This article was written by Monica Rogati. Monica is an independent data science executive and advisor. She built key data products and teams at Jawbone and LinkedIn; she now helps companies make the most out of their data. Do a project you care about. Make it good and share it.

Gradually yet steadily, technology has taken over all aspects of our life. And the financial services sector is no exception. Financial Services spanning investments, lending and management of assets are a fundamental part of fund management for individuals as well as corporations. One of the nuances of this sector is the volatility associated with it owing to factors such as prevailing market conditions, political scenario, performance of stocks, taxation norms, etc. Since this condition is a given, companies dealing with such financial instruments need to glean reams of data before counselling clients on the right investment choice or the right kind of loan to opt, for instance.

There are so many tools, platforms and resources available, MLEs can focus their time on solving problems critical to their field or company instead of worrying about building platforms and hand rolling numerical algorithms. Google Cloud has easy means of building and deploying TensorFlow models including their new TPU support in beta, AWS has an ever evolving suite of deep learning AMIs and Nvidia has a great deep learning SDK. In parallel, Apple's coreML and Android's NN API make is simpler and faster to deploy models on phones; this will continue to push the boundary for developing and releasing ML apps. With all of the above, there is healthy competition among big players in the cloud space pushing the whole ecosystem forward. And yet, most of them are finding ways to collaborate towards open standards like ONNX.

Workers at Amazon have demanded that their employer stop the sale of facial recognition software and other services to the US government. In a letter addressed to Amazon CEO Jeff Bezos and posted on the company's internal wiki, employees said that they "refuse to contribute to tools that violate human rights," citing the mistreatment of refugees and immigrants by ICE and the targeting of black activists by law enforcement. The letter follows similar protests at Google and Microsoft. "As ethically concerned Amazonians, we demand a choice in what we build, and a say in how it is used," says the letter, first reported by The Hill. The employees (it's not clear how many signed the letter) refer to the sale of computer services by IBM to the Nazis as a worrying parallel.

Ibrahim Diallo was fired from his job by the company's AI system, as managers scrambled to figure out the problem and watched him be escorted from the building. In what many are considering a grave warning of the potential consequences of AI in the workplace, a man was fired by his company's AI system. Worst of all, his human managers "stood helpless" throughout the firing process. Nobody had any real answers at first, and no human was able to keep the man from being escorted out of the building by security. Ibrahim Diallo, a programmer for a company in Los Angeles, described his "automated" firing in a viral blog post, described the New Zealand Herald.

The participants in Spafax's first hackathon were split into two teams and given two days to create a basic working prototype for a digital product aimed at improving the passenger experience. One was challenged to experiment with chatbots, while the other looked at new ways to harness machine learning. By applying a new technology called Language Understanding Intelligence Service (LUIS) to a chatbot, the team found that, with a little training the bot could start to intelligently identify new greeting phrases not previously input by the development team. Using Microsoft Azure's Cognitive Services, the team was also able to incorporate speech and image recognition into the chatbot. For example, they created a bot where you could upload an image of a famous actor.

Sixty-two years ago this summer, Dartmouth professor John McCarthy coined the term artificial intelligence. Joi Ito, director of MIT's Media Lab, has come to think it's unhelpful.

To boost sales in an overly competitive environment, many top brands have turned to new, inventive, and unconventional ways. They have embraced the use of Artificial Intelligence as a marketing strategy to create sales content, interact with customers, and for cybersecurity reasons. Sephora, for example, uses their Kik Messenger chatbot to communicate with clientele, and give them suggestions about upcoming Sephora offers based on the client's preference. The popularity of AI in retail is steadily growing, with 60% of ecommerce brands expected to implement the use of AI before 2018 ends. AI's appeals lie in its efficiency to market like or even better than humans, and the ability to help companies cut costs.

The notion of artificial intelligence is something which has long excited technological society. Among the various stories constructed around it are (as in the film Terminator) those of robots ruling the world with humans fighting a losing battle against them. Gary Kasparov, the world chess champion, was matched against an IBM supercomputer named Deep Blue in 1996 and 1997 under tournament conditions and lost in the 1997 rematch. Since chess is, in the popular imagination, the height of intellectual prowess, this created quite a stir and it was anticipated by the popular press that humankind would eventually have to make way for a greater intelligence -- one which it had itself created. 'AI' is a fairly broad term which includes a number of unglamorous capabilities that fall far short of defeating a reigning chess champion.

Since the last couple of years, Microsoft has been working on an ambitious goal to make Artificial Intelligence (AI) accessible and valuable to every individual and organization. The company has been infusing the power of AI across several of its products and services to extend its capabilities and to improve productivity. Here are some of the Office 365 features that are powered by AI. The personalized search experience uses signals from the Microsoft Graph to provide tailored search results across content and people in an organization based on a user's work patterns. The redesigned results are easier to explore and allow users to preview files with support for over 270 different file types without ever leaving the search page.