It seems pretty clear that 2017 is going to be the year that Amazon's Alexa virtual assistant really begins to hit its stride, going from a fast-growing niche product to a mainstream must-have. While Microsoft and especially Google have made their competitive strategies clear -- even Samsung and Baidu have started to make rumbles in the market -- there's one elephant that, notably, isn't in the room yet: Apple, the most valuable company in the world and a notorious latecomer to any new product category. While Apple recently built Siri into the Apple TV, the company is said to be working on a direct competitor to the Amazon Echo -- one that would apparently be more advanced than anything we've seen, down to a possible facial-recognition camera so it knows who's talking. That device, if and when it comes out, would bring Apple's 5-year-old Siri assistant head-to-head with Alexa. And the clock is ticking.

Voice-controlled assistants are having a moment. But there may be an intriguing wrinkle that their makers have to smooth out: users don't seem to be using many apps. The number of Skills--the Amazon name for apps that operate on its Alexa smart assistant software--available for the company's Echo smart speaker have risen significantly in the past six months, from 950 last May to over 8,000 today. But an analysis of the way people use Alexa and and Google's Assistant platforms shows that third-party apps aren't too well used, nor particularly sticky. The analysis, which was carried out voice software start-up Voice Labs, shows that most app don't get any user reviews, which suggests they're not very popular.

Today, with Digitization of everything, 80 percent the data being created is unstructured. Audio, Video, our social footprints, the data generated from conversations between customer service reps, tons of legal document's texts processed in financial sectors are examples of unstructured data stored in Big Data. Organizations are turning to natural language processing (NLP) technology to derive understanding from the myriad of these unstructured data available online and in call-logs. Natural language processing (NLP) is the ability of computers to understand human speech as it is spoken. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact.

You might have detected a "bit" of intentional tongue-in-cheek in my recent coverage of the most "intriguing" products released at the 2017 Consumer Electronics Show, but rest assured that the news wasn't all bizarre. Some truly significant technologies (and products based on them) were both introduced for the first time and notably advanced from prior versions. I thought I'd devote this particular post to showcasing three that particularly stuck with me. Learning goes deep While the world may not need an electric toothbrush that claims to have "artificial intelligence", both that term and the comparable "deep learning" were everywhere at CES, often for good reason. Traditionally, computer vision, audio analysis, and other similar applications have relied on special-purpose algorithms custom-designed to recognize particular patterns.

From creating personalized shopping experiences to offering virtual buying assistants -- AI is improving the online shopping experience for consumers and retailers. Shoppers will be able to easily find the best price for an item and communicate with chatbots for quick customer service. For retailers, they'll be able to better analyze consumer data to predict future buying patterns, create autonomous replenishing systems and also save money and time on customer service by utilizing chatbots. Related: Has Artificial Intelligence Arrived At The Sales Function Yet? A number of companies already employ AI in their ecommerce processes today.

Business intelligence (BI) tools first appeared on the enterprise technology scene several decades ago, at birth clumsy and difficult to use but ultimately improving the flow of data through organizations from their operational systems to decision support. Data warehousing cut the time it took to access data, but even at their full maturity, BI systems could do little more than produce data and reports in a traditional organized way. But with the advancement of artificial intelligence and--more importantly--machine learning, true business intelligence is actually on its way to the enterprise. Such self-learning software will run on servers, be built into bots, drive decision-making systems, be embedded into cars or aircraft, and become the beating heart of mobile devices. Increased data-processing power, the availability of big data, the Internet of Things, and improvements in algorithms are converging to power this actual business intelligence.

Artificial intelligence is likely going to change our world like no other technology ever has. AI might steal our jobs. But what we know for sure is that 2017 is the year artificial intelligence is hitting the mainstream. Smart assistants like Alexa, Cortana, Siri, and Google Assistant live on our phones and in our homes. AI is influencing what we find in Google search results and what we see in the Facebook news feed.

Instant messaging apps have taken over. WhatsApp, iMessage, WeChat, Signal, Slack, Facebook Messenger, Snapchat -- billions of users exchange information in bite-sized chunks on any or all of these platforms on a daily basis. In fact, as of mid-2015, people were spending more time on messaging apps than on social networks, and as messaging apps become increasingly more sophisticated, this trend shows no sign of reversing. Messaging platforms have expanded far beyond simply enabling users to send and receive text messages, photos, and videos. Many of them allow users to exchange documents and files, voice memos, location information, and sometimes even cash.

Join Entrepreneur's The Goal Standard Challenge and make 2017 yours. Artificial intelligence (AI) gets more advanced every year, but there are still some major limitations keeping us from seeing a futuristic reality that includes robot butlers and near-complete societal automation. Fortunately, some of these limitations are on the verge of being overcome, and if you watch and plan carefully, you'll be able to take advantage of those improvements for your business. Right now, most AI systems "learn" new information through a kind of structured force-feeding, relying on information given to those systems by humans. However, this form of "supervised learning" isn't scalable, and doesn't mimic the way that human beings naturally learn.

Here's a fun experiment: Next time you're on a crowded bus, loudly announce, "Hey Siri! Chances are you'll get some horrified looks as your voice awakens iPhones in nearby commuters' pockets and bags. They'll dive for their phones to cancel your command. But what if there was a way to talk to phones with sounds other than words? Unless the phones' owners were prompted for confirmation--and realized what was going on in time to intervene--they'd have no idea that anything was being texted on their behalf. Turns out there's a gap between the kinds of sounds that people and computers understand as human speech. Last summer, a group of Ph.D. candidates at Georgetown and Berkeley exploited that gap: They developed a way to create voice commands that computers can parse--but that sound like meaningless noise to humans.