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.

Traditionally (whatever that means in this context), machine learning has been executed in single processor environments, where algorithmic bottlenecks can lead to substantial delays in model processing, from training, to classification, to distance and error calculations, and beyond. Beyond recent technology-harnessing in neural networking training, much of machine learning - including both off-the-shelf libraries like scikit-learn and DIY algorithm implementation - has been approached without the use of parallel processing. The lack of parallel processing, in this context referring to parallel execution on a shared-memory architecture, inhibits the potential exploitation of large numbers of concurrently-executing threads performing independent tasks in order to achieve economy of performance. The dearth of parallelism is attributable to all sorts of reasons, not the least of which being that parallel programming is hard. Also, parallel processing is not magic, and cannot "just be used" in every situation; there are both practical and theoretical algorithmic design issues that must be considered when even thinking about incorporating parallel processing into a project.

When cars exit the tunnel of the next 15 years, they'll be like giant smartphones. Their sensors will capture sight, sound and motion and transmit the information to the Internet quickly and affordably. The $100-billion app economy built on data from smartphones would look small compared with the $750 billion in revenue produced around cars. The forecast has automakers buzzing. As they accelerate spending on developing self-driving cars, they're devoting enormous attention on what to do with data that those high-tech devices generate -- beyond making the drive automated.

We may be years away from the "AI-enabled Coworker," but the first implementations of machine-learning capabilities are finding their way into the everyday data-analysis tools used by businesses of all types. Cognitive assistance promises to reshape business processes, but only if app development and deployment tools are adapted to support machine learning. While it has become fashionable to hypeAIas the next game-changing technology promising to have an impact greater than either mobile or cloud, the reality is that machine learning will be a long time coming to everyday business analytics. As with any sea change, cognition is likely to sneak its way into applications and processes in drips and drops. It looks like 2017 could be the year many businesses get their first hands-on experience with cognitive-learning business apps.

We're going to be blunt: Chatbots in their current form aren't great. We were promised bots that would change the way we interact with businesses and services, but instead we have interactive bots that perform worse than apps. They are primarily focused on taps or interactive graphical interfaces, and conversing with them using natural language is nearly impossible. Take an example of Poncho Weather on Facebook Messenger. Let's say I'm going to a conference next Monday in San Diego and want to know what the forecast is.