Over the years, Alphabet and Facebook have invested in everything from walking dog robots to autonomous high altitude wind turbine planes. While not always directly related to their core business, these investments provide an outlet for generous cash flows and could open up future business opportunities. At the very least, crazy science projects generate buzz for the company, which certainly doesn't hurt. However, these two aren't the only tech companies that invest heavily in far-off scientific endeavors in hopes of big payoffs. Microsoft (NASDAQ:MSFT) employs hundreds of scientists through the powerful entity known as Microsoft Research, which has contributed to nearly every product sold by the company in the last several decades.

When it was first announced to a sceptical tech press months after a flop phone, the Echo was dismissed separately as a joke and a privacy nightmare. Now the latter may still prove to be the case (the Echo is always listening, and logs every sentence spoken to it), but a joke it is clearly not. In fact many analysts now believe that Amazon has one hand on the future that comes after the smartphone. Alexa is not the only, or even the first, voice-activated virtual assistant – Apple, Google and Microsoft have had their own for years – but it is the first that consumers have truly embraced. While taking out a smartphone in public and speaking to it – as one must with Apple's Siri or Google's Assistant – is awkward, and often slower than simply using a touchscreen, talking to a device in the comfort of one's own home is decidedly less uncomfortable.

Marketers will need to transform from campaigns to real-time, continuous interaction with customers via intelligent agents. So says Forrester Research VP and Principal Analyst Brian Hopkins, co-author (with Adam Silverman) of a new Forrester Research report, The Top Emerging Technologies to Watch: 2017 to 2021 ( 499 for individual purchase). It's about the top 15 developing technologies that will help businesses become more customer-obsessed over the next years. Forrester is obsessed with customer-obsession, which it says is essential to a modern brand and which is characterized by several key principles. According to the research firm, such a customer-focused company is led by insights from and about customers, responds quickly and is thoroughly connected everywhere. The report chose 15 technologies for their impact on companies employing these principles.

Many businesses have already begun to transition themselves towards digital transformation as they realize that it is not something to be left for tomorrow. With this series we want to illustrate, on industry specific use cases, how businesses can re-imagine their business models, processes, products, and services, for the purpose of achieving the benefits of the digital transformation. In order to do this organizations are increasingly looking at technology enablers like Big Data, Mobility, Collaboration, Analytics, and Cloud Computing to adopt. In order for a business to achieve real-time supply chain visibility, it needs to move from manual to a system-based production planning and scheduling, automate sales order management, and enable traceability and visibility of the operations and shipments within and outside the organization. By leveraging Big Data and analytics capabilities, the company can analyze the demand for production, delivery and process times, and can create event based notifications for better monitoring of the supply chain.

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A joint UT Austin/Cornell team has taught a machine learning system based on the free/open Torch library to correctly guess the content of pixellated or blurred redactions with high accuracy: for masked faces that humans correctly guess 0.19% of the time, the system can make a correct guess 83% of the time, when given five tries. Redaction errors have plagued data-releases since the earliest days of the net; who can forget the hilarity of companies and agencies that added black boxes in an overlay to their PDFs, or left Word's document history (including all the deleted passages) intact on their sensitive releases? Or the pedophile whose twirly-faced redaction was de-twirled to catch and prosecute him? These days, the best practice seems to be opening the images in a bitmap editor, then replacing them with black squares. "We're using this off-the-shelf, poor man's approach," says Vitaly Shmatikov, co-author of the paper and professor at Cornell.

Let's be honest, stupid humans are too smart sometimes for their own good and Skynet is going to happen, at least that's what Bill Gates, Stephen Hawking and Elon Musk think, but what do they know? In 2014, Stephen Hawking wrote: "Success in creating AI would be the biggest event in human history, – Unfortunately, it might also be the last, unless we learn how to avoid the risks. In the near term, world militaries are considering autonomous-weapon systems that can choose and eliminate targets." In a separate interview in the same year, he warned: "humans, limited by slow biological evolution, couldn't compete and would be superseded by A.I." In a Reddit Q&A Session in January 2015 Gates said: "I am in the camp that is concerned about super intelligence. First the machines will do a lot of jobs for us and not be super intelligent. That should be positive if we manage it well. A few decades after that though the intelligence is strong enough to be a concern. I agree with Elon Musk and some others on this and don't understand why some people are not concerned."

Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence and an investor in Algorithmia, believes these algorithm can help transform deep learning from a "dark art" practiced only by the giants of the Internet into a technology used by "the 99 percent." Dennis R. Mortensen, the CEO and founder of the artificial intelligence startup x.ai, agrees--up to a point. He adds that these algorithms serve only some needs. As he points out, other companies are providing more complex tools for building deep learning systems, including Google, which recently open sourced a deep learning software engine called TensorFlow. He also points out that deep learning requires enormous amounts of data for training.

While we'll continue to see businesses in and out of the retail space launch with AI at the core, this approach certainly won't make sense for all retailers. Instead, established retailers can apply AI to different business units. Recently, Macy's announced its customer service unit is testing a "mobile companion" tool using AI. The tool enables shoppers to get answers based on the store that they are physically shopping in rather than having to find a sales associate. While this is a great way to leverage AI to engage with current shoppers, what about using the technology to acquire new ones?

Modern radar and communications systems can subtly and quickly change their character, making them harder for U.S. aircraft and other platforms to jam or spoof. That reality is prompting DARPA to lead industry teams to apply artificial intelligence to electronic warfare. The difference between today's tech and that of the 1970s lies in the adoption of readily available digital processing. Such processing effectively allows operators to change aspects of the waveforms that radar and communications systems use. "The problem now is that if we continue to rely on that [old] approach, the radar waveforms we're expecting could be rapidly changed," Tilghman says.