AI is the cornerstone of the next generation of technology applications and is already on its way to infiltrating every part of our daily lives. As such, training AI from a diverse set of perspectives on the world is vital if we hope to advance AI in a way that makes the world a better place. The ever-important task of fostering diversity in the burgeoning AI community is a responsibility that falls upon all of us, not just corporate gatekeepers or select data scientists with advanced technical degrees. Matt Zeiler unveils groundbreaking new technologies that will transform the way AI is "taught" and make both teaching and using AI accessible to anyone in the world.

As Artificial Intelligence race is on, major tech companies are already developing Chatbots to serve their customer in a better way. Many customer services oriented businesses believe that Artificial Intelligence tool could help their companies. But are not sure if their business is sophisticated enough to implement Chatbots in their systems. While there are some imperatives for implementing an AI-based virtual assistant in your organisation, the entry barrier is much lower than many believe. Chatbots and Service Industry can go together till long extend to solve customer queries efficiently saving human cost and giving customers a pleasant and personalised experience.

Deep learning sounds daunting, but it's fast becoming a necessary technology in today's contextual world. Whether you're building simple bots or larger neural networks, a good understanding of artificial intelligence will help you succeed in your endeavor. If you're not even sure what deep learning is, you're definitely not alone. At its core is the building and programming of neural networks that allow machines to decipher speech or text to suit various needs. The best expression of deep learning comes with digital assistants such as Siri or Google Now.

I've been writing code on and off for a long time and used a variety of different languages and supporting technologies. From my perspective, there's been two distinct cycles of development technology during my time in the industry. I started out programming using 3GL's such as C and Pascal. On top of those languages we added ever increasing layers of abstraction to accelerate the development process. Ultimately we ended up using 4GL's such as PowerBuilder and Visual Basic.

The rise of the machines is coming to the sales process. Earlier this week – just hours before the kickoff of Oracle's annual conference OpenWorld – Salesforce unveiled Salesforce Einstein, an artificial intelligence (AI) solution built to work seamlessly across Salesforce platforms. The introduction of AI to the Salesforce ecosystem provides a wide range of uses for sales and marketing teams. But how, specifically, can sales leaders and their teams leverage AI to improve sales team performance and the customer experience? And how eager should sales leaders be about incorporating AI into the sales process?

Artificial intelligence programs, the Internet of Things, and next-level big data analytics tools are likely to start producing a significant impact on healthcare delivery as early as 2017, say participants in a new Silicon Valley Bank survey. The poll, which includes responses from 122 health IT company founders, executives, and investors attending a recent event, indicates a general belief that big data will continue to be a primary driver of innovation in the healthcare industry, but may run into adoption challenges and regulatory hurdles in the near future. Forty-six percent of participants said that big data will have the greatest impact on healthcare over the next year, followed by 35 percent who believe artificial intelligence (AI) will be a major game-changer. AI tools may have a broad range of applications across the healthcare spectrum, including patient engagement and customer relations, chronic disease management, clinical decision support, and sophisticated big data mining and analytics for diagnostics, population health management, and financial modeling. Many current attempts to develop artificial intelligence for healthcare are based on semantic computing or cognitive computing techniques that require vast stores of big data to fuel algorithms that try to mimic the complexity and predictive capabilities of human thought.

If you're a software engineer (or someone who's learning the craft), chances are that you've heard about deep learning (which we'll sometimes abbreviate as "DL"). It's an interesting and rapidly developing field of research that's now being used in industry to address a wide range of problems, from image classification and handwriting recognition, to machine translation and, infamously, beating the world champion Go player in four games out of five. A lot of people think you need a PhD or tons of experience to get a job in deep learning, but if you're already a decent engineer, you can pick up the requisite skills and techniques pretty quickly. Important point: You need to have motivation and be able to code and problem solve well. Here at Deepgram we're using deep learning to tackle the problem of speech search.

Imagine if you could analyze trillions of data points using machine learning algorithms to come up with a list of the absolute best startups to invest in. That's no small task as there are an estimated 23,000 startups in Silicon Valley alone. One startup called SignalFire is doing just that by taking unstructured data from over 2 million data sources and then using machine learning algorithms to pick the best startups to invest in. Wouldn't you be the least bit curious to know which companies they picked? We were extremely interested to know, so we had one of our on-staff PhDs take a look on Crunchbase and lo and behold, 8 startups were listed that SignalFire has invested in so far.

We'll look at some of the most important papers that have been published over the last 5 years and discuss why they're so important. The first half of the list (AlexNet to ResNet) deals with advancements in general network architecture, while the second half is just a collection of interesting papers in other subareas. The one that started it all (Though some may say that Yann LeCun's paper in 1998 was the real pioneering publication). This paper, titled "ImageNet Classification with Deep Convolutional Networks", has been cited a total of 6,184 times and is widely regarded as one of the most influential publications in the field. Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton created a "large, deep convolutional neural network" that was used to win the 2012 ILSVRC (ImageNet Large-Scale Visual Recognition Challenge).

Microsoft announced today that it will make Adobe Marketing Cloud the preferred marketing service for its Dynamics 365 Enterprise offering. The new partnership will give customers a powerful, comprehensive marketing service for intelligent business applications, Microsoft says. First announced in July ahead of its Worldwide Partner Conference, Microsoft Dynamics 365 will become available to customers in the coming weeks. It is a new, Azure cloud-hosted combination of the software giant's CRM (customer relationship management) and ERP (enterprise resource management) solutions. The goal is clear enough: To modernize these capabilities and bring them to market as a public cloud business management service. Like its on-premises Dynamics offerings, Dynamics 365 allows customers to plug a wide array of business apps and services, customizing the solution according to their industry and other needs.