IEEE publishes draft report on'ethically aligned' AI design AMD's strange, cube-shaped device for deep learning packs four'Vega' GPUs Microsoft officially launches Zo.ai, more bot-building tools Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

However, many businesses may ask; are Chat Bots an innovation and investment too far? What should be the strategy for building a successful Bot? How to make the Bot human-like? A human-like Bot will require the use of text analytics, artificial intelligence and sentimental analysis in varying degrees. The study answers these questions and highlights a way to classify and explain the range of available Bot solutions.

At a dinner I attended some years ago, the distinguished differential geometer Eugenio Calabi volunteered to me his tongue-in-cheek distinction between pure and applied mathematicians. A pure mathematician, when stuck on the problem under study, often decides to narrow the problem further and so avoid the obstruction. An applied mathematician interprets being stuck as an indication that it is time to learn more mathematics and find better tools. I have always loved this point of view; it explains how applied mathematicians will always need to make use of the new concepts and structures that are constantly being developed in more foundational mathematics. This is particularly evident today in the ongoing effort to understand "big data"--data sets that are too large or complex to be understood using traditional data-processing techniques.

As the tech world pushes forward with the development of artificial intelligence, the Institute of Electrical and Electronics Engineers (IEEE) is asking everyone to pause and consider the ethical ramifications. It's kicked off that process by publishing the first draft of a document that explores a range of ethical challenges posed by AI. The document, "Ethically Aligned Design: A Vision for Prioritizing Human Wellbeing with Artificial Intelligence and Autonomous Systems (AI/AS)," was drafted by committees of the IEEE's Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems. More than one hundred global thought leaders and experts on AI, ethics and related issues are contributing to the initiative. With the first draft published, the IEEE is inviting public comment on the document. "We need to make sure that these technologies are aligned to humans in terms of our moral values and ethical principles," the report's executive summary says.

Come join us to learn about local Artificial Intelligence (AI) research and the future of AI. Our panelists will discuss their research in machine learning, intelligent interaction, and mechanism design. Our panelists will discuss how their field has evolved, common misconceptions, their thoughts on the rate of progress towards general AI applications, and how advances in AI will impact society.

At long last, Microsoft has set a public target of bringing consumer devices--that aren't PCs--to market in 2017 with its personal digital assistant, Cortana, inside of them. On Tuesday, Microsoft announced a way to challenge Amazon's Alexa-powered Dot and Echo devices, as well as Google's Google Home. Microsoft's New Devices SDK will allow to power a new line of connected-car speakers from Harmon Kardon in 2017, executives said, with presumably more partnerships to come. This follows an earlier announcement that Cortana is coming to Windows 10 IoT, Microsoft's operating system for smart devices. Why this matters: Though Apple, Microsoft, and Google all have prominent artificial-intelligence initiatives, it's been Amazon--and, to a lesser extent, Google--that have designed devices around their technology.

In this post, we are going to introduce you to the Support Vector Machine (SVM) machine learning algorithm. We will follow a similar process to our recent post Naive Bayes for Dummies; A Simple Explanation by keeping it short and not overly-technical. The aim is to give those of you who are new to machine learning a basic understanding of the key concepts of this algorithm. Support Vector Machines – What are they? A Support Vector Machine (SVM) is a supervised machine learning algorithm that can be employed for both classification and regression purposes. SVMs are more commonly used in classification problems and as such, this is what we will focus on in this post. SVMs are based on the idea of finding a hyperplane that best divides a dataset into two classes, as shown in the image below.

It was penned by an AI developed at the University of Toronto, which builds on deep learning capabilities like those of Google's DeepDream. Instead of turning images into hallucination nightmare dogs, this project took seasonal inputs and kicked out its best approximation of a real human carol. It's called "Neural Karaoke," because good science is unnerving science. To prepare, the AI was given 100 hours of Christmas music, 50 hours of song lyrics, and video feed from the game Just Dance. So the thing can dance too.

On a nondescript street in downtown San Francisco, an unremarkable parking garage with dusty walls and painted-over windows seems like an abandoned warehouse. Row upon row of shiny self-driving cars await their moment. At first glance, they look like regular dark-gray Volvo SUVs and white Ford sedans. But they're decked out with rectangular sci-fi contraptions on their roofs. The Uber logo is emblazoned across their doors.

If you're relatively new to Machine Learning and it's applications, you'll more than likely have come across some pretty technical terms that are often difficult for the novice mathematician/scientist to get their head around. Following on from a previous blog, (10 Common NLP Terms Explained for the Text Analysis Novice), we decided to put together a list of 10 Machine Learning terms which have been broken down in simple English, making them easier to understand. So, if you're struggling to understand the difference between Supervised and Un-supervised Learning you'll enjoy this post. A subfield of computer science and artificial intelligence (AI) that focuses on the design of systems that can learn from and make decisions and predictions based on data. Machine learning enables computers to act and make data-driven decisions rather than being explicitly programmed to carry out a certain task.