Microsoft acquires deep learning startup Maluuba
blogs.microsoft.com
Microsoft acquires deep learning startup Maluuba
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Re: Microsoft acquires deep learning startup Maluuba
#2More interesting info on Maluuba's 2 actual, recently-released datasets: http://datasets.maluuba.com/
Their "News QA dataset" contains 120k Q&As collected from CNN articles:
Documents are CNN news articles. Questions are written by human users in natural language. Answers may be multiword passages of the source text. Questions may be unanswerable.
NewsQA is collected using a 3-stage, siloed process. Questioners see only an article's headline and highlights. Answerers see the question and the full article, then select an answer passage. Validators see the article, the question, and a set of answers that they rank. NewsQA is more natural and more challenging than previous datasets.
Their "Frames" dataset contains 1369 dialogues for vacation scheduling:
With this dataset, we also present a new task: frame tracking. Our main observation is that decision-making is tightly linked to memory. In effect, to choose a trip, users and wizards talked about different possibilities, compared them and went back-and-forth between cities, dates, or vacation packages.
Current systems are memory-less. They implement slot-filling for search as a sequential process where the user is asked for constraints one after the other until a database query can be formulated. Only one set of constraints is kept in memory. For instance, in the illustration below, on the left, when the user mentions Montreal, it overwrites Toronto as destination city. However, behaviours observed in Frames imply that slot values should not be overwritten. One use-case is comparisons: it is common that users ask to compare different items and in this case, different sets of constraints are involved (for instance, different destinations). Frame tracking consists of keeping in memory all the different sets of constraints mentioned by the user. It is a generalization of the state tracking task to a setting where not only the current frame is memorized.
Adding this kind of conversational memory is key to building agents which do not simply serve as a natural language interface for searching a database but instead accompany users in their exploration and help them find the best item.
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Can anyone with experience in ML/AI comment on how novel/complex these projects are, and how expensive it would be to build out these datasets? Would be interesting to see what it takes to publish a few datasets trained on 20 day conversations between real people, and get acquired by Microsoft/Apple/Google.
Re: Microsoft acquires deep learning startup Maluuba
#3MS has always shown that they can do research, halolens is one such product and the recent ML enablement spree is another example. It is a good thing that they are going beyond their comfort zone of milking Windows. What is interesting is to see if their efforts make any difference in the status quo. I say this after using AzureML, which I liked, there is no such thing on the internet which allows you to write a ML model without knowing a programming language! It is a webapp which asks you to put data inside it, click a few buttons and it generates python or R code for you. Just brilliant.
Re: Microsoft acquires deep learning startup Maluuba
#4More interesting info on Maluuba's 2 actual, recently-released datasets: http://datasets.maluuba.com/ Their "News QA dataset" contains 120k Q&As collected from CNN articles: Documents are CNN news articles. Questions are written by human users in natural language. Answers may be multiword passages of the source text. Questions may be unanswerable. NewsQA is collected using a 3-stage, siloed process. Questioners see o…
Maluuba does more than publish a few datasets.
Re: Microsoft acquires deep learning startup Maluuba
#5More interesting info on Maluuba's 2 actual, recently-released datasets: http://datasets.maluuba.com/ Their "News QA dataset" contains 120k Q&As collected from CNN articles: Documents are CNN news articles. Questions are written by human users in natural language. Answers may be multiword passages of the source text. Questions may be unanswerable. NewsQA is collected using a 3-stage, siloed process. Questioners see o…
I doubt they were acquired based on their datasets! The "Frames" dataset states 12 users over 20 days.
But if one of the next 12 users was a Microsoft executive who liked what they saw, well then that's an important data point :) Having Bengio as an advisor definitely helps too.
Re: Microsoft acquires deep learning startup Maluuba
#6More interesting info on Maluuba's 2 actual, recently-released datasets: http://datasets.maluuba.com/ Their "News QA dataset" contains 120k Q&As collected from CNN articles: Documents are CNN news articles. Questions are written by human users in natural language. Answers may be multiword passages of the source text. Questions may be unanswerable. NewsQA is collected using a 3-stage, siloed process. Questioners see o…
I doubt they were acquired based on their datasets! The "Frames" dataset states 12 users over 20 days. But if one of the next 12 users was a Microsoft executive who liked what they saw, well then that's an important data point :) Having Bengio as an advisor definitely helps too.
I think you may be confused...they had 12 "wizard" participants converse with each other for 20 days in order to build out the logic needed for the dataset...not that 12 people used the dataset for their own projects/products or as customers.
Re: Microsoft acquires deep learning startup Maluuba
#7More interesting info on Maluuba's 2 actual, recently-released datasets: http://datasets.maluuba.com/ Their "News QA dataset" contains 120k Q&As collected from CNN articles: Documents are CNN news articles. Questions are written by human users in natural language. Answers may be multiword passages of the source text. Questions may be unanswerable. NewsQA is collected using a 3-stage, siloed process. Questioners see o…
Maluuba has real researchers from MILA and university of Toronto. It was an acquihire.
Re: Microsoft acquires deep learning startup Maluuba
#8Earlier quoted context omitted.
I doubt they were acquired based on their datasets! The "Frames" dataset states 12 users over 20 days. But if one of the next 12 users was a Microsoft executive who liked what they saw, well then that's an important data point :) Having Bengio as an advisor definitely helps too.
I think you may be confused...they had 12 "wizard" participants converse with each other for 20 days in order to build out the logic needed for the dataset...not that 12 people used the dataset for their own projects/products or as customers.
You're probably right! It just states "12 particpants over a period of 20 days" on their site - hard to tell what that means.
Re: Microsoft acquires deep learning startup Maluuba
#9More interesting info on Maluuba's 2 actual, recently-released datasets: http://datasets.maluuba.com/ Their "News QA dataset" contains 120k Q&As collected from CNN articles: Documents are CNN news articles. Questions are written by human users in natural language. Answers may be multiword passages of the source text. Questions may be unanswerable. NewsQA is collected using a 3-stage, siloed process. Questioners see o…
Maluuba has real researchers from MILA and university of Toronto. It was an acquihire.
Understood, I'm looking for an analysis - it would cost x to hire the same size team of researchers with comparable research experience for the same amount of time to build out these datasets as proof-of-concept products, plus whatever other costs an ML research team accrues.
Re: Microsoft acquires deep learning startup Maluuba
#10More interesting info on Maluuba's 2 actual, recently-released datasets: http://datasets.maluuba.com/ Their "News QA dataset" contains 120k Q&As collected from CNN articles: Documents are CNN news articles. Questions are written by human users in natural language. Answers may be multiword passages of the source text. Questions may be unanswerable. NewsQA is collected using a 3-stage, siloed process. Questioners see o…
> how expensive it would be to build out these datasets?
Assuming you have the expertise necessary to design and run the process in-house, the major expense is going to be compensating the humans in the loop, which can add up quickly.
This is why organizations that already have access to large datasets have such a huge advantage.
I think that one of the reasons we're seeing such a rush to deploy chatbots is that even a minimally-useful bot will quickly start accumulating extremely useful (and very clean) training data.
There is a lot of noise being made about "democratizing AI", but as long as the best results require a lot of training and huge amounts of training data it will remain the bottleneck.
Look for progress on 1-shot and 0-shot learning to get a better feel for how much progress is made on real democratization.