I have found the dynamic of gut vs. data in venture capital always fascinating. For example, there are top VC firms like Sequoia and Benchmark which both have a knack for identifying top startups and there are also firms like Hone who are using an algorithm to better make decisions on which startups to invest in. I think the answer lies somewhere in the middle, but there is always going to be a human judgment aspect. At the end of the day, ML and AI are replicating human learning and intelligence on a more consistent and larger scale but there seem to always be factors that will just be beyond a machine’s reach for now (before we reach singularity). A machine and algorithm will not be able to pick up certain quirks that a founder demonstrates during a pitch that a seasoned Venture Capitalist could immediately identify and use as a factor to decide whether or not to invest. A saying that I think will resonate well here is that numbers never lie, but they also never tell the full story. AI and ML will augment venture capitalists in the future by serving as a useful tool, but I don’t think AI and ML will replace VC’s in the near future.
I tend to face decision fatigue all the time with the influx of new applications that are supposed to facilitate better decisions but often result in analysis paralysis or option overload. I’ve even come to appreciate restaurants that have an extremely simple menu with less than 20 items which reduces the amount of choices I need to make everyday. I think it’s great that TripAdvisor is looking to leverage AI and ML to better serve personalized recommendations based on my app usage history.
To answer your question, I do agree that by using predictive analytics to help make decisions for humans, we will lose a certain aspect of individual agency. However, does it really matter? There are definitely some decisions which I want to know all the options, pros and cons, and want to spend a lot of time pondering (i.e. moving to a new location, career change, etc.). However, with certain decisions like deciding where to eat on a travel application, or which park to visit, these decisions have little bearing on my long-term happiness, and I would imagine it is similar for most people. Thus, I fully support the use of AI and ML for better help in making more trivial decisions like this everyday.
Very interesting article, I have seen the growth of VIPKid and have been astounded by its growth. I remember the CEO saying, “If the US won’t pay its teachers well, the we will”, which I found extremely powerful in leveraging technology to democratize language learning and education. I think that VIPKid has shown how strong and defensible of a company it is by providing a very customized experience and personal experience to ensure the success of its students.
To answer your question, I think in the near future, basic level education will all be taught with Artificial Intelligence, with limited human oversight. Basic concepts in language, math, science are the same across the realm and with advancements in AI, ML, and language processing, it would be possible to almost identically replicate the student teacher relationship outside of the physical human aspect. I think there will be a lot of backlash initially about this method of teaching as we have seen historically with the advancement of even Massive Open Online Courses (MOOCs) which don’t incorporate a physical teacher. However, I think this will become normalized in the future as long as these companies and schools are able to create the right incentives to use these courses. This biggest challenge these online courses face is not necessarily hurdles in the technology or material, but its the hurdle to get users motivated and disciplined enough to take them. If they become required for grade advancement, this would be a sufficient requirement in my opinion.
Very interesting product, but I’m extremely skeptical. This seems like a higher end fitbit, and the transition from a standalone device to subscription model is a move that every technology company in the world is trying to do. Maybe I need to do some more research into the product and tracking capabilities, but sleep, heart rate, and steps are some of only thousands of factors that would influence my performance. For example, diet is likely a huge factor which would be almost impossible to track unless manually inputted. Blood work would also be another important factor. Mental stress and fatigue would also be factors which wouldn’t necessarily be picked up in the criteria the Whoop tracks.
To answer your question, I completely agree that Whoop should open up its data to allow doctors and physicians to test and use the data to see if there is any statistically significant information in terms of predicting sickness or providing strain on the body. As to this, there should be a combination of both supervised and unsupervised learning. Supervised learning would come in the form of doctors and physicians doing tests as mentioned previously. Unsupervised learning should come in the form of machine learning and using the data to make predictive estimates of fatigue, sickness, and strain on the human body and tracking that against actual results. Lot of exciting potential here, just skeptical given the wave of wearable devices that have come before which have largely fallen short of expectations.
I wasn’t aware John Deere was so entrenched in Machine Learning as it aims to develop its products and am fascinated by the “precision agriculture” concept. It’s so easy to overlook areas such as agriculture and farming when speaking about technologies like machine learning and artificial intelligence but this may actually be one of the most important applications of the technology. As the world population expands, food production will become an ever-increasing important factor and using ML and AI to increase crop yield may prove to sustain human life in the near future.
To answer your question about repairing the machine especially as it becomes increasingly complex, I would envision something similar to the current state of the software + maintenance model we see in technology companies. Historically, you had software companies provide CD-ROMs in physical packages of which a consumer purchased once and then had to figure it out while not receiving any updates. As the technology and connection bandwidth improve on these technologies, I would imagine a subscription fee service for maintenance costs on the software, as well as potential physical maintenance on the machines if and when necessary.
Cool article, and Patreon is a company I have been following for a while. I really value their model of allowing content creators to better focus on their creating their content while earning a supplemental income from passionate users which provides a source of both internal motivation (having an audience to provide quality content to) and external motivation (cash from patrons). I do agree that most likely artists with larger followings will be able to better leverage the company to reap more cash payments but I think the company does fulfill its goals of providing meaningful revenue streams for all users who provide quality content. Patreon seems to be in the business of providing equality of opportunity and not necessarily equality of outcome.
To answer your question, I’m willing to pay for content when that piece of content or artist who created the content can connect with me on an emotional level. If it’s an music artist, a song that makes me feel happy or sad, or if it’s a painter, a painting that evokes sympathy or anger. I think blockchain is actually a very interesting technology that could be used to better democratize content in the future. Instead of large corporations like YouTube who make advertising dollars off of content while giving small percentages to the content creators, there could be a platform where content creators could create their art on the blockchain, and use digital currencies for each listen, like, view, etc. The amounts would be minimal to the content consumer (think 1/100 of a cent) but very meaningful to the content creator at scale.