présentation


Blog: Huawei looks for privacy boost from machine learning - Mobile World Live

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Machine learning was a topic which came up multiple times throughout Apple's WWDC presentation, with the company's CoreML technology giving developers access to on-device processing which will enable apps to "predict, learn and become more intelligent". "Because we control the native apps, the system, the chipsets, we have the capacity to run things on the device and not necessarily to upload information to the cloud," Coutelle continued. Of course, as long as Huawei is running Android, Google will have a significant influence over the products and services offered by the device, which can make differentiation a challenge. Of course, any discussion with Huawei which includes a discussion about privacy needs to include the all-important China question.


Lenovo's Bendable Yoga-Style Laptop Revealed: 5 Features Including Voice-Control, Stylus

International Business Times

Voice-input: The device will work with voice command instead of a traditional track pad. Voice commands on smartphones, despite being highly developed in the form of voice assistants such as the Google Assistants and Siri are not considered the primary way of input. Flexibility: This is the part that makes Lenovo's announcement special -- a foldable, rollable PC is something that hasn't been attempted before and not without reason -- there are many working parts of a PC including a large battery and mostly a fan-based cooling system that will be difficult to put into this kind of a form factor, even if you have a PC-size flexible display. "This is more than just design or look and feel…it's how you can speak to it… or how its speaks to you," the company's presentation stated.


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His team was divided in three levels of hierarchy: besides a couple of highly skilled mathematicians, several mathematicians with less sophisticated skills, he also hired sixty to eighty hairdressers. In 2011 computers were getting one in four images wrong; there were improvements where the red line is (chart), and it only gets better afterwards: image recognition catches up with human abilities. And again with speech recognition (which is particularly interesting considering this wasn't possible for 20 years of using hidden Markov models; ASR only improved when scientists started implementing deep neural networks): For the longest time, one question has lingered: what will automation do to human workforce as computers get better and better? De Prony took humans and employed humans to do mind-numbing tasks.


There is one thing that computers will never beat us at

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Gaspard de Prony, a mathematician and engineer, decided to approach the task by creating logarithmic and trigonometric tables. His team was divided in three levels of hierarchy: besides a couple of highly skilled mathematicians, several mathematicians with less sophisticated skills, he also hired sixty to eighty hairdressers. Since that time, we've made fantastic improvements with this: we've replaced tables with billions of silicon circuits; we've got better and better with algorithms, and the result we've got now in 2017 is that there are many domains where computers are catching up with human abilities. De Prony took humans and employed humans to do mind-numbing tasks.


Halftime: Nairobi Women in Machine Learning and Data Science.

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Fast Forward to about a month later, we met and talked to Jessica Colaco, co-founder, Brave venture labs about our plan to establish a community for people starting out, working in and with interests in Machine Learning and Data Science. The main reason why we set up this community was to create a safe space for women interested in Data Science and Machine Learning to learn, grow, have fun and network. A big thank you to Africa's Talking who have let their cool space be our home as well as Brave Venture labs, Google Kenya and Intel for their support during different events. Also, thanks to Google Kenya, we will in every quarter host a meetup targeted at growing the community of Machine Learning and Data Science experts using Google tools and libraries such as TensorFlow.


Architecture of Data Science Projects

@machinelearnbot

In this article, I summarize the components of any data science / machine learning / statistical project, as well as the cross-dependencies between these components. By models, I mean testing algorithms, selecting, fine-tuning, and combining the best algorithms using techniques such as model fitting, model blending, data reduction, feature selection, and assessing the yield of each model, over the baseline. It also includes calibrating or normalizing data, imputation techniques for missing data, outliers processing, cross-validation, over-fitting avoidance, robustness testing and boosting, and maintenance. Typically, data scientists use Python, R or Java, and SQL.


These Startups Think the Future of the Music Industry is Artificial Intelligence

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The most prominent theme of the day, however, was how companies are innovating the ways in which people make, experience, and monetize music using artificial intelligence. Two of the startups in Techstars Music, Amper Music and PopGun, use AI to create custom music content, though each for different contexts. Since all tracks come from Spotify, artists generate track streams, thus making DJ mixes an actual source of revenue. Speaking to THUMP after the presentation, pop-music AI startup PopGun's Nolan recalled that these concerns surfaced early on during conversations with industry people, causing his team to second-guess themselves.


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Using technologies such as Machine Learning (ML), content can be made available to meet individualized needs. The cloud is emerging as a way to provide services such as storage and AI that can make content, including valuable video content, available for repurposing and monetization. Google's ML tools include TensorFlow and its Cloud Machine Learning Engine. In addition to the Google keynote there was an entire session at CS 2017 looking at the role of AI in creating metadata for video content.


Boost Your Data Munging with R

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Base R introduced the data.frame Unlike commonly used databases which store data row by row, R data.frame stores the data in memory as a column-oriented structure, thus making it more cache-efficient for column operations which are common in analytics. First argument in query order(carrier, -dep_delay) will select data in ascending order on carrier field and descending order on dep_delay measure. Fast overlap join joins datasets based on periods and its overlapping handling by using various overlaping operators: any, within, start, end. Also, the community support has grown over the years, recently reaching the 4000-th question on Stack Overflow data.table The below plot presents the number of data.table This article provides chosen examples for efficient tabular data transformation in R using the data.table The actual figures on performance can be examined by looking for reproducible benchmarks.


The Machines Aren't Coming, They're Already Here Overit

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AI can be used to power voice assistants, chatbots, integrated home technology, robots, and even self-driving cars. Purna later went on to discuss how more and more often, humans are beginning to rely on artificial intelligence and how further development of natural language AI and machine learning would help to achieve higher tech adoption rates. Within her presentation, she demonstrated the latest Cortana features, which could intelligently make calendar scheduling decisions, intuitively power online voice search and interpret natural language conversations much more fluidly than in previous versions. Purna also showed off the machine taught chatbot features that Microsoft is currently powering with its full suite of developer tools, essentially providing a brain to the otherwise useless chatbots we've seen in recent years.