These Five Female Leaders Are Transforming the AI Industry
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Unfortunately, the world's media resources are not always distributed according to importance.
Similarly at the forefront of advancing technology, in the public consciousness, DeepMind's leader Demis Hassabis, who developed AlphaGo that defeated human chess players and AlphaFold which may bring immense potential benefits to humanity, is not as famous as OpenAI's leader Sam Altman who staged a dramatic power struggle.
And the two of them combined might still not be as famous as Musk who tried and failed to invest in both their companies. Elon Musk is undoubtedly outstanding. However, the media resources he receives far exceed those of equally outstanding individuals, and news related to him, even if not highly significant, often makes headlines—before Musk took over Twitter, I really don't remember frequently seeing tech news like "Twitter might add a small feature!" and just might!
Musk dares to act and speak. For every high-profile entrepreneur like Musk, there are ten low-profile contributors like Demis Hassabis, who are renowned within the industry but remain unknown to the general public.
When it comes to women, the situation is often even more severe. Female tech professionals often face a double debuff: on one hand, they tend to maintain low profiles, and on the other, they confront structural societal disadvantages. When accomplishing the same work, women's scientific achievements are often overlooked and credited to their male colleagues working alongside them—this phenomenon has long been recognized and named the Matilda Effect.
For instance, when thinking about programming and artificial intelligence, how many people immediately realize that the earliest computer program in human history was written by Ada Lovelace, a woman, and that the first textbook on AI was authored by Elaine Rich, also a woman? Among those who have long followed the AI field, how many can effortlessly name several outstanding women in AI?
It's okay—before working on this topic, I couldn't immediately list many such remarkable women either. But this doesn't mean there are no outstanding female scientists working in the AI industry. This is precisely the significance of International Women's Day. On March 8th, let's take a few minutes to learn about five outstanding female AI researchers and entrepreneurs.
The AI field's explosive growth has a coherent history in academia, while in the industry, it can almost be traced back to a specific moment: in 2012, the deep learning network AlexNet achieved remarkably high success rates in image recognition.
Since then, AI has gradually entered an era dominated by deep learning, and within a decade, artificial intelligence has become a buzzword in everyone's daily life. The proposal of AlexNet can ultimately be traced back to ImageNet, which was established by Fei-Fei Li in 2009.
Fei-Fei Li was born in Beijing in 1976 and grew up in Chengdu. At the age of 12, she moved to the United States. At that time, she could barely speak English, but within two years, she rapidly achieved a high level of English proficiency while also demonstrating strong mathematical abilities. In 1995, she entered Princeton University on a scholarship. During this period, she returned home almost every weekend to help her family manage a dry-cleaning business they had opened with borrowed money.
In 2007, Fei-Fei Li became an assistant professor at Princeton University. At that time, researchers in the field of computer vision typically had to write one algorithm specifically to identify dogs and another to identify cats. Fei-Fei Li's intuition was: model capability might be sufficient, the problem lies in the data.
She wanted to create a massive database, labeling every possible object in every image. At the time, such a project was almost entirely ignored. She initially hired Princeton students as part-time workers to build ImageNet, but progress was slow. Later, she turned to crowdsourcing platforms, engaging part-time workers worldwide to collaborate on data labeling.
"Online workers aim to earn money in the simplest way possible, right?" she said in her interview with Wired. If you ask them to select pandas from 100 images, how can you prevent them from randomly clicking? To address this, she embedded and tracked certain images, such as photos of golden retrievers already correctly identified as dogs, as control groups. If crowdsourced workers could accurately label these images, it would indicate they were working honestly.
Her ImageNet project initially collected 3.2 million images, later expanding to 15 million. It was on such a database that researchers could compare whose algorithms performed better. The 2012 AlexNet famously rose to prominence through the ImageNet Challenge. It can be said that ImageNet paved the way for the advancement of deep learning. Fields such as autonomous vehicles, facial recognition, and object detection all trace their origins back to ImageNet.
Even today, when people discuss breakthroughs in AI data for a particular field, they often use the phrase, "Is this its ImageNet moment?" to describe it.
In recent years, in addition to continuing her scientific research, Fei-Fei Li has also focused on increasing diversity and inclusivity in AI, advocating for more resources in the AI academic community to prevent it from falling behind the industry. In 2023, her book The World Through My Eyes: Curiosity, Exploration, and Discovery at the Dawn of the AI Era was published. The book narrates her personal scientific experiences and her interpretations of the significant historical moments of AI in this century.
The wave of large models entered the public consciousness perhaps after the emergence of ChatGPT, but the origins of this wave undoubtedly stem from the 2017 paper Attention is All You Need, authored by eight engineers from Google.
This paper introduced the revolutionary Transformer architecture. Today, leading AI companies, including OpenAI's ChatGPT, are almost all built upon the foundation of the Transformer architecture. I don't know about the readers, but I was once misled by the media's portrayal of the "Transformer eight," believing all the authors were male.
This is not the case. The third author of the Transformer paper, Niki Parmar, is actually a female researcher.
Niki Parmar is from India and completed her undergraduate studies at the College of Computer Technology in Pune, India. She moved to the United States in 2013 to pursue a master's degree in Computer Science at the University of Southern California.
Niki developed an interest in machine learning during her undergraduate years: "I took MOOCs on ML and AI taught by Andrew Ng and Peter Norvig, and I was fascinated by the combined power of data, pattern matching, and optimization," she mentioned in an interview.
After graduating in 2015, she joined Google's research division, where she became deeply interested in pure research. By 2017, she had become one of the core authors of the Transformer model. Regarding research, she said, "At first, the overwhelming amount of information and studies around me constantly left me feeling lost. Focusing on a specific problem and exploring it with peers can help you ask the right questions."
Niki Parmar co-founded two companies with Ashish Vaswani, also of Indian descent and the first author of the Transformer paper: Adept AI and Essential AI. She currently primarily manages the latter.
Essential AI secured a new round of funding totaling $56.5 million from tech giants AMD, Google, and Nvidia at the end of last year. Meanwhile, Adept AI previously raised $350 million in funding. A couple of days ago, Anthropic's model, which claims to surpass the capabilities of OpenAI's GPT-4, became a hot topic.
Reports about Anthropic often mention that it was founded by seven researchers who left OpenAI or highlight that its CEO comes from OpenAI, while downplaying the role of Daniela Amodei—Anthropic's president and one of its two co-founders.
In fact, Anthropic was co-founded by Daniela Amodei and Dario Amodei, who are siblings. The new large model released by Anthropic was primarily introduced by Daniela in many television interviews. When promoting its differences, Anthropic often mentions its greater focus on aligning artificial intelligence systems with human values compared to OpenAI, with Daniela Amodei being the former Vice President of Safety and Policy at OpenAI.
Daniela is of Italian descent and grew up in San Francisco. Her career has been relatively diverse. During university, she simultaneously earned bachelor's degrees in English Literature, Political Science, and Music. In her early career, she worked primarily in the political and non-governmental organization sectors, developing strong management skills.
In 2013, she chose to join Stripe, which had just been founded in 2010—at that time Stripe was still a small company. Today, it has reached a valuation of $50 billion, surpassing SpaceX at its peak valuation.
Starting with Stripe, she began applying her management and risk control skills to technology enterprises. At Stripe, she not only led team recruitment but also managed one of the most critical aspects of the payment business—risk management. Collaborating cross-functionally with machine learning, data science, engineering, legal, finance, and vendor management departments, she guided three teams of 26 members each. Together, they analyzed over 7,000 potential cases of fraud, credit, and policy violations, achieving a 72% reduction in loss rates from peak levels—reaching the company’s historical low.
In 2018, she once again demonstrated her exceptional strategic foresight by joining OpenAI, where she directly led two technical teams: OpenAI’s natural language processing and music generation teams, while also overseeing the technical security team.
Beyond these roles, she served as Vice President of People, responsible for recruitment, personnel programs, DEI (Diversity, Equity, and Inclusion), learning and development, and incubating new business operations teams—a true polymath. In 2021, she co-founded Anthropic with Dario Amodei.
While OpenAI is globally renowned, many may not know that its current CTO is a woman, Mira Murati.
Mira Murati joined OpenAI in 2018, was promoted to Senior Vice President overseeing research, products, and partnerships in 2020, and became Chief Technology Officer in 2022. She has played a pivotal role in developing major projects including ChatGPT, DALL-E, and GPT-4. During OpenAI's internal power struggles, she was briefly nominated as the new CEO.
Mira Murati was born in Albania in 1988 and attended high school in Canada.
With an engineering background, she built a hybrid race car as part of her university project while studying engineering at Dartmouth College. After a brief stint in the aerospace industry, Mira joined Tesla as a senior product manager for the Model X, where her work with Autopilot deepened her interest in artificial intelligence.
Her passion for research is evident. In an interview, she once mentioned, 'Boredom is a powerful motivator to pursue and explore the frontiers of anything.'
OpenAI's most important project, ChatGPT, was led by Mira Murati. She has also been deeply involved in many of the company's significant milestones.
In 2023, Microsoft CEO Satya Nadella invested $13 billion in OpenAI through a major partnership managed by Murati, publicly stating that she "demonstrated the ability to build a team with technical expertise, business acumen, and a deep understanding of the importance of AI's mission."
On March 8, the latest news revealed that during the incident where Sam Altman was ousted from OpenAI, both she and Ilya Sutskever expressed concerns about Altman, which significantly influenced the final decision. Unlike Sutskever, however, she does not currently appear to be facing marginalization at OpenAI. These public disclosures certainly don't represent all the facts, but after seeing her, who can say women can't excel in technology or politics?
Recently, Google's model withdrew its text-to-image generation model again due to AI ethics issues.
This inevitably reminds me of the major drama surrounding Google's AI ethics team in 2020.
In 2020, Google's AI ethics researcher, Timnit Gebru, publicly stated she was fired. And the reason for her dismissal?—precisely because she criticized the biases present in large language models.
Timnit Gebru was born in Eritrea and Ethiopia in 1983. In 2014, she earned her Ph.D. in Electrical Engineering from Stanford University, where she studied computer vision and machine learning.
After graduation, she dedicated herself to researching issues related to AI fairness, accountability, transparency, and ethics. She is best known for a groundbreaking co-authored paper that demonstrated facial recognition systems were less accurate in identifying women and people of color. This finding suggested that the use of such AI technologies could ultimately lead to discrimination. Her research eventually influenced Amazon to change its policies. In 2020, Gebru co-authored a paper with another researcher criticizing large language models and the environmental impact of training them. The paper also raised concerns about the lack of diversity and ethical considerations in the development of artificial intelligence technologies.
The article was supposed to be published the following year, but Jeff Dean, head of Google AI, told colleagues in an internal email (which he later posted online) that the paper "did not meet our publication standards." While Gebru was arguing with the company, she found her company email cut off during her vacation.
This caused a sensation at the time. Many prominent researchers, civil rights leaders, and Gebru's colleagues at Google AI publicly defended her on Twitter. A petition in her support received signatures from over 1,500 Google employees and more than 2,000 academics, nonprofit leaders, and industry peers. However, in the end, Timnit Gebru still left Google. After her departure, she announced the establishment of an independent artificial intelligence research institute—"Distributed AI Research" (DAIR). DAIR aims to counter the pervasive influence of large tech companies in artificial intelligence research, development, and deployment.
As a true warrior, she once said: "I can't wait for big tech companies to finally solve the problems brought by AI."
A fundamental fact remains: despite so many outstanding women, the tech and AI circles are still male-dominated domains. To change this situation involves addressing numerous systemic issues: the pressures women face in academia, the unequal treatment they receive in the investment sector, and even the foundational STEM education and workplace support systems available to girls from an early age.
A single article cannot solve these problems.
This is precisely why International Women's Day and numerous women empowerment initiatives continue to exist. Interestingly, the last woman featured in the article, Timnit Gebru, happens to be the protégé of the first researcher mentioned - Fei-Fei Li. Sometimes it can be a beautiful cycle. At the same time, we can still draw strength from these inspiring women on this special day. In this era where media resources are disproportionately allocated to them, let us take a moment on this day to remember them.
Give credit when it's due.