Category Archives: Machine Learning

In 2019, The World Economic Forum forecasted that data analysts would be in high demand by 2020, and so far this year were seeing the prediction become a reality. The fact is, as much as companies would love to hire dozens or even hundreds of highly trained data scientists - even in todays challenging economic climate - the skill set is so highly sought after that it can be both difficult and costly to find and integrate the right people.

This is where the role of the data analyst comes in. Many companies have invested in automated machine learning (AutoML), which has enabled them to automate the process of applying machine learning to solve business challenges. What this means is that a wider variety of data analysts, who are not necessarily highly trained data scientists and who may have broader business skill sets, can access and use data more freely.

The move to AutoML is also being driven by the fact that its becoming increasingly recognised that organisations using AI cannot improve the business-led insight generated from that AI without improving the access to it. More people need access to data sources, the models being fed by data, and to data-driven analytics.

Data needs to be democratised. Were past a point where its acceptable for data access to be restricted only to highly trained data scientists well-versed in manipulating it. If we want to see the mass business benefits of data-driven analytics, data in all its various guises needs to make it outside of the confines of the data science lab and into the hands of a new generation of data analysts and business users.

In this article, we discuss how AutoML and new businesses operational models are influencing and accelerating the rise of the data analyst in this new decade.

The shift has meant that AutoML now has a broader scope to help democratise data science in general, meaning that its becoming easier for data analysts to get involved in the data-to-insights pipeline. While AutoML is not going to replace data scientists, it does mean that data analysts can be self-guided through feature creation, feature selection, model creation and comparison, and even operationalisation. What this means is that AutoML drives self-serve, augmented analytics, which can add efficiency to large swaths of the data pipeline.

At a very high level, AutoML is about automating the process of applying machine learning. Early on, AutoML was almost exclusively used for the automatic selection of the best-performing algorithms for a given task and for tuning the hyperparameters of said algorithms.

While this has been very helpful for data scientists, until recently, it hadnt improved data access or insights for data analysts or business users, who still may be reliant on data scientists to build machine learning based models in code. However, the emphasis on AutoML has shifted to making machine learning more accessible by automatically building models without the help of data scientists.

In the last two years of the previous decade, one of the biggest operational shifts that became apparent in technology-driven businesses was the continued convergence of data science and business intelligence. It was certainly a far cry from more traditional operational models, where organisations employed separate teams standard business intelligence (dashboards, reports, data visualisation, SQL) and data science (statistical models, R/Python.)

Their reasoning is logical: in bringing data science and business intelligence practices together, companies effectively form real-time, centralised access to what may have previously been disparate sources of data. This growing convergence and/or closer collaboration between data science and analytics teams has empowered more people to become data analysts, often referred to as citizen data scientists.

But dont let the term fool you: citizen data scientists come in many forms, and their data analysis skills are empowering business insight in very important ways. Their roles can include the Data Translator, who is bridging the technical expertise of data engineers and data scientists with the operational expertise of marketing, supply chain, manufacturing, risk, and other industry domains.

We are also seeing Data Explorers, who focus on identifying and connecting to new data sources, merging and preparing data, and building production-ready data pipelines. Data Modellers are responsible for building predictive models and generating either a product or a service from those models, and then implementing them.

Regardless of the nature of these new roles, there is a common theme: unlike the data scientists of the previous decade, analysts dont need to master all the intricacies of advanced machine learning and feature engineering. What they bring to the table is an intimate knowledge of the problems at hand and the business questions that need to be answered.

Heads of business units have traditionally had a more difficult time accessing data analytics, and have to specifically request reports and analysis from the data scientists on a case-by-case basis. The next evolution will be for machine learning itself to become more self-serviced. Deployment and maintenance of models will become more and more easy and automated, as will many analytic tasks.

By integrating self-service machine learning into their core business strategies, innovative companies are enabling data analysts to use real-time data at scale to make better and faster decisions throughout their organisations.

Its clear that AI maturity and its resulting data-driven insight cannot improve without expanding the breadth of people that have access to and work with data on a day-to-day basis. Its exciting to see companies prioritise a cultural shift toward a data-driven culture and the economic imperative of data insights. As the new decade progresses, were set to see this continue as one of the more powerful analytics trends that are already transforming business in 2020.

Alexis Fournier, Director of AI Strategy, Dataiku

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The new decade and the rise of AutoML - ITProPortal

Tesla has released a few impressive videos of its Autopilot-powered emergency braking feature helping to avoid running over inattentive pedestrians.

What might be even more impressive is that the automaker says that it sees those events happen every day.

Theres a lot of talk about Tesla Autopilot, but one of the least reported aspects of Teslas semi-autonomous driver-assist system is that it powers a series of safety features that Tesla includes for free in all cars.

One of those features is Emergency Automatic Braking.

We saw the Autopilot-powered safety feature stop for pedestrians in impressive tests by Euro NCAP last year, but now we see it perform in real-world scenarios and avoiding potentially really dangerous situations.

Tesla has now released some examples of its system braking just in time to save pedestrians.

The new videos were released by Andrej Karpathy, Teslas head of AI and computer vision, in a new presentation at the Scaled Machine Learning Conference.

It was held at the end of February, but a video of the presentation was just released (starting when he shows the videos):

In the three video examples, you can see pedestrians emerging from the sides, out of the field of view, and Teslas vehicles braking just in time.

Tesla is able to capture and save those videos, thanks to its integrated TeslaCam dashcam feature.

Karpathy says:

This car might not even have been on the Autopilot, but we continuously monitor the environment around us. We saw that there was a person in front and we slammed on the brake.

The engineer added that Tesla is seeing a lot of those events being prevented by its system:

We see a lot of these tens to hundreds of these per day where we are actually avoiding a collision and not all of them are true positive, but a good fraction of them are.

In the rest of the presentation, Karpathy explains how Tesla is applying machine learning to its system in order to improve it enough to lead to a fully self-driving system.

I think its important to bring attention to these examples considering if an accident happens on Autopilot, it gathers so much attention from the media.

Lets see how many of them run with this story.

But I get it. People love crashes a lot more than a near-miss.

On another note, I really like how Karpathy communicates Teslas self-driving effort. His presentations are always super clear and informative, even for people who are not super experienced in machine learning.

In order for TeslaCam and Sentry Mode to work on a Tesla, you need a few accessories. We recommendJedas Model 3 USB hub(now also available for Model Y) to be able to still use the other plugs and hide your Sentry Mode drive. For the drive, Im now usinga Samsung portable SSD, which you need to format, but it gives you a ton of capacity, and it can be easily hidden in the Jeda hub.

What do you think? Let us know in the comment section below.

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Tesla releases impressive videos of cars avoiding running over pedestrians - Electrek

Microsoft claims a machine learning models its built for software developers can distinguish between security and non-security bugs 99% of the time.

Microsoft plans to open-source the methodology behind a machine learning algorithm that it claims can distinguish between security bugs and non-security bugs with 99% accuracy.

The company developed a machine learning model to help software developers more easily spot security issues and identify which ones need to prioritized.

By pairing the system with human security experts, Microsoft said it was able to develop an algorithm that was not only able to correctly identify security bugs with nearly 100% accuracy, but also correctly flag critical, high priority bugs 97% of the time.

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The company plans to open-source its methodology on GitHub "in the coming months".

According to Microsoft, its team of 47,000 developers generate some 30,000 bugs every month across its AzureDevOps and GitHub silos, causing headaches for security teams whose job it is to ensure critical security vulnerabilities don't go missed.

While tools that automatically flag and triaged bugs are available, sometimes false-positives are tagged or bugs are classified as low-impact issues when they are in fact more severe.

To remedy this, Microsoft set to work building a machine learning model capable of both classifying bugs as security or non-security issues, as well as identifying critical and non-critical bugs "with a level of accuracy that is as close as possible to that of a security expert."

This first involved feeding the model training data that had been approved by security experts, based on statistical sampling of security and non-security bugs. Once the production model had been approved, Microsoft set about programming a two-step learning model that would enable the algorithm to learn how to distinguish between security bugs and non-security bugs, and then assign labels to bugs indicating whether they were low-impact, important or critical.

Crucially, security experts were involved with the production model through every stage of the journey, reviewing and approving data to confirm labels were correct; selecting, training and evaluating modelling techniques; and manually reviewing random samples of bugs to assess the algorithm's accuracy.

Scott Christiansen, Senior Security Program Manager at Microsoft and Mayana Pereira, Microsoft Data and Applied Scientist, explained that the model was automatically re-trained with new data to it kept pace with the Microsoft's internal production cycle.

"The data is still approved by a security expert before the model is retrained, and we continuously monitor the number of bugs generated in production," they said.

"By applying machine learning to our data, we accurately classify which work items are security bugs 99 percent of the time. The model is also 97 percent accurate at labeling critical and non-critical security bugs.

"This level of accuracy gives us confidence that we are catching more security vulnerabilities before they are exploited."

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Developers: This new tool spots critical security bugs 97% of the time - TechRepublic

Microsoft has revealed how it's applying machine learning to the challenge of correctly identifying which bug reports are actually security-related.

Its goal is to correctly identify security bugs at scale using a machine-learning model to analyze just the label of bug reports.

According to Microsoft, its 47,000 developers generate about 30,000 bugs a month, but only some of the flaws have security implications that need to be addressed during the development cycle.

Microsoft says its machine-learning model correctly distinguishes between security and non-security bugs 99% of the time. It can also accurately identify critical security bugs 97% of the time.

SEE: 10 tips for new cybersecurity pros (free PDF)

The model allows Microsoft to label and prioritize bugs without necessarily throwing more human resources at the challenge. Fortunately for Microsoft, it has a trove of 13 million work items and bugs it's collected since 2001 to train its machine-learning model on.

Microsoft used a supervised learning approach to teach a machine-learning model how to classify data from pre-labeled data and then used that model to label data that wasn't already classified.

Importantly, the classifier is able to classify bug reports just from the title of the bug report, allowing it to get around the problem of handling sensitive information within bug reports such as passwords or personal information.

"We train classifiers for the identification of security bug reports (SBRs) based solely on the title of the reports," explain Mayana Pereira, a Microsoft data scientist, and Scott Christiansen from Microsoft's Customer Security and Trust division in a new paper titled Identifying Security Bug Reports Based Solely on Report Titles and Noisy Data.

"To the best of our knowledge this is the first work to do so. Previous works either used the complete bug report or enhanced the bug report with additional complementary features," they write.

"Classifying bugs based solely on the tile is particularly relevant when the complete bug reports cannot be made available due to privacy concerns. For example, it is notorious the case of bug reports that contain passwords and other sensitive data."

SEE: Zoom vs Microsoft Teams? Now even Parliament is trying to decide

Microsoft still relies on security experts who are involved in training, retraining, and evaluating the model, as well as approving training data that its data scientists fed into the machine-learning model.

"By applying machine learning to our data, we accurately classify which work items are security bugs 99% of the time. The model is also 97% accurate at labeling critical and non-critical security bugs. This level of accuracy gives us confidence that we are catching more security vulnerabilities before they are exploited," Pereira and Christiansen said in a blogpost.

Microsoft plans to share its methodology on GitHub in the coming months.

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Microsoft: Our AI can spot security flaws from just the titles of developers' bug reports - ZDNet

Roundup OpenAI Microscope: Neural networks, often described as black boxes, are complicated; its difficult to understand how all the neurons in the different layers interact with one another. As a result, machine learning engineers have a hard time trying to interpret their models.

OpenAI Microscope, a new project launched this week, shows that it is possible to see which groups of neurons are activated in a model when it processes an image. In other words, its possible to see what features these neurons in the different layers are learning. For example, the tools show what parts of a neural network are looking at the wheels or the windows in an image of a car.

There are eight different visualisations that take you through eight popular models - you can explore them all here.

At the moment, its more of an educational resource. The Microscope tools wont help you interpret your own models because they cant be applied to custom neural networks.

Generating the millions of images and underlying data for a Microscope visualization requires running lots of distributed jobs, OpenAI explained. At present, our tooling for doing this isn't usable by anyone other than us and is entangled with other infrastructure.

The researchers hope that their visualisation tools might inspire people to study the connections between neurons. Were excited to see how the community will use Microscope, and we encourage you to reuse these assets. In particular, we think it has a lot of potential in supporting the Circuits collaborationa project to reverse engineer neural networks by analyzing individual neurons and their connectionsor similar work, it concluded.

Don't stand so close to me: Current social distancing guidelines require people to stay at least six feet away from each other to prevent the spread of the novel coronavirus.

But how do you enforce this rule? Well, you cant really but you can try. Landing AI, a Silicon Valley startup led by Andrew Ng, has built what it calls an AI-enabled social distancing detection tool.

Heres how it works: Machine learning software analyses camera footage of people walking around and translates the frames into a birds eye view, where each person is represented as a green dot. A calibration tool estimates how far apart these people or dots are from one another by counting the pixels between them in the images. If theyre less than six feet apart, the dots turn red.

Landing AI said it built the tool to help the manufacturing and pharmaceutical industries. For example, at a factory that produces protective equipment, technicians could integrate this software into their security camera systems to monitor the working environment with easy calibration steps, it said.

The detector could highlight people whose distance is below the minimum acceptable distance in red, and draw a line between to emphasize this. The system will also be able to issue an alert to remind people to keep a safe distance if the protocol is violated.

Landing AI built this prototype at the request of customers whose businesses are deemed essential during this time, a spokesperson told The Register.

The productionization of this system is still early and we are exploring a few ways to notify people when the social distancing protocol is not followed. The methods being explored include issuing an audible alert if people pass too closely to each other on the factory floor, and a nightly report that can help managers get additional insights into their team so that they can make decisions like rearranging the workspace if needed.

You can read more about the prototype here.

Amazon improves Alexas reading voice: Amazon has added a new speaking style for its digital assistant Alexa.

The long-form speaking style will supposedly make Alexa sound more natural when its reading webpages or articles aloud. The feature, built from a text-to-speech AI model, introduces more natural pauses as it recites paragraphs of text or switches from one character to another in dialogues.

Unfortunately, this function is only available for customers in the US at the moment. To learn how to implement the long-form speaking style, follow the rules here.

Zoox settles with Tesla over IP use: Self-driving car startup Zoox announced it had settled its lawsuit with Tesla and agreed to pay Musks auto biz damages of an undisclosed fee.

Zoox acknowledges that certain of its new hires from Tesla were in possession of Tesla documents pertaining to shipping, receiving, and warehouse procedures when they joined Zooxs logistics team, and Zoox regrets the actions of those employees, according to a statement. As part of the settlement, Zoox will also conduct enhanced confidentiality training to ensure that all Zoox employees are aware of and respect their confidentiality obligations.

The case [PDF], initially filed by Teslas lawyers last year, accused the startup and four of its employees of stealing proprietary documents describing its warehouses and operations, and attempting to get more of its employees to join Zoox.

NeurIPS deadline extended: Heres a bit of good news for AI researchers amid all the doom and gloom of the current coronavirus pandemic: the deadline for submitting research papers to the annual NeurIPS AI conference has been extended.

Now, academics have until 27 May to submit their abstracts and 3 June to submit their finished papers. It can be hard to work during current lockdown situations as people juggle looking after children and their jobs.

Due to continued COVID-19 disruption, we have decided to extend the NeurIPS submission deadline by just over three weeks, the program chairs announced this week.

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Who knows the secret of the black magic box? Boffins seek the secrets of AI learning by mapping digital neurons - The Register

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