

• South Korea

• Australia & New Zealand

Latin America Artificial Intelligence (A.I.) in Beauty and Cosmetics Market Estimates Revenue (US$ Billion) by Country, 2025 to 2034

• Brazil

• Mexico

• Rest of Latin America

The Middle East & Africa Artificial Intelligence (A.I.) in Beauty and Cosmetics Market Estimates Revenue (US$ Billion) by Country, 2025 to 2034

• GCC Countries

• South Africa

• Rest of the Middle East & Africa

Get More Information :

https://www.insightaceanalytic.com/report/global-artificial-intelligence-ai-in-beauty-and-cosmetics-market/1051

About Us:

InsightAce Analytic is a market research and consulting firm that enables clients to make strategic decisions. Our qualitative and quantitative market intelligence solutions inform the need for market and competitive intelligence to expand businesses. We help clients gain a competitive advantage by identifying untapped markets, exploring new and competing technologies, segmenting potential markets, and repositioning products. Our expertise is in providing syndicated and custom market intelligence reports with an in-depth analysis with key market insights in a timely and cost-effective manner.https://www.insightaceanalytic.com/images_data/148861653.JPG

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This release was published on openPR.

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AI Insights

AI is eating up Pennsylvania’s power, governor threatens to pull state from the grid — new plants aren’t being built fast enough to keep up with demand

Published

2 hours ago

July 9, 2025

The Editors

Artificial intelligence and the hardware that powers it, is at the heart of a fallout in Pennsylvania, where electricity prices have risen dramatically for wholesalers and consumers due to surging demand. The governor is now threatening to abandon the state’s grid energy provider, PJM Interconnection, via Reuters. He’s demanding that PJM increase energy capacity through the acceleration of new energy plant construction and approval.

Following the launch of ChatGPT in 2023 and the explosion of competitor tools and chatbots in the months that followed, the regional transmission organization, PJM, saw a surge in demand for power as major tech companies scoured the country looking for spare grid capacity to run AI and build new data centers to support them. AI can demand a lot of power, so much so that Elon Musk is shipping an entire power plant to the US.

This couldn’t have come at a worse time, as in 2022, PJM had paused new power plant connections after it faced a huge influx of applications for new renewable projects, which required more engineering oversight before they could be connected to the grid. Although PJM claims that this hasn’t led to a shortfall in supply, it has meant the grid hasn’t expanded like it was expected to. Local opposition to the construction of some of the plants that have been approved has further compounded the issue.

PJM’s power management covers 13 states in total, including Virginia, which plays host to the so-called “Data Center Alley,” a small region with the highest density of data centers in the world. It’s the huge demand from that region and elsewhere in its coverage that PJM blames on its inability to supply power at affordable rates.

Last year’s annual capacity auction for energy capacity saw prices increase by over 800%, and with an impending auction for this year, those prices are expected to rise again. It’s lead to a projected 20% rise in consumer energy prices across Pennsylvania and other PJM-covered territories this summer.

Legislators aren’t happy.

“We need speed from PJM, we need transparency from PJM and we need to keep consumer costs down with PJM,” Pennsylvania Governor Josh Shapiro told Reuters. “I think they’ve taken some steps in that direction which is really encouraging to me and we’re going to continue to work at it.”

PJM’s response to the crisis has been to cap energy prices at $325 per megawatt-day, and it has fast-tracked connections of more than 50 power projects that should help augment its existing capacity, but many of those won’t come online until the early 2030s.

Despite these aims, PJM estimates that the demand issue may only worsen. By 2030, it expects to require an additional 32 gigawatts of power capacity to meet growing demand, with over 30 of these allocated to new data center projects.

In the near term, with concerns about blackouts, the Trump administration has also ordered two oil and gas plants that were planned to close in May, to remain open through the summer.

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