Artificial intelligence and machine learning is rapidly being incorporated into our daily lives, but there's an underlying problem that's not being addressed. Diversity in the machine learning field may minimize these types of incidents by ensuring wholly represented data sets are used during the most critical states of AI development. In a recent New York Times article, Kate Crawford [Principle Researcher at Microsoft and Visiting Professor at the MIT Center for Civic Media] stated that unless we are vigilant about how we design/train machine learning systems, we will "see ingrained forms of bias built into the artificial intelligence of the future." I recognized that I have landed in a great spot to influence change in Artificial Intelligence, by bringing my diverse experience to the research I am working on and encouraging my fellow peers to take an interest in the field as well.

In my role as a machine learning intern, I go to work every day and start my job. I turn on my computer and start looking at my next tasks. But what was quickly unavoidable is the realization that the field of Machine Learning is not very diverse. In this article, I hope to outline why as a black woman, helping to make the next intelligent robot is a massive deal. And why we need to bring more underrepresented groups into this ever important field.

As the final moments of Rutger Hauer's tears in the rain monologue come to a close in Blade Runner, Netflix (or your streaming service of preference) has lined up some recommendations for your next viewing choice. From 2001: A Space Odyssey to The Matrix, the site's algorithms find you similarly cerebral films that you may enjoy…or you may not. The stakes are low in this situation. If you end up watching and disliking The Matrix, chances are you won't cancel your monthly subscription; you will simply be more skeptical of Netflix's algorithmic recommendations in the future and continue on with your day as if nothing happened. In the B2C environment, machine learning is a constant presence in the end user's experience.

For instance, chatbots powered by AI are able to field and answer questions from customers on a variety of subjects, from generating recommendations on gift purchases to locating the nearest Chinese restaurant. As with other forms of automation, some have questioned whether AI will replace customer service reps and people in other types of customer-facing roles. But just as customers have increased their use of digital channels, agents aren't being replaced but instead are relied upon and trained to handle more complex interactions when customers want human assistance. We've only begun to scratch the surface for applying AI and machine learning to the customer experience. Self-driving cars that are powered by AI are moving closer to reality.

Jan: Astonishing increases in computing power and data availability in recent years have been the main benefactors of deep learning technology. Hitoshi: Some of the easily understandable applications, such as image recognition, video captioning and beating the world champion of Go, are pushing people hard to be excited. From a technical perspective, the generality and high accuracy that deep learning has is the main motivation for using it instead of other machine learning methods. In our case, for example, our AI engine learns how traders trade from the technical chart, no matter what kind of strategy or what kind of indicators they use. Alesis: The computational power and tools to utilize that power has definitely enabled the recent advancements in Deep Learning.

We recently held a heated debate at Digital Catapult; would Artificial Intelligence increase the number of jobs? In this blog post, Peter Karney, Head of Product Innovation and Darren Murphy, Digital Communities Manager, go head to head* to explore the big questions surrounding AI. Peter: AI can certainly reduce or eliminate menial activities. One example is a call centre. Currently if you need advice you'll speak to a human being; it's expensive and can be a complete waste of time. But you can put an AI system in that can learn, figure out what you're saying, and do context searches.

Slack's outstanding UX propelled the startup to unicorn status amidst a flurry of competitors, responsive design flourished and gave birth to a new era of mobile friendliness and device agnosticism, and the web as a whole experienced a shift in consciousness as sites became easier to use, apps became more intuitive to navigate, and services became all the more delightful and engaging to interact with. I am proud to say that the field has finally come of age and found itself. At long last, UX Evangelists, Digital Empaths, and Interaction Designers have risen to the highest echelons of the creative class to further the bleeding edge of technology, design, and user delight. With UX Evangelists like Tobias van Schneider, Jennifer Aldrich and Chase Buckley behind the wheel, we are steering towards a brighter future. A future where little big details bring about user delight at every corner, where device agnostic pixel perfection is the norm, and where simple day-to-day experiences engage, excite, and stimulate users in new and innovative ways. So where do you fit into all of this?

Predictive modeling is a formula that transforms a list of input fields or variables into some output of interest. Feature engineering is simply a thoughtful creation of new input fields from existing input fields, either in an automated fashion or manually, with valuable inputs from domain expertise, logical reasoning, or intuition. The new input fields could result in better inferences and insights from data and exponentially increase the performance of predictive models. Feature engineering is one of the most important parts of the data preparation process, where deriving new and meaningful variables takes place. Feature engineering enhances and enriches the ingredients needed for creating a robust model.

The truth is that innovation is really hard. And, by "innovation" I mean real innovation. Not a better mousetrap, but something that the market did not know that it needed, that then becomes adopted (and paid for) in a way in which we could have never imagined our lives without it. So, when it comes to innovation, the thought should be less about what companies are producing innovative products and services, but who - really - is doing the next generation of ideation and exploration. The founder, CEO and CTO of SpaceX, co-founder, CEO and product architect of Tesla, co-founder and chairman of SolarCity, co-chairman of OpenAI and - what many may not even remember - the co-founder of PayPal, is thinking on a whole other level.

Google DeepMind's artificial intelligence AlphaGo made history when it won the complex game of Go against Lee Sedol, one of the greatest world players. As Elon Musk pointed out at the time, experts in the field thought AI was a decade away from reaching that milestone. The momentous event showed that AI was gaining skills typically reserved for humans far faster than we expected. And that very fact could be a problem, Ian Pearson, a futurist with an 85% accuracy track record, told Tech Insider. "You could end up with superhuman machines going down that road," Pearson said.