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Log-in to view content. Working in concert, these techniques and capabilities are transforming the way we engage with machines, data, and each other. At a dinner party, your spouse, across the table, raises an eyebrow ever so slightly. Can we leave yet? Most people recognize this kind of intuitive communication as a shared language that develops over time among people in intimate relationships. We accept it as perfectly natural—but only between humans. It seems a bit farfetched—or, at least, premature—that machines might also be able to recognize the intent behind a subtly raised eyebrow and respond in contextually appropriate ways.
Watch the video. Create a custom PDF. Yet in an emerging technology trend that could redraw—or even erase—boundaries between humans and computers, a new breed of intelligent interfaces is turning the farfetched into reality.
- Watching Her Walk.
- The Song Bird (Siren Publishing Menage Amour).
These interfaces are actually a sophisticated array of data-gathering, processing, and deploying capabilities that, individually or in concert, provide a powerful alternative to traditional modes of human-computer interaction. In this example, the deployed technologies become an intelligent interface between users and systems. And this is only the beginning.
Smartphone data captured in real time can alert retailers that customers are checking online to compare prices for a specific product, suggesting dissatisfaction with store pricing, product selection, or layout. Such potential is fueling a growing demand for a broad range of human-machine interface devices. During the next two years, more B2C and B2B companies will likely embrace aspects of the growing intelligent interfaces trend.
As a first step, they can explore how different approaches can support their customer engagement and operational transformation goals. Companies already on such journeys can further develop use cases and prototypes. Though investments of time, labor, and budget may be required before companies can begin reaping benefits, the steps they take during the next 18 to 24 months will be critical to maintaining future competitiveness.
Intelligent interfaces represent the latest in a series of major technology transformations that began with the transition from mainframes to PCs and continued with the emergence of the web and mobile.
At each stage, the ways in which we interface with technology have become more natural, contextual, and ubiquitous—think of the progression from keyboards to mice to touchscreens, to voice and the consequent changes in the way we manipulate onscreen data. The ongoing competition among these tech giants to dominate the voice systems space is standardizing natural language processing and AI technologies across the interface market—and fueling innovation. Voice use cases are proliferating in warehouse, customer service, and, notably, in field operation deployments where technicians armed with a variety of voice-enabled wearables can interact with company systems and staff without having to hold a phone or printed instructions.
Likewise, we are seeing more organizations explore opportunities to incorporate voice dialog systems into their employee training programs. Their goal is to develop new training methodologies that increase the effectiveness of training, while shortening the amount of time employees spend learning new skills. Though conversational technologies may currently dominate the intelligent interfaces arena, many see a different breed of solutions gaining ground, harnessing the power of advanced sensors, IoT networks, computer vision, analytics, and AI.
To understand how these capabilities could work in concert in an enterprise setting, picture a widely distributed array of IoT sensors collecting data throughout a manufacturing facility, and streaming it rapidly back to a central neural system. For example, microphones embedded in assembly-line motors can detect frequency changes. Enter AI algorithms—acting as a logic-based brain—that derive inferences from the data generated by these and other sensors.
Moreover, by collecting, for example, manufacturing variances in real time versus in batches, the system can accelerate response times and, ultimately, increase operational throughput. To be clear, skilled human observation, combined with machine data, still delivers the most robust and impactful understanding of manufacturing processes or retail operations.
And with intelligent interfaces, the flow of information between humans and machines runs both ways see figure 1. As we have examined in previous editions of Tech Trends, augmented reality AR , virtual reality VR , and mixed reality devices—which act as delivery vehicles for intelligent interfaces—are drawing upon a wide variety of data to provide users information-rich, contextually detailed virtual environments. Rather than being the beginning state of the human-machine interface, we are now the end state.
Any intelligent interface initiative involves underlying technology capabilities to bring it to life. As the fidelity and complexity of these experiences evolve, those foundational elements become even more critical.
If you are collaborating with a colleague in a virtual environment via a head-mounted display, a millisecond delay in a spoken conversation is annoying; if you find yourself waiting a full 10 seconds for a shared visual to load, you will probably lose confidence in the system altogether. Developing the supporting infrastructure necessary to harvest, analyze, and disseminate infinitely more data from more input sources will make or break experiences.
There are also data syndication, capture, storage, compression, and delivery considerations, and this is where having an IT strategy for managing the backbone elements of intelligent interfaces will be crucial. An effective strategy for prioritizing data, breaking it apart, processing it, and then disseminating to systems and network devices should include the following considerations:.
- Network Management Fundamentals.
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- Ein Hundeleben (German Edition).
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Despite the potential of AR to entertain and educate the masses, a barrier to widespread adoption has been developing an interface that is accessible, nondisruptive, and intuitive to use. Snap has found a way to attract hundreds of millions of daily users to its app using AR technology as a hook. There is virtually no learning curve in creating, sending, and viewing a snap, and the result is immediate. And Snap has been working with market leaders to change the boundaries of digital engagement—helping to make interactions seemingly effortless for consumers.
These experiences combine digital reality technology with a cloud-based e-commerce platform and on-demand fulfillment. For engagement, Snap plans to continue to shape the future by delivering intuitive and creative AR experiences to their users. Jackson International Terminal F, direct to an international destination, can check in at kiosks, drop baggage at lobby counters, pass through security checkpoints, and board their flight using facial recognition technology. The airline hopes that implementing biometrics—including fingerprint, along with facial recognition—will improve and expedite the travel experience.
Within three years, Muta says, Delta will explore more technologies to intelligently interact with customers and employees, to help Delta better engage through the travel experience by further mobilizing the workforce, and promoting consistent messaging. Muta is confident that the way Delta is approaching innovation and leveraging biometrics and facial recognition will set a standard not just for Delta but for the industry as a whole. But I think a much more exciting possibility is a future in which people are augmented with intelligent interfaces—thereby elevating and combining human decision-making with machine intelligence.
At the lab, we like to talk about intelligence augmentation rather than artificial intelligence, and we view the future of interaction with our devices as one that is more natural and intimate. There are three ways in which we would like to see our devices change. We are constantly forced to multitask and shift our attention from one to the other. And third, our devices today pick up on only the very deliberate inputs that we give them through type, swipe, and voice. If they had access to more implicit inputs such as our context, behavior, and mental state, they could offer assistance without requiring so much instruction.
But in the future, they will also gather data on the surrounding environment and current situation, perhaps by analyzing what we are looking at or sensing what our hands are doing. This context will enable our devices to provide us with data based on explicit intent and well-defined actions, as well as our state of mind, unspoken preferences, and even desires.
Devices will be able to learn from their interactions with us, which over time will yield much more efficient decision-making and communication between human and device.
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I often joke that the device of tomorrow will know each of us better than our spouse, parents, or best friends because it will always be with us, continually monitor us, and be able to detect even subtle cues from our behavior and environment. Are we focused or absent-minded? What is our stress level? Are we in physical discomfort from a medical condition or injury?
All these factors very much affect engagement but are almost impossible to quantify without improvements in sensing and understanding of the contextual signals around us. Current interfaces such as a computer keyboard or mouse do not adjust automatically to those kinds of cues.
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