Widespread adoption of AI is being held back, in regulated and unregulated industries, by a lack of transparency into AI models. Ople’s latest release opens the black box of the artificial intelligence decision-making process, making Ople the first to market with transparent AI.

On November 28, 2018, the venture-funded, Silicon Valley company is rolling out a feature-rich update that makes it possible for companies to gain the critical buy-in, and deploy AI models with unprecedented transparency into how the models actually work. Ople has added significant and powerful AI transparency tools and reports to ensure model explainability and highlight feature importance. With one-button auto-deployment of AI models, its fastest-in-industry AI modeling software is now even faster. These all enable Ople to make AI streamlined, cost-efficient, and transparent.

Ople allows data science teams to focus on strategic problem formulation, not the grunt work, and enable subject matter experts to incorporate their domain expertise into the AI models. This means that companies can now tackle more projects, in less time, with their existing teams. “AI projects are no longer restricted to high cost, long term investments,” says Pedro Alves, founder and CEO of Ople. “We now have software that lets both seasoned data science teams and less experienced analysts realize material value across more business lines, in less time, and, with minimal investment.”

Ople is rapidly scaling-up its business, having recently closed $8M during its Series A round. Recent hires include engineers and data science experts from major tech companies that include AirBnB and BigCommerce. For the next quarter, Ople is accepting a limited number of new customers who are “Ready for AI” in order to ensure the highest level of product-market fit. “It is essential that we continue to listen to and incorporate customer feedback into Ople”, says Alves. “For the very first time in history, every industry can now take-on their top business challenges with a powerful, yet, easy to use artificial intelligence engine.”

Ople, Inc., a Silicon Valley-based artificial intelligence software company, has secured $8M in Series A funding to expand the capabilities of its AI building AI™ platform. Triage Ventures led the round, with Hack VC joining and seed investors continuing their support.

Ople has grown dramatically in 2018, continuing to lead the fast-changing AI industry with its unique approach and exceeding customer expectations. With more than 10,000 talented applicants from the technical community applying for jobs, the team size more than doubled over the last year, and the company plans to continue the rapid growth. “This investment enables Ople to expand our already strong product team, as well as begin to scale sales and marketing efforts,” explains Todd Hay, Ople COO.

Ople’s software models the behavior, experience, and intuition of elite data scientists. Ople enables Ph.D. Data Scientists, citizen Data Scientists, data analysts, and even business domain experts, to develop production-grade AI-models in minutes. Each model is ready to deploy and deliver quantifiable business impact.

“We are engineering intelligence,” says Pedro Alves, founder and CEO. “Our product is a major leap forward in the simplification and automation of the most laborious tasks in data science. This means that more teams, across more industries, will deliver more AI projects in less time. By using Ople, companies are leaping past their competition, making better decisions - faster, and positioned to seize new market opportunities first.”

With this investment, Ople has now raised more than $10M to power its vision of making Artificial Intelligence easy, cheap and ubiquitous.

About Ople, Inc.

At Ople, we believe that Artificial Intelligence should be easy, cheap, and ubiquitous. Ople enables business leaders to think big, broadly applying Artificial Intelligence across their organization to identify new opportunities and establish strategic advantages. With Ople, you are only limited by your imagination, not by the skillset of your team. Centrally located in Silicon Valley, Ople is led by a team of world-class Data Scientists, engineers and market maker.

Three qualities really define when a technology has matured, when it has truly come into its own and begun to reach its potential. I’m drawing on past technological advances that have transformed into basic utilities, the things we need in our everyday lives to function. Electricity and mobile phones are two fairly recent examples that come to mind.

No matter what the technology, it will not change the way we live our lives or run our businesses until it matures and becomes easy, cheap, and ubiquitous.

These qualities are benchmarks that can help us define AI’s progress from computer science curiosity to requisite tech tool. Though they sound subjective and fuzzy, if we break each benchmark into its constituent elements, they are measurable. Doing so helps us figure out what AI can do for us, where it needs to go, and how it fits into the models and processes of a wide range of businesses. It helps us boil down a vast number of options and nuances into a few handy rules of thumb to guide our decision making.

Benchmark 1: Easy
“Easy” in this context means fewer skills and less time are required. This may seem paradoxical with a bleeding-edge technology like machine learning and AI, but it is an achievable goal.

To reduce the skills required, you need an easy-to-use interface and flawless user experience. Everything you need should be at your fingertips. You should never be required to alter the back-end or open another tool to complete a task. That’s not easy. Instead, you need a product that is intuitive and covers most use cases.

Removing complexity is a critical, but insufficient component of “easy.” Efficiency and the reduction of time spent are an equally important measure. If it takes fifteen hours to wash your car, then it’s not easy, even if it doesn’t require complex or specialized skills.

The efficiency of your AI platform can be determined by looking at how much of the data science pipeline is streamlined and automated. Each step left as an exercise for the user: Data cleansing, feature engineering, model selection, optimization, dev-ops, and deployment to production, adds complexity and time.

Rule of thumb: If you can’t train a reasonably intelligent colleague to use an AI product that covers the entire pipeline in a week, it’s not easy. It’s too hard.

Benchmark 2: Cheap 
“Cheap” is relative. Some projects demand sizable resources, but what matters is the return on investment. If the benefits gained are significantly larger than the investment, it’s cheap.

Complexity and inconsistency are the enemies of cheap. They will quickly eat up your technology budget. If every time you ask your AI software platform to build a model it delivers a different technology, such as random forests or neural networks, you will need to deploy different skills, hardware, and software. It’s not cheap to hire, train, and maintain multiple skill sets or build redundant infrastructure based on the whims of your AI platform.

When you only need to support a single infrastructure, you reduce cost and complexity. When you reduce the skills required to use and maintain your systems, it’s easier to hire and train your workforce, which thus becomes much cheaper. And when you can dramatically increase the output of your existing team, your return on investment will skyrocket. In this case, AI becomes immensely cheaper.

Rule of thumb: Whatever you’re spending now, you should be able to knock off a zero. That’s absolutely possible, if AI is done right.

Benchmark 3: Ubiquitous
“Ubiquitous,” by definition, means you are using it everywhere. If your AI efforts are limited by the data supported (images or text) or by the types of problems you can solve (supervised or unsupervised learning), then it’s not ubiquitous. If you can only refine pre-built models to try and force-fit them to your business, then it’s not ubiquitous. And if the cost and complexity of building models is so high that you are reserving your resources for only a handful of highest impact projects, then it’s not ubiquitous.

The biggest blocks to broad adoption of AI in businesses are data and understanding. A number of companies struggle with finding and preparing the data they need to build effective models. Those that do, run into roadblocks to development because executives, regulators, or other stakeholders cannot understand how the model works, or why it makes certain decisions. Your AI tools need to help, not hinder these common problems in order to be ubiquitous.

Data quality has a significant impact on the accuracy, and even the possibility, of AI models. Many of the tools available to data scientists today expect pristine levels of cleanliness and massive amounts of observations to work properly. On top of that, many of the platforms are limited to the size of datasets they can work with. This alone causes a number of companies to hit a brick wall before even starting.

The software you choose should be able to build robust models with the data you have on hand. If you need to spend months to prepare data for training, that same effort will be required for predictions. More data is always better. Any platform you choose should be able to support you as your data grows. But it should also be able to at least tell you if gathering more data is worth the effort. Nothing prevents adoption more than spending six months gathering, cleaning, and preparing data only to find your hypothesis was wrong.

Finally, the value of AI to your business depends on being able to use it! If, after all that work, you can’t put a model into production because you can’t explain why it works, then you have wasted time and resources. The solutions you choose should help explain how the predictions are being made and which features (data) impact the outcome. When you can apply AI to all areas of your business and quickly put those models into production, then AI becomes ubiquitous.

Rule of thumb: If the AI platform you are considering forces you to limit the number of challenges you can tackle, then you should look elsewhere. The right platform can change the way you think about AI from being a “moonshot” to being as ubiquitous as a spreadsheet.

When AI is accessible to a broad range of skill sets, from Ph.D.’s to subject matter experts, it becomes easy. When more people can deliver AI faster, it becomes cheap. When you can apply AI across your company, it becomes ubiquitous. Only then will AI become the next utility.

Pedro Alves is CEO and Founder at Ople.ai

At Ople, we use AI to build AI. We have developed an artificial intelligence platform that acts, thinks, and learns like a data scientist, providing our customers elite-quality deep-learning models deployed instantly and ready to make predictions in minutes, not months. If you would like to learn more about what you can achieve with Ople, please visit our website to learn more and set-up a private meeting.

AI has the thrilling ability to transform a range of businesses. But let’s be frank: it’s also a beautiful, massive disappointment for many companies.

Here’s a common trajectory for many AI and data science projects in an enterprise: A company decides to incorporate AI into their business. They spend one to two years searching for AI experts to build a team of solid data scientists, but not necessarily industry experts. The team works for a year or so on a project, only for the company to discover that the project is irrelevant and they need very different people. So they restructure the team, winding up back at square one, four years later.

If you’ve been in this situation, you know how hard it is to find data scientists who can do all the tasks required, which range from soft communication skills to hard statistics. In fact, many companies look for data scientists who can comprehend and engineer the data, build and tweak models, communicate with the necessary teams to understand the business, and deliver applicable solutions. This isn’t a run-of-the-mill candidate search. This is a grand quest for AI unicorns.

The other path companies take is to buy a vertical-specific AI solution from a third-party vendor. The software platform looks sleek and pretty and promises to deliver exactly the kind of predictive power the company needs. But looks can be deceiving, and few products have proven their worth at this point. While vendors claim to be vertical experts, these one-size-fits-all types of products often require significant investment to match the companies’ needs. Many of them do not deliver at all. We are simply too early in the AI transformation.

So if building a team is futile and buying your AI is a real gamble, what’s a company to do? Both, but in the right balance. You can build an in-house team based on the right third-party AI platform.  One that will empower existing employee experts or incoming hires to maximize their efficiency and actually solve what matters to your business.

Companies who ask the right questions to enable this hybrid approach save themselves grief and resources. They are also more likely to end up with a viable, business-impacting AI solution.

What data and resources do you already have?

Lots of companies are already formulating problems that can utilize machine learning. Actuaries, sales teams, and a variety of professionals in diverse industries are predicting things and observing patterns via data, addressing issues like risk, resource allocation, or price optimization. They have been doing this since they started, sometimes for centuries, using other techniques. In other words, the knowledge is already there, whether they have AI or not.

If you’re already asking this kind of question, you can better see where AI fits in and what it should deliver for the investment. And If you’re innovative, you can start asking questions in your industry that no one’s asked before, simply because the data analysis techniques were too cumbersome, and create more impact on your business.

What problems will have the biggest impact on your business that also have elements of predictability?

You know your business better than anyone else, but you also know you’re not a data scientist. So how do you formulate your problem for a potential AI project? In machine learning, it’s less about what you ask as about how you ask it. The question should be answerable by someone with infinite time and copious data, and a clear idea of what to predict. By applying machine learning to this type of question, you will get more accurate predictions in less time than before.

Let’s assume you are in ecommerce, and you’re interested in predicting the likelihood of a customer checking out her shopping cart. If you are making the prediction based on factors like the previous purchase history and time spent on site, you will get a reasonable outcome. But if you are looking at factors like the number of red cars on the street at the time or the number of windows in your office, you will never get a reliable answer.

This example is somewhat extreme, but the point is that AI is not magic. Moreover, it requires your expertise because the answers to what matters are often industry- and even company-specific. In other words, you need to rally your teams and figure out the questions and answers you seek before embarking on any AI venture.

What answers and level of accuracy will actually move the needle?
You’ve formulated a problem, you have the data, you have your approach. Now try it out and see how it impacts the business. AI models should not exist simply because they are cool. They need to move the needle for the company.

In many cases, moving the needle means testing a hypothesis. What good is a model with 99 percent accuracy if the prediction doesn’t matter? The biggest project killer does not come from teams failing to hit a certain accuracy. It’s from failing to define the right problem and not knowing how better predictions will impact the company’s business. If it’s a data science success, that’s lovely, but it has to matter to your bottom line, not to scientific research circles. The goal is to change your business. AI can do that, but only if the right tools are given to a team with clear objectives. That’s what a “both” approach can do.

At Ople, we use AI to build AI. We have developed an artificial intelligence platform that acts, thinks, and learns like a data scientist, providing our customers elite-quality deep-learning models deployed instantly and ready to make predictions in minutes, not months. If you would like to learn more about what you can achieve with Ople, please visit our website to download our one-pager and set up time to meet.

Pedro Alves, CEO, Ople, writes about how AI and Machine Learning is helping marketers and advertisers to improve their data and boost creativity

Machine learning, a key component of artificial intelligence (AI), can feel like a moonshot for teams who don’t have huge (or any) data science resources. But when done right, machine learning can liberate minds and let marketers do more of what they love: be creative and engage audiences.

This sounds thrilling, but there are stumbling blocks along the way. One major hurdle is the nitty gritty of machine learning: the data itself. Marketers and advertisers have an abundance of data, but it’s often not in the same place or format. Once the datasets are pulled together, they still need to go through techniques like “dimensionality reduction” to create more manageable - or analyzable - datasets. However, when people “clean” the data, we unknowingly manipulate the data with our own biases. For example, when a person sees a missing cell, they make a decision to fill it with the average or median of the feature or delete the entire instance.

Such decisions are made based on the experience, and while humans think we are good at pattern matching, we are not. In fact, we are far more likely to make biased and thus poorer decisions, as Wharton School IT researcher Alex P. Miller recently argued in Harvard Business Review, looking at studies from finance, lending, criminal justice, and HR contexts. Humans clean data based on these same faulty decision-making processes. As a result, the “cleaned” data ultimately hinders AI’s ability to detect patterns effectively.

Another struggle marketing teams face is the clunkiness of traditional data science approaches. Because the traditional AI approach takes weeks, or even months, to test and build custom models, agencies or teams often choose to use generic “one-size-fits-all” models. However, people’s behaviors change, goals of given campaigns differ, and the data that describes certain behaviors are constantly evolving. These changes are never in the generic model, and accuracy suffers leading to a loss in sales!

Now, you don’t need to have perfect datasets to get valid predictions. You can have holes and inconsistencies and still manage to find the solutions you’re looking for. Basically, if you can continuously tweak models, you can overcome not-so-clean data challenges. You just have to let the machines do the work and learn from it. You can stop focusing on finishing a single model, and start imagining all the predictions you’d like to make.

With this freedom, you can spend your time developing compelling creative assets, studying the nuances of your audience’s preferences, and quickly discovering new and more effective campaigns. You will have actionable predictions regarding execution, as well as more accurate and robust personae for your potential customers, using the data you already have. ROI becomes easier to prove (and frankly easier to "subjugate to the machine") because there are real numbers, and spend can be managed easier.

Artificial intelligence and machine learning have come a long way since the movies of the 80’s. Instead of the fear that many people have of AI replacing their jobs, these latest platforms represent the next major step forward for marketers to truly express their inherent creativity. Machine learning will always be better than humans at the calculations and predictions necessary for good campaign execution. But it’s the creativity of the humans that really makes those campaigns stand out.