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To start, Apple has crafted a system that uses onboard cameras to identify objects even in tricky situations, such as when raindrops cover the lens. It can estimate the position of a pedestrian even if they're hidden by a parked car. Other additions included giving cars direction through simultaneous localization and mapping, creating detailed 3D maps using car sensors and decision-making in urgent situations (say, a wayward pedestrian). It's still not certain if or how Apple will commercialize its self-driving know-how. At the moment, its next goal is to produce driverless employee shuttles.


Hillary Clinton says America is 'totally unprepared' for the impact of AI

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Hillary Clinton has warned that the US is "totally unprepared" for the economic and societal effects of artificial intelligence. Speaking to radio host Hugh Hewitt this week in an interview promoting her recent book, the former Secretary of State said the world was "racing headfirst into a new era of artificial intelligence" that would affect "how we live, how we think, [and] how we relate to each other." In a short segment near the end of the interview, Clinton told Hewitt: "A lot of really smart people, you know, Bill Gates, Elon Musk, Stephen Hawking, a lot of really smart people are sounding an alarm that we're not hearing. And their alarm is artificial intelligence is not our friend." Clinton then mentioned two specific areas of impact: digital surveillance (when "everything we know and everything we say and everything we write is, you know, recorded somewhere") and job automation.


What are the future trends for AI in business? IDG Connect

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Michael Rovatsos is an AI researcher at the University of Edinburgh and former Director of the Centre for Intelligent Systems and Applications. I spoke to him about AI and business and its impact on society and business. I think it's important to distinguish between: There are a lot of'data science' applications that can be envisioned under the second group, whereas the first is something more specific, and, in many cases, much more ambitious. Please login or register to view your article. If you do not have or do not remember your password, please click on the "Forgotten your password?"


JPMorgan's head of data science & machine learning on the reality of his job

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J.P. Morgan is one of the most advanced banks when it comes to data science and machine learning. It hired in Geoffrey Zweig from Microsoft in February 2017 as head of machine learning. It's actually launched a market-making product (LOXM) based on machine learning and it recently promoted Samik Chandarana, a former credit trader, to head its data science and analytics (effectively its machine learning) strategies. Chandarana hasn't actually started his new job yet – he's still a trader, but he'll be starting it soon and in an interview posted on J.P. Morgan's Youtube channel, he expresses various opinions about what it will entail. The bottom line, as Saeed Amen explained in a recent blog, is that machine learning and data science jobs in investment banks aren't necessarily as exciting as they seem.


Top 10 challenges for implementing marketing mix models

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Marketing Mix Modeling refers to statistical methods that attribute product performance to various marketing efforts. In the article below I describe the 10 most difficult challenges my team deals with when tackling these models. In subsequent articles I will discuss the different model choices along with their associated pros/cons. Enjoy the article and please comment at the bottom when you are finished. Below are the top 10 challenges faced by modelers of media mix.


Using Tensorflow and Pytorch in Pycharm on Windows 10

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Pytorch installation on Windows is a pain and Tensorflow isn't available on Python 2.7 for windows which ensues in a nice segue to the solution… You can use this blog post either as a reference guide to reinstall your bash and expiate changing permissions for ssh host keys and messing with chmod on the command line like I did,or as a starting point to put aside dogma and try something new and interesting. Going on a wild goose chase to reinstall modules or packages and restore everything by making a myriad of setting changes, gets me super nettled. It's cumbersome and tedious, so to save my time and sanity, lest it should happen in the future again, I've gathered a cornucopia of commands and install guidelines to ensure a clean and successful workspace for Machine Learning and Deep Learning code. A good IDE is conducive to efficient and effective coding practices. One of the best IDE for all things Python related, that I have come across, is Pycharm.


90% of people think AI will take away the jobs of other people

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In July, we surveyed 1,600 Quartz readers--from 84 countries, though the majority of those who chose to take part came from the US--for their opinions about artificial intelligence, including about their perceptions of job loss to AI and robots. People were anxious; 90% of responders thought that up to half of jobs would be lost to automation within five years. That's a lot, more than most of the studies conclude, include studies conducted by Oxford University (pdf) and McKinsey Global Institute. But, paradoxically, we found that everyone thought it was going to happen to someone else. In our survey, 91% don't think there's any risk to their job and 94% don't think they'll be working for an AI boss--but 48% think they'll have an AI employee (all within five years).


Artificial intelligence institute in pipeline

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Mumbai: The Maharashtra government signed a memorandum of understanding (MoU) with Wadhwani Institute of Artificial Intelligence (WIAI), California through the University of Mumbai (MU) in order to set up a research institute for artificial intelligence (AI) in Mumbai. AI is the theory and development of computer systems that makes them competent for performing tasks that usually require human intelligence. The skills involved include visual perception, speech recognition, decision-making and translating languages. According to MU officials, the artificial institute will be a non-profit for applied research and development projects ranging from pure engineering to future-oriented projects. "The institute will be used for social good and the benefit of the interested students," said an official.


Distributing control of deep learning training delivers 10x performance improvement

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My IBM Research AI team and I recently completed the first formal theoretical study of the convergence rate and communications complexity associated with a decentralized distributed approach in a deep learning training setting. The empirical evidence proves that in specific configurations, a decentralized approach can result in a 10x performance boost over a centralized approach without additional complexity. A paper describing our work has been accepted for oral presentation at the NIPS 2017 Conference, one of the 40 out of 3240 submissions selected for this. Supervised machine learning generally consists of two phases: 1) training (building a model) and 2) inference (making predictions with the model). The training phase involves finding optimal values for a model's parameters such that error on a set of training examples is minimized, and the model generalizes to new data.


3 Ways to Become Better at Machine Learning

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In this video, I give 3 tips on how you can become better at machine learning. The tips are: 1. Stay updated with what's currently state of the art in machine learning through reading research papers and news.