If you've recently had your first interaction with a voice-based personal assistant like Amazon's Alexa or Apple's Siri, you might get the sense that artificial intelligence is just a few years away from being able to talk and act like a human. It will soon be capable of managing our schedules, troubleshooting technical issues, or even holding conversation. According to a recent Wall Street Journal piece titled "Alexa and Cortana May Be Heading to the Office," many businesses share that hope. One startup profiled in the piece uses "an Amazon Echo attached to the office ceiling for such tasks as adding events to their calendars," while another is building a virtual assistant to set meetings on behalf of human users. The belief that natural language processing is right around the corner seems to be widespread: About half of IT professionals in the Spiceworks survey cited in the article said they plan to use intelligent assistants in a corporate setting in the next three years.

The next assistant in many offices could be named Alexa or Cortana. In 2016, Silicon Valley obsessed over how text-based bots in apps like Slack could make employees more efficient, turning complicated tasks or forms into conversational texts. Now, following the success of Amazon Inc.'s Alexa and Alphabet Inc.'s Google Home, people in the technology industry are increasingly thinking about how such voice-activated devices can be made useful in the workplace. The workplace offers challenges that experts say intelligent assistants built for home use so far haven't effectively met, mostly in the area of voice recognition. Workers at Goodwinds Inc. in New York City, for example, have used an Amazon Echo attached to the office ceiling for such tasks as adding events to their calendars and setting reminders for meetings, says Vinay Patankar, chief executive of the workflow-management startup.

Many discussions around artificial intelligence (AI) have focused on the new developments in machine learning, such as assisting with cancer recognition and even detecting earthquakes. However, the use of AI in the creation of a virtual assistant is starting to gain some traction. A recent article from VentureBeat stated that 2017 is the year that "virtual assistants really arrived." In this post, we wanted to look at the rise of virtual assistants and how they could change the way we work. It is hard to imagine an office desk these days without a computer.

For more on advances in machine learning, prediction, and technology, check out the Data science and advanced analytics sessions at Strata Hadoop World London, May 22-25, 2017. Early price ends April 7. You've probably heard by now that machine learning algorithms can use big data to predict whether a donor will give to a charity, whether an infant in a NICU will develop sepsis, whether a customer will respond to an ad, and on and on. Machine learning can even drive cars and predict elections. I believe it can, but these recent high-profile hiccups should leave everyone who works with data (big or not) and machine learning algorithms asking themselves some very hard questions: do I understand my data? Do I understand the model and answers my machine learning algorithm is giving me? And do I trust these answers? Unfortunately, the complexity that bestows the extraordinary predictive abilities on machine learning algorithms also makes the answers the algorithms produce hard to ...

Nvidia has announced a new partnership with Bosch to sell its Drive PX 2 driver-assist platform to automakers. In effect, the deal gives Nvidia a go-to-market strategy for its self-driving hardware and software platform. Bosch joins ZF as the two so-called tier-one suppliers that will sell Nvidia's technology to automakers. Nvidia's technology uses "deep learning" artificial intelligence, which is a fancy way of saying its computer brain learns like a human does: instead of needing to be programmed for every possible driving scenario, it learns what the appropriate behavior is, even for unexpected situations. Theoretically, a car company looking to make its car capable of autonomous driving will also be able to go to Bosch or ZF and buy that technology to integrate into their cars, and sell those to consumers.

Robotics is said to be the next technological revolution. Many seem to agree that robots will have a tremendous impact over the following years, and some are heavily betting on it. Companies are investing billions buying other companies, and public authorities are discussing legal frameworks to enable a coherent growth of robotics. Understanding where the field of robotics is heading is more than mere guesswork. While much public concern focuses on the potential societal issues that will arise with the advent of robots, in this article, we present a review of some of the most relevant milestones that happened in robotics over the last decades.

The data volumes expected from the Internet of Things (IoT) are certain to be large – too large, in fact, for even an army of trained analysts to turn into useful information in a reasonable amount of time. This is why every solution aimed at the IoT relies heavily on automation, simply to manage the flow of information between devices and to centralized storage and analytics systems. But even this is not likely to be enough. To fully leverage the IoT, it's becoming obvious that the enterprise will have to utilize new forms of artificial intelligence and machine learning to basically allow the environment to makes its own use of available data and tell human operators what needs to be done. Already, this is emerging on leading IoT platforms. Software developer C3 recently updated its IoT platform by pushing artificial intelligence to the edge where it can function on an application level for an improved user experience.

The automobile is being dismantled, reimagined, and rebuilt in Silicon Valley. Intel's proposed $15.3 billion acquisition of Mobileye, an Israeli company that supplies carmakers with a computer-vision technology and advanced driver assistance systems, offers a chance to measure the scale of this rebuild. In particular, it shows how valuable on-the-road data is likely to be in the evolution of automated driving. While the price tag might seem steep, especially with so many players in automated driving today, Mobileye has some key technological strengths and strategic advantages. It's also developing new technologies that could help solidify this position.

Genpact (NYSE:G), a global professional services firm focused on delivering digital transformation for clients, has signed a definitive agreement to acquire Rage Frameworks, a leader in knowledge-based automation technology and services providing Artificial Intelligence (AI) for the Enterprise. Terms of the deal are not disclosed. As part of its strategy to drive both digital-led innovation and digital-enabled intelligent operations for its clients, Genpact is investing in leading technologies, such as AI, that are transforming the way companies in many industries compete. Genpact will embed Rage's AI in business operations and apply it to complex enterprise issues to allow clients to generate insights and drive decisions and action, at a scale and speed that humans alone could not achieve. "As advanced technologies such as AI fundamentally change the definition of work, the ability for CXOs to find and leverage new solutions that combine the best elements of human expertise and machine intelligence, will be critical to their ability to gain and sustain competitive advantage," said NV'Tiger' Tyagarajan, president and CEO, Genpact.

Cloud accounting software provider Xero announced a new product that it says will bring machine learning and AI to the process of small business invoicing. The machine learning automation technology is currently without an official title, but Xero says it's designed to simplify and improve the way invoices are filed. More specifically, the technology will automatically suggest the appropriate account codes for an invoice based on past invoicing behavior of the business owner and their advisor. While the use case is indeed specific, Xero posits that small businesses frequently mislabel invoices, which in turn creates more work for themselves and their accountants down the road. "Before we work with a client, we always review their balance sheet to see if items are correctly allocated," said Andrew Erkins, director of technology at Xero Partner Digit Books.