With more than 30 million users, Anaconda is the world's most popular data science platform and the foundation of modern machine learning.

While the creation of machine learning models requires special skills and tools, once the models are created, there is no good reason that they …

… and advancing machine learning for predictive and research analytics. … with deep learning, and tools for data modeling and cohort building.

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As more companies adopt AI, the risks posed by AI are becoming clearer to business leaders. That is driving many companies to hire AI ethicists to help guide them through an ethical minefield. But just as data scientists proved to be as elusive as unicorns, qualified AI ethics are also in very short supply, says Beena Ammanath, executive director of Deloitte's AI Institute. "We've seen different models evolving. It's still very nascent," Ammanath tells Datanami.

From healthcare and government to the financial sector and beyond, advanced data science models and big data projects are unlocking insights that can deliver everything from novel approaches to preventing and treating disease to highly effective financial fraud detection and more. Organizations looking to embark on data collaboration initiatives must overcome obstacles such as data ownership issues, compliance requirements for a variety of regulations and more. In today's data-filled world, ensuring privacy and security is paramount, and the measures to which organizations must go to achieve this can make collaborative data science difficult. The potential consequences of sustaining any kind of privacy or security breach (noncompliance, fines, reputational damage, etc.) can cause organizations to shy away from sharing data sets that could spark the next life-saving medical treatment or momentous public service program. Luckily, organizations across many industries are recognizing just how much upside we're leaving on the table if valuable data sets remain siloed.

Should restrictions be placed on the use of artificial intelligence? Google CEO Sundhar Pichai certainly does, and so do a host of other business leaders, including the CEOs of IBM and H2O.ai, as the chorus of calls for putting limits on the spread of the rapidly evolving technology gets louder. Pichai aired his opinion on the matter in an opinion piece published Monday in the Financial Times, titled "Why Google thinks we need to regulate AI" (story is protected by a paywall). In the story, Pichai, who is also CEO of Google's parent company, Alphabet, shared his lifelong love of technology, as well as the breakthroughs that his company is making in using AI to fight breast cancer, improve weather forecasts, and reduce flight delays. As virtuous as these AI-powered accomplishments are, they don't account for the negative impacts that AI also can have, Pichai wrote.

This number jibes with another finding from the DataRobot survey: that 38% of the organizations surveyed reported they use "black box" machine learning systems that offer no insight into how it makes decisions. The juxtaposition of AI bias concerns and black box systems is enough to warrant serious questions about the direction compaines should take with their machine learning, according to John Giannandrea, Apple's senior vice president of machine learning and AI strategy. "If someone is trying to sell you a black box system… and you don't know how it works or what data was used to train it, then I wouldn't trust it," DataRobot quotes Giannandrea as saying in its report. The survey indicates that organizations are aware of the potential pitfalls and are actively working to mitigate it. DataRobot found that 64% of survey respondents say they're "very to extremely" confident in their ability to identify AI bias.

Nothing is quite so personal for programmers as what language they use. Why a data scientist, engineer, or application developer picks one over the other has as much to do with personal preference and their employers' IT culture as it does the qualities and characteristics of the language itself. But when it comes to big data, there are some definite patterns that emerge. The most important factor in choosing a programming language for a big data project is the goal at hand. If the organization is manipulating data, building analytics, and testing out machine learning models, they will probably choose a language that's best suited for that task.

Like many online retailers, Overstock closely tracks the behavior of visitors on its site. But transforming all those individual page views and clicks into actual revenue is easier said than done. The company recently discussed with Datanami how it overcame challenges in building its own one-to-one marketing analytics system, and what results it's delivered this year. Overstock emerged from the wreckage of the first dot-com boom with a winning business plan: sell the excess merchandise of failed retail outfits at below-wholesale levels, build a loyal following, rinse, and repeat. Now nearly two decades in, Overstock has expanded to sell new products, and it all adds up to nearly $2 billion in revenue annually.