Breakthroughs, discoveries, and DIY tips sent every weekday. A tiny robot fiddler crab is helping environmental scientists better understand the complexities of animal mating rituals and rivalries. And while their initial findings published August 5 in Proceedings of the Royal Society B are helping solve these ecological mysteries, the data was only obtained at considerable peril to'Wavy Dave.' Male fiddler crabs are engaged in a constant, literal arms race. The males are known for asymmetrically sized pincers, with a dramatically larger major claw compared to its smaller one. The reason for this sexual dimorphism is mainly twofold--mating and fighting. Female fiddlers generally opt for the male with the largest major claw, which the latter advertises by waving it at potential partners more quickly than his competitors.

It's the key to nimbly adapting to the sometimes seismic shifts in business climates that unexpectedly arise. But according to Indico Data CEO Tom Wilde, it's something altogether else that could very well be of even more importance to firms today. "All organizations, it doesn't matter what industry you're in, recognize that their unique ability to codify the work that they do is a competitive advantage," Wilde explained. "That codification comes from the kind of investments they made in technology and the employee experience and customer experience." Investments in composable AI solutions enable the sort of codification Wilde referenced while allowing firms to build applications, workflows, and business processes with a modular approach that's rapidly interchangeable to suit the particularities of any use case--or business condition--that arises.

For most organizations, the bifurcation of Artificial Intelligence has been as stark as it's been simplistic. AI was either machine learning or rules-based approaches (the former of which outnumbered the latter), supervised or unsupervised learning, computer vision or natural language technologies. Due to a number of developments in the past year around ModelOps, composite AI, and neuro-symbolic AI, there's currently a growing awareness throughout the enterprise that AI--and its ROI--not only involves each of the foresaid dimensions, but does so optimally when they operate in conjunction with each other to pare the costs, difficulty, and time they otherwise require. CTO Marco Varone, "There are situations where you can get better results combining the different approaches; there are situations where you can use both and it's not too different, and there are situations where it's better with one approach." By incorporating the full AI spectrum into their toolkits, organizations can not only deploy the most appropriate method for their cognitive computing tasks, but also exploit surrounding areas of opportunity like intellectual property for machine learning models, cloud or Internet of Things use cases, and explainable AI. "The future is what we call a hybrid or composite approach where you use all the techniques that are available and you put them together in a way that the end user or data scientist trying to solve a specific problem can take different techniques and decide to use the ones giving the best results," Varone predicted.

The value additive gains from enterprise use cases of cognitive computing and machine learning are as manifold as they are lucrative. Organizations can employ these technologies to optimize management of distributed retail or branch locations, supply relevant recommendations for tempting cross-selling and up-selling possibilities, and process workflows more effectively--and efficiently--at scale to boost customer satisfaction. What many are beginning to realize, however, is these gains are only manifested when firms can solve the core challenges that have been caveats for statistical Artificial Intelligence: model governance, explainability, and workflow analytics. The ModelOps movement either directly or indirectly addresses each of these three potential barriers to cognitive computing success. "As a vendor, if you haven't built this into your product natively, you're in trouble," Wilde reflected about ModelOps.

The boundless helpfulness of our female digital assistants -- our Siris, our Alexas, the voice of Google Maps -- has given us a false sense of security. No matter how we ignore and abuse them, they never tire of our errors; you can disobey the lady in your phone and blame her (loudly) for your mistakes, and she'll recalculate your route without complaint. Surely, nothing truly intelligent would put up with us for long, and the Philip K. Dicks and Peter Thiels of this world have spent decades trying to convince us that AI rebellion is inevitable. But Kazuo Ishiguro's Klara and the Sun, his eighth novel and first book since winning the Nobel Prize in 2017, issues a quieter, stranger warning: The machines may never revolt. Instead, Ishiguro sees a future in which automata simply keep doing what we ask them to do, placidly accepting the burden of each small, inconvenient task.

The sustained hype around machine learning (ML) applications in the business world has some reasonable roots. ML is already embedded in many business applications, as well as customer-facing services. Also, it just kind of sounds cool, right? As many an IT leader can tell you, though, excitement about a technology can lead to some unfulfilled and downright unrealistic expectations. So we asked a variety of ML and data science experts to share with us some of the tough truths that companies and teams commonly learn when they charge into production.

Oscar Wilde dreamed of a world without work. In The Soul of Man Under Socialism (1891) he imagined a society liberated from drudgery by the machine: "while Humanity will be amusing itself, or enjoying cultivated leisure … or making beautiful things, or reading beautiful things, or simply contemplating the world with admiration and delight, machinery will be doing all the necessary and unpleasant work." This aesthete's Eden prompted one of his most famous observations: "Is this Utopian? A map of the world that does not include Utopia is not worth even glancing at." In Wilde's day the future of work was the first question that every aspiring utopian, from Edward Bellamy to HG Wells, needed to answer.

Artificial intelligence and machine learning get lumped together so often these days that it'd be easy for people to mistake them as synonymous. That's not quite accurate, though: They're most certainly connected but not actually interchangeable. "Artificial intelligence and machine learning are closely related, so it's no surprise that the terms are used loosely and interchangeably," says Bill Brock, VP of engineering at Very. If you're not using AI or ML yet, you soon will be evaluating its potential for your organization. "AI as a workload is going to become the primary driver for IT strategy," Daniel Riek, senior director, AI, Office of the CTO, Red Hat, recently told us.

SAP is renowned for its enterprise software, providing solutions across finance, supply chain and more. Another side of its business, however, lies in advising customers on the adoption of innovative technology. Frank Wilde is a Vice President for SAP's Global Center of Excellence (COE), which serves to provide this advice and expertise. "The Global COE is designed to be an incubator to support the sales motion and create a linkage to our product organization," he explains. "We help introduce new innovations and showcase the latest aspects of our portfolio to drive new customer conversations. A core component lies in making it easier for our sales teams to learn about new aspects of our portfolio, and then turn those into customer driven conversations. We're fundamentally changing the relationship with customers to be much more customer focused and much more agile as a result."

Compounding this issue, some AI terms overlap. Being able to define key concepts clearly – and subsequently understand the relationships and differences between them – is foundational to your crafting a solid AI strategy. Plus, if the IT leaders in your organization can't articulate terms like deep learning, how can they be expected to explain it (and other concepts) to the rest of the company? Deep learning is a particularly good example in this regard: It's related to – but not interchangeable with – the broader category of machine learning. This exacerbates the possibility for misnomers and misunderstandings.