The 3 reasons industry doesn't do more AI

‍In 2020 I was royally pissed off.
‍
‍We had just awarded a million dollars to talented data scientists. Our partner, the CEO of a mid-sized mining company, celebrated having 5 years of analysis completed in 6 months. New startups were formed and funded as a result.

I should have been happy and proud. But something wasn’t right.

Here's the story of what I learned, the opportunity it uncovered, and the solution we're announcing today.

Every conversation I had with industry leaders ended in “that’s really interesting, but...”.

We clearly hadn’t cracked it.

The people in charge of producing the materials and energy we all depend on didn’t see what was so bleedingly obvious to us: AI (data science, machine learning, etc.) is the greatest new technology we have for making industry more efficient and sustainable.  

From basic tool-making, to the use of metal, to building complex machinery, to inventing medicines, our ability to discover and share knowledge has elevated humans out of a fight for basic subsistence.  

AI allows us to do this at an unprecedented scale.  

It helps us systematically examine vast amounts of evidence, make and test hypotheses, and discover new knowledge.  In fields from medicine to commerce to energy, it empowers us to augment the knowledge of experts, systematize it, and act on that knowledge at lightning speed.

It’s ready now. And we aren’t putting it to use.

‍Why is this?

‍More than 100 industry leaders have graciously shared with me their frustrations with trying to do more with AI.

One thing stood out from these conversations: there is no lack of data or AI products and tools.

We are witnessing an explosion in both the generation of data and tools for data scientists. This is fantastic: it will improve the work of data scientists and the teams that they work with. But on their own they won’t help industry to get more done.

Overflowing data lakes and sophisticated data science tools aren't yet helping the people that need them to solve our most impactful challenges.

We see three primary reasons for this:

1. Hiring and retaining data scientists is difficult and expensive. There are currently 200,000 unfilled data science roles in the US alone.

2. Managing data science projects is a discipline in its infancy. It requires specialist skills that cross the domains of both data science, stakeholder management, and project management.

3. Despite significant similarities, most use cases are built in a bespoke fashion at each company, resulting in high costs and few reusable, scalable solutions.

‍How can we solve this?

‍We believe that we are on the cusp of a massive change in applied intelligence. If the last 10 years were defined by the explosion of data we generate, then the next 10 will be about giving people the power to put this data to use with collective, augmented intelligence.

It’s not just about new technology - it’s about organizing our teams in new ways so that people and technology together can solve problems more intelligently than ever before.

It’s also not just about connecting jobs to people with skills. Existing freelance platforms connect talent to jobs, but they don’t work well for data science (they don’t address 2. and 3. above). A successful approach must coordinate the efforts of multiple stakeholders with very different skills and backgrounds, and build upon a growing base of reusable assets.  

We believe the solution starts with community.

We have experienced first-hand the power of building an engaged and motivated global community.

Unearthed has grown into the largest community of startups and innovators making the resources and energy sector more efficient and sustainable.  That community continues to drive real impact - surfacing hundreds of novel technology solutions, from methane monitoring, to water treatment, to waste reduction - and creating innovative new companies and thousands of jobs in the process.

What might a global community of data professionals accomplish?

We can’t wait to find out.

That’s why today we’re thrilled to introduce Humyn.ai - we will give every business the power to create data science solutions without adding headcount and every data professional, no matter where they are, opportunities to do great work that makes an impact.

We are changing the way people do data science projects, giving them access to skills and building blocks once reserved for only the biggest tech companies.

How does it work?

‍Humyn.ai data scientists have skills that include predictive analytics, data engineering, deep learning, computer vision, natural language processing, and many more.

Organizations can tap into the growing community of thousands of Humyn.ai data scientists and engineers on demand to get a wide range of data tasks done; from data analysis, proof of concept development, model development, model tuning, to project design.

Any project run with Humyn.ai has a dedicated data science project manager involved  end-to-end. Working with many of the world's largest industrial companies over the past 5 years, we’ve developed an approach and tools for streamlining data science projects that gets results in weeks instead of months.

‍What does it mean for you?

‍If you want to experience the future of data science work with the world’s best data pros, please reach out to us at any time. We’re excited to partner together to solve some of industry's biggest challenges.