On October 14-15, 2016, the NYU Center for Mind, Brain and Consciousness in conjunction with the NYU Center for Bioethics will host a conference on "The Ethics of Artificial Intelligence". Recent progress in artificial intelligence (AI) makes questions about the ethics of AI more pressing than ever. Existing AI systems already raise numerous ethical issues: for example, machine classification systems raise questions about privacy and bias. AI systems in the near-term future raise many more issues: for example, autonomous vehicles and autonomous weapons raise questions about safety and moral responsibility. AI systems in the long-term future raise more issues in turn: for example, human-level artificial general intelligence systems raise questions about the moral status of the systems themselves.

This interview is part of a Machine Learning series. We invited Adam Geitgey, Director of Software Engineering at Groupon, to talk about the difference between machine learning and the older artificial intelligence effort and the progress we've made so far. JAXenter: How are you involved in machine learning? Adam Geitgey: My professional background is primarily in traditional software development, not machine learning. I've worked on scaling large-scale websites, building backend systems, building mobile apps and other things like that.

As machine learning increasingly affects domains protected by anti-discrimination law, there is much interest in the problem of algorithmically measuring and ensuring fairness in machine learning. Across academia and industry, experts are finally embracing this important research direction that has long been marred by sensationalist clickbait overshadowing scientific efforts. This sequence of posts is a sober take on the subtleties and difficulties in engaging productively with the issue of fairness in machine learning. Prudence is necessary, since a poor regulatory proposal could easily do more harm than doing nothing at all. In this first post, I will focus on a sticky idea I call demographic parity that through its many variants has been proposed as a fairness criterion in dozens of papers.

Earlier in the series, we learned that Salesforce (CRM) is increasingly interested in the AI (artificial intelligence) space. Recently, deep learning, machine learning, and AI have all taken the world by storm. AI is an umbrella term that encompasses natural language processing, machine learning, and robotics. Apart from Salesforce and IBM (IBM), Microsoft (MSFT) is also trying to capture AI market share with its recent acquisition of Genee. Plus, Amazon (AMZN) used its Alexa Fund to host a voice technology summit in late August to make a move toward developing its own virtual assistant offerings.

Sydney fintech startup Stockspot has become the first robo-advice platform in Australia to integrate AI and machine learning functions into financial advice. The company has today announced the launch of its new digital financial assistant to help clients optimise their investment behaviour. The digital financial assistant works by analysing thousands of clients and making targeted recommendations. As it learns, the technology will provide consumers with intuitive assistance to help investors optimise their profile and coach them to make better investment decisions. Instead of being a financial advisor that makes recommendations through listening to clients, Stockspot will now give advice based on consumer behaviour and actions.

If life is like a bowl of chocolates, you will never know what you will get, but is there a way to reduce some uncertainty? Dimensionality reduction is the process of reducing the number of random variables impacting your data. Come and explore, but make sure you don't let the chocolates melt.

Conversational Commerce refers to using natural language within a messenger application (Messenger, WhatsApp, WeChat, and others) or using voice assistants (Siri, Amazon Alexa, and others) to interact with a business for an inquiry, purchase, or customer service. The term Conversational Commerce was coined by Chris Messina, Developer Experience Lead at Uber, in a brief post over a year ago. While Conversational Commerce has been around for a while (think IVR and SMS) the recent success is the culmination of three key factors – emergence of the mobile-first customer, domination of messaging apps, and the increasing maturity of Artificial Intelligence. In the IAB Nielsen's Mobile Ratings Report 2015, findings indicate that Australians live in a mobile-first world: While the total time spent on commercial activities is at an average of 3%, this trend is poised for rapid growth as organisations mature their Conversational Commerce capabilities. Messaging apps like WhatsApp and WeChat are no longer just for conversation between friends and family.

Volvo Cars today announced a partnership with Swedish-American vehicle safety systems supplier Autoliv Inc. to form a new jointly-owned company to develop autonomous driving software. The planned company, which has yet to be named, will have its headquarters in Gothenburg, Sweden and an initial workforce of about 200 taken from both firms. The new company is expected to start operations in early 2017, and then grow to more than 600 employees. Autoliv develops and sells automotive safety systems for all major automotive manufacturers around the globe; it has 80 facilities with 60,000 employees in 29 countries. The new Volvo/Autoliv company will develop advanced driver assistance systems (ADAS) and autonomous drive (AD) systems for use in Volvo cars and for sale exclusively by Autoliv to other car makers globally.

The car company announced last week that it would no longer use a vision system provided by MobileEye, an Israeli company that supplies technology to many automakers. This comes a few weeks after the National Highway Traffic Safety Administration announced that it was investigating a fatal accident that occurred while one of Tesla's cars was operating in Autopilot mode, a system designed to enable automated driving under a driver's supervision. It is unclear why Tesla is dropping MobileEye, but one reason may be the emergence of newer approaches to automated driving. MobileEye provides what amounts to an advanced image-recognition system, capable of identifying road signs or obstacles, such as other cars or pedestrians, on the road ahead. The company has said that it uses deep learning, a popular machine-learning technique based on training a many-layered network of simulated neurons to recognize input using a large number of training examples.

Historian Francis Fukuyama in 1992 reckoned with the fall of the Iron Curtain and the replacement of Communist systems behind it with liberal democracies, we had reached the end of history. Can we say the same about software development? Has the arrival of the age of Agile meant that we can now talk about a similar full-stop? Is this the final word in software development? And if it's not, what's going to come after it?