Netflix + data. Accountability in AI. TfL chat bot. http://cognitionx.com/news-briefing/.
As Brexit talks continue, one big question is being hotly debated: what exactly should the closing of borders mean for the movement of EU citizens’ data?
A House of Lords committee has said in a report that restricting the movement of EU citizens’ data after Brexit would hurt trade and security cooperation, and transitional arrangements should be made by the Government to keep information flowing after Britain leaves the bloc.
As TechCrunch reported, the UK’s digital minister, Matt Hancock, said the government’s aim is to “ensure unhindered data flows after Brexit”.
Stewart Room, PwC’s global data protection legal services leader, said that “it is vitally important for businesses and the economy that transitional arrangements are put in place and that the UK continues to have an influential role in Europe on data protection going forwards”.
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Netflix has collected an impressive amount of data on Hollywood entertainment, made possible by tracking the viewing habits of its more than 90 million members. In 2013, Netflix took an educated guess based on that data to stream its own original series, risking its reputation and finances in the process.
Matthew Schroyer from DZone asks: if we could analyse a fraction of the Netflix data, could we gain some insight into Netflix’s overall OS strategy? What could we learn from visualising this data, specifically, in a network diagram? He takes on this challenge, providing great insight on how Netflix is leveraging data to craft awesome original content.
Nauto, the automotive tech startup that uses a combination of artificial intelligence, cameras, motion sensors, and GPS to understand and improve driver behavior, has raised $159 million from a diverse group of investors that includes General Motors, venture capital firm Greylock Partners, and the Softbank Group Corp.
The company has developed an aftermarket inward-facing camera that attaches to the windshield of vehicles. The system captures video and processes the abundant amount of data it collects in real-time and then provides personalized feedback to fleets and their drivers, to help reduce collisions.
Dr Sandra Wachter, in a recent blogpost, discusses the impact GDPR will have on accountability and the transparency of algorithmic decision-making. She argues that it is important to start a public debate now,
before the regulation comes into force. Making the “right to explanation” (of all decisions made by automated or artificially intelligent algorithmic systems) legally binding should be the first step in this direction.
All her research led her (and her research team) to conclude that the GDPR is likely to only grant a ‘right to be informed’ about the existence of automated decision-making and about system functionality (logic involved) as well as the significance (scope) and the envisioned consequences (intended purpose e.g. setting credit rates which can impact payment options) rather than offering reasons for how a decision was reached.
In this post, TransportforLondon explores the reasons for introducing a conversational bot and their learnings around the design of conversation.
By giving the opportunity to ask a question as you would in a normal conversation, they reduce the amount of steps someone needs to take to get to a specific piece of information. Also, the bot is helping them to understand the ways that customers talk about our products and services. For the first time, they are able to see when customers aren’t getting what they expect and change a product to suit specific needs instantly and to respond to
changing needs continuously.
As we mentioned a couple of days ago, Musk doubled down on his stance at a meeting of the National Governors Association this weekend, telling state leaders that AI poses an existential threat to humanity.
To get some perspective on Musk’s comments, Discover reached out to computer scientists and futurists, such as Oren Etzioni and Fei-Fei Li, working on the very kind of AI that the tech CEO warns about. Although there is a general agreement that we need to keep AI in check, some remarked that his comments were ‘alarmist’ or a ‘distraction’ from the real issues.
With a new application announced on Tuesday called Hire, Google is taking aim at the small- and medium-sized business part of the recruiting market. Its pitch is simple: for recruiters who already spend much of their day in Gmail and Google Calendar, they can schedule interviews in Hire much faster and with a lot fewer clicks.
The company plans to announce machine learning tools later this year that will allow, among other use cases, for recruiters to resurface strong candidates for previous positions when new ones open up. The app is the first role-specific attempt of its kind for Google, which announced a Google for Jobs initiative at its I/O conference to tackle job boards and listings.
Zeta Global, the data-driven marketing technology innovator whose SaaS-based marketing cloud helps leading brands acquire, retain and grow customer relationships, announced that it has acquired Boomtrain, a machine learning driven marketing technology platform that uses AI to drive personalized messaging across all digital touchpoints.
Boomtrain developed the first marketing technology platform with machine intelligence at its core, enabling brands to better understand and communicate with their customers.
In the fall of 2016, Spandan Madan was a Teaching Fellow (Harvard’s version of TA) for “Advanced Topics in Data Science (CS209/109)” at Harvard University. He was in charge of designing the class project given to the students, and this tutorial has been built on top of the project he designed for the class.
This tutorial breaks down the whole pipeline and leads the reader through it step by step in an hope to empower you to actually use ML, and not just feel that it was not too hard. Needless to say, this will take much longer than 15-30 minutes.
This infographic created by Villanova University School of Business Online details the challenges of big data, including 1) enormous amounts of data created, 2) the growth of data usage within businesses, 3) Big Data investments, 4) challenges of Big Data initiatives.