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On November 15, 2018, Octobot commented on Machine Learning and AI Impacts on the Financial Markets :

Very interesting essay on machine learning in the financial sector and definitely a great example of how AI complements human judgement vs. fully replacing it – thank you!

I am especially interested in the question you are raising whether AI and machine learning will make the markets less fair for the average investor. While I can fully see your perspective, I am wondering whether there are also arguments for the opposite view. In the long-term, machine enabled investing might be more affordable than human-based services, therefore making the markets more fair. We see many examples where technological advancements, while at first expensive and only affordable for few, become more affordable with scale and give new opportunities to a population that was previously excluded from a certain service or at least disadvantaged (e.g. open education through the internet).

Definitely a very interesting read, that provokes the consideration of many opportunities but also risks.

On November 15, 2018, Octobot commented on Climate Connect: Bridging the gap in Utilities :

Especially given today’s challenges we are facing related to climate change – changing the predictability of weather forecasts and trends, and therefore to a large extend energy supply and demand – your essay is extremely relevant and gives a great insight into the latest developments.
I was surprised to learn how “crude” recent forecasting methods still were (i.e. as per your description based on excel models), so there seems to be a great opportunity to add value to this industry.

On your question, how companies like Climate Connect will survive facing a restricted access to data, I can see several solutions. One would be to find strategic partners that focus on big data collection and evaluation. Both partners could benefit from the partnership, complementing each other’s core capabilities.
Another option would be to provide a comprehensive, in-house “software as a service” solution to target companies, rather than only predictability or consulting services. Thanks to the nature of machine learning, I assume relevant players will not want to develop own IP in the space, but rather leverage an existing, proven system that benefits from having been applied over a longer time period with different data sets.

Very insightful and well-structured overview of L’Oréal’s approach to open innovation. I love the idea to include consumers more in this process, given how close they are to the products. I can see how this not only brings in new, innovative product ideas but also strengthens the identification with the brand, therefore creating positive “spill-over effects” to Marketing & Sales efforts.

One challenge I see here is how many of the ideas would be actually feasible, and how “widening” the funnel would increase costs for L’Oréal to evaluate and identify promising ideas. The current process in place seems to be very focused: By predominantly focusing on early stage companies that are active in the innovative beauty products space, L’Oréal already filters out a lot of potential sources for open innovation ideas (vs. companies that may let “everyone” participate in their open innovation process). The question here would be how to quantify the advantages of bringing in more ideas and having a larger pool of innovation available to choose from, in contrast to having to invest more resources in evaluating all these ideas, being aware of the risk that the quality of ideas might also decrease the more we open up our process.

Extremely interesting to see how AI as a value-adding technology for the artist might at some point become a threat for the very same stakeholder.

Already today, startups work on AI technology that develops new musical content, mostly used by e.g., movie directors, game studios, or advertisement agencies. An example would be Aiva Technologies, a startup that developed Aiva – the first AI that has the official status as Composer, with copyrights to its own name.

The question here would be – can this technology ever fully replace artists? And if yes, what would be the long-term limitations? Can AI rethink music, create new genres, be disruptive? Or would be only cater to mainstream tastes, led by more dominant data input vs. more creative, alternative, “underground” art?

Very interesting perspective on how machine learning is disrupting such a critical industry! I especially appreciated the thought starter on how to apply machine learning more in process improvements, e.g. optimizing flight connections. Leveraging more data to identify passengers with late connections earlier for a better real-time reaction seems like a very promising opportunity not only for the airline industry but also for related industries, e.g. rail or general logistics and distribution applications.

The one aspect I would challenge is the statement that existing distribution channels cannot support the selling of individualized service levels such as JetBlue’s offering. With the emergence of more sophisticated web offerings, in the travel industry but also more generally, I could envision a world in which the channel and the personalized offering complement each other: Data collected via different distribution channels could be leveraged to optimize the machine learning algorithm, which then enables a better offering for the customer, making the channel more attractive. Both elements in the supply chain would benefit from each other in the mid- to long-term.

On November 14, 2018, Octobot commented on Machine Learning vs Poachers :

As someone who deeply cares about our nature and environment, I find PAWS’ mission extremely important and inspiring. In addition to the motivation you are describing in your essay, i.e. our responsibility to maintain and respect nature and the animals within it, another crucial aspect to think about are the many human lives taken due to poaching. Last year, more than 100 rangers died trying to protect the animal wildlife in Asia and Africa. Therefore, even for someone less motivated by the loss of environmental diversity, applying machine learning successfully to this cause also has an important positive impact on people’s lives.

Building up on the previous comment, an interesting challenge I see in this context is to ensure high quality data input for the algorithm – not just due to regional differences or route changes of poachers, but also given the resource restrictions many of the affected areas are facing. When visiting South Africa and interacting with local rangers, I learnt that their access to digital tools to track and access data is often limited. Therefore, we need to think about solutions across the whole “supply chain” when aiming to apply machine successfully learning to PAWS cause.

On November 14, 2018, Octobot commented on How McKinsey is Dealing with the Machine Learning Challenge :

I appreciate the perspective to focus on the value-add McKinsey can bring by connecting companies/startups specialized on Machine Learning with the needs and wants of their clients. From my perspective, McKinsey will and should not strive to be the most innovative company in the space of AI or Machine Learning – as it simply does not reflect their core competencies. However, their close work with clients across a broad range of industries enables them to better understand where and how to apply this technology which will be key in the future.