Iridescent Announces Finalists of Debut AI World Championship

Out Of 200 Submissions, Six Families From Bolivia, Palestine, Pakistan, Spain, The United States and Uzbekistan Selected To Present Their AI Projects To Judges In Silicon Valley

 

Today we are proud to announce the finalists of our inaugural AI World Championship. The championship is the culmination of the AI Family Challenge, a twelve-month global learning program that brings together families, schools, communities and industry mentors so participants can learn, create and play with AI. 7,500 people from 13 countries participated in the first year of the program.

Read more

How many types of engineer are there?

It’s Engineers Week – a chance to highlight engineers and the work they do! But there are many different types of engineers who work on solving different types of problems, using different materials. Some engineers work on the scale of huge buildings, and others work on the micro-biological level of cells and cell parts.

Learn more about five different types of engineers, what they work on, and fun projects you can do to test your own engineering skills.

Read more

Engineers who mentor families: Exploring Artificial Intelligence in Coimbatore, India

It’s National Engineers Week! National Engineers Week is an opportunity to celebrate engineers and their work. In particular, we like to focus on celebrating the amazing engineers who mentor students and families and share their passion and expertise with learners around the world.

Meet Vigneshwer, a data scientist in Coimbatore, India. Vigneshwer volunteered as a mentor for the AI Family Challenge this year, where he worked with local students and their families and taught them about artificial intelligence. Inspired to help people move beyond seeing AI as a “black box they’re not really familiar with” to understanding the ways AI is a tool they can use to make their lives better, Vigneshwar guided families through hands-on projects and lessons about AI concepts and tools, and hoped that they would also tap into their curiosity to keep learning beyond the program.

“You need to learn continuously…that’s the most important thing as a human that you need to do.”

Read more

Machine Learning and Medicine: An Interview with Daphne Koller

Daphne Koller leads Insitro, a company applying machine learning to pharmaceutical drug development. She is also a co-founder of Coursera (one of the world’s largest online education platforms with 30 million + users), a MacArthur Fellowship recipient, and Computer Science Professor at Stanford University.

Recently, she discussed machine learning in the biomedical industry as well as what she thinks are vital characteristics to achieve individual success with Iridescent CEO & Founder Tara Chklovski.

Daphne Koller

Tara Chklovski (TC): Can you share two of the most interesting problems that you’ve tried to tackle in your career and what you’re working on now?

Daphne Koller (DK): My work cuts across a variety of fields including biomedical, education, and problems like computer vision. There are two specific biomedical projects I’m very proud of. The first one involved working with a neonatologist, a doctor for premature babies, to predict survival rates for neonates. Neonates are premature, teeny babies – they’re about the size of your hand and weigh less than 200 grams. They often struggle to survive. The doctor and I worked together to predict the risk of death for these babies. Our hope was to help doctors identify and help the babies at highest risk survive. In our study we didn’t rely on the traditional tests for babies’ health (which we found don’t work well for babies born that early), and we didn’t want to rely on invasive measures like sticking these tiny babies with needles! So we used the data from the bedside monitor. The bedside monitor measures things like the baby’s heart rate, respiratory rate, and their oxygen saturation. It turns out that there are markers in that non-invasive data that can predict survival rates, and that you don’t need that much data to make useful recommendations. We were able to make predictions based on data collected from the first few days of the baby’s life.

Source: Science/AAAS; Nature.com
Example of tumor images that have been stained and then labeled to identify more aggressive tumors.

Another project I did with a PhD student of mine who was an MD/PhD pathologist. We were looking at images of tumors from breast cancer patients and trying to predict five-year survival rates to help organize patients by risk. We took a data-driven approach. Pathologists have been looking at these sort of images for a century. For our project, we included more features in our dataset than the standard features pathologists consider. We put in everything that we could think of, including hundreds of features that no one has ever looked at before. As a result, our predictions were better than pathologists’, and the features that were being used by our predictor were quite different from the ones that pathologists had been looking at. Specifically, they had been looking at features of the tumor cells, and it turned out that the features of the cells that surround the tumor were actually more predictive! Today, this is well recognized as a critical factor in cancer survival, and is called the tumor microenvironment. This microenvironment where cancer lives, and the immune system are factors that are absolutely critical in cancer patient survival. Our paper was one of the earliest pieces of evidence supporting that.

Read more

5 Women You Should Know Working in AI

AI is changing the world – everything from the way we shop for groceries to the way we hire people for jobs, but it doesn’t really reflect the world it’s changing. Wired estimates that only 12% of leading machine learning researchers are women, and we know that a lack of diverse AI researchers means that the technology they build skews towards a white and male default representation of the world, effectively reproducing and worsening existing human bias.

The technology that’s shaping our future needs to be built by people who represent the diversity of humanity, in order to ensure that the future is designed for all of us. Today we’ll start highlighting the many amazing women already doing inspiring work with Artificial Intelligence. In the first of a series highlighting women using technology to solve big problems, here are 5 women in AI you should know, sharing their work, their inspiration, and how they find problems they want to solve.

Read more

An interview with Pierre Bonnet and Alexis Joly: AI, plant recognition, and biodiversity

As part of our AI in Your Community series, I spoke with Pierre Bonnet, a tropical botanist, and Alexis Joly, a computer scientists who have been working on a project called [email protected] for the past ten years. Pierre and Alexis work together to develop tools that teach people about biodiversity and plant identification while also building a collaborative data set that spans continents.

Pierre Bonnet, Alexis Joly and Jean-François Molino, winners of 2016 La Recherche prize | © [email protected] / Rémi Knaff

Tara Chklovski: Let’s start by having you introduce yourselves and tell me a little bit about your work and the problems that you’re trying to solve.

Pierre Bonnet: I’m Pierre Bonnet, I’m a scientist, mainly working in tropical botany. I work at the CIRAD Institute – we conduct research in tropical regions, which are hotspots for biodiversity. I’ve been working in the field of biodiversity informatics for 12 years now. From my point of view, my purpose is to collaborate with computer scientists to design a new approach to solve problems, like the problem presented by identifying hard-to-identify plants at a large scale.

I have worked with developers on tools for plant identification in tropical Africa and southeast Asia, and for the last ten years or so, I’ve been working with Alexis on the [email protected] project. With [email protected] we’re dedicated to trying to solve the problem of identifying plants at a large scale using images. My field is mainly botany so I collaborate with engineers and computer scientists like Alexis – Alexis has been my main collaborator for ten years now. Alexis?

Alexis Joly: My name is Alexis Joly. I’m a computer scientist and part of a research organization in France, called Inria. I’m a specialist of machine learning and computer vision technologies, and I’ve been applying this research to biodiversity and informatics for more than ten years. As for the [email protected] project, at the beginning it was really a research project, with the idea of building and evaluating the technology, and so we have spent many years improving all these technologies and evaluating them at a large scale with researchers.

For three years we have been funded by an educational initiative called Floris’tic, and we have collaborated with similar associations all over the world to do a lot of activities related to education.

Read more

Advice for mentors: 9 tips to help you become a better mentor for girls in tech

Mentors are invaluable! Mentors help their mentees set and achieve goals and act as a sounding board for solutions to tricky problems, and crucially, over time mentors can help their mentees develop their confidence and self-esteem. For young people in particular, mentors can model positive traits and skills around problem solving, conflict resolution, and resilience, and can provide a look into professional workplaces that students might be curious about but unfamiliar with. As the 2019 Technovation season kicked off in January, there are many first-time mentors around the world jumping into the adventure of mentoring girls in tech (or getting started through other programs thanks to National Mentoring Month). It’s an exhilarating and rewarding experience, but it can also be intimidating. For encouragement and guidance, we collected advice for mentors from the people who know best: actual Technovation mentors. Here are some of our favorite “mentorisms”:

Read more

An Interview with Erin Bradner: Using AI to make construction easier

As part of our AI in Your Community series, I sat down to interview Erin Bradner, the Director of Robotics at Autodesk, which aims to solve complex design problems from ecological challenges to smart design practices. Erin has researched topics ranging from from the future of computer-aided design to how to use robots in novel ways to automate processes in manufacturing and construction.

Erin Bradner, Director of Robotics at Autodesk

Tara Chklovski: Tell me about what you’re working on.

Erin Bradner: I’m now the Director of Robotics at Autodesk, where we make professional software for architects, engineers, and animators. And what they’re looking to do is create more flexible manufacturing lines. And in construction, they’re looking to automate aspects of construction that have not been automated before.

In that sense, construction is aiming to be more like manufacturing, with assembly happening off-site to allow you to bring pre-assembled parts onto the construction site and have the construction site become an open-air assembly line. The traditional sort of stick-built architecture where you’re cutting timber on site is inefficient. The construction industry has not received the productivity gains that other fields have received through technology over the last 20 years. It’s been flat, and we’re helping to address that. There are a lot of interesting startups in construction at work too!

Tara Chklovski: Like which ones?

Erin Bradner: Well startups are doing what startups do – they’re laser focused on innovative, focused technology. For example, Built Robotics is looking at autonomous Bobcats to grade a building site. Usually a Bobcat is operated by an engineer and comes in to clear the site, but taking the technology used for autonomous vehicles, like LIDAR and vision sensing, they’ve developed an autonomous Bobcat that can clear the site on its own.

There’s another company called Canvas that’s just getting off the ground and is using soft pneumatic robots that are human-safe and applying them to the construction site to do dirty and repetitive jobs. Their robots are still in development, but they likely requires quite a bit of AI to integrate.

What Autodesk is looking to do, being a software provider, is not to make robots, but rather to connect our CAD software to robots and other machines to make it easier to build what has been designed. Because CAD – computer-aided design software –  is what’s used to specify nearly everything that is manufactured or engineered today. There is CAD to map terrain for those Bobcats, there is CAD for the walls and the floor and other elements of the buildings. We want to bring CAD into these platforms, along with simulations, so you can simulate the robot in its environment before ever running an operation, and also use machine learning to train the robot to complete its tasks.

Read more