Anjali Itzkowitz

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Thank you for this post on a topic that affects millions. Your article left me wondering about the various stakeholders in a project like this, especially in a jurisdiction such as India. Having spent time working in rural development in India, I know how difficult it can be to get a broad spectrum of interest groups to collaborate effectively on an initiative as sweeping as constructing lavatories for millions of people. As such, it is often more efficient to have one commercial enterprise steer the process through to completion. However, I do think that there is potential for open source implementation to work, and to enable the project to reach more people faster. For this to work, a high level of coordination is necessary. Certain organisations in India have managed to do this in collaboration with local government. It will be interesting to see how the Bill and Melinda Gates Foundation advances this project.

On November 15, 2018, Anjali Itzkowitz commented on Open Research: Advancing Innovation in Public Health :

Thank you so much for choosing such an important topic and for raising such difficult questions. I agree with many of those who have already commented that there are benefits to making scientific research less esoteric and more accessible. Choice of research topics is heavily influenced by well funded interest groups, and making research free to access would disempower these powerful lobbying groups. However, the question that you raise around attribution when scientists start building on each other’s work is an important one. We would have to decouple scientists’ career advancement opportunities from the rate at which they publish and perhaps link them to other metrics that incentivise collaboration. For example, scientists could be assessed on how many collaborative projects they have undertaken with colleagues in the field when they are considered for posts.

On November 15, 2018, Anjali Itzkowitz commented on Chanel’s Foray Into 3D Printing :

I love this idea. Thanks Arting for such a great inside look into how one of the most iconic fashion houses is incorporating additive manufacturing into its new product development. I think introducing the technology in their cosmetics line, which has fewer connotations of manual artistry, was a smart move. But as you rightly point out, this technology has implications for its apparel business as well. Given that most of the major fashion houses have already separated haute couture from pret a porter, I don’t see too much risk to the brand if additive manufacturing is introduced in the more mainstream lines but kept out of made to measure couture. The fact that Chanel is among the first of the luxury houses to use this technology to me is in keeping with their pioneering spirit, which started with Coco wearing trousers when other women would never dream of doing so.

On November 15, 2018, Anjali Itzkowitz commented on Made In Space. Literally. :

This was fascinating to read – thank you for writing on such a fascinating topic. I was not even aware that additive manufacturing was being applied to in situ manufacturing in space. The challenges you list associated with this application of additive manufacturing are immense. It does make me wonder whether this sector will ever be truly commercialised, or will remain the preserve of funding grants. It seems to me that until the technology is more advanced, commercial players will be reluctant to enter the space.

On November 14, 2018, Anjali Itzkowitz commented on Machine Learning in Cybersecurity: CyberArk :

Eric, this is fascinating, particularly your suggestion that we could use former offenders as a kind of crack team of hacker thwarters. This is a very intriguing idea, since it seems that for now at least, the hackers have the upper hand in the fight between the ‘bad guys’ and the ‘good guys,’ frequently escaping detection until they have achieved their malicious purpose. The idea does beg the question of how this would work in practice. It would require us to catch offenders and convince them to work for their captors. This made me wonder whether cyberark’s current detection technology is capable of tracing the source of a hack. I would think that as hackers become more sophisticated and their attacks more frequent, identifying them becomes as important as detecting breaches.

On November 14, 2018, Anjali Itzkowitz commented on Machine Learning in Cybersecurity: CyberArk :

Eric, I found this fascinating, particularly your suggestion that CyberArk could hire ex hackers to better defend against sophisticated attacks. This is such an interesting proposition, particularly since when it comes to penetrating cyber defenses, it does seem that the hackers have the upper hand, at least for now. I wonder how this would work in practice, since I would imagine that government agencies would be loathe to hire people with criminal records, not to mention that in order to start working for the ‘good guys’ the hackers would first have to be caught. This raises another question – is hacker identification currently a part of CyberArk’s threat detection? As cyberattacks become more frequent and more sophisticated, it would seem to me that we need to be investing in not just detecting threats, but tracing them.

On November 14, 2018, Anjali Itzkowitz commented on American Express has Struck Gold with Machine Learning :

Really enjoyed reading this. I do think that the use of machine learning in fraud prevention could be effective, but it could also have pitfalls. My card has been suspended while I am travelling, causing enormous inconvenience and denying me access to credit while overseas. I could see this happening with a machine learning tool that observes transactions occurring in a different jurisdiction from the client’s home. Moreover, given the increasing sophistication of hackers, it is not inconceivable that hackers could somehow create fraudulent transaction data on which to train the tool, thereby allowing fraudulent charges to go through undetected. These challenges aside, I do think the implications of machine learning in fraud prevention will be immense, both in the credit card industry and beyond.

On November 14, 2018, Anjali Itzkowitz commented on Beauty Brand with Droves of Data: How Glossier Employs Machine Learning :

This was so interesting to read – I am a huge fan of glossier both as a consumer and as a co-founder of a digitally native skincare brand (aavrani – check out As you mention, the driving force behind the brand’s success has been its sustained high level of customer engagement. This was achievable because Into the Gloss had established credibility with Glossier’s target consumer. I think the question you pose about whether or not the use of machine learning in product development will jeopardise this trust is an excellent one. I also wonder whether the brand’s meteoric rise from indie to mega brand will also alienate consumers.