Women in AI is a biweekly podcast from RE•WORK, meeting with leading female minds in AI, Deep Learning and Machine Learning. We will speak to CEOs, CTOs, Data Scientists, Engineers, Researchers and Industry Professionals to learn about their cutting edge work and advances, as well as their impact on AI and their place in the industry.
In recent years, we are becoming more conscious of our eating habits and our health, and with the help of personalised health platforms, we are able to better monitor our diet, which in turn better our wellbeing. Amice is currently working on her app, eatrite, a personalised food recommendation platform, which, through AI and machine learning algorithms, identifies the dishes on the menu and provides the user with the nutritional value that the dish would provide. This way, users are able to make a better choice when opting for their meal.
Martha’s primary research goal is to develop techniques for adaptive autonomous systems that learn on streams of data with an applied focus on computational sustainability. She focuses on reinforcement learning and representation learning to achieve this goal. In particular, Martha cares about efficient, practical algorithms that enable learning from large amounts of data. Areas of expertise: algorithms & theory, artificial intelligence, machine learning, reinforcement learning.
Vielka is working on supporting underserved students towards their goal of graduation with little debt. At Bridge to College, Vielka is aiming to match students to colleges that will fund and graduate them on-time. In this episode of the podcast, we spoke about her work in education and her journey into AI.
Viola works to create an enriched experience in education and has been doing this in various ways for over 12 years. Viola also won the “Entrepreneur of the Year 2015” award of the Youth Business International, which is one of the world's most honorific awards for young entrepreneurs in over 68 countries. In this episode of the podcast, we spoke about her work as a social entrepreneur and her journey into AI as well as the current landscape of AI in Hong Kong.
Deep learning has enabled significant advances in a variety of domains; however, it relies heavily on large labeled datasets. Chelsea explains how we can use meta-learning, or learning to learn, to enable us to adapt deep models to new tasks with tiny amounts of data, by leveraging data from other tasks.
Jessi has been working in lobbying, policy, communications, reputation and crisis for over a decade. During that time, she has worked for political parties, businesses (start-up to FTSE 100), consultancies, think tanks and NGOs. Her particular area of expertise is highly regulated, highly politicised, technology-driven sectors, including telecoms, energy, and Fintech. She was previously a capital markets solicitor at City firm Linklaters, after graduating from Oxford and subsequently the LSE.
Maroussia works at the crossroads of law and technology. She has a background in interactive arts, having lead interdisciplinary teams at the Obx lab for experimental media within the Hexagram research-creation institute. She was called to the Bar in 2013 and clerked for the Chief Justice at the Quebec Court of Appeal, Canada. She participated in the inquiry commission on the protection of journalists’ sources, investigating law enforcement’s electronic surveillance practices. More recently, she researched AI and human rights at the Digital Inclusion Lab within Global Affairs Canada. She is a member of IEEE’s working group on algorithmic bias.
Giewee has an extensive mathematics background which she uses to resolve complex problems for Upstream E&P. She is a Lead Data Scientist for her division and has strategized and filled the need for constructing a productive advanced data analytics team to meet the demands of upstream advanced data analytics projects. She lectures and participates in Houston Data Analytics, a meetup which she founded in January 2018. She highly values the importance of data preprocessing, data exploration, model validation and model interpretation. She holds the following master degrees: Actuarial Science and Analytics.
Conversational systems like Siri and Google Assistant have been around for several years now; and have recently started to play increasingly ubiquitous roles in people's daily lives, through smart home devices, phones, or social media (like Messenger). Despite this, the conversational experience that these systems provide has evolved only incrementally. At the same time, however, interest in conversational AI from the research community is growing fast, and there’s more potential than ever for using machine learning to power these systems.
Even if you’re not completely clued up on the technicalities of machine learning, you’ve most probably heard of or use Netflix. This means that you’re interacting with a whole myriad of ML every time you use the platform. Julie leads the Machine Learning Infrastructure at Netflix, with the goal of scaling Data Science while increasing innovation. She previously built streaming infrastructure behind the “play” button while Netflix was transitioning from domestic DVD-by-mail service to international streaming service.
Londa is the John L. Hinds Professor of History of Science at Stanford University and directs the EU/US Gendered Innovations in Science, Health & Medicine, Engineering, and Environment project. She is a leading international expert on gender in science and technology and has addressed the United Nations on the topic of “Gender, Science, and Technology.” She is an elected member of the American Academy of Arts and Sciences and the recipient of numerous prizes and awards, including the prestigious Alexander von Humboldt Research Prize and Guggenheim Fellowship. Her work on Gendered Innovations harnesses the creative power of sex and gender analysis to enhance excellence and reproducibility in science and technology.
Anna Bethke is the Head of AI for Social Good of Intel's Artificial Intelligence Products Group where she is establishing partnerships with social impact organizations; enabling their missions with Intel's technologies and AI expertise. She is also actively involved in the AI Ethics discussion, collaborating on research surrounding the design of fair, transparent, ethical, and accessible AI systems.
Deborah, writer at Microsoft, is one of the original architects of the personality for Microsoft’s Cotrana. She crafted the core principles that define Cortana's approach to communication and now helps shepherd those principles as Cortana lights up on other devices, on other operating systems, and in other countries. Today, while the field of AI is still in adolescence, the industry stand in a brilliant position to shape not only technological innovation but also the culture of conversation between humans and machines.
Do our current crop of AI Assistants really use AI? If not, are they still useful? We'll look at the current state of AI Assistants, the challenges of building them, and speculate on what the future might bring. Learn from Cathy, Head of Conversation Design Outreach at Google, and the author of the O'Reilly book, "Designing Voice User Interfaces".
Today AI is being created by a very homogeneous set of people. Not only it is homogeneous in terms of race and color but also in terms of academic backgrounds. Himani explains that she is a very unlikely researcher to become an AI engineer but it is crucial so that AI created by heterogenous set of people is more fair and inclusive.
Hear from Founder & CEO of VineSeluth, Amy Gross, how they use sensory science and apply it to wines to build artificial intelligence-powered learning algorithms for the food and beverage industry. These personal preference learning algorithms and their results can be integrated into client websites, in-store kiosks and applications to hyper-personalize the shopping experience and guide marketing initiatives, increasing sales and reducing waste for the industry.
As Project Manager, Julia is responsible for the research and development of humanoid robotic (Robonaut) and autonomous system capabilities, on the Earth, the International Space Station, and for future exploration, that include dexterous manipulation, autonomous spacecraft control and caretaking, and human-robot interfaces. Julia has a BS from Purdue University, and an MS and PhD from the California Institute of Technology, all in Mechanical Engineering.
Eunice is the founder at DataGig, a digital apprenticeship marketplace that helps connect aspiring data scientists with potential employers for their big data and analytics projects on-demand. Eunice strongly believes in building the next generation of women and minorities in the tech industries.
Laurence joined the Flatiron Institute in September 2018 as member of the CCA. Prior to this, she was a KIPAC postdoctoral fellow at Stanford University, where she conducted research in applications of machine learning methods to cosmology. Laurence completed her PhD degree at the University of Cambridge in DAMTP, where she worked on applications of open effective field theory methods to the formalism of inflation. She received her B.Sc. and M.Sc. degrees from McGill University.
Advances in natural language processing (NLP) are increasingly eroding the barriers between frictionless human-machine interaction, creating opportunities to develop nuanced and natural communication techniques that more accurately convey person-to-person dialogue. In this episode, Margaret discussed how convolutional neural networks and long short-term memory networks are enabling more sophisticated NLP systems and building the necessary components to lay the foundation for advanced dialogue.
Data & Trust Working Group is part of the AI Global Governance Network of Task Forces. Gosia joins us to explain how AI can compute an enormous amount of data helping us understand our own behavioural patterns. We need data sets to train AI to be able to do it.
At the Deep Learning Summit in Toronto earlier this year, we were fortunate enough to be joined by both Sara and Natacha from Google Brain for an exclusive fireside chat. Hear about Sara’s breakthrough work, as well as her journey in AI and Deep Learning.
As we know, deep learning is transforming every industry it touches, but it can be challenging for not only small enterprises, but companies of all shapes and sizes to know where to start and how to apply AI to help optimise their work and save money. Speaking at the Deep Learning Summit in Toronto earlier this month, it was great to chat to Mai to hear how Sightline Innovation’s work has progressed over the last year and to chat about some of the new projects they’re working on.
Yolanda is an AI Policy Researcher at The AI Initiative of The Future Society, a think-and-do-tank incubated at Harvard Kennedy School of Government. Her research focuses on governance and policy to shape the rise of AI to benefit society broadly while mitigating societal risks, including algorithmic bias, fairness, privacy, cybersecurity, safety, and impact on employment and inclusion.
Neural networks have recently been applied to many biological problems, including drug discovery. Applications of DNNs combined with domain expertise can help design de novo druglike compounds and generate large virtual chemical libraries, which can be more efficiently screened for in silico drug discovery purposes. Polina describes aspects of applications of deep adversarial networks and reinforcement learning for molecular de novo design.
Jade is a researcher with the Governance of Artificial Intelligence Program (GovAI) at the Future of Humanity Institute (University of Oxford). Her research focuses on the governance of emerging dual-use technologies, with a specific focus on firm-government relations in the US and China with respect to advanced artificial intelligence. Jade has a background in engineering, international law, and policy design and evaluation.
Mining and analyzing text helps organizations find valuable business insights in corporate data. Too often we find the common text mining techniques are not effective in many real-world corporate settings, in particular when dealing with short snippets, boilerplate text collected in forms and repeated text due to cut and paste authoring. Learn more from Yvonne Li on this week's episode of the podcast.
At the AI in Finance Summit in New York earlier this Autumn, Eunice caught up with one of our speakers, Peggy Tsai. Peggy is Vice President in the Analytics & Data department at Morgan Stanley Wealth Management and is responsible for leading the adoption of Data Governance standards and processes across Wealth Management. Hear what they had to discuss on the podcast.
On this week’s episode, Maithra, PhD Candidate and research scientist at Google Brain discusses her work in the science of Deep Learning. Eunice from the RE•WORK team chats with Maithra about the applications of her current work and how Google Brain are using neural networks in diagnostics, as well as to predict which instances are likely to give rise to maximal expert disagreement in healthcare.
The constant progress of AI leaves society split - some fall into the bracket of excitement, and others are more wary. Whilst these technologies have the potential to transform all industries with a positive effect, we must consider the ramifications if they are not build securely, and with ethical considerations. Mariarosaria joins us to discuss her work in philosophy and ethics at the intersection of AI to share her work in ensuring AI is created with ethics as a priority.
The life insurance industry faces a number of challenges: long-term liabilities that can span decades, outdated legacy infrastructure that limits the potential for innovation, and strict regulations that protect consumers but make quick iteration very difficult. At Haven Life and MassMutual, Laura is using algorithms and machine learning models to streamline the customer experience for policy purchases.
Where am I, and where am I going, and where have I been before? Answering these questions requires cognitive navigation skills--fundamental skills which are employed by every intelligent biological species to find food, evade predators, and return home. On this week's episode of the Podcast, Raia Hadsell discusses her work at DeepMind.
On this week's episode of the podcast we catch up with Global Summit Creator, Ellie Lucy, to hear how she plans her agendas, sources global AI experts, and creates sessions focusing on the most cutting edge work in AI.
We are living in an exciting time where Machine Learning theory for common applications is maturing, open source tools are plenty, and computation is cheap. While this enables us to move faster than ever, it also makes it easy to throw latest technology at any given problem with little preparation. This can lead to overly complex solutions, suboptimal processes, and waste time. In this talk I’ll draw examples from real applications to show the necessity of spending time on defining the problem accurately before diving for solutions.
You only need to open a magazine to be faced with dieting tips and tricks to get in shape, and we’re bombarded with so much contradictory information on how to get the ‘perfect body’ that there’s no wonder it can be overwhelming. Hear from Laura Douglas, Co-Founder at MyLevels is working with her team to overcome this with AI.
Clothes shopping is some people’s idea of a relaxing afternoon well spend, whilst for others it’s a bit of a nightmare. On this episode I'm joined by Sowmiya Chocka Naraynan, Co-Founder and CTO of Lily AI, the first product to use deep emotional intelligence and AI in the commerce market to decode a woman’s emotional and perceptive needs to highlight or draw away from certain parts of the body to help you find the perfect outfit.
Georgia Gkioxari is a research scientist at Facebook AI Research (FAIR). She received a PhD in computer science and electrical engineering from the University of California at Berkeley under the supervision of Jitendra Malik in 2016. Learn about her career in AI as well as her current work.
Advances in robot navigation and fetch and delivery tasks rest to a large extent on robust, efficient and scalable semantic understanding of the surrounding environment. I’m joined today by Jana Kosecka from George Mason University and Google where she’s working on exactly this. Jana is using deep learning to fuel rapid progress in this area to help label data and analyse images, so I’m really excited to learn more about her work in robotics.
Having worked for the likes of NASA’s Jet Propulsion Laboratory, founding Zyrobotics and chairing the Robotics PhD program at Georgia Tech, this week's discussion covers Ayanna Howard's current and upcoming work.
Finding out whether you’re eligible for disability allowance or to be officially registered as having a disability can be a long process, and one that many patients can’t afford to wait for. Recent progressions in NLP have made it possible to speed up this process by identifying disability mentions in text in both medical specialists’ notes and patients’. Learn from Ayah Zirikly from NIH in this week's episode.
Tasha specialises in biologically inspired neural networks, and puts a special emphasis on NLP using clinical text and heads up the AI team to develop state of the art tech that actually understands a doctors thought process through natural language understanding.
Despite the amount of data collected, the healthcare industry as a whole is overwhelmed by the challenges in understanding the information to transform research prototypes into real-world healthcare solutions. IBM Watson Health are working to bring together end-to-end machine learning solutions in healthcare into hospitals and general practice, learn more from Rachita.
Cansu works on ethics of technology and population-level bioethics with an interest in policy questions. Prior to the AI Ethics Lab, she was a lecturer at the University of Hong Kong, and a researcher at the Harvard Law School. Learn more on this week's episode of the podcast.
On this week's episode Sergul, ML Scientist at Amazon discusses her work in neural networks for forecast demand. Hear about how Amazon ensure they have the right quantities of each product to maximise customer experience.
Recent approaches in generative adversarial networks (GANs) can automatically synthesize realistic images from descriptive text. Despite the overall fair quality, the generated images often expose visible flaws that lack structural definition for an object of interest. Miriam joins us today to discuss some of her work to improve this.
This week we’ll be taking another look at AI in finance, and today’s guest is Soledad Galli from LV. So what comes to mind when you think of AI in Finance? Fraud Detection? Algorithmic Trading? These areas are constantly in the news, and AI has already undoubtedly made huge contributions to the efficiency and accuracy of many financial practices, but there are also lots of new ways that AI is being implemented to alter traditional methods within retail and commercial banking, insurance and many more industries.
Recently, at the Deep Learning in Finance Summit in London, Huma Lodhi from Direct Line Group joined us to present her work and explained how deep learning based AI models are showing incredible results for complex problems in both banking and insurance. When you think about all the data in these industries it makes sense that deep learning could be highly effective here, and today Huma chats with us about learning representations from disparate data, like free-from text and structured categorical and numeric data in insurance.
With faster internet and better connectivity, online information has taken a shift from being text-based to visual media such as photos. I’m going to be chatting with Merve Alanyali, PhD Researcher and Academic Assistant at Warwick Business School and The Turing Institute to hear how she’s using these vast amounts of data to quantify human behaviour. Merve’s research focuses on analysing large open data sources with concepts from image processing and machine learning to understand and predict human behaviour on a global scale.
On this week's episode of Women in AI Podcast, we're joined by Kat James, Senior Data Scientist at Royal Mail, who explains how recommender systems help them deliver the 50 million letters across the UK 6 days a week.
How should we consider the societal impact of AI from conception to production? Catherine, Senior Lecturer in Computing and Social Responsibility at De Montfort University shares her work in social responsibilities when creating AI. Learn about bias, ethics & more.
Honglei joins us today to discuss how AI is transforming retail in various areas such as computer vision and pattern recognition for image detection and classification. Honglei Li is currently a senior lecturer in Enterprise Information Systems at Department of Computing & Information Sciences, Northumbria University.
Today, we chat with Ann about her work in NLP and conversational speech recognition as well as her current role as Voice UI/UX Design Leader at Sound United. While doing a PhD in Cognitive Science and Linguistics at UCSD, Ann's interest in phonetics and NLP led to a dissertation using neural networks to model how speakers of a language form new words via paradigm patterning and token analogy.
AI assistants are making a huge impact in healthcare, and another really exciting company that have created a virtual assistant is Sense.ly, whose virtual nurse avatar, Molly, helps people engage with their health. Today i’m going to be chatting to their VP of user experience, Cathy Pearl to hear more about how Molly helps patients. Cathy as worked on everything from helicopter pilot simulators at NASA to a conversational iPad app in which Esquire magazine’s style columnist tells you what you should wear on a first date.
Depression is the leading cause of disability globally, and the cost of mental illness to society has doubled in the last 10 years in every region of the world. Alison Darcy, CEO and Founder of Woebot Labs is joining me today to discuss her work in addressing the mental health crisis with a chatbot that delivers cognitive behaviour therapy.
Is Deep Learning just a field of technical research that won’t affect the general public? Absolutely not - we’ve not seen such a powerful and disruptive technology since the inception of the internet itself - deep learning is transforming every industry it touches from finance to healthcare, self driving cars to smart cities. Mai joins us today to discuss the importance of making the application of deep learning accessible and hassle free for businesses. Even when a business understands the importance of AI and the impact it can have in industry, it’s hard to know where to start and how to afford the relevant staff and technologies.
On this episode, Zeynep from the University of Amsterdam and the Max Planck shares her expertise exploring zero-shot learning as a method to scale up visual category recognition as well talking about discovering and synthesizing novel concepts with minimal supervision.
Aparna joins us to discuss her work in driving Facebook's program to build new algorithms for ranking and personalization, and shipping them to power products for over 2 billion people.
Adriana discusses her work in deep learning and how it has achieved remarkable results in fields such as: computer vision, speech recognition and natural language processing as well as transforming medicine.
Anne Hsu, Assistant Professor at University of London, joins us to discuss the progression and importance of creating psychologically aware AI. These models can understand users’ motivations and provide empathetic dialogues to help create more meaningful and engaging conversations.
In this episode, Noura discusses her work in unsupervised deep learning as well as NLP and optimisation. As Yann LeCun said recently, unsupervised learning is the ‘holy grail’ of AI research as it allows machines to replicate the way humans learn, and we discuss Noura's progressions in unsupervised learning...
However useful AI assistants are, they’re still not perfect. They’re designed to serve us through dialogue, but to be effective they must not only understand natural language, but be able to generate sentences that communicate back in a human-like manner. Layla El Asri from Maluuba, a Canadian AI company teaching machines to think, reason, and communicate with humans joins us on this episode to discuss her work leading a team to improve natural language understanding in these assistants.
In this episode, Doina shares her journey to where she is today in AI, as well as discussing her work at McGill University, and the exciting projects she's involved in at DeepMind Montreal. The landscape of AI in Montreal is rapidly advancing, and we discuss progressions, obstacles, and current research that Doina is working on.
This week Ira Kemelmacher, assistant professor at Allen School of Computer Science and Research Scientist at Facebook joins us to discuss her current work in computer vision. If you open up your internet browser and search for a singing cat or a talking baby, you’ll find edited examples that create the illusion of real speech. These videos provide immense entertainment, but is these a useful application of this?
Understanding public opinion is important in many applications, such as improving company's product or service, marketing research, recommendation systems, decision and policy making and even predicting results of elections. Social media is a very powerful tool to transfer information and express emotions for users and a rich source of data that enables researchers to mine public opinion, but social media is a breeding ground for rumours that get miscommunicated into fact - how can we use AI to help eliminate this?
Antonia is a PhD candidate at Imperial College London, in the Bio-Inspired Computer Vision Group. Her research focuses on unsupervised learning and generative models. She received her masters in Biomedical Engineering from Imperial College London with an exchange year at the University of California, Davis. Antonia has interned at Twitter (Magic Pony), Cortexica and UNMADE. In this episode we discuss the implementation of generative modelling to synthesise new data samples, and the challenges faced in this work.
Nikita Johnson, founder and CEO of RE•WORK discusses the motivation behind the Women in AI series. The episode touches on how she came to found the AI events company, and shares some exciting insights of the upcoming Deep Learning Summit and AI Assistants Summit in London, highlighting some of the influential women who will be sharing their research at the event.