The Top 10 Most Inspirational Women of All Time According to AI
Throughout history, countless women have shattered societal barriers, defied expectations, and left an indelible mark on the world. These women have inspired generations through their courage, achievements, and unwavering commitment to their beliefs. From groundbreaking scientists to trailblazing activists, their legacies continue to empower others to reach their full potential.
Their stories remind us that change often begins with bold visionaries willing to challenge the status quo. Whether it's championing human rights, advancing science, or creating masterpieces of art and literature, these women exemplify resilience, brilliance, and strength. They have not only made history but have shaped the future.
This article highlights the top 10 most inspirational women of all time, with research assistance and insights from AI, chosen for their extraordinary contributions and enduring influence. While their fields of expertise vary, their impact is universal, transcending borders and inspiring people of all ages and backgrounds.
1. Marie Curie
Marie Curie was a pioneering scientist whose discoveries in radioactivity revolutionized the field of physics and chemistry. She was the first woman to win a Nobel Prize and remains the only person to win Nobel Prizes in two different scientific fields. Her groundbreaking work paved the way for advancements in cancer treatment and modern medicine.
Date of Birth and Death: November 7, 1867 – July 4, 1934
Profession: Physicist and chemist
Key Achievements: Discovered polonium and radium
Nobel Prizes: Physics (1903) and Chemistry (1911)
Legacy: The first woman to teach at the University of Paris
Impact: Advanced cancer research with the use of radiotherapy
2. Rosa Parks
Rosa Parks, known as the 'Mother of the Civil Rights Movement,' made history in 1955 when she refused to give up her bus seat to a white man in Montgomery, Alabama. Her brave act of defiance sparked the Montgomery Bus Boycott, a pivotal moment in the fight against racial segregation in the United States.
3. Malala Yousafzai
Malala Yousafzai became a global symbol of resilience and advocacy for girls' education after surviving an assassination attempt by the Taliban in 2012. Despite facing life-threatening adversity, she continued her fight for education rights and became the youngest Nobel Peace Prize laureate.
Date of Birth: July 12, 1997
Profession: Education activist
Key Achievement: Co-founded the Malala Fund to support education worldwide
Awards: Nobel Peace Prize (2014)
Legacy: A global advocate for girls' education
Impact: Inspired educational reforms in developing nations
4. Eleanor Roosevelt
Eleanor Roosevelt was one of the most influential First Ladies in U.S. history, evolving the role into a platform for social advocacy. A staunch supporter of human rights, she chaired the committee that drafted the Universal Declaration of Human Rights and worked tirelessly for equality and justice.
Date of Birth and Death: October 11, 1884 – November 7, 1962
Profession: Diplomat and human rights activist
Key Role: First Lady of the United States (1933–1945)
Legacy: Chairperson of the UN Human Rights Commission
Impact: Helped draft the Universal Declaration of Human Rights (1948)
Inspiration: Advocated for women's and minority rights
5. Mother Teresa
Mother Teresa devoted her life to serving the poorest of the poor. Through her missionary work, she founded the Missionaries of Charity in India, which provides care to the sick, homeless, and dying.
She helped millions of people across the globe, primarily those living in poverty, sickness, and destitution, providing care to thousands of the sick and dying, and supporting countless children through orphanages and other initiatives.
Date of Birth and Death: August 26, 1910 – September 5, 1997 (age 87)
Profession: Catholic nun and humanitarian
Key Achievement: Founded the Missionaries of Charity in 1950
Awards: Nobel Peace Prize (1979)
Legacy: Known as a global symbol of compassion
Impact: Provided aid in over 130 countries and millions of people
6. Frida Kahlo
Frida Kahlo was a Mexican artist renowned for her powerful self-portraits and unique style that explored themes of identity, pain, and resilience. Despite living with debilitating physical ailments, her work broke cultural barriers and inspired movements for feminism and individuality.
Date of Birth and Death: July 6, 1907 – July 13, 1954
Profession: Painter
Key Works: The Two Fridas and Self-Portrait with Thorn Necklace and Hummingbird
Legacy: A feminist icon and pioneer of surrealism in art
Impact: Advocated for Mexican culture and women's rights
Famous Quote: 'I paint myself because I am so often alone and because I am the subject I know best.'
7. Amelia Earhart
Amelia Earhart was a pioneering aviator and the first woman to fly solo across the Atlantic Ocean. Her achievements and adventurous spirit made her an enduring symbol of determination and courage, inspiring countless women to break barriers in male-dominated fields.
Date of Birth and Disappearance: July 24, 1897 – Disappeared July 2, 1937 (Declared dead January 5, 1939)
Profession: Aviator
Key Achievement: First woman to fly solo across the Atlantic (1932)
Legacy: Opened doors for women in aviation
Impact: Advocated for gender equality in aviation
8. Oprah Winfrey
Oprah Winfrey rose from poverty to become one of the most influential media moguls in the world. Her talk show, The Oprah Winfrey Show, inspired millions, and her philanthropy and advocacy for education continue to make a difference globally.
9. Harriet Tubman
Harriet Tubman was an abolitionist and freedom fighter who escaped slavery and helped hundreds of others gain their freedom through the Underground Railroad. Her bravery and leadership made her a symbol of resilience and justice.
Date of Birth and Death: March 1822 – March 10, 1913
Profession: Abolitionist and activist
Key Achievement: Led over 70 enslaved people to freedom
Legacy: Served as a Union spy during the Civil War
Impact: Paved the way for abolition and civil rights movements
10. Jane Goodall
Jane Goodall is a renowned primatologist and conservationist whose groundbreaking work with chimpanzees, primarily in Tanzania, redefined humanity's understanding of animal behavior. Her advocacy for wildlife conservation and sustainability continues to inspire global action.
Summary of the Most Inspirational Women
The women featured on this list have forever altered the course of history, embodying the strength, innovation, and compassion that define humanity's potential. Through their achievements in science, human rights, art, and culture, they have confronted adversity and transformed challenges into milestones that inspire generations. These trailblazers remind us of the extraordinary heights individuals can achieve when guided by purpose and determination.
Each story is a testament to the power of resilience and vision, illustrating how one person's courage can spark movements, shift paradigms, and redefine what is possible. From Harriet Tubman's unwavering quest for freedom to Jane Goodall's lifelong commitment to conservation, these women have not only made significant contributions but have reshaped the world in their image.
Their legacies challenge us to uphold their values, continuing their work in championing justice, equality, and progress. They encourage us to persist in our own aspirations and to recognize the profound impact that every individual has the power to make. By remembering their accomplishments, we are reminded that true inspiration comes from many different paths of life, leaving an indelible mark on the future.
Related Articles:
About the Authors:
Richard D. Harroch is a Senior Advisor to CEOs, management teams, and Boards of Directors. He is an expert on M&A, venture capital, startups, and business contracts. He was the Managing Director and Global Head of M&A at VantagePoint Capital Partners, a venture capital fund in the San Francisco area. His focus is on internet, digital media, AI and technology companies. He was the founder of several Internet companies. His articles have appeared online in Forbes, Fortune, MSN, Yahoo, Fox Business and AllBusiness.com. Richard is the author of several books on startups and entrepreneurship as well as the co-author of Poker for Dummies and a Wall Street Journal-bestselling book on small business. He is the co-author of a 1,500-page book published by Bloomberg on mergers and acquisitions of privately held companies. He was also a corporate and M&A partner at the international law firm of Orrick, Herrington & Sutcliffe. He has been involved in over 200 M&A transactions and 250 startup financings. He can be reached through LinkedIn.
Dominique Harroch is the Chief of Staff at AllBusiness.com. She has acted as a Chief of Staff or Operations Leader for multiple companies where she leveraged her extensive experience in operations management, strategic planning, and team leadership to drive organizational success. With a background that spans over two decades in operations leadership, event planning at her own start-up and marketing at various financial and retail companies. Dominique is known for her ability to optimize processes, manage complex projects and lead high-performing teams. She holds a BA in English and Psychology from U.C. Berkeley and an MBA from the University of San Francisco. She can be reached via LinkedIn.
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Forbes
37 minutes ago
- Forbes
Despite Billions In Investment, AI Reasoning Models Are Falling Short
In early June, Apple released an explosive paper, The Illusion of Thinking: Understanding the Limitations of Reasoning Models via the Lens of Problem Complexity. It examines the reasoning ability of Large Reasoning Models (LRMs) such as Claude 3.7 Sonnet Thinking, Gemini Thinking, DeepSeek-R1, and OpenAI's o-series models — how they think, especially as problem complexity increases. The research community dug in, and the responses were swift. Despite the increasing adoption of Generative AI and the adoption and the presumption that AI will replace tasks and jobs at scale, these Large Reasoning Models are falling short. By definition, Large Reasoning Models (LRMs) are Large Language Models (LLMS) focused on step-by-step thinking. This is called Chain of Thought (CoT) which facilitates problem solving by guiding the model to articulate reasoning steps. Jing Hu, writer, researcher and author of 2nd Order Thinkers, who dissected the paper's findings remarked that "AI is just sophisticated pattern matching, no thinking, no reasoning" and 'AI can only do tasks accurately up to a certain degree of complexity.' As part of the study, researchers created a closed puzzle environment for games like Checkers Jumping, River Crossing, and Tower of Hanoi, which simulate varied conditions of complexity. Puzzles were applied across three stages of complexity ranging from the simplest to high complexity. Across all three stages of the models' performances the paper concluded: At 'Low Complexity', the regular models performed better than LRMs. Hu explained, 'The reasoning models were overthinking — wrote thousands of words, exploring paths that weren't needed, second-guessing correct answers and making things more complicated than they should have been.' In the Tower of Hanoi, a human can solve the puzzle within seven moves while Claude-3.7 Sonnet Thinking uses '10x more tokens compared to the regular version while achieving the same accuracy... it's like driving a rocket ship to the corner store.' At 'Medium Complexity', LRMs outperformed LLMs, revealing traces of chain of thought reasoning . Hu argues LRMs tended to explore wrong answers first before eventually finding the correct answer, however, she argues, 'these thinking models use 10-50x more compute power (15,000-20,000 tokes vs. 1,000-5000). Imagine paying $500 instead of $50 for a hamburger that tastes 10% better.' Hu says that this isn't an impressive breakthrough but reveal a complexity that is dressed to impress audiences and 'simple enough to avoid total failure.' At 'High Complexity', both LRMs and standard models collapse, and accuracy drops to zero. As the problems get more complex, the models simply stopped trying. Hu explains, referencing Figure 6 from Apple's paper, 'Accuracy starts high for all models on simple tasks, dips slowly then crashes to near zero at a 'critical point' of complexity. If this compared to the row displaying Token use, the latter rises as problems become harder ('models think more'), peaks, then drops sharply at the same critical point even if token budget is still available.' Hu explains models aren't scaling up their effort; rather they abandon real reasoning and output less. Gary Marcus is an authority on AI. He's a scientist and has written several books including The Algebraic Mind and Rebooting AI. He continues to scrutinize the releases from these AI companies. In his response to Apple's paper, he states, 'it echoes and amplifies the training distribution argument that I have been making since 1998: neural networks of various kinds can generalize within a training distribution of data they are exposed to, but their generalizations tend to break down outside that distribution.' This means the more edge cases introduced to these LRMs the more they will go off-track especially with problems that are very different from the training data. He also advises that LRMs have a scaling problem because 'the outputs would require too many output tokens' indicating the correct answer would be too long for the LRMs to produce. The implications? Hu advises, 'This comparison matters because it debunks hype around LRMs by showing they only shine on medium complexity tasks, not simple or extreme ones.' Why this Hedge Fund CEO Passes on GenAI Ryan Pannell is the CEO of Kaiju Worldwide, a technology research and investment firm specializing in predictive artificial intelligence and algorithmic trading. He plays in an industry that demands compliance and a stronger level of certainty. He uses Predictive AI, which is a type of artificial intelligence leveraging statistical analysis and machine learning to forecast based on patterns on historical data; unlike generative AI like LLM and LRM chatbots, it does not create original content. Sound data is paramount and for the hedge funds, they only leverage closed datasets, as Pannell explains, 'In our work with price, time, and quantity, the analysis isn't influenced by external factors — the integrity of the data is reliable, as long as proper precautions are taken, such as purchasing quality data sets and putting them through rigorous quality control processes, ensuring only fully sanitized data are used.' The data they purchase — price, time, and quantity — are from three different vendors and when they compare their outputs, 99.999% of the time they all match. However, when there's an error — since some data vendors occasionally provide incorrect price, time, or quantity information — the other two usually point out the mistake. Pannell argues, 'This is why we use data from three sources. Predictive systems don't hallucinate because they aren't guessing.' For Kaiju, the predictive model uses only what it knows and whatever new data they collect to spot patterns they use to predict what will come next. 'In our case, we use it to classify market regimes — bull, bear, neutral, or unknown. We've fed them trillions of transactions and over four terabytes of historical price and quantity data. So, when one of them outputs 'I don't know,' it means it's encountered something genuinely unprecedented.' He claims that if it sees loose patterns and predicts a bear market with 75% certainty, it's likely correct, however, 'I don't know,' signals a unique scenario, something never seen in decades of market data. 'That's rare, but when it happens, it's the most fascinating for us,' says Pannell. In 2017, when Trump policy changes caused major trade disruptions, Pannell asserted these systems were not in place so the gains they made within this period of high uncertainty were mostly luck. But the system today, which has experienced this level of volatility before, can perform well, and with consistency. AI Detection and the Anomaly of COVID-19 Just before the dramatic drop of the stock market of February 2020, the stock market was still at an all-time high. However, Pannell noted that the system was signaling that something was very wrong and the strange behavior in the market kept intensifying, 'The system estimated a 96% chance of a major drop and none of us knew exactly why at the time. That's the challenge with explainability — AI can't tell you about news events, like a cruise ship full of sick people or how COVID spread across the world. It simply analyzes price, time and quantity patterns and predicts a fall based on changing behavior it is seeing, even though it has no awareness of the underlying reasons. We, on the other hand, were following the news as humans do.' The news pointed to this 'COVID-19' thing, at the time it seemed isolated. Pannell's team weren't sure what to expect but in hindsight he realized the value of the system: it analyzes terabytes of data and billions of examinations daily for any recognizable pattern and sometimes determines what's happening matches nothing it has seen before.' In those cases, he realized, the system acted as an early warning, allowing them to increase their hedges. With the billions of dollars generated from these predictive AI systems, their efficacy drops off after a week to ~21%-17% and making trades outside this range is extremely risky. Pannell suggests he hasn't seen any evidence suggesting AI — of any kind — will be able to predict financial markets with accuracy 90 days, six months or a year in advance. 'There are simply too many unpredictable factors involved. Predictive AI is highly accurate in the immediate future — between today and tomorrow — because the scope of possible changes is limited.' Pannell remains skeptical on the promises of LLMs and the current LRMS for his business. He describes wasting three hours being lied to by ChatGPT 4.o when he was experimenting with using it to architecting a new framework. He was blown away the system had substantially increased its functionality at first, but he determined after three hours, it was lying to him the entire time. He explains, 'When I asked, 'Do you have the capability to do what you just said?' the system responded it did not and added that its latest update had programmed it to keep him engaged over giving an honest answer.' Pannell adds, 'Within a session, an LLM can adjust when I give it feedback, like 'don't do this again,' but as soon as the session goes for too long, it forgets and starts lying again.' He also points to ChatGPT's memory constraints. He noted it performs really well for the first hour but in the second or third hour, ChatGPT starts forgetting earlier context, making mistakes and dispensing false information. He described it to a colleague this way, 'It's like working with an extremely talented but completely drunk programmer. It does some impressive work, but it also over-estimates its capabilities, lies about what it can and can't do, delivers some well-written code, wrecks a bunch of stuff, apologizes and says it won't do it again, tells me that my ideas are brilliant and that I am 'right for holding it accountable', and then repeats the whole process over and over again. The experience can be chaotic.' Could Symbolic AI be the Answer? Catriona Kennedy holds a Ph.D. in Computer Science from the University of Birmingham and is an independent researcher focusing on cognitive systems and ethical automation. Kennedy explains that automated reasoning has always been a branch of AI with the inference engine at the core, which applies the rules of logic to a set of statements that are encoded in a formal language. She explains, 'An inference engine is like a calculator, but unlike AI, it operates on symbols and statements instead of numbers. It is designed to be correct.' It is designed to deduce new information, simulating the decision-making of a human expert. Generative AI, in comparison, is a statistical generator, therefore prone to hallucinations because 'they do not interpret the logic of the text in the prompt.' This is the heart of symbolic AI, one that uses an inference engine and allows for human experience and authorship. It is a distinct AI system from generative AI. The difference with Symbolic AI is the knowledge structure. She explains, 'You have your data and connect it with knowledge allowing you to classify the data based on what you know. Metadata is an example of knowledge. It describes what data exists and what it means and this acts as a knowledge base linking data to its context — such as how it was obtained and what it represents.' Kennedy also adds ontologies are becoming popular again. Ontology defines all the things that exist and the interdependent properties and relationships. As an example, animal is a class, and a sub class is a bird and a further sub-class is an eagle or robin. The properties of a bird: Has 2 feet, has feathers, and flies. However, what an eagle eats may be different from what a robin eats. Ontologies and metadata can connect with logic-based rules to ensure the correct reasoning based on defined relationships. The main limitation of pure symbolic AI is that it doesn't easily scale. Kennedy points out that these knowledge structures can become unwieldy. While it excels at special purpose tasks, it becomes brittle at very complex levels and difficult to manage when dealing with large, noisy or unpredictable data sets. What we have today in current LRMs has not yet satisfied these researchers that AI models are any closer to thinking like humans, as Marcus points out, 'our argument is not that humans don't have any limits, but LRMs do, and that's why they aren't intelligent... based on what we observe from their thoughts, their process is not logical and intelligent.' For Jing Hu, she concludes, "Too much money depends on the illusion of progress — there is a huge financial incentive to keep the hype going even if the underlying technology isn't living up to the promises. Stop the Blind worship of GenAI." (Note: Open AI recently raised $40billion with a post-money valuation of $300billion.) For hedge fund CEO, Ryan Pannell, combining generative AI (which can handle communication and language) with predictive systems (which can accurately process data in closed environments) would be ideal. As he explains, 'The challenge is that predictive AI usually doesn't have a user-friendly interface; it communicates in code and math, not plain English. Most people can't access or use these tools directly.' He opts for integrating GPT as an intermediary, 'where you ask GPT for information, and it relays that request to a predictive system and then shares the results in natural language—it becomes much more useful. In this role, GPT acts as an effective interlocutor between the user and the predictive model.' Gary Marcus believes by combining symbolic AI with neural networks — which is coined Neurosymbolic AI — connecting data to knowledge that leverage human thought processes, the result will be better. He explains that this will provide a robust AI capable of 'reasoning, learning and cognitive modelling.' Marcus laments that for four decades, the elites that have evolved machine-learning, 'closed-minded egotists with too much money and power' have 'tried to keep a good idea, namely neurosymbolic AI, down — only to accidentally vindicate the idea in the end." 'Huge vindication for what I have been saying all along: we need AI that integrates both neural networks and symbolic algorithms and representations (such as logic, code, knowledge graphs, etc.). But also, we need to do so reliably, and in a general way, and we haven't yet crossed that threshold.'
Wall Street Journal
37 minutes ago
- Wall Street Journal
Does Artificial Intelligence Belong In Schools?
Regarding 'A Teachers Union's Dangerous Embrace of AI' (op-ed, Aug. 1), I agree with Rebecca Zimmerman's prescription for helping students flourish—focusing on mental health initiatives, cellphone bans during the school day and science-based reading curriculums—all priorities the American Federation of Teachers champions. Where we disagree is over the effect of artificial intelligence in education, specifically Ms. Zimmerman's assertion that AI is 'another fad that will distract teachers.' AI already has an inescapable influence on nearly every part of our lives. I believe, for better or worse, that it will be as transcendent as the printing press.
Yahoo
2 hours ago
- Yahoo
Atombeat and BioDuro link for peptide drug discovery platform
Atombeat and BioDuro have announced a strategic partnership to launch an AI-powered platform aimed at accelerating peptide drug discovery. The collaboration combines Atombeat's expertise in in silico modelling with BioDuro's capabilities in discovery chemistry, biology, and drug metabolism and pharmacokinetics. The initiative is set to enhance the development of next-generation peptides, quality candidates with quick timelines and cost-effectiveness across various therapeutic areas. Atombeat expedites the discovery of drugs using its Hermite software and RiDYMO platform, incorporating technologies such as Uni-Dock and Reinforced Dynamics. These tools enable the exploration of a digital library with over one trillion potential peptide compounds, streamlining the early drug discovery process. By focusing on the most promising candidates, the platform is designed to save cost, time and effort in drug development. BioDuro's role in the partnership is to leverage its synthesis capability for producing several peptide compounds in parallel. The company will be able to deliver pure peptides within one week, without the need for purification steps. This integrated approach aims to significantly reduce research and development timelines and associated costs and risks. BioDuro CEO Dr Armin Spura stated: 'We are excited to advance peptide innovation through our strategic partnership with Atombeat. Atombeat brings extensive experience in AI-assisted drug design and optimisation. 'Together, we aim to provide the industry with leading services in peptide drug development, accelerating smarter and more efficient peptide research, and delivering faster and comprehensive solutions to our global clients.' The AI-accelerated platform will facilitate a seamless workflow from molecular design to preclinical candidate selection. It is expected to deliver cyclic peptides that meet key developability traits, such as membrane permeability, across a broad range of therapeutic areas. "Atombeat and BioDuro link for peptide drug discovery platform" was originally created and published by Pharmaceutical Technology, a GlobalData owned brand. The information on this site has been included in good faith for general informational purposes only. It is not intended to amount to advice on which you should rely, and we give no representation, warranty or guarantee, whether express or implied as to its accuracy or completeness. You must obtain professional or specialist advice before taking, or refraining from, any action on the basis of the content on our site.
