Once upon a time, Artificial Intelligence (AI) was the future. But today, human wants to see even beyond this future. This article try to explain how everyone is thinking about the future of AI in next five years, based on today's emerging trends and developments in IoT, robotics, nanotech and machine learning. We have discussed some AI topics in the previous posts, and it should seem now obvious the extraordinary disruptive impact AI had over the past few years. However, what everyone is now thinking of is where AI will be in five years time. I find it useful then to describe a few emerging trends we start seeing today, as well as make few predictions around machine learning future developments.

Google Photos enhancements provide more incentive to keep memories hosted by Google. The premise of Google Photos is simple enough: one place for all your photos, resulting in a simple way to punch them up or share them. The service's unlimited -- albeit reduced quality -- storage has been a game changer for preserving memories. But Google started hinting at Photos' promise when it introduced tools to simply (or automatically) weave photos into sets of expression, such as collages, movies, or montages that string together photos and clips of video to music. It's similar to what was possible for years with Apple's Instant iMovie feature -- except nobody had time to create that.

The same algorithms that personalize movie recommendations and extract topics from oceans of text could bring doctors closer to diagnosing, treating and preventing disease on the basis of an individual's unique genetic profile. In a study to be published Monday, Nov. 7 in Nature Genetics, researchers at Columbia and Princeton universities describe a new machine-learning algorithm for scanning massive genetic data sets to infer an individual's ancestral makeup, which is key to identifying disease-carrying genetic mutations. On simulated data sets of 10,000 individuals, TeraStructure could estimate population structure more accurately and twice as fast as current state-of-the art algorithms, the study said. TeraStructure alone was capable of analyzing 1 million individuals, orders of magnitude beyond modern software capabilities, researchers said. The algorithm could potentially characterize the structure of world-scale human populations.

We have discussed some AI topics in the previous posts, and it should seem now obvious the extraordinary disruptive impact AI had over the past few years. However, what everyone is now thinking of is where AI will be in five years time. I find it useful then to describe a few emerging trends we start seeing today, as well as make few predictions around machine learning future developments. The following proposed list does not want to be either exhaustive or truth-in-stone, but it comes from a series of personal considerations that might be useful when thinking about the impact of AI on our world. Companies like Vicarious or Geometric Intelligence are working toward reducing the data burden needed to train neural networks.

If you're still getting your arms around deep learning, start with the very basics. This video series by Deep Learning TV provides an introduction that assumes no knowledge of math, programming, or statistics. The series starts with neural networks and deep learning concepts and later gets into techniques such as convolutional nets, restricted Boltzmann machines, deep belief nets, recurrent nets, autoencoders, and recursive neural tensor nets.

San Leandro-based Airspace Systems is making a business out of solving the toughest problems facing the emerging drone industry. The company designed a drone of its own, jam-packed with sensors and machine intelligence, to autonomously intercept threatening drones at high speeds and carry them away from large crowds. If you think this sounds difficult, you would be right. The company employs myriad technologies for its unmanned flying dogfighters that include computer vision, physics and some pretty serious engineering grit. To not only detect enemy drones, but predict where they will be in the future, CTO Guy Bar-Nahum, and a team of machine learning and computer vision experts, devised a creative method of training their machine learning frameworks using simulated test-flights.

As artificial intelligence (AI) continues to move from the summer of hype to the fall tech conference news cycle, mass confusion has begun on what AI can be used for. From fears of SKYNET, to hopes for the computer in StarTrek and Jarvis in Iron Man, the value will come from defining the proper outcomes. AI is more than just a fad. With a market size of $100B by 2025, Constellation sees the AI subsets of machine learning, deep learning, natural language processing, and cognitive computing taking the market by storm (see Figure 1). The disruptive nature of AI comes from the speed, precision, and capacity of augmenting humanity.

Data is the fuel that drives the machine learning algorithmic engines and ultimately provides the business value. In his session at Cloud Expo, Ed Featherston, a director and senior enterprise architect at Collaborative Consulting, discussed the key considerations around quality, volume, timeliness, and pedigree that must be dealt with in order to properly fuel that engine. Speaker Bio Ed Featherston is a director/senior enterprise architect at Collaborative Consulting. He brings 35 years of technology experience in designing, building, and implementing large complex solutions. He has significant expertise in systems integration, Internet/intranet, and cloud technologies, Ed has delivered projects in various industries, including financial services, pharmacy, government and retail.

Intel hosted a conference on Thursday to highlight its latest efforts to sell more microchips used to meet the booming demand for artificial intelligence, machine learning, and related disciplines. The overall strategy made sense to analysts who follow the company, but some of them were concerned that Intel has a ways to go to catch up to competitor Nvidia. The conference agenda included the announcement by Intel of a strategic partnership with Google across the search giant's many AI initiatives. Intel is creating versions of its chips optimized for Google's TensorFlow software that runs machine learning programs and neural networks, for example. Intel also explained how it would integrate a batch of acquisitions it has made in the AI area, such as using machine learning technology acquired with Nervana Systems in August for new chips called Lake Crest and Knights Crest.

General Electric builds jet engines and wind turbines and medical gear. But the 124-year-old industrial giant is also transforming itself for the digital age. It's fashioning software that pulls data from all this hardware, hoping to gain an insight into industrial operations that was never possible in the past. The problem is that analyzing all this data is difficult, and the talent needed to make it happen is scarce. So GE is going shopping.