Toni Campbell & Oliver Badenhorst

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On November 17, 2018, Toni Campbell commented on SenseTime and Public Safety :

Life imitates art perhaps 🙂

On November 15, 2018, Toni Campbell commented on Square’s New Circle: Banking on Data :

So cool! Really enthused that someone wrote about the intersection of machine learning and lending. To answer your question, can data alone be enough to reliably judge the future potential of a business: It depends on the stage and type of business. I think for high growth companies, like 42Technologies :), data alone is not enough to determine the future potential of a business. More qualitative measures such as the quality of the CEO and team are probably more relevant. For more stable / mature businesses, I suspect that data may get a lender 80-90% there. The final 10-20% will still be based on human judgment to understand the “character” of the borrower.

What I love the most about Square’s approach is the granular data that they are able to gather on SMB lending and credit performance in the United States. Despite the veneration of small business owners in the United States as the “backbone of the American economy,” there is a massive dearth of actual data on the disbursement and performance of SMB loans to businesses across the United States. It will be interesting to see how approaches using machine learning like Square will increase the efficiency of underwriting and servicing within this market.

On November 15, 2018, Toni Campbell commented on Spotify: Can machine learning drive content generation? :

As a daily user of Spotify, I think this piece really hits home for me. I’ve been curious and also impressed by the company’s use of AI to create personalized recommendations and playlists. The next step of using AI to select artists and potentially even create content is one that fascinates me.. On one hand, given the similarities between different artists and themes on the US Top 40 lists one might argue that there might be a formula behind the next great earworm hit. On the other hand, I look at the careers of music producers and agents like Jimmy Iovine and Dr. Dre and the often displays of human judgment they had to make to select controversial and innovative artists to back, from Eminem, No Doubt, U2, Trent Reznor, artists who often times were unlike anything or anyone that had come before.

I hope that Spotify can one day find a happy middle if they decide to move into the record label space, where machine learning and AI can predict an artists viability in the market, but final decision on who to produce lies with the human agent. This would allow for the truly innovative and not derivative artists an opportunity to be discovered and promoted.

On November 15, 2018, Toni Campbell commented on Anadarko: Big Oil Meets Big Data :

Super fascinating stuff Volpert. I think you raise an interesting set of questions about the impact of “silicon valley” on “Texan oil” on corporate and industry culture. I suspect these two cultures might actually be complementary. Silicon Valley brings to the table new ideas, values, and ethos can be tested in old industrial spaces, while Texan oil brings tried and true disciplines and methodologies that in many ways can help to better inform tech product development and innovation.

This article also reminds me a lot of a handful of tech companies I’ve come across in the past who are looking to transform the oil and gas value chain with software (https://www.rigup.com/), (http://flowcommand.com/).

The critical unanswered question that all of them are still trying to answer is similar to the one you posed: how culturally resistant to change are the workers, managers, and executives within the natural resources space. Many of them launched in the midst of the collapse in oil prices during 2015-2017 and received significant uptake within the industry, when conventional wisdom would have suggested that an industry contraction should have made these companies less willing to adopt new software vendors and processes.

On November 15, 2018, Toni Campbell commented on The future of energy: forecasting the weather? :

Lori, this was such a cool read! The implications of weather on the production and accessibility of renewable energy is one that I found fascinating and will continue to educate myself on. To answer the questions you posed at the end of your article, I believe that we have an opportunity to do both. We can reduce energy consumption patterns of households across the United States and globally, while also expanding energy storage capacity. I find it remarkable that despite the increase in the number of appliances, devices, electronics in the average American household, energy consumption patterns per household have stayed relatively flat since the 1970s. This indicates to me that the push following the Oil Crises of the late 1970s which spurred a greater awareness on energy efficiency was in fact successful. The challenge moving forward in my mind is how can we leverage machine learning technologies like the one provided by NextEra to bend the consumption curve downwards, while also changing our energy mix away from carbon-sources and towards renewables— a shift that will need the incorporation of energy storage.

On November 15, 2018, Toni Campbell commented on Foxconn: Large Scale Manufacturing in the Machine Learning Age :

Cong, this was a great read and a topic that should be highly relevant to business leaders and policy makers alike as machine learning and automation becomes an ever important part of large scale manufacturing processes. To address the question you posed at the end of your article, where should machine learning experts be placed within FoxConn as an organisation? I suspect it makes the most sense to integrate these machine learning experts into each existing division directly, as opposed to one centralized machine learning division. I believe that direct integration of machine learning experts will go a long way towards increasing the level of engagement needed between line workers and machine learning experts and developers which in turn can help reduce the understandable anxiety held by line workers towards automation, and also accelerate product development of machine learning products as line workers themselves can provide insights on manufacturing processes.

On November 15, 2018, Toni Campbell commented on Improving Public Safety with Machine Learning :

Thank you for writing this piece. I’ve been personally following Shotspotter’s efforts for a number of years and I’m also equally fascinated by some of the questions that you posed towards the end of your article. On the question of Shotspotter’s social consequences, I believe that this might be an example of how the use of big data surveillance is currently shifting the the power balance between citizens and police, a shift that may in fact erode and not improve community trust.

In NY and other metropolitan areas in the US, unreported gun shots are often a symptom of underlying tensions and mistrust held between community members and the police. The use of Shotspotter by police departments may be interpreted by some communities as the further encroachment of law enforcement into their vulnerable communities and could exacerbate and not alleviate the challenges faced by investigators and police officers within these communities.