Digamma.ai CEO Q&A Series: Jarrod Wolf of AddStructure

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Artificial Intelligence / Machine Learning

Digamma.ai CEO Q&A Series: Interview with Jarrod Wolf, co-founder of AddStructure

1. How are you looking to fundamentally change the customer shopping experience through Addstructure?

With our technology we’re hoping to make the shopping experience much more seamless. Imagine looking at your phone and using your voice to speak. As you’re speaking, the product mix that is being displayed to you is updating in real time. You can say something like, “I’m looking for a TV maybe like between 40 and 50 inches, around $600 and that has 3 HDMI ports.”  And, as you’re speaking, the products that are being shown to you are actually updating with that conversational memory.

Next, you can imagine that technology like this could also enable a much better grocery cart building experience. It’s difficult to build a grocery list because you’re building a cart of 50 or 60 products. With our technology you can have your phone in your hand and say, “I’m looking for milk.  Actually I want that to be organic milk.  I want some of the yogurt.  I want some sourdough bread.  I want all the ingredients to make chicken enchiladas.” As you’re speaking, the entire list is just building itself. So, it’s a much faster, natural experience than the experience you have today. Read More

Digamma.ai CEO Q&A Series: Mark Chung of Verdigris

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Artificial Intelligence / Machine Learning

Digamma.ai CEO Q&A Series: Interview with Mark Chung, CEO and Co-Founder at Verdigris.

1. Verdigris provides real-time energy intelligence for facilities managers, enabling them to react faster with device-level monitoring and real-time alerts. What are the implications of your company’s technology on the building industry and to a larger extent, the environment?

We target mission-critical facilities like distribution centers, factories, and even hotels because they are sources of major power consumption and often struggle to mitigate their energy usage. By providing them with a system that allows the building to communicate with its facilities management, it transforms the way we conceptualize buildings. In our paradigm, you have buildings taking care of people instead of people taking care of buildings. The implications of our technology for the future of the building industry, and for the environment as a whole, is one of sustainability through a better understanding of how and when a building consumes electricity. We have created a system that drives down utility costs by reducing energy consumption and avoids operational costs of equipment failures. This technology will give the building industry a powerful tool to take a step in the right direction, one that safeguards our environment. Read More

The Five Food and Restaurant AI Chatbots You Should Know About

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Artificial Intelligence / chatbot / Machine Learning

Facebook Messenger chatbots have major potential, even if the field is relatively nascent one.  With Facebook launching Discover, a hub inside Messenger for discovering new and interesting chatbots to message with, there’s no excuse not to try out a new chatbot this summer — especially a food-related one. From analyzing your receipts to providing awesome restaurant recommendations, the following list represents a veritable freshmen class of powerful, value-adding, food and restaurants AI chatbots.


Lunchcat, created by the machine learning consulting firm Digamma.ai, is an experimental chatbot that helps you and your friends split lunch costs. Simply type how many people you are and what the total bill was and Lunchcat will instantly tell you everyone’s share and tip amount.

Lunchcat’s coolest feature lies in its ability to analyze receipts. Simply upload a photo of your receipt and Lunchcat will automatically split your bill for you, no extra information needed.

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Digamma.ai CEO Q&A Series: Michal Wroczynski of Fido.ai

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Artificial Intelligence
fido artificial intelligence

Digamma.ai CEO Q&A Series: Interview with Michal Wroczynski, CEO and founder at Fido Labs

1. Fido’s technology powers chatbots so that they can learn automatically from the internet and answer complex business questions based on a large volume of text. Tell me about how Fido got started in the first place and your novel approach in teaching machines how to reason more like humans.

In 2003, we co-founded Fido Interactive and implemented many commercial projects of AI systems, including chatbots. However, we slowly started to reach the limits of our capacity of building handcrafted knowledge bases. As a consequence, in 2007, we co-founded an independent AI lab in Europe to tackle this problem. Our focus moved from creating bots to understanding how a chatbot could read and learn by itself. We knew that the incoming trend of statistical learning wouldn’t have been enough as learning without reasoning is only a way to mimic intelligence within very narrow tasks. Instead, we first aimed to teach the computer how language works step-by-step. Then, we taught it to reason based on how people use language to express themselves. This way, we enable it to learn automatically without any data labeling. One of the first systems we created this way was Cerber. It was a public safety system designed to detect any misbehavior of an adult towards kids in online chatrooms.
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Digamma.ai Q&A Series: Vsevolod Dyomkin of (m8n)ware

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Artificial Intelligence

Digamma.ai Q&A Series: Interview with Vsevolod Dyomkin

 1. Grammarly is a commercial product. While developing it, did you encounter any interesting challenges and obtain any interesting research results in the NLP area? Did any interesting academic results arise from the development process or was the work purely an application of existing NLP algorithms?

Grammarly operates in a field that is both down-to-earth and also has a history of relevant academic research.  In addition to its core error correction engine, it relies on a comprehensive set of NLP tools such as language modelling, lemmatization, and parsing, to name a few. Our approach was based on combining the best existing technologies in addition to the internal “secret sauce”, and fine-tuning them to better suit our goals. This resulted in a number of interesting improvements, some of which got into our technical blog:

Some were submitted to conferences, although neither one was accepted, probably due to our immaturity in academic publishing. Meanwhile, the most interesting ones were kept secret as they were too tightly related to our core algorithms.

Also, recently, a novel dataset was released courtesy of the efforts of the Grammarly NLP team, but I was not part of this development.

To sum up, we have definitely faced a lot of research challenges across the whole NLP technology stack and, especially, in error correction. We tried to address those problems with a product-oriented mindset by performing research that would be immediately relevant in improving the quality of Grammarly’s product. This was often quite successful, although most of these solutions will remain in-house at least for some time.

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Machine Learning Consultants Digamma.ai Team Up With Udacity to Address the AI Knowledge Gap

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Artificial Intelligence / Machine Learning

The artificial intelligence and machine learning community is quickly becoming one of the most dynamic, exciting and global ones. VCs have been fast to take notice of what is not just a passing trend, but an indicator of just how much artificial intelligence is positioned to change fundamental aspects of our society, from the division of labour to how we make decisions on a day-to-day basis. In fact, the number of VC-backed investments in AI rose from 160 in 2012 to 658 deals in 2016. What’s more, over 550 startups that rely on AI as a key part of their technology raised $5B in funding in 2016. The funds flowing into AI and machine learning raises the question of where expertise to fulfill opportunities in AI and machine learning are originating from.

A recent article from The Economist sheds light on how leading tech firms often need to recruit researchers and engineers from robotics and machine learning programs at traditional universities due to the lack of deep knowledge and skills in AI in the market. While the field of AI and machine learning is a few decades old, few have built a history of proven expertise in the area. Moreover, the talent shortage is further strained by a shortage of learning opportunities for engineers motivated to learn about machine learning and AI. Read More

Two Artificial Intelligence Trends That Will Change the World — And How To Benefit From Them (Part II)

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Artificial Intelligence / Big Data / Machine Learning

Continued from Part I: Two Artificial Intelligence Trends That Will Change the World — And How To Benefit From Them

2. AI Will Touch and Change Nearly Every Job, Even White Collar Ones

Blue collar jobs previously thought vulnerable to AI are now not the only ones. In fact, AI is already disrupting white collar industries and many professional jobs aren’t as safe as once thought. As Erik Sherman at Fortune commented, “researchers are beginning to see that artificial intelligence, robotics and new disruptive technology are challenging white-collar professions that previously seemed invulnerable.” He cites Frank Tobe, editor and publisher of The Robot Report, a publication that tracks and analyzes the robot industry, who, speaking about Fedex, says that, “they hope that by 2020 they will have a pilot center with three or four pilots that fly the FedEx fleet [of hundreds of planes] around the country.” Even education is not immune either. “I invested in one company that uses robots to teach mathematics in schools,” said Dmitry Grishin, CEO of Russian tech giant Mail.Ru Group and head of robotics VC firm Grishin Robots. One Japanese insurance company, Fukoku Mutual Life Insurance, has even reportedly replaced 34 human insurance claim workers with “IBM Watson Explorer”, as of earlier this year.

So, what are entrepreneurs to do in the looming automation job wave of the future and coming artificial intelligence trends?

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How AI and Machine Learning Will Revolutionize the Health Industry

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Artificial Intelligence / Healthcare / Machine Learning

Imagine if humans could train themselves to give more precise diagnoses. Over time, medical misdiagnosis would abate, save countless lives, and not to mention save hospitals, patients, and their families millions of dollars every year. Pharmaceutical industries, too, would be able to keep their stock orders in far more accurate conditions, keeping their reserves continuously maintained for predictive demand.

Now consider that in the distant future, your surgery could be performed by an enhanced robot. In your post-op appointment you talked, in detail, to a machine about your recovery.

There are many complex interconnections between artificial intelligence (AI), machine learning, and the way they help humans do their jobs more efficiently. We’re adapting to the idea that we can use machine checkouts for our own groceries at the supermarket, but machines commandeering the very state of our health? Many wouldn’t be too keen. Read More

Two AI Trends That Will Change the World — And How To Benefit From Them (Part I)

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Artificial Intelligence / Big Data / Machine Learning

In a recent interview of Andrew Ng and Neil Jacobstein on AI, Wall Street Journal reporter Scott Austin claimed that, “artificial intelligence is shaping up as the next industrial revolution, poised to rapidly reinvent business, the global economy and how people work and interact with each other.” Andrew Ng is chief scientist at Chinese internet giant Baidu Inc. and co-founder of the education startup Coursera. Neil Jacobstein is chair of the artificial intelligence and robotics department at Silicon Valley think tank Singularity University.

The pair share several intriguing predictions and insights. Namely, Ng says that “in a few years everyone will be using speech recognition. It will feel natural. You’ll soon forget what it was like before you could talk to computers.”

Speaking to an even larger looming trend of the impact of AI on jobs, Ng claims that, “things may change in the future, but one rule of thumb today is that almost anything that a typical person can do with less than one second of mental thought we can either do now or in the very near future automate with AI.”

If artificial intelligence and machine learning are truly the technologies that will transform business — and society itself — similar to the overwhelming impact the industrial revolution had on the way we work, live and interact with others, what are the most salient trends entrepreneurs should be aware of as they build the coming AI economy? Read More

5 Ways Artificial Intelligence Can Make Your Business More Profitable

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Artificial Intelligence / Big Data / Machine Learning

1. Automation

Artificial intelligence is becoming so advanced that we’re currently seeing honorary disciplines being aided, though not yet replaced, with computerized assistance. Some customary journalistic tasks are already being carried out by AI, such as summarizing reports, analysis, and pulling data from A to B to gauge further intelligence. Other advancements are being made within the administrative field at a growing rate, with AI able to schedule meetings, note down important items, and take customer requests with minimal supervision.

2. Deeper and Better Insights

Without analysis, a lot of company data is largely useless. However, with more of business’ data being pulled into cloud servers, a growing, vast quota of information – big data – is constantly being surveyed for insights. This kind of new mobility can allow companies to make predictions based on older data, and can even offer prescriptive insights to forecasting future trends. Soon, restaurants could also be able to benefit from utilizing AI to discover specific insights, allowing them to determine which music they should play based on the data profiles of the patrons who’ve made reservations at their locations.
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