We’re proud to be building a world-class machine learning company in one of the country’s most desirable regions: North Carolina’s Research Triangle. There’s a reason companies like Amazon and Apple are seriously thinking about moving to the neighborhood.
Here are a few reasons why we love it here – and why we think you will, too:
CEO Robbie Allen recently spoke about the business impact of machine learning at Adobe’s Think Tank on The Future of AI in the Enterprise.
“I really focus on three areas where I think that machine learning specifically will have an impact,” he said.
“One is reducing costs, and by that I don’t mean that we’re displacing the workforce. What I mean by that is that there’s lot of jobs out there that were never intended for people to do. But the only option was for people to do them, because we didn’t have software that could it on their behalf. So I think there will be a lot of those types of jobs – repetitive – it’s not ideally suited for humans to do in the first place.”
“Second is increasing efficiency, and oftentimes you’ll hear about potentially given people superpowers in their job, where not only can they do their job, they can do it a lot faster, a lot better, and they can be much more productive.”
“And then the third are is achieving new breakthroughs, and this is something I think is going to be really powerful as it relates to what you can get out of machine learning. As a society we have blindspots that we’re not aware of. For any new breakthrough that occurs, that only happened because someone else didn’t think to do that. Machine learning gives us an opportunity to discover new patterns, new ways of working that weren’t obvious before, and so I think we’re going to be able to see all sorts of interesting new capabilities in the enterprise as it relates to that.”
During the talk, Robbie was clear about the need to focus squarely on solving real business problems:
“So I think especiallywhen it comes to the enterprise, it’s less about ‘can we properly define AI,’ and really what problems are we trying to solve,” he said.
“And it goes back to, what are the key differences now between what we can do now versus some of the vendors that we’ve seen before. And it’s the computing power, it’s the availability of data, and it’s the advanced algorithms that have been created.”
“And I think really focusing on what problems can you solve with that, that’s really where it’s at for the enterprise, much more so than ‘are we ever going to create AGI, artificial general intelligence? Are we going to have sentient beings?’ There’s no path for that in the research right now and so to me it’s not even really worth talking about.”
CEO Robbie Allen recently spoke about the importance of data strategy at Adobe’s Think Tank on The Future of AI in the Enterprise.
“People hear about artificial intelligence and all this crazy stuff that it’s doing and so they think that it’s going to be this magical solution that solves all of their problems,” he said.
“In most cases, in my experience, it’s better to walk before you can run when it comes to this, especially if you’ve never done a machine learning style project before which requires a significant amount of data. You first have to to get your data strategy in place first before you can have an AI strategy.”
Later, Robbie spoke about a common data issue many enterprises may not be expecting: the high cost of getting data out of their systems.
“It’s likely that companies will spend just as much money on getting data out of their systems as they did to implement the systems to begin with,” he said.
“What I mean by that are ERPs, CRMs, all their databases. To get the data out of it in a format that’s useful for machine learning algorithms and other things is a nontrivial task.”
“And it turns out to be, in most cases, the long pole in the tent. That is, the hardest aspect of actually implementing a project is just to get the data. So that’s why we were talking about before, data strategy, when do you get it – it’s going to be difficult to do much in the space in the enterprise unless you have access to data in a format that makes it available to somebody to use in terms of implementing algorithms.”
We were proud sponsors of Duke’s first machine learning day, and excited that our Chief Scientist Larry Carin was a speaker. Of course, he’s also Duke’s Vice Provost for Research and a Professor of Electrical and Computer Engineering.
Because one ML Day just isn’t enough, we were proud to sponsor this one too. It also took place at Duke University, and our Chief Scientist Larry Carin spoke once again. Some attendees even scored some swag from our Infinia ML booth!
In a new paper co-authored by Infinia ML Chief Scientist Larry Carin and published in the journal Cell, machine learning gave scientists a new way to understand and treat depressed brains. Today, mice are the subjects. Humans could be next.
DURHAM, N.C., Mar. 1, 2018 — Mice brains and machine learning may lead to a new way to treat depression, according to a new paper published in the journal Cell and co-authored by Infinia ML Chief Scientist Larry Carin, Ph.D.
The paper describes how scientists measured electrical signals in the brains of both observably resilient, active mice and observably depressed, inactive mice. The complexity and scale of the available data, gathered from 18 regions of the brain, then required advanced machine learning for analysis. In effect, scientists trained a learning algorithm to map each brain’s connections. They found a pattern in the resilient mice that differed from the depressed.
“We wanted to understand the traffic flow of a healthy brain,” said Carin, the project’s machine learning lead. “That had not been done before, and machine learning helped us overcome that key technical challenge.”
This new understanding of the brain’s electrical system brings new potential for treatment in mice. More importantly, the research lays groundwork for future advances in human mental health. When scientists measure the relevant patterns in human brains, advanced machine learning could help them assess and treat depression.
Meanwhile, Carin’s company, Infinia ML, is already busy applying machine learning techniques to biological and medical breakthroughs from cancer detection to genetic screening.
“Machine learning offers new ways for us to understand our bodies and minds,” said Carin. “And the best part is, we’re just getting started.”
About Infinia ML
Infinia ML empowers companies to make smarter decisions and automate complex business processes by leveraging the latest breakthroughs in machine learning. Infinia ML has a team of leading AI researchers and deep learning experts that have published hundreds of peer-reviewed papers through top machine learning conferences and journals.
Backed by noted private equity firm Carrick Capital Partners, the Durham, North Carolina company is led by CEO Robbie Allen, an experienced AI entrepreneur, and Chief Scientist Lawrence Carin, Ph.D., the Duke University Vice Provost for Research and Professor of Electrical and Computer Engineering. Learn more.
Are you ready to implement advanced machine learning solutions in your organization?
Your team’s ability to lower costs, increase efficiency, and achieve new breakthroughs will be shaped by its machine learning background, your data policies, and, most importantly, the actual data you’ll use.
Here are some questions to help you think through your machine learning readiness. At Infinia ML, we use a version of this survey before helping potential clients think through the possibilities. Before you work with us – or any machine learning expert – it’s helpful to know where your team is starting from.
Machine Learning Background
What is your biggest machine learning business need? Why do you think machine learning is the solution?
Have you implemented machine learning before? If so, what frameworks did you use (e.g., Tensorflow, PyTorch, etc.)? What was the result?
How many people on your team have machine learning ability at the following levels:
What machine learning techniques most interest you?
Natural Language Processing
What tools or programming languages does your team use to query, manipulate, and report on data?
What are the job titles on the team that works with the data? (Business Analyst, Data Scientist, Software Engineer, etc.)
Do you use third-party or public data sets? If not, are you open to the idea?
What are your data governance processes?
Who owns the data (your company, a third party, the public domain, etc.)?
What kind of data is it?
Other (please describe):
How quickly can your team access the data?
After getting approval
How often is your data updated?
How big is the data set?
Who manages the data?
How is the data stored (local MySQL, AWS S3, Hadoop, etc.)
How sensitive is the data?
Are you ready to talk about advanced machine learning? So are we. We look forward to hearing from you!
If you’re preparing to interview for a technical position at Infinia ML, here’s a heads up on what to expect.
We’re a machine learning company that values learning. In fact, our interview candidates sometimes say things like this:
“. . . I actually learned a lot by thinking through the problems . . .”
“This was a better process than my dissertation defense.”
“I learned more about machine learning during this interview than I have in the last several years.”
As part of the interview, you’ll give a 45-minute presentation to our team. The first five minutes should give an overview of your background. You’ll then have 30 minutes to cover a topic of your choosing, like a project you’ve worked on or your academic research. In addition to technical depth, we want to understand how you think through a problem.10 minutes of Q&A will follow your talk.
You can assume a technical audience, and supporting slides will be helpful. Feel free to bring a computer or email slides in advance to your hiring manager.
Beyond the presentation, we’ll have several small-group discussions to help us assess your:
Theoretical/conceptual understanding of machine learning
Ability to map theory to real-world challenges
Cultural fit with our team
Come prepared to be challenged, but know that our team is very friendly and wants you to succeed.
Our office is located in Research Triangle Park. The address is 1009 Slater Road, Durham, NC 27703. You can find a map on our contact page.
You can park for free in the lot next to our building. Go through the main entrance and up the elevator to the third floor. Turn left to face the hallway, turn right to go down the hallway, and you’ll find Suite 390 in an alcove on the left. Our space is WAY bigger than that door would have you believe. Please ring the bell and we’ll let you in.
We look forward to meeting you soon!
If you have any other questions, please contact your hiring manager.