Progress in artificial intelligence and machine learning has been impressive, but there is still much work to be done to advance learning science. While some progress is being made to bring artificial intelligence to the education space as described above, these efforts pale in comparison to advancements in the non-education space. Most of the exciting breakthroughs in 2015 were in fields outside of education. For example, companies such as Amazon and UPS have been piloting the use of drones to deliver packages and other goods to customers. Google recently purchased an AI software company, DeepMind, from a British startup for half a billion dollars.

The interplay between AI and Sentiment analysis is also a new area. There are already many synergies between AI and Sentiment analysis because many functions of AI apps need sentiment analysis features. "The common interest areas where Artificial Intelligence (AI) meets sentiment analysis can be viewed from four aspects of the problem and the aspects can be grouped as Object identification, Feature extraction, Orientation classification and Integration. The existing reported solutions or available systems are still far from being perfect or fail to meet the satisfaction level of the end users. The main issue may be that there are many conceptual rules that govern sentiment and there are even more clues (possibly unlimited) that can convey these concepts from realization to verbalization of a human being."

These days, all investors want to talk about is artificial intelligence. AI is creeping its way into more and more aspects of the business world and daily life, and global funding for AI-based startups grew some 60% from 2015 to 2016. Many people in Silicon Valley -- some investors included -- don't even understand how AI works. But if they don't see it in your business plan, you're not getting their money. While this may seem like another investor fad, there's plenty of substance behind the expectation.

Chatbots are taking over the world. Companies want them, developers are creating them, and more important, people are eager to try them. The use cases and experiences a chatbot can bring seem to be unlimited -- there are bots for managing group purchases, bots for helping people to obtain their immigration visas, and even bots for tracking flights. So, eventually the hype got me and I decided to build my first chatbot. My idea for this chatbot was to see how I could manually train and use a machine learning model through a chat-based interface, while trying several ideas and concepts that could enhance the experience of using a machine learning model in this way.

Fueled with purpose and optimism they go against all odds to create a product that should exist in the world. But they all make the same mistake. "We're building an AI-driven, machine learning, chat bot, with big data and a conversational interface." The problem isn't their product, it's the way they talk about their product. They talk about its features.

The Islamabad based AI startup is testing "Romulus" as spotted by some users on the Kik messaging platform. The arrival of "Romulus" comes ten months after Microsoft released a millennial-minded chatbot called "Tay" but removed it within 24 hours after the bot expressed racist and inappropriate leanings to some users. Those testing "Romulus" notice a more secure chatbot, and one that doesn't like talking about certain subjects like politics, for example. The chatbot learns from human interactions to respond emotionally and intelligently, offering a viewpoint, in addition to manners and emotional expressions. Till now, Romulus has been found very good at normal conversations.

Nvidia's new partner in bringing AI-powered self-driving tech to the masses definitely has the experience needed to go truly mass-market – it's Bosch, leading tier one auto industry supplier. Bosch will build an AI supercomputer designed for use in vehicles using Nvidia tech, which means Nvidia now has a partner that works as a tier one supplier to all major car maker in the world. It's only the latest partner for Nvidia's AI-powered self-driving car tech, which also include automakers like Audi and Mercedes-Benz, but it's the one that could potentially have the most impact in terms of giving Nvidia reach and influence across the industry. This is the kind of strategic tie-up that lets both partners do what they do best – Nvidia can focus on developing the core AI supercomputing tech, and Bosch can provide relationships and sales operations that offer true scale and reach. Nvidia's deep learning model does not depend on specific rules being coded for each individual situation; instead, it provides the systems with a number of examples from human behavior, and then the AI can determine on its own what to do in specific scenarios. The mid-step implementation of this tech is Nvidia's AI co-pilot, which will allow the vehicle to work with a human driver to understand where their attention is directed and provide warnings about undetected hazards, as well as read a driver's lips and use audio cues to understand commands regardless of the in-vehicle noise environment.

Cater to the "market of the one" -- this has always been the holy grail of marketing. Brands and marketers have always strived to understand individual consumer necessities and tried to cater to them directly through an open dialog, at scale. While this was long a pipe-dream, with the advent of deep neural networks, the current crop of machine learning algorithms, and advancements in artificial intelligence (AI) research, the age-old spray and pray marketing is coming to an end. Now, with machine learning, brands have a good shot of being truly coherent in their narrative and engaging consumers with a consistent voice, tailored to individuals across omnichannel end-points. To break it down, let's take a concrete example of advertising a kid's video game, such as "Plants vs. Zombies -- Garden Warfare 2" and compare the two marketing options. In the marketing world, the best course of action for such a game would involve defining the genre of the game, the intended audience behavior and the market segment to advertise.

"It is mind-boggling the amount of data our media consumption generates across all our devices. Making sense of it all becomes very difficult with a human-sized brain – you get lost in the details" Nicolas Bidon is Xaxis' global president and he's talking about the future of programmatic at Mobile World Congress in Barcelona. MWC is an apt setting for discussions on The Future. Away from these gigabytes of invention, the calm of Xaxis' networking garden is cool relief. So, how has programmatic evolved and what's next for the industry?