Infrastructure investment is on the forefront of political agendas around the world. From the Asian Infrastructure Investment Bank (AIIB) spearheaded by China to the pro-infrastructure themes that united an otherwise divisive presidential campaign in the United States, the policy focus is on transportation, telecommunications, utilities and so on around us. The expected surge in such infrastructure projects is estimated to fill a gap of $8 trillion across Asia alone, and around $1 trillion in the U.S. But this policy spotlight must also now be lifted above us to outer space, where newspace industries are building, extending and connecting a space-based infrastructure to the planet as never before. Newspace is taking it another step forward, transforming the prospects for the economics, societal and national security domains.

Machine learning has tremendous potential to help organizations create powerful and unique customer experiences, as well as make better and more informed decisions. So in October 2016 we conducted a survey to find out what is the state of machine learning and artificial intelligence. Download the infographic to discover some of the highlights.

Algorithms are now used throughout the public and private sectors, informing decisions on everything from education and employment to criminal justice. But despite the potential for efficiency gains, algorithms fed by big data can also amplify structural discrimination, produce errors that deny services to individuals, or even seduce an electorate into a false sense of security. Indeed, there is growing awareness that the public should be wary of the societal risks posed by over-reliance on these systems and work to hold themaccountable. Various industry efforts, including a consortium of Silicon Valley behemoths, are beginning to grapple with the ethics of deploying algorithms that can have unanticipated effects on society. Algorithm developers and product managers need new ways to think about, design, and implement algorithmic systems in publicly accountable ways.

One of the basic features of artificial intelligence (AI) is the ability to recognize images and process them. Companies like Microsoft and Google have debuted tools to show how accurate their image recognition platforms are. Now it seems that Amazon wants in as well as they have announced Amazon Rekognition. This is an image recognition service that is part of a suite of deep-learning services that Amazon has recently announced for developers. For the most part it does what most image recognition services do, which is to identify human faces, identify emotions, and label objects just by looking at it.

Machine learning is an exciting space in the field of artificial intelligence (AI) and has been a stalwart of computing machine efforts since the beginning of integrating computers with enterprises. Yet some organizations are increasingly overwhelmed with too much "big data", new technologies and figuring out how to keep everything secure. As one top IT security expert who wished to remain anonymous noted, "The increase in communications and information accumulation will require not just one program to filter the financial data professionals need from the avalanche of data available but automation of automation of such processes." Such a program, or rather a platform, could scan fundamental information to compile complex datasets together and determine what info is important. Some examples would be determining influencing factors for price movement in global logistics and how weather impacts supply chains. Then the organization could attack weaknesses in that chain to make sure they meet upcoming product demand.

Amazon today announced the launch of its new Amazon AI platform at its re:Invent developer event in Las Vegas. This new service brings many of the machine learning smarts Amazon has developed in-house over the years to devs outside the company. For now, the service only makes three different tools available, but the plan is to add more over time. Amazon Web Services CEO Andy Jassy stressed that Amazon itself has a lot of background in machine learning, even though the company hasn't always talked about it. "We do a lot of AI in our company," he said.

Amazon today announced three new artificial intelligence-related toolkits for developers building apps on Amazon Web Services. At the company's AWS re:invent conference in Las Vegas, Amazon showed how developers can use three new services -- Amazon Lex, Amazon Polly, Amazon Rekognition -- to build artificial intelligence features into apps for platforms like Slack, Facebook Messenger, ZenDesk, and others. The idea is to let developers utilize the machine learning algorithms and technology that Amazon has already created for its own processes and services like Alexa. Instead of developing their own AI software, AWS customers can simply use an API call or the AWS Management Console to incorporate AI features into their own apps. AWS CEO Andy Jassy noted that Amazon has been building AI and machine learning technology for 20 years and said that there are now thousands of people "dedicated to AI in our business."

The key element which separates today's artificial intelligence (AI) systems and what we consider to be human thought and learning processes could be boiled down to no more than an algorithm. That's according to a recent paper published in the journal Frontiers in Systems Neuroscience, which suggests that despite the complexity of the human brain, an algorithm may be all it takes for our technological creations to mimic our way of thinking. As reported by Business Insider, the idea that human thought can be whittled down to an algorithm lies in the "Theory of Connectivity," which proposes that human intelligence is rooted in "a power-of-two-based permutation logic (N 2i-1)" algorithm, capable of producing perceptions, memories, generalized knowledge and flexible actions, according to the paper. First proposed in 2015, the theory suggests that how we acquire and process knowledge can be explained by how different neurons interact and align in separate areas of the brain. It may also be that our brain power is based on "a relatively simple mathematical logic," according to Dr. Joe Tsien, neuroscientist at the Medical College of Georgia at Augusta University and author of the paper. The logic proposed, N 2i-1, relates to how groups of similar neurons come together to handle tasks such as recognizing food, shelter, and threats.

When you think about artificial intelligence (AI), robots, androids and other futuristic technologies may come to mind. And while some of the most successful tech companies like Facebook, Amazon and Google have built their success on industry-changing applications of artificial intelligence, the concept is still fairly new to content marketing teams. In the most basic sense "artificial intelligence" broadly refers to the processes and technologies that are created to teach machines to perform intelligent tasks. For content marketers, "intelligent tasks" typically refer to algorithms designed to process data. This goes far beyond automation, which is where the majority of marketing technology supports marketing teams today.

Technology has progressed by such leaps and bounds that we have computers that untangle spoken language, recognize faces, and even turn down our thermostat when we're away from home. But what does all this have to do with your customer's experience? Unless you closely follow tech trends, you might be surprised to learn that AI is already playing a role in customer service, user experience, inventory planning, and other areas. First, let's consider how AI is changing and improving things on the business side. Some of these we already know about -- for example, the Internet of Things and its mass of interconnected data-gathering devices.