In part 1 of this article we discussed the industry trend of companies wanting to brand themselves as "AI first" and often positioning themselves as deep learning. We highlighted some of the problems building and deploying a deep learning solution presents and suggest that often other machine learning approaches could provide a solution in a simpler and more cost effective way. In this second part we want to outline our own experience building an AI application and reflect on why we chose not to utilise deep learning as the core technology used. At Aiqudo we have built a personal digital assistant for smart phones. Our goal is to understand what users are saying, figure out their intent and execute the correct action for them on their devices..

Over a year ago, OpenAI, the San Francisco–based for-profit AI research lab, announced that it had trained a robotic hand to manipulate a cube with remarkable dexterity. That might not sound earth-shattering. But in the AI world, it was impressive for two reasons. First, the hand had taught itself how to fidget with the cube using a reinforcement-learning algorithm, a technique modeled on the way animals learn. Second, all the training had been done in simulation, but it managed to successfully translate to the real world.

There is a myriad of libraries, platforms and cloud-based services for Artificial Intelligence (AI). But directly programming your AI application on top of them makes your software too dependant of the specific infrastructure you chose. This is dangerous in such a fast-paced environment where new (and better) AI solutions pop up every day. How can you develop AI-enhanced software (also known as smart apps) or just pure AI components (neural networks, image recognition software, chatbots…) for a data science problem without learning each of the specific AI technologies and languages and preserving my ability to easily migrate my software over a new AI infrastructure? The solution won't surprise my readers: modeling AI comes to the rescue.

It's widely acknowledged that the recent successes of Deep Learning rest heavily upon the availability of huge amounts of data. Vision was the first domain in which the promise of DL was realised, probably because of the availability of large datasets such as ImageNet. The recent surge of simulators for RL further illustrates that as we push further to apply these techniques to real-world problems, data scarcity quickly becomes the bottleneck. In commercial contexts, this question comes up a lot. When time and money is at stake, it'd be useful to be able to make some concrete statements about how improvements in model architecture are likely to weigh up against simply gathering more data.

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Fralick chairs McAfee's Analytic Center of Excellence and is responsible for the cybersecurity company's technical analytic strategy that integrates into McAfee consumer and enterprise products. Prior to Intel's divestiture of McAfee, she was Chief Data Scientist in Intel's Internet of Things Group where she developed machine learning and deep learning analytics for over eight different markets. And while broadly applying the term AI makes it easier to market and talk about, for Fralick who has 40 years expertise in the field, each type of model under the AI umbrella has different levels of complexity and intelligence behind them. "As a data scientist and an engineer, I look at AI to be very specific mathematically. I look at deep learning to be very different mathematically from machine learning."

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You may have heard about deep learning and felt like it was an area of data science that is incredibly intimidating. How could you possibly get machines to learn like humans? And, an even scarier notion for some, why would we want machines to exhibit human-like behavior? Here, we look at 10 examples of how deep learning is used in practice that will help you visualize the potential. Both machine and deep learning are subsets of artificial intelligence, but deep learning represents the next evolution of machine learning.

Kuzushiji, a cursive writing style, had been used in Japan for over a thousand years starting from the 8th century. Over 3 millions books on a diverse array of topics, such as literature, science, mathematics and even cooking are preserved. However, following a change to the Japanese writing system in 1900, Kuzushiji has not been included in regular school curricula. Therefore, most Japanese natives nowadays cannot read books written or printed just 150 years ago. Museums and libraries have invested a great deal of effort into creating digital copies of these historical documents as a safeguard against fires, earthquakes and tsunamis. The result has been datasets with hundreds of millions of photographs of historical documents which can only be read by a small number of specially trained experts.