Recommendation systems are evaluated on a variety of tasks.

• Top-N Prediction. N items are predicted for the user. This paper finds that a bunch of existing neural network approaches that use this task are outperformed by simple baselines or not reproducible.
• Rating Prediction. The rating of items are predicted. This apparently has fallen out of favour. Despite being featured on most introductory tutorials to recommendation systems.

• Sequential Prediction. The next item that a user will interact with is predicted. This is featured in some deep neural network approaches that process sequential data.

• And more…

According to the reproducibility paper linked above accuracy on datasets like MovieLens is not informative. However, this is the dataset used in most papers, including the spotlight repository that implements deep algorithms. Many recent papers instead prioritize diversity.

So these are my basic questions, for a baseline system:

• What is a standard reliable dataset?
• What are some good evaluation metrics?
• Which tasks should the system be evaluated on?

I am really struggling to get answers from the literature, as they are quite diverse in all three of these aspects. What do you guys think?

Artificial intelligence (AI) is everywhere, generating excitement about how it could transform our lives in multiple ways. Yet the technology is very likely to be disruptive. Businesses and policymakers must try to capture the full value of what AI has to offer while avoiding any risks.

The concept of AI has been around for more than half a century, and many of us have lived through prior periods of excitement followed by dull stretches of disappointment — “AI winters” — when the promise of A.I tech failed to live up to expectations.

However, recent progress in AI techniques and algorithms, combined with a huge increase in computing power and an explosion in the amount of data available, has created significant and tangible advances, promising to generate massive value for businesses, individuals, and the whole of society.

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Machine Learning is Assisting Multiple Industries

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Companies are currently applying AI techniques in sales and marketing to personalize product recommendations to the desires of individual customers. Also, in manufacturing, AI is improving predictive maintenance by using “deep learning” and applying calculations to high volumes of data from sensors.

By simply deploying algorithms to detect anomalies, firms can decrease the downtime of machinery and equipment, from assembly lines to jet engines.

Recent research has highlighted hundreds of such business cases, which together have the potential to create between €.3.17tn and €5.25tn in revenue every year.

AI can contribute to economic growth by augmenting and substituting labor and capital inputs, spurring innovation, and boosting wealth creation and reinvestment.

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AI Could Increase Global GDP Growth

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It’s estimated that AI and analytics could add as much as €11.78 trillion to total global output by 2030, increasing the yearly rate of global GDP growth by more than one percentage point.

Research suggests AI will be most beneficial if it focuses on innovation-led growth, and if this growth is accompanied by proactive managerial measures — particularly, retraining workers to give them the skills they will need to excel in the new working era.

As AI starts to contribute to faster GDP growth, social welfare is also likely to increase. It’s estimated that AI and related technologies could improve welfare by 0.5%-1% a year between 2020 and 2030.

That would be very similar to the social impact of previous waves of technology adoption, including the internet and communications technology revolution.

AI is likely to help to improve many aspects of wellbeing, from job security and living standards to educational practices and environmental sustainability.

Its most significant positive contribution to human welfare may come in the areas of healthcare and longevity: AI-driven drug discovery is many times faster than conventional research. And AI-based traffic management could reduce the negative impact of air pollution on health by 3%-15%.

AI will also help to address a wide range of social challenges. If implemented carefully, this technology could help the world meet all 17 of the United Nations Sustainable Development Goals.

AI tech that is currently being field-tested includes disease detection systems, smuggler trackers (to combat human trafficking), and tech that helps to predict and aid in disaster relief efforts.

There are still some challenges that must be addressed. These technologies are still very much in their infancy, with more breakthroughs needed to make them applicable on a global scale.

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Challenges and Concerns

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In the fastest possible automation-adoption scenario, up to 375m workers worldwide will have to switch occupational categories by 2030, while some 75m will be affected in some professional capacity. The nature of almost every job type is likely to change, as people are forced to interact with smart machines in the workplace.

That will garner the need for new skills, presenting companies, and policymakers with the challenge of training and retraining the workforce at a massive scale. And as demand for high tech-skill jobs grows, low-skill workers could be left behind, resulting in severe economic imbalance.

The diffusion of AI could also raise challenging ethical questions. Some of these may relate to the use and potential misuse of the technology in areas ranging from public surveillance and advanced military applications to social media.

Algorithms and the data used to train them may introduce new biases, or perpetuate and institutionalize existing types. Other critical concerns include the use of personal information, privacy, cybersecurity, and “deep fakes” used to manipulate some election results or perpetrate large-scale global fraud.

Despite these challenges, AI is likely to generate a tremendous amount of value for all of us, if policymakers and businesses act smartly and swiftly to capture its full benefits…

The much-anticipated AI Spring could be just around the corner!

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You will be happy to know that Fuzzy.one pays Cryptocurrency for posting articles at their website

You will get Cryptocurrency for every post and comments you do. You need to register to do posts. Articles can be at any language even at your own language. Just the articles must be (Driving/Taxicab Driving/Ride Sharing) related

After watching the talks and reading some of the docs it seems that Swfit for TensorFlow addresses most, if not all of the complaints people have about TensorFlow right now. The Swift tooling appears especially helpful with TF’s learning curve, which people often cite as one of the major downsides of TF.

What are your thoughts? Why do/don’t you use it and what would make you change your mind?

I have a (physical) machine that can be tuned by adjusting the values of some parameters A_1, …, A_n (n is around 10). This tuning affects some secondary parameters B_1, …, B_m that cannot be tuned by hand. The machine continuously produces an output X, and by looking at X in a time window, it is possible to decide if the machine was running stable or unstable.

All this information was logged for the past ~10 years, that is roughly around 25M data points.

The tuning of the machine is really complicated, as it can react very sensibly to parameter adjustments and also their influence is not quite well understood, so specialist interventions are needed to keep the machine running at a reasonable performance. The goal is to train a ML model that can support these interventions and generate some insights into how the parameters are related to the stability. For example we would be interested in something like ‘If you raise A_1, you need to lower A_2 in order for the machine to remain stable’ or ‘raising A_1 will increase B_1 in a few hours’.

Up until now we ignored the time component and only ran some clustering to find out which settings were used when the machine was running stable and which were used when it was running unstable. Sadly, the used settings were are greatly (it could have been stable with A_1=100 and A_1=300) and a usually a single setting could lead to a stable as well as an unstable machine, so the time information is crucial.

I am looking for ideas how to approach this task. I was thinking about sub dimensional motif discovery to find typical patterns, but I’m unsure how to link these patterns together.

So I’ll start by saying that I don’t know that much about machine learning so I don’t know how plausible this idea is; that’s why I’m asking.

Essentially, start by downloading the entirety of some hentai site, with artist names and tags. Then feed it into a neural network of some description. Set it up so that you can feed in an artist name and tags and it’ll try to make a manga/doujin in the style of that artist with those tags. (Presumably it would have better results if you stick to tags that actually appear in that artist’s work?)

One question is, would it be best to stick to material in one language so it won’t make speech bubbles in a sort of English-Japanese-Chinese mishmash? (I realize it will have no way of actually knowing the meanings of the words or how they sound out loud but given enough data it should at least be able to correlate written words and phrases to pictures/situations, right?) Similarly, would it be best to stick to material in black and white (or in color)? There are at least 200,000 available mangas/doujins in Japanese in black and white; would that be anywhere close to enough data?

Would it be able to get more out of the training data if the dialogue was all transcribed in text and the pages were panel-by-panel tagged with who’s where, doing what? I realize this would take many man-hours.

How much processing power would I expect this to take? I’m assuming my 2.2 gigahertz, 4 gigabyte RAM laptop would be entirely inadequate.

And finally, if it didn’t end up producing anything coherent, would the results at least be funny?

According to the official website January 6 2020 the results for AISTATS submission should be released. Has anyone received the news yet?

As a PhD student of ML, I recently came into realization that most of the things I learned in college and required courses in PhD weren’t really necessary or useful for research in most areas of ML, and that I learned more relevant things outside of the curriculum by myself by reading recent papers. I think the prerequisite for most papers is usually limited to very few number of easily learnable topics and the cited recent papers, so I think more emphasis should be put on reading recent papers than textbooks of rather irrelevant topics.

I believe it is more efficient to learn whatever you think is necessary during your research rather than learning various things beforehand. So, rather than taking various CS & ML courses and then beginning the research, I believe it is better for people to begin research (e.g. reading the recent papers, implementing various ideas) as soon as possible. This way, while doing your research you would specialize to some specific fields and may find lack of some required knowledge. Then, you can take a course necessary for understanding it or just study it on your own if that works, since that’s what researchers usually do. Meanwhile, you can keep reading the recent papers, implement your ideas and accumulate your knowledge of things you cannot learn from textbooks or lectures.

The target of this curriculum is assumed to know at least single-variable calculus (if you know more, you can skip the topics you know!). This includes some advanced high school students. Since most researchers tend to have been a strong student, I set the pace of the curriculum fast. But it can be slowed down. A sample syllabus is provided for each course (taken from MITOCW and Stanford).

1st semester: Multi-variable Calculus [1], Linear Algebra [2], Elementary Probability & Statistics with emphasis on ML [3] (The syllabii should be modified to focus on ML and incorporate Python & Numpy use.)

2nd semester: Classical ML (covering various classical models quickly) [4], DNN course (focusing on CNN and Transformer (w/ pytorch impl.) with literature review mainly on post 2017 papers at the end) (modified ver. of [5, 6]), some supplementary CS course (covering various miscellaneous things you absolutely need to know).

After these semesters, you would have an understanding of what to specialize on and create your own curriculum. For some of them you need to take some more courses first, whereas others can be studied only by reading papers and/or github libraries. Check daily arxiv feed, check recent papers on twitter/reddit, do literature search, implement your ideas etc.

It is curious to me if advanced high school students would be able to pass this curriculum and do research in a year?

Anyway, I hope I can get any feedback on my post. Thank you for reading.

[1] https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/index.htm

[2] https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/

[3] https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/

[4] http://cs229.stanford.edu/

[5] https://cs230.stanford.edu

[6] http://vision.stanford.edu/teaching/cs231n/

This article doesn’t seem to mention ML research but I’m sure that reproducibility is a major issue in ML research as well, from my own experience.

https://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970

Hey everyone, I played around with the newly released StyleGAN2 recently and created a model that generates faces of K-pop idols.

Website: http://www.thiskpopidoldoesnotexist.xyz/

How I did it: https://medium.com/@hygzhu/this-k-pop-idol-does-not-exist-df2f095c795d

I’m a beginner to machine learning so there were probably many things I could have done better, but I was definitely surprised how fast StyleGAN2 was able to generate decent looking pictures in such little time. Perhaps it is because all these K-pop idols have a mostly homogeneous appearance?

Hi folks,

Just created a subreddit for folks who who want to stay up-to-date with game changing developments in ML and AI one shouldn’t miss. Feel free to join and invite your friends and colleagues. 🙂

Happy New Year!

https://www.reddit.com/r/LatestInML/