"In order for a program to improve itself substantially it would have to have at least a rudimentary understanding of its own problem-solving process and some ability to recognize an improvement when it found one. There is no inherent reason why this should be impossible for a machine. Given a model of its own workings, it could use its problem-solving power to work on the problem of self-improvement. The present programs are not quite smart enough for this purpose; they can only deal with the improvement of programs much simpler than themselves. Once we have devised programs with a genuine capacity for self-improvement a rapid evolutionary process will begin.... Whether or not we could retain some sort of control of the machines, assuming that we would want to, the nature of our activities and aspirations would be changed utterly by the presence on earth of intellectually superior beings. "The audience is tense with excitement as the hero in the film play struggles frantically with the control apparatus of a submarine that is fast sinking to the ocean bottom.

Bill Gates also weighed in, calling intelligent machines dangerous and implying that they may be more trouble than they're worth. Instead, we should feel proud of and satisfied by the enormous progress that we've achieved in AI thus far, especially if we think of the incremental improvements that we'll see in information management based on automatic understanding in even just the next 10 years. Bio: Marco Varone, founder, president and CTO of Expert System, is one of the leading experts on semantic technology and natural language processing. He has worked in the field application of semantic technology in every advanced context: search engines, text analytics, natural language interfaces, Q & A systems, automatic categorization and many others.

Bill Gates also weighed in, calling intelligent machines dangerous and implying that they may be more trouble than they're worth. Instead, we should feel proud of and satisfied by the enormous progress that we've achieved in AI thus far, especially if we think of the incremental improvements that we'll see in information management based on automatic understanding in even just the next 10 years. Bio: Marco Varone, founder, president and CTO of Expert System, is one of the leading experts on semantic technology and natural language processing. He has worked in the field application of semantic technology in every advanced context: search engines, text analytics, natural language interfaces, Q & A systems, automatic categorization and many others.

Last time we wrote an image classifier using TensorFlow for Poets. This time, we'll write a basic one using TF.Learn. To make it easier for you to try this out, I wrote a Jupyter Notebook for this episode -- https://goo.gl/NNlMNu This is a great way to get all the dependencies installed and properly configured. I've linked some additional notebooks below you can try out, too.

Recent cloud statistics and predictions report that worldwide spending on public cloud services will grow from 70 billion in 2015 to more than 141 billion in 2019. Those numbers are positive, indicating that earlier concerns around reliability and security are leveling out. When the public cloud became a bona fide trend about 10 years ago, executives were encouraged at the prospect that Amazon, Microsoft, Google and others could save their company money on licensing, support, management and hardware. IT people were wondering if it would kill their jobs, though the forward-looking ones thought that the cloud might enable IT-based innovation like never before. What has been interesting to see are the business innovations that would have been cost-heavy with an on-premises infrastructure.

As we previously reported, artificial intelligence (AI) is not some Asimovian fantasy, nor an extravagance best left to starch-smocked scientists clinking beakers together in an underground laboratory. It is an opportunity to create tools that save money, save lives and improve life in ways that can't be measured. Stated very simply, AI is the name given to computer systems that attempt to replicate human intelligence and learning. There's great difficulty in finding a satisfying definition for AI because the definition of intelligence itself conjures big questions of consciousness and being that have not yet been resolved by science and philosophy. A practical way to approach AI is to consider why it exists in its current manifestations.

Debt of any kind--if not addressed--is a time bomb waiting to explode. We can easily relate to this with reference to finance. The comparison between technical complexity and debt was first drawn in 1992. In an experience report, Ward Cunningham alerted the industry to the problem and in doing so, coined the term "technical debt." "Shipping first time code is like going into debt. A little debt speeds development so long as it is paid back promptly with a rewrite… The danger occurs when the debt is not repaid. Every minute spent on not-quite-right code counts as interest on that debt. Entire engineering organizations can be brought to a stand-still under the debt load of an unconsolidated implementation, object-oriented or otherwise."--

In today's fast-paced digital realm, the rules are constantly changing. Traditional marketing channels have evolved into new ones, and CMOs are continually challenged. They've spent the last seven years tackling the ins and outs of new mobile and social platforms--investing in new platforms and adjusting media budgets to stay ahead. Now that they finally have everything hashed out, customers are rapidly moving away from standalone mobile apps into the more immediate, intimate world of messaging--to the tune of 100X growth in use YoY in both the US and UK. While some suggest "conversational commerce" is the next big thing--10K developers are already building on wit.ai,

In his ninth post in the series, Marshall Kirkpatrick focuses on the intersection between artificial intelligence and the Internet of Things. By way of reminder, Marshall launched a 30 day series that explores the intersection between AI and the various innovation components on my emerging futures visual. As he has in each post, Marshall identifies the key subject matter experts that sit at the intersection of AI and the visual component in question. In the case of the Internet of Things, the key influencers are: Ajit Jaokar, Michael Cavaretta, Mike Gualtieri and Roger Strukhoff. Here is the foresight and related future scenarios identified at the intersection of Artificial Intelligence and the Internet of Things (taken straight from Marshall's post): Beyond island computing: Connected buildings and devices become hyper efficient by sharing knowledge and experiments.

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