The Path to Impact in AI & Life Sciences
Remember the Useless Box?
The Useless Box came into the limelight in 2010 as a novelty item: it featured a switch that, once turned on, would prompt a finger to reach out and turn off the switch. Press surrounding the Box focused on its uselessness and the amusing nature of it; it functioned as a critique of consumerism with its streamlined design and complete lack of market need. Ultimately, the Useless Box did not achieve a significant impact and was largely left in the past.
For those working in life sciences, working to combine healthcare needs with emerging technologies such as AI, the Useless Box works as an important metaphor. The most beautiful, elegant solutions can have little meaning if there’s no purpose or impact. Solutions must consider market need, and other facets of entrepreneurship: you don’t want to create a useless box, you want to change people’s lives. So how exactly does one successfully navigate the challenge of determining impact?
Part of the answer lies in data. Anyone familiar with AI and machine learning knows the importance of data, but also the challenges of collecting it. Accuracy and ethics are widely debated and, as of late, lead to controversial headlines. How is data being collected, by whom, for whom, and from whom? The latter is especially concerning for Zhenglin Xiong, whose work as CEO and co-founder of Quinditech has offered new insights into data collection.
Quinditech works with both patient and doctor data, which each present new challenges: for patients, concerns of privacy and anonymity are pressing; for doctors, access to their input is expensive and difficult to gain access to.
Data presents a huge opportunity as well, to bring forth solutions and to enlighten the wider purpose of a company as well. The accessibility that machine learning presents also eases the entry to new fields; data scientists could work in fashion, design, or even agriculture. Benjamin De Leener, who himself works in the agricultural sector as a co-founder of ChrysaLabs, initially found it challenging to adjust his experience and knowledge to the demands of the industry. Fortunately, the equalizing power of data meant that he could quickly gain access to the terminology and knowledge he needed to present himself as an expert in the field, allowing him to provide valuable insights to experienced agronomists with ease.
The most impactful solutions are often sculpted by teams. Though we are infatuated with the figure of the intrapreneur, the insight and feedback of a skilled team can chisel beautiful, if impractical, visions into effective and impactful realities. Steve Jobs may be a visionary, but his team at Apple gave him the resources he needed for impact. The same can be said with channeling life sciences into AI: if you’ve got the idea, and the access to data that can back it up, next on your list should be talent.
Zhenglin finds this to be a challenge here in Montreal, as larger, established companies like Google and Facebook set up shop in AI and data science, attracting the latest batch of top talent. It makes sense, for the recently graduated, the opportunity to work at a renowned company can often overwhelm any other option. Zhenglin counters this with a simple statement, but one that’s nevertheless quite interesting to consider: “You always have a chance to work with the giants.”
“You always have a chance to work with the giants.”
With a solid concept and the opportunity presented by data, you have all you need to inspire a team to join your side. Facebook and Google aren’t going anywhere; the chance to work on a potentially world-altering project can, and will, disappear. It’s important to consider the priorities of your potential team, and match theirs to your own. In doing so, you’ll find that your potential impact grows too — not only will your project change the lives of your potential users, but also the lives of those who work on it.
Besting the threat of the Useless Box requires more than just data and a team. Impact also arrives from your own ability to strategize, scale and sell. Naysan Saran, CEO of CANN Forecast, presents this as one of the more difficult aspects of navigating the startup world: you’re expected to do it all. As a founder, you’re in charge of product, but also sales, marketing, strategy and development. Even with her strong background in computing and environmental science, she found herself in the position to learn new things, and expand her skill-set. You have a hand in retaining customers, impressing investors, and fixing bugs, no matter what your background is.
It’s more important now than ever to be a holistic entrepreneur. And that’s a lot of pressure. But with the right help, it’s nothing you can’t best. Accelerators, incubators, communities and more all exist to help you build the competencies you need, meet the experts you’re looking for, and become the entrepreneur you want to be. In one particular program, District 3’s Life Sciences community offers life scientists the opportunity to work with data scientists to conceive of and work on AI-enabled solutions to life science problems. For accomplished researchers with a strong concept, the community offers its resources, and knowledge, to take the idea to where it needs to go.
Sometimes, no matter what you do you’ll feel like you’re building another Useless Box. Self-doubt is a real threat to any entrepreneur, especially if you’re working in AI and life science, crossing several disciplines to bring your concept to its full potential. Fortunately, with the foundations of data to back you up, a team of talented innovators to support you, and a community to guide and equip you with the resources and competencies you need, impact isn’t impossible. Shifting from lab research to entrepreneurship is a difficult pivot, but securing your footing by working through the difficult questions is a surefire way to build an elegant solution that does bring change.
