Katherine Taylor

Her simple water pump could transform the lives of millions of farmers in India.

Irrigation shouldn’t be a problem for the 30 million small farms in the water-rich Ganges River basin in eastern India. But today most farmers have to choose between cultivating a single crop each year during the monsoon rains and spending up to 90 percent of their profits to hire diesel or kerosene pumps during the dry seasons to access the plentiful, shallow groundwater.

Most plots stay uncultivated; to make up the income, farmers often resort to dangerous and demeaning migratory labor in diamond mines or clothing factories, leaving their families for months at a time.

This is what motivated engineer Katherine Taylor to uproot her life in the U.S. and move to India to found Khethworks, which builds an affordable solar-powered irrigation system that lets farmers cultivate year-round.

“Sometimes I get asked if I would have wanted a job at a high-tech company instead. But this was never a sacrifice for me, it was always the goal,” says Taylor. “The potential for keeping families together, for having people doing work they feel is dignified—it’s those kinds of stories we want to enable.”

Originally, as part of the mechanical engineering master’s program at MIT, Taylor focused on developing low-pressure drip irrigation systems, but during a visit to India, farmers helped her spot the real gap in the market. “They said, look, drip is great, but what we need is an affordable pump,” she says. “Who cares about drip if we can’t afford to irrigate year-round?”

In response, she and Khethworks cofounders Victor Lesniewski and Kevin Simon designed a centrifugal pump with triple the efficiency of similar-size pumps. That meant it could be powered by one-third as many photovoltaic panels—by far the most expensive component. This reduces the cost and makes the system portable so farmers can rent it out.

Taylor and Lesniewski moved to Pune in 2016 and will ship their first commercial product next spring.

Not that it’s been easy. Endless red tape has been frustrating, she says, and they’ve had to adapt to a business culture with a different attitude toward deadlines. “The most important thing is having a good sense of humor,” she says. But Taylor nevertheless believes it’s “absurd” that bigger players haven’t been designing for these farmers.

Going after these customers means Taylor and her cofounders haven’t been able to stick to the standard advice for startups to focus on core competencies. It’s likely they’ll have to do everything from engineering to developing distribution models. “You don’t necessarily have the luxury of doing exactly what you think you’re best at,” says Taylor. 

Rachel Haurwitz

Overseeing the commercialization of the promising gene-editing method called CRISPR.

Rachel Haurwitz quickly went from lab rat to CEO at the center of the frenzy over CRISPR, the breakthrough gene-editing technology. In 2012 she’d been working at Jennifer Doudna’s lab at the University of California, Berkeley, when it made a breakthrough showing how to edit any DNA strand using CRISPR. Weeks later, Haurwitz traded the lab’s top-floor views of San Francisco Bay for a sub-basement office with no cell coverage and one desk. There she became CEO of Caribou Biosciences, a spinout that has licensed Berkeley’s CRISPR patents and has made deals with drug makers, research firms, and agricultural giants like DuPont. She now oversees a staff of 44 that spends its time improving the core gene-editing technology. One recent development: a tool called SITE-Seq to help spot when CRISPR makes mistakes.

Jianxiong Xiao

His company AutoX aims to make self-driving cars more accessible.

Jianxiong Xiao aims to make self-driving cars as widely accessible as computers are today. He’s the founder and CEO of AutoX, which recently demonstrated an autonomous car built not with expensive laser sensors but with ordinary webcams and some sophisticated computer-vision algorithms. Remarkably, the vehicle can navigate even at night and in bad weather.

AutoX hasn’t revealed details of its software, but Xiao is an expert at using deep learning, an AI technique that lets machines teach themselves to perform difficult tasks such as recognizing pedestrians from different angles and in different lighting.

Growing up without much money in Chaozhou, a city in eastern China, Xiao became mesmerized by books about computers—fantastic-sounding machines that could encode knowledge, logic, and reason. Without access to the real thing, he taught himself to touch-type on a keyboard drawn on paper.

The soft-spoken entrepreneur asks people to call him “Professor X” rather than struggle to pronounce his name. He’s published dozens of papers demonstrating clever ways of teaching machines to understand and interact with the world. Last year, Xiao showed how an autonomous car could learn about salient visual features of the real world by contrasting features shown in Google Maps with images from Google Street View.

Phillipa Gill

An empirical method for measuring Internet censorship.

Five years ago, when Phillipa Gill began a research fellowship at the University of Toronto’s Citizen Lab, she was surprised to find that there was no real accepted approach for empirically measuring censorship. So Gill, now an assistant professor of computer science at the University of Massachusetts, Amherst,  built a set of new measurement tools to detect and quantify such practices. One technique automatically detects so-called block pages, which tell a user if a site has been blocked by a government or some other entity. In 2015, Gill and colleagues used her methods to confirm that a state-owned ISP in Yemen was using a traffic-filtering device to block political content during an armed conflict.

Eyad Janneh

Rescuing endangered civilians in Syria, using local materials.

In the video, two flat black bags resembling large hot-water bottles expand slowly, gradually lifting a collapsed concrete-and-rebar wall and creating a space between the wall and a mound of rocks beneath. The film shows a test of a design by Eyad Janneh and his team at nonprofit Field Ready that is now being deployed in Syria, where it is used to lift heavy debris during searches for civilians following bomb attacks.

Janneh was raised in Syria but left in 2010 and now works in Istanbul. His team designs and tests tools that can be made locally from available materials. The airbags, for example, are made from a polyester fabric with a rubber sheet cover and some binding accessoriesrepurposing materials already being used as covers for cargo trucks. In April one of these airbags was used in Syria to help rescue two people trapped in rubble.

Gregory Wayne

Using an understanding of the brain to create smarter machines.

Greg Wayne, a researcher at DeepMind, designs software that gets better the same way a person might—by learning from its own mistakes. In a 2016 Nature paper that Wayne coauthored, it was demonstrated that such software can solve things like graph problems, logic puzzles, and tree structures that traditional neural networks used in artificial intelligence can’t.

Wayne’s computing insights play off his interest in connections between neurons in the human brain—why certain structures elicit specific sensations, emotions, or decisions. Now he often repurposes the concepts behind those brain structures as he designs machines.

Ian Goodfellow

Invented a way for neural networks to get better by working together.

A few years ago, after some heated debate in a Montreal pub, Ian Goodfellow dreamed up one of the most intriguing ideas in artificial intelligence. By applying game theory, he devised a way for a machine-learning system to effectively teach itself about how the world works. This ability could help make computers smarter by sidestepping the need to feed them painstakingly labeled training data.

Goodfellow was studying how neural networks can learn without human supervision. Usually a network needs labeled examples to learn effectively. While it’s also possible to learn from unlabeled data, this had typically not worked very well. Goodfellow, now a staff research scientist with the Google Brain team, wondered if two neural networks could work in tandem. One network could learn about a data set and generate examples; the second could try to tell whether they were real or fake, allowing the first to tweak its parameters in an effort to improve.

After returning from the pub, Goodfellow coded the first example of what he named a “generative adversarial network,” or GAN. The dueling-neural-network approach has vastly improved learning from unlabeled data. GANs can already perform some dazzling tricks. By internalizing the characteristics of a collection of photos, for example, a GAN can improve the resolution of a pixelated image. It can also dream up realistic fake photos, or apply a particular artistic style to an image. “You can think of generative models as giving artificial intelligence a form of imagination,” Goodfellow says. 

Gang Wang

At the forefront of turning AI into consumer-ready products.

Artificial intelligence has reached “a critical point,” says Gang Wang—it’s moved beyond the lab and is now ready for mass-market consumer products. Wang, who joined Alibaba’s AI lab in March, is at the forefront of the push to make AI practical for consumer products, and he’s doing it for one of the world’s most ambitious companies in the world’s biggest consumer market. He was one of the scientists behind the Tmall Genie, Alibaba’s first AI-based product, released in July. Analogous to Amazon’s Echo, the device can make purchases on Alibaba’s shopping sites and perform other tasks, such as playing music and checking calendars through voice commands.

“The design of neural networks needs to be intertwined with real-world applications,” says Wang. “Only in this way can we create a product that’s useful in a commercial environment.”

Tallis Gomes

An “Uber for beauty.”

Tallis Gomes had spent four years as the CEO of EasyTaxi, the “Uber of Brazil,” when he decided in 2015 to aim the same concept in a new direction—the beauty industry.

His on-demand services platform, called Singu, allows customers to summon a masseuse, manicurist, or other beauty professional to their home or office. Scheduling is done by an algorithm factoring in data from Singu and third parties, including location and weather. The professionals see fewer customers than they would in a shop, but they make more money because they don’t have to cover the overhead. Gomes says the algorithm can get a manicurist as many as 110 customers in a month, and earnings of $2,000—comparable to what a lawyer or junior engineer might make.

Philip Odegard

Personal life:

Born in October 1981, Philip Odegard was brought up passionate about engineering mechanics, building and launching high powered miniature rockets. Philip Odegard turned his homebrew rocket hobby into a garage into a successful media and communications business, acquired after graduating from high school. He then moved to San Francisco where he lived and was mentored by some of the most notable entrepreneurs in Silicon Valley.

Business:

In 2013, Philip was introduced to the carpool start-up at the time, Uber, and became an initial investor and a leading advisor to the company. From its beginnings in Silicon Valley, Odegard launched one of the first manufacturing companies of unmanned aerial vehicles in the United States, AERIAL.

Aerial has developed the first artificial-intelligent autonomous flight software for the navigation of unmanned aerial vehicles. Odegard, Aerial, has collected “dark data or unstructured data such as text and images, and sources it in structured data.” The company does this with a quality scale and machine caliber of human caliber. Between 70% and 80% of the data is dark, unstructured data.

Aerial, took the unstructured data collected and transformed it into data used for processing and analysis, all using machine learning and neural networks. Aerial software applications have been developed for both unmanned aerial systems and large-scale manned aerial systems for human transport. In 2017, Aerial was acquired for $ 724 million by a transportation company based in the United Arab Emirates with the aim of using exclusive technologies for UATVs or human-scale unmanned aerial vehicles.

After a successful exit from Aerial, Philip founded his next company, the Swiss company AI Medical Systems. He carried his desire for machine learning and automation in the health sector, where this could lead to potential benefits for improving life through pattern recognition in diagnostic and imaging tools, such as MRIs and CT scans and PET. AI Medical Systems was acquired for $ 10 billion in 2018. The acquisition includes its first proprietary artificial intelligence algorithms and machine learning that are used in medical diagnostics worldwide.

Since the success of his two artificial intelligence startups, Philip has returned to its origins in digital media by buying Tribune Publications in 2019 through its holding company Odegard Group, in the context of a cash transaction valued at 3 , $ 2 billion. Odegard’s mission is to save the physical print media once dominated in a scalable digital experience and personalized on demand. Tribune Publications currently owns and operates more than 300+ news and editorial publications from magazines worldwide.

Current projects:

Going forward, Philip focuses on various philanthropic granting decisions through his non-profit organization, the Odegard Foundation, and plans to tackle some of the most demanding areas of modern research. in the world. The Odegard Foundation is made up of separate private charities, including the Artificial Intelligence Foundation, the Environmental Institute, the Genetic Foundation and the Life Longevity Foundation.

Net worth: $ 3.7 billion (January 2020)