Fenwick & West was founded in 1972 in the heart of Silicon Valley--before "Silicon Valley" existed--by four visionary lawyers who left a top-tier New York law firm to pursue their shared belief that technology would revolutionize the business world and to pioneer the legal work for those technological innovations. In order to be most effective, they decided they needed to move to a location close to primary research and technology development. These four attorneys opened their first office in downtown Palo Alto, and Fenwick became one of the first technology law firms in the world.

Thanks to its ability to draw insights from huge and complex data sets at great speed, artificial intelligence (AI) is making the previously obscure into something measurable. It also offers the opportunity to create new experiences for consumers. But what does this mean in practice for marketers? Big data is nothing without equally big intelligence – that's true for people and for machines. It doesn't matter how much you know when you can't systematise and apply that knowledge to achieve something useful.

People respond differently to automated beings. Take my group of friends, for instance... The other day, while browsing online, I came across this clip of a robotic dog. When I shared it with the room, not everyone responded in the same way. Some felt sorry for Spot and sprang to its defence during the kicking episode, some thought it verged on creepy, while others were sceptical of its practical uses.

Artificial intelligence is hot, but also daunting. The latest advances -- known variously as cognitive computing, machine learning, and deep learning -- sound complicated and expensive. And they are, despite the enormous potential they bring to the marketplace. For many companies, the price tag and the commitment of resources are too high a hurdle. And for other reasons, even a giant like Apple is facing challenges.

Many brands these days count personalization as a top priority in customer experience. Customers value their time and look for efficiency, yet they still want experiences tailored to their needs. Automated service delivered through such technologies as IVR and click-to-chat buttons can ensure efficiency, and the prospect of connecting to an agent provides the human touch that customers continue to seek. Yet such practices may soon become a thing of the past with the emergence of chat bots, a power tool for brands that just may deliver the best of both automation and personalization accented by a human touch. Chat bots, defined simply, are a kind of service that people can interact with through a chat interface using textual or audio means. They possess an intelligence capability, such as the kind used in virtual assistant or live chat programs, and provide accurate answers through an artificial intelligence function.

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.