All posts in “machine learning”

Machine learning can tell if you’re wearing swap-meet Louie


A wise man once said “The hat mighta had a L V on the back but at the swap meet that ain’t jack,” and now researchers can ensure that the Louis Vuitton or Prada or Coach you bought is the real deal. The system, which essentially learns the difference between real and fake products over time, uses a small microscope connected to a phone.

“The underlying principle of our system stems from the idea that microscopic characteristics in a genuine product or a class of products-corresponding to the same larger product line-exhibit inherent similarities that can be used to distinguish these products from their corresponding counterfeit versions,” said New York University Professor Lakshminarayanan Subramanian.

The researchers have commercialized the product as Entrupy Inc., a startup founded by Ashlesh Sharma, an NYU doctoral graduate, Vidyuth Srinivasan and Professor Subramanian. You can even buy the product now and run a few dozen authentications per month.

The system is non-invasive and does not damage the merchandise. Because it uses a “dataset of three million images” you can assess a material almost instantly. It takes about 15 seconds to test a product and it can distinguish fabrics, leather, pills, shoes and toys. It can even tell if electronics are authentic.

“The classification accuracy is more than 98 percent, and we show how our system works with a cellphone to verify the authenticity of everyday objects,” said Subramanian.

Entrupy has raised $2.6 million in funding and has apparently authenticated $14 million in real and fake purses, watches and other fancy stuff. I can definitely help out if you get angry and feel the need to begin sockin’ more fools than Patrick Swayze because they are selling bootleg purses.

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Clara Labs nabs $7M Series A as it positions its AI assistant to meet the needs of enterprise teams


Clara Labs, creator of the Clara AI assistant, is announcing a $7 million Series A this morning led by Basis Set Ventures. Slack Fund also joined in the round, alongside existing investors Sequoia and First Round. The startup will be looking to further differentiate within the crowded field of email-centric personal assistants by building in features and integrations to address the needs of enterprise teams.

Founded in 2014, Clara Labs has spent much of the last three years trying to fix email. When CC-ed on emails, the Clara assistant can automatically schedule meetings — reasoning around preferences like location and time.

If this sounds familiar, it’s because you’ve probably come across x.ai or Fin. But while all three startups look similar on paper, each has its own distinct ideology. Where Clara is running toward the needs of teams, Fin embraces the personal pains of travel planning and shopping. Meanwhile, x.ai opts for maximum automation and lower pricing.

That last point around automation needs some extra context. Clara Labs prides itself in its implementation of a learning strategy called human-in-the-loop. For machines to analyze emails, they have to make a lot of decisions — is that date when you want to grab coffee, or is it the start of your vacation when you’ll be unable to meet?

In the open world of natural language, incremental machine learning advances only get you so far. So instead, companies like Clara convert uncertainty into simple questions that can be sent to humans on demand (think proprietary version of Amazon Mechanical Turk). The approach has become a tech trope with the rise of all things AI, but Maran Nelson, CEO of Clara Labs, is adamant that there’s still a meaningful way to implement agile AI.

The trick is ensuring that a feedback mechanism exists for these questions to serve as training materials for uncertain machine learning models. Three years later, Clara Labs is confident that its approach is working.

Bankrolling the human in human-in-the-loop does cost everyone more, but people are willing to pay for performance. After all, even a nosebleed-inducing $399 per month top-tier plan costs a fraction of a real human assistant.

Anyone who has ever experimented with adding new email tools into old workflows understands that Gmail and Outlook have tapped into the dark masochistic part of our brain that remains addicted to inefficiency. It’s tough to switch and the default of trying tools like Clara is often a slow return to the broken way of doing things. Nelson says she’s keeping a keen eye on user engagement and numbers are healthy for now — there’s undoubtedly a connection between accuracy and engagement.

As Clara positions its services around the enterprise, it will need to take into account professional sales and recruiting workflows. Integrations with core systems like Slack, CRMs and job applicant tracking systems will help Clara keep engagement numbers high while feeding machine learning models new edge cases to improve the quality of the entire product.

“Scheduling is different if you’re a sales person and your sales team is measured by the total number of meetings scheduled,” Nelson told me in an interview.

Nelson is planning to make new hires in marketing and sales to push the Clara team beyond its current R&D comfort zone. Meanwhile the technical team will continue to add new features and integrations, like conference room booking, that increase the value-add of the Clara assistant.

Xuezhao Lan of Basis Set Ventures will be joining the Clara Labs board of directors as the company moves into its next phase of growth. Lan will bring both knowledge of machine learning and strategy to the board. Today’s Clara deal is one of the first public deals to involve the recently formed $136 million AI-focused Basis Set fund.

Google Play Music gets more personalized with New Release Radio, customized to your tastes


It’s no longer enough to simply offer on-demand music as part of the value proposition for music streaming services – you have to enable discovery of new tunes and make recommendations, too. While it’s fair to say that Spotify is leading the market in terms of its influential playlist selections, rivals are quickly following suit – including Apple Music and Google Play Music. This week, the latter rolled out its own curated mix, New Release Radio, which it says is crafted with an eye towards the individual listener’s tastes.

The company had first tested the feature through its global partnership with Samsung, announced in April. At that time, Google stepped in to become the default music player and service on new Samsung phones and tablets, in the wake of Samsung’s Milk Music closure in the U.S.

According to Google, it was able to gather feedback from Samsung users through an early access program, and is now able to roll out New Release Radio to all Google Play Music users.

The personalized mix itself is designed to showcase newly released songs from artists you like, or those Google believes you might. The station uses machine learning technology to select from singles and album releases from the past two weeks, based on your listening history on Google Play Music, as well as your broader musical preferences, the company explained in an announcement on Thursday.

The ability to personalize a music service to an end users’ likes is a competitive advantage in today’s streaming music race – an area where Spotify today excels. Its Discover Weekly playlist helped differentiate the service early on, growing to over 40 million listeners as of last year. Spotify then capitalized on that momentum to roll out even more personalized products, including its own Release Radar of suggested new releases you’ll like, which hit last summer; and Daily Mix, a combination of favorite tracks and recommendations, which arrived last fall.

Apple Music also introduced its variations on this theme with personalized playlists like “My New Music Mix” and “My Favorites Mix,” and more recently, a playlist to “Chill” to. 

Google’s New Release Radio is currently available to free radio listeners as well as paying subscribers, and will be continually updated with the latest releases, the company says.

Get the tools to land a machine learning job that robots can’t steal

Image: pixabay

Don’t let sci-fi cautionary tales spook you. Machine learning isn’t quite SkyNet just yet. It’s actually an incredibly innovative field that is making the everyday tools and apps that we rely on even better— from making smarter investment predictions to helping business owners make data-based decisions to recommending products or media that shoppers will like.

Machine learning has the potential to change the world, and the potential to teach robots to do human tasks (and jobs), so there are few smart career paths within the field that are likely to provide lucrative, long-term rewards. 

Machine learning professional is one of them. Prepare to be that “right place, right time” person now with the Complete Machine Learning Bundle. It’ll give you the foundation you need to immerse yourself in machine learning concepts and make your mark on this exciting tech frontier. You’ve likely already encountered machine learning in your everyday life, and it has probably made an impact on you without your knowledge. 

Perhaps Amazon recommended a product that was exactly what you were looking for. Or maybe Netflix offered up a movie that you felt was right up your alley. Both are examples of machine learning in action. That time your bank caught those unauthorized purchases on your account and alerted you immediately? Yup, that was probably machine learning, too. And those self-driving Google cars everyone says we’ll all be zipping around in a few years? That’s where machine learning is taking us next.  

Simply put, machine learning is what happens when computers can build their own analytical models and identify key insights in data without being told what to look for. This allows a much larger amount of data to be analyzed much faster and cheaper than was previously possible, which in turn allows companies to efficiently locate money-making opportunities and avoid risky ventures. 

And since those are every company’s two favorite things to do, you can understand why machine learning engineers are so heavily sought after right now. The Complete Machine Learning Bundle can help you seize those opportunities with an engaging 10-part course of study led by ex-Google engineers and analysts. You’ll learn the concepts that make interactive AIs so lifelike and what makes self-driving cars go on their own. From NLP and sentiment analysis recommendations to quantitative training and computer vision, this bundle leaves no stone unturned.Your course topics include:

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Your introduction to the core concepts of machine learning starts here—with a 14.5-hour course on Python and how you can use it to take your programming skills to the next level.

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Learn to use Python and the Twitter API to build models that track emotional reactions on the ubiquitous social media platform.

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Can you use data predict the survival probability of a passenger aboard the Titanic? It may sound morbid, but this exercise is a great way to learn how to use decision trees and random forests—two common machine learning techniques.

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This is where things get real. This course will introduce you to Deep Learning—the practice of parsing out the high-dimensional data gathered by computer vision and other artificial neural networks.

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MateLabs mixes machine learning with IFTTT


If you’ve ever wanted to train a machine learning model and integrate it with IFTTT, you now can with a new offering from MateLabs. MateVerse, a platform where novices can spin out machine learning models, now works with IFTTT so that you can automatically set up models to run based on conditional statements.

If you’re not familiar with IFTTT, it’s an automation tool for creating your own if/then statements without any programming knowledge. The service makes it possible to say, receive a notification if the temperature outside rises above 50 degrees or post pictures directly to Twitter.

MateLabs’ integration works much the same way, but with machine learning. As of now, the company is offering computer vision and natural language processing tools that can respond to Twitter, Slack, Google Drive, Facebook and more. Hypothetically, you could set up a process to analyze a Twitter mention to determine why the mention occurred.

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Of course you can build your own models — if you would like you can upload your own data on the MateVerse platform and train your own models for specific use cases. All of this is useful for those who might be unfamiliar with complex machine learning frameworks, but that doesn’t mean more advanced developers couldn’t also benefit from the streamlined experience.

As this technology matures it will be cool to see what hackers are able to do with it. I can imagine that one could build some weird IFTTT integrations with hardware — i.e. a camera that can turn on specific lights depending on whether you or your cat walks into a room.

Featured Image: Bryce Durbin