Apple haters have always made the case that the company's massive success is as much the product of marketing and salesmanship as it is any kind of technical innovation. Whatever else Apple cofounder Steve Jobs was, he was the consummate salesman. Maybe the original iPhone could have sold itself back in 2007, but Jobs' legendary introductory event definitely helped. But the world has changed. As smartphone innovation seems to have plateaued, the tech giants of the world, notably Google, Microsoft, and Facebook, have doubled down on machine learning and artificial intelligence -- the trendy technology that's making for smarter, more personalized apps and devices.

Others are fed enough data and leverage machine learning, using natural language processing in a way that can pick up keywords and phrases to understand human language. We have some great technologies like the IBM Watson API, Facebook bot API, and Microsoft Bot Framework that can help developers create interactive bots based on different taxonomies. Soumith Chintala of Facebook AI noted in a recent article, "Deep learning -- neural networks that have several stacked layers of neurons, usually accelerated in computation using GPUs -- has seen huge success recently in many fields such as computer vision, speech recognition, and natural language processing, beating the previous state-of-the-art results on a variety of tasks and domains such as language modeling, translation, speech recognition, and object recognition in images." Google announced that its natural language processing system called Parsey MacParseface is now able to identify 94 percent of word dependencies within an English sentence.

There's a lot of chatter about chatbots these days and how we might be able to use them in the future. The biggest question seems to be whether chatbots can be useful enough to convincingly replace human conversation. Before chatbots can reach that point, they'll need to develop and mature into a technology that enables human communication with a computer using natural language. Most bots today are not at the level where they can flawlessly replicate conversation. Some chatbots today are not fed enough data.

Not content with helping cure cancers and winning Jeopardy, Watson wants to get inside our heads and our homes, whispering instructions into our wireless headsets and helping us do our laundry. IBM will work with appliance maker Whirlpool, TV and camera company Panasonic, wireless headphone designer Bragi and Withings owner Nokia to add Watson's cognitive computing capabilities to their products, the company said. Those cognitive capabilities could help devices talk with one another, or with us. For example, a washing machine could tell a dryer what program to use for the clothes it has just washed, or tell its owner when to order more detergent. Computer vision techniques could help security cameras distinguish between friends and strangers or identify suspicious activity.

Today, a lot of high-profile deep machine learning projects in the cloud are powered by GPUs; specifically NVIDIA GPUs. Even Facebook uses them for its own machine learning work behind-the-scenes. GPUs are able to handle the massive amounts of computing power required to train deep neural networks that facilitate these projects. But Huawei deputy chairman and rotating CEO Eric Xu believes the future of machine learning lies in dedicated processors. Read on to find out more. In the past few years, NVIDIA has made a big push to dethrone CPUs in the deep machine learning space.

Apple haters have always made the case that the company's massive success is as much the product of marketing and salesmanship as it is any kind of technical innovation. Whatever else Apple cofounder Steve Jobs was, he was the consummate salesman. Maybe the original iPhone could have sold itself back in 2007, but Jobs' legendary introductory event definitely helped. But the world has changed. As smartphone innovation seems to have plateaued, the tech giants of the world, notably Google, Microsoft, and Facebook, have doubled down on machine learning and artificial intelligence -- the trendy technology that's making for smarter, more personalized apps and devices.

In this special guest feature, Victor Amin, Data Scientist at SendGrid, advises that businesses implementing machine learning systems focus on data quality first and worry about algorithms later in order to ensure accuracy and reliability in production. After graduating cum laude from Princeton University, Victor earned a PhD studying applications of machine learning to quantum chemistry at Northwestern University. At SendGrid, Victor builds machine learning models to predict engagement and detect abuse in a mailstream that handles over a billion emails per day. It's obvious that you need data before you can implement a machine learning system, but project planners often overlook questions regarding training set collection, cleaning, and maintenance. There are so many sources of big data in today's business systems that it seems like getting enough of the right data ought to be easy!

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We've had robots building cars on assembly lines way before they could drive them. Likewise, marketers have so far been able to automate basic repeatable tasks but the creative or strategic parts of marketing – which include content – have benefited only minimally from advancements in automation. It's only now that they can finally start to turn to artificial intelligence (AI) systems to help them not just work faster but also work smarter. Twenty plus years ago, I created a machine-learning neural network that was designed to predict which stocks were most likely to rise in the next 12 months. The bot – though we didn't call it that at the time – crunched thousands of data points on stock performance, company financials and economy trends to learn correlations that no human beings could establish. In reality, I'm not sure what the system really understood but I did.

Apple's Siri upgrades will let you dictate LinkedIn messages, verbally search for shoe photos on Pinterest, and hail an Uber, hands-free. When Apple unveils its new iPhone Wednesday, it's expected to push hard on software improvements available for its next mobile operating system, iOS 10, available for current iPhones and a predicted new iPhone model. The iPhone's voice-activated digital assistant Siri will be a key part of this upgrade, say tech analysts and developers. Siri, primarily used for voice commands to dial phone numbers, set reminders and get traffic information, has been opened to third-party developers after five years of existence. Apple, via a spokeswoman, said app developers are creating "completely new ways for users to interact" within apps.