Nathalia Barbosa

  • Student

Activity Feed

On November 15, 2018, Nathalia Barbosa commented on What does Machine Learning mean for a technology services vendor? :

Fenomenal article. Impressed how the change of CEO from 2016 was possible to capture 35% of revenue in a new business market, and result in successful transformation for the business.
I would like to point out my concern about “ZenAnalytica has a suite of over 30 business apps ranging from predictive analytics, business intelligence, recommendation engines, etc”. Having a business with already that quantity of apps seems to me a bit lack of focus, and also the need for manual work to input data, that makes it even more probable not many of these 30 apps will survive.
So going into the questions of the article, I would say that investing in AI and machine learning is the right path for Zensar, however I would try to narrow a bit the objectives and how to attack the problem instead of so many options. But investing, is extremely critical for the survival of the business. I would even try to expand the revenue more than 35%.

On November 15, 2018, Nathalia Barbosa commented on Printing: Speed :

Reading the text, I was already biased towards the super positivity of Adidas moving towards the 3D printing. However, after reading this article, I completely agree with many of the pain points addressed and questions raised to guarantee the success of moving forward with this initiative. I would be in favor of Adidas to move forward in the R&D, specially as a competitive advantage and point of difference, and would focus more in high athletes, as mentioned in some of the comments already or a very premium class of costumers. Therefore, I would allow this service to be done in a special boutique office, or be a type of product that Adidas goes to the house of people or athletes clubs to offer this service, obviously charging accordingly. I think it can get a very special niche market.

On November 15, 2018, Nathalia Barbosa commented on UNICEF: Open innovation to tackle humanitarian crises :

Fantastic article! Astonished to realize “as a result of this open innovation platform, UNICEF has developed innovations such as mobile birth registration in Nigeria and drone transportation of blood samples for early infant HIV diagnosis in Malawi”. Very important to understand how the impact of open innovation is bringing transforming and revolutionary positive impacts in the world.
As for the question of the article, I do agree that UNICEF becomes more susceptible to external interests that do not share the same moral imperative. However, I do not see this impact as negative. I would say that being susceptible is their manner to leverage their impact, and actually be able to follow and have a thermometer to see what is the populations matureness in the process, and how is the behavior in the world/population impacting what UNICEF stands for. It is a perfect mechanism to understand the real impacts if they are in the right track. Although hard to implement all initiatives, it is extremely important to keep track of ideas (and if possible, implement some of those), and also know what is the population thinking about.

On November 14, 2018, Nathalia Barbosa commented on Bioprinting: How the Manufacturing of Human Organs Will Disrupt Healthcare :

Intrigued to learn so much from the future of healthcare with this article.
I would first like to build up in the issue already raised by Daniel and TomGirl about FDA. I have a hard time believing that the FDA will actually approve this approach with tremendous facility. However, I disagree with the article that the society discussion is more in the negative side of the lack of accessibility. I truly believe that actually, society will help accelerate FDAs approval of this approach, because it seems to me it is more political correct than testing in animals. I also believe it might yield closer results to the outcomes of humans. Therefore, I would say that I strongly believe this is happening, and it will happen quick. When society understands the benefit of the long run health for human care, they will probably be in the positive side of the equation. However, I do agree, that it is important that these testings and additive manufacturing should be used to help to develop cheap solutions for it to become accessible to the majority of the population in a more exponential manner than other technologies.

On November 14, 2018, Nathalia Barbosa commented on Shooting for the Stars :

This article amazed me. Very interesting to rethink what NASA means, and its value proposition. I always thought the information was private, and that it was more of a competition to the stars than actually to partner, and maximize research for the advancement of the human kind as a whole. I also had a very wrong idea that NASA was super conservative, and reading this article, I am amazed of hoe the organization is managing to reenvent itslef and utilize trends of AI in their daily business.
I would like to point out two main takeaways I have from this article. The first is towards the crowdfunding being able to be used internally and also external of the organization. I had never thought about how a company can use both tools to leverage knowledge. And intrigued to see how impactful it was for NASA.
My second take away is towards how NASA is extremely committed in the advancements of technology. I worked for Braskem for the past 03 years helping a project of 3D Printing for space tools to facilitate the expenses of sending tools to space. And if they are using Additive Manufacturing, as well as Open Innovation, I am sure they are also using machine learning. So was interesting to see how NASA is trying to keep up woth these three megatrends we have been learning in TOM.

To adress the question in the article, I would say that machine learning could actually be one way it could help filter the quantity of ideas, understanding better the sources and how to break the ideas down. I woudl say also, that I would create a team just to tackle ideas, specially because lots of new possibilities that have not been considered in NASA, can actually be very important for research. So I would try to have a team dedicated for open innovation platforms to maximize its value proposition.

On November 14, 2018, Nathalia Barbosa commented on Sotheby’s and machine learning for the arts :

I definitely had not thought about machine learning for the art industry. Very interesting to learn a bi tmore about the trend this industry is reading. So I would beginning stating that I can definetly see the machine learninr business transforming the art industry, in a manner that I actually had never thought of. It makes completely sense that event his industry starts moving towards the machine learning, specially towards predicting and suggesting new paintings for consumer based on their historical preferences, and the market preferences.
However, I would say that art is a controversial industry, where innovation and disruption is crucial for the business. Therefore, if Sotheby uses the input of the historical choices to predict the future, it will be probably be increasing its probability to underestimate the power of disruption in the industry, and be suggesting for consumers arts that to not portray the reality of the new trends of the art industry. In this sense, I would agree that for the more conservative buyers it does make sense the algorithm, but I would be more skeptical in utilizing this algorithm to suggest new art collections for consumers and clients that are more innovative in their art style collection.

On November 14, 2018, Nathalia Barbosa commented on Is machine learning the new wingman? :

Interesting to think that in five years it would be possible to match someone, without even asking the consumer to “scroll, search, swip”. Impressive to imagine that even when it comes to the heart, the machine learning is being used in a rational manner to update your preferences in such an objective way. In this sense, I have a hard time believing that consumers will allow the future algorithms to decide more precisely, without they having a more emotional connection through the process. I was also wondering while reading this article, that the new generations are changing a lot of what was dating in the past, for their preferences, for trying new things, and algo age connections. And I would suggest that machine learning for the future apps consider that the trend is changing, and not use totally biases and historical data to predict and pairing potential couples for the future.