For as long as humans have had consciousness -- two or two hundred millennia depending on who you ask -- human scholars have made great efforts to understand and define what that means. The most facile and purely conceptual description of consciousness might be that it is an awareness of the self within the context of the world. But without an understanding of the underlying mechanism, consciousness keeps chasing its tail. This is, in part, why neuroscientists have successfully interjected themselves in the ongoing conversation about consciousness by pointing to physical phenomena within the brain. But linking the metaphysical to the physical still results in the sort of quasi-scientific, quasi-philosophical overreach that gets academics laughed out of faculty lounges and labeled eccentric.

As we have entered into 2017, the enterprise software industry is at the inflection point for ubiquitous cloud adoption as part of the $4 trillion dollar enterprise IT market transformation. The reasons for enterprise IT to adopt cloud are compelling, and many assume this shift has already happened. We are, however, just at the very beginning of this shift and the impact is fundamentally transforming all elements of IT. Industry experts estimate we are in fact only at about 6% enterprise cloud penetration today [1]. Most businesses have yet to realize the full value of this transformation.

Singapore bank POSB has launched online chat functionality that understands and reacts to its customers' spoken language. It is the latest bank to harness artificial intelligence (AI) to automate customer interactions with the POSB digibank Virtual Assistant, which is available via Facebook Messenger. Download our most popular articles for IT leaders to learn about skills of great CIOs, tech and digital strategy at the board-level, salary survey results, finding funding for digital initiatives and digital opportunities for CIOs. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent.

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.

There are a myriad of questions swirling around the topic of machine learning and artificial intelligence (AI), from how to establish ethics and pursue innovation safely to addressing the monumental task of programming empathy in devices like chatbots. Underneath the collective uncertainty, though, lies a simple truth: AI is a powerful technology that helps humans become more efficient, and many forward-thinking industries are already on board. One such industry is law enforcement. And no, despite what sci-fi movies want you to think, Robocop isn't roaming the streets. Law enforcement agencies across the country are using AI in sophisticated ways to stop crime, find suspects, protect officers, and more.

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.

Google is going to arrive in style in 2017. The tech titan has exciting plans for the maker community. It intends to make a range of smart tools available this year. Google's range of AI and machine learning technology could enable makers to build even more powerful projects. To make this happen, Google needs help from the maker community.

The Chan Zuckerberg Initiative is getting some help from artificial intelligence. The philanthropic organization, founded by Facebook (fb) CEO Mark Zuckerberg and his wife Priscilla Chan in 2015, will buy Meta, a startup focusing on artificial intelligence and related data analytic technology in the healthcare industry. The deal was disclosed in a Facebook post Monday by Chan Zuckerberg Initiative president of science Cornelia Bargmann and the organization's CTO Brian Pinkerton. Terms of the deal were not revealed. Meta specializes in using artificial intelligence technologies like natural language processing and machine learning to sift through millions of scientific and technology papers and identify which papers should presumably be most important to health-care professionals.