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.

The meeting format typically alternates between lecture/paper discussions and lab sessions where we review code. In our lecture sessions we discuss and gain a better understanding of course lectures. In our lab sessions, we walk methodically through code from course assignments. We intend to expand our projects beyond the course material, based on the interests of the group. We welcome all new members and participants, regardless of experience level, who are excited about rolling up their sleeves to dig into Deep Learning.

In machine learning, you may often wish to build predictors that allows to classify things into categories based on some set of associated values. For example, it is possible to provide a diagnosis to a patient based on data from previous patients. Many algorithms have been developed for automated classification, and common ones include random forests, support vector machines, Naïve Bayes classifiers, and many types of neural networks. To get a feel for how classification works, we take a simple example of a classification algorithm – k-Nearest Neighbours (kNN) – and build it from scratch in Python 2. You can use a mostly imperative style of coding, rather than a declarative/functional one with lambda functions and list comprehensions to keep things simple if you are starting with Python. Here, we will provide an introduction to the latter approach.

There are many Python machine learning resources freely available online. Go from zero to Python machine learning hero in 7 steps! The first step is often the hardest to take, and when given too much choice in terms of direction it can often be debilitating. This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way. The prime objective of this outline is to help you wade through the numerous free options that are available; there are many, to be sure, but which are the best?

Businesses have come a long way from implementing conventional methods for successful operation and completion of tasks. With the evolution technology, various tools are increasingly being implemented to make the processes swifter and more efficient. This has helped in improving the productivity of businesses considerably. Artificial Intelligence (AI) is one of these tools that many businesses have benefited from. The rise of automation and machine learning have powered the increased adoption of artificial intelligence by businesses.

There is a pervasive underlying fear from generations raised on dystopian science fiction that artificial intelligence and robotics will be the undoing of humankind. Eventually, the conventional thinking goes -- even the likes of Elon Musk and Stephen Hawking are on board here -- artificial intelligence will become smarter than the organic variety and terrible things will happen as machines take over the planet. In reality, however, it's much more likely AI isn't going to destroy us -- or even take our jobs. In fact, it's very likely going to help us do our jobs better. Think about that for a moment.