Algorithms have become a ubiquitous and essential part of business operations. Uber uses algorithms to determine customer demand and set pricing accordingly, while Amazon and Netflix use algorithms to nudge their customers to purchase a product or stream a new video they might like. And these examples are just the tip of the iceberg. Interestingly, the use of these algorithms can not only increase an enterprise's internal efficiency, but often algorithmic software or machine learning algorithms can also be used to deepen consumer loyalty and trust. Viewers on Netflix trust algorithms to deliver content they'll enjoy, just as customers trust Amazon to offer only useful products for purchase.

Amy is an artificial intelligence-powered personal assistant for scheduling meetings. Users can interact interact with her as they would to any other person – and Amy takes care of all the tedious email ping pong that comes along with scheduling a meeting. Users simply copy their virtual assistant of choice on email with up to four individuals they wish to schedule a meeting, then the assistant takes over and coordinates the schedules using natural language processing, providing an experience that is entirely seamless. The x.ai team rely on MongoDB, the operational database that powers Amy, to provide the speed and schema-flexibility they require to build the platform. As Matt Casey, CTO and a co-founder x.ai explains: "We didn't actually know what that data would look like, so with the schema-less databases, [it was] easy to change our schemas over time very quickly and try new things."

Facebook's new servers for artificial intelligence research, inside the company's data center in Prineville, Oregon (Source: Technology Review) Lee Se-dol, one of the world's top Go players, won just one of the matches against the AlphaGo program, missing out on the 1 million prize up for grabs. (March 2016)

These are just some of the scenarios that Hollywood has dreamt up, which are giving artificial intelligence (AI) a bad name. We've even seen Stephen Hawking express his doubts about AI, saying: "Creating AI will be the biggest event in human history… it might also be the last." It seems that a notion has developed that robots and the AI behind them are out to get us. So how accurate is the glitz and glam of Hollywood, or indeed the doom and gloom of Professor Hawking? It's easy to get caught up in the apocalyptic view Hollywood presents, but the reality, as you would expect, is very different – AI actually offers many benefits.

We are currently looking for an exceptionally talented and passionate Senior Data Scientist to work closely with product engineering teams to identify development opportunities, strategies and architect plans, and develop algorithmic solutions. We are looking for someone who can generate valuable and actionable insights by utilising advanced statistical techniques and data mining approaches to improve decision making. Also analysing and interpreting the results of new product development experiments, with the ability to communicate the findings to key stakeholders, gaining support and'buy in' for any recommendations. The role As a Senior Data Scientist you will build well maintained models of customer behaviours that influence programmatic decision making. Then evolve and enhance systems and tools for agile data analysis and visualisation.

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.