All posts in “machine learning”

This AI tried to write Christmas carols, and the results are hilarious

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Elon Musk is terrified that artificial intelligence might spell the end of humanity.

Thankfully, it looks like we’re not quite there yet. Colorado-based research scientist Janelle Shane has trained a neural network (a type of machine-learning algorithm) to write its own Christmas carols, and the results are…interesting. 

Shane trained the algorithm to imitate a set of 240 popular Christmas carols aggregated by the Times of London. The AI trained itself by continuously attempting to write carols, checking their accuracy against the carols in the dataset, and modifying its process accordingly. 

Here’s an excerpt from one, which Shane posted on her blog: 

The story of the chimney see
Santa baby, and blood and joyous so world and joy and good will to see
Santa baby bore sweet Jesus Christ
Fa la la la la la la, la la la la la la la la.

King of toys and hippopotamuses [sic] full of the light of that stood at the dear Son of Santa Claus
He was born in a wonderful christmas tree

Run, run Rudolph, run, run Rudolph, run, run Rudolph, run, run Rudolph, run, run Rudolph, run, run Rudolph, run, run Rudolph, run, run Rudolf the new born King.

As you can see, the resulting songs look a heck of a lot like Christmas carols, but they don’t make a ton of sense. 

It’s actually somewhat impressive how well Shane’s AI imitates the structure and syntax of a Christmas song. But it’s clearly got a long way to go.

AI alarmists may want to holster their pitchforks for now. The robot overlords may have conquered strategy games, but when it comes to Christmas spirit, humans are still on top. 

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DigitalGenius raises $14.75 million Series A for AI-based customer service solution


When DigitalGenius participated in the TechCrunch Disrupt Battlefield competition in New York City in 2015, there weren’t a lot of companies working on AI and machine learning. Today, it’s become much more commonplace, and the company announced a $14.75 million Series A.

Global Founders Capital led the round. MMC Ventures, Paua Ventures and several other unnamed new investors also participated They also got help from previous investors Salesforce Ventures, Runa Capital, RRE Ventures, Lumia Capital, Compound and Lerer Hippeau Ventures. Today’s investment brings the total to $26 million, according to the company.

DigitalGenius may have been ahead of its time, but the market is finally catching up. Company president and chief strategy officer Mikhail Naumov says the startup has been growing in leaps and bounds going from just two customers last year to 30 this year.

Customers include KLM Royal Dutch Airlines, Unilever, Eurostar and Soylent. Just this year, the company landed its first government customer, which hopes to use DigitalGenius to improve its citizen outreach.

The product uses machine learning and natural language processing to build a lexicon of common customer service interactions for each business using old text and email interactions as training material. In this way, it learns typical kinds of questions and can begin to build reasonable responses.

But DigitalGenius doesn’t see the technology as the be all and end all here. A customer service representative can work with DigitalGenius technology to form a human-technology team. The technology can take the interaction as far as possible before passing off to a human or it can work with the human in the customer service software, offering responses and allowing the CSR to customize the response before sending the email or text.

The company, which has offices in the US and London, wants to use the new capital infusion to expand further. Naumov says the company has hired a chief revenue and they want to grow the number of employees from 60 by around 30-50 percent in the coming year. The exact number will depend on how well they continue to grow, he said.

Learn how to build advanced voice and chatbot interfaces plus more with these online courses

Learn how to code and transform your career.
Learn how to code and transform your career.

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There’s a lot we don’t know about the future, but it’s a pretty good bet that many of our systems and infrastructure will be automated. From thermostats to speakers to robot butlers, the Utopian version of Terminator 2 is almost becoming a reality. And the best part is, you don’t have to be a fancy scientist to take advantage of this new era. 

Familiarize yourself with all the necessary skills with the Voice, Chat, and Vision Automation Bundle. This five-course bundle teaches you enough to hit the ground running and become an automation machine. So, what does it include?

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Voice-activated assistants let you convey complex information to customers in a free-flowing, conversational way and Alexa is the crown jewel. This course will teach you how to build apps with full Alexa integration for popular Amazon devices like Echo, Echo Dot, and FireTV.

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Machines can do things like pattern matching far better than humans, so it’s important to take advantage of them. This course will teach you SikuliX, a popular scripting/automation technology that works with Python and Java. You’ll also learn how to write image recognition software for common machine-learning functions like OCR.

A Search Engine Course

If you think Google is the be-all and end-all of search, think again. New engines like Elasticsearch combine top-notch search engine technology with impressive data warehousing for true next-level performance. This valuable course will improve your search-related programming skills in ways that you didn’t even know were possible. 

A Chatbot Course

DialogFlow, a conversational interface for bots, devices, and applications is popping up everywhere from online shopping websites to technical support chat windows. This course will teach you how to harness its power and build a Chatbot so natural that your friends will think that they’re talking to an actual person.

A Voice Course

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The Voice, Chat, and Vision Automation Bundle normally costs $845, but you can get it now for just $29, a savings of 96%.

Lily raises $2M from NEA and others for a personal stylist service that considers feelings, not just fit


One of the reasons recently IPO’d Stitch Fix became so popular among female shoppers is because of how it pairs the convenience of home try-on for clothing and accessories with a personal styling service that adapts to your tastes over time. But often, personal stylists bring their own subjective takes on fashion to their customers. A new startup called Lily aims to offer a more personalized service that takes into account not just what’s on trend or what looks good, but also how women feel about their bodies and how the right clothing can impact those perceptions.

The company has now closed on $2 million in seed funding from NEA and other investors to further develop its technology, which today involves an iOS application, web app and API platform that retailers can integrate with their own catalogs and digital storefronts.

To better understand a woman’s personal preferences around fashion, Lily uses a combination of algorithms and machine learning techniques to recommend clothing that fits, flatters and makes a woman feel good.

At the start, Lily asks the user a few basic questions about body type and style preferences, but it also asks women how perceive their body.

For example, if Lily asks about bra size, it wouldn’t just ask for the size a woman wears, but also how they think of this body part.

“I’m well-endowed,” a woman might respond, even if she’s only a full B or smaller C – which is not necessarily the reality. This sort of response helps to teach Lily about how the woman thinks of her body and its various parts, to help it craft its recommendations. That same woman may want to minimize her chest, or she may like to show off her cleavage, she may say.

But as she shops Lily’s recommendations in this area, the service learns what sorts of items the woman actually chooses and then adapts accordingly.

This focus on understanding women’s feelings about clothing is something that sets Lily apart.

“Women are looking for clothes to spotlight the parts of their body they feel most comfortable with and hide the ones that make them feel insecure,” explains Lily co-founder and CEO, Purva Gupta. “A customer makes a decision because based on whether a specific cut will hide her belly or downplay a feature they don’t like. Yet stores do nothing to guide women toward these preferences or take the time to understand the reasons behind their selections,” she says.

Gupta came up with the idea for Lily after moving to New York from India, where she felt overwhelmed by the foreign shopping culture. She was surrounded by so much choice, but didn’t know how to find the clothing that would fit her well, or those items that would make her feel good when wearing them.

She wondered if her intimidation was something American women – not just immigrants like herself – also felt. For a year, Gupta interviewed others, asking them one question: what prompted them to buy the last item of clothing they purchased, either online or offline? She learned that those choices were often prompted by emotions.

Being able to create a service that could match up the right clothing based on those feelings was a huge challenge, however.

“I knew that this was a very hard problem, and this was a technology problem,” says Gupta. “There’s only one way to solve this at scale – to use technology, especially artificial intelligence, deep learning and machine learning. That’s going to help me do this at scale at any store.”

To train Lily’s algorithms, the company spent two-and-half years building out its collection of 50 million plus data points and analyzing over a million product recommendations for users. The end result is that an individual item of clothing may have over 1,000 attributes assigned to it, which is then used to match up with the thousands of attributes associated with the user in question.

“This level of detail is not available anywhere,” notes Gupta.

In Lily’s app, which works as something of a demo of the technology at hand, users can shop recommendations from 60 stores, ranging from Forever 21 to Nordstrom, in terms of price. (Lily today makes affiliate revenue from sales).

In addition, the company is now beginning to pilot its technology with a handful of retailers on their own sites – details it plans to announce in a few months’ time. This will allow shoppers to get unique, personalized recommendations online that could also be translated to the offline store in the form of reserved items awaiting you when you’re out shopping.

Though it’s early days for Lily, its hypothesis is proving correct, says Gupta.

“We’ve seen between 10x to 20x conversion rates,” she claims. “That’s what’s very exciting and promising, and why these big retailers are talking to us.”

The pilots tests are paid, but the pricing details for Lily’s service for retailers are not yet set in stone so the company declined to speak about them.

The startup was also co-founded by CTO Sowmiya Chocka Narayanan, previously of Box and Pocket Gems. It’s is now a team of 16 full-time in Palo Alto.

In addition to NEA, other backers include Global Founders Capital, Triplepoint Capital, Think + Ventures, Varsha Rao (Ex-COO of Airbnb, COO of Clover Health), Geoff Donaker (Ex-COO of Yelp), Jed Nachman(COO, Yelp), Unshackled Ventures and others.

PullRequest pulls in $2.3M seed round led by Google’s Gradient Ventures


PullRequest, a Y Combinator Summer 17 graduate, announced a $2.3 million seed round today led by Google’s new AI fund, Gradient Ventures.

The Slack Fund, Fika Ventures, Defy Ventures, Lynett Capital, FundersClub and Joe Montana’s Liquid2 Ventures also participated.

The company has been concentrating on providing code review as a service, a task that often gets lost in today’s high-speed agile development cycles. The company has set up a system of on-demand code reviewers who check for bugs, security issues, coding standards and performance problems.

That wouldn’t seem to have anything to do with the focus of Google’s Gradient Ventures mission, but company founder Lyal Avery says the road map for his company goes far beyond the code reviewer service.

They plan to bring automation to the coding process, so they can pick up issues automatically such as when dependency code, open source pieces in a particular application, have a critical update.

Perhaps it’s not a coincidence that Slack also participated in this round as one of the first automation pieces PullRequest is working on involves a Slack bot to inform developers when one of those dependency pieces requires updating. While that project is still in Alpha testing right now, it’s the general direction of the company, Avery explained.

Avery recognizes that as his company builds up its code reviewing service, it’s going to create a mass of data about the coding process and common reviewer issues. He says that they are scoring and reviewing their code review projects to build that data set. While he doesn’t see a time when humans are removed from the reviewing equation, he does see using this data to automate the repair of common problems. “At the end of the day, it’s about how efficiently we drive code review,” he said.

When we spoke to Avery in August, the company had 200 reviewers and 300 companies on the platform. Today, it has 1000 companies and 1900 reviewers as the company has grown in leaps and bounds since then.

Avery moved back to Austin, Texas after graduating from Y Combinator, and has since hired four more people for a total of six employees. They plan on using this money to continue to grow the company and have plans to double the number of employees next month alone and go from there.

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